System

Abstract

An object of the system according to the embodiment is to perform health management, beauty management, and fashion proposal at home in an integrated manner.SOLUTION: A system according to an embodiment includes a sensor, a generation AI, a health manager, a beauty manager, and a fashion proposing unit. The sensor detects a health condition or a cosmetic condition of the family. The generation AI analyzes the date sensed by the sensor. The health managing unit provides advice related to health based on the AI analyzed by the generating unit. The beauty manager provides advice on beauty based on the AI analyzed by the generator. The fashion suggester provides suggestions regarding fashion based on the AI analyzed by the production data structure.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 technology has made it difficult to provide a unified approach to health management, beauty management, and fashion suggestions within the home.

[0005] The system according to the embodiment aims to provide a unified service for health management, beauty management, and fashion suggestions within the home. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor, a generation AI, a health management unit, a beauty management unit, and a fashion suggestion unit. The sensor detects the health or beauty condition of a family member. The generation AI analyzes the data detected by the sensor. The health management unit provides health advice based on the data analyzed by the generation AI. The beauty management unit provides beauty advice based on the data analyzed by the generation AI. The fashion suggestion unit provides fashion suggestions based on the data analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can provide health management, beauty management, and fashion suggestions in an integrated manner within the home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention combines sensors and generative AI with a home bathroom sink to provide information on health, beauty, and fashion for the family, elevating the bathroom into the center of life. This allows the system to function as a platform for health management, beauty, and fashion for the family.

[0029] The system according to the embodiment includes a sensor, a generation AI, a health management unit, a beauty management unit, and a fashion suggestion unit. The sensor detects the health or beauty condition of a family member. For example, the sensor collects health data such as body temperature, heart rate, and blood pressure. The sensor can also detect skin and hair conditions. The generation AI analyzes the data detected by the sensor. For example, the generation AI can evaluate the health condition using a machine learning algorithm. The generation AI can also evaluate the beauty condition using statistical analysis methods. The health management unit provides health advice based on the data analyzed by the generation AI. For example, if the body temperature is high, the health management unit can provide advice such as, "Your body temperature is high. You may have signs of a cold, so we recommend that you take a rest." If the heart rate is high, the health management unit can provide advice such as, "Your heart rate is high. We recommend that you relax." If the blood pressure is high, the health management unit can provide advice such as, "Your blood pressure is high. We recommend that you limit your salt intake." The beauty management unit provides beauty advice based on the data analyzed by the generation AI. For example, if the skin is dry, the beauty management department can provide advice such as, "Your skin is dry. We recommend using a moisturizing cream." If the hair is damaged, the beauty management department can provide advice such as, "Your hair is damaged. We recommend using a repair shampoo." If the skin moisture content is low, the beauty management department can provide advice such as, "Your skin moisture content is low. We recommend that you stay hydrated." The fashion suggestion department provides fashion suggestions based on data analyzed by the generative AI. For example, the fashion suggestion department can provide a suggestion such as, "It's rainy today, so we recommend wearing a waterproof jacket." The fashion suggestion department can also provide a suggestion such as, "It's cold today, so we recommend wearing warm clothes." The fashion suggestion department can also provide a suggestion such as, "It's sunny today, so we recommend going out in light clothing."As a result, the system according to the embodiment can provide information about health, beauty, and fashion for the family, thereby elevating the bathroom to the center of life.

[0030] The health management unit uses the generating AI to learn the health history of each family member based on health data collected by sensors and make long-term health predictions. The health management unit uses data such as body temperature, heart rate, and blood pressure collected by sensors to learn the health history of each family member and make long-term health predictions. For example, the generating AI can predict future health risks based on past data and suggest preventive measures. The generating AI can also propose optimal health management plans for each family member based on their health history. The generating AI can also analyze health data and monitor changes in health status. This allows the generating AI to learn the health history of each family member and make long-term health predictions.

[0031] The health management unit uses the generation AI to propose individual meal and exercise plans based on health data, supporting daily health management. For example, the health management unit uses the generation AI to propose optimal meal plans for each family member based on health data collected by sensors. For example, the generation AI automatically generates menus that take nutritional balance into consideration. The generation AI can also propose optimal exercise plans for each family member based on health data. For example, the generation AI can propose aerobic exercise and strength training plans. The generation AI can also provide advice to support daily health management based on health data. For example, the generation AI can provide advice such as, "I recommend you exercise today." This allows the generation AI to propose individual meal and exercise plans and support daily health management.

[0032] The health management unit uses the generation AI to analyze the health trends of the entire family based on health data and propose a health program for the entire family. The health management unit uses the generation AI to analyze the health trends of the entire family based on health data collected by sensors, for example. For example, the generation AI monitors fluctuations in the weight and activity levels of each family member and proposes a health program. The generation AI can also propose a health program for the entire family based on the health data. For example, the generation AI can provide advice such as, "We propose an exercise program for the whole family." The generation AI can also provide advice such as, "We recommend that the whole family implement a diet improvement program." The generation AI can also provide advice such as, "We recommend that the whole family undergo health checkups." This makes it possible to analyze the health trends of the entire family and propose a health program for the whole family.

[0033] The health management unit can store data collected by sensors in the cloud and share it with remote doctors to support online medical consultations. For example, the health management unit builds a system that stores health data collected by sensors in the cloud and shares it with remote doctors. For example, the generating AI periodically uploads the data so that doctors can access it. The generating AI can also analyze the data on the cloud and provide diagnosis results to doctors. For example, the generating AI can provide information to doctors based on health data, such as "This patient is at risk of high blood pressure." The generating AI can also use video calls and data sharing platforms to support online medical consultations. For example, the generating AI can conduct video calls between doctors and patients to support medical consultations. The generating AI can also use data sharing platforms to provide detailed health data to doctors. This allows data collected by sensors to be stored in the cloud and shared with remote doctors to support online medical consultations.

[0034] The beauty management unit allows the generation AI to recommend the most suitable beauty products for each individual skin type and hair type based on the beauty data collected by the camera or sensor. The beauty management unit allows the generation AI to recommend the most suitable beauty products based on the skin condition and hair type data collected by the camera or sensor, for example. For example, the generation AI can recommend a moisturizing cream for dry skin. The generation AI can also recommend a repair shampoo for damaged hair. The generation AI can also suggest beauty products that promote hydration if the skin's moisture content is low. This makes it possible to recommend the most suitable beauty products for each individual skin type and hair type.

[0035] The beauty management unit uses the generative AI to provide skin care and hair care advice according to the season and weather based on beauty data. For example, the beauty management unit uses the generative AI to provide skin care advice according to the season and weather based on beauty data collected by cameras and sensors. For example, the generative AI may suggest using sunscreen to protect against UV rays in the summer. The generative AI may also suggest using moisturizing cream to protect against dry skin in the winter. The generative AI may also suggest waterproof hair care products for rainy days. This makes it possible to provide skin care and hair care advice according to the season and weather.

[0036] The beauty management unit uses the generation AI to analyze the beauty trends of each family member based on the beauty data and can suggest a beauty program for the whole family. The beauty management unit uses the generation AI to analyze the beauty trends of each family member based on beauty data collected by cameras and sensors, for example. For example, the generation AI monitors changes in each family member's skin condition and hair quality and suggests a beauty program. The generation AI can also suggest a beauty program for the whole family based on the beauty data. For example, the generation AI can provide advice such as, "We suggest a skin care program for the whole family." The generation AI can also provide advice such as, "We recommend that the whole family implement a hair care program." The generation AI can also provide advice such as, "We recommend that the whole family undergo a beauty check." This makes it possible to analyze the beauty trends of each family member and suggest a beauty program for the whole family.

[0037] The beauty management unit allows the generative AI to automatically make reservations at beauty salons and spas based on data collected by cameras and sensors. The beauty management unit, for example, builds a system in which the generative AI automatically makes reservations at beauty salons and spas based on beauty data collected by cameras and sensors. For example, the generative AI can suggest a beauty salon reservation when your skin is in bad condition. The generative AI can also suggest a spa reservation when your hair is in bad condition. The generative AI can also make reservations at beauty salons and spas at the optimal time based on the beauty data. For example, the generative AI can provide advice such as, "Your skin is in bad condition, so make a reservation at the beauty salon." The generative AI can also provide advice such as, "Your hair is in bad condition, so make a spa reservation." The generative AI can also provide advice such as, "By automatically making reservations at beauty salons and spas, you can save time and effort." This allows reservations at beauty salons and spas to be made automatically.

[0038] The fashion suggestion unit allows the generation AI to suggest outfits that are optimal for each individual's style and preferences based on fashion data collected by the camera. The fashion suggestion unit allows the generation AI to suggest outfits that are optimal for each individual's style and preferences based on, for example, clothing and accessory data collected by the camera. For example, the generation AI makes suggestions based on the user's past fashion history. The generation AI can also suggest outfits that incorporate the latest fashion trends based on fashion data. The generation AI can also suggest outfits that suit the user's preferences. For example, the generation AI can provide advice such as, "For users who like casual styles, we suggest casual outfits." The generation AI can also provide advice such as, "We suggest outfits that are suitable for formal occasions." The generation AI can also provide advice such as, "For users who like sporty styles, we suggest sporty outfits." This makes it possible to suggest outfits that are optimal for each individual's style and preferences.

[0039] The fashion suggestion unit allows the generation AI to provide fashion advice according to the season or event based on the fashion data. The fashion suggestion unit allows the generation AI to provide fashion advice according to the season or event based on fashion data collected by a camera, for example. For example, the generation AI can suggest outfits suitable for a summer beach party. The generation AI can also suggest outfits suitable for a winter formal event. The generation AI can also suggest outfits suitable for a spring casual event. This makes it possible to provide fashion advice according to the season or event.

[0040] The fashion suggestion unit uses the generation AI to analyze the fashion trends of all family members based on fashion data and suggest fashion events that the whole family can enjoy. For example, the generation AI analyzes the fashion trends of all family members based on fashion data collected by a camera. For example, the generation AI identifies trends based on data on the clothing and accessories of all family members. The generation AI can also suggest fashion events that the whole family can enjoy based on the fashion data. For example, the generation AI can provide advice such as, "We suggest a fashion show that the whole family can enjoy." The generation AI can also provide advice such as, "We suggest a fashion party that the whole family can enjoy." The generation AI can also provide advice such as, "By suggesting a fashion event that the whole family can enjoy, you can deepen family bonds." This makes it possible to analyze the fashion trends of all family members and suggest fashion events that the whole family can enjoy.

[0041] The fashion suggestion unit can use the generation AI to support purchases on online shopping sites based on data collected by the camera. The fashion suggestion unit, for example, builds a system in which the generation AI supports purchases on online shopping sites based on fashion data collected by the camera. For example, the generation AI recommends products that match the user's preferences. The generation AI can also recommend products that incorporate the latest fashion trends based on fashion data. The generation AI can also recommend optimal products based on the user's purchase history. For example, the generation AI can provide advice such as "This product suits your preferences." The generation AI can also provide advice such as "This product incorporates the latest fashion trends." The generation AI can also provide advice such as "This product is recommended based on your purchase history." This can support purchases on online shopping sites.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and send instructions to each part of the system. For example, if the user says, "Tell me about my health condition today," the voice recognition unit analyzes the command and sends instructions to the health management unit. The health management unit can report the user's health condition based on data collected by the sensors. Also, if the user says, "Tell me about my fashion advice for today," the voice recognition unit analyzes the command and sends instructions to the fashion suggestion unit. The fashion suggestion unit can provide fashion advice suitable for the user based on the data analyzed by the generation AI. This allows the user to operate the system using voice commands, improving convenience.

[0044] The system can further include a reminder module. The reminder module can provide the user with health management and beauty care reminders based on the data analyzed by the generative AI. For example, the reminder module can provide a reminder such as, "We recommend taking vitamins every day after breakfast." The reminder module can also provide a reminder such as, "We recommend using moisturizing cream every night before going to bed." The reminder module can also provide a reminder such as, "We recommend exercising once a week." This allows the user to remember to manage their daily health and beauty care.

[0045] The system may further include an entertainment unit. The entertainment unit may provide entertainment content according to the user's health and beauty condition. For example, the entertainment unit may provide relaxation music when the user wants to relax. The entertainment unit may also provide exercise videos when the user exercises. The entertainment unit may also provide beauty tutorial videos when the user performs beauty care. This allows the user to enjoy health management and beauty care.

[0046] The system may further include a communication unit. The communication unit may provide a platform for users to share information about health and beauty. For example, the communication unit may provide a bulletin board for users to share their health management results. The communication unit may also provide a chat function for users to exchange beauty care advice. The communication unit may also provide a forum for users to share fashion ideas. This allows users to share information with other users and support each other.

[0047] The system may further include an ecology unit. The ecology unit may provide the user with advice on environmental considerations in health management and beauty care. For example, the ecology unit may suggest eco-friendly beauty products to the user. The ecology unit may also suggest products that use recyclable packaging to the user. The ecology unit may also provide the user with advice on energy conservation. This allows the user to be environmentally conscious while managing their health and beauty care.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The sensor detects the health or beauty condition of the family member. For example, the sensor can collect health data such as body temperature, heart rate, and blood pressure, and can also detect skin condition and hair condition. Step 2: The generative AI analyzes the data detected by the sensors. For example, the generative AI can use machine learning algorithms to evaluate health status and statistical analysis methods to evaluate beauty status. Step 3: The health management department provides health advice based on the data analyzed by the generative AI. For example, if the patient has a high body temperature, the department may provide advice such as, "Your body temperature is high. You may have symptoms of a cold, so we recommend that you take a rest." If the patient has a high heart rate, the department may provide advice such as, "Your heart rate is high. We recommend that you relax." If the patient has high blood pressure, the department may provide advice such as, "Your blood pressure is high. We recommend that you limit your salt intake." Step 4: The beauty management department provides beauty advice based on the data analyzed by the generative AI. For example, if the skin is dry, the department can provide advice such as "Your skin is dry. We recommend using a moisturizing cream." If the hair is damaged, the department can provide advice such as "Your hair is damaged. We recommend using a repair shampoo." If the skin's moisture content is low, the department can provide advice such as "Your skin's moisture content is low. We recommend that you stay hydrated." Step 5: The fashion suggestion unit provides fashion suggestions based on the data analyzed by the generative AI. For example, it can provide suggestions such as "It's raining today, so we recommend wearing a waterproof jacket," "It's cold today, so we recommend wearing warm clothes," or "It's sunny today, so we recommend going out in light clothing."

[0050] (Example 2) The system according to the embodiment of the present invention combines sensors and generative AI with a home bathroom sink to provide information on health, beauty, and fashion for the family, elevating the bathroom into the center of life. This allows the system to function as a platform for health management, beauty, and fashion for the family.

[0051] The system according to the embodiment includes a sensor, a generation AI, a health management unit, a beauty management unit, and a fashion suggestion unit. The sensor detects the health or beauty condition of a family member. For example, the sensor collects health data such as body temperature, heart rate, and blood pressure. The sensor can also detect skin and hair conditions. The generation AI analyzes the data detected by the sensor. For example, the generation AI can evaluate the health condition using a machine learning algorithm. The generation AI can also evaluate the beauty condition using statistical analysis methods. The health management unit provides health advice based on the data analyzed by the generation AI. For example, if the body temperature is high, the health management unit can provide advice such as, "Your body temperature is high. You may have signs of a cold, so we recommend that you take a rest." If the heart rate is high, the health management unit can provide advice such as, "Your heart rate is high. We recommend that you relax." If the blood pressure is high, the health management unit can provide advice such as, "Your blood pressure is high. We recommend that you limit your salt intake." The beauty management unit provides beauty advice based on the data analyzed by the generation AI. For example, if the skin is dry, the beauty management department can provide advice such as, "Your skin is dry. We recommend using a moisturizing cream." If the hair is damaged, the beauty management department can provide advice such as, "Your hair is damaged. We recommend using a repair shampoo." If the skin moisture content is low, the beauty management department can provide advice such as, "Your skin moisture content is low. We recommend that you stay hydrated." The fashion suggestion department provides fashion suggestions based on data analyzed by the generative AI. For example, the fashion suggestion department can provide a suggestion such as, "It's rainy today, so we recommend wearing a waterproof jacket." The fashion suggestion department can also provide a suggestion such as, "It's cold today, so we recommend wearing warm clothes." The fashion suggestion department can also provide a suggestion such as, "It's sunny today, so we recommend going out in light clothing."As a result, the system according to the embodiment can provide information about health, beauty, and fashion for the family, thereby elevating the bathroom to the center of life.

[0052] The health management unit uses the generating AI to learn the health history of each family member based on health data collected by sensors and make long-term health predictions. The health management unit uses data such as body temperature, heart rate, and blood pressure collected by sensors to learn the health history of each family member and make long-term health predictions. For example, the generating AI can predict future health risks based on past data and suggest preventive measures. The generating AI can also propose optimal health management plans for each family member based on their health history. The generating AI can also analyze health data and monitor changes in health status. This allows the generating AI to learn the health history of each family member and make long-term health predictions.

[0053] The health management unit uses the generation AI to propose individual meal and exercise plans based on health data, supporting daily health management. For example, the health management unit uses the generation AI to propose optimal meal plans for each family member based on health data collected by sensors. For example, the generation AI automatically generates menus that take nutritional balance into consideration. The generation AI can also propose optimal exercise plans for each family member based on health data. For example, the generation AI can propose aerobic exercise and strength training plans. The generation AI can also provide advice to support daily health management based on health data. For example, the generation AI can provide advice such as, "I recommend you exercise today." This allows the generation AI to propose individual meal and exercise plans and support daily health management.

[0054] The health management unit can use the emotion estimation function to analyze the user's emotional state and suggest relaxation methods based on the user's stress level. For example, the health management unit uses a generation AI to analyze the user's emotional state and assess their stress level based on data collected by sensors. For example, the generation AI analyzes heart rate and facial expression data to detect signs of stress. The generation AI can also use the emotion estimation function to monitor the user's emotional state in real time. For example, the generation AI can use voice analysis technology to analyze the tone and speed of the user's voice to assess their emotional state. The generation AI can also use the emotion estimation function to suggest relaxation methods based on the user's emotional state. For example, the generation AI can provide advice such as, "When stress is high, we recommend meditating." The generation AI can also provide advice such as, "We recommend taking deep breaths to relax." The generation AI can also provide advice such as, "We recommend trying aromatherapy." This allows the system to analyze the user's emotional state and suggest relaxation methods based on their stress level.

[0055] The health management unit uses the generation AI to analyze the health trends of the entire family based on health data and propose a health program for the entire family. The health management unit uses the generation AI to analyze the health trends of the entire family based on health data collected by sensors, for example. For example, the generation AI monitors fluctuations in the weight and activity levels of each family member and proposes a health program. The generation AI can also propose a health program for the entire family based on the health data. For example, the generation AI can provide advice such as, "We propose an exercise program for the whole family." The generation AI can also provide advice such as, "We recommend that the whole family implement a diet improvement program." The generation AI can also provide advice such as, "We recommend that the whole family undergo health checkups." This makes it possible to analyze the health trends of the entire family and propose a health program for the whole family.

[0056] The health management unit can store data collected by sensors in the cloud and share it with remote doctors to support online medical consultations. For example, the health management unit builds a system that stores health data collected by sensors in the cloud and shares it with remote doctors. For example, the generating AI periodically uploads the data so that doctors can access it. The generating AI can also analyze the data on the cloud and provide diagnosis results to doctors. For example, the generating AI can provide information to doctors based on health data, such as "This patient is at risk of high blood pressure." The generating AI can also use video calls and data sharing platforms to support online medical consultations. For example, the generating AI can conduct video calls between doctors and patients to support medical consultations. The generating AI can also use data sharing platforms to provide detailed health data to doctors. This allows data collected by sensors to be stored in the cloud and shared with remote doctors to support online medical consultations.

[0057] The health management unit can use the emotion estimation function to provide health advice based on the user's emotional state and elicit positive emotions. For example, the health management unit uses the emotion estimation function to analyze the user's emotional state in real time and provide health advice. For example, the generation AI can suggest relaxation methods when stress is high. The generation AI can also use the emotion estimation function to provide health advice based on the user's emotional state. For example, the generation AI can provide advice such as, "When stress is high, we recommend taking deep breaths to relax." The generation AI can also provide advice such as, "We recommend meditating to elicit positive emotions." The generation AI can also provide advice such as, "We recommend trying aromatherapy to increase your sense of happiness." In this way, health advice based on the user's emotional state can be provided and positive emotions can be elicited.

[0058] The beauty management unit allows the generation AI to recommend the most suitable beauty products for each individual skin type and hair type based on the beauty data collected by the camera or sensor. The beauty management unit allows the generation AI to recommend the most suitable beauty products based on the skin condition and hair type data collected by the camera or sensor, for example. For example, the generation AI can recommend a moisturizing cream for dry skin. The generation AI can also recommend a repair shampoo for damaged hair. The generation AI can also suggest beauty products that promote hydration if the skin's moisture content is low. This makes it possible to recommend the most suitable beauty products for each individual skin type and hair type.

[0059] The beauty management unit uses the generative AI to provide skin care and hair care advice according to the season and weather based on beauty data. For example, the beauty management unit uses the generative AI to provide skin care advice according to the season and weather based on beauty data collected by cameras and sensors. For example, the generative AI may suggest using sunscreen to protect against UV rays in the summer. The generative AI may also suggest using moisturizing cream to protect against dry skin in the winter. The generative AI may also suggest waterproof hair care products for rainy days. This makes it possible to provide skin care and hair care advice according to the season and weather.

[0060] The beauty management unit can use the emotion estimation function to analyze the user's emotional state and suggest beauty treatments with a refreshing effect. For example, the beauty management unit can use the emotion estimation function to analyze the user's emotional state in real time and suggest beauty treatments with a refreshing effect. For example, the generation AI can suggest an aroma massage when stress is high. The generation AI can also use the emotion estimation function to suggest beauty treatments according to the user's emotional state. For example, the generation AI can provide advice such as, "When stress is high, we recommend a relaxing facial massage." The generation AI can also provide advice such as, "We recommend trying a hair treatment to change your mood." The generation AI can also provide advice such as, "We recommend trying aromatherapy to enhance the refreshing effect." This makes it possible to analyze the user's emotional state and suggest beauty treatments with a refreshing effect.

[0061] The beauty management unit uses the generation AI to analyze the beauty trends of each family member based on the beauty data and can suggest a beauty program for the whole family. The beauty management unit uses the generation AI to analyze the beauty trends of each family member based on beauty data collected by cameras and sensors, for example. For example, the generation AI monitors changes in each family member's skin condition and hair quality and suggests a beauty program. The generation AI can also suggest a beauty program for the whole family based on the beauty data. For example, the generation AI can provide advice such as, "We suggest a skin care program for the whole family." The generation AI can also provide advice such as, "We recommend that the whole family implement a hair care program." The generation AI can also provide advice such as, "We recommend that the whole family undergo a beauty check." This makes it possible to analyze the beauty trends of each family member and suggest a beauty program for the whole family.

[0062] The beauty management unit allows the generative AI to automatically make reservations at beauty salons and spas based on data collected by cameras and sensors. The beauty management unit, for example, builds a system in which the generative AI automatically makes reservations at beauty salons and spas based on beauty data collected by cameras and sensors. For example, the generative AI can suggest a beauty salon reservation when your skin is in bad condition. The generative AI can also suggest a spa reservation when your hair is in bad condition. The generative AI can also make reservations at beauty salons and spas at the optimal time based on the beauty data. For example, the generative AI can provide advice such as, "Your skin is in bad condition, so make a reservation at the beauty salon." The generative AI can also provide advice such as, "Your hair is in bad condition, so make a spa reservation." The generative AI can also provide advice such as, "By automatically making reservations at beauty salons and spas, you can save time and effort." This allows reservations at beauty salons and spas to be made automatically.

[0063] The beauty management unit uses the emotion estimation function to provide beauty advice based on the user's emotional state, thereby eliciting positive emotions. For example, the beauty management unit uses the emotion estimation function to analyze the user's emotional state in real time and provide beauty advice. For example, the generation AI suggests skin care products that are relaxing when stress is high. The generation AI can also use the emotion estimation function to provide beauty advice based on the user's emotional state. For example, the generation AI can provide advice such as, "When stress is high, we recommend a relaxing facial massage." The generation AI can also provide advice such as, "We recommend trying aromatherapy to elicit positive emotions." The generation AI can also provide advice such as, "We recommend trying a hair treatment to increase your sense of happiness." This makes it possible to provide beauty advice based on the user's emotional state and elicit positive emotions.

[0064] The fashion suggestion unit allows the generation AI to suggest outfits that are optimal for each individual's style and preferences based on fashion data collected by the camera. The fashion suggestion unit allows the generation AI to suggest outfits that are optimal for each individual's style and preferences based on, for example, clothing and accessory data collected by the camera. For example, the generation AI makes suggestions based on the user's past fashion history. The generation AI can also suggest outfits that incorporate the latest fashion trends based on fashion data. The generation AI can also suggest outfits that suit the user's preferences. For example, the generation AI can provide advice such as, "For users who like casual styles, we suggest casual outfits." The generation AI can also provide advice such as, "We suggest outfits that are suitable for formal occasions." The generation AI can also provide advice such as, "For users who like sporty styles, we suggest sporty outfits." This makes it possible to suggest outfits that are optimal for each individual's style and preferences.

[0065] The fashion suggestion unit allows the generation AI to provide fashion advice according to the season or event based on the fashion data. The fashion suggestion unit allows the generation AI to provide fashion advice according to the season or event based on fashion data collected by a camera, for example. For example, the generation AI can suggest outfits suitable for a summer beach party. The generation AI can also suggest outfits suitable for a winter formal event. The generation AI can also suggest outfits suitable for a spring casual event. This makes it possible to provide fashion advice according to the season or event.

[0066] The fashion suggestion unit can use the emotion estimation function to analyze the user's emotional state and make fashion suggestions that will lift the user's mood. For example, the fashion suggestion unit can use the emotion estimation function to analyze the user's emotional state in real time and make fashion suggestions that will lift the user's mood. For example, the generation AI can suggest bright-colored clothing when the user is feeling depressed. The generation AI can also use the emotion estimation function to make fashion suggestions based on the user's emotional state. For example, the generation AI can provide advice such as, "When you are feeling depressed, we recommend wearing bright-colored clothing." The generation AI can also provide advice such as, "To elicit positive emotions, we recommend wearing clothing made from comfortable materials." The generation AI can also provide advice such as, "To increase your sense of happiness, we recommend wearing clothing with a design you like." This makes it possible to analyze the user's emotional state and make fashion suggestions that will lift the user's mood.

[0067] The fashion suggestion unit uses the generation AI to analyze the fashion trends of all family members based on fashion data and suggest fashion events that the whole family can enjoy. For example, the generation AI analyzes the fashion trends of all family members based on fashion data collected by a camera. For example, the generation AI identifies trends based on data on the clothing and accessories of all family members. The generation AI can also suggest fashion events that the whole family can enjoy based on the fashion data. For example, the generation AI can provide advice such as, "We suggest a fashion show that the whole family can enjoy." The generation AI can also provide advice such as, "We suggest a fashion party that the whole family can enjoy." The generation AI can also provide advice such as, "By suggesting a fashion event that the whole family can enjoy, you can deepen family bonds." This makes it possible to analyze the fashion trends of all family members and suggest fashion events that the whole family can enjoy.

[0068] The fashion suggestion unit can use the generation AI to support purchases on online shopping sites based on data collected by the camera. The fashion suggestion unit, for example, builds a system in which the generation AI supports purchases on online shopping sites based on fashion data collected by the camera. For example, the generation AI recommends products that match the user's preferences. The generation AI can also recommend products that incorporate the latest fashion trends based on fashion data. The generation AI can also recommend optimal products based on the user's purchase history. For example, the generation AI can provide advice such as "This product suits your preferences." The generation AI can also provide advice such as "This product incorporates the latest fashion trends." The generation AI can also provide advice such as "This product is recommended based on your purchase history." This can support purchases on online shopping sites.

[0069] The fashion suggestion unit uses the emotion estimation function to provide fashion advice according to the user's emotional state and elicit positive emotions. The fashion suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide fashion advice. For example, the generation AI suggests a casual style that is relaxing when stress is high. The generation AI can also use the emotion estimation function to provide fashion advice according to the user's emotional state. For example, the generation AI can provide advice such as, "When stress is high, we recommend a casual style that is relaxing." The generation AI can also provide advice such as, "To elicit positive emotions, we recommend wearing brightly colored clothing." The generation AI can also provide advice such as, "To increase your sense of happiness, we recommend wearing clothing made of comfortable materials." In this way, fashion advice according to the user's emotional state can be provided and positive emotions can be elicited.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The system can further include a voice recognition unit. The voice recognition unit can analyze the user's voice commands and send instructions to each part of the system. For example, if the user says, "Tell me about my health condition today," the voice recognition unit analyzes the command and sends instructions to the health management unit. The health management unit can report the user's health condition based on data collected by the sensors. Also, if the user says, "Tell me about my fashion advice for today," the voice recognition unit analyzes the command and sends instructions to the fashion suggestion unit. The fashion suggestion unit can provide fashion advice suitable for the user based on the data analyzed by the generation AI. This allows the user to operate the system using voice commands, improving convenience.

[0072] The system can further include a reminder module. The reminder module can provide the user with health management and beauty care reminders based on the data analyzed by the generative AI. For example, the reminder module can provide a reminder such as, "We recommend taking vitamins every day after breakfast." The reminder module can also provide a reminder such as, "We recommend using moisturizing cream every night before going to bed." The reminder module can also provide a reminder such as, "We recommend exercising once a week." This allows the user to remember to manage their daily health and beauty care.

[0073] The system may further include an entertainment unit. The entertainment unit may provide entertainment content according to the user's health and beauty condition. For example, the entertainment unit may provide relaxation music when the user wants to relax. The entertainment unit may also provide exercise videos when the user exercises. The entertainment unit may also provide beauty tutorial videos when the user performs beauty care. This allows the user to enjoy health management and beauty care.

[0074] The system may further include a communication unit. The communication unit may provide a platform for users to share information about health and beauty. For example, the communication unit may provide a bulletin board for users to share their health management results. The communication unit may also provide a chat function for users to exchange beauty care advice. The communication unit may also provide a forum for users to share fashion ideas. This allows users to share information with other users and support each other.

[0075] The system may further include an ecology unit. The ecology unit may provide the user with advice on environmental considerations in health management and beauty care. For example, the ecology unit may suggest eco-friendly beauty products to the user. The ecology unit may also suggest products that use recyclable packaging to the user. The ecology unit may also provide the user with advice on energy conservation. This allows the user to be environmentally conscious while managing their health and beauty care.

[0076] The health management unit can use the emotion estimation function to analyze the user's emotional state and suggest meal and exercise plans that correspond to the emotion. For example, the generation AI can suggest a meal plan that has a relaxing effect when the user is feeling stressed. The generation AI can also suggest a meal plan that will replenish energy when the user is tired. The generation AI can also suggest an exercise plan that will lift the user's spirits when the user is feeling down. This allows the system to provide meal and exercise plans that correspond to the user's emotional state and support their overall health.

[0077] The beauty management unit uses the emotion estimation function to analyze the user's emotional state and suggest beauty care products that correspond to the emotion. For example, the generation AI can suggest skin care products with a relaxing effect when the user is feeling stressed. The generation AI can also suggest hair care products with a refreshing effect when the user is tired. The generation AI can also suggest aromatherapy to lift the user's mood when the user is feeling down. This allows the system to provide beauty care that corresponds to the user's emotional state and support overall beauty.

[0078] The fashion suggestion unit uses the emotion estimation function to analyze the user's emotional state and provide fashion advice according to the emotion. For example, the generation AI can suggest a relaxing casual style when the user is feeling stressed. The generation AI can also suggest clothing made of comfortable materials when the user is tired. The generation AI can also suggest bright-colored clothing when the user is feeling down. This allows the system to provide fashion advice according to the user's emotional state and improve their overall mood.

[0079] The system can further use its emotion estimation function to provide entertainment content that corresponds to the user's emotional state. For example, the generation AI can provide relaxing music when the user is feeling stressed. The generation AI can also provide a refreshing video when the user is tired. The generation AI can also provide entertainment content that lifts the user's mood when the user is feeling depressed. This allows the system to provide entertainment content that corresponds to the user's emotional state and improve the user's overall mood.

[0080] The system can further use its emotion estimation function to provide communication support according to the user's emotional state. For example, the generation AI can suggest conversations that have a relaxing effect when the user is feeling stressed. The generation AI can also provide words of encouragement when the user is tired. The generation AI can also provide communication support that lifts the user's mood when the user is feeling down. This allows the system to provide communication support according to the user's emotional state and improve their overall mood.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The sensor detects the health or beauty condition of the family member. For example, the sensor can collect health data such as body temperature, heart rate, and blood pressure, and can also detect skin condition and hair condition. Step 2: The generative AI analyzes the data detected by the sensors. For example, the generative AI can use machine learning algorithms to evaluate health status and statistical analysis methods to evaluate beauty status. Step 3: The health management department provides health advice based on the data analyzed by the generative AI. For example, if the patient has a high body temperature, the department may provide advice such as, "Your body temperature is high. You may have symptoms of a cold, so we recommend that you take a rest." If the patient has a high heart rate, the department may provide advice such as, "Your heart rate is high. We recommend that you relax." If the patient has high blood pressure, the department may provide advice such as, "Your blood pressure is high. We recommend that you limit your salt intake." Step 4: The beauty management department provides beauty advice based on the data analyzed by the generative AI. For example, if the skin is dry, the department can provide advice such as "Your skin is dry. We recommend using a moisturizing cream." If the hair is damaged, the department can provide advice such as "Your hair is damaged. We recommend using a repair shampoo." If the skin's moisture content is low, the department can provide advice such as "Your skin's moisture content is low. We recommend that you stay hydrated." Step 5: The fashion suggestion unit provides fashion suggestions based on the data analyzed by the generative AI. For example, it can provide suggestions such as "It's raining today, so we recommend wearing a waterproof jacket," "It's cold today, so we recommend wearing warm clothes," or "It's sunny today, so we recommend going out in light clothing."

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 sensor, Generative AI and Health Management Department and Beauty Management Department and A fashion proposal department, The sensor Detect your family's health or beauty condition, The generated AI is Analyzing data sensed by the sensor; The health management department Providing health advice based on the data analyzed by the generating AI; The beauty management department Providing beauty advice based on the data analyzed by the generating AI, The fashion suggestion department Providing fashion suggestions based on data analyzed by the generative AI A system characterized by:

2. The health management department Based on the health data collected by the sensors, the generative AI learns the health history of each family member and makes long-term health predictions.

2. The system of claim 1.

3. The health management department Based on health data, the generative AI analyzes the health trends of the entire family and proposes a health program for the whole family.

2. The system of claim 1.

4. The beauty management department Based on the beauty data collected by the camera or the sensor, the generative AI recommends beauty products that are best suited to individual skin types and hair textures.

2. The system of claim 1.

5. The fashion suggestion department Based on fashion data collected by the camera, the generative AI will suggest outfits that best suit individual styles and preferences.

2. The system of claim 1.

6. The health management department Analyzes the user's emotional state and suggests relaxation methods according to stress levels 2. The system of claim 1.

7. The beauty management department Analyzes the user's emotional state and suggests beauty care with a refreshing effect 2. The system of claim 1.

8. The fashion suggestion department Analyzes the user's emotional state and suggests fashion that will boost their mood 2. The system of claim 1.

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