system

Abstract

The system according to the embodiment aims to propose optimal nail designs based on the user's preferences and personal history. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a simulation unit, and a recommendation unit. The collection unit collects a user's image creation and personal history. The analysis unit analyzes the data collected by the collection unit and proposes nail designs. The simulation unit simulates the designs proposed by the analysis unit. The recommendation unit analyzes the user's preferences based on the designs simulated by the simulation unit and provides a recommendation function.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to propose optimal nail designs based on a user's preferences and personal history.

[0005] The system according to the embodiment aims to propose optimal nail designs based on the user's preferences and personal history. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a simulation unit, and a recommendation unit. The collection unit collects the user's image generation and personal history. The analysis unit analyzes the data collected by the collection unit and proposes nail designs. The simulation unit simulates the designs proposed by the analysis unit. The recommendation unit analyzes the user's preferences based on the designs simulated by the simulation unit and provides a recommendation function. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal nail designs based on the user's preferences and personal history. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A nail art system according to an embodiment of the present invention proposes optimal nail designs based on a user's image creation and personal history, and provides simulation and recommendation functions. This nail art system collects the user's image creation and personal history, analyzes the collected data, and proposes optimal nail designs. The user can simulate the proposed designs and try out nail designs that suit their hands. The system also analyzes the user's preferences based on their personal search history and chat history, and provides more sophisticated designs through a recommendation function. For example, the nail art system collects images of nail designs previously searched by the user and chat history. This allows the system to understand the user's preferences and trends. The system then analyzes the collected data to determine the user's hand shape and preferences. For example, the system uses an image analysis algorithm to analyze the user's hand shape and propose optimal nail designs. The system also uses a machine learning algorithm to analyze the user's preferences and provide more sophisticated designs through a recommendation function. The system then simulates the proposed designs. The user can simulate the proposed designs on their own hand to see how they would actually look. This allows the AI ​​to automatically propose optimal designs without the user having to specify the desired nail design or style. In addition, the system can analyze users' preferences based on their personal search and chat history and provide more sophisticated designs through its recommendation function. This allows users to easily enjoy unique and stylish nail art. This allows the nail art system to propose optimal nail designs based on the user's image generation and personal history, and provide simulation and recommendation functions.

[0029] A nail art system according to an embodiment includes a collection unit, an analysis unit, a simulation unit, and a recommendation unit. The collection unit collects a user's image generation and personal history. The user's image generation includes, but is not limited to, images of nail designs previously searched by the user and chat histories. The collection unit, for example, collects images of nail designs previously searched by the user. The collection unit can also collect chat histories. For example, the content of conversations between users in chats is collected to understand the user's preferences. The analysis unit analyzes the data collected by the collection unit and proposes an optimal nail design. The analysis unit, for example, analyzes the shape of the user's hand using an image analysis algorithm. The analysis unit can also analyze the user's preferences using a machine learning algorithm. For example, the image analysis algorithm analyzes the shape of the user's hand using techniques such as deep learning and edge detection. The machine learning algorithm analyzes the user's preferences using techniques such as support vector machines and neural networks. The simulation unit simulates the design proposed by the analysis unit. For example, the simulation unit simulates the proposed nail design on the user's hand. The simulation unit can also adjust the accuracy of the simulation. For example, the simulation unit optimizes the simulation algorithm based on the shape and size of the user's hand. The recommendation unit analyzes the user's preferences based on the design simulated by the simulation unit and provides a recommendation function. The recommendation unit can analyze the user's preferences, for example, from the user's search history or chat history. The recommendation unit can also estimate the user's emotions and adjust the recommendation method based on the estimated user's emotions. For example, the recommendation unit provides detailed recommendations when the user is relaxed. As a result, the nail art system according to the embodiment can propose optimal nail designs based on the user's image generation and personal history, and provide simulation and recommendation functions.

[0030] The collection unit can collect images of nail designs previously searched by the user or chat history. For example, the collection unit collects images of nail designs previously searched by the user. For example, the collection unit collects images based on the image format and resolution. The collection unit can also collect chat history. For example, the collection unit collects chat history based on the collection period and the target chat app. This allows for more accurate nail design suggestions by collecting data based on the user's past search history and chat history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past search history into a generation AI and cause the generation AI to collect data.

[0031] The analysis unit can analyze the shape of the user's hand using an image analysis algorithm and suggest a nail design. The analysis unit, for example, analyzes the shape of the user's hand using an image analysis algorithm. For example, the analysis unit can analyze the shape of the user's hand using deep learning. The analysis unit can also analyze the shape of the user's hand using edge detection. For example, the analysis unit can analyze the shape of the user's hand in detail using deep learning and suggest an optimal nail design. The analysis unit can also extract the outline of the user's hand using edge detection and suggest a nail design. In this way, by using an image analysis algorithm, an optimal nail design based on the shape of the user's hand can be suggested. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data of the user's hand to a generation AI and cause the generation AI to analyze the shape of the hand.

[0032] The analysis unit can analyze user preferences using a machine learning algorithm and provide a refined design using a recommendation function. The analysis unit can analyze user preferences using, for example, a machine learning algorithm. For example, the analysis unit can analyze user preferences using a support vector machine. The analysis unit can also analyze user preferences using a neural network. For example, the analysis unit can analyze a user's past search history and chat history using a support vector machine to identify the user's preferences. The analysis unit can also analyze user preferences in detail using a neural network and provide a refined design using a recommendation function. In this way, by using a machine learning algorithm, a more refined design based on the user's preferences can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input user preference data into a generation AI and have the generation AI perform a preference analysis.

[0033] The simulation unit can simulate the proposed nail design on the user's hand. For example, the simulation unit simulates the proposed nail design on the user's hand. For example, the simulation unit performs the simulation by overlaying the proposed nail design on an image of the user's hand. The simulation unit can also adjust the accuracy of the simulation. For example, the simulation unit optimizes the simulation algorithm based on the shape and size of the user's hand. This allows the user to confirm how the proposed nail design will actually look on the user's hand by simulating it. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without AI. For example, the simulation unit can input image data of the user's hand and the proposed nail design into a generation AI and have the generation AI perform the simulation.

[0034] The recommendation unit can analyze a user's preferences from the user's search history and chat history and provide sophisticated designs using the recommendation function. The recommendation unit can analyze a user's preferences from, for example, the user's search history and chat history. For example, the recommendation unit can analyze images of nail designs that the user has previously searched for and their chat history to identify the user's preferences. The recommendation unit can also estimate the user's emotions and adjust the recommendation method based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. This can analyze the user's preferences based on the user's search history and chat history and provide more sophisticated designs. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input the user's search history and chat history into a generation AI and have the generation AI perform a preference analysis.

[0035] The collection unit can analyze the user's past nail design history and select a collection method. The collection unit, for example, analyzes the user's past nail design history and selects the optimal collection method. For example, the collection unit prioritizes collection of designs that the user has frequently searched for in the past. The collection unit can also focus on collecting designs that the user has previously given high ratings. Furthermore, the collection unit can also collect designs based on designs that the user has previously shared. This allows for the analysis of the user's past nail design history to select the optimal collection method and enable efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past nail design history into a generation AI and have the generation AI select a collection method.

[0036] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can collect designs that match the user's current season. The collection unit can also collect designs related to events in which the user is interested. Furthermore, the collection unit can collect designs that match the user's current lifestyle. This allows for more relevant data to be collected by filtering data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest into a generation AI, and have the generation AI perform filtering.

[0037] The collection unit can select a collection means according to the user's input method when collecting data. For example, the collection unit selects a collection means according to the user's input method when collecting data. For example, when the user uses voice input, the collection unit collects data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect data using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect data using image analysis technology. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's input data to a generation AI, causing the generation AI to select a collection means.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit collects trending designs in the area where the user is currently located. The collection unit can also collect designs of places the user frequently visits. Furthermore, the collection unit can prioritize collecting related designs based on the user's geographical location. This makes it possible to prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to a generation AI and cause the generation AI to collect data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect designs shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related designs. Furthermore, the collection unit can collect related designs by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related data can be efficiently collected. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's social media activities into a generation AI and cause the generation AI to collect data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit prioritizes collecting designs that the user has previously rated highly. The collection unit can also exclude designs that the user has previously rated poorly when collecting data. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback data into a generation AI and have the generation AI customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the nail design during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the nail design during analysis. For example, the analysis unit performs a detailed analysis of important designs. The analysis unit can also perform a concise analysis of common designs. Furthermore, the analysis unit can perform a detailed analysis of designs in which the user is particularly interested. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the nail design. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the importance of the nail design to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the nail design category during analysis. For example, the analysis unit applies different analysis algorithms depending on the nail design category during analysis. For example, the analysis unit applies a specific analysis algorithm for French nails. The analysis unit can also apply a different analysis algorithm for gradient nails. Furthermore, the analysis unit can apply yet another analysis algorithm for art nails. By applying different analysis algorithms depending on the nail design category, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the nail design category into a generation AI and have the generation AI apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into a generation AI, and have the generation AI improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the submission date of the nail design during analysis. For example, the analysis unit can prioritize the analysis based on the submission date of the nail design during analysis. For example, the analysis unit prioritizes the analysis of the most recent nail designs. The analysis unit can also determine the analysis priority based on a deadline specified by the user. Furthermore, the analysis unit can also determine the analysis priority by referring to the user's past submission dates. This enables efficient analysis by determining the analysis priority based on the submission date of the nail design. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the submission date of the nail design into the generation AI, causing the generation AI to determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of nail designs during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of nail designs during analysis. For example, the analysis unit prioritizes analysis of highly relevant designs. The analysis unit can also prioritize analysis of designs in which the user is interested. Furthermore, the analysis unit can adjust the order of analysis based on the user's past selection history. This enables efficient analysis by adjusting the order of analysis based on the relevance of nail designs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of nail designs to a generation AI and have the generation AI adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can use simple terms. If the user is an intermediate user, the analysis unit can also use appropriate technical terms. If the user is an advanced user, the analysis unit can also use detailed technical terms. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI, causing the generation AI to adjust the use of technical terms.

[0047] The simulation unit can adjust the accuracy of the simulation based on the level of detail of the nail design during the simulation. For example, the simulation unit can adjust the accuracy of the simulation based on the level of detail of the nail design during the simulation. For example, the simulation unit can perform a detailed simulation for important designs. The simulation unit can also perform a concise simulation for general designs. Furthermore, the simulation unit can perform a detailed simulation for designs in which the user is particularly interested. This allows for efficient simulation by adjusting the accuracy of the simulation based on the level of detail of the nail design. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input data on the level of detail of the nail design to a generation AI, causing the generation AI to adjust the accuracy of the simulation.

[0048] The simulation unit can optimize the simulation algorithm according to the shape and size of the user's hand during the simulation. For example, the simulation unit optimizes the simulation algorithm according to the shape and size of the user's hand during the simulation. For example, the simulation unit optimizes the simulation algorithm based on the shape of the user's hand. The simulation unit can also optimize the simulation algorithm based on the size of the user's hand. Furthermore, the simulation unit can optimize the simulation algorithm based on the characteristics of the user's hand. This enables more accurate simulation by optimizing the simulation algorithm according to the shape and size of the user's hand. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the shape and size of the user's hand into a generation AI, and cause the generation AI to optimize the simulation algorithm.

[0049] The simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results during the simulation. For example, the simulation unit can optimize a simulation algorithm based on the user's past simulation results. The simulation unit can also improve the accuracy of the simulation by referring to the user's past simulation results. Furthermore, the simulation unit can analyze the user's past simulation results and identify areas for improvement in the simulation. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the simulation unit can input data of the user's past simulation results into a generation AI, and have the generation AI improve the accuracy of the simulation.

[0050] The simulation unit can determine the priority of simulations based on the submission date of the nail designs during the simulation. For example, the simulation unit determines the priority of simulations based on the submission date of the nail designs during the simulation. For example, the simulation unit prioritizes the most recent designs. The simulation unit can also determine the priority of simulations based on a deadline specified by the user. Furthermore, the simulation unit can determine the priority of simulations by referring to the user's past submission dates. This enables efficient simulations by determining the priority of simulations based on the submission date of the nail designs. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the submission date of the nail designs into the generation AI, causing the generation AI to determine the priority of simulations.

[0051] The simulation unit can adjust the order of simulations based on the relevance of nail designs during simulation. For example, the simulation unit can adjust the order of simulations based on the relevance of nail designs during simulation. For example, the simulation unit prioritizes simulation of highly relevant designs. The simulation unit can also prioritize simulation of designs in which the user is interested. Furthermore, the simulation unit can adjust the order of simulations based on the user's past selection history. This allows for efficient simulation by adjusting the order of simulations based on the relevance of nail designs. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input data on the relevance of nail designs to a generation AI and have the generation AI adjust the order of simulations.

[0052] The simulation unit can adjust the level of detail of the simulation during the simulation according to the user's level of expertise. For example, the simulation unit can adjust the level of detail of the simulation during the simulation according to the user's level of expertise. For example, the simulation unit can provide a simple simulation if the user is a beginner. The simulation unit can also provide a simulation with an appropriate level of detail if the user is an intermediate user. Furthermore, the simulation unit can also provide a detailed simulation if the user is an advanced user. In this way, by adjusting the level of detail of the simulation according to the user's level of expertise, it is possible to provide an optimal simulation result for the user. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the simulation unit can input data on the user's level of expertise to a generation AI and cause the generation AI to adjust the level of detail of the simulation.

[0053] The recommendation unit can select the optimal recommendation method by analyzing the user's past nail design history when making a recommendation. For example, the recommendation unit can select the optimal recommendation method by analyzing the user's past nail design history when making a recommendation. For example, the recommendation unit can prioritize recommending designs that the user has previously rated highly. The recommendation unit can also prioritize recommending designs that the user has frequently searched for in the past. Furthermore, the recommendation unit can select the optimal recommendation method based on the user's past selection history. This allows the optimal recommendation method to be selected by analyzing the user's past nail design history, enabling efficient recommendations. Some or all of the above-described processing in the recommendation unit can be performed, for example, using AI or without AI. For example, the recommendation unit can input data on the user's past nail design history into a generation AI, causing the generation AI to select a recommendation method.

[0054] The recommendation unit can customize the recommendation method based on the user's current living situation and areas of interest when making a recommendation. For example, the recommendation unit customizes the recommendation method based on the user's current living situation and areas of interest when making a recommendation. For example, the recommendation unit recommends designs that match the user's current season. The recommendation unit can also recommend designs related to events in which the user is interested. Furthermore, the recommendation unit can recommend designs that match the user's current lifestyle. This enables more relevant recommendations by customizing the recommendation method based on the user's current living situation and areas of interest. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's living situation and areas of interest into a generation AI, allowing the generation AI to customize the recommendation method.

[0055] The recommendation unit can improve the recommendation method by reflecting user feedback when making a recommendation. The recommendation unit, for example, improves the recommendation method by reflecting user feedback when making a recommendation. For example, the recommendation unit prioritizes recommending designs that the user has previously rated highly. The recommendation unit can also recommend designs that the user has previously rated poorly. Furthermore, the recommendation unit can optimize the recommendation method based on the user's past feedback. This improves the recommendation method by reflecting user feedback, enabling more accurate recommendations. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input user feedback data into a generation AI, which can then improve the recommendation method.

[0056] The recommendation unit can select the optimal recommendation method by taking into account the user's geographical location information when making a recommendation. For example, the recommendation unit selects the optimal recommendation method by taking into account the user's geographical location information when making a recommendation. For example, the recommendation unit recommends trendy designs in the area where the user is currently located. The recommendation unit can also recommend designs for places the user frequently visits. Furthermore, the recommendation unit can preferentially recommend related designs based on the user's geographical location. This makes it possible to make highly relevant recommendations by taking into account the user's geographical location information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's geographical location information into a generation AI, causing the generation AI to select a recommendation method.

[0057] The recommendation unit can analyze the user's social media activity and suggest a means of recommendation when making a recommendation. For example, the recommendation unit can analyze the user's social media activity and suggest a means of recommendation when making a recommendation. For example, the recommendation unit recommends designs shared by the user on social media. The recommendation unit can also analyze the content of the user's social media posts and recommend related designs. Furthermore, the recommendation unit can recommend related designs by referring to the activity of the user's friends on social media. This makes it possible to make highly relevant recommendations by analyzing the user's social media activity. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's social media activity into a generation AI, and have the generation AI execute the suggestion of a means of recommendation.

[0058] The recommendation unit can customize the recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit, for example, customizes the recommendation method by reflecting the user's past feedback when making a recommendation. For example, the recommendation unit prioritizes recommending designs that the user has previously rated highly. The recommendation unit can also recommend designs that the user has previously rated poorly. Furthermore, the recommendation unit can optimize the recommendation method based on the user's past feedback. This allows the recommendation method to be customized by reflecting the user's past feedback, enabling more accurate recommendations. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data of the user's past feedback into a generation AI, and have the generation AI customize the recommendation method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The nail art system may further include a fashion analysis unit that analyzes the user's fashion style. The fashion analysis unit analyzes the user's past fashion history and current fashion trends and suggests nail designs based on this. For example, if the user prefers a casual style, a simple and easy-to-use design may be suggested. Alternatively, if the user plans to attend a formal event, an elegant design may be suggested. This makes it possible to provide nail designs that match the user's fashion style.

[0061] The nail art system may further include a music analysis unit that analyzes the user's musical preferences. The music analysis unit analyzes the genres and artists of music that the user often listens to and suggests nail designs based on this. For example, if the user likes rock music, a dark and edgy design may be suggested. Alternatively, if the user likes classical music, an elegant and sophisticated design may be suggested. This allows the system to provide nail designs that match the user's musical preferences.

[0062] The nail art system can further include a travel analysis unit that analyzes the user's travel history. The travel analysis unit analyzes the places the user has visited in the past and their travel habits, and suggests nail designs based on this. For example, if the user likes beach resorts, it can suggest designs that evoke the ocean and sand. If the user likes urban sightseeing, it can suggest modern and stylish designs. This makes it possible to provide nail designs that match the user's travel history.

[0063] The nail art system can further include a food analysis unit that analyzes the user's food preferences. The food analysis unit analyzes the user's favorite dishes and ingredients and suggests nail designs based on this. For example, if the user likes Japanese food, a Japanese-style design can be suggested. Also, if the user likes Italian food, a traditional Italian design can be suggested. This makes it possible to provide nail designs that match the user's food preferences.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects the user's image creation and personal history. Specifically, it collects images of nail designs the user has searched for in the past, chat history, etc. This allows the system to understand the user's preferences and past behavior. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal nail design. The analysis unit uses image analysis algorithms and machine learning algorithms to analyze the user's hand shape and preferences. For example, it uses deep learning and edge detection technology to analyze the hand shape, and support vector machines and neural networks to analyze the user's preferences. Step 3: The simulation unit simulates the design proposed by the analysis unit. Specifically, the proposed nail design is simulated on the user's hand and the accuracy of the simulation is adjusted. For example, the simulation algorithm is optimized based on the shape and size of the user's hand. Step 4: The recommendation unit analyzes the user's preferences based on the design simulated by the simulation unit and provides a recommendation function. Specifically, it analyzes the user's preferences from the user's search history and chat history, estimates the user's emotions, and adjusts the recommendation method. For example, if the user is relaxed, it provides detailed recommendations.

[0066] (Example 2) A nail art system according to an embodiment of the present invention proposes optimal nail designs based on a user's image creation and personal history, and provides simulation and recommendation functions. This nail art system collects the user's image creation and personal history, analyzes the collected data, and proposes optimal nail designs. The user can simulate the proposed designs and try out nail designs that suit their hands. The system also analyzes the user's preferences based on their personal search history and chat history, and provides more sophisticated designs through a recommendation function. For example, the nail art system collects images of nail designs previously searched by the user and chat history. This allows the system to understand the user's preferences and trends. The system then analyzes the collected data to determine the user's hand shape and preferences. For example, the system uses an image analysis algorithm to analyze the user's hand shape and propose optimal nail designs. The system also uses a machine learning algorithm to analyze the user's preferences and provide more sophisticated designs through a recommendation function. The system then simulates the proposed designs. The user can simulate the proposed designs on their own hand to see how they would actually look. This allows the AI ​​to automatically propose optimal designs without the user having to specify the desired nail design or style. In addition, the system can analyze users' preferences based on their personal search and chat history and provide more sophisticated designs through its recommendation function. This allows users to easily enjoy unique and stylish nail art. This allows the nail art system to propose optimal nail designs based on the user's image generation and personal history, and provide simulation and recommendation functions.

[0067] A nail art system according to an embodiment includes a collection unit, an analysis unit, a simulation unit, and a recommendation unit. The collection unit collects a user's image generation and personal history. The user's image generation includes, but is not limited to, images of nail designs previously searched by the user and chat histories. The collection unit, for example, collects images of nail designs previously searched by the user. The collection unit can also collect chat histories. For example, the content of conversations between users in chats is collected to understand the user's preferences. The analysis unit analyzes the data collected by the collection unit and proposes an optimal nail design. The analysis unit, for example, analyzes the shape of the user's hand using an image analysis algorithm. The analysis unit can also analyze the user's preferences using a machine learning algorithm. For example, the image analysis algorithm analyzes the shape of the user's hand using techniques such as deep learning and edge detection. The machine learning algorithm analyzes the user's preferences using techniques such as support vector machines and neural networks. The simulation unit simulates the design proposed by the analysis unit. For example, the simulation unit simulates the proposed nail design on the user's hand. The simulation unit can also adjust the accuracy of the simulation. For example, the simulation unit optimizes the simulation algorithm based on the shape and size of the user's hand. The recommendation unit analyzes the user's preferences based on the design simulated by the simulation unit and provides a recommendation function. The recommendation unit can analyze the user's preferences, for example, from the user's search history or chat history. The recommendation unit can also estimate the user's emotions and adjust the recommendation method based on the estimated user's emotions. For example, the recommendation unit provides detailed recommendations when the user is relaxed. As a result, the nail art system according to the embodiment can propose optimal nail designs based on the user's image generation and personal history, and provide simulation and recommendation functions.

[0068] The collection unit can collect images of nail designs previously searched by the user or chat history. For example, the collection unit collects images of nail designs previously searched by the user. For example, the collection unit collects images based on the image format and resolution. The collection unit can also collect chat history. For example, the collection unit collects chat history based on the collection period and the target chat app. This allows for more accurate nail design suggestions by collecting data based on the user's past search history and chat history. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past search history into a generation AI and cause the generation AI to collect data.

[0069] The analysis unit can analyze the shape of the user's hand using an image analysis algorithm and suggest a nail design. The analysis unit, for example, analyzes the shape of the user's hand using an image analysis algorithm. For example, the analysis unit can analyze the shape of the user's hand using deep learning. The analysis unit can also analyze the shape of the user's hand using edge detection. For example, the analysis unit can analyze the shape of the user's hand in detail using deep learning and suggest an optimal nail design. The analysis unit can also extract the outline of the user's hand using edge detection and suggest a nail design. In this way, by using an image analysis algorithm, an optimal nail design based on the shape of the user's hand can be suggested. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data of the user's hand to a generation AI and cause the generation AI to analyze the shape of the hand.

[0070] The analysis unit can analyze user preferences using a machine learning algorithm and provide a refined design using a recommendation function. The analysis unit can analyze user preferences using, for example, a machine learning algorithm. For example, the analysis unit can analyze user preferences using a support vector machine. The analysis unit can also analyze user preferences using a neural network. For example, the analysis unit can analyze a user's past search history and chat history using a support vector machine to identify the user's preferences. The analysis unit can also analyze user preferences in detail using a neural network and provide a refined design using a recommendation function. In this way, by using a machine learning algorithm, a more refined design based on the user's preferences can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input user preference data into a generation AI and have the generation AI perform a preference analysis.

[0071] The simulation unit can simulate the proposed nail design on the user's hand. For example, the simulation unit simulates the proposed nail design on the user's hand. For example, the simulation unit performs the simulation by overlaying the proposed nail design on an image of the user's hand. The simulation unit can also adjust the accuracy of the simulation. For example, the simulation unit optimizes the simulation algorithm based on the shape and size of the user's hand. This allows the user to confirm how the proposed nail design will actually look on the user's hand by simulating it. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without AI. For example, the simulation unit can input image data of the user's hand and the proposed nail design into a generation AI and have the generation AI perform the simulation.

[0072] The recommendation unit can analyze a user's preferences from the user's search history and chat history and provide sophisticated designs using the recommendation function. The recommendation unit can analyze a user's preferences from, for example, the user's search history and chat history. For example, the recommendation unit can analyze images of nail designs that the user has previously searched for and their chat history to identify the user's preferences. The recommendation unit can also estimate the user's emotions and adjust the recommendation method based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. This can analyze the user's preferences based on the user's search history and chat history and provide more sophisticated designs. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input the user's search history and chat history into a generation AI and have the generation AI perform a preference analysis.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit immediately collects data to reduce the user's stress. If the user is busy, the collection unit can postpone data collection to reduce the user's burden. If the user is excited, the collection unit can quickly collect data to maintain the user's interest. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0074] The collection unit can analyze the user's past nail design history and select a collection method. The collection unit, for example, analyzes the user's past nail design history and selects the optimal collection method. For example, the collection unit prioritizes collection of designs that the user has frequently searched for in the past. The collection unit can also focus on collecting designs that the user has previously given high ratings. Furthermore, the collection unit can also collect designs based on designs that the user has previously shared. This allows for the analysis of the user's past nail design history to select the optimal collection method and enable efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past nail design history into a generation AI and have the generation AI select a collection method.

[0075] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can collect designs that match the user's current season. The collection unit can also collect designs related to events in which the user is interested. Furthermore, the collection unit can collect designs that match the user's current lifestyle. This allows for more relevant data to be collected by filtering data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's living situation and areas of interest into a generation AI, and have the generation AI perform filtering.

[0076] The collection unit can select a collection means according to the user's input method when collecting data. For example, the collection unit selects a collection means according to the user's input method when collecting data. For example, when the user uses voice input, the collection unit collects data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect data using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect data using image analysis technology. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the user's input data to a generation AI, causing the generation AI to select a collection means.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed data. Furthermore, when the user is busy, the collection unit can prioritize collecting important data. Furthermore, when the user is excited, the collection unit can prioritize collecting interesting data. This enables efficient data collection by determining the priority of data to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0078] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit collects trending designs in the area where the user is currently located. The collection unit can also collect designs of places the user frequently visits. Furthermore, the collection unit can prioritize collecting related designs based on the user's geographical location. This makes it possible to prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to a generation AI and cause the generation AI to collect data.

[0079] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect designs shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related designs. Furthermore, the collection unit can collect related designs by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related data can be efficiently collected. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's social media activities into a generation AI and cause the generation AI to collect data.

[0080] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit prioritizes collecting designs that the user has previously rated highly. The collection unit can also exclude designs that the user has previously rated poorly when collecting data. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. This allows the collection method to be customized by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback data into a generation AI and have the generation AI customize the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is busy. The analysis unit can also provide visually appealing analysis results when the user is excited. By adjusting the presentation method of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the nail design during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the nail design during analysis. For example, the analysis unit performs a detailed analysis of important designs. The analysis unit can also perform a concise analysis of common designs. Furthermore, the analysis unit can perform a detailed analysis of designs in which the user is particularly interested. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the nail design. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the importance of the nail design to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the nail design category during analysis. For example, the analysis unit applies different analysis algorithms depending on the nail design category during analysis. For example, the analysis unit applies a specific analysis algorithm for French nails. The analysis unit can also apply a different analysis algorithm for gradient nails. Furthermore, the analysis unit can apply yet another analysis algorithm for art nails. By applying different analysis algorithms depending on the nail design category, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the nail design category into a generation AI and have the generation AI apply the analysis algorithm.

[0084] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and identify areas for improvement in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into a generation AI, and have the generation AI improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is busy. The analysis unit can also perform a visually appealing analysis when the user is excited. By adjusting the length of the analysis based on the user's emotions, it is possible to provide an optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0086] The analysis unit can determine the analysis priority based on the submission date of the nail design during analysis. For example, the analysis unit can prioritize the analysis based on the submission date of the nail design during analysis. For example, the analysis unit prioritizes the analysis of the most recent nail designs. The analysis unit can also determine the analysis priority based on a deadline specified by the user. Furthermore, the analysis unit can also determine the analysis priority by referring to the user's past submission dates. This enables efficient analysis by determining the analysis priority based on the submission date of the nail design. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the submission date of the nail design into the generation AI, causing the generation AI to determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of nail designs during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of nail designs during analysis. For example, the analysis unit prioritizes analysis of highly relevant designs. The analysis unit can also prioritize analysis of designs in which the user is interested. Furthermore, the analysis unit can adjust the order of analysis based on the user's past selection history. This enables efficient analysis by adjusting the order of analysis based on the relevance of nail designs. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of nail designs to a generation AI and have the generation AI adjust the order of analysis.

[0088] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can use simple terms. If the user is an intermediate user, the analysis unit can also use appropriate technical terms. If the user is an advanced user, the analysis unit can also use detailed technical terms. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI, causing the generation AI to adjust the use of technical terms.

[0089] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user emotions. For example, the simulation unit estimates the user's emotions and adjusts the display method of the simulation based on the estimated user emotions. For example, the simulation unit provides a detailed simulation when the user is relaxed. The simulation unit can also provide a concise simulation when the user is busy. The simulation unit can also provide a visually appealing simulation when the user is excited. This allows the optimal simulation result to be provided for the user by adjusting the display method of the simulation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the simulation unit may be performed using an AI, for example, or without an AI. For example, the simulation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the simulation.

[0090] The simulation unit can adjust the accuracy of the simulation based on the level of detail of the nail design during the simulation. For example, the simulation unit can adjust the accuracy of the simulation based on the level of detail of the nail design during the simulation. For example, the simulation unit can perform a detailed simulation for important designs. The simulation unit can also perform a concise simulation for general designs. Furthermore, the simulation unit can perform a detailed simulation for designs in which the user is particularly interested. This allows for efficient simulation by adjusting the accuracy of the simulation based on the level of detail of the nail design. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input data on the level of detail of the nail design to a generation AI, causing the generation AI to adjust the accuracy of the simulation.

[0091] The simulation unit can optimize the simulation algorithm according to the shape and size of the user's hand during the simulation. For example, the simulation unit optimizes the simulation algorithm according to the shape and size of the user's hand during the simulation. For example, the simulation unit optimizes the simulation algorithm based on the shape of the user's hand. The simulation unit can also optimize the simulation algorithm based on the size of the user's hand. Furthermore, the simulation unit can optimize the simulation algorithm based on the characteristics of the user's hand. This enables more accurate simulation by optimizing the simulation algorithm according to the shape and size of the user's hand. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the shape and size of the user's hand into a generation AI, and cause the generation AI to optimize the simulation algorithm.

[0092] The simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results during the simulation. For example, the simulation unit can optimize a simulation algorithm based on the user's past simulation results. The simulation unit can also improve the accuracy of the simulation by referring to the user's past simulation results. Furthermore, the simulation unit can analyze the user's past simulation results and identify areas for improvement in the simulation. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the simulation unit can input data of the user's past simulation results into a generation AI, and have the generation AI improve the accuracy of the simulation.

[0093] The simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated user emotions. For example, the simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated user emotions. For example, the simulation unit can provide a detailed simulation when the user is relaxed. The simulation unit can also provide a concise simulation when the user is busy. The simulation unit can also provide a visually appealing simulation when the user is excited. By adjusting the length of the simulation based on the user's emotions, it is possible to provide an optimal simulation result for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the simulation unit can be performed using an AI, for example, or without an AI. For example, the simulation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the simulation.

[0094] The simulation unit can determine the priority of simulations based on the submission date of the nail designs during the simulation. For example, the simulation unit determines the priority of simulations based on the submission date of the nail designs during the simulation. For example, the simulation unit prioritizes the most recent designs. The simulation unit can also determine the priority of simulations based on a deadline specified by the user. Furthermore, the simulation unit can determine the priority of simulations by referring to the user's past submission dates. This enables efficient simulations by determining the priority of simulations based on the submission date of the nail designs. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can input data on the submission date of the nail designs into the generation AI, causing the generation AI to determine the priority of simulations.

[0095] The simulation unit can adjust the order of simulations based on the relevance of nail designs during simulation. For example, the simulation unit can adjust the order of simulations based on the relevance of nail designs during simulation. For example, the simulation unit prioritizes simulation of highly relevant designs. The simulation unit can also prioritize simulation of designs in which the user is interested. Furthermore, the simulation unit can adjust the order of simulations based on the user's past selection history. This allows for efficient simulation by adjusting the order of simulations based on the relevance of nail designs. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can input data on the relevance of nail designs to a generation AI and have the generation AI adjust the order of simulations.

[0096] The simulation unit can adjust the level of detail of the simulation during the simulation according to the user's level of expertise. For example, the simulation unit can adjust the level of detail of the simulation during the simulation according to the user's level of expertise. For example, the simulation unit can provide a simple simulation if the user is a beginner. The simulation unit can also provide a simulation with an appropriate level of detail if the user is an intermediate user. Furthermore, the simulation unit can also provide a detailed simulation if the user is an advanced user. In this way, by adjusting the level of detail of the simulation according to the user's level of expertise, it is possible to provide an optimal simulation result for the user. Some or all of the above-described processing in the simulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the simulation unit can input data on the user's level of expertise to a generation AI and cause the generation AI to adjust the level of detail of the simulation.

[0097] The recommendation unit can estimate a user's emotions and adjust the recommendation method based on the estimated user emotions. For example, the recommendation unit can estimate a user's emotions and adjust the recommendation method based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. The recommendation unit can also provide concise recommendations when the user is busy. The recommendation unit can also provide visually appealing recommendations when the user is excited. This allows the recommendation method to be adjusted based on the user's emotions, thereby providing optimal recommendation results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI or without AI. For example, the recommendation unit can input user emotional data into the generation AI, causing the generation AI to adjust the recommendation method.

[0098] The recommendation unit can select the optimal recommendation method by analyzing the user's past nail design history when making a recommendation. For example, the recommendation unit can select the optimal recommendation method by analyzing the user's past nail design history when making a recommendation. For example, the recommendation unit can prioritize recommending designs that the user has previously rated highly. The recommendation unit can also prioritize recommending designs that the user has frequently searched for in the past. Furthermore, the recommendation unit can select the optimal recommendation method based on the user's past selection history. This allows the optimal recommendation method to be selected by analyzing the user's past nail design history, enabling efficient recommendations. Some or all of the above-described processing in the recommendation unit can be performed, for example, using AI or without AI. For example, the recommendation unit can input data on the user's past nail design history into a generation AI, causing the generation AI to select a recommendation method.

[0099] The recommendation unit can customize the recommendation method based on the user's current living situation and areas of interest when making a recommendation. For example, the recommendation unit customizes the recommendation method based on the user's current living situation and areas of interest when making a recommendation. For example, the recommendation unit recommends designs that match the user's current season. The recommendation unit can also recommend designs related to events in which the user is interested. Furthermore, the recommendation unit can recommend designs that match the user's current lifestyle. This enables more relevant recommendations by customizing the recommendation method based on the user's current living situation and areas of interest. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's living situation and areas of interest into a generation AI, allowing the generation AI to customize the recommendation method.

[0100] The recommendation unit can improve the recommendation method by reflecting user feedback when making a recommendation. The recommendation unit, for example, improves the recommendation method by reflecting user feedback when making a recommendation. For example, the recommendation unit prioritizes recommending designs that the user has previously rated highly. The recommendation unit can also recommend designs that the user has previously rated poorly. Furthermore, the recommendation unit can optimize the recommendation method based on the user's past feedback. This improves the recommendation method by reflecting user feedback, enabling more accurate recommendations. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input user feedback data into a generation AI, which can then improve the recommendation method.

[0101] The recommendation unit can estimate a user's emotions and determine the priority of recommendations based on the estimated user emotions. The recommendation unit, for example, estimates a user's emotions and determines the priority of recommendations based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can prioritize detailed recommendations. Also, if the user is busy, the recommendation unit can prioritize important recommendations. Also, if the user is excited, the recommendation unit can prioritize visually appealing recommendations. By determining the priority of recommendations based on the user's emotions, it is possible to provide optimal recommendation results for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI or without AI. For example, the recommendation unit can input user emotional data into the generation AI, allowing the generation AI to determine the priority of recommendations.

[0102] The recommendation unit can select the optimal recommendation method by taking into account the user's geographical location information when making a recommendation. For example, the recommendation unit selects the optimal recommendation method by taking into account the user's geographical location information when making a recommendation. For example, the recommendation unit recommends trendy designs in the area where the user is currently located. The recommendation unit can also recommend designs for places the user frequently visits. Furthermore, the recommendation unit can preferentially recommend related designs based on the user's geographical location. This makes it possible to make highly relevant recommendations by taking into account the user's geographical location information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's geographical location information into a generation AI, causing the generation AI to select a recommendation method.

[0103] The recommendation unit can analyze the user's social media activity and suggest a means of recommendation when making a recommendation. For example, the recommendation unit can analyze the user's social media activity and suggest a means of recommendation when making a recommendation. For example, the recommendation unit recommends designs shared by the user on social media. The recommendation unit can also analyze the content of the user's social media posts and recommend related designs. Furthermore, the recommendation unit can recommend related designs by referring to the activity of the user's friends on social media. This makes it possible to make highly relevant recommendations by analyzing the user's social media activity. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's social media activity into a generation AI, and have the generation AI execute the suggestion of a means of recommendation.

[0104] The recommendation unit can customize the recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit, for example, customizes the recommendation method by reflecting the user's past feedback when making a recommendation. For example, the recommendation unit prioritizes recommending designs that the user has previously rated highly. The recommendation unit can also recommend designs that the user has previously rated poorly. Furthermore, the recommendation unit can optimize the recommendation method based on the user's past feedback. This allows the recommendation method to be customized by reflecting the user's past feedback, enabling more accurate recommendations. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data of the user's past feedback into a generation AI, and have the generation AI customize the recommendation method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, simulation unit, and recommendation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's image generation and personal history using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and proposes an optimal nail design. The simulation unit, realized, for example, by the control unit 46A of the smart device 14, simulates the proposed design on the user's hand. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the user's preferences, and provides a recommendation function. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, simulation unit, and recommendation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's image generation and personal history using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and proposes an optimal nail design. The simulation unit, realized, for example, by the control unit 46A of the smart glasses 214, simulates the proposed design on the user's hand. The recommendation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the user's preferences, and provides a recommendation function. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, simulation unit, and recommendation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's image generation and personal history using the camera 42 and communication I / F 44 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to propose an optimal nail design. The simulation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and simulates the proposed design on the user's hand. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's preferences and provides a recommendation function. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, simulation unit, and recommendation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's image generation and personal history using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to propose an optimal nail design. The simulation unit is realized, for example, by the control unit 46A of the robot 414, and simulates the proposed design on the user's hand. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's preferences and provides a recommendation function.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The nail art system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures the user's heart rate and stress level and provides this data to the analysis unit. For example, if the user is in a high-stress state, the analysis unit may suggest a nail design that has a relaxing effect. Alternatively, if the user's heart rate is stable, the analysis unit may suggest a bolder design. This allows the system to provide the optimal nail design based on the user's health condition.

[0107] The nail art system may further include a fashion analysis unit that analyzes the user's fashion style. The fashion analysis unit analyzes the user's past fashion history and current fashion trends and suggests nail designs based on this. For example, if the user prefers a casual style, a simple and easy-to-use design may be suggested. Alternatively, if the user plans to attend a formal event, an elegant design may be suggested. This makes it possible to provide nail designs that match the user's fashion style.

[0108] The nail art system may further include a music analysis unit that analyzes the user's musical preferences. The music analysis unit analyzes the genres and artists of music that the user often listens to and suggests nail designs based on this. For example, if the user likes rock music, a dark and edgy design may be suggested. Alternatively, if the user likes classical music, an elegant and sophisticated design may be suggested. This allows the system to provide nail designs that match the user's musical preferences.

[0109] The nail art system can further include a travel analysis unit that analyzes the user's travel history. The travel analysis unit analyzes the places the user has visited in the past and their travel habits, and suggests nail designs based on this. For example, if the user likes beach resorts, it can suggest designs that evoke the ocean and sand. If the user likes urban sightseeing, it can suggest modern and stylish designs. This makes it possible to provide nail designs that match the user's travel history.

[0110] The nail art system can further include a food analysis unit that analyzes the user's food preferences. The food analysis unit analyzes the user's favorite dishes and ingredients and suggests nail designs based on this. For example, if the user likes Japanese food, a Japanese-style design can be suggested. Also, if the user likes Italian food, a traditional Italian design can be suggested. This makes it possible to provide nail designs that match the user's food preferences.

[0111] The nail art system can further estimate the user's emotions and adjust the colors of the nail design based on the estimated emotions. For example, if the user is relaxed, a design with calm colors can be suggested. On the other hand, if the user is excited, a design with vibrant colors can be suggested. This allows the system to provide nail designs with colors based on the user's emotions.

[0112] The nail art system can further estimate the user's emotions and adjust the theme of the nail design based on the estimated emotions. For example, if the user is sad, a bright and cheerful theme design can be suggested to cheer them up. If the user is happy, a bright and cheerful theme design can be suggested to further enhance the user's emotions. In this way, it is possible to provide a themed nail design based on the user's emotions.

[0113] The nail art system can further estimate the user's emotions and adjust the decoration of the nail design based on the estimated emotions. For example, if the user is relaxed, a design with simple and calm decoration can be suggested. On the other hand, if the user is excited, a design with flashy and eye-catching decoration can be suggested. In this way, it is possible to provide a nail design with decoration based on the user's emotions.

[0114] The nail art system can further estimate the user's emotions and adjust the nail design pattern based on the estimated emotions. For example, if the user is relaxed, a calm pattern design can be suggested. If the user is excited, a dynamic pattern design can be suggested. This makes it possible to provide nail designs with patterns based on the user's emotions.

[0115] The nail art system can further estimate the user's emotions and adjust the materials of the nail design based on the estimated emotions. For example, if the user is relaxed, a design made of soft materials can be suggested. On the other hand, if the user is excited, a design made of hard and shiny materials can be suggested. This allows the system to provide nail designs made of materials based on the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects the user's image creation and personal history. Specifically, it collects images of nail designs the user has searched for in the past, chat history, etc. This allows the system to understand the user's preferences and past behavior. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal nail design. The analysis unit uses image analysis algorithms and machine learning algorithms to analyze the user's hand shape and preferences. For example, it uses deep learning and edge detection technology to analyze the hand shape, and support vector machines and neural networks to analyze the user's preferences. Step 3: The simulation unit simulates the design proposed by the analysis unit. Specifically, the proposed nail design is simulated on the user's hand and the accuracy of the simulation is adjusted. For example, the simulation algorithm is optimized based on the shape and size of the user's hand. Step 4: The recommendation unit analyzes the user's preferences based on the design simulated by the simulation unit and provides a recommendation function. Specifically, it analyzes the user's preferences from the user's search history and chat history, estimates the user's emotions, and adjusts the recommendation method. For example, if the user is relaxed, it provides detailed recommendations.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user image generation and personal history; an analysis unit that analyzes the data collected by the collection unit and proposes nail designs; a simulation unit that simulates the design proposed by the analysis unit; a recommendation unit that analyzes user preferences based on the design simulated by the simulation unit and provides a recommendation function. A system characterized by:

2. The collecting unit Collect images of nail designs that users have previously searched for or chat history 2. The system of claim 1.

3. The analysis unit It uses an image analysis algorithm to analyze the shape of the user's hand and suggest nail designs.

2. The system of claim 1.

4. The analysis unit Using machine learning algorithms to analyze user preferences and provide sophisticated designs with recommendation functions 2. The system of claim 1.

5. The simulation unit Simulate the proposed nail design on the user's hand 2. The system of claim 1.

6. The recommendation unit Analyzes user preferences based on search and chat history, and provides sophisticated designs through recommendation functions 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the user's past nail design history and select the collection method 2. The system of claim 1.

Images