System

Abstract

An object of a system according to an embodiment is to make a proposal for improving a health condition or a life rhythm of a user.SOLUTION: A system includes a face color measurement part, a weight measurement part, an analysis part, and a provision part. The face color measurement unit measures a face color of a user. The body weight measurement unit measures a body weight of a user. The analysis part analyzes the data measured by the face color measurement part and the body weight measurement part, and proposes improvement of a health condition and a life rhythm. The providing unit provides the user with the proposal obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately collect and analyze data to suggest improvements to a user's health condition and lifestyle, and there is room for improvement.

[0005] The system according to the embodiment aims to make suggestions for improving the health condition and lifestyle of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a complexion measurement unit, a weight measurement unit, an analysis unit, and a provision unit. The complexion measurement unit measures the complexion of the user. The weight measurement unit measures the weight of the user. The analysis unit analyzes the data measured by the complexion measurement unit and the weight measurement unit and makes suggestions for improving the user's health condition and lifestyle rhythm. The provision unit provides the suggestions obtained by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide suggestions for improving the user's health condition and lifestyle. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A smart mirror according to an embodiment of the present invention is a system that measures a user's complexion and weight, and a generation AI uses the data to suggest improvements to their health and lifestyle. The smart mirror measures the user's complexion and weight, and the generation AI analyzes the data to suggest improvements to their health and lifestyle. The smart mirror also generates advice incorporating the latest health information and research findings. Furthermore, by connecting with external devices such as smartwatches, the number of measurement items can be increased to generate advice that includes mental and physical care. For example, when a user stands in front of the smart mirror, the mirror uses image recognition technology to measure their complexion and weight. For example, it can detect changes in complexion and weight gain or loss. This data is then input into the generation AI. The generation AI then analyzes the input data and makes suggestions for improving that day's health and lifestyle. For example, if the user's complexion is poor, the AI ​​may point out possible causes of sleep deprivation or stress and suggest improvements. Furthermore, if the user is gaining weight, the AI ​​may provide dietary and exercise advice. Furthermore, the generation AI generates advice incorporating the latest health information and research findings. For example, the AI ​​provides appropriate advice to the user based on health methods and nutritional information revealed in the latest research. In addition, the number of measurement items can be increased by connecting to external devices such as smartwatches. For example, heart rate and sleep data can be obtained from a smartwatch, and the generation AI can analyze this data to provide more detailed suggestions for improving health and lifestyle habits. This allows the smart mirror to comprehensively grasp the user's health status and provide appropriate advice, thereby achieving well-being. For example, when a user stands in front of the mirror, image recognition technology is used to measure their complexion and weight. Changes in complexion and weight gain or loss can be detected. This data is input into the generation AI, which analyzes the input data and makes suggestions for improving that day's health and lifestyle habits. If the user's complexion is poor, it will point out possible reasons for lack of sleep or stress and suggest improvements. If the user is gaining weight, it will provide dietary and exercise advice. The generation AI generates advice based on the latest health information and research findings.The smart mirror provides users with appropriate advice based on health and nutritional information revealed by the latest research. It can also measure more items by connecting to external devices such as smartwatches. Heart rate and sleep data are acquired from the smartwatch, and the AI ​​analyzes this data to provide more detailed health status and lifestyle improvement suggestions. This allows the smart mirror to comprehensively grasp the user's health status and provide appropriate advice, thereby achieving well-being.

[0029] A smart mirror according to an embodiment includes a complexion measurement unit, a weight measurement unit, an analysis unit, and a providing unit. The complexion measurement unit measures the user's complexion. The complexion measurement unit analyzes facial color tone using, for example, an RGB camera. The complexion measurement unit can also detect changes in the user's complexion. For example, the complexion measurement unit can analyze facial color tone and detect changes in complexion. The weight measurement unit measures the user's weight. The weight measurement unit uses, for example, a scale built into the smart mirror. The weight measurement unit can also detect weight gain or loss. For example, the weight measurement unit can detect weight gain or loss. The analysis unit uses a generating AI to analyze data measured by the complexion measurement unit and the weight measurement unit and make suggestions for improving the user's health condition and lifestyle rhythm. For example, the analysis unit uses a generating AI to analyze data measured by the complexion measurement unit and the weight measurement unit and make suggestions for improving the user's health condition and lifestyle rhythm. The analysis unit can also generate advice that incorporates the latest health information and research results. For example, the analysis unit provides appropriate advice to the user based on health methods and nutritional information revealed in the latest research. The provision unit provides the user with the suggestions obtained by the analysis unit. The provision unit provides appropriate advice to the user based on, for example, data analyzed by the generation AI. This allows the smart mirror according to the embodiment to measure the user's complexion and weight and make suggestions for improving their health condition and lifestyle. For example, it can measure the user's complexion and weight and make suggestions for improving their health condition and lifestyle. This allows the smart mirror to comprehensively grasp the user's health condition and provide appropriate advice.

[0030] The facial color measurement unit can analyze facial color tone using an RGB camera. Specifications of the RGB camera include, but are not limited to, resolution and frame rate. The facial color measurement unit can analyze facial color tone using an RGB camera. For example, the facial color measurement unit can analyze facial color tone using an RGB camera and detect changes in facial color. The facial color measurement unit can also analyze facial color tone using an RGB camera and detect changes in facial color. Thus, using an RGB camera improves the accuracy of facial color analysis. Some or all of the above-described processing in the facial color measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial color measurement unit can input facial image data acquired by the RGB camera to the generation AI and cause the generation AI to analyze facial color.

[0031] The weight measurement unit may use a scale built into the smart mirror. The smart mirror may have specifications such as, but not limited to, the type of built-in sensor and display function. The weight measurement unit may use, for example, a scale built into the smart mirror. For example, the weight measurement unit may measure weight using the scale built into the smart mirror. The weight measurement unit may also detect weight gain or loss using the scale built into the smart mirror. This makes weight measurement easier by using the scale built into the smart mirror. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the weight measurement unit may input weight data acquired by the scale built into the smart mirror into the generation AI, and have the generation AI analyze the weight.

[0032] The analysis unit can generate advice incorporating the latest health information and research results. Examples of the latest health information include, but are not limited to, reliable databases and the latest research papers. The analysis unit can generate advice incorporating the latest health information and research results. For example, the analysis unit provides appropriate advice to the user based on health practices and nutritional information revealed in the latest research. The analysis unit can also generate advice incorporating the latest health information and research results. By incorporating the latest health information and research results, more appropriate advice can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the latest health information and research results into the generation AI and cause the generation AI to generate advice.

[0033] The analysis unit can acquire heart rate and sleep data from a smartwatch or other external device and analyze the data to provide detailed suggestions for improving health and lifestyle rhythms. Smartwatches may have specifications, such as compatible manufacturers and measurable data types, but these examples are not limited to these. The analysis unit can acquire heart rate and sleep data from a smartwatch or other external device and analyze the data to provide detailed suggestions for improving health and lifestyle rhythms. For example, the analysis unit can acquire heart rate data from the smartwatch and analyze it to evaluate health. The analysis unit can also acquire sleep data from the smartwatch and analyze it to provide suggestions for improving lifestyle rhythms. This allows for more detailed suggestions for improving health and lifestyle rhythms by incorporating data from external devices. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input heart rate data and sleep data acquired from the smartwatch into a generation AI and have the generation AI analyze the data.

[0034] The providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI. The generation AI includes, for example, specifications such as the machine learning model to be used and training data, but is not limited to these examples. The providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI. For example, the providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI. The providing unit can also provide appropriate advice to the user based on the data analyzed by the generation AI. This makes it possible to provide appropriate advice to the user based on the data analyzed by the generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI.

[0035] The complexion measurement unit can improve the accuracy of the complexion measurement by referring to the user's past complexion data. Examples of past complexion data include, but are not limited to, specifications such as database construction and data update frequency. The complexion measurement unit can improve the accuracy of the complexion measurement by referring to the user's past complexion data. For example, the complexion measurement unit can improve the accuracy of the complexion measurement by referring to the user's past complexion data. For example, the complexion measurement unit can improve the accuracy of the measurement by referring to the user's past complexion data and taking into account daily fluctuations. The complexion measurement unit can also analyze the user's past complexion data and make corrections according to seasonal and environmental changes. The complexion measurement unit can also improve the accuracy of the measurement by predicting complexion changes in specific time periods or situations based on the user's past complexion data. Thus, by referring to the past complexion data, the measurement accuracy is improved. Some or all of the above-described processing in the complexion measurement unit may be performed using, or without, AI. For example, the complexion measurement unit can input past complexion data into the generation AI and cause the generation AI to improve the measurement accuracy.

[0036] The facial color measurement unit can apply an algorithm to correct for the influence of the user's skin type and ambient light when measuring facial color. Examples of skin types include, but are not limited to, classification methods such as dry skin and oily skin. Examples of ambient light include, but are not limited to, the influence of light intensity and color temperature. The facial color measurement unit can apply an algorithm to correct for the influence of the user's skin type and ambient light when measuring facial color. For example, the facial color measurement unit can register the user's skin type (dry skin, oily skin, etc.) in advance and perform correction when measuring facial color based on that information. The facial color measurement unit can also detect the color temperature and brightness of ambient light in real time and perform correction when measuring facial color. The facial color measurement unit can also simultaneously consider the user's skin type and the influence of ambient light and apply an optimal correction algorithm. This improves measurement accuracy by correcting for the influence of skin type and ambient light. Some or all of the above-described processing in the facial color measurement unit can be performed using, for example, AI, or without AI. For example, the complexion measurement unit can input data on skin type and ambient light into the generation AI and have the generation AI apply a correction algorithm.

[0037] The facial color measurement unit can detect changes in the user's facial expression during facial color measurement and estimate the stress level. Examples of detection methods for changes in facial expression include, but are not limited to, movements of facial features and types of facial expressions. Examples of evaluation criteria for stress levels include, but are not limited to, heart rate variability and skin galvanic response. For example, the facial color measurement unit can detect changes in the user's facial expression during facial color measurement and estimate the stress level. For example, the facial color measurement unit can detect changes in the user's facial expression in real time and estimate the stress level. The facial color measurement unit can also analyze fluctuations in the stress level by referencing the user's past facial expression data. The facial color measurement unit can also combine changes in the user's facial expression and changes in facial color to more accurately estimate the stress level. This allows the stress level to be estimated by detecting changes in facial expression. Some or all of the above-described processing in the facial color measurement unit may be performed using, for example, AI, or without AI. For example, the facial color measurement unit can input data on changes in facial expression into the generation AI and have the generation AI estimate the stress level.

[0038] The complexion measurement unit can correct environmental factors based on the user's geographical location information when measuring complexion. Examples of geographical location information include, but are not limited to, GPS data and location information services. The complexion measurement unit corrects environmental factors based on the user's geographical location information when measuring complexion. For example, if the user is at high altitude, the complexion measurement unit corrects the complexion measurement taking into account the influence of oxygen concentration. Furthermore, if the user is in an urban area, the complexion measurement unit can correct the complexion measurement taking into account the influence of air pollution. Furthermore, if the user is at the seaside, the complexion measurement unit can correct the complexion measurement taking into account the influence of humidity. By taking the geographical location information into account, environmental factors are corrected, improving measurement accuracy. Some or all of the above-described processing in the complexion measurement unit may be performed using, or without, AI. For example, the complexion measurement unit can input the geographical location information into the generation AI and cause the generation AI to correct the environmental factors.

[0039] The facial color measurement unit can analyze the user's social media activity during facial color measurement to detect signs of stress or fatigue. Social media activity includes, but is not limited to, analysis methods such as posting content and activity frequency. The facial color measurement unit can analyze the user's social media activity during facial color measurement to detect signs of stress or fatigue. For example, the facial color measurement unit can analyze the content of the user's social media posts to detect signs of stress or fatigue. The facial color measurement unit can also analyze the frequency of the user's social media activity to detect signs of stress or fatigue. The facial color measurement unit can also analyze the user's interactions with friends on social media to detect signs of stress or fatigue. In this way, signs of stress or fatigue can be detected by analyzing social media activity. Some or all of the above-described processing in the facial color measurement unit can be performed using, for example, AI, or without AI. For example, the facial color measurement unit can input social media activity data into a generation AI and cause the generation AI to detect signs of stress or fatigue.

[0040] The complexion measurement unit can customize the measurement method by reflecting the user's past feedback when measuring complexion. Examples of past feedback include, but are not limited to, methods of obtaining feedback such as survey results and user reviews. The complexion measurement unit can customize the measurement method by reflecting the user's past feedback when measuring complexion. For example, the complexion measurement unit customizes the complexion measurement method based on feedback provided by the user in the past. The complexion measurement unit can also analyze the user's past feedback and make adjustments to improve measurement accuracy. The complexion measurement unit can also optimize the measurement timing and method by reflecting the user's past feedback. By reflecting the past feedback, the measurement method can be customized and accuracy can be improved. Some or all of the above-described processing in the complexion measurement unit may be performed using, for example, AI, or without AI. For example, the complexion measurement unit can input past feedback into a generation AI and have the generation AI customize the measurement method.

[0041] The weight measurement unit can improve measurement accuracy by referring to the user's past weight data when measuring weight. Past weight data includes, but is not limited to, specifications such as database construction and data update frequency. For example, the weight measurement unit can improve measurement accuracy by referring to the user's past weight data when measuring weight. For example, the weight measurement unit can refer to the user's weight data from the past week and improve measurement accuracy by taking daily fluctuations into account. The weight measurement unit can also analyze the user's weight data from the past month and make corrections according to seasonal and environmental changes. The weight measurement unit can also predict weight changes in specific time periods or situations based on the user's past weight data, thereby improving measurement accuracy. By referring to past weight data, measurement accuracy is improved. Some or all of the above-described processing in the weight measurement unit may be performed using, or without, AI. For example, the weight measurement unit can input past weight data into a generation AI and have the generation AI improve measurement accuracy.

[0042] The weight measurement unit can analyze the cause of weight fluctuation based on the user's diet and exercise history when measuring weight. The diet history can be obtained, for example, through a diet record app or calorie calculation, but is not limited to these examples. The exercise history can be obtained, for example, through a fitness tracker or exercise log, but is not limited to these examples. The weight measurement unit can analyze the cause of weight fluctuation based on the user's diet and exercise history when measuring weight. For example, the weight measurement unit can refer to the user's diet history to analyze the cause of weight fluctuation. The weight measurement unit can also refer to the user's exercise history to analyze the cause of weight fluctuation. The weight measurement unit can also combine the user's diet and exercise history to comprehensively analyze the cause of weight fluctuation. This allows the cause of weight fluctuation to be analyzed by taking the diet and exercise history into consideration. Some or all of the above-described processing in the weight measurement unit can be performed using, for example, AI, or without AI. For example, the weight measurement unit can input diet and exercise history data into a generation AI and have the generation AI analyze the cause of weight fluctuation.

[0043] The weight measurement unit may be added with a function to simultaneously measure the user's body fat percentage and muscle mass when measuring weight. Measurement methods for body fat percentage include, but are not limited to, bioimpedance and dual-energy X-ray absorptiometry. Measurement methods for muscle mass include, but are not limited to, muscle mass meters and MRI scans. The weight measurement unit may be added with a function to simultaneously measure the user's body fat percentage and muscle mass when measuring weight. For example, the weight measurement unit may simultaneously measure the body fat percentage when measuring weight to evaluate the overall health status. The weight measurement unit may also simultaneously measure muscle mass when measuring weight to evaluate the effectiveness of exercise. The weight measurement unit may also simultaneously measure the body fat percentage and muscle mass when measuring weight to evaluate a balanced health status. By simultaneously measuring the body fat percentage and muscle mass, the overall health status can be evaluated. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or without AI. For example, the weight measurement unit can input data on body fat percentage and muscle mass into the generation AI, allowing the generation AI to perform an overall health assessment.

[0044] The weight measurement unit can provide dietary and exercise advice by taking into account the user's geographical location information when measuring the user's weight. Examples of geographical location information include, but are not limited to, methods of obtaining the information, such as GPS data and location information services. For example, the weight measurement unit can provide dietary and exercise advice by taking into account the user's geographical location information when measuring the user's weight. For example, if the user is in an urban area, the weight measurement unit can provide information on nearby gyms and health food stores. Furthermore, if the user is in a suburban area, the weight measurement unit can also suggest exercise methods that utilize natural environments. Furthermore, if the user is traveling, the weight measurement unit can also suggest local healthy dietary and exercise methods. By taking the geographical location information into account, more appropriate dietary and exercise advice can be provided. Some or all of the above-described processing in the weight measurement unit may be performed, for example, using AI, or may be performed without AI. For example, the weight measurement unit can input geographical location information into a generation AI and cause the generation AI to provide dietary and exercise advice.

[0045] The weight measurement unit can analyze the user's social media activity at the time of weight measurement to detect dietary and exercise patterns. Social media activity includes, but is not limited to, analysis methods such as posting content and activity frequency. For example, the weight measurement unit can analyze the user's social media activity at the time of weight measurement to detect dietary and exercise patterns. For example, the weight measurement unit can analyze the user's social media posting content to detect dietary and exercise patterns. The weight measurement unit can also analyze the frequency of the user's social media activity to detect dietary and exercise patterns. The weight measurement unit can also analyze the user's interactions with friends on social media to detect dietary and exercise patterns. In this way, dietary and exercise patterns can be detected by analyzing social media activity. Some or all of the above-described processing in the weight measurement unit can be performed, for example, using AI or without AI. For example, the weight measurement unit can input social media activity data to a generation AI and cause the generation AI to detect dietary and exercise patterns.

[0046] The weight measurement unit can customize the measurement method by reflecting the user's past feedback when measuring weight. Past feedback includes, but is not limited to, methods of obtaining survey results and user reviews. The weight measurement unit can customize the measurement method by reflecting the user's past feedback when measuring weight. For example, the weight measurement unit customizes the weight measurement method based on feedback provided by the user in the past. The weight measurement unit can also analyze the user's past feedback and make adjustments to improve measurement accuracy. The weight measurement unit can also optimize the measurement timing and method by reflecting the user's past feedback. In this way, the measurement method can be customized by reflecting the past feedback, thereby improving accuracy. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the weight measurement unit can input past feedback into a generation AI and have the generation AI customize the measurement method.

[0047] The analysis unit may apply an algorithm that incorporates the latest health information and research results in real time during analysis. Examples of the latest health information include, but are not limited to, reliable databases and the latest research papers. Examples of research results include, but are not limited to, methods of obtaining academic papers and clinical trial results. For example, the analysis unit may apply an algorithm that incorporates the latest health information and research results in real time during analysis. For example, the analysis unit may acquire the latest health information in real time and reflect it in the analysis results. The analysis unit may also acquire the latest research results in real time and reflect it in the analysis results. The analysis unit may also combine the latest health information and research results to generate optimal advice. By incorporating the latest health information and research results in real time, more appropriate advice can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit may input the latest health information and research results into the generation AI and cause the generation AI to incorporate them in real time.

[0048] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during analysis. Examples of past health data include, but are not limited to, data obtained from electronic medical records or health management apps. The analysis unit can improve the accuracy of the analysis by, for example, referring to the user's past health data during analysis. For example, the analysis unit can refer to the user's past week's health data and improve the accuracy of the analysis by taking daily fluctuations into account. The analysis unit can also analyze the user's past month's health data and make corrections based on seasonal or environmental changes. The analysis unit can also predict the user's health status for specific time periods or situations based on the user's past health data, thereby improving the accuracy of the analysis. By referring to past health data, the accuracy of the analysis can be improved. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input past health data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0049] The analysis unit can generate personalized advice by taking into account the user's lifestyle and habits during analysis. Lifestyle includes, but is not limited to, evaluation methods such as sleep patterns and meal timing. Habits include, but are not limited to, evaluation methods such as exercise habits and smoking habits. The analysis unit can generate personalized advice by taking into account the user's lifestyle and habits during analysis. For example, the analysis unit generates optimal advice by taking into account the user's lifestyle (wake-up time, bedtime, etc.). The analysis unit can also generate personalized advice by taking into account the user's habits (meal times, exercise times, etc.). The analysis unit can also generate comprehensive advice by combining the user's lifestyle and habits. This allows personalized advice to be provided by taking into account the user's lifestyle and habits. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the lifestyle and habits into a generation AI and cause the generation AI to generate personalized advice.

[0050] During analysis, the analysis unit can reflect environmental factors in the analysis by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, acquisition methods such as GPS data and location information services. During analysis, the analysis unit can reflect environmental factors in the analysis by taking into account the user's geographical location information. For example, when the user is at high altitude, the analysis unit can consider the influence of oxygen concentration when performing the analysis. Furthermore, when the user is in an urban area, the analysis unit can also consider the influence of air pollution when performing the analysis. Furthermore, when the user is at the seaside, the analysis unit can also consider the influence of humidity when performing the analysis. By taking geographical location information into account, environmental factors are reflected in the analysis, improving the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical location information into the generation AI and cause the generation AI to analyze environmental factors.

[0051] The analysis unit can analyze the user's social media activity during the analysis and reflect signs of stress or fatigue in the analysis. Social media activity analysis methods include, but are not limited to, the content of posts and the frequency of activity. For example, the analysis unit can analyze the user's social media activity during the analysis and reflect signs of stress or fatigue in the analysis. For example, the analysis unit can analyze the content of the user's social media posts and reflect signs of stress or fatigue in the analysis. The analysis unit can also analyze the frequency of the user's social media activity and reflect signs of stress or fatigue in the analysis. The analysis unit can also analyze the user's interactions with friends on social media and reflect signs of stress or fatigue in the analysis. This allows signs of stress or fatigue to be reflected in the analysis by analyzing social media activity, improving the accuracy of the analysis. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input social media activity data into a generation AI and have the generation AI analyze signs of stress or fatigue.

[0052] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback during analysis. Examples of past feedback include, but are not limited to, methods of obtaining survey results and user reviews. The analysis unit can customize the analysis algorithm by reflecting the user's past feedback during analysis. For example, the analysis unit customizes the analysis algorithm based on feedback previously provided by the user. The analysis unit can also analyze the user's past feedback and make adjustments to improve analysis accuracy. The analysis unit can also optimize the timing and method of analysis by reflecting the user's past feedback. By doing so, the analysis algorithm can be customized and accuracy improved by reflecting the past feedback. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past feedback into a generation AI and have the generation AI customize the analysis algorithm.

[0053] The providing unit can provide optimal advice by referring to the user's past advice history when providing the advice. Examples of past advice history include, but are not limited to, methods of obtaining advice records and history databases. For example, the providing unit can provide optimal advice by referring to the user's past advice history when providing the advice. For example, the providing unit can provide optimal advice by referring to the user's advice history for the past week. The providing unit can also analyze the user's advice history for the past month and provide advice according to changes in the season or environment. The providing unit can also provide optimal advice for a specific time period or situation based on the user's past advice history. This allows optimal advice to be provided by referring to the past advice history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input past advice history into a generation AI and cause the generation AI to provide optimal advice.

[0054] The providing unit can customize the advice based on the user's current living situation and areas of interest when providing the advice. Examples of the current living situation include, but are not limited to, methods for evaluating lifestyle habits and health status. Examples of areas of interest include, but are not limited to, methods for identifying hobbies and topics of interest. The providing unit, for example, customizes the advice based on the user's current living situation and areas of interest when providing the advice. For example, the providing unit provides optimal advice taking into account the user's current living situation (work, family, etc.). The providing unit can also provide personalized advice taking into account the user's areas of interest (health, fitness, etc.). The providing unit can also provide comprehensive advice by combining the user's living situation and areas of interest. This allows for more appropriate advice to be provided by customizing the advice based on the user's current living situation and areas of interest. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data on the user's current living situation and areas of interest into a generation AI and cause the generation AI to customize the advice.

[0055] The providing unit can improve the content of the advice by reflecting user feedback when providing the advice. Examples of feedback include, but are not limited to, methods of obtaining survey results and user reviews. For example, the providing unit can improve the content of the advice by reflecting user feedback when providing the advice. For example, the providing unit can improve the content of the advice based on feedback previously provided by the user. The providing unit can also analyze the user's past feedback and make adjustments to improve the accuracy of the advice. The providing unit can also optimize the timing and method of providing the advice by reflecting the user's past feedback. By reflecting the feedback, the content of the advice is improved and its accuracy is improved. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input feedback to a generating AI and cause the generating AI to improve the content of the advice.

[0056] The providing unit can provide optimal advice by taking into account the user's geographical location information when providing the advice. Examples of geographical location information include, but are not limited to, acquisition methods such as GPS data and location information services. For example, the providing unit can provide optimal advice by taking into account the user's geographical location information when providing the advice. For example, if the user is in an urban area, the providing unit can provide information on nearby gyms and health food stores. Furthermore, if the user is in a suburban area, the providing unit can also suggest exercise methods that utilize natural environments. Furthermore, if the user is traveling, the providing unit can also suggest local healthy diets and exercise methods. This allows for more appropriate advice to be provided by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information into a generating AI and cause the generating AI to provide optimal advice.

[0057] The providing unit can analyze the user's social media activity at the time of providing the advice and provide relevant advice. Social media activity includes, but is not limited to, analysis methods such as post content and activity frequency. For example, the providing unit can analyze the user's social media activity at the time of providing the advice and provide relevant advice. For example, the providing unit can analyze the user's social media post content and provide relevant advice. The providing unit can also analyze the frequency of the user's social media activity and provide relevant advice. The providing unit can also analyze the user's interactions with friends on social media and provide relevant advice. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input social media activity data to a generation AI and cause the generation AI to provide relevant advice.

[0058] The providing unit can customize the content of the advice by reflecting the user's past feedback when providing the advice. Examples of past feedback include, but are not limited to, methods of obtaining survey results and user reviews. The providing unit, for example, customizes the content of the advice by reflecting the user's past feedback when providing the advice. For example, the providing unit customizes the content of the advice based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and make adjustments to improve the accuracy of the advice. The providing unit can also optimize the timing and method of providing the advice by reflecting the user's past feedback. In this way, the content of the advice is customized and its accuracy is improved by reflecting the past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past feedback into a generating AI and cause the generating AI to customize the content of the advice.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The complexion measurement unit can simultaneously measure the moisture content of the user's skin when measuring the user's complexion. For example, the complexion measurement unit measures the moisture content of the skin while measuring the complexion and evaluates the condition of dry or oily skin. The complexion measurement unit can also track changes in the moisture content of the skin and provide skin care advice according to changes in the season or environment. Furthermore, the complexion measurement unit can send the moisture content data of the user's skin to the analysis unit, which can be used to evaluate the user's overall health condition. This allows the complexion measurement unit to grasp the user's skin condition in more detail and provide appropriate advice.

[0061] The weight measurement unit can measure the user's bone density at the same time as measuring the user's weight. For example, the weight measurement unit measures bone density at the time of weight measurement to evaluate the health of the bones. The weight measurement unit can also track changes in bone density and provide advice on calcium intake and exercise. Furthermore, the weight measurement unit can transmit the user's bone density data to the analysis unit to help evaluate the user's overall health condition. This allows the weight measurement unit to grasp the user's bone health condition in more detail and provide appropriate advice.

[0062] The weight measurement unit can measure the user's body temperature at the same time as measuring the user's weight. For example, the weight measurement unit can measure the user's body temperature when measuring the user's weight and detect signs of fever or poor health. The weight measurement unit can also track changes in body temperature and provide health management advice according to changes in season or environment. Furthermore, the weight measurement unit can send the user's body temperature data to the analysis unit to help evaluate the user's overall health condition. This allows the weight measurement unit to take the user's body temperature into consideration when measuring the user's weight and provide appropriate advice.

[0063] The analysis unit can generate personalized advice taking into account the user's lifestyle and habits. For example, the analysis unit generates optimal advice taking into account the user's sleep patterns and meal timings. The analysis unit can also generate personalized advice taking into account the user's exercise habits and smoking habits. Furthermore, the analysis unit can combine the user's lifestyle and habits to generate comprehensive advice. This allows personalized advice to be provided by taking into account the user's lifestyle and habits.

[0064] The providing unit can provide optimal advice by referring to the user's past advice history. For example, the providing unit can provide optimal advice by referring to the user's advice history for the past week. The providing unit can also analyze the user's advice history for the past month and provide advice according to changes in the season or environment. Furthermore, the providing unit can provide optimal advice for a specific time period or situation based on the user's past advice history. In this way, optimal advice can be provided by referring to the past advice history.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The facial color measurement unit measures the user's facial color. For example, it can use an RGB camera to analyze the color tone of the face and detect changes in facial color. Step 2: The weight measurement unit measures the user's weight. For example, weight gain or loss can be detected using a scale built into the smart mirror. Step 3: The analysis unit uses the generation AI to analyze the data measured by the complexion measurement unit and weight measurement unit, and makes suggestions for improving health and lifestyle habits. For example, it can generate advice that incorporates the latest health information and research findings. Step 4: The providing unit provides the user with the suggestions obtained by the analyzing unit. For example, the providing unit provides the user with appropriate advice based on the data analyzed by the generating AI.

[0067] (Example 2) A smart mirror according to an embodiment of the present invention is a system that measures a user's complexion and weight, and a generation AI uses the data to suggest improvements to their health and lifestyle. The smart mirror measures the user's complexion and weight, and the generation AI analyzes the data to suggest improvements to their health and lifestyle. The smart mirror also generates advice incorporating the latest health information and research findings. Furthermore, by connecting with external devices such as smartwatches, the number of measurement items can be increased to generate advice that includes mental and physical care. For example, when a user stands in front of the smart mirror, the mirror uses image recognition technology to measure their complexion and weight. For example, it can detect changes in complexion and weight gain or loss. This data is then input into the generation AI. The generation AI then analyzes the input data and makes suggestions for improving that day's health and lifestyle. For example, if the user's complexion is poor, the AI ​​may point out possible causes of sleep deprivation or stress and suggest improvements. Furthermore, if the user is gaining weight, the AI ​​may provide dietary and exercise advice. Furthermore, the generation AI generates advice incorporating the latest health information and research findings. For example, the AI ​​provides appropriate advice to the user based on health methods and nutritional information revealed in the latest research. In addition, the number of measurement items can be increased by connecting to external devices such as smartwatches. For example, heart rate and sleep data can be obtained from a smartwatch, and the generation AI can analyze this data to provide more detailed suggestions for improving health and lifestyle habits. This allows the smart mirror to comprehensively grasp the user's health status and provide appropriate advice, thereby achieving well-being. For example, when a user stands in front of the mirror, image recognition technology is used to measure their complexion and weight. Changes in complexion and weight gain or loss can be detected. This data is input into the generation AI, which analyzes the input data and makes suggestions for improving that day's health and lifestyle habits. If the user's complexion is poor, it will point out possible reasons for lack of sleep or stress and suggest improvements. If the user is gaining weight, it will provide dietary and exercise advice. The generation AI generates advice based on the latest health information and research findings.The smart mirror provides users with appropriate advice based on health and nutritional information revealed by the latest research. It can also measure more items by connecting to external devices such as smartwatches. Heart rate and sleep data are acquired from the smartwatch, and the AI ​​analyzes this data to provide more detailed health status and lifestyle improvement suggestions. This allows the smart mirror to comprehensively grasp the user's health status and provide appropriate advice, thereby achieving well-being.

[0068] A smart mirror according to an embodiment includes a complexion measurement unit, a weight measurement unit, an analysis unit, and a providing unit. The complexion measurement unit measures the user's complexion. The complexion measurement unit analyzes facial color tone using, for example, an RGB camera. The complexion measurement unit can also detect changes in the user's complexion. For example, the complexion measurement unit can analyze facial color tone and detect changes in complexion. The weight measurement unit measures the user's weight. The weight measurement unit uses, for example, a scale built into the smart mirror. The weight measurement unit can also detect weight gain or loss. For example, the weight measurement unit can detect weight gain or loss. The analysis unit uses a generating AI to analyze data measured by the complexion measurement unit and the weight measurement unit and make suggestions for improving the user's health condition and lifestyle rhythm. For example, the analysis unit uses a generating AI to analyze data measured by the complexion measurement unit and the weight measurement unit and make suggestions for improving the user's health condition and lifestyle rhythm. The analysis unit can also generate advice that incorporates the latest health information and research results. For example, the analysis unit provides appropriate advice to the user based on health methods and nutritional information revealed in the latest research. The provision unit provides the user with the suggestions obtained by the analysis unit. The provision unit provides appropriate advice to the user based on, for example, data analyzed by the generation AI. This allows the smart mirror according to the embodiment to measure the user's complexion and weight and make suggestions for improving their health condition and lifestyle. For example, it can measure the user's complexion and weight and make suggestions for improving their health condition and lifestyle. This allows the smart mirror to comprehensively grasp the user's health condition and provide appropriate advice.

[0069] The facial color measurement unit can analyze facial color tone using an RGB camera. Specifications of the RGB camera include, but are not limited to, resolution and frame rate. The facial color measurement unit can analyze facial color tone using an RGB camera. For example, the facial color measurement unit can analyze facial color tone using an RGB camera and detect changes in facial color. The facial color measurement unit can also analyze facial color tone using an RGB camera and detect changes in facial color. Thus, using an RGB camera improves the accuracy of facial color analysis. Some or all of the above-described processing in the facial color measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial color measurement unit can input facial image data acquired by the RGB camera to the generation AI and cause the generation AI to analyze facial color.

[0070] The weight measurement unit may use a scale built into the smart mirror. The smart mirror may have specifications such as, but not limited to, the type of built-in sensor and display function. The weight measurement unit may use, for example, a scale built into the smart mirror. For example, the weight measurement unit may measure weight using the scale built into the smart mirror. The weight measurement unit may also detect weight gain or loss using the scale built into the smart mirror. This makes weight measurement easier by using the scale built into the smart mirror. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the weight measurement unit may input weight data acquired by the scale built into the smart mirror into the generation AI, and have the generation AI analyze the weight.

[0071] The analysis unit can generate advice incorporating the latest health information and research results. Examples of the latest health information include, but are not limited to, reliable databases and the latest research papers. The analysis unit can generate advice incorporating the latest health information and research results. For example, the analysis unit provides appropriate advice to the user based on health practices and nutritional information revealed in the latest research. The analysis unit can also generate advice incorporating the latest health information and research results. By incorporating the latest health information and research results, more appropriate advice can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the latest health information and research results into the generation AI and cause the generation AI to generate advice.

[0072] The analysis unit can acquire heart rate and sleep data from a smartwatch or other external device and analyze the data to provide detailed suggestions for improving health and lifestyle rhythms. Smartwatches may have specifications, such as compatible manufacturers and measurable data types, but these examples are not limited to these. The analysis unit can acquire heart rate and sleep data from a smartwatch or other external device and analyze the data to provide detailed suggestions for improving health and lifestyle rhythms. For example, the analysis unit can acquire heart rate data from the smartwatch and analyze it to evaluate health. The analysis unit can also acquire sleep data from the smartwatch and analyze it to provide suggestions for improving lifestyle rhythms. This allows for more detailed suggestions for improving health and lifestyle rhythms by incorporating data from external devices. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input heart rate data and sleep data acquired from the smartwatch into a generation AI and have the generation AI analyze the data.

[0073] The providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI. The generation AI includes, for example, specifications such as the machine learning model to be used and training data, but is not limited to these examples. The providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI. For example, the providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI. The providing unit can also provide appropriate advice to the user based on the data analyzed by the generation AI. This makes it possible to provide appropriate advice to the user based on the data analyzed by the generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can provide appropriate advice to the user based on the data analyzed by the generation AI.

[0074] The facial color measurement unit can estimate the user's emotions and adjust the timing of facial color measurement based on the estimated user emotions. Emotion estimation includes, but is not limited to, methods such as facial expression analysis and voice analysis. For example, the facial color measurement unit estimates the user's emotions and adjusts the timing of facial color measurement based on the estimated user emotions. For example, if the user is feeling stressed, the facial color measurement unit sets the measurement timing to nighttime to measure the user's facial color in a relaxed state. Furthermore, if the user is relaxed, the facial color measurement unit can measure the user's facial color in the morning and evaluate the impact on daytime activities. Furthermore, if the user is in a hurry, the facial color measurement unit can measure the user's facial color in a short time and provide a quick result. This enables more accurate measurement by adjusting the timing of facial color measurement based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the facial color measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial color measurement unit may input user emotion data to the generation AI and cause the generation AI to adjust the measurement timing based on the emotion.

[0075] The complexion measurement unit can improve the accuracy of the complexion measurement by referring to the user's past complexion data. Examples of past complexion data include, but are not limited to, specifications such as database construction and data update frequency. The complexion measurement unit can improve the accuracy of the complexion measurement by referring to the user's past complexion data. For example, the complexion measurement unit can improve the accuracy of the complexion measurement by referring to the user's past complexion data. For example, the complexion measurement unit can improve the accuracy of the measurement by referring to the user's past complexion data and taking into account daily fluctuations. The complexion measurement unit can also analyze the user's past complexion data and make corrections according to seasonal and environmental changes. The complexion measurement unit can also improve the accuracy of the measurement by predicting complexion changes in specific time periods or situations based on the user's past complexion data. Thus, by referring to the past complexion data, the measurement accuracy is improved. Some or all of the above-described processing in the complexion measurement unit may be performed using, or without, AI. For example, the complexion measurement unit can input past complexion data into the generation AI and cause the generation AI to improve the measurement accuracy.

[0076] The facial color measurement unit can apply an algorithm to correct for the influence of the user's skin type and ambient light when measuring facial color. Examples of skin types include, but are not limited to, classification methods such as dry skin and oily skin. Examples of ambient light include, but are not limited to, the influence of light intensity and color temperature. The facial color measurement unit can apply an algorithm to correct for the influence of the user's skin type and ambient light when measuring facial color. For example, the facial color measurement unit can register the user's skin type (dry skin, oily skin, etc.) in advance and perform correction when measuring facial color based on that information. The facial color measurement unit can also detect the color temperature and brightness of ambient light in real time and perform correction when measuring facial color. The facial color measurement unit can also simultaneously consider the user's skin type and the influence of ambient light and apply an optimal correction algorithm. This improves measurement accuracy by correcting for the influence of skin type and ambient light. Some or all of the above-described processing in the facial color measurement unit can be performed using, for example, AI, or without AI. For example, the complexion measurement unit can input data on skin type and ambient light into the generation AI and have the generation AI apply a correction algorithm.

[0077] The facial color measurement unit can detect changes in the user's facial expression during facial color measurement and estimate the stress level. Examples of detection methods for changes in facial expression include, but are not limited to, movements of facial features and types of facial expressions. Examples of evaluation criteria for stress levels include, but are not limited to, heart rate variability and skin galvanic response. For example, the facial color measurement unit can detect changes in the user's facial expression during facial color measurement and estimate the stress level. For example, the facial color measurement unit can detect changes in the user's facial expression in real time and estimate the stress level. The facial color measurement unit can also analyze fluctuations in the stress level by referencing the user's past facial expression data. The facial color measurement unit can also combine changes in the user's facial expression and changes in facial color to more accurately estimate the stress level. This allows the stress level to be estimated by detecting changes in facial expression. Some or all of the above-described processing in the facial color measurement unit may be performed using, for example, AI, or without AI. For example, the facial color measurement unit can input data on changes in facial expression into the generation AI and have the generation AI estimate the stress level.

[0078] The facial color measurement unit can estimate the user's emotions and monitor changes in facial color in real time based on the estimated user emotions. Real-time includes, but is not limited to, specifications such as data update frequency and delay time. The facial color measurement unit can estimate the user's emotions and monitor changes in facial color in real time based on the estimated user emotions. For example, if the user is nervous, the facial color measurement unit can monitor changes in facial color in real time to evaluate the stress level. If the user is relaxed, the facial color measurement unit can also monitor changes in facial color in real time to evaluate the degree of relaxation. If the user is excited, the facial color measurement unit can also monitor changes in facial color in real time to evaluate the level of excitement. This enables a more accurate understanding of the user's health condition by monitoring changes in facial color in real time based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the facial color measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial color measurement unit may input user emotion data to the generation AI and cause the generation AI to perform real-time monitoring of changes in facial color.

[0079] The complexion measurement unit can correct environmental factors based on the user's geographical location information when measuring complexion. Examples of geographical location information include, but are not limited to, GPS data and location information services. The complexion measurement unit corrects environmental factors based on the user's geographical location information when measuring complexion. For example, if the user is at high altitude, the complexion measurement unit corrects the complexion measurement taking into account the influence of oxygen concentration. Furthermore, if the user is in an urban area, the complexion measurement unit can correct the complexion measurement taking into account the influence of air pollution. Furthermore, if the user is at the seaside, the complexion measurement unit can correct the complexion measurement taking into account the influence of humidity. By taking the geographical location information into account, environmental factors are corrected, improving measurement accuracy. Some or all of the above-described processing in the complexion measurement unit may be performed using, or without, AI. For example, the complexion measurement unit can input the geographical location information into the generation AI and cause the generation AI to correct the environmental factors.

[0080] The facial color measurement unit can analyze the user's social media activity during facial color measurement to detect signs of stress or fatigue. Social media activity includes, but is not limited to, analysis methods such as posting content and activity frequency. The facial color measurement unit can analyze the user's social media activity during facial color measurement to detect signs of stress or fatigue. For example, the facial color measurement unit can analyze the content of the user's social media posts to detect signs of stress or fatigue. The facial color measurement unit can also analyze the frequency of the user's social media activity to detect signs of stress or fatigue. The facial color measurement unit can also analyze the user's interactions with friends on social media to detect signs of stress or fatigue. In this way, signs of stress or fatigue can be detected by analyzing social media activity. Some or all of the above-described processing in the facial color measurement unit can be performed using, for example, AI, or without AI. For example, the facial color measurement unit can input social media activity data into a generation AI and cause the generation AI to detect signs of stress or fatigue.

[0081] The complexion measurement unit can customize the measurement method by reflecting the user's past feedback when measuring complexion. Examples of past feedback include, but are not limited to, methods of obtaining feedback such as survey results and user reviews. The complexion measurement unit can customize the measurement method by reflecting the user's past feedback when measuring complexion. For example, the complexion measurement unit customizes the complexion measurement method based on feedback provided by the user in the past. The complexion measurement unit can also analyze the user's past feedback and make adjustments to improve measurement accuracy. The complexion measurement unit can also optimize the measurement timing and method by reflecting the user's past feedback. By reflecting the past feedback, the measurement method can be customized and accuracy can be improved. Some or all of the above-described processing in the complexion measurement unit may be performed using, for example, AI, or without AI. For example, the complexion measurement unit can input past feedback into a generation AI and have the generation AI customize the measurement method.

[0082] The weight measurement unit can estimate the user's emotions and adjust the timing of weight measurement based on the estimated user emotions. Emotion estimation includes, but is not limited to, methods such as facial expression analysis and voice analysis. For example, the weight measurement unit can estimate the user's emotions and adjust the timing of weight measurement based on the estimated user emotions. For example, if the user is feeling stressed, the weight measurement unit can set the measurement timing to nighttime in order to measure the user's weight in a relaxed state. Furthermore, if the user is relaxed, the weight measurement unit can measure the user's weight in the morning and evaluate the impact on daytime activities. Furthermore, if the user is in a hurry, the weight measurement unit can measure the user's weight in a short time and provide the result quickly. This enables more appropriate weight measurement by adjusting the timing of weight measurement based on the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or without AI. For example, the weight measurement unit can input the user's emotional data into the generation AI and have the generation AI adjust the measurement timing based on the emotion.

[0083] The weight measurement unit can improve measurement accuracy by referring to the user's past weight data when measuring weight. Past weight data includes, but is not limited to, specifications such as database construction and data update frequency. For example, the weight measurement unit can improve measurement accuracy by referring to the user's past weight data when measuring weight. For example, the weight measurement unit can refer to the user's weight data from the past week and improve measurement accuracy by taking daily fluctuations into account. The weight measurement unit can also analyze the user's weight data from the past month and make corrections according to seasonal and environmental changes. The weight measurement unit can also predict weight changes in specific time periods or situations based on the user's past weight data, thereby improving measurement accuracy. By referring to past weight data, measurement accuracy is improved. Some or all of the above-described processing in the weight measurement unit may be performed using, or without, AI. For example, the weight measurement unit can input past weight data into a generation AI and have the generation AI improve measurement accuracy.

[0084] The weight measurement unit can analyze the cause of weight fluctuation based on the user's diet and exercise history when measuring weight. The diet history can be obtained, for example, through a diet record app or calorie calculation, but is not limited to these examples. The exercise history can be obtained, for example, through a fitness tracker or exercise log, but is not limited to these examples. The weight measurement unit can analyze the cause of weight fluctuation based on the user's diet and exercise history when measuring weight. For example, the weight measurement unit can refer to the user's diet history to analyze the cause of weight fluctuation. The weight measurement unit can also refer to the user's exercise history to analyze the cause of weight fluctuation. The weight measurement unit can also combine the user's diet and exercise history to comprehensively analyze the cause of weight fluctuation. This allows the cause of weight fluctuation to be analyzed by taking the diet and exercise history into consideration. Some or all of the above-described processing in the weight measurement unit can be performed using, for example, AI, or without AI. For example, the weight measurement unit can input diet and exercise history data into a generation AI and have the generation AI analyze the cause of weight fluctuation.

[0085] The weight measurement unit may be added with a function to simultaneously measure the user's body fat percentage and muscle mass when measuring weight. Measurement methods for body fat percentage include, but are not limited to, bioimpedance and dual-energy X-ray absorptiometry. Measurement methods for muscle mass include, but are not limited to, muscle mass meters and MRI scans. The weight measurement unit may be added with a function to simultaneously measure the user's body fat percentage and muscle mass when measuring weight. For example, the weight measurement unit may simultaneously measure the body fat percentage when measuring weight to evaluate the overall health status. The weight measurement unit may also simultaneously measure muscle mass when measuring weight to evaluate the effectiveness of exercise. The weight measurement unit may also simultaneously measure the body fat percentage and muscle mass when measuring weight to evaluate a balanced health status. By simultaneously measuring the body fat percentage and muscle mass, the overall health status can be evaluated. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or without AI. For example, the weight measurement unit can input data on body fat percentage and muscle mass into the generation AI, allowing the generation AI to perform an overall health assessment.

[0086] The weight measurement unit can estimate the user's emotions and identify the cause of weight fluctuations based on the estimated user emotions. Emotion estimation includes, but is not limited to, methods such as facial expression analysis and voice analysis. The weight measurement unit can estimate the user's emotions and identify the cause of weight fluctuations based on the estimated user emotions. For example, if the user is feeling stressed, the weight measurement unit can identify the possibility that stress is the cause of weight fluctuations. Furthermore, if the user is relaxed, the weight measurement unit can also identify the effects of diet and exercise. Furthermore, if the user is in a hurry, the weight measurement unit can identify the cause of short-term weight fluctuations. This enables more appropriate advice by identifying the cause of weight fluctuations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or without AI. For example, the weight measurement unit can input the user's emotional data into the generation AI and have the generation AI identify the cause of weight fluctuations.

[0087] The weight measurement unit can provide dietary and exercise advice by taking into account the user's geographical location information when measuring the user's weight. Examples of geographical location information include, but are not limited to, methods of obtaining the information, such as GPS data and location information services. For example, the weight measurement unit can provide dietary and exercise advice by taking into account the user's geographical location information when measuring the user's weight. For example, if the user is in an urban area, the weight measurement unit can provide information on nearby gyms and health food stores. Furthermore, if the user is in a suburban area, the weight measurement unit can also suggest exercise methods that utilize natural environments. Furthermore, if the user is traveling, the weight measurement unit can also suggest local healthy dietary and exercise methods. By taking the geographical location information into account, more appropriate dietary and exercise advice can be provided. Some or all of the above-described processing in the weight measurement unit may be performed, for example, using AI, or may be performed without AI. For example, the weight measurement unit can input geographical location information into a generation AI and cause the generation AI to provide dietary and exercise advice.

[0088] The weight measurement unit can analyze the user's social media activity at the time of weight measurement to detect dietary and exercise patterns. Social media activity includes, but is not limited to, analysis methods such as posting content and activity frequency. For example, the weight measurement unit can analyze the user's social media activity at the time of weight measurement to detect dietary and exercise patterns. For example, the weight measurement unit can analyze the user's social media posting content to detect dietary and exercise patterns. The weight measurement unit can also analyze the frequency of the user's social media activity to detect dietary and exercise patterns. The weight measurement unit can also analyze the user's interactions with friends on social media to detect dietary and exercise patterns. In this way, dietary and exercise patterns can be detected by analyzing social media activity. Some or all of the above-described processing in the weight measurement unit can be performed, for example, using AI or without AI. For example, the weight measurement unit can input social media activity data to a generation AI and cause the generation AI to detect dietary and exercise patterns.

[0089] The weight measurement unit can customize the measurement method by reflecting the user's past feedback when measuring weight. Past feedback includes, but is not limited to, methods of obtaining survey results and user reviews. The weight measurement unit can customize the measurement method by reflecting the user's past feedback when measuring weight. For example, the weight measurement unit customizes the weight measurement method based on feedback provided by the user in the past. The weight measurement unit can also analyze the user's past feedback and make adjustments to improve measurement accuracy. The weight measurement unit can also optimize the measurement timing and method by reflecting the user's past feedback. In this way, the measurement method can be customized by reflecting the past feedback, thereby improving accuracy. Some or all of the above-described processing in the weight measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the weight measurement unit can input past feedback into a generation AI and have the generation AI customize the measurement method.

[0090] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis result based on the estimated user's emotion. Emotion estimation includes, but is not limited to, methods such as facial expression analysis and voice analysis. The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis result based on the estimated user's emotion. For example, the analysis unit can provide a simple, highly visible analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise analysis result when the user is in a hurry. By adjusting the presentation method of the analysis result based on the user's emotion, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis results are expressed.

[0091] The analysis unit may apply an algorithm that incorporates the latest health information and research results in real time during analysis. Examples of the latest health information include, but are not limited to, reliable databases and the latest research papers. Examples of research results include, but are not limited to, methods of obtaining academic papers and clinical trial results. For example, the analysis unit may apply an algorithm that incorporates the latest health information and research results in real time during analysis. For example, the analysis unit may acquire the latest health information in real time and reflect it in the analysis results. The analysis unit may also acquire the latest research results in real time and reflect it in the analysis results. The analysis unit may also combine the latest health information and research results to generate optimal advice. By incorporating the latest health information and research results in real time, more appropriate advice can be provided. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit may input the latest health information and research results into the generation AI and cause the generation AI to incorporate them in real time.

[0092] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during analysis. Examples of past health data include, but are not limited to, data obtained from electronic medical records or health management apps. The analysis unit can improve the accuracy of the analysis by, for example, referring to the user's past health data during analysis. For example, the analysis unit can refer to the user's past week's health data and improve the accuracy of the analysis by taking daily fluctuations into account. The analysis unit can also analyze the user's past month's health data and make corrections based on seasonal or environmental changes. The analysis unit can also predict the user's health status for specific time periods or situations based on the user's past health data, thereby improving the accuracy of the analysis. By referring to past health data, the accuracy of the analysis can be improved. Some or all of the above-described processing by the analysis unit can be performed using, or without, AI. For example, the analysis unit can input past health data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0093] The analysis unit can generate personalized advice by taking into account the user's lifestyle and habits during analysis. Lifestyle includes, but is not limited to, evaluation methods such as sleep patterns and meal timing. Habits include, but are not limited to, evaluation methods such as exercise habits and smoking habits. The analysis unit can generate personalized advice by taking into account the user's lifestyle and habits during analysis. For example, the analysis unit generates optimal advice by taking into account the user's lifestyle (wake-up time, bedtime, etc.). The analysis unit can also generate personalized advice by taking into account the user's habits (meal times, exercise times, etc.). The analysis unit can also generate comprehensive advice by combining the user's lifestyle and habits. This allows personalized advice to be provided by taking into account the user's lifestyle and habits. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the lifestyle and habits into a generation AI and cause the generation AI to generate personalized advice.

[0094] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. Emotion estimation can be performed using, for example, facial expression analysis or voice analysis, but is not limited to these examples. The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is relaxed, the analysis unit can sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that highlight the main points. This prioritizes the analysis results based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the analysis results.

[0095] During analysis, the analysis unit can reflect environmental factors in the analysis by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, acquisition methods such as GPS data and location information services. During analysis, the analysis unit can reflect environmental factors in the analysis by taking into account the user's geographical location information. For example, when the user is at high altitude, the analysis unit can consider the influence of oxygen concentration when performing the analysis. Furthermore, when the user is in an urban area, the analysis unit can also consider the influence of air pollution when performing the analysis. Furthermore, when the user is at the seaside, the analysis unit can also consider the influence of humidity when performing the analysis. By taking geographical location information into account, environmental factors are reflected in the analysis, improving the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical location information into the generation AI and cause the generation AI to analyze environmental factors.

[0096] The analysis unit can analyze the user's social media activity during the analysis and reflect signs of stress or fatigue in the analysis. Social media activity analysis methods include, but are not limited to, the content of posts and the frequency of activity. For example, the analysis unit can analyze the user's social media activity during the analysis and reflect signs of stress or fatigue in the analysis. For example, the analysis unit can analyze the content of the user's social media posts and reflect signs of stress or fatigue in the analysis. The analysis unit can also analyze the frequency of the user's social media activity and reflect signs of stress or fatigue in the analysis. The analysis unit can also analyze the user's interactions with friends on social media and reflect signs of stress or fatigue in the analysis. This allows signs of stress or fatigue to be reflected in the analysis by analyzing social media activity, improving the accuracy of the analysis. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input social media activity data into a generation AI and have the generation AI analyze signs of stress or fatigue.

[0097] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback during analysis. Examples of past feedback include, but are not limited to, methods of obtaining survey results and user reviews. The analysis unit can customize the analysis algorithm by reflecting the user's past feedback during analysis. For example, the analysis unit customizes the analysis algorithm based on feedback previously provided by the user. The analysis unit can also analyze the user's past feedback and make adjustments to improve analysis accuracy. The analysis unit can also optimize the timing and method of analysis by reflecting the user's past feedback. By doing so, the analysis algorithm can be customized and accuracy improved by reflecting the past feedback. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past feedback into a generation AI and have the generation AI customize the analysis algorithm.

[0098] The providing unit can estimate the user's emotion and adjust the way the advice is presented based on the estimated user's emotion. Emotion estimation can be performed, for example, by facial expression analysis or voice analysis, but is not limited to these examples. The providing unit can estimate the user's emotion and adjust the way the advice is presented based on the estimated user's emotion. For example, the providing unit can provide simple, highly visible advice when the user is nervous. The providing unit can also provide detailed advice when the user is relaxed. The providing unit can also provide advice that focuses on the main points when the user is in a hurry. By adjusting the way the advice is presented based on the user's emotion, more appropriate advice can be provided. Emotion estimation can be achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-described processing in the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the way the advice is expressed.

[0099] The providing unit can provide optimal advice by referring to the user's past advice history when providing the advice. Examples of past advice history include, but are not limited to, methods of obtaining advice records and history databases. For example, the providing unit can provide optimal advice by referring to the user's past advice history when providing the advice. For example, the providing unit can provide optimal advice by referring to the user's advice history for the past week. The providing unit can also analyze the user's advice history for the past month and provide advice according to changes in the season or environment. The providing unit can also provide optimal advice for a specific time period or situation based on the user's past advice history. This allows optimal advice to be provided by referring to the past advice history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input past advice history into a generation AI and cause the generation AI to provide optimal advice.

[0100] The providing unit can customize the advice based on the user's current living situation and areas of interest when providing the advice. Examples of the current living situation include, but are not limited to, methods for evaluating lifestyle habits and health status. Examples of areas of interest include, but are not limited to, methods for identifying hobbies and topics of interest. The providing unit, for example, customizes the advice based on the user's current living situation and areas of interest when providing the advice. For example, the providing unit provides optimal advice taking into account the user's current living situation (work, family, etc.). The providing unit can also provide personalized advice taking into account the user's areas of interest (health, fitness, etc.). The providing unit can also provide comprehensive advice by combining the user's living situation and areas of interest. This allows for more appropriate advice to be provided by customizing the advice based on the user's current living situation and areas of interest. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input data on the user's current living situation and areas of interest into a generation AI and cause the generation AI to customize the advice.

[0101] The providing unit can improve the content of the advice by reflecting user feedback when providing the advice. Examples of feedback include, but are not limited to, methods of obtaining survey results and user reviews. For example, the providing unit can improve the content of the advice by reflecting user feedback when providing the advice. For example, the providing unit can improve the content of the advice based on feedback previously provided by the user. The providing unit can also analyze the user's past feedback and make adjustments to improve the accuracy of the advice. The providing unit can also optimize the timing and method of providing the advice by reflecting the user's past feedback. By reflecting the feedback, the content of the advice is improved and its accuracy is improved. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input feedback to a generating AI and cause the generating AI to improve the content of the advice.

[0102] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression analysis and voice analysis. The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, the providing unit can prioritize important advice when the user is nervous. Furthermore, the providing unit can sequentially display detailed advice when the user is relaxed. Furthermore, the providing unit can prioritize advice that focuses on the main points when the user is in a hurry. Thus, by determining the priority of advice based on the user's emotions, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotional data into the generating AI and have the generating AI determine the priority of advice.

[0103] The providing unit can provide optimal advice by taking into account the user's geographical location information when providing the advice. Examples of geographical location information include, but are not limited to, acquisition methods such as GPS data and location information services. For example, the providing unit can provide optimal advice by taking into account the user's geographical location information when providing the advice. For example, if the user is in an urban area, the providing unit can provide information on nearby gyms and health food stores. Furthermore, if the user is in a suburban area, the providing unit can also suggest exercise methods that utilize natural environments. Furthermore, if the user is traveling, the providing unit can also suggest local healthy diets and exercise methods. This allows for more appropriate advice to be provided by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information into a generating AI and cause the generating AI to provide optimal advice.

[0104] The providing unit can analyze the user's social media activity at the time of providing the advice and provide relevant advice. Social media activity includes, but is not limited to, analysis methods such as post content and activity frequency. For example, the providing unit can analyze the user's social media activity at the time of providing the advice and provide relevant advice. For example, the providing unit can analyze the user's social media post content and provide relevant advice. The providing unit can also analyze the frequency of the user's social media activity and provide relevant advice. The providing unit can also analyze the user's interactions with friends on social media and provide relevant advice. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input social media activity data to a generation AI and cause the generation AI to provide relevant advice.

[0105] The providing unit can customize the content of the advice by reflecting the user's past feedback when providing the advice. Examples of past feedback include, but are not limited to, methods of obtaining survey results and user reviews. The providing unit, for example, customizes the content of the advice by reflecting the user's past feedback when providing the advice. For example, the providing unit customizes the content of the advice based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and make adjustments to improve the accuracy of the advice. The providing unit can also optimize the timing and method of providing the advice by reflecting the user's past feedback. In this way, the content of the advice is customized and its accuracy is improved by reflecting the past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past feedback into a generating AI and cause the generating AI to customize the content of the advice. === Hard Collateral 1-1 === Each of the multiple elements including the above-described complexion measurement unit, weight measurement unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the complexion measurement unit can measure the user's complexion using the camera 42 of the smart device 14. For example, the weight measurement unit can measure the user's weight using a scale built into the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and can analyze data measured by the complexion measurement unit and the weight measurement unit to provide suggestions for improving the user's health condition and lifestyle rhythm. For example, the provision unit is realized by the control unit 46A of the smart device 14 and can provide the user with suggestions obtained by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements including the above-described complexion measurement unit, weight measurement unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the complexion measurement unit can measure the user's complexion using the camera 42 of the smart glasses 214. For example, the weight measurement unit can measure the user's weight using a scale built into the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and can analyze data measured by the complexion measurement unit and the weight measurement unit to provide suggestions for improving the user's health condition and lifestyle rhythm. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 and can provide the user with suggestions obtained by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned complexion measurement unit, weight measurement unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the complexion measurement unit can measure the user's complexion using the camera 42 of the headset-type terminal 314. For example, the weight measurement unit can measure the user's weight using a scale built into the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and can analyze data measured by the complexion measurement unit and the weight measurement unit and make suggestions for improving the user's health condition and lifestyle rhythm. For example, the provision unit is realized by the control unit 46A of the headset-type terminal 314, and can provide the user with suggestions obtained by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned complexion measurement unit, weight measurement unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the complexion measurement unit can measure the user's complexion using the camera 42 of the robot 414. For example, the weight measurement unit can measure the user's weight using a scale built into the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and can analyze data measured by the complexion measurement unit and the weight measurement unit and make suggestions for improving the user's health condition and lifestyle rhythm. For example, the provision unit is realized by the control unit 46A of the robot 414 and can provide the user with suggestions obtained by the analysis unit.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The complexion measurement unit can simultaneously measure the moisture content of the user's skin when measuring the user's complexion. For example, the complexion measurement unit measures the moisture content of the skin while measuring the complexion and evaluates the condition of dry or oily skin. The complexion measurement unit can also track changes in the moisture content of the skin and provide skin care advice according to changes in the season or environment. Furthermore, the complexion measurement unit can send the moisture content data of the user's skin to the analysis unit, which can be used to evaluate the user's overall health condition. This allows the complexion measurement unit to grasp the user's skin condition in more detail and provide appropriate advice.

[0108] The weight measurement unit can measure the user's bone density at the same time as measuring the user's weight. For example, the weight measurement unit measures bone density at the time of weight measurement to evaluate the health of the bones. The weight measurement unit can also track changes in bone density and provide advice on calcium intake and exercise. Furthermore, the weight measurement unit can transmit the user's bone density data to the analysis unit to help evaluate the user's overall health condition. This allows the weight measurement unit to grasp the user's bone health condition in more detail and provide appropriate advice.

[0109] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can display simple, highly visible analysis results. If the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display analysis results that focus on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate analysis results can be provided.

[0110] The providing unit can estimate the user's emotions and adjust the content of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice that has a relaxing effect. Also, if the user is relaxed, the providing unit can provide advice that encourages proactive action. Furthermore, if the user is in a hurry, the providing unit can provide advice that can be implemented in a short time. In this way, by adjusting the content of advice based on the user's emotions, more appropriate advice can be provided.

[0111] When measuring the user's facial color, the facial color measurement unit can analyze the user's facial expression and estimate the user's emotion. For example, the facial color measurement unit performs facial expression analysis when measuring the user's facial color to estimate the user's emotion. The facial color measurement unit can also evaluate changes in facial color based on the estimated emotion and detect signs of stress or fatigue. Furthermore, the facial color measurement unit can transmit the user's emotion data to the analysis unit and use it to evaluate the user's overall health condition. This allows the facial color measurement unit to measure the user's facial color while taking the user's emotion into consideration and provide appropriate advice.

[0112] The weight measurement unit can measure the user's body temperature at the same time as measuring the user's weight. For example, the weight measurement unit can measure the user's body temperature when measuring the user's weight and detect signs of fever or poor health. The weight measurement unit can also track changes in body temperature and provide health management advice according to changes in season or environment. Furthermore, the weight measurement unit can send the user's body temperature data to the analysis unit to help evaluate the user's overall health condition. This allows the weight measurement unit to take the user's body temperature into consideration when measuring the user's weight and provide appropriate advice.

[0113] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. Also, if the user is relaxed, the analysis unit can sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. In this way, by prioritizing the analysis results based on the user's emotions, more appropriate analysis results can be provided.

[0114] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize displaying important advice. Also, if the user is relaxed, the providing unit can sequentially display detailed advice. Furthermore, if the user is in a hurry, the providing unit can prioritize displaying advice that focuses on the main points. In this way, by determining the priority of advice based on the user's emotions, more appropriate advice can be provided.

[0115] The analysis unit can generate personalized advice taking into account the user's lifestyle and habits. For example, the analysis unit generates optimal advice taking into account the user's sleep patterns and meal timings. The analysis unit can also generate personalized advice taking into account the user's exercise habits and smoking habits. Furthermore, the analysis unit can combine the user's lifestyle and habits to generate comprehensive advice. This allows personalized advice to be provided by taking into account the user's lifestyle and habits.

[0116] The providing unit can provide optimal advice by referring to the user's past advice history. For example, the providing unit can provide optimal advice by referring to the user's advice history for the past week. The providing unit can also analyze the user's advice history for the past month and provide advice according to changes in the season or environment. Furthermore, the providing unit can provide optimal advice for a specific time period or situation based on the user's past advice history. In this way, optimal advice can be provided by referring to the past advice history.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The facial color measurement unit measures the user's facial color. For example, it can use an RGB camera to analyze the color tone of the face and detect changes in facial color. Step 2: The weight measurement unit measures the user's weight. For example, weight gain or loss can be detected using a scale built into the smart mirror. Step 3: The analysis unit uses the generation AI to analyze the data measured by the complexion measurement unit and weight measurement unit, and makes suggestions for improving health and lifestyle habits. For example, it can generate advice that incorporates the latest health information and research findings. Step 4: The providing unit provides the user with the suggestions obtained by the analyzing unit. For example, the providing unit provides the user with appropriate advice based on the data analyzed by the generating AI.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a complexion measurement unit that measures the complexion of a user; a weight measurement unit that measures the weight of a user; an analysis unit that analyzes the data measured by the complexion measurement unit and the weight measurement unit and makes suggestions for improving health conditions and lifestyle rhythms; a providing unit that provides the proposal obtained by the analysis unit to the user. A system characterized by:

2. The complexion measurement unit Analyzing facial color using an RGB camera 2. The system of claim 1.

3. The weight measurement unit Using a scale built into a smart mirror 2. The system of claim 1.

4. The analysis unit Generate advice incorporating the latest health information and research findings 2. The system of claim 1.

5. The analysis unit Acquire heart rate and sleep data from your smartwatch or other external devices, and analyze this data to provide detailed health status and lifestyle improvement suggestions 2. The system of claim 1.

6. The providing unit Generative AI provides appropriate advice to users based on the data analyzed.

2. The system of claim 1.

7. The complexion measurement unit Estimate the user's emotions and adjust the timing of facial color measurement based on the estimated user emotions.

2. The system of claim 1.

8. The complexion measurement unit When measuring complexion, the accuracy of the measurement is improved by referencing the user's past complexion data.

2. The system of claim 1.

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