system

Abstract

The system according to this embodiment aims to provide a personalized tanning plan that is tailored to the user's skin. [Solution] The system according to the embodiment comprises a skin analysis unit, a weather data acquisition unit, a generation unit, a monitoring unit, and a notification unit. The skin analysis unit analyzes an image of the user's skin. The weather data acquisition unit acquires weather data. The generation unit generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit. The monitoring unit monitors the tanning time in real time based on the tanning plan generated by the generation unit. The notification unit provides a notification when the tanning time monitored by the monitoring unit ends.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide an individualized sunburn plan and there is a risk of skin trouble.

[0005] The system according to the embodiment aims to provide an individualized sunburn plan suitable for the user's skin.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a skin analysis unit, a weather data acquisition unit, a generation unit, a monitoring unit, and a notification unit. The skin analysis unit analyzes an image of the user's skin. The weather data acquisition unit acquires weather data. The generation unit generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit. The monitoring unit monitors the tanning time in real time based on the tanning plan generated by the generation unit. The notification unit provides a notification when the tanning time monitored by the monitoring unit ends. [Effects of the Invention]

[0007] The system according to this embodiment can provide a personalized tanning plan tailored to the user's skin. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[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 tanning plan provision system according to an embodiment of the present invention is a system that provides personalized tanning plans using generative AI. This tanning plan provision system was developed to solve the problem that conventional tanning methods are not personalized and carry the risk of skin problems. It also solves the problem that it is difficult to plan a tan according to the weather and location, and that there is a lack of information to efficiently and safely obtain the desired skin tone. This system first analyzes the user's skin image using a skin analysis app and obtains weather data. Next, the generative AI analyzes this data and proposes a custom tanning plan to the user. The user selects a color sample, and the generative AI considers the intensity of sunlight, time, and angle to indicate the necessary tanning time. Furthermore, it provides a function to monitor the tanning time in real time via a smartphone and notify the user when it is finished. Adjustments are also made based on daily weather fluctuations. For example, the tanning plan provision system uses a skin analysis app to analyze the user's skin image. For example, the user uploads a skin image they have taken to the app, and the app analyzes the image and evaluates the condition of the skin. It also uses a weather data acquisition API to obtain weather data. For example, it obtains current weather data based on the user's location information. The AI ​​analyzes this data and proposes the optimal tanning plan for the user. For example, based on the color swatch selected by the user, it suggests tanning time and sunscreen application methods. Furthermore, the tanning plan system provides a function to monitor tanning time in real time via smartphone and notify the user when it is finished. For example, once the user starts tanning, the system counts down the tanning time and notifies the user when it is finished. This allows the user to enjoy a safe and personalized tanning experience, preventing excessive tanning and achieving the ideal skin tone. In addition, the AI ​​automatically creates the optimal plan according to the weather and location, saving the user time and effort. The real-time notification function allows the user to enjoy the tanning process with peace of mind. In summary, the tanning plan system provides a tanning plan tailored to the user's skin, and by monitoring and notifying in real time, it enables safe and efficient tanning.

[0029] The tanning plan provision system according to the embodiment comprises a skin analysis unit, a weather data acquisition unit, a generation unit, a monitoring unit, and a notification unit. The skin analysis unit analyzes an image of the user's skin. For example, the skin analysis unit analyzes an image of the user's skin and evaluates the condition of the skin. The skin analysis unit performs the analysis considering, for example, the resolution of the skin, shooting conditions, image format, etc. The weather data acquisition unit acquires weather data. For example, the weather data acquisition unit acquires weather data based on the user's location information. For example, the weather data acquisition unit acquires weather data such as temperature, humidity, and ultraviolet index. The generation unit generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit. For example, the generation unit generates a custom tanning plan based on a color sample selected by the user. For example, the generation unit proposes tanning time, sunscreen usage method, etc. The monitoring unit monitors the tanning time in real time based on the tanning plan generated by the generation unit. For example, the monitoring unit counts down the tanning time and provides notification when it is finished. The monitoring unit performs monitoring, taking into consideration factors such as the frequency of monitoring and the sensors used. The notification unit provides notification when the tanning time monitored by the monitoring unit ends. The notification unit provides notification by means such as email or app notification. The notification unit provides notification considering factors such as the details of the notification content. As a result, the tanning plan provision system according to the embodiment can provide a tanning plan that suits the user's skin, and by performing real-time monitoring and notification, it can achieve safe and efficient tanning.

[0030] The Skin Analysis Department analyzes images of the user's skin. Specifically, users upload images of their skin taken with their smartphones or digital cameras, and the department analyzes these images. The Skin Analysis Department performs the analysis considering factors such as image resolution, shooting conditions, and image format. For example, the higher the resolution of the skin image, the more detailed the analysis, and the more appropriate the shooting conditions, the more accurate the evaluation. The Skin Analysis Department uses AI to perform image analysis and evaluate skin tone, blemishes, wrinkles, and pore condition. Based on a large amount of pre-trained skin image data, the AI ​​analyzes the user's skin condition with high accuracy. For example, by analyzing skin tone, it identifies the user's skin type and detects the presence or absence of blemishes and wrinkles. It also evaluates the health of the skin by analyzing the condition of the pores. As a result, the Skin Analysis Department can gain a detailed understanding of the user's skin condition and provide the data necessary to generate a custom tanning plan. Furthermore, the Skin Analysis Department can also provide feedback on the analysis results to the user and offer skincare advice tailored to their skin condition. For example, it can advise users with dry skin on moisturizing care and users with many blemishes on whitening care. This allows the skin analysis unit to comprehensively evaluate the user's skin condition and suggest appropriate care methods.

[0031] The weather data acquisition unit acquires weather data. Specifically, it acquires current and predicted weather data based on the user's location information. The weather data acquisition unit acquires data in real time from weather data provision services and collects detailed weather information such as temperature, humidity, UV index, and wind speed. For example, if the user is at a beach, the weather data acquisition unit acquires the UV index for that area and assesses the risk of sunburn. In addition, by acquiring temperature and humidity data, it can predict the rate of sunburn progression and its impact on the skin. The weather data acquisition unit regularly updates this data to provide the latest weather information. Furthermore, the weather data acquisition unit can also accumulate historical weather data and analyze long-term weather patterns. This allows it to predict the risk of sunburn in specific seasons and regions and provide appropriate advice to the user. For example, based on historical data, it can predict fluctuations in the UV index in a specific region and suggest the optimal timing for sunburn to the user. As a result, the weather data acquisition unit can provide accurate weather data based on the user's location information and provide the information necessary to generate a custom sunburn plan.

[0032] The generation unit generates a custom tanning plan based on data obtained from the skin analysis unit and the weather data acquisition unit. Specifically, it creates an optimal tanning plan considering the user's skin condition and current weather conditions. The generation unit uses AI to automatically generate a plan tailored to the user's skin type and tanning goals. For example, for users with sensitive skin, it recommends short tanning sessions and provides detailed instructions on how and how often to use sunscreen. It also suggests specific tanning times and methods to achieve the target skin tone based on the color swatch selected by the user. The generation unit provides a detailed plan including tanning time, sunscreen usage, and after-tanning care. Furthermore, the generation unit considers information such as the UV index and temperature obtained from the weather data acquisition unit to provide advice to minimize the risk of sunburn. For example, on days with a high UV index, it suggests shortening tanning time and recommends reapplying sunscreen. In this way, the generation unit can provide a custom tanning plan tailored to the user's skin condition and weather conditions, supporting safe and effective tanning.

[0033] The monitoring unit monitors tanning time in real time based on the tanning plan generated by the generation unit. Specifically, it counts down the tanning time from the moment the user starts tanning and notifies the user when it ends. The monitoring unit uses sensors from smartphones and wearable devices to monitor the user's location and activity in real time. For example, if a user is tanning on the beach, the monitoring unit identifies the user's location based on GPS data and accurately measures the tanning time. It also uses acceleration sensors and gyroscope sensors to detect the user's movements and understand their activity level while tanning. Based on this data, the monitoring unit displays the progress of the tanning time in real time and notifies the user at the appropriate time. For example, it issues an alert a few minutes before the tanning time ends, prompting the user to reapply sunscreen or move to the shade. This allows the monitoring unit to accurately manage the user's tanning time and prevent excessive tanning. Furthermore, the monitoring unit can collect user feedback and continuously improve the accuracy of monitoring and the timing of notifications. This allows the monitoring unit to support safe and effective tanning for users.

[0034] The notification unit notifies the user when the monitoring time, as monitored by the monitoring unit, has ended. Specifically, it notifies the user via means such as email, app notifications, and voice alerts. The notification unit selects the most appropriate notification method according to the user's settings and reliably delivers the information. For example, it uses smartphone app notifications to inform the user that their sun exposure time has ended and to suggest post-sun exposure care methods. It also uses voice alerts to notify the user in a way that makes them more likely to notice while sun exposure is in progress. The notification unit considers the details of the notification content and accurately conveys the necessary information to the user. For example, it may notify not only that the sun exposure time has ended, but also to include advice on reapplying sunscreen and staying hydrated. This allows the notification unit to support users in ending sun exposure at the appropriate time and performing post-sun exposure care. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy of notification content and timing. This allows the notification unit to provide information to users quickly and reliably, supporting safe and effective sun exposure.

[0035] The generation unit can generate a custom tanning plan based on a color sample selected by the user. For example, the generation unit generates the optimal tanning plan based on the color sample selected by the user. The generation unit generates a custom tanning plan considering, for example, the format of the color sample, the selection criteria, etc. The generation unit provides, for example, a custom tanning plan that suits the user's preferences. This makes it possible to provide a custom tanning plan that suits the user's preferences. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the color sample selected by the user into the generation AI, and the generation AI generates a custom tanning plan.

[0036] The monitoring unit can adjust the tanning time based on daily weather fluctuations. For example, the monitoring unit adjusts the tanning time based on daily weather fluctuations. For example, the monitoring unit adjusts the tanning time considering changes in temperature, sudden changes in weather, etc. For example, the monitoring unit can achieve a safer and more effective tan by adjusting the tanning time in response to weather fluctuations. This makes it possible to achieve a safer and more effective tan by adjusting the tanning time in response to weather fluctuations. Some or all of the above processing in the monitoring unit is performed using a generation AI. For example, the monitoring unit inputs weather data into the generation AI, and the generation AI adjusts the tanning time.

[0037] The notification unit can notify the user when the sunbathing time is over. The notification unit notifies the user when the sunbathing time is over, for example. The notification unit notifies the user by means such as email or app notification. The notification unit notifies the user when the sunbathing time is over, for example. This allows the user to prevent excessive sunburn by notifying them when the sunbathing time is over. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the end of the sunbathing time to the generation AI, and the generation AI makes the notification.

[0038] The skin analysis unit can perform analysis while considering the user's skin type and allergy history. For example, the skin analysis unit considers the user's skin type and allergy history when performing analysis. For example, the skin analysis unit considers skin types such as dry skin, oily skin, and combination skin when performing analysis. For example, the skin analysis unit considers allergy history such as medical records and self-reported data when performing analysis. This allows for more accurate skin analysis by considering the user's skin type and allergy history. Some or all of the above processing in the skin analysis unit is performed using a generating AI. For example, the skin analysis unit inputs the user's skin type and allergy history into the generating AI, and the generating AI performs the analysis.

[0039] The weather data acquisition unit can acquire weather data based on the user's location information. For example, the weather data acquisition unit acquires weather data based on the user's location information. The weather data acquisition unit acquires weather data using, for example, GPS data, location information services, etc. This allows for the provision of a more accurate sunbathing plan by acquiring weather data based on the user's location information. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's location information into the generating AI, and the generating AI acquires the weather data.

[0040] The skin analysis unit can analyze the user's past skin trouble history and select the optimal analysis method. For example, the skin analysis unit selects a specific analysis method based on the types of skin troubles the user has experienced in the past. For example, the skin analysis unit adjusts the frequency of analysis based on the frequency of the user's skin troubles. For example, the skin analysis unit identifies the cause of the user's skin troubles and selects an analysis method based on that. In this way, the optimal analysis method can be selected by analyzing the user's past skin trouble history. Some or all of the above processes in the skin analysis unit are performed using a generating AI. For example, the skin analysis unit inputs the user's past skin trouble history into the generating AI, and the generating AI selects the optimal analysis method.

[0041] The skin analysis unit can filter the results based on the user's current lifestyle and diet during skin analysis. For example, the skin analysis unit can reflect deficiencies in specific nutrients based on the user's diet. For example, the skin analysis unit can evaluate the skin condition based on the user's lifestyle (sleep duration, exercise level, etc.). For example, the skin analysis unit can analyze the skin condition based on the user's stress level. This allows for more accurate analysis results by performing skin analysis based on the user's lifestyle and diet. Some or all of the above processes in the skin analysis unit are performed using a generative AI. For example, the skin analysis unit inputs the user's lifestyle and diet into the generative AI, which then performs the filtering.

[0042] The skin analysis unit can prioritize the acquisition of highly relevant analysis data by considering the user's geographical location information during skin analysis. For example, if the user is in a high-humidity area, the skin analysis unit will reflect the effect of humidity on the skin in its analysis. For example, if the user is in a dry area, the skin analysis unit will reflect the effect of dryness on the skin in its analysis. For example, if the user is in an area with strong ultraviolet radiation, the skin analysis unit will reflect the effect of ultraviolet radiation on the skin in its analysis. In this way, by considering the user's geographical location information, the skin analysis unit can prioritize the acquisition of highly relevant analysis data. Some or all of the above processing in the skin analysis unit is performed using a generation AI. For example, the skin analysis unit inputs the user's geographical location information into the generation AI, and the generation AI prioritizes the acquisition of highly relevant analysis data.

[0043] The skin analysis unit can analyze a user's social media activity and acquire relevant skin data during skin analysis. For example, the skin analysis unit can analyze images shared by the user on social media and evaluate the condition of the skin. For example, the skin analysis unit can acquire analysis data based on skin problems mentioned by the user on social media. For example, the skin analysis unit can estimate lifestyle habits and stress levels from the user's social media activity and incorporate them into the analysis. In this way, relevant skin data can be acquired by analyzing the user's social media activity. Some or all of the above processes in the skin analysis unit are performed using generative AI. For example, the skin analysis unit inputs the user's social media activity into the generative AI, and the generative AI acquires relevant skin data.

[0044] The weather data acquisition unit can analyze past weather data and select the optimal acquisition method. For example, the weather data acquisition unit can acquire weather data at specific time periods based on past weather data. For example, the weather data acquisition unit can analyze past weather data to identify weather variation patterns. For example, the weather data acquisition unit can prioritize acquiring weather data for specific regions based on past weather data. This allows the optimal acquisition method to be selected by analyzing past weather data. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs past weather data into the generating AI, and the generating AI selects the optimal acquisition method.

[0045] The weather data acquisition unit can filter weather data based on the user's current activity status when acquiring it. For example, if the user is active outdoors, the weather data acquisition unit acquires weather data in real time. For example, if the user is active indoors, the weather data acquisition unit reduces the frequency of weather data acquisition. For example, if the user is participating in a specific event, the weather data acquisition unit prioritizes acquiring weather data related to that event. This allows for the acquisition of more relevant data by filtering weather data based on the user's current activity status. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's current activity status into the generating AI, and the generating AI performs the filtering.

[0046] The weather data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring weather data. For example, if the user is in a high-humidity area, the weather data acquisition unit will prioritize the acquisition of humidity data. For example, if the user is in a dry area, the weather data acquisition unit will prioritize the acquisition of dryness data. For example, if the user is in an area with strong ultraviolet radiation, the weather data acquisition unit will prioritize the acquisition of ultraviolet radiation data. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's geographical location information into the generating AI, and the generating AI prioritizes the acquisition of highly relevant data.

[0047] The weather data acquisition unit can analyze the user's social media activity and acquire relevant weather data when acquiring weather data. For example, the weather data acquisition unit can analyze images shared by the user on social media and acquire weather data. For example, the weather data acquisition unit can acquire weather data based on the weather mentioned by the user on social media. For example, the weather data acquisition unit can acquire weather data of interest from the user's social media activity. In this way, relevant weather data can be acquired by analyzing the user's social media activity. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's social media activity into the generating AI, and the generating AI acquires relevant weather data.

[0048] The generation unit can adjust the level of detail in a tanning plan based on the user's skin condition when generating the plan. For example, if the user's skin is sensitive, the generation unit generates a detailed tanning plan. For example, if the user's skin is healthy, the generation unit generates a simple tanning plan. The generation unit adjusts the level of detail in the tanning plan according to the user's skin condition. This allows for the provision of a more appropriate tanning plan by adjusting the level of detail according to the user's skin condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's skin condition into the generation AI, and the generation AI adjusts the level of detail in the plan.

[0049] The generation unit can apply different generation algorithms depending on the color sample selected by the user when generating a tanning plan. For example, the generation unit generates the optimal tanning plan based on the color sample selected by the user. For example, the generation unit applies different generation algorithms depending on the user's color sample. For example, the generation unit adjusts the details of the tanning plan based on the user's color sample. By applying different generation algorithms depending on the color sample selected by the user, it is possible to provide a tanning plan that better suits the user's preferences. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's color sample into the generation AI, and the generation AI applies different generation algorithms.

[0050] The generation unit can determine the priority of tanning plans based on the user's past plan history when generating tanning plans. For example, the generation unit generates the optimal plan based on tanning plans the user has used in the past. For example, the generation unit determines the priority from the user's past plan history. For example, the generation unit analyzes the user's past plan history and generates the most efficient plan. This allows for the provision of more effective tanning plans by determining the priority of plans based on the user's past plan history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past plan history into the generation AI, and the generation AI determines the priority of the plans.

[0051] The generation unit can adjust the order of tanning plans based on user relevance when generating tanning plans. For example, the generation unit determines the optimal order of plans based on user relevance. For example, the generation unit adjusts the order of plans according to user relevance. For example, the generation unit adjusts the details of the plans based on user relevance. By adjusting the order of plans based on user relevance, a more effective tanning plan can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user relevance to the generation AI, and the generation AI adjusts the order of the plans.

[0052] The monitoring unit can improve the accuracy of monitoring by analyzing the user's skin condition in real time during monitoring. For example, the monitoring unit measures the user's skin moisture content in real time and reflects it in the monitoring. For example, the monitoring unit analyzes the user's skin pigmentation in real time and reflects it in the monitoring. For example, the monitoring unit measures the user's skin elasticity in real time and reflects it in the monitoring. In this way, the accuracy of monitoring can be improved by analyzing the user's skin condition in real time. Some or all of the above processing in the monitoring unit is performed using a generative AI. For example, the monitoring unit inputs the user's skin condition into the generative AI, which analyzes it in real time.

[0053] The monitoring unit can perform monitoring while taking into account the user's activity status. For example, if the user is exercising, the monitoring unit will take into account sweating due to exercise. For example, if the user is resting, the monitoring unit will take into account the skin's recovery status. For example, if the user is outdoors, the monitoring unit will take into account the influence of the external environment. This allows for more accurate monitoring by taking into account the user's activity status. Some or all of the above processing in the monitoring unit is performed using a generation AI. For example, the monitoring unit inputs the user's activity status into the generation AI, and the generation AI performs the monitoring.

[0054] The monitoring unit can perform monitoring while considering the geographical distribution of users. For example, if a user is in a high-humidity area, the monitoring unit will consider the effect of humidity during monitoring. For example, if a user is in a dry area, the monitoring unit will consider the effect of dryness during monitoring. For example, if a user is in an area with strong ultraviolet radiation, the monitoring unit will consider the effect of ultraviolet radiation during monitoring. This allows for more accurate monitoring by considering the geographical distribution of users. Some or all of the above processing in the monitoring unit is performed using a generation AI. For example, the monitoring unit inputs the geographical distribution of users into the generation AI, and the generation AI performs the monitoring.

[0055] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature for the user during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature on the user's skin type. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature on the user's skin problems. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature on the user's lifestyle. In this way, the accuracy of monitoring can be improved by referring to relevant literature for the user. Some or all of the above processing in the monitoring unit is performed using a generating AI. For example, the monitoring unit inputs relevant literature for the user into the generating AI, and the generating AI improves the accuracy of monitoring.

[0056] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has preferred to use in the past. For example, the notification unit may select the optimal notification timing from the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's past notification history into the generation AI, and the generation AI selects the optimal notification method.

[0057] The notification unit can adjust the timing of notifications based on the user's current activity status. For example, if the user is exercising, the notification unit will send a notification after the exercise is finished. If the user is resting, the notification unit will send a notification during the rest period. If the user is out, the notification unit will send a notification while they are out. By adjusting the timing of notifications based on the user's current activity status, notifications can be sent at a more appropriate time. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's current activity status into the generation AI, and the generation AI adjusts the timing of the notification.

[0058] The notification unit can select the optimal notification method by considering the user's geographical location information when sending a notification. For example, if the user is in a high-humidity area, the notification unit will prioritize notifications related to humidity. For example, if the user is in a dry area, the notification unit will prioritize notifications related to dryness. For example, if the user is in an area with strong ultraviolet radiation, the notification unit will prioritize notifications related to ultraviolet radiation. In this way, the notification unit can select the optimal notification method by considering the user's geographical location information. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's geographical location information into the generation AI, and the generation AI selects the optimal notification method.

[0059] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will send a notification optimized for the smartphone. For example, if the user is using a tablet, the notification unit will send a notification optimized for the tablet. For example, if the user is using a smartwatch, the notification unit will send a notification optimized for the smartwatch. In this way, the notification unit can select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's device information into the generation AI, and the generation AI selects the optimal notification method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The skin analysis unit can measure the user's skin moisture level in real time when analyzing images of the user's skin and incorporate this into the analysis results. For example, the skin analysis unit uses a sensor to measure the user's skin moisture level and evaluate the dryness of the skin. This allows for more accurate skin analysis based on the user's skin moisture level. The skin analysis unit can also suggest moisturizing care if the user's skin moisture level is low. Furthermore, the skin analysis unit can monitor changes in the user's skin moisture level and notify them of moisturizing care at the appropriate time. This allows the user to prevent skin dryness and maintain healthy skin.

[0062] The generator can adjust a custom tanning plan based on the user's selected color swatch, taking into account the user's past skin trouble history. For example, it can generate a plan with specific precautions based on the types of sun-induced skin problems the user has experienced in the past. This allows the user to receive specific advice to avoid past problems. The generator can also adjust tanning time and sunscreen application based on the frequency of the user's skin problems. Furthermore, it can identify the cause of the user's skin problems and optimize the plan accordingly. This allows the user to enjoy a safer and more effective tanning plan.

[0063] The monitoring unit can adjust the tanning time based on daily weather changes, taking into account the user's current activity level. For example, if the user is exercising, the monitoring unit can shorten the tanning time to account for sweating caused by exercise. This allows the user to avoid excessive sunburn. Furthermore, if the user is resting, the monitoring unit can extend the tanning time to account for skin recovery. Additionally, if the user is outdoors, the monitoring unit can adjust the tanning time to account for the effects of the external environment. This allows the user to achieve a safer and more effective tan.

[0064] The skin analysis unit can filter the analysis based on the user's lifestyle and diet, taking into account the user's skin type and allergy history. For example, it can reflect deficiencies in specific nutrients based on the user's diet, allowing users to receive specific advice tailored to their own eating habits. The skin analysis unit can also evaluate the user's skin condition based on their lifestyle (sleep duration, exercise level, etc.). Furthermore, it can analyze the skin condition based on the user's stress level, enabling users to receive a more accurate skin analysis based on their own lifestyle.

[0065] The notification unit can select the optimal notification method by referring to the user's past notification history when notifying the user at the end of their tanning time. For example, the notification unit can prioritize notification methods that the user has preferred in the past. This allows the user to receive notifications tailored to their preferences. The notification unit can also select the optimal notification timing based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. This allows the user to receive notifications that are best suited to their past experiences.

[0066] The skin analysis unit can prioritize obtaining highly relevant analysis data by considering the user's geographical location when analyzing images of the user's skin. For example, if the user is in a high-humidity area, the skin analysis unit can reflect the effects of humidity on the skin in its analysis. This allows the user to receive specific advice based on their environment. Furthermore, if the user is in a dry area, the skin analysis unit can reflect the effects of dryness on the skin. Additionally, if the user is in an area with strong ultraviolet radiation, the skin analysis unit can reflect the effects of ultraviolet radiation on the skin. This allows the user to receive a more accurate skin analysis based on their geographical location.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The skin analysis unit analyzes the user's skin image. For example, it analyzes a skin image taken by the user and evaluates the skin's condition. The analysis takes into account factors such as skin resolution, shooting conditions, and image format. Step 2: The weather data acquisition unit acquires weather data. For example, it acquires weather data such as temperature, humidity, and UV index based on the user's location information. Step 3: The generation unit generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit. For example, it suggests tanning time and sunscreen usage methods based on the color swatch selected by the user. Step 4: The monitoring unit monitors the tanning time in real time based on the tanning plan generated by the generation unit. For example, it counts down the tanning time and sends a notification when it is finished. Step 5: The notification unit sends a notification when the sunburn time monitored by the monitoring unit ends. For example, the notification may be sent via email or app notification.

[0069] (Example of form 2) The tanning plan provision system according to an embodiment of the present invention is a system that provides personalized tanning plans using generative AI. This tanning plan provision system was developed to solve the problem that conventional tanning methods are not personalized and carry the risk of skin problems. It also solves the problem that it is difficult to plan a tan according to the weather and location, and that there is a lack of information to efficiently and safely obtain the desired skin tone. This system first analyzes the user's skin image using a skin analysis app and obtains weather data. Next, the generative AI analyzes this data and proposes a custom tanning plan to the user. The user selects a color sample, and the generative AI considers the intensity of sunlight, time, and angle to indicate the necessary tanning time. Furthermore, it provides a function to monitor the tanning time in real time via a smartphone and notify the user when it is finished. Adjustments are also made based on daily weather fluctuations. For example, the tanning plan provision system uses a skin analysis app to analyze the user's skin image. For example, the user uploads a skin image they have taken to the app, and the app analyzes the image and evaluates the condition of the skin. It also uses a weather data acquisition API to obtain weather data. For example, it obtains current weather data based on the user's location information. The AI ​​analyzes this data and proposes the optimal tanning plan for the user. For example, based on the color swatch selected by the user, it suggests tanning time and sunscreen application methods. Furthermore, the tanning plan system provides a function to monitor tanning time in real time via smartphone and notify the user when it is finished. For example, once the user starts tanning, the system counts down the tanning time and notifies the user when it is finished. This allows the user to enjoy a safe and personalized tanning experience, preventing excessive tanning and achieving the ideal skin tone. In addition, the AI ​​automatically creates the optimal plan according to the weather and location, saving the user time and effort. The real-time notification function allows the user to enjoy the tanning process with peace of mind. In summary, the tanning plan system provides a tanning plan tailored to the user's skin, and by monitoring and notifying in real time, it enables safe and efficient tanning.

[0070] The tanning plan provision system according to the embodiment comprises a skin analysis unit, a weather data acquisition unit, a generation unit, a monitoring unit, and a notification unit. The skin analysis unit analyzes an image of the user's skin. For example, the skin analysis unit analyzes an image of the user's skin and evaluates the condition of the skin. The skin analysis unit performs the analysis considering, for example, the resolution of the skin, shooting conditions, image format, etc. The weather data acquisition unit acquires weather data. For example, the weather data acquisition unit acquires weather data based on the user's location information. For example, the weather data acquisition unit acquires weather data such as temperature, humidity, and ultraviolet index. The generation unit generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit. For example, the generation unit generates a custom tanning plan based on a color sample selected by the user. For example, the generation unit proposes tanning time, sunscreen usage method, etc. The monitoring unit monitors the tanning time in real time based on the tanning plan generated by the generation unit. For example, the monitoring unit counts down the tanning time and provides notification when it is finished. The monitoring unit performs monitoring, taking into consideration factors such as the frequency of monitoring and the sensors used. The notification unit provides notification when the tanning time monitored by the monitoring unit ends. The notification unit provides notification by means such as email or app notification. The notification unit provides notification considering factors such as the details of the notification content. As a result, the tanning plan provision system according to the embodiment can provide a tanning plan that suits the user's skin, and by performing real-time monitoring and notification, it can achieve safe and efficient tanning.

[0071] The Skin Analysis Department analyzes images of the user's skin. Specifically, users upload images of their skin taken with their smartphones or digital cameras, and the department analyzes these images. The Skin Analysis Department performs the analysis considering factors such as image resolution, shooting conditions, and image format. For example, the higher the resolution of the skin image, the more detailed the analysis, and the more appropriate the shooting conditions, the more accurate the evaluation. The Skin Analysis Department uses AI to perform image analysis and evaluate skin tone, blemishes, wrinkles, and pore condition. Based on a large amount of pre-trained skin image data, the AI ​​analyzes the user's skin condition with high accuracy. For example, by analyzing skin tone, it identifies the user's skin type and detects the presence or absence of blemishes and wrinkles. It also evaluates the health of the skin by analyzing the condition of the pores. As a result, the Skin Analysis Department can gain a detailed understanding of the user's skin condition and provide the data necessary to generate a custom tanning plan. Furthermore, the Skin Analysis Department can also provide feedback on the analysis results to the user and offer skincare advice tailored to their skin condition. For example, it can advise users with dry skin on moisturizing care and users with many blemishes on whitening care. This allows the skin analysis unit to comprehensively evaluate the user's skin condition and suggest appropriate care methods.

[0072] The weather data acquisition unit acquires weather data. Specifically, it acquires current and predicted weather data based on the user's location information. The weather data acquisition unit acquires data in real time from weather data provision services and collects detailed weather information such as temperature, humidity, UV index, and wind speed. For example, if the user is at a beach, the weather data acquisition unit acquires the UV index for that area and assesses the risk of sunburn. In addition, by acquiring temperature and humidity data, it can predict the rate of sunburn progression and its impact on the skin. The weather data acquisition unit regularly updates this data to provide the latest weather information. Furthermore, the weather data acquisition unit can also accumulate historical weather data and analyze long-term weather patterns. This allows it to predict the risk of sunburn in specific seasons and regions and provide appropriate advice to the user. For example, based on historical data, it can predict fluctuations in the UV index in a specific region and suggest the optimal timing for sunburn to the user. As a result, the weather data acquisition unit can provide accurate weather data based on the user's location information and provide the information necessary to generate a custom sunburn plan.

[0073] The generation unit generates a custom tanning plan based on data obtained from the skin analysis unit and the weather data acquisition unit. Specifically, it creates an optimal tanning plan considering the user's skin condition and current weather conditions. The generation unit uses AI to automatically generate a plan tailored to the user's skin type and tanning goals. For example, for users with sensitive skin, it recommends short tanning sessions and provides detailed instructions on how and how often to use sunscreen. It also suggests specific tanning times and methods to achieve the target skin tone based on the color swatch selected by the user. The generation unit provides a detailed plan including tanning time, sunscreen usage, and after-tanning care. Furthermore, the generation unit considers information such as the UV index and temperature obtained from the weather data acquisition unit to provide advice to minimize the risk of sunburn. For example, on days with a high UV index, it suggests shortening tanning time and recommends reapplying sunscreen. In this way, the generation unit can provide a custom tanning plan tailored to the user's skin condition and weather conditions, supporting safe and effective tanning.

[0074] The monitoring unit monitors tanning time in real time based on the tanning plan generated by the generation unit. Specifically, it counts down the tanning time from the moment the user starts tanning and notifies the user when it ends. The monitoring unit uses sensors from smartphones and wearable devices to monitor the user's location and activity in real time. For example, if a user is tanning on the beach, the monitoring unit identifies the user's location based on GPS data and accurately measures the tanning time. It also uses acceleration sensors and gyroscope sensors to detect the user's movements and understand their activity level while tanning. Based on this data, the monitoring unit displays the progress of the tanning time in real time and notifies the user at the appropriate time. For example, it issues an alert a few minutes before the tanning time ends, prompting the user to reapply sunscreen or move to the shade. This allows the monitoring unit to accurately manage the user's tanning time and prevent excessive tanning. Furthermore, the monitoring unit can collect user feedback and continuously improve the accuracy of monitoring and the timing of notifications. This allows the monitoring unit to support safe and effective tanning for users.

[0075] The notification unit notifies the user when the monitoring time, as monitored by the monitoring unit, has ended. Specifically, it notifies the user via means such as email, app notifications, and voice alerts. The notification unit selects the most appropriate notification method according to the user's settings and reliably delivers the information. For example, it uses smartphone app notifications to inform the user that their sun exposure time has ended and to suggest post-sun exposure care methods. It also uses voice alerts to notify the user in a way that makes them more likely to notice while sun exposure is in progress. The notification unit considers the details of the notification content and accurately conveys the necessary information to the user. For example, it may notify not only that the sun exposure time has ended, but also to include advice on reapplying sunscreen and staying hydrated. This allows the notification unit to support users in ending sun exposure at the appropriate time and performing post-sun exposure care. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy of notification content and timing. This allows the notification unit to provide information to users quickly and reliably, supporting safe and effective sun exposure.

[0076] The generation unit can generate a custom tanning plan based on a color sample selected by the user. For example, the generation unit generates the optimal tanning plan based on the color sample selected by the user. The generation unit generates a custom tanning plan considering, for example, the format of the color sample, the selection criteria, etc. The generation unit provides, for example, a custom tanning plan that suits the user's preferences. This makes it possible to provide a custom tanning plan that suits the user's preferences. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the color sample selected by the user into the generation AI, and the generation AI generates a custom tanning plan.

[0077] The monitoring unit can adjust the tanning time based on daily weather fluctuations. For example, the monitoring unit adjusts the tanning time based on daily weather fluctuations. For example, the monitoring unit adjusts the tanning time considering changes in temperature, sudden changes in weather, etc. For example, the monitoring unit can achieve a safer and more effective tan by adjusting the tanning time in response to weather fluctuations. This makes it possible to achieve a safer and more effective tan by adjusting the tanning time in response to weather fluctuations. Some or all of the above processing in the monitoring unit is performed using a generation AI. For example, the monitoring unit inputs weather data into the generation AI, and the generation AI adjusts the tanning time.

[0078] The notification unit can notify the user when the sunbathing time is over. The notification unit notifies the user when the sunbathing time is over, for example. The notification unit notifies the user by means such as email or app notification. The notification unit notifies the user when the sunbathing time is over, for example. This allows the user to prevent excessive sunburn by notifying them when the sunbathing time is over. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the end of the sunbathing time to the generation AI, and the generation AI makes the notification.

[0079] The skin analysis unit can perform analysis while considering the user's skin type and allergy history. For example, the skin analysis unit considers the user's skin type and allergy history when performing analysis. For example, the skin analysis unit considers skin types such as dry skin, oily skin, and combination skin when performing analysis. For example, the skin analysis unit considers allergy history such as medical records and self-reported data when performing analysis. This allows for more accurate skin analysis by considering the user's skin type and allergy history. Some or all of the above processing in the skin analysis unit is performed using a generating AI. For example, the skin analysis unit inputs the user's skin type and allergy history into the generating AI, and the generating AI performs the analysis.

[0080] The weather data acquisition unit can acquire weather data based on the user's location information. For example, the weather data acquisition unit acquires weather data based on the user's location information. The weather data acquisition unit acquires weather data using, for example, GPS data, location information services, etc. This allows for the provision of a more accurate sunbathing plan by acquiring weather data based on the user's location information. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's location information into the generating AI, and the generating AI acquires the weather data.

[0081] The skin analysis unit can estimate the user's emotions and adjust the timing of the skin analysis based on the estimated emotions. For example, if the user is stressed, the skin analysis unit will adjust the timing to perform the skin analysis during a time when the user is relaxed. For example, if the user is relaxed, the skin analysis unit will adjust the timing to perform the skin analysis immediately. For example, if the user is busy, the skin analysis unit will adjust the timing to match the user's schedule. By adjusting the timing of the skin analysis according to the user's emotions, the skin analysis can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skin analysis unit is performed using generative AI. For example, the skin analysis unit inputs the user's emotions into the generative AI, and the generative AI adjusts the timing of the skin analysis.

[0082] The skin analysis unit can analyze the user's past skin trouble history and select the optimal analysis method. For example, the skin analysis unit selects a specific analysis method based on the types of skin troubles the user has experienced in the past. For example, the skin analysis unit adjusts the frequency of analysis based on the frequency of the user's skin troubles. For example, the skin analysis unit identifies the cause of the user's skin troubles and selects an analysis method based on that. In this way, the optimal analysis method can be selected by analyzing the user's past skin trouble history. Some or all of the above processes in the skin analysis unit are performed using a generating AI. For example, the skin analysis unit inputs the user's past skin trouble history into the generating AI, and the generating AI selects the optimal analysis method.

[0083] The skin analysis unit can filter the results based on the user's current lifestyle and diet during skin analysis. For example, the skin analysis unit can reflect deficiencies in specific nutrients based on the user's diet. For example, the skin analysis unit can evaluate the skin condition based on the user's lifestyle (sleep duration, exercise level, etc.). For example, the skin analysis unit can analyze the skin condition based on the user's stress level. This allows for more accurate analysis results by performing skin analysis based on the user's lifestyle and diet. Some or all of the above processes in the skin analysis unit are performed using a generative AI. For example, the skin analysis unit inputs the user's lifestyle and diet into the generative AI, which then performs the filtering.

[0084] The skin analysis unit can estimate the user's emotions and prioritize which skin areas to analyze based on the estimated emotions. For example, if the user is stressed, the skin analysis unit will prioritize analyzing areas that are easily affected by stress. For example, if the user is relaxed, the skin analysis unit will analyze the overall condition of the skin. For example, if the user is anxious about a particular area, the skin analysis unit will prioritize analyzing that area. This allows for more effective skin analysis by prioritizing the skin areas to be analyzed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the skin analysis unit are performed using generative AI. For example, the skin analysis unit inputs the user's emotions into the generative AI, which then prioritizes which skin areas to analyze.

[0085] The skin analysis unit can prioritize the acquisition of highly relevant analysis data by considering the user's geographical location information during skin analysis. For example, if the user is in a high-humidity area, the skin analysis unit will reflect the effect of humidity on the skin in its analysis. For example, if the user is in a dry area, the skin analysis unit will reflect the effect of dryness on the skin in its analysis. For example, if the user is in an area with strong ultraviolet radiation, the skin analysis unit will reflect the effect of ultraviolet radiation on the skin in its analysis. In this way, by considering the user's geographical location information, the skin analysis unit can prioritize the acquisition of highly relevant analysis data. Some or all of the above processing in the skin analysis unit is performed using a generation AI. For example, the skin analysis unit inputs the user's geographical location information into the generation AI, and the generation AI prioritizes the acquisition of highly relevant analysis data.

[0086] The skin analysis unit can analyze a user's social media activity and acquire relevant skin data during skin analysis. For example, the skin analysis unit can analyze images shared by the user on social media and evaluate the condition of the skin. For example, the skin analysis unit can acquire analysis data based on skin problems mentioned by the user on social media. For example, the skin analysis unit can estimate lifestyle habits and stress levels from the user's social media activity and incorporate them into the analysis. In this way, relevant skin data can be acquired by analyzing the user's social media activity. Some or all of the above processes in the skin analysis unit are performed using generative AI. For example, the skin analysis unit inputs the user's social media activity into the generative AI, and the generative AI acquires relevant skin data.

[0087] The weather data acquisition unit can estimate the user's emotions and adjust the timing of weather data acquisition based on the estimated emotions. For example, if the user is stressed, the weather data acquisition unit will acquire weather data during a time when the user can relax. If the user is relaxed, the weather data acquisition unit will acquire weather data immediately. If the user is busy, the weather data acquisition unit will acquire weather data according to the user's schedule. By adjusting the timing of weather data acquisition according to the user's emotions, weather data can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the weather data acquisition unit is performed using the generative AI. For example, the weather data acquisition unit inputs the user's emotions into the generative AI, and the generative AI adjusts the timing of weather data acquisition.

[0088] The weather data acquisition unit can analyze past weather data and select the optimal acquisition method. For example, the weather data acquisition unit can acquire weather data at specific time periods based on past weather data. For example, the weather data acquisition unit can analyze past weather data to identify weather variation patterns. For example, the weather data acquisition unit can prioritize acquiring weather data for specific regions based on past weather data. This allows the optimal acquisition method to be selected by analyzing past weather data. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs past weather data into the generating AI, and the generating AI selects the optimal acquisition method.

[0089] The weather data acquisition unit can filter weather data based on the user's current activity status when acquiring it. For example, if the user is active outdoors, the weather data acquisition unit acquires weather data in real time. For example, if the user is active indoors, the weather data acquisition unit reduces the frequency of weather data acquisition. For example, if the user is participating in a specific event, the weather data acquisition unit prioritizes acquiring weather data related to that event. This allows for the acquisition of more relevant data by filtering weather data based on the user's current activity status. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's current activity status into the generating AI, and the generating AI performs the filtering.

[0090] The weather data acquisition unit can estimate the user's emotions and determine the priority of weather data to acquire based on the estimated user emotions. For example, if the user is feeling stressed, the weather data acquisition unit will prioritize acquiring relaxing weather data. For example, if the user is relaxed, the weather data acquisition unit will prioritize acquiring general weather data. For example, if the user is interested in a particular weather condition, the weather data acquisition unit will prioritize acquiring that weather data. In this way, by determining the priority of weather data to acquire according to the user's emotions, more important data can be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the weather data acquisition unit is performed using generative AI. For example, the weather data acquisition unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of weather data to acquire.

[0091] The weather data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring weather data. For example, if the user is in a high-humidity area, the weather data acquisition unit will prioritize the acquisition of humidity data. For example, if the user is in a dry area, the weather data acquisition unit will prioritize the acquisition of dryness data. For example, if the user is in an area with strong ultraviolet radiation, the weather data acquisition unit will prioritize the acquisition of ultraviolet radiation data. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's geographical location information into the generating AI, and the generating AI prioritizes the acquisition of highly relevant data.

[0092] The weather data acquisition unit can analyze the user's social media activity and acquire relevant weather data when acquiring weather data. For example, the weather data acquisition unit can analyze images shared by the user on social media and acquire weather data. For example, the weather data acquisition unit can acquire weather data based on the weather mentioned by the user on social media. For example, the weather data acquisition unit can acquire weather data of interest from the user's social media activity. In this way, relevant weather data can be acquired by analyzing the user's social media activity. Some or all of the above processing in the weather data acquisition unit is performed using a generating AI. For example, the weather data acquisition unit inputs the user's social media activity into the generating AI, and the generating AI acquires relevant weather data.

[0093] The generation unit can estimate the user's emotions and adjust how the tanning plan is presented based on those emotions. For example, if the user is relaxed, the generation unit will generate a tanning plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit will generate a tanning plan that emphasizes the shortest route. If the user is excited, the generation unit will generate a tanning plan with visually stimulating effects. By adjusting how the tanning plan is presented according to the user's emotions, a plan more suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotions into the generation AI, and the generation AI adjusts how the tanning plan is presented.

[0094] The generation unit can adjust the level of detail in a tanning plan based on the user's skin condition when generating the plan. For example, if the user's skin is sensitive, the generation unit generates a detailed tanning plan. For example, if the user's skin is healthy, the generation unit generates a simple tanning plan. The generation unit adjusts the level of detail in the tanning plan according to the user's skin condition. This allows for the provision of a more appropriate tanning plan by adjusting the level of detail according to the user's skin condition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's skin condition into the generation AI, and the generation AI adjusts the level of detail in the plan.

[0095] The generation unit can apply different generation algorithms depending on the color sample selected by the user when generating a tanning plan. For example, the generation unit generates the optimal tanning plan based on the color sample selected by the user. For example, the generation unit applies different generation algorithms depending on the user's color sample. For example, the generation unit adjusts the details of the tanning plan based on the user's color sample. By applying different generation algorithms depending on the color sample selected by the user, it is possible to provide a tanning plan that better suits the user's preferences. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's color sample into the generation AI, and the generation AI applies different generation algorithms.

[0096] The generation unit can estimate the user's emotions and adjust the length of the tanning plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise tanning plan. If the user is relaxed, the generation unit will generate a longer tanning plan with detailed explanations. If the user is excited, the generation unit will generate a tanning plan with visually stimulating effects. By adjusting the length of the tanning plan according to the user's emotions, a more appropriate tanning plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotions into the generation AI, and the generation AI adjusts the length of the tanning plan.

[0097] The generation unit can determine the priority of tanning plans based on the user's past plan history when generating tanning plans. For example, the generation unit generates the optimal plan based on tanning plans the user has used in the past. For example, the generation unit determines the priority from the user's past plan history. For example, the generation unit analyzes the user's past plan history and generates the most efficient plan. This allows for the provision of more effective tanning plans by determining the priority of plans based on the user's past plan history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's past plan history into the generation AI, and the generation AI determines the priority of the plans.

[0098] The generation unit can adjust the order of tanning plans based on user relevance when generating tanning plans. For example, the generation unit determines the optimal order of plans based on user relevance. For example, the generation unit adjusts the order of plans according to user relevance. For example, the generation unit adjusts the details of the plans based on user relevance. By adjusting the order of plans based on user relevance, a more effective tanning plan can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user relevance to the generation AI, and the generation AI adjusts the order of the plans.

[0099] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is tense, the monitoring unit provides simple and easily visible monitoring criteria. For example, if the user is relaxed, the monitoring unit provides monitoring criteria that include detailed information. For example, if the user is in a hurry, the monitoring unit provides concise monitoring criteria. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit are performed using generative AI. For example, the monitoring unit inputs the user's emotions into the generative AI, and the generative AI adjusts the monitoring criteria.

[0100] The monitoring unit can improve the accuracy of monitoring by analyzing the user's skin condition in real time during monitoring. For example, the monitoring unit measures the user's skin moisture content in real time and reflects it in the monitoring. For example, the monitoring unit analyzes the user's skin pigmentation in real time and reflects it in the monitoring. For example, the monitoring unit measures the user's skin elasticity in real time and reflects it in the monitoring. In this way, the accuracy of monitoring can be improved by analyzing the user's skin condition in real time. Some or all of the above processing in the monitoring unit is performed using a generative AI. For example, the monitoring unit inputs the user's skin condition into the generative AI, which analyzes it in real time.

[0101] The monitoring unit can perform monitoring while taking into account the user's activity status. For example, if the user is exercising, the monitoring unit will take into account sweating due to exercise. For example, if the user is resting, the monitoring unit will take into account the skin's recovery status. For example, if the user is outdoors, the monitoring unit will take into account the influence of the external environment. This allows for more accurate monitoring by taking into account the user's activity status. Some or all of the above processing in the monitoring unit is performed using a generation AI. For example, the monitoring unit inputs the user's activity status into the generation AI, and the generation AI performs the monitoring.

[0102] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated emotions. For example, if the user is tense, the monitoring unit will display important results first. If the user is relaxed, the monitoring unit will display detailed results sequentially. If the user is in a hurry, the monitoring unit will display concise results first. By adjusting the order in which the monitoring results are displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit is performed using generative AI. For example, the monitoring unit inputs the user's emotions into the generative AI, and the generative AI adjusts the order in which the monitoring results are displayed.

[0103] The monitoring unit can perform monitoring while considering the geographical distribution of users. For example, if a user is in a high-humidity area, the monitoring unit will consider the effect of humidity during monitoring. For example, if a user is in a dry area, the monitoring unit will consider the effect of dryness during monitoring. For example, if a user is in an area with strong ultraviolet radiation, the monitoring unit will consider the effect of ultraviolet radiation during monitoring. This allows for more accurate monitoring by considering the geographical distribution of users. Some or all of the above processing in the monitoring unit is performed using a generation AI. For example, the monitoring unit inputs the geographical distribution of users into the generation AI, and the generation AI performs the monitoring.

[0104] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature for the user during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature on the user's skin type. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature on the user's skin problems. For example, the monitoring unit can improve the accuracy of monitoring by referring to literature on the user's lifestyle. In this way, the accuracy of monitoring can be improved by referring to relevant literature for the user. Some or all of the above processing in the monitoring unit is performed using a generating AI. For example, the monitoring unit inputs relevant literature for the user into the generating AI, and the generating AI improves the accuracy of monitoring.

[0105] The notification unit can estimate the user's emotions and adjust how notifications are displayed based on those emotions. For example, if the user is stressed, the notification unit displays a simple, highly visible notification. If the user is relaxed, the notification unit displays a notification containing detailed information. If the user is in a hurry, the notification unit displays a notification that gets straight to the point. This allows for more appropriate notifications by adjusting how notifications are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit are performed using generative AI. For example, the notification unit inputs the user's emotions into the generative AI, which then adjusts how notifications are displayed.

[0106] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has preferred to use in the past. For example, the notification unit may select the optimal notification timing from the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's past notification history into the generation AI, and the generation AI selects the optimal notification method.

[0107] The notification unit can adjust the timing of notifications based on the user's current activity status. For example, if the user is exercising, the notification unit will send a notification after the exercise is finished. If the user is resting, the notification unit will send a notification during the rest period. If the user is out, the notification unit will send a notification while they are out. By adjusting the timing of notifications based on the user's current activity status, notifications can be sent at a more appropriate time. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's current activity status into the generation AI, and the generation AI adjusts the timing of the notification.

[0108] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit will sequentially display detailed notifications. If the user is in a hurry, the notification unit will prioritize notifications that get straight to the point. In this way, by determining the priority of notifications according to the user's emotions, more important notifications can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit is performed using generative AI. For example, the notification unit inputs the user's emotions into the generative AI, and the generative AI determines the priority of notifications.

[0109] The notification unit can select the optimal notification method by considering the user's geographical location information when sending a notification. For example, if the user is in a high-humidity area, the notification unit will prioritize notifications related to humidity. For example, if the user is in a dry area, the notification unit will prioritize notifications related to dryness. For example, if the user is in an area with strong ultraviolet radiation, the notification unit will prioritize notifications related to ultraviolet radiation. In this way, the notification unit can select the optimal notification method by considering the user's geographical location information. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's geographical location information into the generation AI, and the generation AI selects the optimal notification method.

[0110] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will send a notification optimized for the smartphone. For example, if the user is using a tablet, the notification unit will send a notification optimized for the tablet. For example, if the user is using a smartwatch, the notification unit will send a notification optimized for the smartwatch. In this way, the notification unit can select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit is performed using a generation AI. For example, the notification unit inputs the user's device information into the generation AI, and the generation AI selects the optimal notification method.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The skin analysis unit can measure the user's skin moisture level in real time when analyzing images of the user's skin and incorporate this into the analysis results. For example, the skin analysis unit uses a sensor to measure the user's skin moisture level and evaluate the dryness of the skin. This allows for more accurate skin analysis based on the user's skin moisture level. The skin analysis unit can also suggest moisturizing care if the user's skin moisture level is low. Furthermore, the skin analysis unit can monitor changes in the user's skin moisture level and notify them of moisturizing care at the appropriate time. This allows the user to prevent skin dryness and maintain healthy skin.

[0113] The generator can adjust a custom tanning plan based on the user's selected color swatch, taking into account the user's past skin trouble history. For example, it can generate a plan with specific precautions based on the types of sun-induced skin problems the user has experienced in the past. This allows the user to receive specific advice to avoid past problems. The generator can also adjust tanning time and sunscreen application based on the frequency of the user's skin problems. Furthermore, it can identify the cause of the user's skin problems and optimize the plan accordingly. This allows the user to enjoy a safer and more effective tanning plan.

[0114] The monitoring unit can adjust the tanning time based on daily weather changes, taking into account the user's current activity level. For example, if the user is exercising, the monitoring unit can shorten the tanning time to account for sweating caused by exercise. This allows the user to avoid excessive sunburn. Furthermore, if the user is resting, the monitoring unit can extend the tanning time to account for skin recovery. Additionally, if the user is outdoors, the monitoring unit can adjust the tanning time to account for the effects of the external environment. This allows the user to achieve a safer and more effective tan.

[0115] The notification system can estimate the user's mood when notifying them at the end of their tanning session and adjust how the notification is displayed based on that mood. For example, if the user is feeling stressed, the notification system can display a simple, highly visible notification, ensuring they don't miss important information. If the user is relaxed, the notification system can display a more detailed notification. Furthermore, if the user is in a hurry, the notification system can display a concise notification. This allows the user to receive the most appropriate notification for their situation.

[0116] The skin analysis unit can filter the analysis based on the user's lifestyle and diet, taking into account the user's skin type and allergy history. For example, it can reflect deficiencies in specific nutrients based on the user's diet, allowing users to receive specific advice tailored to their own eating habits. The skin analysis unit can also evaluate the user's skin condition based on their lifestyle (sleep duration, exercise level, etc.). Furthermore, it can analyze the skin condition based on the user's stress level, enabling users to receive a more accurate skin analysis based on their own lifestyle.

[0117] The weather data acquisition unit can estimate the user's emotions when acquiring weather data based on the user's location information, and adjust the timing of weather data acquisition based on the estimated emotions. For example, if the user is feeling stressed, the weather data acquisition unit can acquire weather data during a time when the user is relaxed. This allows the user to receive weather data while reducing stress. The weather data acquisition unit can also acquire weather data immediately if the user is relaxed. Furthermore, if the user is busy, the weather data acquisition unit can acquire weather data according to the user's schedule. This allows the user to receive weather data at the optimal time according to their situation.

[0118] The generator can estimate the user's emotions when creating a custom tanning plan based on the user's selected color swatch, and adjust how the tanning plan is presented based on those emotions. For example, if the user is relaxed, the generator can create a tanning plan that progresses at a leisurely pace, allowing the user to enjoy tanning in a relaxed state. If the user is in a hurry, the generator can create a tanning plan that emphasizes the shortest route. Furthermore, if the user is excited, the generator can create a tanning plan with visually stimulating effects. This allows the user to receive a tanning plan that is optimally suited to their emotions.

[0119] The monitoring unit can estimate the user's emotions when adjusting tanning time based on daily weather variations, and adjust the monitoring criteria based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide simple and highly visible monitoring criteria. This ensures the user receives important information without missing anything. Furthermore, if the user is relaxed, the monitoring unit can provide monitoring criteria that include more detailed information. Additionally, if the user is in a hurry, the monitoring unit can provide concise monitoring criteria. This ensures the user receives optimal monitoring tailored to their situation.

[0120] The notification unit can select the optimal notification method by referring to the user's past notification history when notifying the user at the end of their tanning time. For example, the notification unit can prioritize notification methods that the user has preferred in the past. This allows the user to receive notifications tailored to their preferences. The notification unit can also select the optimal notification timing based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. This allows the user to receive notifications that are best suited to their past experiences.

[0121] The skin analysis unit can prioritize obtaining highly relevant analysis data by considering the user's geographical location when analyzing images of the user's skin. For example, if the user is in a high-humidity area, the skin analysis unit can reflect the effects of humidity on the skin in its analysis. This allows the user to receive specific advice based on their environment. Furthermore, if the user is in a dry area, the skin analysis unit can reflect the effects of dryness on the skin. Additionally, if the user is in an area with strong ultraviolet radiation, the skin analysis unit can reflect the effects of ultraviolet radiation on the skin. This allows the user to receive a more accurate skin analysis based on their geographical location.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The skin analysis unit analyzes the user's skin image. For example, it analyzes a skin image taken by the user and evaluates the skin's condition. The analysis takes into account factors such as skin resolution, shooting conditions, and image format. Step 2: The weather data acquisition unit acquires weather data. For example, it acquires weather data such as temperature, humidity, and UV index based on the user's location information. Step 3: The generation unit generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit. For example, it suggests tanning time and sunscreen usage methods based on the color swatch selected by the user. Step 4: The monitoring unit monitors the tanning time in real time based on the tanning plan generated by the generation unit. For example, it counts down the tanning time and sends a notification when it is finished. Step 5: The notification unit sends a notification when the sunburn time monitored by the monitoring unit ends. For example, the notification may be sent via email or app notification.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the skin analysis unit, weather data acquisition unit, generation unit, monitoring unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the skin analysis unit acquires an image of the user's skin using the camera 42 of the smart device 14 and performs analysis using the control unit 46A. The weather data acquisition unit acquires weather data via a weather data acquisition API using the specific processing unit 290 of the data processing unit 12. The generation unit generates an optimal tanning plan for the user using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the tanning time in real time using the control unit 46A of the smart device 14. The notification unit notifies the user when the tanning time ends using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the skin analysis unit, weather data acquisition unit, generation unit, monitoring unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the skin analysis unit acquires an image of the user's skin using the camera 42 of the smart glasses 214 and performs analysis using the control unit 46A. The weather data acquisition unit acquires weather data via a weather data acquisition API using the specific processing unit 290 of the data processing unit 12. The generation unit generates an optimal tanning plan for the user using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors tanning time in real time using the control unit 46A of the smart glasses 214. The notification unit notifies the user when tanning time ends using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the skin analysis unit, weather data acquisition unit, generation unit, monitoring unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the skin analysis unit acquires an image of the user's skin using the camera 42 of the headset terminal 314 and performs analysis using the control unit 46A. The weather data acquisition unit acquires weather data via a weather data acquisition API using the specific processing unit 290 of the data processing unit 12. The generation unit generates an optimal tanning plan for the user using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors tanning time in real time using the control unit 46A of the headset terminal 314. The notification unit notifies the user when tanning time ends using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the skin analysis unit, weather data acquisition unit, generation unit, monitoring unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the skin analysis unit acquires an image of the user's skin using the camera 42 of the robot 414 and performs analysis using the control unit 46A. The weather data acquisition unit acquires weather data via a weather data acquisition API using the specific processing unit 290 of the data processing unit 12. The generation unit generates an optimal tanning plan for the user using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the tanning time in real time using the control unit 46A of the robot 414. The notification unit notifies the user when the tanning time is over using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) The skin analysis unit analyzes images of the user's skin, A weather data acquisition unit that acquires weather data, A generation unit that generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit, A monitoring unit that monitors tanning time in real time based on the tanning plan generated by the generation unit, The system includes a notification unit that provides notification when the sunburn time monitored by the monitoring unit has ended. A system characterized by the following features. (Note 2) The generating unit is Generate a custom tanning plan based on the color swatch selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, Adjust sun exposure time based on daily weather changes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, The system notifies the user when their sunbathing time is over. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned skin analysis unit is The analysis takes into account the user's skin type and allergy history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned weather data acquisition unit, Obtain weather data based on the user's location information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned skin analysis unit is The system estimates the user's emotions and adjusts the timing of skin analysis based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned skin analysis unit is The system analyzes the user's past skin trouble history and selects the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned skin analysis unit is During skin analysis, filtering is performed based on the user's current lifestyle and diet. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned skin analysis unit is It estimates the user's emotions and prioritizes skin areas for analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned skin analysis unit is During skin analysis, the system prioritizes obtaining highly relevant analysis data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned skin analysis unit is During skin analysis, the system analyzes the user's social media activity and obtains relevant skin data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned weather data acquisition unit, The system estimates the user's emotions and adjusts the timing of weather data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned weather data acquisition unit, Analyze past weather data and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned weather data acquisition unit, When acquiring weather data, filtering is performed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned weather data acquisition unit, The system estimates the user's emotions and determines the priority of weather data to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned weather data acquisition unit, When acquiring weather data, the system prioritizes acquiring data that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned weather data acquisition unit, When acquiring weather data, the system analyzes the user's social media activity and retrieves relevant weather data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the user's emotions and adjusts how the tanning plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a tanning plan, adjust the level of detail in the plan based on the user's skin condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a tanning plan, different generation algorithms are applied depending on the color swatch selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the tanning plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a tanning plan, the system prioritizes plans based on the user's past plan history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating tanning plans, the order of the plans is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, The system estimates user sentiment and adjusts monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, the system analyzes the user's skin condition in real time to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, the monitoring process takes into account the user's activity status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, It estimates the user's emotions and adjusts the order in which monitoring results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, the monitoring process takes into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, referencing relevant user literature improves the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, the timing of the notifications will be adjusted based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The skin analysis unit analyzes images of the user's skin, A weather data acquisition unit that acquires weather data, A generation unit that generates a custom tanning plan based on the data obtained by the skin analysis unit and the weather data acquisition unit, A monitoring unit that monitors tanning time in real time based on the tanning plan generated by the generation unit, The system includes a notification unit that provides notification when the sunburn time monitored by the monitoring unit has ended. A system characterized by the following features.

2. The generating unit is Generate a custom tanning plan based on the color swatch selected by the user. The system according to feature 1.

3. The monitoring unit, Adjust sun exposure time based on daily weather changes. The system according to feature 1.

4. The aforementioned notification unit, The system notifies the user when their sunbathing time is over. The system according to feature 1.

5. The aforementioned skin analysis unit is The analysis takes into account the user's skin type and allergy history. The system according to feature 1.

6. The aforementioned weather data acquisition unit, Obtain weather data based on the user's location information. The system according to feature 1.

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

8. The aforementioned skin analysis unit is The system analyzes the user's past skin trouble history and selects the optimal analysis method. The system according to feature 1.

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