system

Abstract

The system according to this embodiment aims to provide personalized skincare that responds to the ever-changing condition of the skin. [Solution] The system according to the embodiment comprises a camera, an analysis unit, a suggestion unit, an allergy consideration unit, and a daily analysis unit. The camera captures the user's face. The analysis unit analyzes the image captured by the camera and evaluates the skin condition. The suggestion unit proposes a personalized skincare schedule based on the skin condition evaluated by the analysis unit. The allergy consideration unit proposes care points based on allergy information, based on the schedule proposed by the suggestion unit. The daily analysis unit proposes appropriate care for today based on the skin condition today evaluated by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to provide appropriate skin care according to the daily-changing skin condition.

[0005] The system according to the embodiment aims to provide personalized skin care according to the daily-changing skin condition.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a camera, an analysis unit, a suggestion unit, an allergy consideration unit, and a daily analysis unit. The camera captures the user's face. The analysis unit analyzes the image captured by the camera and evaluates the skin condition. The suggestion unit proposes a personalized skincare schedule based on the skin condition evaluated by the analysis unit. The allergy consideration unit proposes care points based on allergy information, based on the schedule proposed by the suggestion unit. The daily analysis unit proposes appropriate care for the day based on the skin condition for the day evaluated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized skincare that responds to the ever-changing condition of the skin. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network). [[ID=第十九]]

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The skincare advice system according to an embodiment of the present invention is a system that provides optimal skincare to individual users in accordance with the ever-changing external environment (such as weather) and internal environment (such as physical condition). This skincare advice system allows users to show their face through the built-in camera, and the AI ​​analyzes the skin condition and provides a detailed report. The analysis includes information on blemishes, wrinkles, elasticity, and pore condition, and continuous progress is also tracked. Next, it proposes a personalized skincare schedule that takes into account the user's lifestyle, skin condition, and seasonality. Furthermore, it proposes care points that also take into account allergy information. Finally, it analyzes the user's skin condition for the day, evaluates the daily condition such as roughness, swelling, and dark circles, and proposes the optimal care for that day. This allows users to maintain healthy skin at all times. In addition, natural language processing is utilized to enable easy voice-based communication. For example, when a user shows their face through the built-in camera, the AI ​​analyzes the skin condition and provides a detailed report. The analysis includes information on blemishes, wrinkles, elasticity, and pore condition, and continuous progress is also tracked. Next, it proposes a personalized skincare schedule that takes into account the user's lifestyle, skin condition, and seasonality. Furthermore, it suggests care points that take allergy information into consideration. Finally, it analyzes the user's skin condition for the day, evaluating its state for each day, such as roughness, swelling, and dark circles, and suggests the optimal care for that day. This allows users to maintain healthy skin at all times. In addition, it utilizes natural language processing to enable easy voice-based conversational support. As a result, the skincare advice system can analyze the user's skin condition in detail and provide optimal skincare.

[0029] The skincare advice system according to the embodiment comprises a camera, an analysis unit, a suggestion unit, an allergy consideration unit, and a day analysis unit. The camera captures the user's face. The camera, for example, uses a high-resolution camera to photograph the user's face. The camera, for example, can photograph the entire face using a wide-angle lens. The camera, for example, can photograph at night using an infrared camera. The analysis unit analyzes the image captured by the camera and evaluates the condition of the skin. The analysis unit, for example, evaluates the size and color of blemishes. The analysis unit, for example, can evaluate the depth of wrinkles. The analysis unit, for example, can measure elasticity. The analysis unit, for example, can evaluate the condition of pores. The suggestion unit proposes a personalized skincare schedule based on the skin condition evaluated by the analysis unit. The suggestion unit, for example, proposes a skincare schedule based on lifestyle. The suggestion unit, for example, can propose a skincare schedule based on skin condition. The suggestion unit, for example, can propose a skincare schedule based on seasonality. The allergy consideration unit proposes care points that take allergy information into account, based on the schedule proposed by the suggestion unit. The allergy consideration unit suggests care points based on the user's allergy history, for example. The allergy consideration unit can suggest care points based on the type of allergen, for example. The allergy consideration unit can suggest care points to reduce allergy risk, for example. The current analysis unit suggests the optimal care for today based on the current skin condition evaluated by the analysis unit. The current analysis unit can evaluate the degree of skin irritation, for example. The current analysis unit can evaluate the location and degree of swelling, for example. The current analysis unit can evaluate the color and size of dark circles under the eyes, for example. As a result, the skincare advice system according to the embodiment can analyze the user's skin condition in detail and provide optimal skincare.

[0030] The camera captures the user's face. For example, a high-resolution camera can capture the user's face. High-resolution cameras can capture even the smallest details clearly, allowing for accurate detection of subtle skin changes and problems. The camera can also capture the entire face using a wide-angle lens. Wide-angle lenses capture the entire face at once, enabling a comprehensive assessment of skin condition, including areas often missed in partial shots. The camera can also shoot at night using an infrared camera. Infrared cameras provide clear images even in dark environments, allowing for accurate skin condition assessment even at night or in dimly lit conditions. This enables the camera to consistently capture and analyze the user's skin condition with high accuracy, regardless of time or location. Furthermore, the camera has a facial movement tracking function, allowing for blur-free images even when the user moves. This ensures the user is captured in a natural state, enabling a more accurate assessment of skin condition. The camera also has multiple shooting modes, such as standard mode, magnified mode, and infrared mode, allowing for shooting at various angles and under different conditions. This allows the camera to capture the user's skin condition from multiple angles, improving the quality of the data provided to the analysis unit.

[0031] The analysis unit analyzes images captured by the camera to evaluate the condition of the skin. For example, the analysis unit can evaluate the size and color of blemishes. To evaluate blemishes, it uses image processing technology to detect the outline of the blemishes and quantify their area and color intensity. The analysis unit can also evaluate the depth of wrinkles. To evaluate wrinkles, it uses 3D image analysis technology to measure the depth and length of wrinkles and evaluate the progression of wrinkles. The analysis unit can also measure elasticity. To measure elasticity, it combines image analysis and physical measurement data to quantify the elasticity of the skin and evaluate the health of the skin. The analysis unit can also evaluate the condition of pores. To evaluate pores, it uses high-resolution image analysis to detect the size and degree of clogging of pores and evaluate the health of the pores. In this way, the analysis unit can evaluate the user's skin condition from multiple angles and provide detailed analysis results. Furthermore, the analysis unit can utilize AI technology to track changes in the skin by comparing it with past data. For example, by analyzing regularly taken images and monitoring the increase or decrease of blemishes and wrinkles, it is possible to understand changes in the user's skin condition and use this information to suggest appropriate skincare. Furthermore, the analysis unit can provide more personalized analysis results by taking into account information such as the user's skin type, age, and lifestyle. This allows the analysis unit to comprehensively evaluate the user's skin condition and contribute to suggesting optimal skincare.

[0032] The suggestion department proposes a personalized skincare schedule based on the skin condition evaluated by the analysis department. For example, the suggestion department can propose a skincare schedule based on the user's lifestyle. Lifestyle includes the user's daily rhythm, diet, and exercise habits, and based on this information, it proposes the optimal timing and method of skincare. The suggestion department can also propose a skincare schedule based on the user's skin condition. Skin condition includes blemishes, wrinkles, elasticity, and pore condition, and based on these evaluation results, it proposes the optimal skincare products and methods. The suggestion department can also propose a skincare schedule based on seasonality. Seasonality includes temperature, humidity, and UV radiation levels, and considering these environmental factors, it proposes skincare methods appropriate for each season. In this way, the suggestion department can provide a skincare schedule that meets the user's individual needs and support effective skincare. Furthermore, the suggestion department can use AI to analyze the user's past skincare history and effects to make more accurate suggestions. For example, it can evaluate the effects of skincare products used in the past and propose products that can be expected to have similar effects. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with an optimal skincare schedule and support them in maintaining healthy skin.

[0033] The Allergy Consideration Unit proposes care points that take allergy information into account, based on the schedule proposed by the Proposal Unit. For example, the Allergy Consideration Unit proposes care points based on the user's allergy history. The user's allergy history includes ingredients and products that have caused allergic reactions in the past, and based on this information, it proposes skincare products and methods to reduce allergy risk. For example, the Allergy Consideration Unit can propose care points based on the type of allergen. The types of allergens include specific chemical components, plant extracts, fragrances, etc., and it proposes skincare products and methods to avoid these ingredients. For example, the Allergy Consideration Unit can propose care points to reduce allergy risk. To reduce allergy risk, it is important to use hypoallergenic products and choose products that do not contain allergens, and based on this information, it proposes skincare products and methods that are suitable for the user. In this way, the Allergy Consideration Unit can reduce the user's allergy risk and support safe and effective skincare. Furthermore, the Allergy Consideration Unit can use AI to analyze the user's allergy reaction patterns and make more accurate suggestions. For example, based on past allergy reaction data, it can evaluate sensitivity to specific ingredients and make suggestions to avoid high-risk ingredients. Furthermore, the allergy-conscious unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the allergy-conscious unit to provide users with safe and effective skincare and support them in maintaining healthy skin.

[0034] Today's analysis unit proposes the optimal care for today based on the current state of the skin, as evaluated by the unit. For example, today's analysis unit can evaluate the degree of skin irritation. To evaluate skin irritation, image analysis technology is used to quantify the redness and dryness of the skin and assess the degree of irritation. Today's analysis unit can also evaluate the location and degree of swelling. To evaluate swelling, 3D image analysis technology is used to measure the contours and swelling of the face and assess the location and degree of swelling. Today's analysis unit can also evaluate the color and size of dark circles under the eyes. To evaluate dark circles under the eyes, image color analysis technology is used to quantify the intensity and size of the dark circles and assess their condition. As a result, today's analysis unit can evaluate the user's skin condition in detail and propose the optimal skincare for today. Furthermore, today's analysis unit can utilize AI to track changes in the user's skin condition by comparing it with past data. For example, it can evaluate the current condition based on past data on skin irritation and swelling and propose appropriate care methods. In addition, today's analysis unit can provide more personalized care suggestions by considering the user's lifestyle and environmental factors. This allows today's analysis department to provide users with optimal skincare and support them in maintaining healthy skin.

[0035] The analysis unit can evaluate blemishes, wrinkles, elasticity, and pore condition, and track continuous progress. For example, the analysis unit can evaluate the size and color of blemishes. For example, the analysis unit can evaluate the depth of wrinkles. For example, the analysis unit can measure elasticity. For example, the analysis unit can evaluate the condition of pores. This allows for a detailed understanding of the user's skin condition by evaluating blemishes, wrinkles, elasticity, pore condition, etc., and tracking continuous progress. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the size and color of blemishes into a generative AI and have the generative AI perform the evaluation of blemishes.

[0036] The suggestion unit can propose a personalized skincare schedule based on lifestyle, skin condition, and seasonality. For example, the suggestion unit can propose a skincare schedule based on lifestyle. For example, the suggestion unit can propose a skincare schedule based on skin condition. For example, the suggestion unit can propose a skincare schedule based on seasonality. This allows the user to receive optimal skincare by proposing a personalized skincare schedule that takes lifestyle, skin condition, and seasonality into consideration. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input a skincare schedule based on lifestyle into a generative AI and have the generative AI execute the schedule proposal.

[0037] The allergy-considering unit can propose care points based on allergy information. For example, the allergy-considering unit can propose care points based on the user's allergy history. For example, the allergy-considering unit can propose care points based on the type of allergen. For example, the allergy-considering unit can propose care points to reduce allergy risk. In this way, the user's allergy risk can be reduced by proposing care points that take allergy information into consideration. Some or all of the above processing in the allergy-considering unit may be performed using AI, for example, or without using AI. For example, the allergy-considering unit can input the user's allergy history into a generating AI and have the generating AI execute the proposal of care points.

[0038] Today's analysis unit can evaluate skin roughness, swelling, dark circles, and other daily conditions, and suggest appropriate care for the day. For example, today's analysis unit can evaluate the degree of skin roughness. For example, today's analysis unit can evaluate the location and degree of swelling. For example, today's analysis unit can evaluate the color and size of dark circles. In this way, by evaluating the daily conditions such as skin roughness, swelling, and dark circles, and suggesting the optimal care for the day, the user's skin health can be maintained. Some or all of the above processing in today's analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, today's analysis unit can input the degree of skin roughness into a generative AI and have the generative AI perform the evaluation of skin roughness.

[0039] The system can utilize natural language processing to enable simple voice-based conversations. For example, the system can use speech recognition technology to convert user speech into text. For example, the system can use text analysis technology to analyze the content of user speech. For example, the system can use dialogue generation technology to generate responses to user speech. This improves user convenience by utilizing natural language processing to enable easy voice-based conversations. Some or all of the above processes in the system may be performed using, for example, a generative AI, or without a generative AI. For example, the system can input user speech into a generative AI and have the generative AI perform analysis of the speech content.

[0040] The analysis unit can improve the accuracy of the analysis based on the user's past skin condition data during the analysis. For example, the analysis unit can more accurately evaluate the current skin condition based on the user's past skin condition data. For example, the analysis unit can refer to past data, analyze trends in skin changes, and reflect them in the analysis results. For example, the analysis unit can use the user's past skin condition data to evaluate the long-term effects of skincare. This improves the accuracy of the analysis by referring to the user's past skin condition data. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating 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 improvement of the analysis accuracy.

[0041] The analysis unit can correct the analysis results based on the user's lifestyle and diet during the analysis. For example, the analysis unit can correct the analysis results by considering the user's lifestyle (sleep duration, exercise level, etc.). For example, the analysis unit can adjust the analysis results based on the user's diet (nutritional balance, calorie intake, etc.). For example, the analysis unit can correct the analysis results by considering the user's stress level and hormone balance. This improves the accuracy of the analysis results by considering the user's lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's lifestyle and diet into a generating AI and have the generating AI perform the correction of the analysis results.

[0042] The analysis unit can adjust the analysis results based on the user's geographical location information during analysis. For example, the analysis unit can adjust the analysis results by considering the climate conditions of the user's place of residence. For example, the analysis unit can correct the analysis results based on the weather information of the user's current location. For example, the analysis unit can provide analysis results that take into account region-specific skin problems based on the user's geographical location. In this way, by considering the user's geographical location information, it is possible to provide analysis results that address region-specific skin problems. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the analysis results.

[0043] The analysis unit can analyze the user's social media activity during analysis and obtain relevant skin condition information. For example, the analysis unit can infer stress levels and lifestyle habits from the user's social media posts and reflect them in the analysis results. For example, the analysis unit can correct the analysis results based on the user's usage of skincare products shared. For example, the analysis unit can adjust the analysis results by considering events (travel, parties, etc.) that affect skin condition based on the user's social media activity. This allows for obtaining more detailed skin condition information by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI and have the generative AI perform the acquisition of relevant skin condition information.

[0044] The suggestion unit can improve the accuracy of its suggestions based on the user's past skincare history. For example, the suggestion unit can suggest the most suitable skincare products based on the user's past skincare history. For example, the suggestion unit can refer to past history and prioritize suggesting skincare methods that have been effective. For example, the suggestion unit can use the user's past skincare history to suggest a long-term skincare plan. This improves the accuracy of the suggestions by referring to the user's past skincare history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past skincare history into a generative AI and have the generative AI perform the improvement of the accuracy of the suggestions.

[0045] The suggestion unit can adjust its suggestions based on the user's lifestyle and dietary habits. For example, the suggestion unit can adjust its suggestions by considering the user's lifestyle (sleep duration, exercise level, etc.). For example, the suggestion unit can adjust its suggestions based on the user's dietary habits (nutritional balance, calorie intake, etc.). For example, the suggestion unit can adjust its suggestions by considering the user's stress level and hormone balance. This improves the accuracy of the suggestions by considering the user's lifestyle and dietary habits. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's lifestyle and dietary habits into a generative AI and have the generative AI perform the adjustments to the suggestions.

[0046] The suggestion unit can adjust the suggested content based on the user's geographical location information when making a suggestion. For example, the suggestion unit can adjust the suggested content considering the climate conditions of the user's place of residence. For example, the suggestion unit can correct the suggested content based on the weather information of the user's current location. For example, the suggestion unit can suggest region-specific skincare methods based on the user's geographical location. In this way, by considering the user's geographical location information, region-specific skincare methods can be suggested. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI perform the adjustment of the suggested content.

[0047] The suggestion unit can analyze the user's social media activity and suggest relevant skincare information when making suggestions. For example, the suggestion unit can infer the usage status of skincare products from the user's social media posts and reflect this in the suggestions. For example, the suggestion unit can suggest effective skincare methods based on skincare methods shared by the user. For example, the suggestion unit can reflect skincare trend information from the user's social media activity in the suggestions. This allows for the provision of more detailed skincare information by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI generate suggestions for relevant skincare information.

[0048] The allergy consideration unit can improve accuracy when considering allergy information based on the user's past allergy reaction history. For example, the allergy consideration unit can evaluate the current allergy risk based on the user's past allergy reaction history. For example, the allergy consideration unit can identify components with a high allergy risk by referring to the past allergy reaction history. For example, the allergy consideration unit can evaluate the long-term allergy risk using the user's past allergy reaction history. This improves the accuracy of allergy information by referring to the user's past allergy reaction history. Some or all of the above processing in the allergy consideration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the allergy consideration unit can input the user's past allergy reaction history into a generating AI and have the generating AI perform the accuracy improvement.

[0049] The allergy consideration unit can provide relevant allergy information based on the user's geographical location when considering allergy information. For example, the allergy consideration unit can provide information considering the allergy risk of the user's place of residence. For example, the allergy consideration unit can correct the information based on the allergy risk of the user's current location. For example, the allergy consideration unit can provide region-specific allergy information based on the user's geographical location. In this way, region-specific allergy information can be provided by considering the user's geographical location. Some or all of the above processing in the allergy consideration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the allergy consideration unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of allergy information.

[0050] Today's analysis unit can improve the accuracy of its analysis based on the user's past skin condition data during today's analysis. For example, today's analysis unit can more accurately evaluate the user's current skin condition based on the user's past skin condition data. For example, today's analysis unit can refer to past data, analyze trends in skin changes, and reflect them in today's analysis results. For example, today's analysis unit can use the user's past skin condition data to evaluate the long-term effects of skincare. This improves the accuracy of today's analysis by referring to the user's past skin condition data. Some or all of the above processes in today's analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, today's analysis unit can input the user's past skin condition data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0051] The analysis unit can correct the analysis results based on the user's lifestyle and diet during the analysis. For example, the analysis unit can correct the analysis results by considering the user's lifestyle (sleep duration, exercise level, etc.). For example, the analysis unit can adjust the analysis results based on the user's diet (nutritional balance, calorie intake, etc.). For example, the analysis unit can correct the analysis results by considering the user's stress level and hormone balance. This improves the accuracy of the analysis results by considering the user's lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's lifestyle and diet into a generating AI and have the generating AI perform the correction of the analysis results.

[0052] Today's analysis unit can adjust the analysis results based on the user's geographical location information during the analysis. Today's analysis unit can adjust the analysis results by considering the climate conditions of the user's place of residence, for example. Today's analysis unit can correct the analysis results based on the weather information of the user's current location, for example. Today's analysis unit can provide analysis results that take into account region-specific skin problems based on the user's geographical location, for example. This makes it possible to provide analysis results that address region-specific skin problems by considering the user's geographical location information. Some or all of the above processing in today's analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, today's analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the adjustment of the analysis results.

[0053] Today's analysis unit can analyze a user's social media activity and obtain relevant skin condition information during the analysis. For example, today's analysis unit can infer stress levels and lifestyle habits from a user's social media posts and reflect them in the analysis results. For example, today's analysis unit can adjust today's analysis results based on the usage status of skincare products shared by the user. For example, today's analysis unit can adjust today's analysis results by considering events (travel, parties, etc.) that affect skin condition based on the user's social media activity. This allows for obtaining more detailed skin condition information by analyzing the user's social media activity. Some or all of the above processing in today's analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, today's analysis unit can input the user's social media activity into a generative AI and have the generative AI perform the acquisition of relevant skin condition information.

[0054] The natural language processing unit can improve the accuracy of conversations based on the user's past conversation history. For example, the natural language processing unit can provide appropriate skincare advice based on the user's past conversation history. For example, the natural language processing unit can refer to past conversation history and conduct conversations tailored to the user's preferences and needs. For example, the natural language processing unit can use the user's past conversation history to propose a long-term skincare plan. This improves the accuracy of conversations by referring to the user's past conversation history. Some or all of the above processing in the natural language processing unit may be performed using, for example, generative AI, or without generative AI. For example, the natural language processing unit can input the user's past conversation history into a generative AI and have the generative AI perform the conversation accuracy improvement.

[0055] The natural language processing unit can adjust the content of a conversation based on the user's geographical location information. For example, the natural language processing unit can adjust the content of a conversation by considering the climate conditions of the user's place of residence. For example, the natural language processing unit can correct the content of a conversation based on the weather information of the user's current location. For example, the natural language processing unit can provide region-specific skincare advice based on the user's geographical location. This allows for the provision of region-specific skincare advice by considering the user's geographical location information. Some or all of the above-described processes in the natural language processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the natural language processing unit can input the user's geographical location information into a generative AI and have the generative AI perform the adjustment of the conversation content.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] A skincare advice system can improve its analytical accuracy based on the user's past skin condition data. For example, it can more accurately assess the user's current skin condition based on their past data. It can also analyze trends in skin changes by referring to past data and reflect these trends in the analysis results. Furthermore, it can evaluate the long-term effects of skincare using the user's past skin condition data. In short, referring to the user's past skin condition data improves analytical accuracy.

[0058] The skincare advice system can adjust its analysis results based on the user's lifestyle and diet. For example, it can adjust the results by considering the user's lifestyle (sleep duration, exercise level, etc.). It can also adjust the results based on the user's diet (nutritional balance, calorie intake, etc.). Furthermore, it can adjust the results by considering the user's stress level and hormone balance. This improves the accuracy of the analysis results by taking into account the user's lifestyle and diet.

[0059] The skincare advice system can adjust its analysis results based on the user's geographical location. For example, it can adjust the results to take into account the climate conditions of the user's place of residence. It can also correct the results based on the weather information of the user's current location. Furthermore, it can provide analysis results that take into account region-specific skin problems based on the user's geographical location. In this way, by considering the user's geographical location, it can provide analysis results that address region-specific skin problems.

[0060] The skincare advice system can analyze a user's social media activity and obtain relevant skin condition information. For example, it can infer stress levels and lifestyle habits from a user's social media posts and reflect this in the analysis results. It can also adjust the analysis results based on the user's shared usage of skincare products. Furthermore, it can adjust the analysis results by considering events that affect skin condition (travel, parties, etc.) based on the user's social media activity. In this way, by analyzing a user's social media activity, more detailed skin condition information can be obtained.

[0061] A skincare advice system can improve the accuracy of its recommendations based on the user's past skincare history. For example, it can suggest the most suitable skincare products based on the user's past skincare history. It can also prioritize suggesting skincare methods that have been effective by referring to past history. Furthermore, it can propose a long-term skincare plan using the user's past skincare history. In this way, the accuracy of recommendations is improved by referring to the user's past skincare history.

[0062] The skincare advice system can adjust its recommendations based on the user's lifestyle and diet. For example, it can adjust recommendations based on the user's lifestyle (sleep duration, exercise level, etc.). It can also adjust recommendations based on the user's diet (nutritional balance, calorie intake, etc.). Furthermore, it can adjust recommendations based on the user's stress level and hormone balance. This improves the accuracy of the recommendations by taking into account the user's lifestyle and diet.

[0063] The skincare advice system can adjust its recommendations based on the user's geographical location. For example, it can adjust recommendations considering the climate conditions of the user's place of residence. It can also correct recommendations based on the weather information of the user's current location. Furthermore, it can suggest region-specific skincare methods based on the user's geographical location. In this way, by considering the user's geographical location, it can suggest region-specific skincare methods.

[0064] The skincare advice system can analyze a user's social media activity and suggest relevant skincare information. For example, it can infer the usage status of skincare products from a user's social media posts and reflect this in its suggestions. It can also suggest effective skincare methods based on skincare routines shared by the user. Furthermore, it can incorporate skincare trend information from the user's social media activity into its suggestions. In this way, by analyzing the user's social media activity, it can provide more detailed skincare information.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The camera captures the user's face. For example, a high-resolution camera can be used to capture the user's face, and a wide-angle lens can be used to capture the entire face. Infrared cameras can also be used to capture images at night. Step 2: The analysis unit analyzes the images captured by the camera and evaluates the condition of the skin. For example, it can evaluate the size and color of blemishes, the depth of wrinkles, elasticity, and the condition of pores. Step 3: The suggestion unit proposes a personalized skincare schedule based on the skin condition evaluated by the analysis unit. For example, it can propose a skincare schedule based on lifestyle, skin condition, and seasonality. Step 4: The allergy consideration unit proposes care points that take allergy information into account, based on the schedule proposed by the proposal unit. For example, it can propose the user's allergy history, types of allergens, and care points to reduce allergy risk. Step 5: Today's analysis unit will suggest the optimal care for today based on the skin condition evaluated by the unit. For example, it can evaluate the degree of skin irritation, the location and degree of swelling, and the color and size of dark circles under the eyes.

[0067] (Example of form 2) The skincare advice system according to an embodiment of the present invention is a system that provides optimal skincare to individual users in accordance with the ever-changing external environment (such as weather) and internal environment (such as physical condition). This skincare advice system allows users to show their face through the built-in camera, and the AI ​​analyzes the skin condition and provides a detailed report. The analysis includes information on blemishes, wrinkles, elasticity, and pore condition, and continuous progress is also tracked. Next, it proposes a personalized skincare schedule that takes into account the user's lifestyle, skin condition, and seasonality. Furthermore, it proposes care points that also take into account allergy information. Finally, it analyzes the user's skin condition for the day, evaluates the daily condition such as roughness, swelling, and dark circles, and proposes the optimal care for that day. This allows users to maintain healthy skin at all times. In addition, natural language processing is utilized to enable easy voice-based communication. For example, when a user shows their face through the built-in camera, the AI ​​analyzes the skin condition and provides a detailed report. The analysis includes information on blemishes, wrinkles, elasticity, and pore condition, and continuous progress is also tracked. Next, it proposes a personalized skincare schedule that takes into account the user's lifestyle, skin condition, and seasonality. Furthermore, it suggests care points that take allergy information into consideration. Finally, it analyzes the user's skin condition for the day, evaluating its state for each day, such as roughness, swelling, and dark circles, and suggests the optimal care for that day. This allows users to maintain healthy skin at all times. In addition, it utilizes natural language processing to enable easy voice-based conversational support. As a result, the skincare advice system can analyze the user's skin condition in detail and provide optimal skincare.

[0068] The skincare advice system according to the embodiment comprises a camera, an analysis unit, a suggestion unit, an allergy consideration unit, and a day analysis unit. The camera captures the user's face. The camera, for example, uses a high-resolution camera to photograph the user's face. The camera, for example, can photograph the entire face using a wide-angle lens. The camera, for example, can photograph at night using an infrared camera. The analysis unit analyzes the image captured by the camera and evaluates the condition of the skin. The analysis unit, for example, evaluates the size and color of blemishes. The analysis unit, for example, can evaluate the depth of wrinkles. The analysis unit, for example, can measure elasticity. The analysis unit, for example, can evaluate the condition of pores. The suggestion unit proposes a personalized skincare schedule based on the skin condition evaluated by the analysis unit. The suggestion unit, for example, proposes a skincare schedule based on lifestyle. The suggestion unit, for example, can propose a skincare schedule based on skin condition. The suggestion unit, for example, can propose a skincare schedule based on seasonality. The allergy consideration unit proposes care points that take allergy information into account, based on the schedule proposed by the suggestion unit. The allergy consideration unit suggests care points based on the user's allergy history, for example. The allergy consideration unit can suggest care points based on the type of allergen, for example. The allergy consideration unit can suggest care points to reduce allergy risk, for example. The current analysis unit suggests the optimal care for today based on the current skin condition evaluated by the analysis unit. The current analysis unit can evaluate the degree of skin irritation, for example. The current analysis unit can evaluate the location and degree of swelling, for example. The current analysis unit can evaluate the color and size of dark circles under the eyes, for example. As a result, the skincare advice system according to the embodiment can analyze the user's skin condition in detail and provide optimal skincare.

[0069] The camera captures the user's face. For example, a high-resolution camera can capture the user's face. High-resolution cameras can capture even the smallest details clearly, allowing for accurate detection of subtle skin changes and problems. The camera can also capture the entire face using a wide-angle lens. Wide-angle lenses capture the entire face at once, enabling a comprehensive assessment of skin condition, including areas often missed in partial shots. The camera can also shoot at night using an infrared camera. Infrared cameras provide clear images even in dark environments, allowing for accurate skin condition assessment even at night or in dimly lit conditions. This enables the camera to consistently capture and analyze the user's skin condition with high accuracy, regardless of time or location. Furthermore, the camera has a facial movement tracking function, allowing for blur-free images even when the user moves. This ensures the user is captured in a natural state, enabling a more accurate assessment of skin condition. The camera also has multiple shooting modes, such as standard mode, magnified mode, and infrared mode, allowing for shooting at various angles and under different conditions. This allows the camera to capture the user's skin condition from multiple angles, improving the quality of the data provided to the analysis unit.

[0070] The analysis unit analyzes images captured by the camera to evaluate the condition of the skin. For example, the analysis unit can evaluate the size and color of blemishes. To evaluate blemishes, it uses image processing technology to detect the outline of the blemishes and quantify their area and color intensity. The analysis unit can also evaluate the depth of wrinkles. To evaluate wrinkles, it uses 3D image analysis technology to measure the depth and length of wrinkles and evaluate the progression of wrinkles. The analysis unit can also measure elasticity. To measure elasticity, it combines image analysis and physical measurement data to quantify the elasticity of the skin and evaluate the health of the skin. The analysis unit can also evaluate the condition of pores. To evaluate pores, it uses high-resolution image analysis to detect the size and degree of clogging of pores and evaluate the health of the pores. In this way, the analysis unit can evaluate the user's skin condition from multiple angles and provide detailed analysis results. Furthermore, the analysis unit can utilize AI technology to track changes in the skin by comparing it with past data. For example, by analyzing regularly taken images and monitoring the increase or decrease of blemishes and wrinkles, it is possible to understand changes in the user's skin condition and use this information to suggest appropriate skincare. Furthermore, the analysis unit can provide more personalized analysis results by taking into account information such as the user's skin type, age, and lifestyle. This allows the analysis unit to comprehensively evaluate the user's skin condition and contribute to suggesting optimal skincare.

[0071] The suggestion department proposes a personalized skincare schedule based on the skin condition evaluated by the analysis department. For example, the suggestion department can propose a skincare schedule based on the user's lifestyle. Lifestyle includes the user's daily rhythm, diet, and exercise habits, and based on this information, it proposes the optimal timing and method of skincare. The suggestion department can also propose a skincare schedule based on the user's skin condition. Skin condition includes blemishes, wrinkles, elasticity, and pore condition, and based on these evaluation results, it proposes the optimal skincare products and methods. The suggestion department can also propose a skincare schedule based on seasonality. Seasonality includes temperature, humidity, and UV radiation levels, and considering these environmental factors, it proposes skincare methods appropriate for each season. In this way, the suggestion department can provide a skincare schedule that meets the user's individual needs and support effective skincare. Furthermore, the suggestion department can use AI to analyze the user's past skincare history and effects to make more accurate suggestions. For example, it can evaluate the effects of skincare products used in the past and propose products that can be expected to have similar effects. In addition, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with an optimal skincare schedule and support them in maintaining healthy skin.

[0072] The Allergy Consideration Unit proposes care points that take allergy information into account, based on the schedule proposed by the Proposal Unit. For example, the Allergy Consideration Unit proposes care points based on the user's allergy history. The user's allergy history includes ingredients and products that have caused allergic reactions in the past, and based on this information, it proposes skincare products and methods to reduce allergy risk. For example, the Allergy Consideration Unit can propose care points based on the type of allergen. The types of allergens include specific chemical components, plant extracts, fragrances, etc., and it proposes skincare products and methods to avoid these ingredients. For example, the Allergy Consideration Unit can propose care points to reduce allergy risk. To reduce allergy risk, it is important to use hypoallergenic products and choose products that do not contain allergens, and based on this information, it proposes skincare products and methods that are suitable for the user. In this way, the Allergy Consideration Unit can reduce the user's allergy risk and support safe and effective skincare. Furthermore, the Allergy Consideration Unit can use AI to analyze the user's allergy reaction patterns and make more accurate suggestions. For example, based on past allergy reaction data, it can evaluate sensitivity to specific ingredients and make suggestions to avoid high-risk ingredients. Furthermore, the allergy-conscious unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the allergy-conscious unit to provide users with safe and effective skincare and support them in maintaining healthy skin.

[0073] Today's analysis unit proposes the optimal care for today based on the current state of the skin, as evaluated by the unit. For example, today's analysis unit can evaluate the degree of skin irritation. To evaluate skin irritation, image analysis technology is used to quantify the redness and dryness of the skin and assess the degree of irritation. Today's analysis unit can also evaluate the location and degree of swelling. To evaluate swelling, 3D image analysis technology is used to measure the contours and swelling of the face and assess the location and degree of swelling. Today's analysis unit can also evaluate the color and size of dark circles under the eyes. To evaluate dark circles under the eyes, image color analysis technology is used to quantify the intensity and size of the dark circles and assess their condition. As a result, today's analysis unit can evaluate the user's skin condition in detail and propose the optimal skincare for today. Furthermore, today's analysis unit can utilize AI to track changes in the user's skin condition by comparing it with past data. For example, it can evaluate the current condition based on past data on skin irritation and swelling and propose appropriate care methods. In addition, today's analysis unit can provide more personalized care suggestions by considering the user's lifestyle and environmental factors. This allows today's analysis department to provide users with optimal skincare and support them in maintaining healthy skin.

[0074] The analysis unit can evaluate blemishes, wrinkles, elasticity, and pore condition, and track continuous progress. For example, the analysis unit can evaluate the size and color of blemishes. For example, the analysis unit can evaluate the depth of wrinkles. For example, the analysis unit can measure elasticity. For example, the analysis unit can evaluate the condition of pores. This allows for a detailed understanding of the user's skin condition by evaluating blemishes, wrinkles, elasticity, pore condition, etc., and tracking continuous progress. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the size and color of blemishes into a generative AI and have the generative AI perform the evaluation of blemishes.

[0075] The suggestion unit can propose a personalized skincare schedule based on lifestyle, skin condition, and seasonality. For example, the suggestion unit can propose a skincare schedule based on lifestyle. For example, the suggestion unit can propose a skincare schedule based on skin condition. For example, the suggestion unit can propose a skincare schedule based on seasonality. This allows the user to receive optimal skincare by proposing a personalized skincare schedule that takes lifestyle, skin condition, and seasonality into consideration. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input a skincare schedule based on lifestyle into a generative AI and have the generative AI execute the schedule proposal.

[0076] The allergy-considering unit can propose care points based on allergy information. For example, the allergy-considering unit can propose care points based on the user's allergy history. For example, the allergy-considering unit can propose care points based on the type of allergen. For example, the allergy-considering unit can propose care points to reduce allergy risk. In this way, the user's allergy risk can be reduced by proposing care points that take allergy information into consideration. Some or all of the above processing in the allergy-considering unit may be performed using AI, for example, or without using AI. For example, the allergy-considering unit can input the user's allergy history into a generating AI and have the generating AI execute the proposal of care points.

[0077] Today's analysis unit can evaluate skin roughness, swelling, dark circles, and other daily conditions, and suggest appropriate care for the day. For example, today's analysis unit can evaluate the degree of skin roughness. For example, today's analysis unit can evaluate the location and degree of swelling. For example, today's analysis unit can evaluate the color and size of dark circles. In this way, by evaluating the daily conditions such as skin roughness, swelling, and dark circles, and suggesting the optimal care for the day, the user's skin health can be maintained. Some or all of the above processing in today's analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, today's analysis unit can input the degree of skin roughness into a generative AI and have the generative AI perform the evaluation of skin roughness.

[0078] The system can utilize natural language processing to enable simple voice-based conversations. For example, the system can use speech recognition technology to convert user speech into text. For example, the system can use text analysis technology to analyze the content of user speech. For example, the system can use dialogue generation technology to generate responses to user speech. This improves user convenience by utilizing natural language processing to enable easy voice-based conversations. Some or all of the above processes in the system may be performed using, for example, a generative AI, or without a generative AI. For example, the system can input user speech into a generative AI and have the generative AI perform analysis of the speech content.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can display the analysis results in a simple and easy-to-understand format. For example, if the user is relaxed, the analysis unit can provide detailed analysis results and include additional skincare advice. For example, if the user is in a hurry, the analysis unit can display concise analysis results that get straight to the point. By adjusting the presentation of the analysis results based on the user's emotions, the analysis unit can provide results 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 processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can improve the accuracy of the analysis based on the user's past skin condition data during the analysis. For example, the analysis unit can more accurately evaluate the current skin condition based on the user's past skin condition data. For example, the analysis unit can refer to past data, analyze trends in skin changes, and reflect them in the analysis results. For example, the analysis unit can use the user's past skin condition data to evaluate the long-term effects of skincare. This improves the accuracy of the analysis by referring to the user's past skin condition data. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating 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 improvement of the analysis accuracy.

[0081] The analysis unit can correct the analysis results based on the user's lifestyle and diet during the analysis. For example, the analysis unit can correct the analysis results by considering the user's lifestyle (sleep duration, exercise level, etc.). For example, the analysis unit can adjust the analysis results based on the user's diet (nutritional balance, calorie intake, etc.). For example, the analysis unit can correct the analysis results by considering the user's stress level and hormone balance. This improves the accuracy of the analysis results by considering the user's lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's lifestyle and diet into a generating AI and have the generating AI perform the correction of the analysis results.

[0082] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit can display detailed analysis results sequentially. For example, if the user is in a hurry, the analysis unit can display the most important analysis results first. This allows for the priority of important information to be provided by prioritizing the analysis results based on the user's emotions. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can adjust the analysis results based on the user's geographical location information during analysis. For example, the analysis unit can adjust the analysis results by considering the climate conditions of the user's place of residence. For example, the analysis unit can correct the analysis results based on the weather information of the user's current location. For example, the analysis unit can provide analysis results that take into account region-specific skin problems based on the user's geographical location. In this way, by considering the user's geographical location information, it is possible to provide analysis results that address region-specific skin problems. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the analysis results.

[0084] The analysis unit can analyze the user's social media activity during analysis and obtain relevant skin condition information. For example, the analysis unit can infer stress levels and lifestyle habits from the user's social media posts and reflect them in the analysis results. For example, the analysis unit can correct the analysis results based on the user's usage of skincare products shared. For example, the analysis unit can adjust the analysis results by considering events (travel, parties, etc.) that affect skin condition based on the user's social media activity. This allows for obtaining more detailed skin condition information by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI and have the generative AI perform the acquisition of relevant skin condition information.

[0085] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can offer simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can offer detailed suggestions, including additional skincare advice. If the user is in a hurry, the suggestion unit can offer concise and to-the-point suggestions. By adjusting the way it presents suggestions based on the user's emotions, it can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The suggestion unit can improve the accuracy of its suggestions based on the user's past skincare history. For example, the suggestion unit can suggest the most suitable skincare products based on the user's past skincare history. For example, the suggestion unit can refer to past history and prioritize suggesting skincare methods that have been effective. For example, the suggestion unit can use the user's past skincare history to suggest a long-term skincare plan. This improves the accuracy of the suggestions by referring to the user's past skincare history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past skincare history into a generative AI and have the generative AI perform the improvement of the accuracy of the suggestions.

[0087] The suggestion unit can adjust its suggestions based on the user's lifestyle and dietary habits. For example, the suggestion unit can adjust its suggestions by considering the user's lifestyle (sleep duration, exercise level, etc.). For example, the suggestion unit can adjust its suggestions based on the user's dietary habits (nutritional balance, calorie intake, etc.). For example, the suggestion unit can adjust its suggestions by considering the user's stress level and hormone balance. This improves the accuracy of the suggestions by considering the user's lifestyle and dietary habits. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's lifestyle and dietary habits into a generative AI and have the generative AI perform the adjustments to the suggestions.

[0088] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying important suggestions. If the user is relaxed, the suggestion unit can display detailed suggestions sequentially. If the user is in a hurry, the suggestion unit can display the most important suggestions first. This allows for the priority provision of important information by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The suggestion unit can adjust the suggested content based on the user's geographical location information when making a suggestion. For example, the suggestion unit can adjust the suggested content considering the climate conditions of the user's place of residence. For example, the suggestion unit can correct the suggested content based on the weather information of the user's current location. For example, the suggestion unit can suggest region-specific skincare methods based on the user's geographical location. In this way, by considering the user's geographical location information, region-specific skincare methods can be suggested. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI perform the adjustment of the suggested content.

[0090] The suggestion unit can analyze the user's social media activity and suggest relevant skincare information when making suggestions. For example, the suggestion unit can infer the usage status of skincare products from the user's social media posts and reflect this in the suggestions. For example, the suggestion unit can suggest effective skincare methods based on skincare methods shared by the user. For example, the suggestion unit can reflect skincare trend information from the user's social media activity in the suggestions. This allows for the provision of more detailed skincare information by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI generate suggestions for relevant skincare information.

[0091] The allergy-aware unit can estimate the user's emotions and adjust how allergy information is displayed based on the estimated emotions. For example, if the user is stressed, the allergy-aware unit can display simple and easy-to-understand allergy information. If the user is relaxed, the allergy-aware unit can provide detailed allergy information and include additional care points. If the user is in a hurry, the allergy-aware unit can display concise and to-the-point allergy information. By adjusting how allergy information is displayed based on the user's emotions, the system can provide information that is 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the allergy-aware unit may be performed using AI or not using AI. For example, the allergy-aware unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The allergy consideration unit can improve accuracy when considering allergy information based on the user's past allergy reaction history. For example, the allergy consideration unit can evaluate the current allergy risk based on the user's past allergy reaction history. For example, the allergy consideration unit can identify components with a high allergy risk by referring to the past allergy reaction history. For example, the allergy consideration unit can evaluate the long-term allergy risk using the user's past allergy reaction history. This improves the accuracy of allergy information by referring to the user's past allergy reaction history. Some or all of the above processing in the allergy consideration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the allergy consideration unit can input the user's past allergy reaction history into a generating AI and have the generating AI perform the accuracy improvement.

[0093] The allergy consideration unit can estimate the user's emotions and prioritize allergy information based on the estimated emotions. For example, if the user is stressed, the allergy consideration unit will prioritize displaying important allergy information. For example, if the user is relaxed, the allergy consideration unit can display detailed allergy information sequentially. For example, if the user is in a hurry, the allergy consideration unit can display the most important allergy information first. In this way, important information can be prioritized by prioritizing allergy information based on the user's emotions. 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 allergy consideration unit may be performed using AI, for example, or without AI. For example, the allergy consideration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The allergy consideration unit can provide relevant allergy information based on the user's geographical location when considering allergy information. For example, the allergy consideration unit can provide information considering the allergy risk of the user's place of residence. For example, the allergy consideration unit can correct the information based on the allergy risk of the user's current location. For example, the allergy consideration unit can provide region-specific allergy information based on the user's geographical location. In this way, region-specific allergy information can be provided by considering the user's geographical location. Some or all of the above processing in the allergy consideration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the allergy consideration unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of allergy information.

[0095] Today's analysis unit can estimate the user's emotions and adjust the presentation of today's analysis results based on the estimated emotions. For example, if the user is stressed, today's analysis unit can display today's analysis results in a simple and easy-to-understand format. For example, if the user is relaxed, today's analysis unit can provide detailed analysis results and include additional skincare advice. For example, if the user is in a hurry, today's analysis unit can display concise analysis results that get straight to the point. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of today's analysis results based on the user's emotions. 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 today's analysis unit may be performed using AI, for example, or not using AI. For example, today's analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] Today's analysis unit can improve the accuracy of its analysis based on the user's past skin condition data during today's analysis. For example, today's analysis unit can more accurately evaluate the user's current skin condition based on the user's past skin condition data. For example, today's analysis unit can refer to past data, analyze trends in skin changes, and reflect them in today's analysis results. For example, today's analysis unit can use the user's past skin condition data to evaluate the long-term effects of skincare. This improves the accuracy of today's analysis by referring to the user's past skin condition data. Some or all of the above processes in today's analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, today's analysis unit can input the user's past skin condition data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0097] The analysis unit can correct the analysis results based on the user's lifestyle and diet during the analysis. For example, the analysis unit can correct the analysis results by considering the user's lifestyle (sleep duration, exercise level, etc.). For example, the analysis unit can adjust the analysis results based on the user's diet (nutritional balance, calorie intake, etc.). For example, the analysis unit can correct the analysis results by considering the user's stress level and hormone balance. This improves the accuracy of the analysis results by considering the user's lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's lifestyle and diet into a generating AI and have the generating AI perform the correction of the analysis results.

[0098] Today's analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, today's analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, today's analysis unit can display detailed analysis results sequentially. For example, if the user is in a hurry, today's analysis unit can display the most important analysis results first. In this way, by prioritizing the analysis results based on the user's emotions, important information can be provided preferentially. 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 today's analysis unit may be performed using AI, for example, or without AI. For example, today's analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] Today's analysis unit can adjust the analysis results based on the user's geographical location information during the analysis. Today's analysis unit can adjust the analysis results by considering the climate conditions of the user's place of residence, for example. Today's analysis unit can correct the analysis results based on the weather information of the user's current location, for example. Today's analysis unit can provide analysis results that take into account region-specific skin problems based on the user's geographical location, for example. This makes it possible to provide analysis results that address region-specific skin problems by considering the user's geographical location information. Some or all of the above processing in today's analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, today's analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the adjustment of the analysis results.

[0100] Today's analysis unit can analyze a user's social media activity and obtain relevant skin condition information during the analysis. For example, today's analysis unit can infer stress levels and lifestyle habits from a user's social media posts and reflect them in the analysis results. For example, today's analysis unit can adjust today's analysis results based on the usage status of skincare products shared by the user. For example, today's analysis unit can adjust today's analysis results by considering events (travel, parties, etc.) that affect skin condition based on the user's social media activity. This allows for obtaining more detailed skin condition information by analyzing the user's social media activity. Some or all of the above processing in today's analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, today's analysis unit can input the user's social media activity into a generative AI and have the generative AI perform the acquisition of relevant skin condition information.

[0101] The natural language processing unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated emotions. For example, if the user is stressed, the natural language processing unit can proceed with the conversation in a calm tone. For example, if the user is relaxed, the natural language processing unit can proceed with the conversation in a friendly tone. For example, if the user is in a hurry, the natural language processing unit can conduct a concise and to-the-point conversation. In this way, by adjusting the way the conversation is expressed based on the user's emotions, it is possible to provide a conversation that is 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 processing in the natural language processing unit may be performed using AI, for example, or not using AI. For example, the natural language processing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The natural language processing unit can improve the accuracy of conversations based on the user's past conversation history. For example, the natural language processing unit can provide appropriate skincare advice based on the user's past conversation history. For example, the natural language processing unit can refer to past conversation history and conduct conversations tailored to the user's preferences and needs. For example, the natural language processing unit can use the user's past conversation history to propose a long-term skincare plan. This improves the accuracy of conversations by referring to the user's past conversation history. Some or all of the above processing in the natural language processing unit may be performed using, for example, generative AI, or without generative AI. For example, the natural language processing unit can input the user's past conversation history into a generative AI and have the generative AI perform the conversation accuracy improvement.

[0103] The natural language processing unit can estimate the user's emotions and prioritize conversation content based on the estimated emotions. For example, if the user is stressed, the natural language processing unit can prioritize providing important conversation content. For example, if the user is relaxed, the natural language processing unit can provide detailed conversation content sequentially. For example, if the user is in a hurry, the natural language processing unit can provide the most important conversation content first. In this way, important information can be prioritized by prioritizing conversation content based on the user's emotions. 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 natural language processing unit may be performed using AI or not using AI. For example, the natural language processing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The natural language processing unit can adjust the content of a conversation based on the user's geographical location information. For example, the natural language processing unit can adjust the content of a conversation by considering the climate conditions of the user's place of residence. For example, the natural language processing unit can correct the content of a conversation based on the weather information of the user's current location. For example, the natural language processing unit can provide region-specific skincare advice based on the user's geographical location. This allows for the provision of region-specific skincare advice by considering the user's geographical location information. Some or all of the above-described processes in the natural language processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the natural language processing unit can input the user's geographical location information into a generative AI and have the generative AI perform the adjustment of the conversation content.

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

[0106] The skincare advice system can analyze a user's skin condition, estimate their emotions, and adjust the presentation of the analysis results based on those emotions. For example, if the user is stressed, the analysis results can be displayed in a simple and easy-to-understand format. If the user is relaxed, detailed analysis results can be provided, including additional skincare advice. Furthermore, if the user is in a hurry, concise analysis results can be displayed. By adjusting the presentation of analysis results based on the user's emotions, the system can provide results that are easy for the user to understand.

[0107] A skincare advice system can improve its analytical accuracy based on the user's past skin condition data. For example, it can more accurately assess the user's current skin condition based on their past data. It can also analyze trends in skin changes by referring to past data and reflect these trends in the analysis results. Furthermore, it can evaluate the long-term effects of skincare using the user's past skin condition data. In short, referring to the user's past skin condition data improves analytical accuracy.

[0108] The skincare advice system can adjust its analysis results based on the user's lifestyle and diet. For example, it can adjust the results by considering the user's lifestyle (sleep duration, exercise level, etc.). It can also adjust the results based on the user's diet (nutritional balance, calorie intake, etc.). Furthermore, it can adjust the results by considering the user's stress level and hormone balance. This improves the accuracy of the analysis results by taking into account the user's lifestyle and diet.

[0109] The skincare advice system can adjust its analysis results based on the user's geographical location. For example, it can adjust the results to take into account the climate conditions of the user's place of residence. It can also correct the results based on the weather information of the user's current location. Furthermore, it can provide analysis results that take into account region-specific skin problems based on the user's geographical location. In this way, by considering the user's geographical location, it can provide analysis results that address region-specific skin problems.

[0110] The skincare advice system can analyze a user's social media activity and obtain relevant skin condition information. For example, it can infer stress levels and lifestyle habits from a user's social media posts and reflect this in the analysis results. It can also adjust the analysis results based on the user's shared usage of skincare products. Furthermore, it can adjust the analysis results by considering events that affect skin condition (travel, parties, etc.) based on the user's social media activity. In this way, by analyzing a user's social media activity, more detailed skin condition information can be obtained.

[0111] A skincare advice system can estimate the user's emotions and adjust the way it presents its recommendations based on those emotions. For example, if the user is stressed, it can offer simple and easy-to-understand recommendations. If the user is relaxed, it can provide more detailed recommendations and include additional skincare advice. Furthermore, if the user is in a hurry, it can offer concise and to-the-point recommendations. By adjusting the way recommendations are presented based on the user's emotions, the system can provide recommendations that are easy for the user to understand.

[0112] A skincare advice system can improve the accuracy of its recommendations based on the user's past skincare history. For example, it can suggest the most suitable skincare products based on the user's past skincare history. It can also prioritize suggesting skincare methods that have been effective by referring to past history. Furthermore, it can propose a long-term skincare plan using the user's past skincare history. In this way, the accuracy of recommendations is improved by referring to the user's past skincare history.

[0113] The skincare advice system can adjust its recommendations based on the user's lifestyle and diet. For example, it can adjust recommendations based on the user's lifestyle (sleep duration, exercise level, etc.). It can also adjust recommendations based on the user's diet (nutritional balance, calorie intake, etc.). Furthermore, it can adjust recommendations based on the user's stress level and hormone balance. This improves the accuracy of the recommendations by taking into account the user's lifestyle and diet.

[0114] The skincare advice system can adjust its recommendations based on the user's geographical location. For example, it can adjust recommendations considering the climate conditions of the user's place of residence. It can also correct recommendations based on the weather information of the user's current location. Furthermore, it can suggest region-specific skincare methods based on the user's geographical location. In this way, by considering the user's geographical location, it can suggest region-specific skincare methods.

[0115] The skincare advice system can analyze a user's social media activity and suggest relevant skincare information. For example, it can infer the usage status of skincare products from a user's social media posts and reflect this in its suggestions. It can also suggest effective skincare methods based on skincare routines shared by the user. Furthermore, it can incorporate skincare trend information from the user's social media activity into its suggestions. In this way, by analyzing the user's social media activity, it can provide more detailed skincare information.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The camera captures the user's face. For example, a high-resolution camera can be used to capture the user's face, and a wide-angle lens can be used to capture the entire face. Infrared cameras can also be used to capture images at night. Step 2: The analysis unit analyzes the images captured by the camera and evaluates the condition of the skin. For example, it can evaluate the size and color of blemishes, the depth of wrinkles, elasticity, and the condition of pores. Step 3: The suggestion unit proposes a personalized skincare schedule based on the skin condition evaluated by the analysis unit. For example, it can propose a skincare schedule based on lifestyle, skin condition, and seasonality. Step 4: The allergy consideration unit proposes care points that take allergy information into account, based on the schedule proposed by the proposal unit. For example, it can propose the user's allergy history, types of allergens, and care points to reduce allergy risk. Step 5: Today's analysis unit will suggest the optimal care for today based on the skin condition evaluated by the unit. For example, it can evaluate the degree of skin irritation, the location and degree of swelling, and the color and size of dark circles under the eyes.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the camera, analysis unit, suggestion unit, allergy consideration unit, and today's analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the camera is implemented by the camera 42 of the smart device 14 and captures the user's face. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the captured image to evaluate the skin condition. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a personalized skincare schedule based on the analysis results. The allergy consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care points that take allergy information into consideration. The today's analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the current skin condition and proposes optimal care. 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.

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0135] 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.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0137] Each of the multiple elements described above, including the camera, analysis unit, suggestion unit, allergy consideration unit, and today's analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the camera is implemented by the camera 42 of the smart glasses 214 and captures the user's face. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the captured image to evaluate the skin condition. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a personalized skincare schedule based on the analysis results. The allergy consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care points that take allergy information into consideration. The today's analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the current skin condition and proposes optimal care. 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.

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

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0153] Each of the multiple elements described above, including the camera, analysis unit, suggestion unit, allergy consideration unit, and today's analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the camera is implemented by the camera 42 of the headset terminal 314 and captures the user's face. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the captured image to evaluate the skin condition. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a personalized skincare schedule based on the analysis results. The allergy consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care points that take allergy information into consideration. The today's analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the current skin condition and proposes optimal care. 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0168] 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.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0170] Each of the multiple elements described above, including the camera, analysis unit, suggestion unit, allergy consideration unit, and today's analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the camera is implemented by the camera 42 of the robot 414 and captures the user's face. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the captured image to evaluate the skin condition. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a personalized skincare schedule based on the analysis results. The allergy consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes care points that take allergy information into consideration. The today's analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the current skin condition and proposes optimal care. 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] (Note 1) Equipped with a camera that captures the user's face, An analysis unit analyzes images captured by the aforementioned camera and evaluates the condition of the skin, Based on the skin condition evaluated by the aforementioned analysis unit, a proposal unit proposes a personalized skincare schedule. Based on the schedule proposed by the aforementioned proposal unit, the allergy consideration unit proposes care points based on allergy information, The system comprises: a "Today's Analysis Unit" that proposes appropriate care for today based on the current skin condition evaluated by the aforementioned analysis unit; and a "Today's Analysis Unit" that proposes appropriate care for today. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Evaluate blemishes, wrinkles, elasticity, and pore condition, and track continuous progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose a personalized skincare schedule based on your lifestyle, skin condition, and seasonality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned allergy consideration section is We propose care points based on allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The analysis unit mentioned above today is, We assess your skin condition each day, including roughness, swelling, and dark circles, and suggest appropriate care for the day. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned system, By utilizing natural language processing, it enables simple conversational communication via voice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's past skin condition data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the analysis results are corrected based on the user's lifestyle and dietary habits. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, 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, During analysis, the analysis results are adjusted based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the user's social media activity is analyzed to obtain relevant skin condition information. 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 the 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, improve the accuracy of the suggestions based on the user's past skincare history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the proposal based on the user's lifestyle and dietary habits. 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 prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the proposal content based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a recommendation, we analyze the user's social media activity and suggest relevant skincare information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned allergy consideration section is The system estimates the user's emotions and adjusts how allergy information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned allergy consideration section is When considering allergy information, accuracy is improved based on the user's past allergy reaction history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned allergy consideration section is The system estimates the user's emotions and prioritizes allergy information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned allergy consideration section is When considering allergy information, provide relevant allergy information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The analysis unit mentioned above today is, We estimate the user's emotions and adjust how today's analysis results are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The analysis unit mentioned above today is, Today's analysis will improve accuracy based on the user's past skin condition data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The analysis unit mentioned above today is, During today's analysis, the results will be adjusted based on the user's lifestyle and dietary habits. The system described in Appendix 1, characterized by the features described herein. (Note 26) The analysis unit mentioned above today is, It estimates user sentiment and prioritizes today's analysis results based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The analysis unit mentioned above today is, During today's analysis, we will adjust the analysis results based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The analysis unit mentioned above today is, Today's analysis will analyze the user's social media activity and obtain relevant skin condition information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned natural language processing unit, It estimates the user's emotions and adjusts the way the conversation is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned natural language processing unit, During conversations, the accuracy of the conversation is improved based on the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned natural language processing unit, It estimates the user's emotions and prioritizes conversation topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned natural language processing unit, During conversations, the content of the conversation is adjusted based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0190] 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. Equipped with a camera that captures the user's face, An analysis unit analyzes images captured by the aforementioned camera and evaluates the condition of the skin, Based on the skin condition evaluated by the aforementioned analysis unit, a proposal unit proposes a personalized skincare schedule. Based on the schedule proposed by the aforementioned proposal unit, the allergy consideration unit proposes care points based on allergy information, The system comprises: a "Today's Analysis Unit" that proposes appropriate care for today based on the current skin condition evaluated by the aforementioned analysis unit; and a "Today's Analysis Unit" that proposes appropriate care for today. A system characterized by the following features.

2. The aforementioned analysis unit, Evaluate blemishes, wrinkles, elasticity, and pore condition, and track continuous progress. The system according to feature 1.

3. The aforementioned proposal section is, We propose a personalized skincare schedule based on your lifestyle, skin condition, and seasonality. The system according to feature 1.

4. The aforementioned allergy consideration section is We propose care points based on allergy information. The system according to feature 1.

5. The aforementioned analysis unit today is, We assess your skin condition each day, including roughness, swelling, and dark circles, and suggest appropriate care for the day. The system according to feature 1.

6. The aforementioned system, By utilizing natural language processing, it enables simple conversational communication via voice. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are presented based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's past skin condition data. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the analysis results are corrected based on the user's lifestyle and dietary habits. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

Images