system

Abstract

The system according to this embodiment aims to analyze the user's skin condition and suggest optimal makeup and skincare. [Solution] The system according to the embodiment comprises an analysis unit, a suggestion unit, and a recording unit. The analysis unit analyzes the user's skin. The suggestion unit suggests the optimal makeup and skincare for the user based on the skin condition analyzed by the analysis unit. The recording unit records the results of the makeup and skincare suggested by the suggestion unit and performs a before-and-after comparison.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, optimal makeup and skincare proposals based on the user's skin condition have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's skin condition and propose optimal makeup and skincare.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a suggestion unit, and a recording unit. The analysis unit analyzes the user's skin. The suggestion unit suggests the optimal makeup and skincare for the user based on the skin condition analyzed by the analysis unit. The recording unit records the results of the makeup and skincare suggested by the suggestion unit and performs a before-and-after comparison. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's skin condition and suggest optimal makeup and skincare. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. [[ID=http: / / www.example.com / 15]]

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI-powered mirror system according to an embodiment of the present invention is a system that enables salon-quality skin care at home. This AI-powered mirror system analyzes the user's skin and diagnoses skin type and skin age. Next, the AI ​​suggests the optimal makeup and skincare routine for the user. Furthermore, it saves the user's skincare and makeup records and learns and updates personalized beauty methods by comparing before and after results. For example, when the user stands in front of the mirror, the AI-powered mirror system scans the skin condition through a camera and performs a detailed analysis. This allows for an accurate understanding of the user's skin condition. Next, the AI-powered mirror system suggests skincare methods tailored to the skin condition and makeup that suits the user's personal color (warm or cool undertones). It can also introduce relevant commercially available products and salons. This allows the user to learn the optimal beauty methods for themselves. Furthermore, the AI-powered mirror system saves the user's skincare and makeup records, allowing them to check changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. This allows the user to feel the effects of their beauty routine and increase their motivation. The AI ​​also learns and updates personalized beauty methods from the records. This ensures that the user always has access to the latest beauty information. For example, it allows users to easily perform self-care even amidst a busy daily life. Furthermore, it enables users to select beauty methods based on reliable information provided by AI. This allows users to find the optimal beauty methods for themselves and effectively perform self-care. The AI-powered mirror system analyzes the user's skin, suggests optimal makeup and skincare, and records and compares the results to provide personalized beauty methods.

[0029] The AI-equipped mirror system according to this embodiment comprises an analysis unit, a suggestion unit, and a recording unit. The analysis unit analyzes the user's skin. For example, the analysis unit diagnoses skin type (dry skin, oily skin, combination skin) and skin age (moisture content, tone, pores, wrinkles, blemishes, etc.). For example, when a user stands in front of the mirror, the analysis unit scans the skin condition through a camera and performs a detailed analysis. This allows for an accurate understanding of the user's skin condition. The suggestion unit suggests the optimal makeup and skincare for the user based on the skin condition analyzed by the analysis unit. For example, the suggestion unit suggests skincare methods tailored to the skin condition and makeup that suits the user's personal color (warm or cool undertones). The suggestion unit can also introduce relevant commercially available products and salons. This allows the user to learn the best beauty methods for themselves. The recording unit records the results of the makeup and skincare suggested by the suggestion unit and performs a Before & After comparison. For example, the recording unit saves records of the user's skincare and makeup, allowing for the confirmation of changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. This allows users to experience the beauty effects firsthand and boost their motivation. Furthermore, the recording unit learns and updates personalized beauty methods from the recorded data. This ensures users always have access to the latest beauty information. As a result, the AI-powered mirror system according to this embodiment can analyze the user's skin, suggest optimal makeup and skincare, and record and compare the results to provide personalized beauty methods.

[0030] The analysis unit analyzes the user's skin. Specifically, when the user stands in front of the mirror, the analysis unit scans the skin condition through a built-in high-resolution camera and performs a detailed analysis. The camera is equipped with a special sensor that can detect not only the surface of the skin but also the condition of the deeper layers, allowing it to accurately determine the skin's moisture content, oil content, tone, pore condition, and the presence or absence of wrinkles and blemishes. Furthermore, the analysis unit uses AI to analyze this data in real time and diagnose the user's skin type (dry, oily, or combination) and skin age. The AI ​​can perform a more accurate diagnosis by comparing it with past data and data from other users. For example, the AI ​​analyzes the user's skin tone and tracks daily changes to perform a diagnosis that takes into account the influence of seasons and lifestyle habits. The analysis unit can also periodically scan the user's skin condition and monitor long-term changes. This allows the user to always know the condition of their skin and take appropriate care. In addition, the analysis unit can save the user's skin condition to the cloud and link with other devices. This allows the user to check their skin condition anytime via smartphone or tablet.

[0031] The Proposal Department suggests optimal makeup and skincare products to users based on their skin condition analyzed by the Analysis Department. Specifically, the Proposal Department uses AI to analyze the user's skin condition and suggests the most suitable skincare methods and makeup. For example, for users with dry skin, it suggests highly moisturizing skincare products and gentle makeup products. It can also perform a personal color analysis and suggest makeup colors that best suit the user's skin tone. The Proposal Department can provide specific product names and usage instructions according to the user's skin condition and preferences, and can also introduce relevant commercially available products and salons. This allows users to learn the beauty methods best suited to them and perform effective care. Furthermore, the Proposal Department can collect user feedback and continuously improve its suggestions. For example, it can record the results of users using the suggested skincare products and analyze their effects to make future suggestions more accurate. The Proposal Department can also suggest appropriate care methods according to seasonal and environmental changes. This allows users to always have access to the latest beauty information and perform effective care.

[0032] The recording unit records the results of makeup and skincare suggested by the suggestion unit and performs before-and-after comparisons. Specifically, the recording unit meticulously records the results of the user's use of suggested skincare products and makeup, and can compare them to the state one month or three months prior. For example, it records how much the user's skin moisture level improved after using a suggested moisturizing cream and displays this visually in graphs and charts. It can also compare changes such as the reduction of blemishes and wrinkles, and skin tone improvement, using photographs. This allows users to feel the effects of their beauty routine and boost their motivation. Furthermore, the recording unit accumulates data on the user's skin condition and the products used, and uses AI to learn and update personalized beauty methods. For example, based on past data, it can analyze which skin types a particular product is effective for and reflect this in future suggestions. The recording unit can also save user data to the cloud and link with other devices. This allows users to check their beauty history anytime via smartphone or tablet. In addition, the recording unit can collect user feedback and continuously improve the accuracy and effectiveness of the recorded content. This allows users to always have access to the latest beauty information and perform effective skincare.

[0033] The analysis unit can diagnose skin type and skin age. For example, the analysis unit can diagnose skin type (dry skin, oily skin, combination skin). For example, the analysis unit can evaluate the user's skin moisture content, tone, pores, wrinkles, and blemishes, and diagnose skin age. This allows for an accurate understanding of the user's skin condition by diagnosing skin type and skin age. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skin image data into a generating AI and have the generating AI perform a diagnosis of skin type and skin age.

[0034] The suggestion unit can propose skincare methods tailored to the user's skin condition and makeup that suits their personal color. For example, the suggestion unit can propose skincare methods tailored to the user's skin condition. For example, the suggestion unit can propose appropriate skincare products based on the user's skin's dryness and oil content. The suggestion unit can also propose makeup that suits the user's personal color (warm or cool undertones). For example, the suggestion unit can propose optimal makeup products based on the user's skin tone and color. This allows the unit to provide effective beauty methods by proposing the most suitable skincare and makeup for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's skin condition data into a generating AI and have the generating AI execute suggestions for skincare methods and makeup.

[0035] The suggestion unit can introduce relevant commercially available products and salons. For example, the suggestion unit can introduce the most suitable commercially available products based on the user's skin condition. For example, the suggestion unit can introduce appropriate skincare products based on the user's skin dryness and oiliness. The suggestion unit can also introduce the most suitable makeup products based on the user's skin tone and color. Furthermore, the suggestion unit can also introduce relevant salons. For example, the suggestion unit can introduce the most suitable hair salon or beauty salon based on the user's skin condition. In this way, by introducing the user to the most suitable commercially available products and salons, effective beauty methods can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's skin condition data into a generating AI and have the generating AI perform the introduction of commercially available products and salons.

[0036] The recording unit saves records of the user's skincare and makeup, and can check for changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. For example, the recording unit can save records of the user's skincare and makeup. For example, the recording unit can save information on the skincare and makeup products the user has used. The recording unit can also check for changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. For example, the recording unit can periodically scan the user's skin condition and compare it with past data to check for the effects of blemish reduction and skin tone improvement. This makes it easier for the user to feel the beauty effects by checking the effects of their skincare and makeup. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the user's skin condition data into a generating AI and have the generating AI perform a Before / After comparison.

[0037] The recording unit can learn and update personalized beauty methods from the records. For example, the recording unit learns personalized beauty methods based on the user's skincare and makeup records. For example, the recording unit can analyze the effects of the skincare and makeup products used by the user and learn the optimal beauty methods. The recording unit can also update the learned beauty methods. For example, the recording unit can periodically update the data and provide the latest beauty information. In this way, by learning and updating personalized beauty methods, it can always provide the latest beauty information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's skincare and makeup record data into a generating AI and have the generating AI perform the learning and updating of beauty methods.

[0038] The analysis unit can improve the accuracy of its analysis by referring to the user's past skin condition data during the analysis. For example, the analysis unit can compare the user's past skin condition data with the user's current skin condition. For example, the analysis unit can refer to the user's past skin condition data to analyze seasonal changes in skin. The analysis unit can also use the user's past skin condition data to grasp long-term skin trends. In this way, the accuracy of the analysis can be improved by referring to past skin condition data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past skin condition data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0039] The analysis unit can diagnose the skin condition by considering the user's lifestyle habits during analysis. For example, the analysis unit can analyze the user's diet and evaluate its impact on the skin condition. For example, the analysis unit can diagnose the nutritional status of the skin based on the user's diet. The analysis unit can also consider the user's sleep patterns and diagnose the skin's regenerative capacity. For example, the analysis unit can evaluate the skin's regenerative capacity based on the user's sleep data. The analysis unit can also evaluate the user's stress level and analyze its impact on the skin condition. For example, the analysis unit can diagnose the skin's health condition based on the user's stress data. This allows for a more accurate diagnosis of the skin condition by considering lifestyle habits. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform a diagnosis of the skin condition.

[0040] The analysis unit can diagnose the skin condition by considering the user's geographical environment during analysis. For example, the analysis unit can evaluate the degree of skin dryness based on climate data of the user's place of residence. For example, the analysis unit can diagnose the skin's moisture retention capacity based on climate data of the user's place of residence. The analysis unit can also diagnose the skin's moisture retention capacity by referring to humidity data of the user's place of residence. For example, the analysis unit can evaluate the skin's moisture retention capacity based on humidity data of the user's place of residence. The analysis unit can also evaluate skin damage by considering the pollution level of the user's place of residence. For example, the analysis unit can diagnose the skin's health condition based on the pollution level of the user's place of residence. This allows for a more accurate diagnosis of the skin condition by considering the geographical environment. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical environment data into a generating AI and have the generating AI perform the diagnosis of the skin condition.

[0041] The analysis unit can analyze users' social media activity during analysis and reflect relevant skin condition trends. For example, the analysis unit can analyze users' social media posts to understand trends related to skin condition. For example, the analysis unit can reflect the latest trends related to skin condition based on users' social media posts. The analysis unit can also refer to posts from beauty influencers that users follow and provide the latest information on skin condition. For example, the analysis unit can reflect the latest trends related to skin condition based on posts from beauty influencers that users follow. The analysis unit can also identify factors that influence skin condition from users' social media activity. For example, the analysis unit can identify factors that influence skin condition based on users' social media activity and reflect them in the analysis. In this way, by analyzing social media activity, the latest trends in skin condition can be reflected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user social media data into a generating AI and have the generating AI perform the reflection of skin condition trends.

[0042] The suggestion unit can make optimal suggestions by referring to the user's past skincare history at the time of suggestion. For example, the suggestion unit can suggest the most suitable skincare method for the user's current skin condition based on the user's past skincare history. For example, the suggestion unit can refer to the user's past skincare history and suggest effective skincare products. The suggestion unit can also analyze the user's past skincare history and suggest a long-term skincare plan. For example, the suggestion unit can suggest a long-term skincare plan based on the user's past skincare history. In this way, optimal suggestions can be made by referring to past skincare history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past skincare history data into a generating AI and have the generating AI execute the optimal suggestion.

[0043] The suggestion unit can propose skincare methods that take into account the user's lifestyle habits when making suggestions. For example, the suggestion unit can consider the user's diet and propose skincare methods that include ingredients beneficial to the skin. For example, the suggestion unit can propose skincare methods that include ingredients beneficial to the skin based on the user's diet. The suggestion unit can also refer to the user's sleep patterns and propose skincare methods that enhance the skin's regenerative power. For example, the suggestion unit can propose skincare methods that enhance the skin's regenerative power based on the user's sleep data. The suggestion unit can also evaluate the user's stress level and propose skincare methods that help reduce stress. For example, the suggestion unit can propose skincare methods that help reduce stress based on the user's stress data. In this way, by taking lifestyle habits into account, more effective skincare methods can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI execute the skincare method proposal.

[0044] The suggestion unit can propose the most suitable skincare method by considering the user's geographical environment. For example, the suggestion unit can propose a skincare method to prevent skin dryness based on climate data of the user's place of residence. The suggestion unit can also propose a skincare method to enhance the skin's moisture retention capacity by referring to humidity data of the user's place of residence. The suggestion unit can also propose a skincare method to reduce skin damage by considering the pollution level of the user's place of residence. In this way, by considering the geographical environment, a more effective skincare method can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical environment data into a generating AI and have the generating AI execute the skincare method proposal.

[0045] The suggestion unit can analyze the user's social media activity when making suggestions and reflect relevant beauty trends. For example, the suggestion unit can analyze the user's social media posts and suggest skincare methods based on beauty trends. For example, the suggestion unit can suggest skincare methods based on beauty trends based on the user's social media posts. The suggestion unit can also refer to posts from beauty influencers that the user follows and reflect the latest beauty trends. For example, the suggestion unit can reflect the latest beauty trends based on posts from beauty influencers that the user follows. The suggestion unit can also identify the user's interests in beauty from their social media activity and make suggestions based on those interests. For example, the suggestion unit can identify the user's interests in beauty from their social media activity and make suggestions based on those interests. This allows the suggestion unit to reflect the latest beauty trends by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI perform the task of reflecting beauty trends.

[0046] The recording unit can improve the accuracy of Before / After comparisons by referring to the user's past skincare history during recording. For example, the recording unit can compare the current skin condition with the user's past skincare history. For example, the recording unit can refer to the user's past skincare history to compare seasonal changes in skin. The recording unit can also use the user's past skincare history to grasp long-term skin trends. For example, the recording unit can grasp long-term skin trends based on the user's past skincare history. This improves the accuracy of Before / After comparisons by referring to past skincare history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's past skincare history data into a generating AI and have the generating AI perform the Before / After comparison.

[0047] The recording unit can evaluate the beauty effects by considering the user's lifestyle habits during recording. For example, the recording unit can analyze the user's diet and evaluate its impact on the beauty effects. For example, the recording unit can evaluate the impact on the beauty effects based on the user's diet. The recording unit can also evaluate the beauty effects by considering the user's sleep patterns. For example, the recording unit can evaluate the beauty effects based on the user's sleep data. The recording unit can also evaluate the user's stress level and analyze its impact on the beauty effects. For example, the recording unit can analyze the impact on the beauty effects based on the user's stress data. By considering lifestyle habits, a more accurate evaluation of beauty effects can be achieved. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's lifestyle data into a generating AI and have the generating AI perform the evaluation of beauty effects.

[0048] The recording unit can evaluate the beauty effects while considering the user's geographical environment during recording. For example, the recording unit can evaluate the beauty effects based on climate data of the user's place of residence. The recording unit can also evaluate the beauty effects by referring to humidity data of the user's place of residence. The recording unit can also evaluate the beauty effects by considering the pollution level of the user's place of residence. This allows for a more accurate evaluation of beauty effects by considering the geographical environment. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's geographical environment data into a generating AI and have the generating AI perform the evaluation of beauty effects.

[0049] The recording unit can analyze the user's social media activity during recording and reflect relevant beauty trends. For example, the recording unit can analyze the user's social media posts and evaluate the beauty effects based on beauty trends. For example, the recording unit can evaluate the beauty effects based on beauty trends based on the user's social media posts. The recording unit can also refer to posts from beauty influencers that the user follows and reflect the latest beauty trends. For example, the recording unit can reflect the latest beauty trends based on posts from beauty influencers that the user follows. The recording unit can also identify beauty interests from the user's social media activity and evaluate the beauty effects based on those interests. For example, the recording unit can identify beauty interests based on the user's social media activity and evaluate the beauty effects based on those interests. In this way, by analyzing social media activity, the latest beauty trends can be reflected. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's social media data into a generating AI and have the generating AI perform the reflection of beauty trends.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The AI-powered mirror system can further diagnose skin condition by considering the user's lifestyle. For example, the analysis unit can analyze the user's diet and evaluate its impact on skin condition. Specifically, it considers the balance of nutrients the user consumes and the frequency of meals to diagnose skin health. It can also consider the user's sleep patterns and evaluate skin regeneration. For example, it analyzes the user's sleep duration and quality to assess skin regeneration capacity. Furthermore, it can evaluate the user's stress level and analyze its impact on skin condition. For example, it diagnoses skin health based on the user's stress data. By considering lifestyle, it can diagnose skin condition more accurately.

[0052] The AI-powered mirror system can diagnose skin condition while considering the user's geographical environment. For example, the analysis unit can evaluate the degree of skin dryness based on climate data of the user's place of residence. Specifically, it diagnoses the skin's moisture retention capacity based on climate data of the user's place of residence. It can also diagnose skin moisture retention capacity by referring to humidity data of the user's place of residence. For example, it evaluates skin moisture retention capacity based on humidity data of the user's place of residence. Furthermore, it can assess skin damage by considering the pollution level of the user's place of residence. For example, it diagnoses skin health based on the pollution level of the user's place of residence. In this way, by considering the geographical environment, a more accurate diagnosis of skin condition can be made.

[0053] The AI-powered mirror system can analyze users' social media activity and reflect relevant skin condition trends. For example, the analysis unit can analyze users' social media posts to understand trends related to skin condition. Specifically, it reflects the latest trends related to skin condition based on users' social media posts. It can also refer to posts from beauty influencers that users follow and provide the latest information on skin condition. For example, it reflects the latest trends related to skin condition based on posts from beauty influencers that users follow. Furthermore, it can identify factors that influence skin condition from users' social media activity. For example, it identifies factors that influence skin condition based on users' social media activity and reflects them in the analysis. In this way, by analyzing social media activity, it can reflect the latest trends in skin condition.

[0054] The AI-powered mirror system can provide optimal suggestions by referencing the user's past skincare history. For example, the suggestion function can propose the most suitable skincare method for the user's current skin condition based on their past skincare history. Specifically, it refers to the user's past skincare history and suggests effective skincare products. It can also analyze the user's past skincare history and propose a long-term skincare plan. For example, it proposes a long-term skincare plan based on the user's past skincare history. In this way, it can provide optimal suggestions by referring to past skincare history.

[0055] The AI-powered mirror system can suggest optimal skincare methods considering the user's geographical environment. For example, the suggestion function can propose skincare methods to prevent skin dryness based on climate data of the user's place of residence. Specifically, it can suggest skincare methods to prevent skin dryness based on climate data of the user's place of residence. It can also refer to humidity data of the user's place of residence and suggest skincare methods to enhance the skin's moisture retention capacity. For example, it can suggest skincare methods to enhance the skin's moisture retention capacity based on humidity data of the user's place of residence. Furthermore, it can consider the pollution level of the user's place of residence and suggest skincare methods to reduce skin damage. For example, it can suggest skincare methods to reduce skin damage based on the pollution level of the user's place of residence. In this way, by considering the geographical environment, it can suggest more effective skincare methods.

[0056] The AI-powered mirror system can analyze users' social media activity and reflect relevant beauty trends. For example, the suggestion function can analyze users' social media posts and suggest skincare methods based on beauty trends. Specifically, it suggests skincare methods based on beauty trends based on users' social media posts. It can also refer to posts from beauty influencers that users follow and reflect the latest beauty trends. For example, it reflects the latest beauty trends based on posts from beauty influencers that users follow. Furthermore, it can identify users' beauty interests from their social media activity and make suggestions based on those interests. For example, it identifies users' beauty interests based on their social media activity and makes suggestions based on those interests. In this way, by analyzing social media activity, it can reflect the latest beauty trends.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis unit analyzes the user's skin. Specifically, it diagnoses skin type (dry, oily, combination) and skin age (moisture level, tone, pores, wrinkles, blemishes, etc.). When the user stands in front of the mirror, the analysis unit scans the skin condition via camera and performs a detailed analysis. Step 2: The Proposal Department proposes the most suitable makeup and skincare for the user based on the skin condition analyzed by the Analysis Department. Specifically, they propose skincare methods tailored to the skin condition and makeup that suits the user's personal color (warm or cool undertones). They can also introduce relevant commercially available products and salons. Step 3: The recording unit records the results of the makeup and skincare suggested by the suggestion unit and performs a before-and-after comparison. Specifically, it saves the user's skincare and makeup records, allowing users to see changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. The recording unit also learns and updates personalized beauty methods based on the records.

[0059] (Example of form 2) An AI-powered mirror system according to an embodiment of the present invention is a system that enables salon-quality skin care at home. This AI-powered mirror system analyzes the user's skin and diagnoses skin type and skin age. Next, the AI ​​suggests the optimal makeup and skincare routine for the user. Furthermore, it saves the user's skincare and makeup records and learns and updates personalized beauty methods by comparing before and after results. For example, when the user stands in front of the mirror, the AI-powered mirror system scans the skin condition through a camera and performs a detailed analysis. This allows for an accurate understanding of the user's skin condition. Next, the AI-powered mirror system suggests skincare methods tailored to the skin condition and makeup that suits the user's personal color (warm or cool undertones). It can also introduce relevant commercially available products and salons. This allows the user to learn the optimal beauty methods for themselves. Furthermore, the AI-powered mirror system saves the user's skincare and makeup records, allowing them to check changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. This allows the user to feel the effects of their beauty routine and increase their motivation. The AI ​​also learns and updates personalized beauty methods from the records. This ensures that the user always has access to the latest beauty information. For example, it allows users to easily perform self-care even amidst a busy daily life. Furthermore, it enables users to select beauty methods based on reliable information provided by AI. This allows users to find the optimal beauty methods for themselves and effectively perform self-care. The AI-powered mirror system analyzes the user's skin, suggests optimal makeup and skincare, and records and compares the results to provide personalized beauty methods.

[0060] The AI-equipped mirror system according to this embodiment comprises an analysis unit, a suggestion unit, and a recording unit. The analysis unit analyzes the user's skin. For example, the analysis unit diagnoses skin type (dry skin, oily skin, combination skin) and skin age (moisture content, tone, pores, wrinkles, blemishes, etc.). For example, when a user stands in front of the mirror, the analysis unit scans the skin condition through a camera and performs a detailed analysis. This allows for an accurate understanding of the user's skin condition. The suggestion unit suggests the optimal makeup and skincare for the user based on the skin condition analyzed by the analysis unit. For example, the suggestion unit suggests skincare methods tailored to the skin condition and makeup that suits the user's personal color (warm or cool undertones). The suggestion unit can also introduce relevant commercially available products and salons. This allows the user to learn the best beauty methods for themselves. The recording unit records the results of the makeup and skincare suggested by the suggestion unit and performs a Before & After comparison. For example, the recording unit saves records of the user's skincare and makeup, allowing for the confirmation of changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. This allows users to experience the beauty effects firsthand and boost their motivation. Furthermore, the recording unit learns and updates personalized beauty methods from the recorded data. This ensures users always have access to the latest beauty information. As a result, the AI-powered mirror system according to this embodiment can analyze the user's skin, suggest optimal makeup and skincare, and record and compare the results to provide personalized beauty methods.

[0061] The analysis unit analyzes the user's skin. Specifically, when the user stands in front of the mirror, the analysis unit scans the skin condition through a built-in high-resolution camera and performs a detailed analysis. The camera is equipped with a special sensor that can detect not only the surface of the skin but also the condition of the deeper layers, allowing it to accurately determine the skin's moisture content, oil content, tone, pore condition, and the presence or absence of wrinkles and blemishes. Furthermore, the analysis unit uses AI to analyze this data in real time and diagnose the user's skin type (dry, oily, or combination) and skin age. The AI ​​can perform a more accurate diagnosis by comparing it with past data and data from other users. For example, the AI ​​analyzes the user's skin tone and tracks daily changes to perform a diagnosis that takes into account the influence of seasons and lifestyle habits. The analysis unit can also periodically scan the user's skin condition and monitor long-term changes. This allows the user to always know the condition of their skin and take appropriate care. In addition, the analysis unit can save the user's skin condition to the cloud and link with other devices. This allows the user to check their skin condition anytime via smartphone or tablet.

[0062] The Proposal Department suggests optimal makeup and skincare products to users based on their skin condition analyzed by the Analysis Department. Specifically, the Proposal Department uses AI to analyze the user's skin condition and suggests the most suitable skincare methods and makeup. For example, for users with dry skin, it suggests highly moisturizing skincare products and gentle makeup products. It can also perform a personal color analysis and suggest makeup colors that best suit the user's skin tone. The Proposal Department can provide specific product names and usage instructions according to the user's skin condition and preferences, and can also introduce relevant commercially available products and salons. This allows users to learn the beauty methods best suited to them and perform effective care. Furthermore, the Proposal Department can collect user feedback and continuously improve its suggestions. For example, it can record the results of users using the suggested skincare products and analyze their effects to make future suggestions more accurate. The Proposal Department can also suggest appropriate care methods according to seasonal and environmental changes. This allows users to always have access to the latest beauty information and perform effective care.

[0063] The recording unit records the results of makeup and skincare suggested by the suggestion unit and performs before-and-after comparisons. Specifically, the recording unit meticulously records the results of the user's use of suggested skincare products and makeup, and can compare them to the state one month or three months prior. For example, it records how much the user's skin moisture level improved after using a suggested moisturizing cream and displays this visually in graphs and charts. It can also compare changes such as the reduction of blemishes and wrinkles, and skin tone improvement, using photographs. This allows users to feel the effects of their beauty routine and boost their motivation. Furthermore, the recording unit accumulates data on the user's skin condition and the products used, and uses AI to learn and update personalized beauty methods. For example, based on past data, it can analyze which skin types a particular product is effective for and reflect this in future suggestions. The recording unit can also save user data to the cloud and link with other devices. This allows users to check their beauty history anytime via smartphone or tablet. In addition, the recording unit can collect user feedback and continuously improve the accuracy and effectiveness of the recorded content. This allows users to always have access to the latest beauty information and perform effective skincare.

[0064] The analysis unit can diagnose skin type and skin age. For example, the analysis unit can diagnose skin type (dry skin, oily skin, combination skin). For example, the analysis unit can evaluate the user's skin moisture content, tone, pores, wrinkles, and blemishes, and diagnose skin age. This allows for an accurate understanding of the user's skin condition by diagnosing skin type and skin age. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skin image data into a generating AI and have the generating AI perform a diagnosis of skin type and skin age.

[0065] The suggestion unit can propose skincare methods tailored to the user's skin condition and makeup that suits their personal color. For example, the suggestion unit can propose skincare methods tailored to the user's skin condition. For example, the suggestion unit can propose appropriate skincare products based on the user's skin's dryness and oil content. The suggestion unit can also propose makeup that suits the user's personal color (warm or cool undertones). For example, the suggestion unit can propose optimal makeup products based on the user's skin tone and color. This allows the unit to provide effective beauty methods by proposing the most suitable skincare and makeup for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's skin condition data into a generating AI and have the generating AI execute suggestions for skincare methods and makeup.

[0066] The suggestion unit can introduce relevant commercially available products and salons. For example, the suggestion unit can introduce the most suitable commercially available products based on the user's skin condition. For example, the suggestion unit can introduce appropriate skincare products based on the user's skin dryness and oiliness. The suggestion unit can also introduce the most suitable makeup products based on the user's skin tone and color. Furthermore, the suggestion unit can also introduce relevant salons. For example, the suggestion unit can introduce the most suitable hair salon or beauty salon based on the user's skin condition. In this way, by introducing the user to the most suitable commercially available products and salons, effective beauty methods can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's skin condition data into a generating AI and have the generating AI perform the introduction of commercially available products and salons.

[0067] The recording unit saves records of the user's skincare and makeup, and can check for changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. For example, the recording unit can save records of the user's skincare and makeup. For example, the recording unit can save information on the skincare and makeup products the user has used. The recording unit can also check for changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. For example, the recording unit can periodically scan the user's skin condition and compare it with past data to check for the effects of blemish reduction and skin tone improvement. This makes it easier for the user to feel the beauty effects by checking the effects of their skincare and makeup. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input the user's skin condition data into a generating AI and have the generating AI perform a Before / After comparison.

[0068] The recording unit can learn and update personalized beauty methods from the records. For example, the recording unit learns personalized beauty methods based on the user's skincare and makeup records. For example, the recording unit can analyze the effects of the skincare and makeup products used by the user and learn the optimal beauty methods. The recording unit can also update the learned beauty methods. For example, the recording unit can periodically update the data and provide the latest beauty information. In this way, by learning and updating personalized beauty methods, it can always provide the latest beauty information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's skincare and makeup record data into a generating AI and have the generating AI perform the learning and updating of beauty methods.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the skin analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the system to adjust the display method according to the user's emotions, providing a user-friendly display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the display method.

[0070] The analysis unit can improve the accuracy of its analysis by referring to the user's past skin condition data during the analysis. For example, the analysis unit can compare the user's past skin condition data with the user's current skin condition. For example, the analysis unit can refer to the user's past skin condition data to analyze seasonal changes in skin. The analysis unit can also use the user's past skin condition data to grasp long-term skin trends. In this way, the accuracy of the analysis can be improved by referring to past skin condition data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past skin condition data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0071] The analysis unit can diagnose the skin condition by considering the user's lifestyle habits during analysis. For example, the analysis unit can analyze the user's diet and evaluate its impact on the skin condition. For example, the analysis unit can diagnose the nutritional status of the skin based on the user's diet. The analysis unit can also consider the user's sleep patterns and diagnose the skin's regenerative capacity. For example, the analysis unit can evaluate the skin's regenerative capacity based on the user's sleep data. The analysis unit can also evaluate the user's stress level and analyze its impact on the skin condition. For example, the analysis unit can diagnose the skin's health condition based on the user's stress data. This allows for a more accurate diagnosis of the skin condition by considering lifestyle habits. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform a diagnosis of the skin condition.

[0072] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the system to prioritize the analysis results according to the user's emotions, thereby providing the user with information that is important to them. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and priority determination.

[0073] The analysis unit can diagnose the skin condition by considering the user's geographical environment during analysis. For example, the analysis unit can evaluate the degree of skin dryness based on climate data of the user's place of residence. For example, the analysis unit can diagnose the skin's moisture retention capacity based on climate data of the user's place of residence. The analysis unit can also diagnose the skin's moisture retention capacity by referring to humidity data of the user's place of residence. For example, the analysis unit can evaluate the skin's moisture retention capacity based on humidity data of the user's place of residence. The analysis unit can also evaluate skin damage by considering the pollution level of the user's place of residence. For example, the analysis unit can diagnose the skin's health condition based on the pollution level of the user's place of residence. This allows for a more accurate diagnosis of the skin condition by considering the geographical environment. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical environment data into a generating AI and have the generating AI perform the diagnosis of the skin condition.

[0074] The analysis unit can analyze users' social media activity during analysis and reflect relevant skin condition trends. For example, the analysis unit can analyze users' social media posts to understand trends related to skin condition. For example, the analysis unit can reflect the latest trends related to skin condition based on users' social media posts. The analysis unit can also refer to posts from beauty influencers that users follow and provide the latest information on skin condition. For example, the analysis unit can reflect the latest trends related to skin condition based on posts from beauty influencers that users follow. The analysis unit can also identify factors that influence skin condition from users' social media activity. For example, the analysis unit can identify factors that influence skin condition based on users' social media activity and reflect them in the analysis. In this way, by analyzing social media activity, the latest trends in skin condition can be reflected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user social media data into a generating AI and have the generating AI perform the reflection of skin condition trends.

[0075] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on changes in facial expressions. The proposal unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the proposal unit can analyze the tone and speed of the voice and calculate an emotion score. The proposal unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on fluctuations in heart rate. By adjusting the way the proposal is presented according to the user's emotions, it is possible to provide proposals that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the expression method.

[0076] The suggestion unit can make optimal suggestions by referring to the user's past skincare history at the time of suggestion. For example, the suggestion unit can suggest the most suitable skincare method for the user's current skin condition based on the user's past skincare history. For example, the suggestion unit can refer to the user's past skincare history and suggest effective skincare products. The suggestion unit can also analyze the user's past skincare history and suggest a long-term skincare plan. For example, the suggestion unit can suggest a long-term skincare plan based on the user's past skincare history. In this way, optimal suggestions can be made by referring to past skincare history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past skincare history data into a generating AI and have the generating AI execute the optimal suggestion.

[0077] The suggestion unit can propose skincare methods that take into account the user's lifestyle habits when making suggestions. For example, the suggestion unit can consider the user's diet and propose skincare methods that include ingredients beneficial to the skin. For example, the suggestion unit can propose skincare methods that include ingredients beneficial to the skin based on the user's diet. The suggestion unit can also refer to the user's sleep patterns and propose skincare methods that enhance the skin's regenerative power. For example, the suggestion unit can propose skincare methods that enhance the skin's regenerative power based on the user's sleep data. The suggestion unit can also evaluate the user's stress level and propose skincare methods that help reduce stress. For example, the suggestion unit can propose skincare methods that help reduce stress based on the user's stress data. In this way, by taking lifestyle habits into account, more effective skincare methods can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI execute the skincare method proposal.

[0078] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expressions. The suggestion unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on fluctuations in heart rate. By determining the priority of suggestions according to the user's emotions, it is possible to prioritize providing information that is important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and priority determination.

[0079] The suggestion unit can propose the most suitable skincare method by considering the user's geographical environment. For example, the suggestion unit can propose a skincare method to prevent skin dryness based on climate data of the user's place of residence. The suggestion unit can also propose a skincare method to enhance the skin's moisture retention capacity by referring to humidity data of the user's place of residence. The suggestion unit can also propose a skincare method to reduce skin damage by considering the pollution level of the user's place of residence. In this way, by considering the geographical environment, a more effective skincare method can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical environment data into a generating AI and have the generating AI execute the skincare method proposal.

[0080] The suggestion unit can analyze the user's social media activity when making suggestions and reflect relevant beauty trends. For example, the suggestion unit can analyze the user's social media posts and suggest skincare methods based on beauty trends. For example, the suggestion unit can suggest skincare methods based on beauty trends based on the user's social media posts. The suggestion unit can also refer to posts from beauty influencers that the user follows and reflect the latest beauty trends. For example, the suggestion unit can reflect the latest beauty trends based on posts from beauty influencers that the user follows. The suggestion unit can also identify the user's interests in beauty from their social media activity and make suggestions based on those interests. For example, the suggestion unit can identify the user's interests in beauty from their social media activity and make suggestions based on those interests. This allows the suggestion unit to reflect the latest beauty trends by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI perform the task of reflecting beauty trends.

[0081] The recording unit can estimate the user's emotions and adjust the display method of the recording based on the estimated user emotions. For example, the recording unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the recording unit can calculate an emotion score based on changes in facial expressions. The recording unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the recording unit can analyze the tone and speed of the voice and calculate an emotion score. The recording unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the recording unit can calculate an emotion score based on fluctuations in heart rate. This allows the display method to be adjusted according to the user's emotions, providing a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the display method.

[0082] The recording unit can improve the accuracy of Before / After comparisons by referring to the user's past skincare history during recording. For example, the recording unit can compare the current skin condition with the user's past skincare history. For example, the recording unit can refer to the user's past skincare history to compare seasonal changes in skin. The recording unit can also use the user's past skincare history to grasp long-term skin trends. For example, the recording unit can grasp long-term skin trends based on the user's past skincare history. This improves the accuracy of Before / After comparisons by referring to past skincare history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's past skincare history data into a generating AI and have the generating AI perform the Before / After comparison.

[0083] The recording unit can evaluate the beauty effects by considering the user's lifestyle habits during recording. For example, the recording unit can analyze the user's diet and evaluate its impact on the beauty effects. For example, the recording unit can evaluate the impact on the beauty effects based on the user's diet. The recording unit can also evaluate the beauty effects by considering the user's sleep patterns. For example, the recording unit can evaluate the beauty effects based on the user's sleep data. The recording unit can also evaluate the user's stress level and analyze its impact on the beauty effects. For example, the recording unit can analyze the impact on the beauty effects based on the user's stress data. By considering lifestyle habits, a more accurate evaluation of beauty effects can be achieved. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's lifestyle data into a generating AI and have the generating AI perform the evaluation of beauty effects.

[0084] The recording unit can estimate the user's emotions and determine recording priorities based on the estimated emotions. For example, the recording unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the recording unit can calculate an emotion score based on changes in facial expressions. The recording unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the recording unit can analyze the tone and speed of the voice and calculate an emotion score. The recording unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the recording unit can calculate an emotion score based on fluctuations in heart rate. This allows the system to prioritize recordings according to the user's emotions, thereby providing users with priority access to information important to them. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and priority determination.

[0085] The recording unit can evaluate the beauty effects while considering the user's geographical environment during recording. For example, the recording unit can evaluate the beauty effects based on climate data of the user's place of residence. The recording unit can also evaluate the beauty effects by referring to humidity data of the user's place of residence. The recording unit can also evaluate the beauty effects by considering the pollution level of the user's place of residence. This allows for a more accurate evaluation of beauty effects by considering the geographical environment. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's geographical environment data into a generating AI and have the generating AI perform the evaluation of beauty effects.

[0086] The recording unit can analyze the user's social media activity during recording and reflect relevant beauty trends. For example, the recording unit can analyze the user's social media posts and evaluate the beauty effects based on beauty trends. For example, the recording unit can evaluate the beauty effects based on beauty trends based on the user's social media posts. The recording unit can also refer to posts from beauty influencers that the user follows and reflect the latest beauty trends. For example, the recording unit can reflect the latest beauty trends based on posts from beauty influencers that the user follows. The recording unit can also identify beauty interests from the user's social media activity and evaluate the beauty effects based on those interests. For example, the recording unit can identify beauty interests based on the user's social media activity and evaluate the beauty effects based on those interests. In this way, by analyzing social media activity, the latest beauty trends can be reflected. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's social media data into a generating AI and have the generating AI perform the reflection of beauty trends.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The AI-powered mirror system can further diagnose skin condition by considering the user's lifestyle. For example, the analysis unit can analyze the user's diet and evaluate its impact on skin condition. Specifically, it considers the balance of nutrients the user consumes and the frequency of meals to diagnose skin health. It can also consider the user's sleep patterns and evaluate skin regeneration. For example, it analyzes the user's sleep duration and quality to assess skin regeneration capacity. Furthermore, it can evaluate the user's stress level and analyze its impact on skin condition. For example, it diagnoses skin health based on the user's stress data. By considering lifestyle, it can diagnose skin condition more accurately.

[0089] The AI-powered mirror system can estimate the user's emotions and adjust the display method of skin analysis results based on those estimated emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates emotions using an emotion estimation algorithm. Specifically, it calculates an emotion score based on changes in facial expressions. It can also record the user's voice and estimate emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice to calculate an emotion score. Furthermore, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. By adjusting the display method according to the user's emotions, it can provide a display that is easy for the user to see.

[0090] The AI-powered mirror system can diagnose skin condition while considering the user's geographical environment. For example, the analysis unit can evaluate the degree of skin dryness based on climate data of the user's place of residence. Specifically, it diagnoses the skin's moisture retention capacity based on climate data of the user's place of residence. It can also diagnose skin moisture retention capacity by referring to humidity data of the user's place of residence. For example, it evaluates skin moisture retention capacity based on humidity data of the user's place of residence. Furthermore, it can assess skin damage by considering the pollution level of the user's place of residence. For example, it diagnoses skin health based on the pollution level of the user's place of residence. In this way, by considering the geographical environment, a more accurate diagnosis of skin condition can be made.

[0091] The AI-powered mirror system can analyze users' social media activity and reflect relevant skin condition trends. For example, the analysis unit can analyze users' social media posts to understand trends related to skin condition. Specifically, it reflects the latest trends related to skin condition based on users' social media posts. It can also refer to posts from beauty influencers that users follow and provide the latest information on skin condition. For example, it reflects the latest trends related to skin condition based on posts from beauty influencers that users follow. Furthermore, it can identify factors that influence skin condition from users' social media activity. For example, it identifies factors that influence skin condition based on users' social media activity and reflects them in the analysis. In this way, by analyzing social media activity, it can reflect the latest trends in skin condition.

[0092] The AI-powered mirror system can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Specifically, it calculates an emotion score based on changes in facial expressions. It can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice to calculate an emotion score. Furthermore, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are easy for the user to understand.

[0093] The AI-powered mirror system can provide optimal suggestions by referencing the user's past skincare history. For example, the suggestion function can propose the most suitable skincare method for the user's current skin condition based on their past skincare history. Specifically, it refers to the user's past skincare history and suggests effective skincare products. It can also analyze the user's past skincare history and propose a long-term skincare plan. For example, it proposes a long-term skincare plan based on the user's past skincare history. In this way, it can provide optimal suggestions by referring to past skincare history.

[0094] The AI-powered mirror system can estimate the user's emotions and prioritize suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Specifically, it calculates an emotion score based on changes in facial expressions. It can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of their voice to calculate an emotion score. Furthermore, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. By prioritizing suggestions according to the user's emotions, the system can prioritize providing information that is important to the user.

[0095] The AI-powered mirror system can suggest optimal skincare methods considering the user's geographical environment. For example, the suggestion function can propose skincare methods to prevent skin dryness based on climate data of the user's place of residence. Specifically, it can suggest skincare methods to prevent skin dryness based on climate data of the user's place of residence. It can also refer to humidity data of the user's place of residence and suggest skincare methods to enhance the skin's moisture retention capacity. For example, it can suggest skincare methods to enhance the skin's moisture retention capacity based on humidity data of the user's place of residence. Furthermore, it can consider the pollution level of the user's place of residence and suggest skincare methods to reduce skin damage. For example, it can suggest skincare methods to reduce skin damage based on the pollution level of the user's place of residence. In this way, by considering the geographical environment, it can suggest more effective skincare methods.

[0096] The AI-powered mirror system can analyze users' social media activity and reflect relevant beauty trends. For example, the suggestion function can analyze users' social media posts and suggest skincare methods based on beauty trends. Specifically, it suggests skincare methods based on beauty trends based on users' social media posts. It can also refer to posts from beauty influencers that users follow and reflect the latest beauty trends. For example, it reflects the latest beauty trends based on posts from beauty influencers that users follow. Furthermore, it can identify users' beauty interests from their social media activity and make suggestions based on those interests. For example, it identifies users' beauty interests based on their social media activity and makes suggestions based on those interests. In this way, by analyzing social media activity, it can reflect the latest beauty trends.

[0097] The AI-powered mirror system can estimate the user's emotions and adjust the display method of the recording based on those emotions. For example, the recording unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Specifically, it calculates an emotion score based on changes in facial expressions. It can also record the user's voice and estimate emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice to calculate an emotion score. Furthermore, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. By adjusting the display method according to the user's emotions, it can provide a display that is easy for the user to view.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The analysis unit analyzes the user's skin. Specifically, it diagnoses skin type (dry, oily, combination) and skin age (moisture level, tone, pores, wrinkles, blemishes, etc.). When the user stands in front of the mirror, the analysis unit scans the skin condition via camera and performs a detailed analysis. Step 2: The Proposal Department proposes the most suitable makeup and skincare for the user based on the skin condition analyzed by the Analysis Department. Specifically, they propose skincare methods tailored to the skin condition and makeup that suits the user's personal color (warm or cool undertones). They can also introduce relevant commercially available products and salons. Step 3: The recording unit records the results of the makeup and skincare suggested by the suggestion unit and performs a before-and-after comparison. Specifically, it saves the user's skincare and makeup records, allowing users to see changes such as a reduction in blemishes or an improvement in skin tone compared to one month ago. The recording unit also learns and updates personalized beauty methods based on the records.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the analysis unit, proposal unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit scans the user's skin using the camera 42 of the smart device 14 and diagnoses the skin type and skin age using the identification processing unit 290 of the data processing unit 12. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes optimal makeup and skincare based on the skin condition. The recording unit is implemented in the control unit 46A of the smart device 14 and saves records of the user's skincare and makeup and performs Before / After comparisons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the analysis unit, proposal unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit scans the user's skin using the camera 42 of the smart glasses 214 and diagnoses the skin type and skin age using the identification processing unit 290 of the data processing unit 12. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes optimal makeup and skincare based on the skin condition. The recording unit is implemented in the control unit 46A of the smart glasses 214 and saves records of the user's skincare and makeup and performs Before / After comparisons. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the analysis unit, proposal unit, and recording unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit scans the user's skin using the camera 42 of the headset terminal 314 and diagnoses the skin type and skin age using the identification processing unit 290 of the data processing unit 12. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes optimal makeup and skincare based on the skin condition. The recording unit is implemented in the control unit 46A of the headset terminal 314 and saves records of the user's skincare and makeup, and performs Before / After comparisons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the analysis unit, proposal unit, and recording unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit scans the user's skin using the camera 42 of the robot 414 and diagnoses the skin type and skin age using the identification processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes optimal makeup and skincare based on the skin condition. The recording unit is implemented by, for example, the control unit 46A of the robot 414 and saves records of the user's skincare and makeup and performs Before / After comparisons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The analysis department analyzes the user's skin, Based on the skin condition analyzed by the aforementioned analysis unit, the proposal unit suggests the most suitable makeup and skincare products for the user. The system includes a recording unit that records the results of the makeup and skincare suggested by the aforementioned proposal unit and performs a Before / After comparison. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Diagnose your skin type and skin age The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We offer skincare methods tailored to your skin condition and makeup that suits your personal color. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, This section introduces relevant commercially available products and salons. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recording unit is, The system saves users' skincare and makeup records and allows them to check for changes such as a reduction in blemishes or an improvement in skin tone compared to one month prior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recording unit is, Learn and update personalized beauty methods from records. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is The system estimates the user's emotions and adjusts how the skin analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is The system improves analysis accuracy by referencing the user's past skin condition data during analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is The skin condition is diagnosed while taking into account the user's lifestyle habits during analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is The skin condition is diagnosed while taking the user's geographical environment into consideration during analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, the system analyzes users' social media activity to reflect related skin condition trends. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, we refer to the user's past skincare history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, we suggest skincare methods that take into account the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, we take the user's geographical environment into consideration to suggest the most suitable skincare method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, we analyze users' social media activity and reflect relevant beauty trends. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recording unit is, It estimates the user's emotions and adjusts how the records are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recording unit is, The system improves the accuracy of before-and-after comparisons by referencing the user's past skincare history during recording. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recording unit is, When recording, the beauty effects are evaluated while taking into account the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recording unit is, The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recording unit is, When recording, the beauty effects are evaluated while taking into account the user's geographical environment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recording unit is, Analyze users' social media activity during recording to reflect relevant beauty trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the user's skin, Based on the skin condition analyzed by the aforementioned analysis unit, the proposal unit suggests the most suitable makeup and skincare products for the user. The system includes a recording unit that records the results of the makeup and skincare suggested by the aforementioned proposal unit and performs a Before / After comparison. A system characterized by the following features.

2. The aforementioned analysis unit is Diagnose your skin type and skin age The system according to feature 1.

3. The aforementioned proposal section is, We offer skincare methods tailored to your skin condition and makeup that suits your personal color. The system according to feature 1.

4. The aforementioned proposal section is, This section introduces relevant commercially available products and salons. The system according to feature 1.

5. The aforementioned recording unit is The system saves users' skincare and makeup records and allows them to check for changes such as a reduction in blemishes or an improvement in skin tone compared to one month prior. The system according to feature 1.

6. The aforementioned recording unit is Learn and update personalized beauty methods from records. The system according to feature 1.

7. The aforementioned analysis unit is The system estimates the user's emotions and adjusts how the skin analysis results are displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit is The system improves analysis accuracy by referencing the user's past skin condition data during analysis. The system according to feature 1.

Images