system

Abstract

The system according to this embodiment aims to suggest and provide the optimal sunscreen for the user according to their skin condition and the season. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a reception unit. The collection unit collects the user's skin data. The analysis unit analyzes the data collected by the collection unit and suggests sunscreen. The provision unit provides the user with the sunscreen suggested by the analysis unit. The reception unit receives advice from a makeup instructor or coordinator.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to propose an optimal sunscreen according to the user's skin condition and season.

[0005] The system according to the embodiment aims to propose and provide an optimal sunscreen according to the user's skin condition and season.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a provision unit, and a reception unit. The data collection unit collects the user's skin data. The analysis unit analyzes the data collected by the data collection unit and suggests sunscreen. The provision unit provides the sunscreen suggested by the analysis unit to the user. The reception unit receives advice from a makeup instructor or coordinator. [Effects of the Invention]

[0007] The system according to this embodiment can suggest and provide the optimal sunscreen for the user according to their skin condition and the season. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The subscription service according to an embodiment of the present invention is a system that uses a generating AI to deliver sunscreen tailored to the user's needs to their home every month. This system records the user's skin age, skin type, color, etc., each month, analyzes this data using the generating AI, and proposes the most suitable sunscreen. The generating AI reads a large amount of data from the web and provides optimal advice. Furthermore, the generating AI creates a report based on the user's data and delivers it along with the advice. It is also possible to receive direct advice from a makeup instructor or coordinator, and recommended sunscreens are sent to the user. For example, the user records their skin age, skin type, color, etc., each month. This data is analyzed by the generating AI, and the most suitable sunscreen is proposed. The generating AI reads a large amount of data from the web and provides optimal advice from the outset. Furthermore, the generating AI creates a report based on the user's data and delivers it along with the advice. It is also possible to receive direct advice from a makeup instructor or coordinator, and recommended sunscreens are sent to the user. This allows the user to easily obtain the sunscreen best suited to them. This will enable subscription services to efficiently collect, analyze, provide, and offer advice on users' skin data.

[0029] The subscription service according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a reception unit. The collection unit collects the user's skin data. The user's skin data includes, but is not limited to, examples of skin moisture content, oil content, and elasticity. The collection unit measures skin moisture content using, for example, a sensor. The collection unit can also collect the user's skin type using a questionnaire. For example, the collection unit asks the user questions about their skin condition and collects the answers as data. Furthermore, the collection unit can use a dedicated measuring instrument to measure the elasticity of the user's skin. For example, the collection unit provides the user with a device for measuring skin elasticity and collects the data. The analysis unit uses generative AI to analyze the data collected by the collection unit and proposes the optimal sunscreen. The analysis is performed using, for example, statistical analysis of data or machine learning algorithms, but is not limited to these examples. For example, the analysis unit proposes the optimal sunscreen according to the user's skin type based on the collected data. The analysis unit can also propose sunscreen according to the season. For example, the analysis department might suggest sunscreens with high UV protection in the summer and sunscreens with high moisturizing effects in the winter. Furthermore, the analysis department can also suggest sunscreens that address specific skin problems based on the user's skin data. For example, the analysis department might analyze the user's skin data and suggest a sunscreen suitable for sensitive skin. The delivery department provides the user with the sunscreen suggested by the analysis department. Delivery includes, but is not limited to, online delivery or delivery by mail. For example, the delivery department might mail sunscreen ordered online by the user. The delivery department can also create a report using generative AI and provide it to the user along with advice. For example, the delivery department might send a report created by generative AI to the user, providing advice on how to use sunscreen and skincare. The reception department receives advice from makeup instructors and coordinators and provides that information to the analysis department. Reception includes, but is not limited to, online chat or telephone consultations. For example, the reception department receives the content of a user's consultation with a makeup instructor via online chat and provides that information to the analysis department.Furthermore, the reception department can receive inquiries from users to coordinators by telephone and provide that information to the analysis department. This enables the subscription service according to the embodiment to efficiently collect, analyze, provide, and receive advice on users' skin data.

[0030] The data collection unit collects user skin data. This data includes, but is not limited to, skin moisture content, oil content, and elasticity. For example, the data collection unit measures skin moisture content using a sensor. Specifically, it uses a moisture sensor that comes into direct contact with the skin to accurately measure moisture content from the skin's surface. This sensor detects skin moisture content using minute electrical signals and collects the data in real time. The data collection unit can also collect user skin type data using questionnaires. For example, the data collection unit asks users questions about their skin condition and collects the answers as data. The questionnaires are conducted via online forms or dedicated apps and are designed to be easy for users to answer. Furthermore, the data collection unit can use a dedicated measuring instrument to measure the elasticity of the user's skin. For example, the data collection unit provides the user with a device to measure skin elasticity and collects the data. This device measures elasticity by lightly pressing it against the skin and records the results as digital data. This allows the data collection unit to comprehensively understand the user's skin condition and collect detailed data. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit and propose the most suitable sunscreen. The analysis is performed using, for example, statistical data analysis or machine learning algorithms, but is not limited to these examples. Specifically, the generative AI receives user skin data as input and proposes the most suitable sunscreen based on that data. First, the generative AI analyzes data such as the user's skin's moisture content, oil content, and elasticity to identify the user's skin type. Next, based on past data and statistical information, it identifies sunscreens that have been effective for other users with similar skin types. Furthermore, the generative AI proposes the most suitable sunscreen considering seasonal and regional climate conditions. For example, it proposes a sunscreen with high UV protection in the summer and a sunscreen with high moisturizing effects in the winter. The generative AI can also propose sunscreens that address specific skin problems based on the user's skin data. For example, it analyzes the user's skin data and proposes a sunscreen suitable for sensitive skin. This allows the analysis unit to quickly and accurately analyze the collected data and propose the most suitable sunscreen for the user. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict skin fluctuations under specific seasons or environmental conditions based on past skin data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service department provides users with sunscreens recommended by the analysis department. This service includes, but is not limited to, online or postal delivery. Specifically, the service department can deliver sunscreens ordered online by the user via postal delivery. The service department receives user orders and, in conjunction with the inventory management system, ensures prompt shipment. The service department can also create and provide users with reports generated by AI, along with advice. For example, the service department could send users reports generated by AI, providing advice on sunscreen usage and skincare. These reports would include detailed advice based on the user's skin data and effective sunscreen application methods. Furthermore, the service department can collect user feedback to improve the service. For example, it could collect feedback on the effects and problems users experienced after using the sunscreen and provide this information to the analysis department. This allows the service department to continue providing high-quality service to users. Additionally, the service department can offer multiple delivery methods to enhance user convenience. For example, in addition to online ordering, it could offer a regular subscription service, allowing users to automatically receive sunscreen. This allows the service department to deliver products to users quickly and reliably, thereby increasing customer satisfaction.

[0033] The reception department receives advice from makeup instructors and coordinators and provides this information to the analytics department. Reception includes, but is not limited to, online chat and telephone consultations. Specifically, the reception department receives inquiries from users to makeup instructors via online chat and provides this information to the analytics department. Online chat provides an interface that allows users to ask questions and seek advice in real time, and makeup instructors respond quickly. The reception department can also receive inquiries from users to coordinators by telephone and provide this information to the analytics department. Telephone consultations allow users to speak directly with coordinators and receive more detailed advice. Furthermore, the reception department can collect user feedback to improve the service. For example, it can collect feedback on the effectiveness and satisfaction level of the advice received by users and provide this information to the analytics department. This allows the reception department to continue providing high-quality advice to users. In addition, the reception department can provide multiple reception methods to improve user convenience. For example, it can accept inquiries not only via online chat and telephone consultations, but also via email and social media. This allows the reception department to provide users with quick and reliable advice, thereby increasing their satisfaction.

[0034] The data collection unit can collect data such as the user's skin age, skin type, and color. For example, to measure the user's skin age, the data collection unit can measure the depth of wrinkles and the number of blemishes. For example, the data collection unit can measure the depth of wrinkles using a dedicated measuring instrument and collect the data. The data collection unit can also classify the user's skin type into categories such as dry skin, oily skin, and combination skin. For example, the data collection unit can measure the amount of oil in the user's skin and classify the skin type based on the result. Furthermore, the data collection unit can use criteria such as hue, lightness, and saturation to measure the user's skin color. For example, the data collection unit can measure the skin color using a dedicated colorimeter and collect the data. By collecting data such as the user's skin age, skin type, and color, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's skin data into a generating AI, which can then collect and analyze the data.

[0035] The analysis unit can read multiple data from the web and suggest sunscreens. For example, the analysis unit can read data such as product reviews, ingredient information, and usage instructions from the web. For example, the analysis unit can use a generative AI to collect product reviews from the web and analyze that data. The analysis unit can also collect ingredient information from the web and suggest the most suitable sunscreen. For example, the analysis unit can use a generative AI to analyze the ingredient information of sunscreens and suggest products that suit the user's skin type. Furthermore, the analysis unit can collect data on usage methods from the web and suggest appropriate usage methods to the user. For example, the analysis unit can use a generative AI to analyze information on how to use sunscreens and provide specific advice to the user. This improves the accuracy of suggesting the most suitable sunscreen by reading a large amount of data from the web. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input data collected from the web into a generative AI, which can then analyze the data and make suggestions.

[0036] The service provider can create reports using generative AI and provide them to users along with advice. For example, the service provider can use generative AI to create reports based on the user's skin data. For example, the service provider can create reports on the user's skin condition and the most suitable sunscreen based on data analyzed by the generative AI. The service provider can also provide skincare advice along with the generative AI reports. For example, the service provider can recommend specific skincare methods and products to use to the user based on the reports created by the generative AI. Furthermore, the service provider can provide the generative AI reports online. For example, the service provider can upload the reports created by the generative AI to the user's account so that the user can access them at any time. This allows the service provider to provide users with more detailed advice by creating reports using generative AI. Some or all of the above processes in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can provide users with reports created by generative AI and offer advice.

[0037] The reception department can receive advice from makeup instructors and coordinators and provide that information to the analysis department. The reception department can receive advice from makeup instructors and coordinators through, for example, online chat or telephone consultations. For example, the reception department can receive the content of consultations that users have with makeup instructors via online chat and provide that information to the analysis department. The reception department can also receive the content of consultations that users have with coordinators by telephone and provide that information to the analysis department. Furthermore, the reception department can record the advice from makeup instructors and coordinators and save it to the user's account. For example, the reception department can save the content of the advice received by the user to a database so that it can be referenced later. This allows for the provision of more specialized advice by receiving advice from makeup instructors and coordinators. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or not using generative AI. For example, the reception department can provide the advice received by generative AI to the analysis department for analysis.

[0038] The data collection unit can analyze the user's past skin data and select the optimal collection method. For example, the data collection unit can select the most effective collection method from the user's past skin data. For example, the data collection unit can select the optimal sensor or measuring instrument based on past measurement results. The data collection unit can also analyze the fluctuation patterns of the user's skin data and determine the optimal collection timing. For example, the data collection unit can analyze the fluctuation patterns of the user's skin data and determine the optimal collection timing according to the season and time of day. Furthermore, the data collection unit can customize the collection method based on the trends in the user's skin data. For example, the data collection unit can analyze the trends in the user's skin data and select a collection method to address specific skin problems. In this way, the optimal collection method can be selected by analyzing the user's past skin data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past skin data into a generating AI, which can then analyze the data and select the collection method.

[0039] The data collection unit can filter skin data based on the user's current living environment and climate conditions. For example, the data collection unit can filter skin data based on the user's living environment (indoors / outdoors, humidity, etc.). For example, the data collection unit collects data related to the user's living environment and filters skin data based on that data. The data collection unit can also collect skin data considering the user's current climate conditions (temperature, UV radiation level, etc.). For example, the data collection unit collects data related to climate conditions and filters skin data based on that data. Furthermore, the data collection unit can collect optimal skin data in accordance with the user's daily rhythm. For example, the data collection unit collects data related to the user's daily rhythm and filters skin data based on that data. This allows for the collection of more accurate data by filtering based on the user's living environment and climate conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to the living environment and climate conditions into a generating AI, and the generating AI can filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting skin data. For example, if the user is in a high-UV area, the data collection unit will prioritize the collection of skin data related to UV radiation. For example, the data collection unit can obtain the user's geographical location from GPS data and collect skin data based on that information. The data collection unit can also prioritize the collection of skin data related to dryness if the user is in a dry area. For example, the data collection unit can obtain the user's geographical location from a location information service and collect skin data based on that information. Furthermore, if the user is in a high-humidity area, the data collection unit can also prioritize the collection of skin data related to humidity. For example, the data collection unit can collect humidity-related data based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI, and the generating AI can collect and analyze the data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting skin data. For example, the data collection unit can collect information about the user's skin condition from the user's social media posts. For example, the data collection unit can use generative AI to analyze the user's social media posts and collect data about the user's skin condition. The data collection unit can also collect data about lifestyle habits from the user's social media activity. For example, the data collection unit can use generative AI to analyze the user's social media activity and collect data about lifestyle habits. Furthermore, the data collection unit can analyze changes in the user's skin from photos on social media and collect data. For example, the data collection unit can use generative AI to analyze photos on social media and collect data about changes in the skin. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input data about social media activity into generative AI, and the generative AI can collect and analyze the data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the skin data during the analysis. For example, the analysis unit performs a detailed analysis on important skin data. For example, the analysis unit evaluates the reliability and impact of the user's skin data and performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general skin data. For example, the analysis unit performs a simplified analysis based on the importance of the user's skin data. Furthermore, the analysis unit can focus its analysis on data related to specific skin problems. For example, the analysis unit focuses its analysis on data related to the user's skin problems and provides detailed analysis results. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the skin data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the skin data into a generating AI, which can then perform the data analysis and adjust the level of detail.

[0043] The analysis unit can apply different analysis algorithms depending on the skin category during analysis. For example, the analysis unit can apply a specific analysis algorithm to sensitive skin. For instance, the analysis unit can analyze the data using a machine learning algorithm suitable for sensitive skin. The analysis unit can also apply a different analysis algorithm to dry skin. For example, the analysis unit can analyze the data using a statistical analysis algorithm suitable for dry skin. Furthermore, the analysis unit can apply yet another analysis algorithm to oily skin. For example, the analysis unit can analyze the data using an analysis algorithm suitable for oily skin. By applying different analysis algorithms depending on the skin category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data corresponding to the skin category into a generating AI, and the generating AI can perform data analysis and apply algorithms.

[0044] The analysis unit can determine the priority of analysis based on when the skin data was collected. For example, the analysis unit may prioritize the analysis of recently collected skin data. For example, the analysis unit may prioritize the analysis of the latest data based on when the user's skin data was collected. The analysis unit can also determine the priority of analysis by considering seasonal skin data. For example, the analysis unit may prioritize the analysis of skin data corresponding to the season. Furthermore, the analysis unit may also prioritize the analysis of skin data collected before a specific event. For example, the analysis unit may prioritize the analysis based on data collected before a specific event. This allows for the prioritization of the analysis based on when the skin data was collected, ensuring that the latest data is analyzed first. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the skin data collection period into a generating AI, which can then perform data analysis and determine the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of skin data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant skin data. For instance, it can evaluate the correlation of the user's skin data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant skin data. For example, it can evaluate the co-occurrence frequency of the user's skin data and postpone the analysis of less relevant data. Furthermore, the analysis unit can prioritize the analysis of data related to specific skin problems. For example, it can prioritize the analysis of data related to the user's skin problems and provide detailed analysis results. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of skin data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of skin data into a generating AI, which can then perform the data analysis and adjust the order.

[0046] The service provider can select the optimal sunscreen by analyzing the user's past usage history at the time of delivery. For example, the service provider can analyze the effectiveness of sunscreens the user has used in the past and select the optimal sunscreen. For example, the service provider can select a highly effective sunscreen based on the user's past usage history. The service provider can also select a sunscreen considering the user's preferred scent and texture based on their past usage history. For example, the service provider can analyze the user's past usage history and select a sunscreen with the user's preferred scent and texture. Furthermore, the service provider can also select the optimal sunscreen to avoid allergic reactions based on the user's past usage history. For example, the service provider can analyze the user's past usage history and select a sunscreen that does not contain ingredients that would cause allergic reactions. In this way, the optimal sunscreen can be selected by analyzing the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history into a generating AI, which can then analyze the data and select the sunscreen.

[0047] The service provider can customize the selection of sunscreen based on the user's current lifestyle at the time of delivery. For example, if the user is engaged in outdoor activities, the service provider can provide a highly water-resistant sunscreen. For example, the service provider can collect data on the user's lifestyle and select a highly water-resistant sunscreen based on that data. The service provider can also provide a sunscreen with a light texture if the user spends a lot of time indoors. For example, the service provider can collect data on the user's lifestyle and select a sunscreen with a light texture based on that data. Furthermore, if the user is traveling, the service provider can provide a sunscreen in a portable size. For example, the service provider can collect data on the user's lifestyle and select a sunscreen in a portable size based on that data. In this way, by customizing the selection of sunscreen based on the user's current lifestyle, a more appropriate sunscreen can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's lifestyle into a generating AI, which can then analyze the data and select the sunscreen.

[0048] The service provider can select the most suitable sunscreen by considering the user's geographical location information at the time of delivery. For example, if the user is in a high-UV area, the service provider can provide a sunscreen with high UV protection. For example, the service provider can obtain the user's geographical location information from GPS data and select a sunscreen with high UV protection based on that information. The service provider can also provide a sunscreen with high moisturizing effects if the user is in a dry area. For example, the service provider can obtain the user's geographical location information from a location information service and select a sunscreen with high moisturizing effects based on that information. Furthermore, if the user is in a high-humidity area, the service provider can provide a sunscreen with a light texture. For example, the service provider can select a sunscreen with a light texture based on the user's geographical location information. In this way, the service provider can provide the most suitable sunscreen by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI, and the generating AI can perform data analysis and sunscreen selection.

[0049] The service provider can select sunscreen by analyzing the user's social media activity at the time of delivery. For example, the service provider can analyze the user's preferred brands and products from their social media posts and select sunscreen. For example, the service provider can use generative AI to analyze the user's social media posts and analyze their preferred brands and products. The service provider can also select sunscreen that suits the user's lifestyle based on their social media activity. For example, the service provider can use generative AI to analyze the user's social media activity and select sunscreen that suits their lifestyle. Furthermore, the service provider can analyze the user's skin condition from their social media photos and select the most suitable sunscreen. For example, the service provider can use generative AI to analyze the user's social media photos and analyze their skin condition. This allows for the selection of a more appropriate sunscreen by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input data related to social media activity into generative AI, which can then analyze the data and select sunscreen.

[0050] The reception department can select the optimal reception method by referring to the user's past advice history when an advice request is received. For example, the reception department may prioritize suggesting advice reception methods that the user has frequently used in the past. For example, the reception department may select the optimal reception method based on the user's past advice history. The reception department can also predict and suggest reception methods to be used during specific time periods based on the user's past advice history. For example, the reception department may analyze the user's past advice history and select the optimal reception method for a specific time period. Furthermore, the reception department may select the most effective reception method based on the user's past advice history. For example, the reception department may analyze the user's past advice history and select the most effective reception method. This allows the reception department to select the optimal reception method by referring to the user's past advice history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input past advice history into a generating AI, which can then analyze the data and select a reception method.

[0051] The reception unit can select the optimal reception method when receiving advice, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method adapted to the screen size. For example, based on the user's device information, the reception unit can provide an interface optimized for smartphones. The reception unit can also provide a reception method optimized for larger screens if the user is using a tablet. For example, based on the user's device information, the reception unit can provide an interface optimized for tablets. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. For example, based on the user's device information, the reception unit can provide an interface optimized for smartwatches. This allows the reception unit to select the optimal reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input device information into a generating AI, which can then analyze the data and select the reception method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The analysis unit can consider data on the user's diet and exercise habits when analyzing the user's skin data. For example, the analysis unit can evaluate the balance of nutrients the user consumes and identify factors that affect the condition of the skin. It can also analyze the user's exercise habits and evaluate the impact of exercise on the skin. Furthermore, the analysis unit can provide advice that helps improve the user's skin based on their diet and exercise habits. This enables analysis that takes into account the user's entire lifestyle, allowing for more accurate recommendations for sunscreen.

[0054] The data collection unit can consider the user's sleep patterns when collecting user skin data. For example, the data collection unit can monitor the user's sleep duration and quality and evaluate their impact on skin condition. It can also determine the optimal data collection timing based on the user's sleep patterns. Furthermore, the data collection unit can provide data on the user's sleep patterns to the analysis unit, which can then be incorporated into the analysis results. This enables data collection that takes the user's sleep patterns into account, resulting in more accurate skin data.

[0055] The service provider can offer different sunscreens for each season based on the user's skin data. For example, they can offer a sunscreen with high UV protection in the summer and a sunscreen with high moisturizing properties in the winter. They can also offer sunscreens with allergy-fighting properties in the spring and fall. Furthermore, they can suggest sunscreens that address seasonal skin changes based on the user's skin data. This allows them to provide the optimal sunscreen for each season, protecting the user's skin more effectively.

[0056] The reception desk can provide advice tailored to specific events and situations based on the user's skin data. For example, it can suggest the optimal skincare routine when a user is attending a special event such as a wedding or party. It can also advise on how to choose and use sunscreen when a user is traveling or engaging in outdoor activities. Furthermore, it can suggest relaxing skincare routines when a user is feeling stressed or unwell. This allows for the provision of advice tailored to the user's specific situation, enabling a more personalized service.

[0057] The service provider can analyze a user's past usage history and select the most suitable sunscreen. For example, it can analyze the effectiveness of sunscreens the user has used in the past and select the most suitable one. It can also select a sunscreen considering the user's preferred scent and texture based on their past usage history. Furthermore, it can select a sunscreen that avoids allergic reactions based on the user's past usage history. In this way, the service provider can select the most suitable sunscreen by analyzing the user's past usage history.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects the user's skin data. This data includes, for example, skin moisture content, oil content, and elasticity. The data collection unit can also measure skin moisture content using sensors and collect the user's skin type using questionnaires. It can also measure skin elasticity using a dedicated measuring instrument. Step 2: The analysis unit uses generational AI to analyze the data collected by the collection unit and propose the optimal sunscreen. The analysis is performed using statistical data analysis and machine learning algorithms. For example, it proposes sunscreens tailored to the user's skin type and the season, and sunscreens that address specific skin problems. Step 3: The delivery department provides the user with the sunscreen suggested by the analysis department. Delivery can be online or by mail. For example, sunscreen ordered online by the user can be delivered by mail. It is also possible to create a report using AI generation and provide it along with advice. Step 4: The reception department receives advice from makeup instructors and coordinators and provides that information to the analytics department. Reception includes online chat and telephone consultations. For example, it can receive questions from users who consult with makeup instructors via online chat and provide that information to the analytics department. It can also receive questions from users who consult with coordinators by telephone and provide that information to the analytics department.

[0060] (Example of form 2) The subscription service according to an embodiment of the present invention is a system that uses a generating AI to deliver sunscreen tailored to the user's needs to their home every month. This system records the user's skin age, skin type, color, etc., each month, analyzes this data using the generating AI, and proposes the most suitable sunscreen. The generating AI reads a large amount of data from the web and provides optimal advice. Furthermore, the generating AI creates a report based on the user's data and delivers it along with the advice. It is also possible to receive direct advice from a makeup instructor or coordinator, and recommended sunscreens are sent to the user. For example, the user records their skin age, skin type, color, etc., each month. This data is analyzed by the generating AI, and the most suitable sunscreen is proposed. The generating AI reads a large amount of data from the web and provides optimal advice from the outset. Furthermore, the generating AI creates a report based on the user's data and delivers it along with the advice. It is also possible to receive direct advice from a makeup instructor or coordinator, and recommended sunscreens are sent to the user. This allows the user to easily obtain the sunscreen best suited to them. This will enable subscription services to efficiently collect, analyze, provide, and offer advice on users' skin data.

[0061] The subscription service according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a reception unit. The collection unit collects the user's skin data. The user's skin data includes, but is not limited to, examples of skin moisture content, oil content, and elasticity. The collection unit measures skin moisture content using, for example, a sensor. The collection unit can also collect the user's skin type using a questionnaire. For example, the collection unit asks the user questions about their skin condition and collects the answers as data. Furthermore, the collection unit can use a dedicated measuring instrument to measure the elasticity of the user's skin. For example, the collection unit provides the user with a device for measuring skin elasticity and collects the data. The analysis unit uses generative AI to analyze the data collected by the collection unit and proposes the optimal sunscreen. The analysis is performed using, for example, statistical analysis of data or machine learning algorithms, but is not limited to these examples. For example, the analysis unit proposes the optimal sunscreen according to the user's skin type based on the collected data. The analysis unit can also propose sunscreen according to the season. For example, the analysis department might suggest sunscreens with high UV protection in the summer and sunscreens with high moisturizing effects in the winter. Furthermore, the analysis department can also suggest sunscreens that address specific skin problems based on the user's skin data. For example, the analysis department might analyze the user's skin data and suggest a sunscreen suitable for sensitive skin. The delivery department provides the user with the sunscreen suggested by the analysis department. Delivery includes, but is not limited to, online delivery or delivery by mail. For example, the delivery department might mail sunscreen ordered online by the user. The delivery department can also create a report using generative AI and provide it to the user along with advice. For example, the delivery department might send a report created by generative AI to the user, providing advice on how to use sunscreen and skincare. The reception department receives advice from makeup instructors and coordinators and provides that information to the analysis department. Reception includes, but is not limited to, online chat or telephone consultations. For example, the reception department receives the content of a user's consultation with a makeup instructor via online chat and provides that information to the analysis department.Furthermore, the reception department can receive inquiries from users to coordinators by telephone and provide that information to the analysis department. This enables the subscription service according to the embodiment to efficiently collect, analyze, provide, and receive advice on users' skin data.

[0062] The data collection unit collects user skin data. This data includes, but is not limited to, skin moisture content, oil content, and elasticity. For example, the data collection unit measures skin moisture content using a sensor. Specifically, it uses a moisture sensor that comes into direct contact with the skin to accurately measure moisture content from the skin's surface. This sensor detects skin moisture content using minute electrical signals and collects the data in real time. The data collection unit can also collect user skin type data using questionnaires. For example, the data collection unit asks users questions about their skin condition and collects the answers as data. The questionnaires are conducted via online forms or dedicated apps and are designed to be easy for users to answer. Furthermore, the data collection unit can use a dedicated measuring instrument to measure the elasticity of the user's skin. For example, the data collection unit provides the user with a device to measure skin elasticity and collects the data. This device measures elasticity by lightly pressing it against the skin and records the results as digital data. This allows the data collection unit to comprehensively understand the user's skin condition and collect detailed data. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0063] The analysis unit uses generative AI to analyze data collected by the data collection unit and propose the most suitable sunscreen. The analysis is performed using, for example, statistical data analysis or machine learning algorithms, but is not limited to these examples. Specifically, the generative AI receives user skin data as input and proposes the most suitable sunscreen based on that data. First, the generative AI analyzes data such as the user's skin's moisture content, oil content, and elasticity to identify the user's skin type. Next, based on past data and statistical information, it identifies sunscreens that have been effective for other users with similar skin types. Furthermore, the generative AI proposes the most suitable sunscreen considering seasonal and regional climate conditions. For example, it proposes a sunscreen with high UV protection in the summer and a sunscreen with high moisturizing effects in the winter. The generative AI can also propose sunscreens that address specific skin problems based on the user's skin data. For example, it analyzes the user's skin data and proposes a sunscreen suitable for sensitive skin. This allows the analysis unit to quickly and accurately analyze the collected data and propose the most suitable sunscreen for the user. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict skin fluctuations under specific seasons or environmental conditions based on past skin data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0064] The service department provides users with sunscreens recommended by the analysis department. This service includes, but is not limited to, online or postal delivery. Specifically, the service department can deliver sunscreens ordered online by the user via postal delivery. The service department receives user orders and, in conjunction with the inventory management system, ensures prompt shipment. The service department can also create and provide users with reports generated by AI, along with advice. For example, the service department could send users reports generated by AI, providing advice on sunscreen usage and skincare. These reports would include detailed advice based on the user's skin data and effective sunscreen application methods. Furthermore, the service department can collect user feedback to improve the service. For example, it could collect feedback on the effects and problems users experienced after using the sunscreen and provide this information to the analysis department. This allows the service department to continue providing high-quality service to users. Additionally, the service department can offer multiple delivery methods to enhance user convenience. For example, in addition to online ordering, it could offer a regular subscription service, allowing users to automatically receive sunscreen. This allows the service department to deliver products to users quickly and reliably, thereby increasing customer satisfaction.

[0065] The reception department receives advice from makeup instructors and coordinators and provides this information to the analytics department. Reception includes, but is not limited to, online chat and telephone consultations. Specifically, the reception department receives inquiries from users to makeup instructors via online chat and provides this information to the analytics department. Online chat provides an interface that allows users to ask questions and seek advice in real time, and makeup instructors respond quickly. The reception department can also receive inquiries from users to coordinators by telephone and provide this information to the analytics department. Telephone consultations allow users to speak directly with coordinators and receive more detailed advice. Furthermore, the reception department can collect user feedback to improve the service. For example, it can collect feedback on the effectiveness and satisfaction level of the advice received by users and provide this information to the analytics department. This allows the reception department to continue providing high-quality advice to users. In addition, the reception department can provide multiple reception methods to improve user convenience. For example, it can accept inquiries not only via online chat and telephone consultations, but also via email and social media. This allows the reception department to provide users with quick and reliable advice, thereby increasing their satisfaction.

[0066] The data collection unit can collect data such as the user's skin age, skin type, and color. For example, to measure the user's skin age, the data collection unit can measure the depth of wrinkles and the number of blemishes. For example, the data collection unit can measure the depth of wrinkles using a dedicated measuring instrument and collect the data. The data collection unit can also classify the user's skin type into categories such as dry skin, oily skin, and combination skin. For example, the data collection unit can measure the amount of oil in the user's skin and classify the skin type based on the result. Furthermore, the data collection unit can use criteria such as hue, lightness, and saturation to measure the user's skin color. For example, the data collection unit can measure the skin color using a dedicated colorimeter and collect the data. By collecting data such as the user's skin age, skin type, and color, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's skin data into a generating AI, which can then collect and analyze the data.

[0067] The analysis unit can read multiple data from the web and suggest sunscreens. For example, the analysis unit can read data such as product reviews, ingredient information, and usage instructions from the web. For example, the analysis unit can use a generative AI to collect product reviews from the web and analyze that data. The analysis unit can also collect ingredient information from the web and suggest the most suitable sunscreen. For example, the analysis unit can use a generative AI to analyze the ingredient information of sunscreens and suggest products that suit the user's skin type. Furthermore, the analysis unit can collect data on usage methods from the web and suggest appropriate usage methods to the user. For example, the analysis unit can use a generative AI to analyze information on how to use sunscreens and provide specific advice to the user. This improves the accuracy of suggesting the most suitable sunscreen by reading a large amount of data from the web. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input data collected from the web into a generative AI, which can then analyze the data and make suggestions.

[0068] The service provider can create reports using generative AI and provide them to users along with advice. For example, the service provider can use generative AI to create reports based on the user's skin data. For example, the service provider can create reports on the user's skin condition and the most suitable sunscreen based on data analyzed by the generative AI. The service provider can also provide skincare advice along with the generative AI reports. For example, the service provider can recommend specific skincare methods and products to use to the user based on the reports created by the generative AI. Furthermore, the service provider can provide the generative AI reports online. For example, the service provider can upload the reports created by the generative AI to the user's account so that the user can access them at any time. This allows the service provider to provide users with more detailed advice by creating reports using generative AI. Some or all of the above processes in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can provide users with reports created by generative AI and offer advice.

[0069] The reception department can receive advice from makeup instructors and coordinators and provide that information to the analysis department. The reception department can receive advice from makeup instructors and coordinators through, for example, online chat or telephone consultations. For example, the reception department can receive the content of consultations that users have with makeup instructors via online chat and provide that information to the analysis department. The reception department can also receive the content of consultations that users have with coordinators by telephone and provide that information to the analysis department. Furthermore, the reception department can record the advice from makeup instructors and coordinators and save it to the user's account. For example, the reception department can save the content of the advice received by the user to a database so that it can be referenced later. This allows for the provision of more specialized advice by receiving advice from makeup instructors and coordinators. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or not using generative AI. For example, the reception department can provide the advice received by generative AI to the analysis department for analysis.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of skin data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can adjust the timing to collect skin data during relaxed periods. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also collect skin data immediately if the user is relaxed. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is busy, the data collection unit can adjust the skin data collection timing to match the user's schedule. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for data collection at a more appropriate time by adjusting the timing of skin data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0071] The data collection unit can analyze the user's past skin data and select the optimal collection method. For example, the data collection unit can select the most effective collection method from the user's past skin data. For example, the data collection unit can select the optimal sensor or measuring instrument based on past measurement results. The data collection unit can also analyze the fluctuation patterns of the user's skin data and determine the optimal collection timing. For example, the data collection unit can analyze the fluctuation patterns of the user's skin data and determine the optimal collection timing according to the season and time of day. Furthermore, the data collection unit can customize the collection method based on the trends in the user's skin data. For example, the data collection unit can analyze the trends in the user's skin data and select a collection method to address specific skin problems. In this way, the optimal collection method can be selected by analyzing the user's past skin data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past skin data into a generating AI, which can then analyze the data and select the collection method.

[0072] The data collection unit can filter skin data based on the user's current living environment and climate conditions. For example, the data collection unit can filter skin data based on the user's living environment (indoors / outdoors, humidity, etc.). For example, the data collection unit collects data related to the user's living environment and filters skin data based on that data. The data collection unit can also collect skin data considering the user's current climate conditions (temperature, UV radiation level, etc.). For example, the data collection unit collects data related to climate conditions and filters skin data based on that data. Furthermore, the data collection unit can collect optimal skin data in accordance with the user's daily rhythm. For example, the data collection unit collects data related to the user's daily rhythm and filters skin data based on that data. This allows for the collection of more accurate data by filtering based on the user's living environment and climate conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to the living environment and climate conditions into a generating AI, and the generating AI can filter the data.

[0073] The data collection unit can estimate the user's emotions and prioritize the skin data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related skin data. For example, the data collection unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also collect overall skin data in a balanced manner if the user is relaxed. For example, the data collection unit may record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user has a specific skin problem, the data collection unit can prioritize collecting data related to that problem. For example, the data collection unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the priority collection of important data by prioritizing skin data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting skin data. For example, if the user is in a high-UV area, the data collection unit will prioritize the collection of skin data related to UV radiation. For example, the data collection unit can obtain the user's geographical location from GPS data and collect skin data based on that information. The data collection unit can also prioritize the collection of skin data related to dryness if the user is in a dry area. For example, the data collection unit can obtain the user's geographical location from a location information service and collect skin data based on that information. Furthermore, if the user is in a high-humidity area, the data collection unit can also prioritize the collection of skin data related to humidity. For example, the data collection unit can collect humidity-related data based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI, and the generating AI can collect and analyze the data.

[0075] The data collection unit can analyze the user's social media activity and collect relevant data when collecting skin data. For example, the data collection unit can collect information about the user's skin condition from the user's social media posts. For example, the data collection unit can use generative AI to analyze the user's social media posts and collect data about the user's skin condition. The data collection unit can also collect data about lifestyle habits from the user's social media activity. For example, the data collection unit can use generative AI to analyze the user's social media activity and collect data about lifestyle habits. Furthermore, the data collection unit can analyze changes in the user's skin from photos on social media and collect data. For example, the data collection unit can use generative AI to analyze photos on social media and collect data about changes in the skin. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input data about social media activity into generative AI, and the generative AI can collect and analyze the data.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the skin data during the analysis. For example, the analysis unit performs a detailed analysis on important skin data. For example, the analysis unit evaluates the reliability and impact of the user's skin data and performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general skin data. For example, the analysis unit performs a simplified analysis based on the importance of the user's skin data. Furthermore, the analysis unit can focus its analysis on data related to specific skin problems. For example, the analysis unit focuses its analysis on data related to the user's skin problems and provides detailed analysis results. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the skin data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the skin data into a generating AI, which can then perform the data analysis and adjust the level of detail.

[0078] The analysis unit can apply different analysis algorithms depending on the skin category during analysis. For example, the analysis unit can apply a specific analysis algorithm to sensitive skin. For instance, the analysis unit can analyze the data using a machine learning algorithm suitable for sensitive skin. The analysis unit can also apply a different analysis algorithm to dry skin. For example, the analysis unit can analyze the data using a statistical analysis algorithm suitable for dry skin. Furthermore, the analysis unit can apply yet another analysis algorithm to oily skin. For example, the analysis unit can analyze the data using an analysis algorithm suitable for oily skin. By applying different analysis algorithms depending on the skin category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data corresponding to the skin category into a generating AI, and the generating AI can perform data analysis and apply algorithms.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to provide analysis results tailored to the user's situation by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0080] The analysis unit can determine the priority of analysis based on when the skin data was collected. For example, the analysis unit may prioritize the analysis of recently collected skin data. For example, the analysis unit may prioritize the analysis of the latest data based on when the user's skin data was collected. The analysis unit can also determine the priority of analysis by considering seasonal skin data. For example, the analysis unit may prioritize the analysis of skin data corresponding to the season. Furthermore, the analysis unit may also prioritize the analysis of skin data collected before a specific event. For example, the analysis unit may prioritize the analysis based on data collected before a specific event. This allows for the prioritization of the analysis based on when the skin data was collected, ensuring that the latest data is analyzed first. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the skin data collection period into a generating AI, which can then perform data analysis and determine the priority.

[0081] The analysis unit can adjust the order of analysis based on the relevance of skin data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant skin data. For instance, it can evaluate the correlation of the user's skin data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant skin data. For example, it can evaluate the co-occurrence frequency of the user's skin data and postpone the analysis of less relevant data. Furthermore, the analysis unit can prioritize the analysis of data related to specific skin problems. For example, it can prioritize the analysis of data related to the user's skin problems and provide detailed analysis results. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of skin data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of skin data into a generating AI, which can then perform the data analysis and adjust the order.

[0082] The service provider can estimate the user's emotions and select a sunscreen to offer based on those emotions. For example, if the user is stressed, the service provider can offer a sunscreen with relaxing properties. For example, the service provider can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the service provider can offer a sunscreen with high moisturizing properties. For example, the service provider can record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is excited, the service provider can offer a sunscreen with visually appealing packaging. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the selection of a more appropriate sunscreen based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0083] The service provider can select the optimal sunscreen by analyzing the user's past usage history at the time of delivery. For example, the service provider can analyze the effectiveness of sunscreens the user has used in the past and select the optimal sunscreen. For example, the service provider can select a highly effective sunscreen based on the user's past usage history. The service provider can also select a sunscreen considering the user's preferred scent and texture based on their past usage history. For example, the service provider can analyze the user's past usage history and select a sunscreen with the user's preferred scent and texture. Furthermore, the service provider can also select the optimal sunscreen to avoid allergic reactions based on the user's past usage history. For example, the service provider can analyze the user's past usage history and select a sunscreen that does not contain ingredients that would cause allergic reactions. In this way, the optimal sunscreen can be selected by analyzing the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history into a generating AI, which can then analyze the data and select the sunscreen.

[0084] The service provider can customize the selection of sunscreen based on the user's current lifestyle at the time of delivery. For example, if the user is engaged in outdoor activities, the service provider can provide a highly water-resistant sunscreen. For example, the service provider can collect data on the user's lifestyle and select a highly water-resistant sunscreen based on that data. The service provider can also provide a sunscreen with a light texture if the user spends a lot of time indoors. For example, the service provider can collect data on the user's lifestyle and select a sunscreen with a light texture based on that data. Furthermore, if the user is traveling, the service provider can provide a sunscreen in a portable size. For example, the service provider can collect data on the user's lifestyle and select a sunscreen in a portable size based on that data. In this way, by customizing the selection of sunscreen based on the user's current lifestyle, a more appropriate sunscreen can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's lifestyle into a generating AI, which can then analyze the data and select the sunscreen.

[0085] The service provider can estimate the user's emotions and determine the priority of sunscreens to offer based on those emotions. For example, if the user is stressed, the service provider will prioritize offering sunscreens with relaxing effects. For instance, the service provider might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is relaxed, the service provider can prioritize offering sunscreens with high moisturizing effects. For example, the service provider might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the service provider can prioritize offering sunscreens with visually appealing packaging. For example, the service provider might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to offer more appropriate sunscreens by prioritizing them based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user image data captured by a camera into a generative AI and have the generative AI perform an estimation of the user's emotions.

[0086] The service provider can select the most suitable sunscreen by considering the user's geographical location information at the time of delivery. For example, if the user is in a high-UV area, the service provider can provide a sunscreen with high UV protection. For example, the service provider can obtain the user's geographical location information from GPS data and select a sunscreen with high UV protection based on that information. The service provider can also provide a sunscreen with high moisturizing effects if the user is in a dry area. For example, the service provider can obtain the user's geographical location information from a location information service and select a sunscreen with high moisturizing effects based on that information. Furthermore, if the user is in a high-humidity area, the service provider can provide a sunscreen with a light texture. For example, the service provider can select a sunscreen with a light texture based on the user's geographical location information. In this way, the service provider can provide the most suitable sunscreen by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI, and the generating AI can perform data analysis and sunscreen selection.

[0087] The service provider can select sunscreen by analyzing the user's social media activity at the time of delivery. For example, the service provider can analyze the user's preferred brands and products from their social media posts and select sunscreen. For example, the service provider can use generative AI to analyze the user's social media posts and analyze their preferred brands and products. The service provider can also select sunscreen that suits the user's lifestyle based on their social media activity. For example, the service provider can use generative AI to analyze the user's social media activity and select sunscreen that suits their lifestyle. Furthermore, the service provider can analyze the user's skin condition from their social media photos and select the most suitable sunscreen. For example, the service provider can use generative AI to analyze the user's social media photos and analyze their skin condition. This allows for the selection of a more appropriate sunscreen by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can input data related to social media activity into generative AI, which can then analyze the data and select sunscreen.

[0088] The reception system can estimate the user's emotions and adjust the advice delivery method based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize input steps. For example, the reception system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. For example, the reception system can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the reception system can prioritize voice input and provide advice quickly. For example, the reception system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate advice to be provided by adjusting the advice delivery method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI and have the generative AI perform an estimation of the user's emotions.

[0089] The reception department can select the optimal reception method by referring to the user's past advice history when an advice request is received. For example, the reception department may prioritize suggesting advice reception methods that the user has frequently used in the past. For example, the reception department may select the optimal reception method based on the user's past advice history. The reception department can also predict and suggest reception methods to be used during specific time periods based on the user's past advice history. For example, the reception department may analyze the user's past advice history and select the optimal reception method for a specific time period. Furthermore, the reception department may select the most effective reception method based on the user's past advice history. For example, the reception department may analyze the user's past advice history and select the most effective reception method. This allows the reception department to select the optimal reception method by referring to the user's past advice history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input past advice history into a generating AI, which can then analyze the data and select a reception method.

[0090] The reception desk can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the reception desk will prioritize providing relaxing advice. For instance, the reception desk might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception desk can prioritize providing detailed advice. For example, the reception desk might record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is in a hurry, the reception desk can prioritize providing quick and concise advice. For example, the reception desk might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate advice to be provided by prioritizing advice based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI and have the generative AI perform an estimation of the user's emotions.

[0091] The reception unit can select the optimal reception method when receiving advice, taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method adapted to the screen size. For example, based on the user's device information, the reception unit can provide an interface optimized for smartphones. The reception unit can also provide a reception method optimized for larger screens if the user is using a tablet. For example, based on the user's device information, the reception unit can provide an interface optimized for tablets. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible reception method. For example, based on the user's device information, the reception unit can provide an interface optimized for smartwatches. This allows the reception unit to select the optimal reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input device information into a generating AI, which can then analyze the data and select the reception method.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The analysis unit can consider data on the user's diet and exercise habits when analyzing the user's skin data. For example, the analysis unit can evaluate the balance of nutrients the user consumes and identify factors that affect the condition of the skin. It can also analyze the user's exercise habits and evaluate the impact of exercise on the skin. Furthermore, the analysis unit can provide advice that helps improve the user's skin based on their diet and exercise habits. This enables analysis that takes into account the user's entire lifestyle, allowing for more accurate recommendations for sunscreen.

[0094] The data collection unit can consider the user's sleep patterns when collecting user skin data. For example, the data collection unit can monitor the user's sleep duration and quality and evaluate their impact on skin condition. It can also determine the optimal data collection timing based on the user's sleep patterns. Furthermore, the data collection unit can provide data on the user's sleep patterns to the analysis unit, which can then be incorporated into the analysis results. This enables data collection that takes the user's sleep patterns into account, resulting in more accurate skin data.

[0095] The service provider can offer different sunscreens for each season based on the user's skin data. For example, they can offer a sunscreen with high UV protection in the summer and a sunscreen with high moisturizing properties in the winter. They can also offer sunscreens with allergy-fighting properties in the spring and fall. Furthermore, they can suggest sunscreens that address seasonal skin changes based on the user's skin data. This allows them to provide the optimal sunscreen for each season, protecting the user's skin more effectively.

[0096] The reception desk can provide advice tailored to specific events and situations based on the user's skin data. For example, it can suggest the optimal skincare routine when a user is attending a special event such as a wedding or party. It can also advise on how to choose and use sunscreen when a user is traveling or engaging in outdoor activities. Furthermore, it can suggest relaxing skincare routines when a user is feeling stressed or unwell. This allows for the provision of advice tailored to the user's specific situation, enabling a more personalized service.

[0097] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is excited, it can provide visually appealing analysis results. By adjusting the presentation of analysis results based on the user's emotions, it is possible to provide more appropriate analysis results.

[0098] The data collection unit can estimate the user's emotions and adjust the timing of skin data collection based on those emotions. For example, if the user is stressed, it can adjust the timing to collect skin data during a relaxed period. If the user is relaxed, skin data can be collected immediately. Furthermore, if the user is busy, skin data can be collected according to the user's schedule. By adjusting the timing of skin data collection based on the user's emotions, data can be collected at a more appropriate time.

[0099] The service provider can estimate the user's emotions and select a sunscreen to offer based on those emotions. For example, if the user is stressed, a sunscreen with relaxing properties can be offered. If the user is relaxed, a sunscreen with high moisturizing properties can be offered. Furthermore, if the user is excited, a sunscreen with visually appealing packaging can be offered. By selecting a sunscreen based on the user's emotions, a more appropriate sunscreen can be provided.

[0100] The reception system can estimate the user's emotions and adjust the advice delivery method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input and provide advice quickly. In this way, by adjusting the advice delivery method based on the user's emotions, more appropriate advice can be provided.

[0101] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is in a hurry, it can provide a short, concise analysis. If the user is relaxed, it can provide a detailed analysis. Furthermore, if the user is excited, it can provide a visually appealing analysis. By adjusting the length of the analysis based on the user's emotions, the system can provide analysis results tailored to the user's situation.

[0102] The service provider can analyze a user's past usage history and select the most suitable sunscreen. For example, it can analyze the effectiveness of sunscreens the user has used in the past and select the most suitable one. It can also select a sunscreen considering the user's preferred scent and texture based on their past usage history. Furthermore, it can select a sunscreen that avoids allergic reactions based on the user's past usage history. In this way, the service provider can select the most suitable sunscreen by analyzing the user's past usage history.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The data collection unit collects the user's skin data. This data includes, for example, skin moisture content, oil content, and elasticity. The data collection unit can also measure skin moisture content using sensors and collect the user's skin type using questionnaires. It can also measure skin elasticity using a dedicated measuring instrument. Step 2: The analysis unit uses generational AI to analyze the data collected by the collection unit and propose the optimal sunscreen. The analysis is performed using statistical data analysis and machine learning algorithms. For example, it proposes sunscreens tailored to the user's skin type and the season, and sunscreens that address specific skin problems. Step 3: The delivery department provides the user with the sunscreen suggested by the analysis department. Delivery can be online or by mail. For example, sunscreen ordered online by the user can be delivered by mail. It is also possible to create a report using AI generation and provide it along with advice. Step 4: The reception department receives advice from makeup instructors and coordinators and provides that information to the analytics department. Reception includes online chat and telephone consultations. For example, it can receive questions from users who consult with makeup instructors via online chat and provide that information to the analytics department. It can also receive questions from users who consult with coordinators by telephone and provide that information to the analytics department.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] For example, the data collection unit can collect the user's skin data using the sensors and camera 42 of the smart device 14. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and suggests the optimal sunscreen. For example, the supply unit can provide sunscreen to the user through the output device 40 of the smart device 14. For example, the reception unit can receive advice from makeup instructors and coordinators using the reception device 38 of the smart device 14 and provide that information to the analysis unit. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] For example, the data collection unit can collect the user's skin data using the sensors and camera 42 of the smart glasses 214. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and suggests the optimal sunscreen. For example, the supply unit can provide sunscreen to the user through the speaker 240 of the smart glasses 214. For example, the reception unit can receive advice from a makeup instructor or coordinator using the microphone 238 of the smart glasses 214 and provide that information to the analysis unit. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] For example, the data collection unit can collect the user's skin data using the sensors and camera 42 of the headset terminal 314. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and suggests the optimal sunscreen. For example, the supply unit can provide sunscreen to the user through the display 343 of the headset terminal 314. For example, the reception unit can receive advice from a makeup instructor or coordinator using the microphone 238 of the headset terminal 314 and provide that information to the analysis unit. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] For example, the data collection unit can collect the user's skin data using the sensors and camera 42 of the robot 414. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and suggests the optimal sunscreen. For example, the supply unit can provide sunscreen to the user through the speaker 240 of the robot 414. For example, the reception unit can receive advice from makeup instructors and coordinators using the microphone 238 of the robot 414 and provide that information to the analysis unit. The correspondence between each unit and the device and control unit is not limited to the examples described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A data collection unit that collects user skin data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that proposes sunscreens, A supply unit that provides the user with the sunscreen proposed by the analysis unit, It includes a reception area where customers can receive advice from a makeup instructor or coordinator. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on the user's skin age, skin type, color, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Reads multiple data points from the web and suggests sunscreens. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We generate reports using AI and provide them to users along with advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We receive advice from makeup instructors and coordinators and provide that information to the analysis department. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of skin data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We analyze the user's past skin data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting skin data, filtering is performed based on the user's current living environment and climate conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of skin data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting skin data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting skin data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the skin data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the skin category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the skin data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the skin data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and selects sunscreens to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the product, the system analyzes the user's past usage history to select the most suitable sunscreen. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the product, the selection of sunscreen is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of sunscreens to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the product, the system selects the most suitable sunscreen based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the product, the selection of sunscreen is made by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is The system estimates the user's emotions and adjusts how advice is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is When receiving advice, the system will refer to the user's past advice history to select the most suitable method of submission. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is When receiving advice, the optimal method of receiving it will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user skin data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that proposes sunscreens, A supply unit that provides the user with the sunscreen proposed by the analysis unit, It includes a reception area where customers can receive advice from a makeup instructor or coordinator. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on the user's skin age, skin type, color, etc. The system according to feature 1.

3. The aforementioned analysis unit, Reads multiple data points from the web and suggests sunscreens. The system according to feature 1.

4. The aforementioned supply unit is, We generate reports using AI and provide them to users along with advice. The system according to feature 1.

5. The aforementioned reception unit is The system receives advice from makeup instructors and coordinators and provides that information to the aforementioned analysis unit. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of skin data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is We analyze the user's past skin data and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting skin data, filtering is performed based on the user's current living environment and climate conditions. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of skin data to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting skin data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

Images