System

Abstract

To provide a system capable of proposing a personalized skincare routine on the basis of the skin type and lifestyle of a user.SOLUTION: A system according to an embodiment comprises a collection unit, a proposal unit, an advice unit, a detection unit, and a feedback unit. The collection unit collects information about a skin type, concerns, and a lifestyle of a user. The proposal unit proposes a user skin care routine on the basis of the information collected by the collection unit. The advice unit provides the user with a skin care advice on the basis of the current weather conditions. The detection unit detects a user skin state. The feedback unit feeds back changes or improvements in the skin state detected by the detection 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide personalized skin care recommendations based on a user's skin type and lifestyle, and there is room for improvement.

[0005] The system of the embodiment aims to suggest a personalized skin care routine based on the user's skin type and lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a suggestion unit, an advice unit, a detection unit, and a feedback unit. The collection unit collects information on a user's skin type, concerns, and lifestyle. The suggestion unit suggests a skin care routine for the user based on the information collected by the collection unit. The advice unit provides skin care advice to the user based on current weather conditions. The detection unit detects the user's skin condition. The feedback unit provides feedback on changes or improvements in the skin condition detected by the detection unit. Effect of the Invention

[0007] In accordance with an embodiment, the system can suggest a personalized skin care routine based on the user's skin type and lifestyle. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A skin care advisory system according to an embodiment of the present invention is a system that collects information such as a user's skin type, concerns, and lifestyle, and proposes an individual skin care routine. The system collects information on the user's skin type, concerns, and lifestyle, and proposes an optimal skin care routine to the user based on the collected information. In addition, the system provides skin care advice based on the current weather, season, and amount of UV rays, analyzes the user's skin condition with a camera or sensor, and tracks changes and improvements and provides feedback. This system allows the user to find the optimal skin care routine for themselves and improve their skin condition. For example, a skin care product with high moisturizing effect is proposed to a user with dry skin, and the use of sunscreen is advised in seasons with strong UV rays. In addition, by regularly checking the skin condition and providing feedback on improvements, the user can easily grasp the condition of his or her skin. In this way, the skin care advisory system can improve the user's skin condition.

[0029] The skin care advisory system according to the embodiment includes a collection unit, a suggestion unit, an advice unit, a detection unit, and a feedback unit. The collection unit collects information on the user's skin type, concerns, and lifestyle. The collection unit can collect information using, for example, a questionnaire or a sensor. The suggestion unit suggests an optimal skin care routine to the user based on the information collected by the collection unit. The suggestion unit analyzes the user's past skin care history and current skin condition using, for example, AI, and generates an optimal skin care routine. The advice unit provides the user with skin care advice based on the current weather, season, and amount of UV rays. The advice unit obtains real-time weather information in cooperation with, for example, a weather database, and provides the user with appropriate skin care advice. The detection unit detects the user's skin condition based on data from a camera or a sensor. The detection unit obtains an image of the user's skin using, for example, a camera, and analyzes the skin condition using AI. The feedback unit feeds back changes or improvements in the skin condition detected by the detection unit. The feedback unit generates, for example, a graph or chart to visually show the user the changes in the skin condition. As a result, the skin care advisory system according to the embodiment can improve the user's skin condition.

[0030] The collection unit collects information on the user's skin type, concerns, and lifestyle. The collection unit can collect information using, for example, a questionnaire or a sensor. Specifically, the questionnaire is provided through an online form or an app, and the user answers in detail about their skin type (dry skin, oily skin, combination skin, etc.), skin concerns (blemishes, wrinkles, acne, etc.), and lifestyle (diet, sleep habits, stress level, etc.). The sensor is installed in a smartphone or a dedicated device and collects data such as the moisture content, oil content, and elasticity of the skin in real time. These data are sent to a cloud server and stored in a central database. In addition, the collection unit also collects information on the user's past skin care history and the skin care products used. This allows the collection unit to build a comprehensive data set on the user's skin condition and lifestyle, which can be used for subsequent analysis and suggestions. The collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, more detailed information can be obtained by increasing the frequency of data collection at the change of seasons or before a specific event. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0031] The suggestion unit suggests an optimal skin care routine to the user based on the information collected by the collection unit. The suggestion unit, for example, uses AI to analyze the user's past skin care history and current skin condition, and generates an optimal skin care routine. Specifically, the AI ​​uses a machine learning algorithm to analyze the collected data and identify skin care products and procedures that are optimal for the user's skin type and concerns. For example, it suggests products with high moisturizing effects to users with dry skin, and products that control oil content to users with oily skin. The AI ​​also takes into account the user's lifestyle data, and suggests skin care methods with a relaxing effect to users who are under a lot of stress, for example. The suggestion unit presents a specific skin care routine to the user based on this information. For example, it provides detailed explanations of morning and evening skin care procedures, the order and amount of products to be used, additional care methods for specific skin concerns, and the like. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. As a result, the suggestion unit can provide the user with an optimal skin care routine and provide specific advice to improve the skin condition.

[0032] The advice unit provides the user with skin care advice based on the current weather, season, and amount of ultraviolet rays. For example, the advice unit cooperates with a weather database to obtain real-time weather information and provide the user with appropriate skin care advice. Specifically, based on the weather information and ultraviolet ray amount data obtained from the weather database, the advice unit provides the user with advice emphasizing the importance of using sunscreen and moisturizing. For example, on days with high ultraviolet ray amount, the use of sunscreen is recommended, and in dry seasons, the use of moisturizing cream is recommended. In addition, at the change of seasons, the skin condition is likely to change, so a special care method is suggested. Based on this information, the advice unit provides the user with specific skin care advice. For example, the advice unit provides detailed explanations on how to select and use skin care products according to specific weather conditions, skin care procedures for each season, etc. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice content. As a result, the advice unit can provide the user with optimal skin care advice and provide specific guidance for maintaining and improving the skin condition.

[0033] The detection unit detects the user's skin condition based on data from a camera or a sensor. For example, the detection unit uses a camera to capture an image of the user's skin and uses AI to analyze the skin condition. Specifically, the camera takes high-resolution images, and the AI ​​uses image recognition technology to analyze the skin tone, texture, blemishes, wrinkles, pore condition, and the like. In addition, the sensor collects data such as the skin's moisture content, oil content, and elasticity in real time, and the AI ​​integrates these data to evaluate the overall skin condition. Based on this information, the detection unit identifies the user's skin health condition and problems, and provides basic data for suggesting an appropriate skin care method. For example, if the skin's moisture content is low, it indicates the need for moisturizing, and if the skin's oil content is high, it recommends the use of a product that controls oil. In addition, the detection unit can periodically monitor the skin condition and track changes and improvements. This allows the detection unit to accurately grasp the user's skin condition and provide important data for suggesting an appropriate skin care method.

[0034] The feedback unit feeds back changes or improvements in the skin condition detected by the detection unit. The feedback unit, for example, generates graphs and charts to visually show the user changes in the skin condition. Specifically, data such as the amount of moisture, oil, elasticity, and number of spots and wrinkles on the skin are displayed in chronological order, allowing the user to understand changes in his or her own skin condition at a glance. In addition, the feedback unit evaluates the effectiveness of the user's skin care routine and suggests improvements and next steps. For example, if the amount of moisture on the skin increases after using a particular skin care product, the system recommends continued use of the product. Also, if the skin condition does not improve, the system advises the user to try a different product or method. The feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the entire system. As a result, the feedback unit can provide the user with specific and effective skin care advice and support for maintaining and improving the skin condition.

[0035] The detection unit can detect the user's skin condition based on data from a camera or a sensor. The detection unit, for example, uses a camera to acquire an image of the user's skin and analyzes the skin condition using AI. For example, the resolution of the camera is high and can detect even the finest details of the skin. In addition, the amount of moisture and oil in the skin can be measured using a sensor. As a result, the user's skin condition can be accurately detected by using a camera or a sensor. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input image data acquired by the camera to the generation AI and cause the generation AI to perform an analysis of the skin condition from the image data.

[0036] The suggestion unit can suggest a skin care routine to the user based on the information collected by the collection unit and the user's past skin care history. The suggestion unit, for example, uses AI to analyze the user's past skin care history and generate an optimal skin care routine. For example, the effect of a skin care product used by the user in the past is analyzed and a similar product is suggested. In addition, the reaction to a specific ingredient can be analyzed from the user's past skin care history and a related product can be suggested. This makes it possible to suggest a more appropriate skin care routine by taking into account the user's past skin care history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input past skin care history data to the generation AI and cause the generation AI to generate an optimal skin care routine.

[0037] The suggestion unit can suggest a skin care routine to the user based on the information collected by the collection unit and the user's location information. The suggestion unit, for example, uses AI to analyze the user's location information and generate a skin care routine suitable for the local climate and environment. For example, if the user lives in a dry area, a skin care product with a high moisturizing effect can be suggested. Also, if the user lives in a humid area, a skin care product that suppresses oiliness can be suggested. In this way, by taking the user's location information into consideration, a skin care routine suitable for the local climate and environment can be suggested. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input location information data to the generation AI and cause the generation AI to generate an optimal skin care routine.

[0038] The feedback unit can generate a graph or chart to visualize the change in the user's skin condition. The feedback unit uses, for example, a simple graph to visually display the change in the user's skin condition in an easy-to-understand manner. For example, a line graph is used to display the change in the moisture content and oil content of the skin in chronological order. A color-coded chart can also be used to highlight the improvement of the user's skin. This makes it easier for the user to confirm the effect of their own skin care by visually grasping the change in the skin condition. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input skin condition data to the generation AI and cause the generation AI to generate a graph or chart.

[0039] The suggestion unit can cooperate with a weather database to generate a skin care routine for each season. The suggestion unit cooperates with the weather database to generate an appropriate skin care routine for each season. For example, the suggestion unit suggests skin care products with high moisturizing effects in winter and suggests the use of sunscreen in summer. In addition, the skin care routine for each season can be automatically updated based on data acquired from the weather database. In this way, by cooperating with the weather database, an appropriate skin care routine for each season can be suggested. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input weather data to the generation AI and cause the generation AI to generate a skin care routine for each season.

[0040] The feedback unit can provide statistical data for comparing the improvement status of the user's skin with other users. The feedback unit, for example, compares the improvement status of the user's skin with other users and provides statistical data. For example, the user's skin condition is compared with the average of other users and feedback is provided. In addition, the improvement points of the user's skin can be compared with the data of other users and specific advice can be provided. By providing comparison data with other users, the user can objectively evaluate the improvement status of his / her own skin. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input data of other users to the generation AI and cause the generation AI to generate statistical data.

[0041] The suggestion unit can suggest a skin care routine according to the user's budget. The suggestion unit, for example, uses AI to analyze the user's budget and generate an effective skin care routine while reducing the economic burden. For example, the suggestion unit suggests optimal skin care products based on the user's monthly budget or annual budget. In addition, it is also possible to suggest products with high cost performance according to the user's budget. In this way, by taking the user's budget into consideration, it is possible to suggest an effective skin care routine while reducing the economic burden. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input budget data to the generation AI and cause the generation AI to generate an optimal skin care routine.

[0042] The feedback unit can conduct periodic questionnaires to evaluate the effectiveness of the user's skin care routine. The feedback unit, for example, conducts periodic questionnaires to evaluate the effectiveness of the user's skin care routine. For example, questions in the questionnaire include the feeling of using a skin care product, the feeling of effectiveness, changes in skin condition, and the like. In addition, the answer format of the questionnaire may be multiple choice or free description, and the user's opinions can be collected in detail. As a result, the effectiveness of the user's skin care routine can be continuously evaluated by conducting periodic questionnaires. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input questionnaire data into the generation AI and cause the generation AI to execute an evaluation of the effectiveness of the skin care routine.

[0043] The collection unit can analyze the user's past skin care history and select the optimal information collection method. The collection unit, for example, uses AI to analyze the user's past skin care history and select the optimal information collection method. For example, the effect of a skin care product used by the user in the past is analyzed and similar product information is collected. Effective information can also be collected based on the skin care method the user has tried in the past. Furthermore, it is also possible to analyze the reaction to a specific ingredient from the user's past skin care history and collect related information. In this way, a more effective information collection method can be selected by analyzing the past skin care history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input past skin care history data to the generation AI and cause the generation AI to select the optimal information collection method.

[0044] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting information. The collection unit, for example, uses AI to analyze the user's current living situation and field of interest and filters the information. For example, if the user feels stressed in the current living situation, skin care information with a relaxing effect is preferentially collected. In addition, if the user has a specific field of interest (e.g., organic products), information related to that field can also be collected. Furthermore, simple skin care methods can be collected in accordance with the user's living situation (e.g., busy schedule). This makes it possible to collect more relevant information by filtering information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the living situation and field of interest to the generation AI and cause the generation AI to filter the information.

[0045] When collecting information, the collection unit can preferentially collect highly relevant information taking into account the geographical location information of the user. The collection unit, for example, uses AI to analyze the geographical location information of the user and preferentially collect highly relevant information. For example, if the user lives in a dry area, skin care information with high moisturizing effect can be preferentially collected. In addition, if the user lives in a humid area, skin care information that suppresses oiliness can also be preferentially collected. Furthermore, if the user lives in an area with strong ultraviolet rays, information on sunscreen can also be preferentially collected. In this way, by taking into account the geographical location information of the user, information suitable for the local climate and environment can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to execute the determination of the priority order of information.

[0046] The collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit can, for example, use AI to analyze the user's social media activity and collect related information. For example, information on skin care products in which the user has shown interest on social media can be collected. Skin care information recommended by influencers followed by the user can also be collected. Furthermore, information that is a hot topic in skin care-related communities in which the user participates can also be collected. This allows more relevant information to be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media activity data to the generation AI and cause the generation AI to collect related information.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the skin care product when making a proposal. The suggestion unit, for example, uses AI to evaluate the importance of the skin care product and adjust the level of detail of the proposal. For example, for basic skin care products (e.g., cleanser), a concise proposal is made. In addition, for specialized skin care products (e.g., anti-aging serum), a proposal including a detailed explanation can be made. Furthermore, for products in which the user is particularly interested, a detailed proposal can be made. As a result, by adjusting the level of detail of the proposal based on the importance of the skin care product, it is possible to make an optimal proposal for the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the skin care product to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the skin care product when making a suggestion. The suggestion unit, for example, classifies the category of the skin care product using AI and applies different suggestion algorithms. For example, for moisturizing products, the suggestion is made based on the user's skin type. For sunscreen products, the suggestion can be made based on the current amount of UV rays. Furthermore, for anti-aging products, the suggestion can be made based on the user's age and skin condition. This allows more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of the skin care product. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input category data of the skin care product to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0049] The suggestion unit can determine the priority of the suggestions based on the submission time of the skin care products at the time of suggestion. The suggestion unit, for example, uses AI to evaluate the submission time of the skin care products and determine the priority of the suggestions. For example, at the turn of the season, skin care products suitable for the season are preferentially suggested. Also, before a specific event (e.g., a wedding), products that require special care can be preferentially suggested. Furthermore, the priority of the suggestions can be adjusted according to the timing when the user tries a new skin care product. This enables more appropriate suggestions by determining the priority of the suggestions based on the submission time of the skin care products. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the submission time data to the generation AI and cause the generation AI to execute the determination of the priority of the suggestions.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the skin care products when making suggestions. The suggestion unit, for example, uses AI to evaluate the relevance of the skin care products and adjust the order of suggestions. For example, the product most relevant to the user's skin type is suggested first. The product most relevant to the user's concerns can also be suggested first. Furthermore, the product most relevant to the user's lifestyle can also be suggested first. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of skin care products. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] The advice unit can adjust the level of detail of the advice based on the weather and seasonal fluctuations when giving advice. The advice unit, for example, uses AI to evaluate the weather and seasonal fluctuations and adjust the level of detail of the advice. For example, when it is raining, detailed advice on skin care products with high moisturizing effects is given. Also, when it is sunny, detailed advice on the use of sunscreen can be given. Furthermore, when the seasons change, detailed advice on skin care products suitable for the season can be given. As a result, by adjusting the level of detail of the advice based on the weather and seasonal fluctuations, more appropriate advice can be given. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input weather and seasonal data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0052] The advice unit can apply different advice algorithms according to the fluctuation of the amount of ultraviolet rays when giving advice. The advice unit, for example, uses AI to evaluate the fluctuation of the amount of ultraviolet rays and applies different advice algorithms. For example, when the amount of ultraviolet rays is high, advice on the use of sunscreen is given priority. Also, when the amount of ultraviolet rays is low, advice on skin care products with high moisturizing effects can be given priority. Furthermore, advice on the frequency of use of skin care products can be adjusted according to the fluctuation of the amount of ultraviolet rays. This makes it possible to give more appropriate advice by applying different advice algorithms according to the fluctuation of the amount of ultraviolet rays. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input ultraviolet ray amount data to the generation AI and cause the generation AI to apply the advice algorithm.

[0053] The advice unit can determine the priority of advice based on weather and seasonal fluctuations when giving advice. The advice unit, for example, uses AI to evaluate weather and seasonal fluctuations and determine the priority of advice. For example, in rainy weather, advice on skin care products with high moisturizing effects is given priority. In addition, in sunny weather, advice on the use of sunscreen can be given priority. Furthermore, at the turn of the seasons, advice on skin care products suitable for the season can be given priority. As a result, by determining the priority of advice based on weather and seasonal fluctuations, more appropriate advice can be given. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input weather and seasonal data to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0054] The advice unit can adjust the order of advice based on the fluctuation of the amount of ultraviolet light when giving advice. The advice unit, for example, uses AI to evaluate the fluctuation of the amount of ultraviolet light and adjust the order of advice. For example, when the amount of ultraviolet light is high, advice on the use of sunscreen is given first. Also, when the amount of ultraviolet light is low, advice on skin care products with high moisturizing effects can be given first. Furthermore, the order of advice on the frequency of use of skin care products can be adjusted according to the fluctuation of the amount of ultraviolet light. As a result, more appropriate advice can be given by adjusting the order of advice based on the fluctuation of the amount of ultraviolet light. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input ultraviolet light amount data to the generation AI and cause the generation AI to adjust the order of advice.

[0055] The detection unit can apply a correction algorithm for improving the accuracy of the camera or sensor at the time of detection. The detection unit applies a correction algorithm for improving the accuracy of the camera or sensor, for example, using AI. For example, an algorithm for automatically adjusting the exposure of the camera according to the lighting conditions is applied. In addition, the sensor can be calibrated periodically to maintain accuracy. Furthermore, the resolution of the camera can be optimized so that even the finest details of the skin can be detected. This improves the accuracy of the camera or sensor, enabling more accurate detection of the skin condition. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the correction algorithm to the generation AI and cause the generation AI to improve the accuracy of the camera or sensor.

[0056] The detection unit can improve the detection accuracy by referring to the user's past skin condition data at the time of detection. The detection unit, for example, uses AI to refer to the user's past skin condition data and improves the detection accuracy. For example, the current skin condition is compared based on the user's past skin condition data to improve the detection accuracy. In addition, a specific pattern can be extracted from the user's past skin condition data to improve the detection accuracy. Furthermore, abnormal changes can be detected by referring to the user's past skin condition data. As a result, more accurate skin condition detection is possible by referring to the user's past skin condition data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input past skin condition data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0057] The detection unit can improve the detection accuracy by considering the geographical location information of the user at the time of detection. The detection unit, for example, uses AI to analyze the geographical location information of the user and improve the detection accuracy. For example, if the user lives in a dry area, skin care information with high moisturizing effect can be collected preferentially. In addition, if the user lives in a humid area, skin care information that suppresses oiliness can be collected preferentially. Furthermore, if the user lives in an area with strong ultraviolet rays, information on sunscreen can be collected preferentially. This makes it possible to detect the skin condition more accurately by considering the geographical location information of the user. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input geographical location information data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0058] The detection unit can analyze the user's social media activities at the time of detection to improve the detection accuracy. The detection unit can, for example, use AI to analyze the user's social media activities to improve the detection accuracy. For example, the detection unit can collect information on skin care products in which the user has shown interest on social media. In addition, the detection unit can collect skin care information recommended by influencers followed by the user. In addition, the detection unit can collect information on topics in skin care-related communities in which the user participates. This allows for more accurate skin condition detection by analyzing the user's social media activities. Some or all of the above-mentioned processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input social media activity data to the generation AI and cause the generation AI to improve the detection accuracy.

[0059] The feedback unit can optimize the design of a graph or chart for visualizing the change in skin condition at the time of feedback. The feedback unit optimizes the design of a graph or chart for visualizing the change in skin condition, for example, using AI. For example, a simple graph is used to visually display the change in the user's skin condition in an easy-to-understand manner. A color-coded chart can also be used to highlight the improvement of the user's skin. Furthermore, a line graph can be used to track the user's skin condition over time. This makes it easier for the user to check the effect of their skin care by visualizing the change in the skin condition. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input design data of a graph or chart to the generation AI and cause the generation AI to execute design optimization.

[0060] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback history at the time of feedback. The feedback unit, for example, uses AI to refer to the user's past feedback history and improves the accuracy of the feedback. For example, the current feedback is compared based on the user's past feedback history to improve the accuracy. In addition, a specific pattern can be extracted from the user's past feedback history to improve the accuracy of the feedback. Furthermore, the user's past feedback history can be referred to to detect abnormal changes. As a result, more accurate feedback can be provided by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input past feedback history data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the feedback.

[0061] The feedback unit can provide statistical data for comparison with the improvement status of the skin of other users at the time of feedback. The feedback unit provides statistical data for comparison with the improvement status of the skin of other users, for example, using AI. For example, the improvement status of the skin of the user is compared with other users and statistical data is provided. The user's skin condition can also be compared with the average of other users and feedback can be provided. Furthermore, the improvement points of the user's skin can be compared with the data of other users and specific advice can be provided. In this way, by providing comparison data with other users, the user can objectively evaluate the improvement status of his / her own skin. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input data of other users to the generation AI and cause the generation AI to execute the generation of statistical data.

[0062] The feedback unit can analyze the user's social media activity at the time of feedback to improve the accuracy of the feedback. The feedback unit can, for example, use AI to analyze the user's social media activity to improve the accuracy of the feedback. For example, information about skin care products in which the user has shown interest on social media can be collected and reflected in the feedback. In addition, skin care information recommended by influencers followed by the user can be collected and reflected in the feedback. Furthermore, information that is a hot topic in skin care-related communities in which the user participates can be collected and reflected in the feedback. This allows for more accurate feedback by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input social media activity data to the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can also collect the user's diet and nutritional intake information to detect the user's skin condition. For example, it can be linked to an app that allows the user to record their daily dietary intake and analyze the intake of vitamins and minerals. In addition, if the user takes a specific supplement, the effect of the supplement can be evaluated in relation to the skin condition. This allows for more comprehensive skin care advice to be provided by taking into account diet and nutritional intake information.

[0065] The detection unit can take into account the user's sleep patterns when detecting the user's skin condition. For example, the detection unit can obtain the user's sleep time and quality from a device such as a smartwatch and analyze the correlation with the skin condition. It can also evaluate the effect of lack of sleep on the skin and provide appropriate skin care advice. By taking the sleep pattern into account, more accurate skin condition detection is possible.

[0066] The suggestion unit can suggest a skin care routine based on the user's life events. For example, before a wedding or an important presentation, the suggestion unit can suggest skin care products that require special care. Also, before a trip or outdoor activity, the suggestion unit can suggest products with high sunscreen and moisturizing effects. Furthermore, at the change of seasons, the suggestion unit can suggest skin care products suitable for the season. This allows for more appropriate suggestions by suggesting a skin care routine according to the user's life events.

[0067] The suggestion unit can suggest a skin care routine based on the user's exercise habits. For example, if the user exercises regularly, post-exercise skin care products can be suggested. If the user does not exercise, daily skin care products can be suggested. Furthermore, if the user plays a specific sport, skin care products suitable for that sport can be suggested. This allows for more appropriate suggestions by suggesting a skin care routine according to the user's exercise habits.

[0068] The collection unit can adjust the method of collecting information based on the user's hobbies and interests. For example, if the user is interested in beauty, beauty-related information can be collected preferentially. Also, if the user is interested in health, health-related information can be collected preferentially. Furthermore, if the user is interested in a particular brand, information related to the brand can be collected. In this way, by adjusting the method of collecting information based on the user's hobbies and interests, more relevant information can be collected.

[0069] The feedback unit can generate a 3D model to visualize changes in the user's skin condition. For example, a 3D model of the user's face can be generated to display changes in the skin condition in a three-dimensional manner. In addition, the skin condition of a specific part can be enlarged and detailed changes can be confirmed. Furthermore, a time lapse function can be provided to compare past and current conditions. This allows the user to check the effects of their skin care in more detail by visualizing changes in skin condition in a three-dimensional manner.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The collection unit collects information on the user's skin type, concerns, and lifestyle. The collection unit can collect information by using, for example, a questionnaire or a sensor. Step 2: The suggestion unit suggests an optimal skin care routine to the user based on the information collected by the collection unit. For example, the suggestion unit uses AI to analyze the user's past skin care history and current skin condition, and generates an optimal skin care routine. Step 3: The advice unit provides the user with skin care advice based on the current weather, season, and UV amount. The advice unit, for example, cooperates with a weather database to obtain real-time weather information and provides the user with appropriate skin care advice. Step 4: The detection unit detects the user's skin condition based on data from the camera or sensor. For example, the detection unit uses a camera to acquire an image of the user's skin and analyzes the skin condition using AI. Step 5: The feedback unit feeds back the changes or improvements in the skin condition detected by the detection unit, for example by generating a graph or chart to visually show the changes in the skin condition to the user.

[0072] (Example 2) A skin care advisory system according to an embodiment of the present invention is a system that collects information such as a user's skin type, concerns, and lifestyle, and proposes an individual skin care routine. The system collects information on the user's skin type, concerns, and lifestyle, and proposes an optimal skin care routine to the user based on the collected information. In addition, the system provides skin care advice based on the current weather, season, and amount of UV rays, analyzes the user's skin condition with a camera or sensor, and tracks changes and improvements and provides feedback. This system allows the user to find the optimal skin care routine for themselves and improve their skin condition. For example, a skin care product with high moisturizing effect is proposed to a user with dry skin, and the use of sunscreen is advised in seasons with strong UV rays. In addition, by regularly checking the skin condition and providing feedback on improvements, the user can easily grasp the condition of his or her skin. In this way, the skin care advisory system can improve the user's skin condition.

[0073] The skin care advisory system according to the embodiment includes a collection unit, a suggestion unit, an advice unit, a detection unit, and a feedback unit. The collection unit collects information on the user's skin type, concerns, and lifestyle. The collection unit can collect information using, for example, a questionnaire or a sensor. The suggestion unit suggests an optimal skin care routine to the user based on the information collected by the collection unit. The suggestion unit analyzes the user's past skin care history and current skin condition using, for example, AI, and generates an optimal skin care routine. The advice unit provides the user with skin care advice based on the current weather, season, and amount of UV rays. The advice unit obtains real-time weather information in cooperation with, for example, a weather database, and provides the user with appropriate skin care advice. The detection unit detects the user's skin condition based on data from a camera or a sensor. The detection unit obtains an image of the user's skin using, for example, a camera, and analyzes the skin condition using AI. The feedback unit feeds back changes or improvements in the skin condition detected by the detection unit. The feedback unit generates, for example, a graph or chart to visually show the user the changes in the skin condition. As a result, the skin care advisory system according to the embodiment can improve the user's skin condition.

[0074] The collection unit collects information on the user's skin type, concerns, and lifestyle. The collection unit can collect information using, for example, a questionnaire or a sensor. Specifically, the questionnaire is provided through an online form or an app, and the user answers in detail about their skin type (dry skin, oily skin, combination skin, etc.), skin concerns (blemishes, wrinkles, acne, etc.), and lifestyle (diet, sleep habits, stress level, etc.). The sensor is installed in a smartphone or a dedicated device and collects data such as the moisture content, oil content, and elasticity of the skin in real time. These data are sent to a cloud server and stored in a central database. In addition, the collection unit also collects information on the user's past skin care history and the skin care products used. This allows the collection unit to build a comprehensive data set on the user's skin condition and lifestyle, which can be used for subsequent analysis and suggestions. The collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, more detailed information can be obtained by increasing the frequency of data collection at the change of seasons or before a specific event. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0075] The suggestion unit suggests an optimal skin care routine to the user based on the information collected by the collection unit. The suggestion unit, for example, uses AI to analyze the user's past skin care history and current skin condition, and generates an optimal skin care routine. Specifically, the AI ​​uses a machine learning algorithm to analyze the collected data and identify skin care products and procedures that are optimal for the user's skin type and concerns. For example, it suggests products with high moisturizing effects to users with dry skin, and products that control oil content to users with oily skin. The AI ​​also takes into account the user's lifestyle data, and suggests skin care methods with a relaxing effect to users who are under a lot of stress, for example. The suggestion unit presents a specific skin care routine to the user based on this information. For example, it provides detailed explanations of morning and evening skin care procedures, the order and amount of products to be used, additional care methods for specific skin concerns, and the like. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. As a result, the suggestion unit can provide the user with an optimal skin care routine and provide specific advice to improve the skin condition.

[0076] The advice unit provides the user with skin care advice based on the current weather, season, and amount of ultraviolet rays. For example, the advice unit cooperates with a weather database to obtain real-time weather information and provide the user with appropriate skin care advice. Specifically, based on the weather information and ultraviolet ray amount data obtained from the weather database, the advice unit provides the user with advice emphasizing the importance of using sunscreen and moisturizing. For example, on days with high ultraviolet ray amount, the use of sunscreen is recommended, and in dry seasons, the use of moisturizing cream is recommended. In addition, at the change of seasons, the skin condition is likely to change, so a special care method is suggested. Based on this information, the advice unit provides the user with specific skin care advice. For example, the advice unit provides detailed explanations on how to select and use skin care products according to specific weather conditions, skin care procedures for each season, etc. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice content. As a result, the advice unit can provide the user with optimal skin care advice and provide specific guidance for maintaining and improving the skin condition.

[0077] The detection unit detects the user's skin condition based on data from a camera or a sensor. For example, the detection unit uses a camera to capture an image of the user's skin and uses AI to analyze the skin condition. Specifically, the camera takes high-resolution images, and the AI ​​uses image recognition technology to analyze the skin tone, texture, blemishes, wrinkles, pore condition, and the like. In addition, the sensor collects data such as the skin's moisture content, oil content, and elasticity in real time, and the AI ​​integrates these data to evaluate the overall skin condition. Based on this information, the detection unit identifies the user's skin health condition and problems, and provides basic data for suggesting an appropriate skin care method. For example, if the skin's moisture content is low, it indicates the need for moisturizing, and if the skin's oil content is high, it recommends the use of a product that controls oil. In addition, the detection unit can periodically monitor the skin condition and track changes and improvements. This allows the detection unit to accurately grasp the user's skin condition and provide important data for suggesting an appropriate skin care method.

[0078] The feedback unit feeds back changes or improvements in the skin condition detected by the detection unit. The feedback unit, for example, generates graphs and charts to visually show the user changes in the skin condition. Specifically, data such as the amount of moisture, oil, elasticity, and number of spots and wrinkles on the skin are displayed in chronological order, allowing the user to understand changes in his or her own skin condition at a glance. In addition, the feedback unit evaluates the effectiveness of the user's skin care routine and suggests improvements and next steps. For example, if the amount of moisture on the skin increases after using a particular skin care product, the system recommends continued use of the product. Also, if the skin condition does not improve, the system advises the user to try a different product or method. The feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the entire system. As a result, the feedback unit can provide the user with specific and effective skin care advice and support for maintaining and improving the skin condition.

[0079] The detection unit can detect the user's skin condition based on data from a camera or a sensor. The detection unit, for example, uses a camera to acquire an image of the user's skin and analyzes the skin condition using AI. For example, the resolution of the camera is high and can detect even the finest details of the skin. In addition, the amount of moisture and oil in the skin can be measured using a sensor. As a result, the user's skin condition can be accurately detected by using a camera or a sensor. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input image data acquired by the camera to the generation AI and cause the generation AI to perform an analysis of the skin condition from the image data.

[0080] The suggestion unit can suggest a skin care routine to the user based on the information collected by the collection unit and the user's past skin care history. The suggestion unit, for example, uses AI to analyze the user's past skin care history and generate an optimal skin care routine. For example, the effect of a skin care product used by the user in the past is analyzed and a similar product is suggested. In addition, the reaction to a specific ingredient can be analyzed from the user's past skin care history and a related product can be suggested. This makes it possible to suggest a more appropriate skin care routine by taking into account the user's past skin care history. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input past skin care history data to the generation AI and cause the generation AI to generate an optimal skin care routine.

[0081] The suggestion unit can suggest a skin care routine to the user based on the information collected by the collection unit and the user's location information. The suggestion unit, for example, uses AI to analyze the user's location information and generate a skin care routine suitable for the local climate and environment. For example, if the user lives in a dry area, a skin care product with a high moisturizing effect can be suggested. Also, if the user lives in a humid area, a skin care product that suppresses oiliness can be suggested. In this way, by taking the user's location information into consideration, a skin care routine suitable for the local climate and environment can be suggested. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input location information data to the generation AI and cause the generation AI to generate an optimal skin care routine.

[0082] The feedback unit can generate a graph or chart to visualize the change in the user's skin condition. The feedback unit uses, for example, a simple graph to visually display the change in the user's skin condition in an easy-to-understand manner. For example, a line graph is used to display the change in the moisture content and oil content of the skin in chronological order. A color-coded chart can also be used to highlight the improvement of the user's skin. This makes it easier for the user to confirm the effect of their own skin care by visually grasping the change in the skin condition. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input skin condition data to the generation AI and cause the generation AI to generate a graph or chart.

[0083] The suggestion unit can cooperate with a weather database to generate a skin care routine for each season. The suggestion unit cooperates with the weather database to generate an appropriate skin care routine for each season. For example, the suggestion unit suggests skin care products with high moisturizing effects in winter and suggests the use of sunscreen in summer. In addition, the skin care routine for each season can be automatically updated based on data acquired from the weather database. In this way, by cooperating with the weather database, an appropriate skin care routine for each season can be suggested. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input weather data to the generation AI and cause the generation AI to generate a skin care routine for each season.

[0084] The feedback unit can provide statistical data for comparing the improvement status of the user's skin with other users. The feedback unit, for example, compares the improvement status of the user's skin with other users and provides statistical data. For example, the user's skin condition is compared with the average of other users and feedback is provided. In addition, the improvement points of the user's skin can be compared with the data of other users and specific advice can be provided. By providing comparison data with other users, the user can objectively evaluate the improvement status of his / her own skin. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input data of other users to the generation AI and cause the generation AI to generate statistical data.

[0085] The suggestion unit can suggest a skin care routine according to the user's budget. The suggestion unit, for example, uses AI to analyze the user's budget and generate an effective skin care routine while reducing the economic burden. For example, the suggestion unit suggests optimal skin care products based on the user's monthly budget or annual budget. In addition, it is also possible to suggest products with high cost performance according to the user's budget. In this way, by taking the user's budget into consideration, it is possible to suggest an effective skin care routine while reducing the economic burden. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input budget data to the generation AI and cause the generation AI to generate an optimal skin care routine.

[0086] The feedback unit can conduct periodic questionnaires to evaluate the effectiveness of the user's skin care routine. The feedback unit, for example, conducts periodic questionnaires to evaluate the effectiveness of the user's skin care routine. For example, questions in the questionnaire include the feeling of using a skin care product, the feeling of effectiveness, changes in skin condition, and the like. In addition, the answer format of the questionnaire may be multiple choice or free description, and the user's opinions can be collected in detail. As a result, the effectiveness of the user's skin care routine can be continuously evaluated by conducting periodic questionnaires. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input questionnaire data into the generation AI and cause the generation AI to execute an evaluation of the effectiveness of the skin care routine.

[0087] The collection unit can estimate the user's emotion and adjust the timing of information collection based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, an emotion estimation algorithm. For example, the collection unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. In addition, the collection unit can also estimate the emotion by analyzing the tone and speed of the user's voice using voice analysis technology. Furthermore, the collection unit can estimate the user's emotion using self-report data. This allows more effective information collection by adjusting the timing of information collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0088] The collection unit can analyze the user's past skin care history and select the optimal information collection method. The collection unit, for example, uses AI to analyze the user's past skin care history and select the optimal information collection method. For example, the effect of a skin care product used by the user in the past is analyzed and similar product information is collected. Effective information can also be collected based on the skin care method the user has tried in the past. Furthermore, it is also possible to analyze the reaction to a specific ingredient from the user's past skin care history and collect related information. This makes it possible to select a more effective information collection method by analyzing the past skin care history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past skin care history data to the generation AI and cause the generation AI to select the optimal information collection method.

[0089] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting information. The collection unit, for example, uses AI to analyze the user's current living situation and field of interest and filters the information. For example, if the user feels stressed in the current living situation, skin care information with a relaxing effect is preferentially collected. In addition, if the user has a specific field of interest (e.g., organic products), information related to that field can also be collected. Furthermore, simple skin care methods can be collected in accordance with the user's living situation (e.g., busy schedule). This makes it possible to collect more relevant information by filtering information based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data on the living situation and field of interest to the generation AI and cause the generation AI to filter the information.

[0090] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. The collection unit, for example, uses an emotion estimation algorithm to estimate the user's emotion and determine the priority of information to be collected. For example, when the user is stressed, skin care information with a relaxing effect is preferentially collected. In addition, when the user is relaxed, it is also possible to preferentially collect the latest skin care trend information. Furthermore, when the user is busy, it is also possible to preferentially collect skin care information that is effective in a short time. This enables more effective information collection by determining the priority of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] When collecting information, the collection unit can preferentially collect highly relevant information taking into account the geographical location information of the user. The collection unit, for example, uses AI to analyze the geographical location information of the user and preferentially collect highly relevant information. For example, if the user lives in a dry area, skin care information with high moisturizing effect can be preferentially collected. In addition, if the user lives in a humid area, skin care information that suppresses oiliness can also be preferentially collected. Furthermore, if the user lives in an area with strong ultraviolet rays, information on sunscreen can also be preferentially collected. In this way, by taking into account the geographical location information of the user, information suitable for the local climate and environment can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to execute the determination of the priority order of information.

[0092] The collection unit can analyze the user's social media activity and collect related information when collecting information. The collection unit can, for example, use AI to analyze the user's social media activity and collect related information. For example, information on skin care products in which the user has shown interest on social media can be collected. Skin care information recommended by influencers followed by the user can also be collected. Furthermore, information that is a hot topic in skin care-related communities in which the user participates can also be collected. This allows more relevant information to be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input social media activity data to the generation AI and cause the generation AI to collect related information.

[0093] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. The suggestion unit, for example, uses an emotion estimation algorithm to estimate the user's emotion and adjust the way of expressing the suggestion. For example, if the user is stressed, a skin care product with a relaxing effect is suggested in gentle words. Also, if the user is relaxed, a suggestion including a detailed explanation can be made. Furthermore, if the user is in a hurry, a concise suggestion that covers the main points can be made. This allows for more effective suggestions by adjusting the way of expressing the suggestion according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the emotion.

[0094] The suggestion unit can adjust the level of detail of the proposal based on the importance of the skin care product when making a proposal. The suggestion unit, for example, uses AI to evaluate the importance of the skin care product and adjust the level of detail of the proposal. For example, for basic skin care products (e.g., cleanser), a concise proposal is made. In addition, for specialized skin care products (e.g., anti-aging serum), a proposal including a detailed explanation can be made. Furthermore, for products in which the user is particularly interested, a detailed proposal can be made. As a result, by adjusting the level of detail of the proposal based on the importance of the skin care product, it is possible to make an optimal proposal for the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the importance data of the skin care product to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0095] The suggestion unit can apply different suggestion algorithms depending on the category of the skin care product when making a suggestion. The suggestion unit, for example, classifies the category of the skin care product using AI and applies different suggestion algorithms. For example, for moisturizing products, the suggestion is made based on the user's skin type. For sunscreen products, the suggestion can be made based on the current amount of UV rays. Furthermore, for anti-aging products, the suggestion can be made based on the user's age and skin condition. This allows more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of the skin care product. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input category data of the skin care product to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0096] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit, for example, uses an emotion estimation algorithm to estimate the user's emotion and adjust the length of the proposal. For example, if the user is in a hurry, a short and to the point proposal is made. Also, if the user is relaxed, a longer proposal including a detailed explanation can be made. Furthermore, if the user is excited, a proposal with a visually stimulating effect can be made. This allows for more effective proposals by adjusting the length of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0097] The suggestion unit can determine the priority of the suggestions based on the submission time of the skin care products at the time of suggestion. The suggestion unit, for example, uses AI to evaluate the submission time of the skin care products and determine the priority of the suggestions. For example, at the turn of the season, skin care products suitable for the season are preferentially suggested. Also, before a specific event (e.g., a wedding), products that require special care can be preferentially suggested. Furthermore, the priority of the suggestions can be adjusted according to the timing when the user tries a new skin care product. This enables more appropriate suggestions by determining the priority of the suggestions based on the submission time of the skin care products. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the submission time data to the generation AI and cause the generation AI to execute the determination of the priority of the suggestions.

[0098] The suggestion unit can adjust the order of suggestions based on the relevance of the skin care products when making suggestions. The suggestion unit, for example, uses AI to evaluate the relevance of the skin care products and adjust the order of suggestions. For example, the product most relevant to the user's skin type is suggested first. The product most relevant to the user's concerns can also be suggested first. Furthermore, the product most relevant to the user's lifestyle can also be suggested first. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of skin care products. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0099] The advice unit can estimate the user's emotion and adjust the way of expressing the advice based on the estimated user's emotion. The advice unit can estimate the user's emotion using, for example, an emotion estimation algorithm and adjust the way of expressing the advice. For example, if the user is feeling stressed, the advice unit can provide relaxing advice in gentle words. If the user is relaxed, the advice unit can provide advice including detailed explanations. If the user is in a hurry, the advice unit can provide concise advice that is to the point. This allows for more effective advice by adjusting the way of expressing the advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0100] The advice unit can adjust the level of detail of the advice based on the weather and seasonal fluctuations when giving advice. The advice unit, for example, uses AI to evaluate the weather and seasonal fluctuations and adjust the level of detail of the advice. For example, when it is raining, detailed advice on skin care products with high moisturizing effects is given. Also, when it is sunny, detailed advice on the use of sunscreen can be given. Furthermore, when the seasons change, detailed advice on skin care products suitable for the season can be given. As a result, by adjusting the level of detail of the advice based on the weather and seasonal fluctuations, more appropriate advice can be given. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input weather and seasonal data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0101] The advice unit can apply different advice algorithms according to the fluctuation of the amount of ultraviolet rays when giving advice. The advice unit, for example, uses AI to evaluate the fluctuation of the amount of ultraviolet rays and applies different advice algorithms. For example, when the amount of ultraviolet rays is high, advice on the use of sunscreen is given priority. Also, when the amount of ultraviolet rays is low, advice on skin care products with high moisturizing effects can be given priority. Furthermore, advice on the frequency of use of skin care products can be adjusted according to the fluctuation of the amount of ultraviolet rays. This makes it possible to give more appropriate advice by applying different advice algorithms according to the fluctuation of the amount of ultraviolet rays. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input ultraviolet ray amount data to the generation AI and cause the generation AI to apply the advice algorithm.

[0102] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The advice unit can estimate the user's emotion using, for example, an emotion estimation algorithm and adjust the length of the advice. For example, if the user is in a hurry, short advice that is to the point can be provided. Also, if the user is relaxed, longer advice including detailed explanations can be provided. Furthermore, if the user is excited, advice with visually stimulating effects can be provided. This allows for more effective advice by adjusting the length of the advice according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The advice unit can determine the priority of advice based on weather and seasonal fluctuations when giving advice. The advice unit, for example, uses AI to evaluate weather and seasonal fluctuations and determine the priority of advice. For example, in rainy weather, advice on skin care products with high moisturizing effects is given priority. In addition, in sunny weather, advice on the use of sunscreen can be given priority. Furthermore, at the turn of the seasons, advice on skin care products suitable for the season can be given priority. As a result, by determining the priority of advice based on weather and seasonal fluctuations, more appropriate advice can be given. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input weather and seasonal data to the generation AI and cause the generation AI to execute the determination of the priority of advice.

[0104] The advice unit can adjust the order of advice based on the fluctuation of the amount of ultraviolet light when giving advice. The advice unit, for example, uses AI to evaluate the fluctuation of the amount of ultraviolet light and adjust the order of advice. For example, when the amount of ultraviolet light is high, advice on the use of sunscreen is given first. Also, when the amount of ultraviolet light is low, advice on skin care products with high moisturizing effects can be given first. Furthermore, the order of advice on the frequency of use of skin care products can be adjusted according to the fluctuation of the amount of ultraviolet light. As a result, more appropriate advice can be given by adjusting the order of advice based on the fluctuation of the amount of ultraviolet light. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input ultraviolet light amount data to the generation AI and cause the generation AI to adjust the order of advice.

[0105] The detection unit can estimate the user's emotion and adjust the timing of skin condition detection based on the estimated user's emotion. The detection unit, for example, uses an emotion estimation algorithm to estimate the user's emotion and adjust the timing of skin condition detection. For example, when the user is relaxed, the skin condition is detected immediately. Also, when the user is stressed, the skin condition can be detected during a relaxed time period. Furthermore, when the user is busy, the skin condition can be detected during an unoccupied time period. This allows for more effective detection by adjusting the timing of skin condition detection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0106] The detection unit can apply a correction algorithm for improving the accuracy of the camera or sensor at the time of detection. The detection unit applies a correction algorithm for improving the accuracy of the camera or sensor, for example, using AI. For example, an algorithm for automatically adjusting the exposure of the camera according to the lighting conditions is applied. In addition, the sensor can be calibrated periodically to maintain accuracy. Furthermore, the resolution of the camera can be optimized so that even the finest details of the skin can be detected. This improves the accuracy of the camera or sensor, enabling more accurate detection of the skin condition. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the correction algorithm to the generation AI and cause the generation AI to improve the accuracy of the camera or sensor.

[0107] The detection unit can improve the detection accuracy by referring to the user's past skin condition data at the time of detection. The detection unit, for example, uses AI to refer to the user's past skin condition data and improves the detection accuracy. For example, the current skin condition is compared based on the user's past skin condition data to improve the detection accuracy. In addition, a specific pattern can be extracted from the user's past skin condition data to improve the detection accuracy. Furthermore, abnormal changes can be detected by referring to the user's past skin condition data. As a result, more accurate skin condition detection is possible by referring to the user's past skin condition data. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input past skin condition data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0108] The detection unit can estimate the user's emotion and adjust the display method of the detection result based on the estimated user's emotion. The detection unit, for example, estimates the user's emotion using an emotion estimation algorithm and adjusts the display method of the detection result. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more effective display by adjusting the display method of the detection result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0109] The detection unit can improve the detection accuracy by considering the geographical location information of the user at the time of detection. The detection unit, for example, uses AI to analyze the geographical location information of the user and improve the detection accuracy. For example, if the user lives in a dry area, skin care information with high moisturizing effect can be collected preferentially. In addition, if the user lives in a humid area, skin care information that suppresses oiliness can be collected preferentially. Furthermore, if the user lives in an area with strong ultraviolet rays, information on sunscreen can be collected preferentially. This makes it possible to detect the skin condition more accurately by considering the geographical location information of the user. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input geographical location information data to the generation AI and cause the generation AI to execute the improvement of the detection accuracy.

[0110] The detection unit can analyze the user's social media activities at the time of detection to improve the detection accuracy. The detection unit can, for example, use AI to analyze the user's social media activities to improve the detection accuracy. For example, the detection unit can collect information on skin care products in which the user has shown interest on social media. In addition, the detection unit can collect skin care information recommended by influencers followed by the user. In addition, the detection unit can collect information on topics in skin care-related communities in which the user participates. This allows for more accurate skin condition detection by analyzing the user's social media activities. Some or all of the above-mentioned processing in the detection unit can be performed, for example, using AI or without AI. For example, the detection unit can input social media activity data to the generation AI and cause the generation AI to improve the detection accuracy.

[0111] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. The feedback unit, for example, uses an emotion estimation algorithm to estimate the user's emotion and adjust the feedback expression method. For example, if the user is stressed, feedback with a relaxing effect is provided in gentle words. If the user is relaxed, feedback including detailed explanations can be provided. Furthermore, if the user is in a hurry, feedback that is concise and to the point can be provided. This enables more effective feedback by adjusting the feedback expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0112] The feedback unit can optimize the design of a graph or chart for visualizing the change in skin condition at the time of feedback. The feedback unit optimizes the design of a graph or chart for visualizing the change in skin condition, for example, using AI. For example, a simple graph is used to visually display the change in the user's skin condition in an easy-to-understand manner. A color-coded chart can also be used to highlight the improvement of the user's skin. Furthermore, a line graph can be used to track the user's skin condition over time. This makes it easier for the user to check the effect of their skin care by visualizing the change in the skin condition. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input design data of a graph or chart to the generation AI and cause the generation AI to execute design optimization.

[0113] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback history at the time of feedback. The feedback unit, for example, uses AI to refer to the user's past feedback history and improves the accuracy of the feedback. For example, the current feedback is compared based on the user's past feedback history to improve the accuracy. In addition, a specific pattern can be extracted from the user's past feedback history to improve the accuracy of the feedback. Furthermore, the user's past feedback history can be referred to to detect abnormal changes. As a result, more accurate feedback can be provided by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input past feedback history data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the feedback.

[0114] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion using an emotion estimation algorithm and adjusts the length of the feedback. For example, if the user is in a hurry, short feedback that is to the point can be provided. Also, if the user is relaxed, longer feedback including detailed explanations can be provided. Furthermore, if the user is excited, feedback with visually stimulating effects can be provided. This allows for more effective feedback by adjusting the length of the feedback according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0115] The feedback unit can provide statistical data for comparison with the improvement status of the skin of other users at the time of feedback. The feedback unit provides statistical data for comparison with the improvement status of the skin of other users, for example, using AI. For example, the improvement status of the skin of the user is compared with other users and statistical data is provided. The user's skin condition can also be compared with the average of other users and feedback can be provided. Furthermore, the improvement points of the user's skin can be compared with the data of other users and specific advice can be provided. In this way, by providing comparison data with other users, the user can objectively evaluate the improvement status of his / her own skin. Part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI or may be performed without using AI. For example, the feedback unit can input data of other users to the generation AI and cause the generation AI to execute the generation of statistical data.

[0116] The feedback unit can analyze the user's social media activity at the time of feedback to improve the accuracy of the feedback. The feedback unit can, for example, use AI to analyze the user's social media activity to improve the accuracy of the feedback. For example, information about skin care products in which the user has shown interest on social media can be collected and reflected in the feedback. In addition, skin care information recommended by influencers followed by the user can be collected and reflected in the feedback. Furthermore, information that is a hot topic in skin care-related communities in which the user participates can be collected and reflected in the feedback. This allows for more accurate feedback by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI, or can be performed without using AI. For example, the feedback unit can input social media activity data to the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The collection unit can also collect the user's diet and nutritional intake information to detect the user's skin condition. For example, it can be linked to an app that allows the user to record their daily dietary intake and analyze the intake of vitamins and minerals. In addition, if the user takes a specific supplement, the effect of the supplement can be evaluated in relation to the skin condition. This allows for more comprehensive skin care advice to be provided by taking into account diet and nutritional intake information.

[0119] The detection unit can take into account the user's sleep patterns when detecting the user's skin condition. For example, the detection unit can obtain the user's sleep time and quality from a device such as a smartwatch and analyze the correlation with the skin condition. It can also evaluate the effect of lack of sleep on the skin and provide appropriate skin care advice. By taking the sleep pattern into account, more accurate skin condition detection is possible.

[0120] The suggestion unit can estimate the user's emotion and adjust the suggested content of the skin care routine based on the estimated emotion of the user. For example, if the user is stressed, a skin care product with a relaxing effect can be suggested. Also, if the user is relaxed, a more active skin care product can be suggested. Furthermore, if the user is excited, a skin care product containing a stimulating ingredient can be suggested. This allows more effective suggestions to be made by adjusting the suggested content of the skin care routine according to the user's emotion.

[0121] The suggestion unit can suggest a skin care routine based on the user's life events. For example, before a wedding or an important presentation, the suggestion unit can suggest skin care products that require special care. Also, before a trip or outdoor activity, the suggestion unit can suggest products with high sunscreen and moisturizing effects. Furthermore, at the change of seasons, the suggestion unit can suggest skin care products suitable for the season. This allows for more appropriate suggestions by suggesting a skin care routine according to the user's life events.

[0122] The feedback unit can estimate the user's emotion and adjust the timing of the feedback based on the estimated user's emotion. For example, if the user is relaxed, the feedback can be provided immediately. If the user is stressed, the feedback can be provided during a relaxed time period. If the user is busy, the feedback can be provided during a free time period. This allows for more effective feedback by adjusting the timing of the feedback according to the user's emotion.

[0123] The suggestion unit can suggest a skin care routine based on the user's exercise habits. For example, if the user exercises regularly, post-exercise skin care products can be suggested. If the user does not exercise, daily skin care products can be suggested. Furthermore, if the user plays a specific sport, skin care products suitable for that sport can be suggested. This allows for more appropriate suggestions by suggesting a skin care routine according to the user's exercise habits.

[0124] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, if the user is stressed, feedback with a relaxing effect can be provided. If the user is relaxed, feedback including detailed explanations can be provided. Furthermore, if the user is excited, feedback with a visually stimulating effect can be provided. This allows for more effective feedback by adjusting the content of the feedback according to the user's emotion.

[0125] The collection unit can adjust the method of collecting information based on the user's hobbies and interests. For example, if the user is interested in beauty, beauty-related information can be collected preferentially. Also, if the user is interested in health, health-related information can be collected preferentially. Furthermore, if the user is interested in a particular brand, information related to the brand can be collected. In this way, by adjusting the method of collecting information based on the user's hobbies and interests, more relevant information can be collected.

[0126] The suggestion unit can estimate the user's emotion and adjust the timing of the suggestion based on the estimated user's emotion. For example, if the user is relaxed, the suggestion can be made immediately. Also, if the user is stressed, the suggestion can be made during a relaxed time period. Furthermore, if the user is busy, the suggestion can be made during an unoccupied time period. In this way, more effective suggestions can be made by adjusting the timing of the suggestion according to the user's emotion.

[0127] The feedback unit can generate a 3D model to visualize changes in the user's skin condition. For example, a 3D model of the user's face can be generated to display changes in the skin condition in a three-dimensional manner. In addition, the skin condition of a specific part can be enlarged and detailed changes can be confirmed. Furthermore, a time lapse function can be provided to compare past and current conditions. This allows the user to check the effects of their skin care in more detail by visualizing changes in skin condition in a three-dimensional manner.

[0128] The process flow of the second embodiment will be briefly described below.

[0129] Step 1: The collection unit collects information on the user's skin type, concerns, and lifestyle. The collection unit can collect information by using, for example, a questionnaire or a sensor. Step 2: The suggestion unit suggests an optimal skin care routine to the user based on the information collected by the collection unit. For example, the suggestion unit uses AI to analyze the user's past skin care history and current skin condition, and generates an optimal skin care routine. Step 3: The advice unit provides the user with skin care advice based on the current weather, season, and UV amount. The advice unit, for example, cooperates with a weather database to obtain real-time weather information and provides the user with appropriate skin care advice. Step 4: The detection unit detects the user's skin condition based on data from the camera or sensor. For example, the detection unit uses a camera to acquire an image of the user's skin and analyzes the skin condition using AI. Step 5: The feedback unit feeds back the changes or improvements in the skin condition detected by the detection unit, for example by generating a graph or chart to visually show the changes in the skin condition to the user.

[0130] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0132] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] For example, the collection unit can collect information on the user's skin type, concerns, and lifestyle using the camera 42 and microphone 38B of the smart device 14. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal skin care routine based on the collected information. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and cooperates with a weather database to obtain real-time weather information and provide the user with appropriate skin care advice. The detection unit detects the user's skin condition using the camera 42 of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit is realized by the specific processing unit 290 of the data processing device 12, and feeds back the detected changes and improvements in the skin condition to the user. The collection unit can estimate the user's emotions using an emotion estimation algorithm, and adjust the timing of information collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0137] 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.

[0138] 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 the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] 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 performed in a secure state.

[0141] Fig. 4 shows an example of 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.

[0142] 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.

[0143] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0147] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] For example, the collection unit can collect information on the user's skin type, concerns, and lifestyle using the camera 42 and microphone 238 of the smart glasses 214. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal skin care routine based on the collected information. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and cooperates with a weather database to obtain real-time weather information and provide appropriate skin care advice to the user. The detection unit detects the user's skin condition using the camera 42 of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit is realized by the specific processing unit 290 of the data processing device 12, and feeds back the detected changes and improvements in the skin condition to the user. The collection unit can estimate the user's emotions using an emotion estimation algorithm, and adjust the timing of information collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0153] 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.

[0154] 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 the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0156] 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 performed in a secure state.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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.

[0159] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0165] For example, the collection unit can collect information on the user's skin type, concerns, and lifestyle using the camera 42 and microphone 238 of the headset type terminal 314. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal skin care routine based on the collected information. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and cooperates with a weather database to obtain real-time weather information and provide the user with appropriate skin care advice. The detection unit detects the user's skin condition using the camera 42 of the headset type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit is realized by the specific processing unit 290 of the data processing device 12, and feeds back the detected changes and improvements in the skin condition to the user. The collection unit can estimate the user's emotions using an emotion estimation algorithm, and adjust the timing of information collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0169] 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.

[0170] 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 the voice according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0172] 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 performed in a secure state.

[0173] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0174] 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.

[0175] 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.

[0176] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0177] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0178] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0179] 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 a voice indicating a user input for 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.

[0180] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0181] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0182] For example, the collection unit can collect information on the user's skin type, concerns, and lifestyle using the camera 42 and microphone 238 of the robot 414. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal skin care routine based on the collected information. The advice unit is realized by the specific processing unit 290 of the data processing device 12, and cooperates with a weather database to obtain real-time weather information and provide the user with appropriate skin care advice. The detection unit detects the user's skin condition using the camera 42 of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The feedback unit is realized by the specific processing unit 290 of the data processing device 12, and feeds back the detected changes and improvements in the skin condition to the user. The collection unit can estimate the user's emotions using an emotion estimation algorithm, and adjust the timing of information collection based on the estimated emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0183] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0184] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0186] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0188] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0190] 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.

[0191] 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 Universal Serial Bus (USB) 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.

[0192] In addition, 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 upon request from the data processing device 12.

[0193] 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.

[0194] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0195] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0196] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0197] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0199] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0201] (Appendix 1) a collection unit that collects information on a user's skin type, concerns, and lifestyle; a suggestion unit that suggests a skin care routine for the user based on the information collected by the collection unit; an advice unit that provides the user with skin care advice based on current weather conditions; A detection unit that detects a skin condition of the user; A feedback unit that feeds back the change or improvement in the skin condition detected by the detection unit. A system characterized by: (Appendix 2) The detection unit is Detecting a skin condition of the user based on data from a camera or a sensor 2. The system of claim 1, (Appendix 3) The suggestion unit, Suggesting a skin care routine to the user based on the information collected by the collection unit and the user's past skin care history. 2. The system of claim 1, (Appendix 4) The suggestion unit, Suggesting a skin care routine to the user based on the information collected by the collection unit and location information of the user. 2. The system of claim 1, (Appendix 5) The feedback unit is Generate a graph or chart to visualize changes in the user's skin condition. 2. The system of claim 1, (Appendix 6) The suggestion unit, Integrate with weather databases to generate seasonal skin care routines 2. The system of claim 1, (Appendix 7) The feedback unit is Providing statistical data for comparing the user's skin improvement with other users 2. The system of claim 1, (Appendix 8) The suggestion unit, Suggest a skin care routine that fits the user's budget 2. The system of claim 1, (Appendix 9) The feedback unit is Conducting periodic surveys to assess the effectiveness of the user's skin care routine 2. The system of claim 1, (Appendix 10) The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Analyze the user's past skin care history and select the optimal method of collecting information 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting information, it filters it based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting information, the system prioritizes collecting information that is highly relevant by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting information, we analyze your social media activity and collect relevant information. 2. The system of claim 1, (Appendix 16) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the skin care product 2. The system of claim 1, (Appendix 18) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of skin care products. 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 20) The suggestion unit, When submitting proposals, we will prioritize proposals based on when the skin care products were submitted. 2. The system of claim 1, (Appendix 21) The suggestion unit, During suggestions, adjust the order of suggestions based on skin care product relevance 2. The system of claim 1, (Appendix 22) The advice unit, Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The advice unit, When giving advice, adjust the level of detail based on weather and seasonal variations 2. The system of claim 1, (Appendix 24) The advice unit, When giving advice, different advice algorithms are applied depending on the fluctuation of UV radiation levels. 2. The system of claim 1, (Appendix 25) The advice unit is Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 26) The advice unit is When providing advice, prioritize advice based on weather and seasonal variations 2. The system of claim 1, (Appendix 27) The advice unit is When giving advice, adjust the order of advice based on fluctuations in UV levels 2. The system of claim 1, (Appendix 28) The detection unit is A user's emotion is estimated, and the timing of skin condition detection is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The detection unit is Upon detection, apply correction algorithms to improve the accuracy of cameras and sensors 2. The system of claim 1, (Appendix 30) The detection unit is During detection, the accuracy of detection is improved by referencing the user's past skin condition data. 2. The system of claim 1, (Appendix 31) The detection unit is Infer user emotions and adjust the way detection results are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 32) The detection unit is During detection, the geographic location of the user is taken into account to improve detection accuracy. 2. The system of claim 1, (Appendix 33) The detection unit is Upon detection, we analyze users' social media activity to improve detection accuracy. 2. The system of claim 1, (Appendix 34) The feedback unit is Infer user emotions and adjust feedback presentation based on the estimated user emotions 2. The system of claim 1, (Appendix 35) The feedback unit is Optimize the design of graphs and charts to visualize changes in skin condition upon feedback 2. The system of claim 1, (Appendix 36) The feedback unit is When providing feedback, improve the accuracy of the feedback by referring to the user's past feedback history 2. The system of claim 1, (Appendix 37) The feedback unit is Estimate the user's emotions and adjust the length of the feedback based on the estimated user emotions. 2. The system of claim 1, (Appendix 38) The feedback unit is When you provide feedback, we provide statistical data to compare your skin improvement with that of other users. 2. The system of claim 1, (Appendix 39) The feedback unit is When providing feedback, analyze users' social media activity to improve the accuracy of the feedback 2. The system of claim 1, [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information on a user's skin type, concerns, and lifestyle; a suggestion unit that suggests a skin care routine for the user based on the information collected by the collection unit; an advice unit that provides the user with skin care advice based on current weather conditions; A detection unit that detects a skin condition of the user; A feedback unit that feeds back the change or improvement in the skin condition detected by the detection unit. A system characterized by:

2. The detection unit is Detecting a skin condition of the user based on data from a camera or a sensor 2. The system of claim 1 .

3. The suggestion unit, Suggesting a skin care routine to the user based on the information collected by the collection unit and the user's past skin care history.

2. The system of claim 1 .

4. The suggestion unit, Suggesting a skin care routine to the user based on the information collected by the collection unit and location information of the user.

2. The system of claim 1 .

5. The feedback unit is Generate a graph or chart to visualize changes in the user's skin condition.

2. The system of claim 1 .

6. The suggestion unit, Integrate with weather databases to generate seasonal skin care routines 2. The system of claim 1 .

7. The feedback unit is Providing statistical data for comparing the user's skin improvement with other users 2. The system of claim 1 .

8. The suggestion unit, Suggest a skin care routine that fits the user's budget 2. The system of claim 1 .

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