system

Abstract

The system according to this embodiment aims to propose and realize nail art designs that suit the user's preferences and personality. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a selection unit, a reservation unit, and a fitting unit. The collection unit collects user information. The generation unit analyzes the information collected by the collection unit and generates nail art designs. The selection unit selects nail artists and salons that can realize the designs generated by the generation unit. The reservation unit makes reservations for the nail artists and salons selected by the selection unit. The fitting unit allows the user to virtually try on the designs 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, and includes 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 to efficiently propose and realize a nail art design that matches the user's preference and personality.

[0005] The system according to the embodiment aims to propose and realize a nail art design that matches the user's preference and personality.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a selection unit, a reservation unit, and a fitting unit. The collection unit collects user information. The generation unit analyzes the information collected by the collection unit and generates nail art designs. The selection unit selects nail technicians and salons that can realize the designs generated by the generation unit. The reservation unit makes reservations for the nail technicians and salons selected by the selection unit. The fitting unit allows users to virtually try on the designs generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose and realize nail art designs that suit the user's preferences and personality. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The nail art design suggestion system according to an embodiment of the present invention is a system that uses a generative AI to suggest a nail art design that perfectly suits the user's personality and preferences. This system allows the user to upload their own photos, favorite illustrations, nail art designs, and art designs from around the world. The generative AI analyzes this information and generates the optimal nail art design for the user. Furthermore, it can select a nail artist and salon capable of realizing the generated design and even make a reservation. The suggested nail art can be virtually tried on using an AR (Augmented Reality) fitting function, and the system suggests designs that reflect seasonal trends and the user's events. It also provides recommendations for when to use care products along with the nails, product recommendations suitable for skin type, and appropriate timing for re-treatment. This system is provided via a smartphone / tablet app or website. For example, the user uploads their own photos, favorite illustrations, nail art designs, and art designs from around the world. The user can upload photos that reflect their facial features, atmosphere, clothing, etc. For example, by uploading images of their favorite artwork or designs, the generative AI generates a design based on that information. Next, the generative AI analyzes the uploaded information and generates the optimal nail art design for the user. The generative AI suggests designs based on the user's personality and preferences. For example, the system analyzes photos and illustrations uploaded by users and generates nail art designs that match their style. Furthermore, it can select nail artists and salons that can realize the generated designs and even allow users to make reservations. The generation AI searches a database for nail artists and salons that can realize the suggested designs and provides the user with the best options. Users can then choose from these options and make reservations. The suggested nail art can be virtually tried on using an AR (Augmented Reality) fitting function. Users can virtually try on the suggested designs on their hands using their smartphones or tablets. This allows them to check the design before actually getting the treatment. The system also suggests designs that are in line with seasonal trends and the user's events.The generating AI suggests optimal designs based on the latest trend information and the user's event information. For example, it can suggest designs tailored to special events such as weddings and coming-of-age ceremonies. Furthermore, it provides recommendations for the timing of using care products along with nails, product guidance suitable for skin type, and appropriate timing for re-treatment. The generating AI analyzes the user's skin type and nail condition to suggest the optimal timing for using care products and re-treatment. This allows users to perform nail care effectively. This system is provided through a smartphone / tablet app or website. Users can easily access it anytime, anywhere, and find the perfect nail art design for them. As a result, the nail art design suggestion system suggests the optimal nail art design based on the user's information and allows for a consistent process from booking to trying on.

[0029] The nail art design suggestion system according to this embodiment comprises a collection unit, a generation unit, a selection unit, a reservation unit, and a fitting unit. The collection unit collects user information. User information includes, but is not limited to, age, gender, preferences, and past nail art history. The collection unit collects, for example, photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the collection unit will immediately collect information to prevent the user from feeling stressed. If the user is busy, the collection unit can postpone information collection and collect it when the user is calm. If the user is excited, the collection unit can quickly collect information and suggest designs before the user's excitement subsides. The generation unit analyzes the information collected by the collection unit and generates nail art designs. The generation unit generates nail art designs based on, for example, photos and illustrations uploaded by the user, nail art designs, and art designs from around the world, using a generation AI. The generation AI suggests designs based on the user's personality and preferences. For example, the generation AI analyzes photos and illustrations uploaded by the user and generates nail art designs that match the atmosphere. The generation unit can also suggest optimal designs based on seasonal trends and the user's event information. For example, the generation unit suggests designs tailored to special events such as weddings or coming-of-age ceremonies. The selection unit selects nail artists and salons that can realize the designs generated by the generation unit. For example, the selection unit searches a database for nail artists and salons that can realize the generated designs and provides the user with the best option. The selection unit can also analyze the user's past booking history and select the optimal selection method. For example, the selection unit applies the same selection criteria based on nail artists and salons the user has used in the past. The selection unit can also prioritize selecting nail artists and salons that the user was most satisfied with based on their past booking history. The booking unit makes reservations for the nail artists and salons selected by the selection unit.The booking section can, for example, estimate the user's emotions and adjust the timing of the booking based on those emotions. For instance, if the user is relaxed, the booking section can make the booking immediately to avoid stressing the user. Alternatively, if the user is busy, the booking section can postpone the booking until the user is calmer. Furthermore, if the user is excited, the booking section can make the booking quickly to complete it before the user's excitement subsides. The fitting section allows users to virtually try on designs generated by the generation section. For example, the fitting section provides the ability to virtually try on suggested designs using a smartphone or tablet. The fitting section can also estimate the user's emotions and adjust how the fitting is displayed based on those emotions. For instance, if the user is relaxed, the fitting section can provide detailed fitting options for the user to choose from. If the user is in a hurry, the fitting section can provide options to quickly complete the fitting. Furthermore, if the user is excited, the fitting section can provide visually appealing fitting options. As a result, the nail art design suggestion system according to the embodiment can suggest the optimal nail art design based on user information and handle everything from booking to trying on in a consistent manner.

[0030] The data collection unit collects user information. This information includes, but is not limited to, age, gender, preferences, and past nail art history. The data collection unit collects, for example, photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. Specifically, it analyzes images and illustrations uploaded by the user via smartphone or computer to understand the user's preferences and trends. The data collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the data collection unit is relaxed, it will immediately collect information to avoid stressing the user. This can be achieved using AI technology that analyzes the user's facial expressions and voice tone. The data collection unit can also postpone information collection if the user is busy and collect it when the user is calm. For example, it can integrate with the user's calendar or schedule management app to collect information at the optimal time. Furthermore, if the data collection unit is excited, it can quickly collect information and provide design suggestions while the user's excitement is still high. This allows the data collection unit to flexibly collect information according to the user's status and environment, and acquire data at the optimal time for the user. Furthermore, the data collection unit can store the collected data on a cloud server and share it with other departments. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The generation unit analyzes the information collected by the collection unit and generates nail art designs. For example, the generation unit uses a generation AI to generate nail art designs based on photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The generation AI uses deep learning technology to suggest designs based on the user's personality and preferences. Specifically, the generation AI analyzes photos and illustrations uploaded by the user and generates nail art designs that match the atmosphere. For example, it can generate a flower-themed nail art design based on a photo of flowers uploaded by the user. The generation unit can also suggest the most suitable design based on seasonal trends and the user's event information. For example, the generation unit can suggest designs tailored to special events such as weddings and coming-of-age ceremonies. This uses an algorithm in which the generation AI learns from past data and trend information to generate the optimal design. Furthermore, the generation unit can continuously improve the accuracy of the designs and the quality of the suggestions by collecting user feedback and using it as training data for the generation AI. As a result, the generation unit can provide high-quality nail art designs that meet the user's preferences and needs.

[0032] The selection unit selects nail technicians and salons that can realize the designs generated by the generation unit. For example, the selection unit searches a database for nail technicians and salons that can realize the generated designs and provides the user with the best option. Specifically, the selection unit makes selections based on factors such as the skill level and past performance of the nail technicians and salons, and user reviews. For example, the selection unit applies the same selection criteria to nail technicians and salons that the user has used in the past. The selection unit can also prioritize selecting nail technicians and salons that have given the user the highest satisfaction based on their past booking history. This can be done using machine learning algorithms to predict the user's preferences and satisfaction levels and make the optimal selection. Furthermore, the selection unit can also select the nearest and most convenient nail technician or salon based on the user's current location information. As a result, the selection unit can quickly and accurately select the best nail technician or salon for the user, improving user satisfaction.

[0033] The booking department makes reservations for nail technicians and salons selected by the selection department. For example, the booking department can estimate the user's emotions and adjust the timing of reservations based on those emotions. Specifically, if the user is relaxed, the booking department will make a reservation immediately to minimize stress. This can be achieved using AI technology that analyzes the user's facial expressions and tone of voice. Furthermore, if the user is busy, the booking department can postpone the reservation until the user is calmer. For example, it can integrate with the user's calendar or schedule management app to determine the optimal timing for the reservation. If the user is excited, the booking department can also make a reservation quickly to complete it before the user's excitement subsides. This allows the booking department to flexibly make reservations according to the user's state and environment, completing them at the optimal time for the user. In addition, the booking department can provide an easy-to-use interface for procedures such as confirming, changing, and canceling reservations, improving user convenience. This allows the booking department to provide users with a stress-free and smooth booking experience.

[0034] The fitting section allows users to virtually try on designs generated by the generation section. For example, the fitting section provides the functionality to virtually try on suggested designs using a smartphone or tablet. Specifically, it provides a realistic fitting experience by taking a photo of the user's hand and overlaying the generated nail art design onto it. The fitting section can also estimate the user's emotions and adjust the display method based on that estimation. For example, if the user is relaxed, it can provide detailed fitting options for the user to choose from. If the user is in a hurry, it can provide options to quickly complete the fitting, such as an interface that allows for easy completion. Furthermore, if the user is excited, it can provide visually appealing fitting options, such as using animations and effects to make the fitting experience more enjoyable. This allows the fitting section to flexibly provide a fitting experience according to the user's state and environment, helping the user choose the best design. Additionally, the fitting section provides a function to save fitting results, allowing users to review them later. This allows the fitting room to provide users with a convenient and enjoyable fitting experience.

[0035] The generation unit can generate nail art designs based on photos, illustrations, nail art designs uploaded by the user, and art designs from around the world. For example, the generation unit can analyze photos and illustrations uploaded by the user and generate a nail art design that matches their style. For example, the generation unit can generate a similar design based on a nail art design uploaded by the user. For example, the generation unit can refer to art designs from around the world and generate a nail art design based on them. This allows the generation unit to generate designs based on the user's preferences and personality. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs photos and illustrations uploaded by the user into the generation AI, which analyzes that information and generates a nail art design.

[0036] The selection unit can search a database for nail technicians and salons that can realize the generated design and provide the user with the best option. For example, the selection unit can search a database for nail technicians and salons that can realize the generated design and provide the user with the best option. The selection unit can also provide the best option based on, for example, the nail technician's skill information and the salon's equipment information. The selection unit can also provide the best option based on, for example, the user's rating, distance, price, etc. In this way, the selection unit can provide the user with the best nail technician or salon. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs information on nail technicians and salons that can realize the generated design into the AI, and the AI ​​analyzes that information to provide the best option.

[0037] The fitting room can provide a function that allows users to virtually try on suggested designs using a smartphone or tablet. For example, the fitting room can use a smartphone or tablet to virtually try on suggested designs. The fitting room can also use augmented reality (AR) technology to virtually try on suggested designs. The fitting room can also use 3D modeling to virtually try on suggested designs. This allows the fitting room to allow users to check the design before actually receiving treatment. Some or all of the above processes in the fitting room may be performed using AI or not. For example, the fitting room can input suggested designs into an AI, which then analyzes that information and provides a function to virtually try them on.

[0038] The generation unit can propose optimal designs based on seasonal trends and user event information. For example, the generation unit can propose seasonal designs based on the latest trend information. The generation unit can also propose designs tailored to special events based on user event information. For example, the generation unit can propose designs tailored to special events such as weddings or coming-of-age ceremonies. In this way, the generation unit can propose designs that match the season and events. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs seasonal trend information and user event information into the generation AI, which analyzes that information and proposes the optimal design.

[0039] The generation unit can analyze the user's skin type and nail condition and suggest the optimal timing for using care products and for retreatment. For example, the generation unit can analyze the user's skin type and suggest the optimal timing for using care products. The generation unit can also analyze the user's nail condition and suggest the optimal timing for retreatment. For example, the generation unit can suggest the timing for using care products tailored to skin type, such as dry or oily skin. For example, the generation unit can suggest the timing for retreatment tailored to the condition of the nails, such as their tendency to break. In this way, the generation unit can suggest care tailored to the user's skin type and nail condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's skin type and nail condition into the generation AI, which analyzes this information and suggests the optimal timing for using care products and for retreatment.

[0040] The data collection unit can analyze the user's past upload history and select the optimal data collection method. For example, the data collection unit can analyze trends in images previously uploaded by the user and prioritize the collection of similar images. For example, the data collection unit can analyze the time periods in which images previously uploaded by the user and collect information during those time periods. For example, the data collection unit can analyze the categories of images previously uploaded by the user and prioritize the collection of information in the same category. This allows the data collection unit to collect information optimally based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past upload history into the AI, which will analyze that information and select the optimal data collection method.

[0041] The data collection unit can filter information based on the user's current fashion and lifestyle. For example, the data collection unit can prioritize collecting relevant designs based on the clothes the user is currently wearing. The data collection unit can also collect appropriate designs based on the user's lifestyle (e.g., whether they are outdoorsy or indoorsy). The data collection unit can also collect relevant designs based on the user's current fashion style (e.g., casual or formal). This allows the data collection unit to collect information tailored to the user's current situation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information about the user's current fashion and lifestyle into an AI, which then analyzes and filters that information.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information. For example, the data collection unit can prioritize the collection of trend information in the area where the user is currently located. For example, the data collection unit can also prioritize the collection of information on nearby nail technicians and salons based on the user's geographical location. For example, the data collection unit can also prioritize the collection of region-specific designs and styles based on the user's geographical location. This allows the data collection unit to collect the most relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's geographical location information into the AI, which analyzes that information and prioritizes the collection of highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information when gathering data. For example, the data collection unit can collect relevant information based on designs that the user has "liked" on social media. The data collection unit can also analyze posts from accounts that the user follows on social media and collect relevant information. The data collection unit can also collect relevant information based on designs that the user has shared on social media. This allows the data collection unit to collect the most relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's social media activity into an AI, which can then analyze that information and collect relevant information.

[0044] The generation unit can adjust the level of detail in a design based on the user's preferences and personality during the design generation process. For example, if the user prefers a simple design, the generation unit will generate a simple design. If the user prefers a complex design, the generation unit can also generate a detailed design. If the user prefers a specific theme, the generation unit can also generate a design based on that theme. This allows the generation unit to generate detailed designs tailored to the user's preferences. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs information about the user's preferences and personality into the generation AI, which then analyzes this information to adjust the level of detail in the design.

[0045] The generation unit can apply different generation algorithms depending on the category of the image uploaded by the user when generating a design. For example, if the image uploaded by the user is a work of art, the generation unit will apply an algorithm to generate a design based on that work of art. For example, if the image uploaded by the user is a fashion item, the generation unit can also apply an algorithm to generate a design based on that fashion item. For example, if the image uploaded by the user is a natural landscape, the generation unit can also apply an algorithm to generate a design based on that natural landscape. This allows the generation unit to generate the optimal design according to the image uploaded by the user. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the category information of the image uploaded by the user into the generation AI, and the generation AI analyzes that information and applies a different generation algorithm.

[0046] The generation unit can determine design priorities based on the user's past preferences when generating designs. For example, the generation unit can prioritize generating similar designs based on designs the user has liked in the past. The generation unit can also exclude designs the user has avoided in the past. For example, the generation unit can analyze the user's past preferences and prioritize generating designs that are most likely to be preferred. This allows the generation unit to generate the optimal design based on the user's past preferences. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's past preference data into the generation AI, which analyzes that information to determine design priorities.

[0047] The generation unit can generate designs by referencing the user's relevant art and design trends. For example, the generation unit can generate designs by referencing the latest works of artists the user follows. The generation unit can also generate the latest designs based on the design trends the user is interested in. For example, the generation unit can analyze the design trends the user has liked in the past and generate related designs. This allows the generation unit to generate the latest designs based on the user's interests. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs information on the user's relevant art and design trends into the generation AI, which then analyzes that information and generates designs.

[0048] The selection unit can analyze the user's past booking history to select the optimal selection method. For example, the selection unit can apply similar selection criteria based on the nail technicians and salons the user has used in the past. For example, the selection unit can prioritize selecting the nail technicians and salons that the user was most satisfied with based on their past booking history. For example, the selection unit can analyze the user's past booking history to derive the optimal selection criteria. This allows the selection unit to select the best nail technician or salon based on the user's past history. Some or all of the above processes in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past booking history into AI, which then analyzes that information to select the optimal selection method.

[0049] The selection unit can filter the results based on the user's current lifestyle and event information during the selection process. For example, if a user is preparing for a wedding, the selection unit will prioritize selecting nail technicians and salons suitable for weddings. If a user is busy, the selection unit can also prioritize selecting nail technicians and salons that can respond quickly. If a user wants to relax, the selection unit can also prioritize selecting salons with a relaxing atmosphere. In this way, the selection unit can select nail technicians and salons that match the user's current situation. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the user's current lifestyle and event information into the AI, which then analyzes and filters that information.

[0050] The selection unit can prioritize selecting highly relevant nail technicians and salons by considering the user's geographical location information during the selection process. For example, the selection unit can prioritize selecting nail technicians and salons in the area where the user is currently located. The selection unit can also prioritize selecting nearby nail technicians and salons based on the user's geographical location. The selection unit can also prioritize selecting nail technicians and salons that offer region-specific services based on the user's geographical location. In this way, the selection unit can select the most suitable nail technician or salon based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the user's geographical location information into the AI, which analyzes that information and prioritizes selecting highly relevant nail technicians and salons.

[0051] The selection unit can analyze the user's social media activity during the selection process and select relevant nail technicians and salons. For example, the selection unit can select relevant nail technicians and salons based on the nail technicians and salons that the user has "liked" on social media. The selection unit can also select relevant nail technicians and salons based on the nail technicians and salons that the user follows on social media. The selection unit can also select relevant nail technicians and salons based on the nail technicians and salons that the user has shared on social media. In this way, the selection unit can select the most suitable nail technicians and salons based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input data on the user's social media activity into AI, and the AI ​​can analyze that information to select relevant nail technicians and salons.

[0052] The reservation department can analyze a user's past reservation history to select the optimal reservation method at the time of booking. For example, the reservation department can suggest a similar reservation method based on the user's past reservation methods (online, telephone, etc.). For example, the reservation department can also prioritize suggesting the reservation method that the user was most satisfied with based on their past reservation history. For example, the reservation department can analyze a user's past reservation history to derive the optimal reservation method. This allows the reservation department to select the optimal reservation method based on the user's past history. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's past reservation history into the AI, which then analyzes that information to select the optimal reservation method.

[0053] The reservation system can filter reservations based on the user's current schedule and event information. For example, it can refer to the user's calendar information and make reservations for available time slots. The reservation system can also make reservations at the optimal time based on the user's event information (such as a wedding or coming-of-age ceremony). The reservation system can also analyze the user's schedule and make reservations for the most convenient time slot. This allows the reservation system to make optimal reservations tailored to the user's current situation. Some or all of the above processes in the reservation system may be performed using AI or not. For example, the reservation system can input the user's current schedule and event information into the AI, which then analyzes and filters that information.

[0054] The reservation system can prioritize highly relevant reservations by considering the user's geographical location information during the reservation process. For example, the reservation system can prioritize reservations with nail technicians or salons in the user's current location. The reservation system can also prioritize reservations with nearby nail technicians or salons based on the user's geographical location. The reservation system can also prioritize reservations with nail technicians or salons that offer region-specific services based on the user's geographical location. This allows the reservation system to make optimal reservations based on the user's geographical location. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input the user's geographical location information into an AI, which then analyzes that information and prioritizes highly relevant reservations.

[0055] The reservation department can analyze a user's social media activity when they make a reservation and make a relevant reservation. For example, the reservation department can make a relevant reservation based on nail artists and salons that the user has "liked" on social media. The reservation department can also make a relevant reservation based on nail artists and salons that the user follows on social media. The reservation department can also make a relevant reservation based on nail artists and salons that the user has shared on social media. This allows the reservation department to make the most optimal reservation based on the user's social media activity. Some or all of the above processing in the reservation department may be performed using AI or not. For example, the reservation department can input data on the user's social media activity into an AI, which will analyze that information and make a relevant reservation.

[0056] The fitting room unit can analyze the user's past fitting history to select the optimal fitting method during the fitting process. For example, the fitting room unit can prioritize trying on similar designs based on designs the user has tried on in the past. For example, the fitting room unit can also prioritize trying on designs that the user was most satisfied with based on their past fitting history. For example, the fitting room unit can analyze the user's past fitting history to derive the optimal fitting method. This allows the fitting room unit to select the optimal fitting method based on the user's past history. Some or all of the above processes in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input the user's past fitting history into an AI, which then analyzes that information to select the optimal fitting method.

[0057] The fitting room unit can filter the user's current fashion and lifestyle during the fitting process. For example, the fitting room unit prioritizes trying on designs relevant to the clothes the user is currently wearing. The fitting room unit can also try on appropriate designs based on the user's lifestyle (e.g., whether they are outdoorsy or indoorsy). The fitting room unit can also try on relevant designs based on the user's current fashion style (e.g., casual or formal). This allows the fitting room unit to perform the optimal fitting tailored to the user's current situation. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit inputs information about the user's current fashion and lifestyle into the AI, which then analyzes and filters that information.

[0058] The fitting room function can prioritize fittings that are highly relevant to the user, taking into account the user's geographical location. For example, the fitting room function can prioritize fittings of relevant designs based on trend information in the user's current location. For example, the fitting room function can also prioritize fittings of designs from nearby nail artists or salons based on the user's geographical location. For example, the fitting room function can also prioritize fittings of regionally specific designs or styles based on the user's geographical location. This allows the fitting room function to perform optimal fittings based on the user's geographical location. Some or all of the above processing in the fitting room function may be performed using AI or not. For example, the fitting room function can input the user's geographical location information into AI, which then analyzes that information and prioritizes fittings that are highly relevant.

[0059] The fitting room unit can analyze the user's social media activity during the fitting process and perform relevant fittings. For example, the fitting room unit can perform relevant fittings based on designs the user has "liked" on social media. It can also perform relevant fittings based on designs the user follows on social media. It can also perform relevant fittings based on designs the user has shared on social media. This allows the fitting room unit to perform optimal fittings based on the user's social media activity. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input data on the user's social media activity into the AI, which then analyzes that information and performs relevant fittings.

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

[0061] The nail art design suggestion system can prioritize designs based on the user's past preferences. For example, the generation unit can prioritize generating similar designs based on designs the user has liked in the past. It can also exclude designs the user has avoided in the past. Furthermore, it can analyze the user's past preferences and prioritize generating designs that are most likely to be preferred. In this way, the generation unit can generate the optimal design based on the user's past preferences. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past preference data into the generation AI, which analyzes that information to determine the design priority.

[0062] The nail art design suggestion system can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the collection unit can prioritize the collection of trend information for the area where the user is currently located. It can also prioritize the collection of information on nearby nail technicians and salons based on the user's geographical location. Furthermore, it can prioritize the collection of region-specific designs and styles based on the user's geographical location. In this way, the collection unit can collect the most relevant information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the user's geographical location information into the AI, which analyzes that information and prioritizes the collection of highly relevant information.

[0063] The nail art design suggestion system can analyze a user's social media activity and collect relevant information. For example, the collection unit can collect relevant information based on designs that the user has "liked" on social media. It can also analyze posts from accounts that the user follows on social media and collect relevant information. Furthermore, it can collect relevant information based on designs that the user has shared on social media. This allows the collection unit to collect the most relevant information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs data on the user's social media activity into the AI, which then analyzes that information to collect relevant information.

[0064] The nail art design suggestion system can analyze a user's past booking history to select the optimal method. For example, the selection unit can apply similar selection criteria based on the nail technicians and salons the user has used in the past. It can also prioritize selecting the nail technicians and salons that the user was most satisfied with based on their past booking history. Furthermore, it can analyze the user's past booking history to derive the optimal selection criteria. As a result, the selection unit can select the most suitable nail technician and salon based on the user's past history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the user's past booking history into the AI, which then analyzes that information to select the optimal method.

[0065] The nail art design suggestion system can filter based on the user's current schedule and event information. For example, the booking system can refer to the user's calendar information and make reservations during available time slots. It can also make reservations at the optimal time based on the user's event information (wedding, coming-of-age ceremony, etc.). Furthermore, it can analyze the user's schedule and make reservations at the most convenient time slot. This allows the booking system to make optimal reservations tailored to the user's current situation. Some or all of the above processing in the booking system may be performed using AI or not. For example, the booking system inputs the user's current schedule and event information into the AI, which then analyzes and filters that information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects user information. This information includes age, gender, preferences, and past nail art history. The data collection unit collects photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The data collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, information collection is performed immediately to avoid stressing the user. If the user is busy, information collection is postponed and performed when the user is calm. If the user is excited, information collection is performed quickly and design suggestions are made while the user's excitement is still high. Step 2: The generation unit analyzes the information collected by the collection unit and generates nail art designs. The generation unit uses a generation AI to generate nail art designs based on photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The generation AI suggests designs based on the user's personality and preferences. For example, it analyzes photos and illustrations uploaded by the user and generates nail art designs that match the atmosphere. It can also suggest the most suitable designs based on seasonal trends and the user's event information. For example, it can suggest designs tailored to special events such as weddings or coming-of-age ceremonies. Step 3: The selection unit selects nail technicians and salons that can realize the designs generated by the generation unit. The selection unit searches its database for nail technicians and salons that can realize the generated designs and provides the user with the best option. The selection unit can also analyze the user's past booking history to select the optimal selection method. For example, it can apply the same selection criteria based on nail technicians and salons the user has used in the past. It can also prioritize selecting nail technicians and salons that the user was most satisfied with based on their past booking history. Step 4: The booking department makes reservations for nail technicians and salons selected by the selection department. The booking department can estimate the user's emotions and adjust the timing of the reservation based on the estimated emotions. For example, if the user is relaxed, the reservation can be made immediately to avoid stressing the user. If the user is busy, the reservation can be postponed and made when the user is calmer. If the user is excited, the reservation can be made quickly and completed before the user's excitement subsides. Step 5: The fitting room allows users to virtually try on designs generated by the generation room. The fitting room provides the ability to virtually try on suggested designs using a smartphone or tablet. The fitting room can also estimate the user's emotions and adjust how the fitting is displayed based on those emotions. For example, if the user is relaxed, it may provide detailed fitting options for the user to choose from. If the user is in a hurry, it may provide options to quickly complete the fitting. If the user is excited, it may also provide visually appealing fitting options.

[0068] (Example of form 2) The nail art design suggestion system according to an embodiment of the present invention is a system that uses a generative AI to suggest a nail art design that perfectly suits the user's personality and preferences. This system allows the user to upload their own photos, favorite illustrations, nail art designs, and art designs from around the world. The generative AI analyzes this information and generates the optimal nail art design for the user. Furthermore, it can select a nail artist and salon capable of realizing the generated design and even make a reservation. The suggested nail art can be virtually tried on using an AR (Augmented Reality) fitting function, and the system suggests designs that reflect seasonal trends and the user's events. It also provides recommendations for when to use care products along with the nails, product recommendations suitable for skin type, and appropriate timing for re-treatment. This system is provided via a smartphone / tablet app or website. For example, the user uploads their own photos, favorite illustrations, nail art designs, and art designs from around the world. The user can upload photos that reflect their facial features, atmosphere, clothing, etc. For example, by uploading images of their favorite artwork or designs, the generative AI generates a design based on that information. Next, the generative AI analyzes the uploaded information and generates the optimal nail art design for the user. The generative AI suggests designs based on the user's personality and preferences. For example, the system analyzes photos and illustrations uploaded by users and generates nail art designs that match their style. Furthermore, it can select nail artists and salons that can realize the generated designs and even allow users to make reservations. The generation AI searches a database for nail artists and salons that can realize the suggested designs and provides the user with the best options. Users can then choose from these options and make reservations. The suggested nail art can be virtually tried on using an AR (Augmented Reality) fitting function. Users can virtually try on the suggested designs on their hands using their smartphones or tablets. This allows them to check the design before actually getting the treatment. The system also suggests designs that are in line with seasonal trends and the user's events.The generating AI suggests optimal designs based on the latest trend information and the user's event information. For example, it can suggest designs tailored to special events such as weddings and coming-of-age ceremonies. Furthermore, it provides recommendations for the timing of using care products along with nails, product guidance suitable for skin type, and appropriate timing for re-treatment. The generating AI analyzes the user's skin type and nail condition to suggest the optimal timing for using care products and re-treatment. This allows users to perform nail care effectively. This system is provided through a smartphone / tablet app or website. Users can easily access it anytime, anywhere, and find the perfect nail art design for them. As a result, the nail art design suggestion system suggests the optimal nail art design based on the user's information and allows for a consistent process from booking to trying on.

[0069] The nail art design suggestion system according to this embodiment comprises a collection unit, a generation unit, a selection unit, a reservation unit, and a fitting unit. The collection unit collects user information. User information includes, but is not limited to, age, gender, preferences, and past nail art history. The collection unit collects, for example, photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the collection unit will immediately collect information to prevent the user from feeling stressed. If the user is busy, the collection unit can postpone information collection and collect it when the user is calm. If the user is excited, the collection unit can quickly collect information and suggest designs before the user's excitement subsides. The generation unit analyzes the information collected by the collection unit and generates nail art designs. The generation unit generates nail art designs based on, for example, photos and illustrations uploaded by the user, nail art designs, and art designs from around the world, using a generation AI. The generation AI suggests designs based on the user's personality and preferences. For example, the generation AI analyzes photos and illustrations uploaded by the user and generates nail art designs that match the atmosphere. The generation unit can also suggest optimal designs based on seasonal trends and the user's event information. For example, the generation unit suggests designs tailored to special events such as weddings or coming-of-age ceremonies. The selection unit selects nail artists and salons that can realize the designs generated by the generation unit. For example, the selection unit searches a database for nail artists and salons that can realize the generated designs and provides the user with the best option. The selection unit can also analyze the user's past booking history and select the optimal selection method. For example, the selection unit applies the same selection criteria based on nail artists and salons the user has used in the past. The selection unit can also prioritize selecting nail artists and salons that the user was most satisfied with based on their past booking history. The booking unit makes reservations for the nail artists and salons selected by the selection unit.The booking section can, for example, estimate the user's emotions and adjust the timing of the booking based on those emotions. For instance, if the user is relaxed, the booking section can make the booking immediately to avoid stressing the user. Alternatively, if the user is busy, the booking section can postpone the booking until the user is calmer. Furthermore, if the user is excited, the booking section can make the booking quickly to complete it before the user's excitement subsides. The fitting section allows users to virtually try on designs generated by the generation section. For example, the fitting section provides the ability to virtually try on suggested designs using a smartphone or tablet. The fitting section can also estimate the user's emotions and adjust how the fitting is displayed based on those emotions. For instance, if the user is relaxed, the fitting section can provide detailed fitting options for the user to choose from. If the user is in a hurry, the fitting section can provide options to quickly complete the fitting. Furthermore, if the user is excited, the fitting section can provide visually appealing fitting options. As a result, the nail art design suggestion system according to the embodiment can suggest the optimal nail art design based on user information and handle everything from booking to trying on in a consistent manner.

[0070] The data collection unit collects user information. This information includes, but is not limited to, age, gender, preferences, and past nail art history. The data collection unit collects, for example, photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. Specifically, it analyzes images and illustrations uploaded by the user via smartphone or computer to understand the user's preferences and trends. The data collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the data collection unit is relaxed, it will immediately collect information to avoid stressing the user. This can be achieved using AI technology that analyzes the user's facial expressions and voice tone. The data collection unit can also postpone information collection if the user is busy and collect it when the user is calm. For example, it can integrate with the user's calendar or schedule management app to collect information at the optimal time. Furthermore, if the data collection unit is excited, it can quickly collect information and provide design suggestions while the user's excitement is still high. This allows the data collection unit to flexibly collect information according to the user's status and environment, and acquire data at the optimal time for the user. Furthermore, the data collection unit can store the collected data on a cloud server and share it with other departments. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0071] The generation unit analyzes the information collected by the collection unit and generates nail art designs. For example, the generation unit uses a generation AI to generate nail art designs based on photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The generation AI uses deep learning technology to suggest designs based on the user's personality and preferences. Specifically, the generation AI analyzes photos and illustrations uploaded by the user and generates nail art designs that match the atmosphere. For example, it can generate a flower-themed nail art design based on a photo of flowers uploaded by the user. The generation unit can also suggest the most suitable design based on seasonal trends and the user's event information. For example, the generation unit can suggest designs tailored to special events such as weddings and coming-of-age ceremonies. This uses an algorithm in which the generation AI learns from past data and trend information to generate the optimal design. Furthermore, the generation unit can continuously improve the accuracy of the designs and the quality of the suggestions by collecting user feedback and using it as training data for the generation AI. As a result, the generation unit can provide high-quality nail art designs that meet the user's preferences and needs.

[0072] The selection unit selects nail technicians and salons that can realize the designs generated by the generation unit. For example, the selection unit searches a database for nail technicians and salons that can realize the generated designs and provides the user with the best option. Specifically, the selection unit makes selections based on factors such as the skill level and past performance of the nail technicians and salons, and user reviews. For example, the selection unit applies the same selection criteria to nail technicians and salons that the user has used in the past. The selection unit can also prioritize selecting nail technicians and salons that have given the user the highest satisfaction based on their past booking history. This can be done using machine learning algorithms to predict the user's preferences and satisfaction levels and make the optimal selection. Furthermore, the selection unit can also select the nearest and most convenient nail technician or salon based on the user's current location information. As a result, the selection unit can quickly and accurately select the best nail technician or salon for the user, improving user satisfaction.

[0073] The booking department makes reservations for nail technicians and salons selected by the selection department. For example, the booking department can estimate the user's emotions and adjust the timing of reservations based on those emotions. Specifically, if the user is relaxed, the booking department will make a reservation immediately to minimize stress. This can be achieved using AI technology that analyzes the user's facial expressions and tone of voice. Furthermore, if the user is busy, the booking department can postpone the reservation until the user is calmer. For example, it can integrate with the user's calendar or schedule management app to determine the optimal timing for the reservation. If the user is excited, the booking department can also make a reservation quickly to complete it before the user's excitement subsides. This allows the booking department to flexibly make reservations according to the user's state and environment, completing them at the optimal time for the user. In addition, the booking department can provide an easy-to-use interface for procedures such as confirming, changing, and canceling reservations, improving user convenience. This allows the booking department to provide users with a stress-free and smooth booking experience.

[0074] The fitting section allows users to virtually try on designs generated by the generation section. For example, the fitting section provides the functionality to virtually try on suggested designs using a smartphone or tablet. Specifically, it provides a realistic fitting experience by taking a photo of the user's hand and overlaying the generated nail art design onto it. The fitting section can also estimate the user's emotions and adjust the display method based on that estimation. For example, if the user is relaxed, it can provide detailed fitting options for the user to choose from. If the user is in a hurry, it can provide options to quickly complete the fitting, such as an interface that allows for easy completion. Furthermore, if the user is excited, it can provide visually appealing fitting options, such as using animations and effects to make the fitting experience more enjoyable. This allows the fitting section to flexibly provide a fitting experience according to the user's state and environment, helping the user choose the best design. Additionally, the fitting section provides a function to save fitting results, allowing users to review them later. This allows the fitting room to provide users with a convenient and enjoyable fitting experience.

[0075] The generation unit can generate nail art designs based on photos, illustrations, nail art designs uploaded by the user, and art designs from around the world. For example, the generation unit can analyze photos and illustrations uploaded by the user and generate a nail art design that matches their style. For example, the generation unit can generate a similar design based on a nail art design uploaded by the user. For example, the generation unit can refer to art designs from around the world and generate a nail art design based on them. This allows the generation unit to generate designs based on the user's preferences and personality. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs photos and illustrations uploaded by the user into the generation AI, which analyzes that information and generates a nail art design.

[0076] The selection unit can search a database for nail technicians and salons that can realize the generated design and provide the user with the best option. For example, the selection unit can search a database for nail technicians and salons that can realize the generated design and provide the user with the best option. The selection unit can also provide the best option based on, for example, the nail technician's skill information and the salon's equipment information. The selection unit can also provide the best option based on, for example, the user's rating, distance, price, etc. In this way, the selection unit can provide the user with the best nail technician or salon. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs information on nail technicians and salons that can realize the generated design into the AI, and the AI ​​analyzes that information to provide the best option.

[0077] The fitting room can provide a function that allows users to virtually try on suggested designs using a smartphone or tablet. For example, the fitting room can use a smartphone or tablet to virtually try on suggested designs. The fitting room can also use augmented reality (AR) technology to virtually try on suggested designs. The fitting room can also use 3D modeling to virtually try on suggested designs. This allows the fitting room to allow users to check the design before actually receiving treatment. Some or all of the above processes in the fitting room may be performed using AI or not. For example, the fitting room can input suggested designs into an AI, which then analyzes that information and provides a function to virtually try them on.

[0078] The generation unit can propose optimal designs based on seasonal trends and user event information. For example, the generation unit can propose seasonal designs based on the latest trend information. The generation unit can also propose designs tailored to special events based on user event information. For example, the generation unit can propose designs tailored to special events such as weddings or coming-of-age ceremonies. In this way, the generation unit can propose designs that match the season and events. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs seasonal trend information and user event information into the generation AI, which analyzes that information and proposes the optimal design.

[0079] The generation unit can analyze the user's skin type and nail condition and suggest the optimal timing for using care products and for retreatment. For example, the generation unit can analyze the user's skin type and suggest the optimal timing for using care products. The generation unit can also analyze the user's nail condition and suggest the optimal timing for retreatment. For example, the generation unit can suggest the timing for using care products tailored to skin type, such as dry or oily skin. For example, the generation unit can suggest the timing for retreatment tailored to the condition of the nails, such as their tendency to break. In this way, the generation unit can suggest care tailored to the user's skin type and nail condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's skin type and nail condition into the generation AI, which analyzes this information and suggests the optimal timing for using care products and for retreatment.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect information immediately to avoid causing the user stress. For example, if the user is busy, the data collection unit can postpone information collection and collect it when the user is calm. For example, if the user is excited, the data collection unit can collect information quickly and provide design suggestions before the user's excitement subsides. This allows the data collection unit to collect information at the optimal time 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs user emotion data into the AI, which analyzes the information and adjusts the timing of information collection.

[0081] The data collection unit can analyze the user's past upload history and select the optimal data collection method. For example, the data collection unit can analyze trends in images previously uploaded by the user and prioritize the collection of similar images. For example, the data collection unit can analyze the time periods in which images previously uploaded by the user and collect information during those time periods. For example, the data collection unit can analyze the categories of images previously uploaded by the user and prioritize the collection of information in the same category. This allows the data collection unit to collect information optimally based on the user's past history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past upload history into the AI, which will analyze that information and select the optimal data collection method.

[0082] The data collection unit can filter information based on the user's current fashion and lifestyle. For example, the data collection unit can prioritize collecting relevant designs based on the clothes the user is currently wearing. The data collection unit can also collect appropriate designs based on the user's lifestyle (e.g., whether they are outdoorsy or indoorsy). The data collection unit can also collect relevant designs based on the user's current fashion style (e.g., casual or formal). This allows the data collection unit to collect information tailored to the user's current situation. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input information about the user's current fashion and lifestyle into an AI, which then analyzes and filters that information.

[0083] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. If the user is in a hurry, for example, the data collection unit may prioritize collecting important information. If the user is excited, for example, the data collection unit may prioritize collecting visually appealing information. This allows the data collection unit to prioritize the collection of information that is most appropriate for the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs user emotion data into an AI, and the AI ​​analyzes that information to determine the priority of information to collect.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information. For example, the data collection unit can prioritize the collection of trend information in the area where the user is currently located. For example, the data collection unit can also prioritize the collection of information on nearby nail technicians and salons based on the user's geographical location. For example, the data collection unit can also prioritize the collection of region-specific designs and styles based on the user's geographical location. This allows the data collection unit to collect the most relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's geographical location information into the AI, which analyzes that information and prioritizes the collection of highly relevant information.

[0085] The data collection unit can analyze the user's social media activity and collect relevant information when gathering data. For example, the data collection unit can collect relevant information based on designs that the user has "liked" on social media. The data collection unit can also analyze posts from accounts that the user follows on social media and collect relevant information. The data collection unit can also collect relevant information based on designs that the user has shared on social media. This allows the data collection unit to collect the most relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's social media activity into an AI, which can then analyze that information and collect relevant information.

[0086] The generation unit can estimate the user's emotions and adjust the design's expression based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a design with soft colors. If the user is excited, for example, the generation unit can also generate a design with vibrant colors. If the user is depressed, for example, the generation unit can also generate a design with bright colors. This allows the generation unit to generate the optimal design 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 generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, which analyzes that information and adjusts the design's expression.

[0087] The generation unit can adjust the level of detail in a design based on the user's preferences and personality during the design generation process. For example, if the user prefers a simple design, the generation unit will generate a simple design. If the user prefers a complex design, the generation unit can also generate a detailed design. If the user prefers a specific theme, the generation unit can also generate a design based on that theme. This allows the generation unit to generate detailed designs tailored to the user's preferences. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs information about the user's preferences and personality into the generation AI, which then analyzes this information to adjust the level of detail in the design.

[0088] The generation unit can apply different generation algorithms depending on the category of the image uploaded by the user when generating a design. For example, if the image uploaded by the user is a work of art, the generation unit will apply an algorithm to generate a design based on that work of art. For example, if the image uploaded by the user is a fashion item, the generation unit can also apply an algorithm to generate a design based on that fashion item. For example, if the image uploaded by the user is a natural landscape, the generation unit can also apply an algorithm to generate a design based on that natural landscape. This allows the generation unit to generate the optimal design according to the image uploaded by the user. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the category information of the image uploaded by the user into the generation AI, and the generation AI analyzes that information and applies a different generation algorithm.

[0089] The generation unit can estimate the user's emotions and adjust the length and complexity of the design based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a long and complex design. If the user is in a hurry, for example, the generation unit can also generate a short and simple design. If the user is excited, for example, the generation unit can also generate a visually stimulating design. In this way, the generation unit can generate the optimal design 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 generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, which analyzes that information and adjusts the length and complexity of the design.

[0090] The generation unit can determine design priorities based on the user's past preferences when generating designs. For example, the generation unit can prioritize generating similar designs based on designs the user has liked in the past. The generation unit can also exclude designs the user has avoided in the past. For example, the generation unit can analyze the user's past preferences and prioritize generating designs that are most likely to be preferred. This allows the generation unit to generate the optimal design based on the user's past preferences. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's past preference data into the generation AI, which analyzes that information to determine design priorities.

[0091] The generation unit can generate designs by referencing the user's relevant art and design trends. For example, the generation unit can generate designs by referencing the latest works of artists the user follows. The generation unit can also generate the latest designs based on the design trends the user is interested in. For example, the generation unit can analyze the design trends the user has liked in the past and generate related designs. This allows the generation unit to generate the latest designs based on the user's interests. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs information on the user's relevant art and design trends into the generation AI, which then analyzes that information and generates designs.

[0092] The selection unit can estimate the user's emotions and adjust the selection criteria for nail technicians and salons based on the estimated emotions. For example, if the user is relaxed, the selection unit will prioritize salons with a relaxing atmosphere. If the user is in a hurry, the selection unit may also prioritize nail technicians who can respond quickly. If the user is excited, the selection unit may also prioritize salons that are visually appealing. In this way, the selection unit can select the most suitable nail technician and salon 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs user emotion data into the AI, and the AI ​​analyzes that information to adjust the selection criteria for nail technicians and salons.

[0093] The selection unit can analyze the user's past booking history to select the optimal selection method. For example, the selection unit can apply similar selection criteria based on the nail technicians and salons the user has used in the past. For example, the selection unit can prioritize selecting the nail technicians and salons that the user was most satisfied with based on their past booking history. For example, the selection unit can analyze the user's past booking history to derive the optimal selection criteria. This allows the selection unit to select the best nail technician or salon based on the user's past history. Some or all of the above processes in the selection unit may be performed using AI or not. For example, the selection unit can input the user's past booking history into AI, which then analyzes that information to select the optimal selection method.

[0094] The selection unit can filter the results based on the user's current lifestyle and event information during the selection process. For example, if a user is preparing for a wedding, the selection unit will prioritize selecting nail technicians and salons suitable for weddings. If a user is busy, the selection unit can also prioritize selecting nail technicians and salons that can respond quickly. If a user wants to relax, the selection unit can also prioritize selecting salons with a relaxing atmosphere. In this way, the selection unit can select nail technicians and salons that match the user's current situation. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the user's current lifestyle and event information into the AI, which then analyzes and filters that information.

[0095] The selection unit can estimate the user's emotions and determine the priority of nail technicians and salons based on the estimated emotions. For example, if the user is relaxed, the selection unit will prioritize salons with a relaxing atmosphere. If the user is in a hurry, the selection unit may also prioritize nail technicians who can respond quickly. If the user is excited, the selection unit may also prioritize salons that are visually appealing. In this way, the selection unit can prioritize selecting the most suitable nail technician and salon 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs user emotion data into the AI, and the AI ​​analyzes that information to determine the priority of nail technicians and salons.

[0096] The selection unit can prioritize selecting highly relevant nail technicians and salons by considering the user's geographical location information during the selection process. For example, the selection unit can prioritize selecting nail technicians and salons in the area where the user is currently located. The selection unit can also prioritize selecting nearby nail technicians and salons based on the user's geographical location. The selection unit can also prioritize selecting nail technicians and salons that offer region-specific services based on the user's geographical location. In this way, the selection unit can select the most suitable nail technician or salon based on the user's geographical location. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the user's geographical location information into the AI, which analyzes that information and prioritizes selecting highly relevant nail technicians and salons.

[0097] The selection unit can analyze the user's social media activity during the selection process and select relevant nail technicians and salons. For example, the selection unit can select relevant nail technicians and salons based on the nail technicians and salons that the user has "liked" on social media. The selection unit can also select relevant nail technicians and salons based on the nail technicians and salons that the user follows on social media. The selection unit can also select relevant nail technicians and salons based on the nail technicians and salons that the user has shared on social media. In this way, the selection unit can select the most suitable nail technicians and salons based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input data on the user's social media activity into AI, and the AI ​​can analyze that information to select relevant nail technicians and salons.

[0098] The reservation system can estimate the user's emotions and adjust the timing of reservations based on those emotions. For example, if the user is relaxed, the reservation system can make a reservation immediately to avoid stressing the user. If the user is busy, the reservation system can postpone the reservation until the user is calmer. If the user is excited, the reservation system can make a reservation quickly to complete it before the user's excitement subsides. This allows the reservation system to make reservations at the optimal time 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system inputs user emotion data into the AI, which analyzes that information to adjust the timing of reservations.

[0099] The reservation department can analyze a user's past reservation history to select the optimal reservation method at the time of booking. For example, the reservation department can suggest a similar reservation method based on the user's past reservation methods (online, telephone, etc.). For example, the reservation department can also prioritize suggesting the reservation method that the user was most satisfied with based on their past reservation history. For example, the reservation department can analyze a user's past reservation history to derive the optimal reservation method. This allows the reservation department to select the optimal reservation method based on the user's past history. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's past reservation history into the AI, which then analyzes that information to select the optimal reservation method.

[0100] The reservation system can filter reservations based on the user's current schedule and event information. For example, it can refer to the user's calendar information and make reservations for available time slots. The reservation system can also make reservations at the optimal time based on the user's event information (such as a wedding or coming-of-age ceremony). The reservation system can also analyze the user's schedule and make reservations for the most convenient time slot. This allows the reservation system to make optimal reservations tailored to the user's current situation. Some or all of the above processes in the reservation system may be performed using AI or not. For example, the reservation system can input the user's current schedule and event information into the AI, which then analyzes and filters that information.

[0101] The booking system can estimate the user's emotions and prioritize bookings based on those emotions. For example, if the user is relaxed, the booking system can provide detailed booking options for the user to choose from. If the user is in a hurry, the booking system can also provide options to complete the booking quickly. If the user is excited, the booking system can also provide visually appealing booking options. This allows the booking system to prioritize the most suitable bookings 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 processing in the booking system may be performed using AI or not. For example, the booking system inputs user emotion data into an AI, which analyzes that information to determine booking priorities.

[0102] The reservation system can prioritize highly relevant reservations by considering the user's geographical location information during the reservation process. For example, the reservation system can prioritize reservations with nail technicians or salons in the user's current location. The reservation system can also prioritize reservations with nearby nail technicians or salons based on the user's geographical location. The reservation system can also prioritize reservations with nail technicians or salons that offer region-specific services based on the user's geographical location. This allows the reservation system to make optimal reservations based on the user's geographical location. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input the user's geographical location information into an AI, which then analyzes that information and prioritizes highly relevant reservations.

[0103] The reservation department can analyze a user's social media activity when they make a reservation and make a relevant reservation. For example, the reservation department can make a relevant reservation based on nail artists and salons that the user has "liked" on social media. The reservation department can also make a relevant reservation based on nail artists and salons that the user follows on social media. The reservation department can also make a relevant reservation based on nail artists and salons that the user has shared on social media. This allows the reservation department to make the most optimal reservation based on the user's social media activity. Some or all of the above processing in the reservation department may be performed using AI or not. For example, the reservation department can input data on the user's social media activity into an AI, which will analyze that information and make a relevant reservation.

[0104] The fitting room unit can estimate the user's emotions and adjust how the fitting room is displayed based on the estimated emotions. For example, if the user is relaxed, the fitting room unit can provide detailed fitting options for the user to choose from. If the user is in a hurry, the fitting room unit can also provide options to quickly complete the fitting. If the user is excited, the fitting room unit can also provide visually appealing fitting options. This allows the fitting room unit to provide the optimal fitting method 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit inputs user emotion data into the AI, and the AI ​​analyzes that information to adjust how the fitting room is displayed.

[0105] The fitting room unit can analyze the user's past fitting history to select the optimal fitting method during the fitting process. For example, the fitting room unit can prioritize trying on similar designs based on designs the user has tried on in the past. For example, the fitting room unit can also prioritize trying on designs that the user was most satisfied with based on their past fitting history. For example, the fitting room unit can analyze the user's past fitting history to derive the optimal fitting method. This allows the fitting room unit to select the optimal fitting method based on the user's past history. Some or all of the above processes in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input the user's past fitting history into an AI, which then analyzes that information to select the optimal fitting method.

[0106] The fitting room unit can filter the user's current fashion and lifestyle during the fitting process. For example, the fitting room unit prioritizes trying on designs relevant to the clothes the user is currently wearing. The fitting room unit can also try on appropriate designs based on the user's lifestyle (e.g., whether they are outdoorsy or indoorsy). The fitting room unit can also try on relevant designs based on the user's current fashion style (e.g., casual or formal). This allows the fitting room unit to perform the optimal fitting tailored to the user's current situation. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit inputs information about the user's current fashion and lifestyle into the AI, which then analyzes and filters that information.

[0107] The fitting room unit can estimate the user's emotions and determine fitting priority based on the estimated emotions. For example, if the user is relaxed, the fitting room unit can provide detailed fitting options for the user to choose from. If the user is in a hurry, the fitting room unit can also provide options to complete the fitting quickly. If the user is excited, the fitting room unit can also provide visually appealing fitting options. This allows the fitting room unit to prioritize the most suitable fitting 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 processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit inputs user emotion data into the AI, which analyzes that information to determine fitting priority.

[0108] The fitting room function can prioritize fittings that are highly relevant to the user, taking into account the user's geographical location. For example, the fitting room function can prioritize fittings of relevant designs based on trend information in the user's current location. For example, the fitting room function can also prioritize fittings of designs from nearby nail artists or salons based on the user's geographical location. For example, the fitting room function can also prioritize fittings of regionally specific designs or styles based on the user's geographical location. This allows the fitting room function to perform optimal fittings based on the user's geographical location. Some or all of the above processing in the fitting room function may be performed using AI or not. For example, the fitting room function can input the user's geographical location information into AI, which then analyzes that information and prioritizes fittings that are highly relevant.

[0109] The fitting room unit can analyze the user's social media activity during the fitting process and perform relevant fittings. For example, the fitting room unit can perform relevant fittings based on designs the user has "liked" on social media. It can also perform relevant fittings based on designs the user follows on social media. It can also perform relevant fittings based on designs the user has shared on social media. This allows the fitting room unit to perform optimal fittings based on the user's social media activity. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input data on the user's social media activity into the AI, which then analyzes that information and performs relevant fittings.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The nail art design suggestion system can estimate the user's emotions and adjust the design's color scheme based on those emotions. For example, if the user is relaxed, the generation unit can generate a design with soft colors. If the user is excited, the generation unit can generate a design with vibrant colors. Furthermore, if the user is depressed, the generation unit can generate a design with bright colors. This allows the generation unit to generate the optimal design according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generative AI. For example, the generation unit inputs the user's emotion data into the generative AI, which analyzes that information and adjusts the design's color scheme.

[0112] The nail art design suggestion system can prioritize designs based on the user's past preferences. For example, the generation unit can prioritize generating similar designs based on designs the user has liked in the past. It can also exclude designs the user has avoided in the past. Furthermore, it can analyze the user's past preferences and prioritize generating designs that are most likely to be preferred. In this way, the generation unit can generate the optimal design based on the user's past preferences. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past preference data into the generation AI, which analyzes that information to determine the design priority.

[0113] The nail art design suggestion system can estimate the user's emotions and adjust the length and complexity of the design based on those emotions. For example, if the user is relaxed, the generation unit can generate a long and complex design. If the user is in a hurry, the generation unit can generate a short and simple design. Furthermore, if the user is excited, the generation unit can generate a visually stimulating design. In this way, the generation unit can generate the optimal design according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generative AI. For example, the generation unit inputs the user's emotion data into the generative AI, which analyzes that information and adjusts the length and complexity of the design.

[0114] The nail art design suggestion system can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the collection unit can prioritize the collection of trend information for the area where the user is currently located. It can also prioritize the collection of information on nearby nail technicians and salons based on the user's geographical location. Furthermore, it can prioritize the collection of region-specific designs and styles based on the user's geographical location. In this way, the collection unit can collect the most relevant information based on the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the user's geographical location information into the AI, which analyzes that information and prioritizes the collection of highly relevant information.

[0115] The nail art design suggestion system can analyze a user's social media activity and collect relevant information. For example, the collection unit can collect relevant information based on designs that the user has "liked" on social media. It can also analyze posts from accounts that the user follows on social media and collect relevant information. Furthermore, it can collect relevant information based on designs that the user has shared on social media. This allows the collection unit to collect the most relevant information based on the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs data on the user's social media activity into the AI, which then analyzes that information to collect relevant information.

[0116] The nail art design suggestion system can estimate the user's emotions and adjust the selection criteria for nail technicians and salons based on those estimated emotions. For example, if the user is relaxed, the selection unit can prioritize salons with a relaxing atmosphere. If the user is in a hurry, it can prioritize nail technicians who can respond quickly. Furthermore, if the user is excited, it can prioritize salons that are visually appealing. In this way, the selection unit can select the most suitable nail technician and salon according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the user's emotion data into the AI, and the AI ​​analyzes that information to adjust the selection criteria for nail technicians and salons.

[0117] The nail art design suggestion system can analyze a user's past booking history to select the optimal method. For example, the selection unit can apply similar selection criteria based on the nail technicians and salons the user has used in the past. It can also prioritize selecting the nail technicians and salons that the user was most satisfied with based on their past booking history. Furthermore, it can analyze the user's past booking history to derive the optimal selection criteria. As a result, the selection unit can select the most suitable nail technician and salon based on the user's past history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs the user's past booking history into the AI, which then analyzes that information to select the optimal method.

[0118] The nail art design suggestion system can estimate the user's emotions and adjust the timing of appointments based on those emotions. For example, if the user is relaxed, the appointment system can make an appointment immediately to avoid stressing the user. If the user is busy, the appointment can be postponed until the user is calmer. Furthermore, if the user is excited, the appointment can be made quickly to complete it before the user's excitement subsides. This allows the appointment system to make appointments at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the appointment system may be performed using AI or not. For example, the appointment system inputs user emotion data into the AI, which analyzes that information and adjusts the timing of appointments.

[0119] The nail art design suggestion system can filter based on the user's current schedule and event information. For example, the booking system can refer to the user's calendar information and make reservations during available time slots. It can also make reservations at the optimal time based on the user's event information (wedding, coming-of-age ceremony, etc.). Furthermore, it can analyze the user's schedule and make reservations at the most convenient time slot. This allows the booking system to make optimal reservations tailored to the user's current situation. Some or all of the above processing in the booking system may be performed using AI or not. For example, the booking system inputs the user's current schedule and event information into the AI, which then analyzes and filters that information.

[0120] The nail art design suggestion system can estimate the user's emotions and adjust the display method of the try-on based on the estimated emotions. For example, if the user is relaxed, the try-on section can provide detailed try-on options for the user to choose from. If the user is in a hurry, it can also provide options to complete the try-on quickly. Furthermore, if the user is excited, it can provide visually appealing try-on options. In this way, the try-on section can provide the optimal try-on method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the try-on section may be performed using AI or not. For example, the try-on section inputs the user's emotion data into the AI, and the AI ​​analyzes that information to adjust the display method of the try-on.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects user information. This information includes age, gender, preferences, and past nail art history. The data collection unit collects photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The data collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, information collection is performed immediately to avoid stressing the user. If the user is busy, information collection is postponed and performed when the user is calm. If the user is excited, information collection is performed quickly and design suggestions are made while the user's excitement is still high. Step 2: The generation unit analyzes the information collected by the collection unit and generates nail art designs. The generation unit uses a generation AI to generate nail art designs based on photos and illustrations uploaded by the user, nail art designs, and art designs from around the world. The generation AI suggests designs based on the user's personality and preferences. For example, it analyzes photos and illustrations uploaded by the user and generates nail art designs that match the atmosphere. It can also suggest the most suitable designs based on seasonal trends and the user's event information. For example, it can suggest designs tailored to special events such as weddings or coming-of-age ceremonies. Step 3: The selection unit selects nail technicians and salons that can realize the designs generated by the generation unit. The selection unit searches its database for nail technicians and salons that can realize the generated designs and provides the user with the best option. The selection unit can also analyze the user's past booking history to select the optimal selection method. For example, it can apply the same selection criteria based on nail technicians and salons the user has used in the past. It can also prioritize selecting nail technicians and salons that the user was most satisfied with based on their past booking history. Step 4: The booking department makes reservations for nail technicians and salons selected by the selection department. The booking department can estimate the user's emotions and adjust the timing of the reservation based on the estimated emotions. For example, if the user is relaxed, the reservation can be made immediately to avoid stressing the user. If the user is busy, the reservation can be postponed and made when the user is calmer. If the user is excited, the reservation can be made quickly and completed before the user's excitement subsides. Step 5: The fitting room allows users to virtually try on designs generated by the generation room. The fitting room provides the ability to virtually try on suggested designs using a smartphone or tablet. The fitting room can also estimate the user's emotions and adjust how the fitting is displayed based on those emotions. For example, if the user is relaxed, it may provide detailed fitting options for the user to choose from. If the user is in a hurry, it may provide options to quickly complete the fitting. If the user is excited, it may also provide visually appealing fitting options.

[0123] 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.

[0124] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, generation unit, selection unit, reservation unit, and fitting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates nail art designs by analyzing the collected information. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12 and searches the database 24 for nail artists and salons that can realize the generated designs. The reservation unit is implemented in the specific processing unit 290 of the data processing unit 12 and searches the database 24 for nail artists and salons that can realize the generated designs. The reservation unit is implemented in the specific processing unit 46A of the smart device 14 and makes reservations for the selected nail artists and salons. The fitting unit is implemented in the specific processing unit 46A of the smart device 14 and provides a function to virtually try on the proposed designs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, generation unit, selection unit, reservation unit, and fitting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates nail art designs by analyzing the collected information. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12 and searches the database 24 for nail artists and salons that can realize the generated designs. The reservation unit is implemented in the specific processing unit 290 of the data processing unit 12 and searches the database 24 for nail artists and salons that can realize the generated designs. The reservation unit is implemented in the specific processing unit 46A of the smart glasses 214 and makes reservations for the selected nail artists and salons. The fitting unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides a function to virtually try on the proposed designs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, generation unit, selection unit, reservation unit, and fitting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates nail art designs by analyzing the collected information. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12 and searches the database 24 for nail artists and salons that can realize the generated designs. The reservation unit is implemented in the specific processing unit 290 of the data processing unit 12 and searches the database 24 for nail artists and salons that can realize the generated designs. The reservation unit is implemented in the specific processing unit 46A of the headset terminal 314 and makes reservations for the selected nail artists and salons. The fitting unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides a function to virtually try on the proposed designs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, generation unit, selection unit, reservation unit, and fitting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates nail art designs by analyzing the collected information. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and searches the database 24 for nail artists and salons that can realize the generated designs. The reservation unit is implemented, for example, by the control unit 46A of the robot 414, and makes reservations for the selected nail artists and salons. The fitting unit is implemented, for example, by the control unit 46A of the robot 414, and provides a function to virtually try on the proposed designs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects user information, A generation unit analyzes the information collected by the aforementioned collection unit and generates a nail art design, A selection unit that selects nail technicians and salons capable of realizing the designs generated by the generation unit, The reservation department makes reservations for nail technicians and salons selected by the aforementioned selection department, The system includes a fitting unit that virtually tries on the designs generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is This service generates nail art designs based on user-uploaded photos and illustrations, nail art designs, and art designs from around the world. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is The system searches a database for nail technicians and salons that can realize the generated designs, providing users with the best possible options. The system described in Appendix 1, characterized by the features described herein. (Note 4) The fitting area is, The service provides a feature that allows users to virtually try on suggested designs using their smartphones or tablets. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We propose the optimal design based on seasonal trends and user event information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system analyzes the user's skin type and nail condition to suggest the optimal timing for using care products and for follow-up treatments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past upload history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current fashion and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, we analyze users' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the design's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During design generation, the level of detail in the design is adjusted based on the user's preferences and personality. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating designs, different generation algorithms are applied depending on the category of the user-uploaded image. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length and complexity of the design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating designs, design priorities are determined based on the user's past preferences. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During design generation, the system references the user's relevant art and design trends to generate the design. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is We estimate user emotions and adjust the selection criteria for nail technicians and salons based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is During the selection process, the system analyzes the user's past booking history to determine the optimal selection method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is During the selection process, filtering is performed based on the user's current lifestyle and event information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is It estimates user emotions and determines the priority of nail technicians and salons based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is During the selection process, we prioritize selecting highly relevant nail technicians and salons, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is During the selection process, we analyze the user's social media activity and select relevant nail technicians and salons. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation section is, It estimates the user's emotions and adjusts the timing of reservations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reservation section is, When making a reservation, filtering is performed based on the user's current schedule and event information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reservation section is, When making a reservation, the system prioritizes highly relevant reservations by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's social media activity and makes relevant reservations. The system described in Appendix 1, characterized by the features described herein. (Note 31) The fitting area is, The system estimates the user's emotions and adjusts how the try-on display is based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The fitting area is, During the fitting process, the system analyzes the user's past fitting history to select the optimal fitting method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The fitting area is, When trying on clothes, filtering is performed based on the user's current fashion and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The fitting area is, The system estimates the user's emotions and determines the priority of try-on based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The fitting area is, During the try-on process, the system prioritizes highly relevant try-ons by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The fitting area is, During the try-on process, the system analyzes the user's social media activity and suggests relevant try-ons. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 collection unit that collects user information, A generation unit analyzes the information collected by the aforementioned collection unit and generates a nail art design, A selection unit that selects nail technicians and salons capable of realizing the designs generated by the generation unit, The reservation department makes reservations for nail technicians and salons selected by the aforementioned selection department, The system includes a fitting unit that virtually tries on the designs generated by the generation unit. A system characterized by the following features.

2. The generating unit is This service generates nail art designs based on user-uploaded photos and illustrations, nail art designs, and art designs from around the world. The system according to feature 1.

3. The aforementioned selection unit is The system searches a database for nail technicians and salons that can realize the generated designs, providing users with the best possible options. The system according to feature 1.

4. The fitting area is, The service provides a feature that allows users to virtually try on suggested designs using their smartphones or tablets. The system according to feature 1.

5. The generating unit is We propose the optimal design based on seasonal trends and user event information. The system according to feature 1.

6. The generating unit is The system analyzes the user's skin type and nail condition to suggest the optimal timing for using care products and for follow-up treatments. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past upload history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current fashion and lifestyle. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

Images