System

Abstract

An object of a system according to an embodiment is to provide optimum beauty advice based on a skin condition of a user.SOLUTION: A system according to an embodiment includes a skin condition analyzer and a beauty advice provider. The skin condition analyzer analyzes photographs and information of the skin provided by the user. The beauty advice providing unit provides optimum beauty advice to the user on the basis of a result of the analysis by the skin condition analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been sufficient in providing optimal beauty advice based on the user's skin condition, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal beauty advice based on the user's skin condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a skin condition analysis unit and a beauty advice providing unit. The skin condition analysis unit analyzes skin photos and information provided by a user. The beauty advice providing unit provides optimal beauty advice to the user based on the results of the analysis by the skin condition analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal beauty advice based on the user's skin condition. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A beauty care support system according to an embodiment of the present invention is a system that analyzes a user's skin condition and provides customized beauty advice. As a result, the beauty care support system can analyze the user's skin condition in detail and provide optimal beauty advice.

[0029] A beauty care support system according to an embodiment includes a skin condition analysis unit and a beauty advice providing unit. The skin condition analysis unit analyzes skin photos and information provided by a user. For example, when a user uploads a skin photo to an app, the generation AI analyzes the photo and identifies skin problems such as blemishes, wrinkles, and dryness. The skin condition analysis unit also performs analysis based on the skin information provided by the user. For example, the generation AI can analyze the skin condition and past skin care history entered by the user to identify skin problems. The beauty advice providing unit provides optimal beauty advice to the user based on the results of the analysis by the skin condition analysis unit. For example, the generation AI can recommend skin care products with high moisturizing effects to a user with dry skin, and suggest products with whitening effects to a user who is concerned about blemishes. The beauty advice providing unit also generates advice based on information about the user's skin condition. For example, the generation AI can suggest skin care products and lifestyle improvements based on the user's skin condition. This allows the beauty care support system according to an embodiment to perform a detailed analysis of the user's skin condition and provide optimal beauty advice. For example, a user can improve their skin health by using skin care products that are optimal for their skin condition. In addition, you can maintain your skin condition by receiving suggestions for improving your lifestyle habits.

[0030] The skin condition analysis unit can identify factors that cause skin condition fluctuations based on the user's lifestyle and dietary information. For example, the generation AI analyzes the user's lifestyle and dietary information to identify factors that cause skin condition fluctuations. For example, when the user inputs their daily diet and sleep time, the generation AI analyzes factors that cause skin condition fluctuations based on that information. The skin condition analysis unit can also identify factors that cause skin fluctuations based on the user's lifestyle and dietary information. For example, the generation AI can analyze the user's exercise habits and stress level to identify factors that cause skin fluctuations. This enables more accurate analysis of skin condition by taking the user's lifestyle and dietary information into consideration.

[0031] The skin condition analysis unit can make specific lifestyle improvement suggestions to the user based on the analysis results. For example, the generation AI can make specific lifestyle improvement suggestions to the user based on the analysis results. For example, it can recommend extending sleep time or consuming specific foods. The skin condition analysis unit can also make specific lifestyle improvement suggestions to the user based on the analysis results. For example, the generation AI can make suggestions to improve the user's exercise habits or stress management methods. As a result, by making specific lifestyle improvement suggestions to the user, improvement in skin condition can be expected.

[0032] The skin condition analysis unit can evaluate the characteristics of the skin condition due to genetic factors based on the user's genetic information. For example, the generation AI analyzes the user's genetic information and evaluates the characteristics of the skin condition due to genetic factors. For example, it identifies a genetic tendency to be prone to dry skin. The skin condition analysis unit can also evaluate the characteristics of the skin condition due to genetic factors based on the user's genetic information. For example, the generation AI can analyze the user's genetic information and identify a genetic tendency to be prone to developing spots. In this way, it is possible to evaluate the characteristics of the skin condition due to genetic factors by taking the genetic information into consideration.

[0033] The skin condition analysis unit can provide seasonal skin care advice to the user based on the analysis results. For example, the generation AI of the skin condition analysis unit provides seasonal skin care advice to the user based on the analysis results. For example, it can recommend products with high moisturizing effects in winter. The skin condition analysis unit can also provide seasonal skin care advice to the user based on the analysis results. For example, the generation AI can recommend UV protection measures in summer. In this way, by providing seasonal skin care advice, appropriate care according to the season is possible.

[0034] The beauty advice providing unit can provide more accurate advice by reflecting the user's past beauty care history. For example, the generation AI analyzes the user's past beauty care history and provides more accurate advice based on that information. For example, it takes into account the effectiveness of products used in the past. The beauty advice providing unit can also provide more accurate advice by reflecting the user's past beauty care history. For example, the generation AI can re-recommend products that have been effective in the past. In this way, more accurate advice can be provided by reflecting the user's past beauty care history.

[0035] The beauty advice providing unit can recommend products to avoid allergic reactions based on the user's allergy information. For example, the generation AI analyzes the user's allergy information and recommends products to avoid allergic reactions. For example, it can suggest products that do not contain specific ingredients. The beauty advice providing unit can also recommend products to avoid allergic reactions based on the user's allergy information. For example, the generation AI can suggest low-allergen products. This makes it possible to recommend products to avoid allergic reactions by taking allergy information into consideration.

[0036] The beauty advice providing unit can provide region-specific skin care advice based on the climate information of the user's region. For example, the generation AI analyzes the climate information of the user's region and provides region-specific skin care advice. For example, in regions with high humidity, light moisturizing products are recommended. The beauty advice providing unit can also provide region-specific skin care advice based on the climate information of the user's region. For example, in dry regions, the generation AI can suggest products with high moisturizing effects. In this way, region-specific skin care advice can be provided by taking into account the climate information of the region.

[0037] The beauty advice providing unit can provide specialized advice according to the user's age or gender. For example, the generation AI analyzes the user's age and gender and provides specialized beauty advice. For example, it recommends anti-aging products according to age. The beauty advice providing unit can also provide specialized advice according to the user's age and gender. For example, the generation AI can suggest skin care products according to gender. This allows for more appropriate beauty care by providing specialized advice according to age and gender.

[0038] The beauty advice providing unit can select the most suitable skin care product by reflecting the user's past usage history and its effects. For example, the generation AI of the beauty advice providing unit analyzes the user's past usage history and selects the most suitable product by reflecting its effects. For example, it may recommend a product that was effective in the past. The beauty advice providing unit can also select the most suitable skin care product by reflecting the user's past usage history and its effects. For example, the generation AI can consider the effects of products used in the past and suggest the most suitable product. This allows the most suitable skin care product to be selected by reflecting the user's past usage history and its effects.

[0039] The beauty advice providing unit can suggest cost-effective products based on the user's budget information. For example, the generation AI analyzes the user's budget information and suggests cost-effective products. For example, it recommends the most effective product within the budget. The beauty advice providing unit can also suggest cost-effective products based on the user's budget information. For example, the generation AI can consider the balance between price and effectiveness and suggest the optimal product. This makes it possible to suggest cost-effective products by taking budget information into consideration.

[0040] The beauty advice providing unit can suggest products that suit the lifestyle based on the user's lifestyle information. For example, the generation AI analyzes the user's lifestyle information and suggests products that suit the lifestyle. For example, it can recommend products that are easy to use to a busy user. The beauty advice providing unit can also suggest products that suit the lifestyle based on the user's lifestyle information. For example, the generation AI can suggest products that are easy to carry to a user with an active lifestyle. In this way, by taking lifestyle information into consideration, it can suggest products that suit the lifestyle.

[0041] The beauty advice providing unit can suggest specialized products according to the user's skin type. For example, the generation AI analyzes the user's skin type and suggests specialized products. For example, it recommends hypoallergenic products to users with sensitive skin. The beauty advice providing unit can also suggest specialized products according to the user's skin type. For example, the generation AI can suggest products with high moisturizing effects to users with dry skin. This allows for more appropriate skin care by suggesting specialized products according to skin type.

[0042] The beauty advice providing unit can manage the progress of beauty care based on the user's lifestyle habits and dietary information. For example, the generation AI of the beauty advice providing unit analyzes the user's lifestyle habits and dietary information and identifies factors that affect progress. For example, it evaluates the impact of dietary content and amount of exercise on the effectiveness of beauty care. The beauty advice providing unit can also manage the progress of beauty care based on the user's lifestyle habits and dietary information. For example, the generation AI can manage progress based on the user's lifestyle habits and dietary information and suggest effective care. This allows for more accurate management of beauty care progress by taking lifestyle habits and dietary information into consideration.

[0043] The beauty advice providing unit can provide the user with specific suggestions for improving lifestyle habits based on the progress management results. For example, the generation AI of the beauty advice providing unit makes specific suggestions for improving lifestyle habits to the user based on the progress management results. For example, it recommends extending sleep time or consuming specific foods. The beauty advice providing unit can also provide the user with specific suggestions for improving lifestyle habits based on the progress management results. For example, the generation AI can make suggestions for improving the user's exercise habits or stress management methods. In this way, by making specific suggestions for improving lifestyle habits based on the progress management results, the effectiveness of the user's beauty care can be improved.

[0044] The beauty advice providing unit can evaluate progress characteristics due to genetic factors based on the user's genetic information. For example, the generation AI analyzes the user's genetic information and evaluates progress characteristics due to genetic factors. For example, it identifies a genetic tendency for beauty care to be effective. The beauty advice providing unit can also evaluate progress characteristics due to genetic factors based on the user's genetic information. For example, the generation AI can analyze the user's genetic information and identify a genetic tendency for beauty care to be less effective. In this way, it is possible to evaluate progress characteristics due to genetic factors by taking genetic information into consideration.

[0045] The beauty advice providing unit can provide seasonal care advice to the user based on the progress management results. For example, the generation AI in the beauty advice providing unit provides seasonal care advice to the user based on the progress management results. For example, it can recommend products with high moisturizing effects in winter. The beauty advice providing unit can also provide seasonal care advice to the user based on the progress management results. For example, the generation AI can recommend UV protection in summer. In this way, seasonal care advice can be provided based on the progress management results, enabling appropriate care according to the season.

[0046] The beauty advice providing unit can provide personalized trend information by reflecting the user's past interests and search history. For example, the generation AI analyzes the user's past interests and search history to provide personalized trend information. For example, it provides the latest information related to products or topics searched for in the past. The beauty advice providing unit can also provide personalized trend information by reflecting the user's past interests and search history. For example, the generation AI can provide information related to topics that are likely to interest the user. In this way, personalized trend information can be provided by reflecting the user's past interests and search history.

[0047] The beauty advice providing unit can provide region-specific trend information based on the beauty trends in the user's region. For example, the generation AI analyzes beauty trends in the user's region and provides region-specific trend information. For example, it introduces skin care products and beauty methods that are popular in the region. The beauty advice providing unit can also provide region-specific trend information based on the beauty trends in the user's region. For example, the generation AI can provide beauty trends that correspond to the climate and culture of the region. This makes it possible to provide region-specific trend information by taking into account regional beauty trends.

[0048] The beauty advice providing unit can provide specialized trend information according to the user's age or gender. For example, the generation AI analyzes the user's age and gender and provides specialized trend information. For example, it introduces anti-aging trends according to age. The beauty advice providing unit can also provide specialized trend information according to the user's age and gender. For example, the generation AI can provide beauty trends according to gender. This makes it possible to provide more appropriate beauty information by providing specialized trend information according to age and gender.

[0049] The beauty advice providing unit can provide trend information that suits the lifestyle based on the user's lifestyle information. For example, the generation AI analyzes the user's lifestyle information and provides trend information that suits the lifestyle. For example, it can introduce beauty trends that are easy to incorporate to busy users. The beauty advice providing unit can also provide trend information that suits the lifestyle based on the user's lifestyle information. For example, the generation AI can suggest products that are easy to carry to users with active lifestyles. In this way, by taking lifestyle information into consideration, it is possible to provide trend information that suits the lifestyle.

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

[0051] The beauty care support system can further include a sensor unit that monitors the user's skin condition in real time. The sensor unit is a device that is worn directly on the user's skin and measures the skin's moisture content, oil content, temperature, and other factors in real time. For example, even while the user is out during the day, the sensor unit can continuously monitor the user's skin condition and immediately notify the user of any changes. The sensor unit can also measure the skin's reaction immediately after the user uses a skin care product and evaluate its effectiveness in real time. This allows the user to constantly monitor their skin condition and take appropriate care.

[0052] The beauty care support system can further include an environmental data acquisition unit that takes environmental data into account when analyzing the user's skin condition. The environmental data acquisition unit collects environmental data such as the temperature, humidity, and amount of UV rays around the user and provides it to the skin condition analysis unit. For example, if the user is in a hot and humid environment, the amount of oil on the skin may increase, so the environmental data acquisition unit reflects this information in the analysis. In addition, advice on UV protection can be provided to users who live in areas with high levels of UV rays. This enables more accurate skin condition analysis that takes environmental data into account.

[0053] The beauty care support system can further include a genetic information acquisition unit that takes genetic information into account when analyzing the user's skin condition. The genetic information acquisition unit collects the user's genetic information and provides it to the skin condition analysis unit. For example, a product with a particularly high moisturizing effect can be recommended to a user who is genetically prone to dry skin. Also, a product with a whitening effect can be suggested to a user who is genetically prone to developing spots. This makes it possible to provide more personalized beauty advice that takes genetic information into account.

[0054] The beauty care support system can further include a dietary information acquisition unit that takes dietary information into account when analyzing the user's skin condition. The dietary information acquisition unit collects the user's dietary details and provides them to the skin condition analysis unit. For example, a user who eats a diet rich in vitamin C can expect a whitening effect, so this information can be reflected in the analysis. In addition, a user who eats a diet high in fat can have an increased amount of oil on the skin, so this information can be reflected in the analysis. This enables more accurate skin condition analysis that takes dietary information into account.

[0055] The beauty care support system can further include an exercise information acquisition unit that takes exercise information into account when analyzing the user's skin condition. The exercise information acquisition unit collects the user's exercise habits and exercise amount and provides them to the skin condition analysis unit. For example, a user who exercises regularly may have improved blood circulation and improved skin condition, so this information can be reflected in the analysis. In addition, a user who does not exercise enough may have delayed skin turnover, so this information can be reflected in the analysis. This enables more accurate skin condition analysis that takes exercise information into account.

[0056] The beauty care support system can further include a sleep information acquisition unit that takes sleep information into account when analyzing the user's skin condition. The sleep information acquisition unit collects the user's sleep time and sleep quality and provides the information to the skin condition analysis unit. For example, a user who gets enough sleep may have a higher skin recovery ability, so this information can be reflected in the analysis. In addition, a user who is sleep-deprived may have a delayed skin turnover, so this information can be reflected in the analysis. This enables more accurate skin condition analysis that takes sleep information into account.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The skin condition analysis unit analyzes the skin photos and information provided by the user. For example, when a user uploads a photo of their skin to the app, the generation AI analyzes the photo and identifies skin problems such as blemishes, wrinkles, and dryness. The skin condition analysis unit also performs analysis based on the skin information provided by the user. For example, the generation AI can analyze the skin condition and past care history entered by the user to identify skin problems. Step 2: The beauty advice provider provides optimal beauty advice to the user based on the results of the analysis by the skin condition analyzer. For example, it may recommend skin care products with high moisturizing effects to a user with dry skin, or suggest products with whitening effects to a user who is concerned about blemishes. The beauty advice provider also generates advice based on information about the user's skin condition. For example, the generation AI can suggest skin care products and lifestyle improvements based on the user's skin condition.

[0059] (Example 2) A beauty care support system according to an embodiment of the present invention is a system that analyzes a user's skin condition and provides customized beauty advice. As a result, the beauty care support system can analyze the user's skin condition in detail and provide optimal beauty advice.

[0060] A beauty care support system according to an embodiment includes a skin condition analysis unit and a beauty advice providing unit. The skin condition analysis unit analyzes skin photos and information provided by a user. For example, when a user uploads a skin photo to an app, the generation AI analyzes the photo and identifies skin problems such as blemishes, wrinkles, and dryness. The skin condition analysis unit also performs analysis based on the skin information provided by the user. For example, the generation AI can analyze the skin condition and past skin care history entered by the user to identify skin problems. The beauty advice providing unit provides optimal beauty advice to the user based on the results of the analysis by the skin condition analysis unit. For example, the generation AI can recommend skin care products with high moisturizing effects to a user with dry skin, and suggest products with whitening effects to a user who is concerned about blemishes. The beauty advice providing unit also generates advice based on information about the user's skin condition. For example, the generation AI can suggest skin care products and lifestyle improvements based on the user's skin condition. This allows the beauty care support system according to an embodiment to perform a detailed analysis of the user's skin condition and provide optimal beauty advice. For example, a user can improve their skin health by using skin care products that are optimal for their skin condition. In addition, you can maintain your skin condition by receiving suggestions for improving your lifestyle habits.

[0061] The skin condition analysis unit can identify factors that cause skin condition fluctuations based on the user's lifestyle and dietary information. For example, the generation AI analyzes the user's lifestyle and dietary information to identify factors that cause skin condition fluctuations. For example, when the user inputs their daily diet and sleep time, the generation AI analyzes factors that cause skin condition fluctuations based on that information. The skin condition analysis unit can also identify factors that cause skin fluctuations based on the user's lifestyle and dietary information. For example, the generation AI can analyze the user's exercise habits and stress level to identify factors that cause skin fluctuations. This enables more accurate analysis of skin condition by taking the user's lifestyle and dietary information into consideration.

[0062] The skin condition analysis unit can make specific lifestyle improvement suggestions to the user based on the analysis results. For example, the generation AI can make specific lifestyle improvement suggestions to the user based on the analysis results. For example, it can recommend extending sleep time or consuming specific foods. The skin condition analysis unit can also make specific lifestyle improvement suggestions to the user based on the analysis results. For example, the generation AI can make suggestions to improve the user's exercise habits or stress management methods. As a result, by making specific lifestyle improvement suggestions to the user, improvement in skin condition can be expected.

[0063] The skin condition analysis unit can use the emotion estimation function to analyze the relationship between the user's emotional state and the skin condition, and evaluate the impact of stress or emotional fluctuations on the skin. In the skin condition analysis unit, for example, the generation AI analyzes the user's emotional state and evaluates the association with the skin condition. For example, it can identify that skin becomes drier during periods of high stress. The skin condition analysis unit can also use the emotion estimation function to analyze the association between the user's emotional state and the skin condition, and evaluate the impact of stress or emotional fluctuations on the skin. For example, the generation AI can analyze the user's emotional state and evaluate the impact of stress on the skin. In this way, by analyzing the association between the emotional state and the skin condition, it is possible to evaluate the impact of stress or emotional fluctuations on the skin.

[0064] The skin condition analysis unit can evaluate the characteristics of the skin condition due to genetic factors based on the user's genetic information. For example, the generation AI analyzes the user's genetic information and evaluates the characteristics of the skin condition due to genetic factors. For example, it identifies a genetic tendency to be prone to dry skin. The skin condition analysis unit can also evaluate the characteristics of the skin condition due to genetic factors based on the user's genetic information. For example, the generation AI can analyze the user's genetic information and identify a genetic tendency to be prone to developing spots. In this way, it is possible to evaluate the characteristics of the skin condition due to genetic factors by taking the genetic information into consideration.

[0065] The skin condition analysis unit can provide seasonal skin care advice to the user based on the analysis results. For example, the generation AI of the skin condition analysis unit provides seasonal skin care advice to the user based on the analysis results. For example, it can recommend products with high moisturizing effects in winter. The skin condition analysis unit can also provide seasonal skin care advice to the user based on the analysis results. For example, the generation AI can recommend UV protection measures in summer. In this way, by providing seasonal skin care advice, appropriate care according to the season is possible.

[0066] The skin condition analysis unit uses the emotion estimation function to monitor the emotional state of the user in real time when performing beauty care and make suggestions that will elicit positive emotions. For example, the generation AI in the skin condition analysis unit monitors the emotional state of the user in real time when performing beauty care and make suggestions that will elicit positive emotions. For example, it recommends relaxing music. The skin condition analysis unit also uses the emotion estimation function to monitor the emotional state of the user in real time when performing beauty care and make suggestions that will elicit positive emotions. For example, the generation AI can suggest an environment in which the user can relax when performing beauty care. This makes it possible to improve the effectiveness of beauty care by monitoring the user's emotional state in real time and making suggestions that will elicit positive emotions.

[0067] The beauty advice providing unit can provide more accurate advice by reflecting the user's past beauty care history. For example, the generation AI analyzes the user's past beauty care history and provides more accurate advice based on that information. For example, it takes into account the effectiveness of products used in the past. The beauty advice providing unit can also provide more accurate advice by reflecting the user's past beauty care history. For example, the generation AI can re-recommend products that have been effective in the past. In this way, more accurate advice can be provided by reflecting the user's past beauty care history.

[0068] The beauty advice providing unit can recommend products to avoid allergic reactions based on the user's allergy information. For example, the generation AI analyzes the user's allergy information and recommends products to avoid allergic reactions. For example, it can suggest products that do not contain specific ingredients. The beauty advice providing unit can also recommend products to avoid allergic reactions based on the user's allergy information. For example, the generation AI can suggest low-allergen products. This makes it possible to recommend products to avoid allergic reactions by taking allergy information into consideration.

[0069] The beauty advice providing unit uses the emotion estimation function to provide beauty advice according to the user's emotional state and can suggest care methods that will allow the user to relax. For example, the generation AI in the beauty advice providing unit analyzes the user's emotional state and suggests care methods that will allow the user to relax. For example, during times of high stress, the generation AI can recommend products that have a relaxing effect. The beauty advice providing unit also uses the emotion estimation function to provide beauty advice according to the user's emotional state and can suggest care methods that will allow the user to relax. For example, the generation AI can suggest an environment where the user can relax. In this way, beauty advice according to the emotional state can be provided and care methods that will allow the user to relax can be suggested.

[0070] The beauty advice providing unit can provide region-specific skin care advice based on the climate information of the user's region. For example, the generation AI analyzes the climate information of the user's region and provides region-specific skin care advice. For example, in regions with high humidity, light moisturizing products are recommended. The beauty advice providing unit can also provide region-specific skin care advice based on the climate information of the user's region. For example, in dry regions, the generation AI can suggest products with high moisturizing effects. In this way, region-specific skin care advice can be provided by taking into account the climate information of the region.

[0071] The beauty advice providing unit can provide specialized advice according to the user's age or gender. For example, the generation AI analyzes the user's age and gender and provides specialized beauty advice. For example, it recommends anti-aging products according to age. The beauty advice providing unit can also provide specialized advice according to the user's age and gender. For example, the generation AI can suggest skin care products according to gender. This allows for more appropriate beauty care by providing specialized advice according to age and gender.

[0072] The beauty advice providing unit can use the emotion estimation function to monitor the emotional state of the user when performing beauty care and provide advice to elicit positive emotions. For example, the generation AI of the beauty advice providing unit can monitor the emotional state of the user when performing beauty care and provide advice to elicit positive emotions. For example, relaxing music can be recommended. The beauty advice providing unit can also use the emotion estimation function to monitor the emotional state of the user when performing beauty care and provide advice to elicit positive emotions. For example, the generation AI can suggest an environment in which the user can relax when performing beauty care. This can improve the effectiveness of beauty care by monitoring the emotional state and providing advice to elicit positive emotions.

[0073] The beauty advice providing unit can select the most suitable skin care product by reflecting the user's past usage history and its effects. For example, the generation AI of the beauty advice providing unit analyzes the user's past usage history and selects the most suitable product by reflecting its effects. For example, it may recommend a product that was effective in the past. The beauty advice providing unit can also select the most suitable skin care product by reflecting the user's past usage history and its effects. For example, the generation AI can consider the effects of products used in the past and suggest the most suitable product. This allows the most suitable skin care product to be selected by reflecting the user's past usage history and its effects.

[0074] The beauty advice providing unit can suggest cost-effective products based on the user's budget information. For example, the generation AI analyzes the user's budget information and suggests cost-effective products. For example, it recommends the most effective product within the budget. The beauty advice providing unit can also suggest cost-effective products based on the user's budget information. For example, the generation AI can consider the balance between price and effectiveness and suggest the optimal product. This makes it possible to suggest cost-effective products by taking budget information into consideration.

[0075] The beauty advice providing unit uses the emotion estimation function to recommend products according to the user's emotional state and can select products that will give the user a sense of satisfaction. For example, the generation AI in the beauty advice providing unit analyzes the user's emotional state and recommends products that will give the user a sense of satisfaction. For example, it can suggest products that have a relaxing effect. The beauty advice providing unit also uses the emotion estimation function to recommend products according to the user's emotional state and can select products that will give the user a sense of satisfaction. For example, the generation AI can suggest products that will help the user relax. In this way, by recommending products according to the emotional state, it is possible to select products that will give the user a sense of satisfaction.

[0076] The beauty advice providing unit can suggest products that suit the lifestyle based on the user's lifestyle information. For example, the generation AI analyzes the user's lifestyle information and suggests products that suit the lifestyle. For example, it can recommend products that are easy to use to a busy user. The beauty advice providing unit can also suggest products that suit the lifestyle based on the user's lifestyle information. For example, the generation AI can suggest products that are easy to carry to a user with an active lifestyle. In this way, by taking lifestyle information into consideration, it can suggest products that suit the lifestyle.

[0077] The beauty advice providing unit can suggest specialized products according to the user's skin type. For example, the generation AI analyzes the user's skin type and suggests specialized products. For example, it recommends hypoallergenic products to users with sensitive skin. The beauty advice providing unit can also suggest specialized products according to the user's skin type. For example, the generation AI can suggest products with high moisturizing effects to users with dry skin. This allows for more appropriate skin care by suggesting specialized products according to skin type.

[0078] The beauty advice providing unit can use the emotion estimation function to monitor the emotional state of the user when using a product and suggest products that elicit positive emotions. For example, the beauty advice providing unit can use the generation AI to monitor the emotional state of the user when using a product and suggest products that elicit positive emotions. For example, the beauty advice providing unit can recommend products that have a relaxing effect. The beauty advice providing unit can also use the emotion estimation function to monitor the emotional state of the user when using a product and suggest products that elicit positive emotions. For example, the generation AI can suggest products that will help the user relax. In this way, by monitoring the emotional state and suggesting products that elicit positive emotions, user satisfaction can be increased.

[0079] The beauty advice providing unit can manage the progress of beauty care based on the user's lifestyle habits and dietary information. For example, the generation AI of the beauty advice providing unit analyzes the user's lifestyle habits and dietary information and identifies factors that affect progress. For example, it evaluates the impact of dietary content and amount of exercise on the effectiveness of beauty care. The beauty advice providing unit can also manage the progress of beauty care based on the user's lifestyle habits and dietary information. For example, the generation AI can manage progress based on the user's lifestyle habits and dietary information and suggest effective care. This allows for more accurate management of beauty care progress by taking lifestyle habits and dietary information into consideration.

[0080] The beauty advice providing unit can provide the user with specific suggestions for improving lifestyle habits based on the progress management results. For example, the generation AI of the beauty advice providing unit makes specific suggestions for improving lifestyle habits to the user based on the progress management results. For example, it recommends extending sleep time or consuming specific foods. The beauty advice providing unit can also provide the user with specific suggestions for improving lifestyle habits based on the progress management results. For example, the generation AI can make suggestions for improving the user's exercise habits or stress management methods. In this way, by making specific suggestions for improving lifestyle habits based on the progress management results, the effectiveness of the user's beauty care can be improved.

[0081] The beauty advice providing unit can use the emotion estimation function to analyze the relationship between the user's emotional state and the progress of beauty care, and evaluate the impact of emotional fluctuations on the care. In the beauty advice providing unit, for example, the generation AI analyzes the user's emotional state and evaluates the association with the progress of beauty care. For example, it can identify that the effectiveness of beauty care decreases during periods of high stress. The beauty advice providing unit can also use the emotion estimation function to analyze the relationship between the user's emotional state and the progress of beauty care, and evaluate the impact of emotional fluctuations on the care. For example, the generation AI can analyze the user's emotional state and evaluate the impact of stress on beauty care. In this way, by analyzing the relationship between the emotional state and the progress of beauty care, it is possible to evaluate the impact of emotional fluctuations on the care.

[0082] The beauty advice providing unit can evaluate progress characteristics due to genetic factors based on the user's genetic information. For example, the generation AI analyzes the user's genetic information and evaluates progress characteristics due to genetic factors. For example, it identifies a genetic tendency for beauty care to be effective. The beauty advice providing unit can also evaluate progress characteristics due to genetic factors based on the user's genetic information. For example, the generation AI can analyze the user's genetic information and identify a genetic tendency for beauty care to be less effective. In this way, it is possible to evaluate progress characteristics due to genetic factors by taking genetic information into consideration.

[0083] The beauty advice providing unit can provide seasonal care advice to the user based on the progress management results. For example, the generation AI in the beauty advice providing unit provides seasonal care advice to the user based on the progress management results. For example, it can recommend products with high moisturizing effects in winter. The beauty advice providing unit can also provide seasonal care advice to the user based on the progress management results. For example, the generation AI can recommend UV protection in summer. In this way, seasonal care advice can be provided based on the progress management results, enabling appropriate care according to the season.

[0084] The beauty advice providing unit can provide personalized trend information by reflecting the user's past interests and search history. For example, the generation AI analyzes the user's past interests and search history to provide personalized trend information. For example, it provides the latest information related to products or topics searched for in the past. The beauty advice providing unit can also provide personalized trend information by reflecting the user's past interests and search history. For example, the generation AI can provide information related to topics that are likely to interest the user. In this way, personalized trend information can be provided by reflecting the user's past interests and search history.

[0085] The beauty advice providing unit can provide region-specific trend information based on the beauty trends in the user's region. For example, the generation AI analyzes beauty trends in the user's region and provides region-specific trend information. For example, it introduces skin care products and beauty methods that are popular in the region. The beauty advice providing unit can also provide region-specific trend information based on the beauty trends in the user's region. For example, the generation AI can provide beauty trends that correspond to the climate and culture of the region. This makes it possible to provide region-specific trend information by taking into account regional beauty trends.

[0086] The beauty advice providing unit uses the emotion estimation function to provide trend information according to the user's emotional state and can select information that is likely to interest the user. For example, the generation AI analyzes the user's emotional state and provides trend information that is likely to interest the user. For example, it introduces beauty trends that evoke positive emotions. The beauty advice providing unit also uses the emotion estimation function to provide trend information according to the user's emotional state and can select information that is likely to interest the user. For example, the generation AI can provide information related to topics that are likely to interest the user. In this way, by providing trend information according to the user's emotional state, it is possible to select information that is likely to interest the user.

[0087] The beauty advice providing unit can provide specialized trend information according to the user's age or gender. For example, the generation AI analyzes the user's age and gender and provides specialized trend information. For example, it introduces anti-aging trends according to age. The beauty advice providing unit can also provide specialized trend information according to the user's age and gender. For example, the generation AI can provide beauty trends according to gender. This makes it possible to provide more appropriate beauty information by providing specialized trend information according to age and gender.

[0088] The beauty advice providing unit can provide trend information that suits the lifestyle based on the user's lifestyle information. For example, the generation AI analyzes the user's lifestyle information and provides trend information that suits the lifestyle. For example, it can introduce beauty trends that are easy to incorporate to busy users. The beauty advice providing unit can also provide trend information that suits the lifestyle based on the user's lifestyle information. For example, the generation AI can suggest products that are easy to carry to users with active lifestyles. In this way, by taking lifestyle information into consideration, it is possible to provide trend information that suits the lifestyle.

[0089] The beauty advice providing unit can use the emotion estimation function to monitor the emotional state of the user when receiving trend information and provide information that elicits positive emotions. For example, the generation AI of the beauty advice providing unit can monitor the emotional state of the user when receiving trend information and provide information that elicits positive emotions. For example, the generation AI can introduce beauty trends that have a relaxing effect. The beauty advice providing unit can also use the emotion estimation function to monitor the emotional state of the user when receiving trend information and provide information that elicits positive emotions. For example, the generation AI can provide trend information that helps the user relax. This can increase user satisfaction by monitoring the emotional state and providing information that elicits positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The beauty care support system can further include a sensor unit that monitors the user's skin condition in real time. The sensor unit is a device that is worn directly on the user's skin and measures the skin's moisture content, oil content, temperature, and other factors in real time. For example, even while the user is out during the day, the sensor unit can continuously monitor the user's skin condition and immediately notify the user of any changes. The sensor unit can also measure the skin's reaction immediately after the user uses a skin care product and evaluate its effectiveness in real time. This allows the user to constantly monitor their skin condition and take appropriate care.

[0092] The beauty care support system can further include an environmental data acquisition unit that takes environmental data into account when analyzing the user's skin condition. The environmental data acquisition unit collects environmental data such as the temperature, humidity, and amount of UV rays around the user and provides it to the skin condition analysis unit. For example, if the user is in a hot and humid environment, the amount of oil on the skin may increase, so the environmental data acquisition unit reflects this information in the analysis. In addition, advice on UV protection can be provided to users who live in areas with high levels of UV rays. This enables more accurate skin condition analysis that takes environmental data into account.

[0093] The beauty care support system can further include a genetic information acquisition unit that takes genetic information into account when analyzing the user's skin condition. The genetic information acquisition unit collects the user's genetic information and provides it to the skin condition analysis unit. For example, a product with a particularly high moisturizing effect can be recommended to a user who is genetically prone to dry skin. Also, a product with a whitening effect can be suggested to a user who is genetically prone to developing spots. This makes it possible to provide more personalized beauty advice that takes genetic information into account.

[0094] The beauty care support system may further include an emotion estimation unit that estimates the user's emotional state and provides beauty advice according to the emotional state. The emotion estimation unit estimates the user's emotion from the user's facial expression, tone of voice, input text, etc., and provides this information to the beauty advice providing unit. For example, if the user is feeling stressed, it may recommend a skin care product with a relaxing effect. Also, if the user is feeling positive, it may suggest a beauty care method to maintain that emotion. This makes it possible to provide more effective beauty advice according to the user's emotional state.

[0095] The beauty care support system may further include a relaxation suggestion unit that estimates the user's emotional state and suggests relaxation methods according to the emotional state. The relaxation suggestion unit analyzes the user's emotional state and suggests ways to relax. For example, if the user is feeling stressed, it may recommend music or aromas that have a relaxing effect. Furthermore, if the user is relaxed, it may suggest environmental settings or skin care methods to maintain that state. This allows the effectiveness of beauty care to be improved by providing relaxation methods according to the user's emotional state.

[0096] The beauty care support system can further include a dietary information acquisition unit that takes dietary information into account when analyzing the user's skin condition. The dietary information acquisition unit collects the user's dietary details and provides them to the skin condition analysis unit. For example, a user who eats a diet rich in vitamin C can expect a whitening effect, so this information can be reflected in the analysis. In addition, a user who eats a diet high in fat can have an increased amount of oil on the skin, so this information can be reflected in the analysis. This enables more accurate skin condition analysis that takes dietary information into account.

[0097] The beauty care support system may further include a progress management unit that estimates the user's emotional state and manages the progress of beauty care in accordance with the emotional state. The progress management unit analyzes the user's emotional state and manages the progress of beauty care based on that information. For example, if the user is feeling positive, the effectiveness of beauty care may be enhanced, and this information can be reflected in the progress management. Also, if the user is feeling stressed, the effectiveness of beauty care may be reduced, and this information can be reflected in the progress management. This enables more effective progress management of beauty care in accordance with the user's emotional state.

[0098] The beauty care support system can further include an exercise information acquisition unit that takes exercise information into account when analyzing the user's skin condition. The exercise information acquisition unit collects the user's exercise habits and exercise amount and provides them to the skin condition analysis unit. For example, a user who exercises regularly may have improved blood circulation and improved skin condition, so this information can be reflected in the analysis. In addition, a user who does not exercise enough may have delayed skin turnover, so this information can be reflected in the analysis. This enables more accurate skin condition analysis that takes exercise information into account.

[0099] The beauty care support system may further include a product suggestion unit that estimates the user's emotional state and suggests beauty care products according to the emotional state. The product suggestion unit analyzes the user's emotional state and suggests beauty care products based on that information. For example, if the user feels like relaxing, it may recommend a skin care product with a relaxing effect. Also, if the user is feeling energetic, it may suggest a product to help maintain that mood. This allows it to suggest more appropriate beauty care products according to the user's emotional state.

[0100] The beauty care support system can further include a sleep information acquisition unit that takes sleep information into account when analyzing the user's skin condition. The sleep information acquisition unit collects the user's sleep time and sleep quality and provides the information to the skin condition analysis unit. For example, a user who gets enough sleep may have a higher skin recovery ability, so this information can be reflected in the analysis. In addition, a user who is sleep-deprived may have a delayed skin turnover, so this information can be reflected in the analysis. This enables more accurate skin condition analysis that takes sleep information into account.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The skin condition analysis unit analyzes the skin photos and information provided by the user. For example, when a user uploads a photo of their skin to the app, the generation AI analyzes the photo and identifies skin problems such as blemishes, wrinkles, and dryness. The skin condition analysis unit also performs analysis based on the skin information provided by the user. For example, the generation AI can analyze the skin condition and past care history entered by the user to identify skin problems. Step 2: The beauty advice provider provides optimal beauty advice to the user based on the results of the analysis by the skin condition analyzer. For example, it may recommend skin care products with high moisturizing effects to a user with dry skin, or suggest products with whitening effects to a user who is concerned about blemishes. The beauty advice provider also generates advice based on information about the user's skin condition. For example, the generation AI can suggest skin care products and lifestyle improvements based on the user's skin condition.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a skin condition analysis unit that analyzes skin photos and information provided by a user; a beauty advice providing unit that provides optimal beauty advice to the user based on the results of the analysis by the skin condition analyzing unit. A system characterized by:

2. The skin condition analysis unit Analyzing the relationship between the emotional state of the user and the skin condition, and evaluating the effect of stress or emotional fluctuations on the skin.

2. The system of claim 1.

3. The skin condition analysis unit Evaluating characteristics of skin conditions due to genetic factors based on the genetic information of the user 2. The system of claim 1.

4. The beauty advice providing unit Reflecting the user's past beauty care history to provide more accurate advice 2. The system of claim 1.

5. The beauty advice providing unit Recommending products according to the emotional state of the user and selecting the products that will give the user a sense of satisfaction.

2. The system of claim 1.

6. The beauty advice providing unit Analyzing the relationship between the emotional state of the user and the progress of beauty care, and evaluating the influence of emotional fluctuations on the care.

2. The system of claim 1.

7. The beauty advice providing unit Providing trend information according to the emotional state of the user and selecting information that is likely to interest the user 2. The system of claim 1.

8. The beauty advice providing unit Monitor the emotional state of the user when receiving trend information and provide the information to elicit positive emotions.

2. The system of claim 1.

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