System

Abstract

An object of a system according to an embodiment is to recommend an appropriate restaurant menu on the basis of a health condition of a user.SOLUTION: A system includes a camera, a data collection part, a health condition analysis part, and a recommendation part. The camera acquires data. The data collection unit collects data acquired by the camera. The health condition analysis unit analyzes a health condition of the user on the basis of the data collected by the data collection unit. The recommendation unit recommends a restaurant menu on the basis of the health condition analyzed by the health condition analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to select an appropriate restaurant menu based on a user's health condition.

[0005] The system according to the embodiment aims to recommend appropriate restaurant menus based on the user's health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a data collection unit, a health condition analysis unit, and a recommendation unit. The camera acquires data. The data collection unit collects the data acquired by the camera. The health condition analysis unit analyzes the user's health condition based on the data collected by the data collection unit. The recommendation unit recommends restaurant menu items based on the health condition analyzed by the health condition analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend appropriate restaurant menus based on the user's health condition. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A recommendation system according to an embodiment of the present invention uses a camera to predict a user's complexion, body temperature, height, and weight, and recommends optimal menu items from restaurant menus based on that data. This allows the recommendation system to provide optimal restaurant menu items based on the user's health condition.

[0029] A recommendation system according to an embodiment includes a camera, a data collection unit, a health condition analysis unit, and a recommendation unit. The camera detects a user's complexion, body temperature, height, and weight. For example, the camera scans the user's face and estimates the user's health condition from the complexion. It also measures the user's body temperature using an infrared sensor to estimate the user's height and weight. The data collection unit collects data acquired by the camera. For example, it collects image data and video data acquired by the camera and stores them for analysis. The health condition analysis unit analyzes the user's health condition based on the data collected by the data collection unit. For example, a pale complexion may indicate fatigue or stress, and a high body temperature may indicate a fever. The recommendation unit recommends restaurant menu items based on the user's health condition analyzed by the health condition analysis unit. For example, if fatigue is determined to be present, a menu item rich in vitamins and minerals may be suggested. This allows the recommendation system according to an embodiment to recommend optimal restaurant menu items based on the user's health condition.

[0030] The data collection unit can use a camera to scan the user's facial color and infer their health condition. For example, the data collection unit uses a camera to scan the user's face and analyze changes in facial expression in real time. For example, it detects subtle changes in facial expression, such as smiling or furrowing the brow, and infers their emotional state. It also uses facial expression analysis technology to infer the user's emotional state in real time. For example, it analyzes eye movements and the degree to which the corners of the mouth are turned up to identify emotions such as joy or sadness. The camera also continuously monitors changes in the user's facial expression and records changes in their emotional state in real time. For example, it can detect signs of stress or fatigue through long-term observation. This makes it possible to infer the user's health condition from their facial color.

[0031] The data collection unit can measure the user's body temperature using an infrared sensor and infer their health condition. For example, the data collection unit uses a camera to analyze the user's movements and estimate their fatigue level. For example, it detects changes in walking speed and posture to identify signs of fatigue. It also uses posture analysis technology to estimate the user's stress level. For example, it analyzes the position of the shoulders and the degree of curvature of the back to detect signs of stress. The camera also continuously monitors the user's movements and records changes in fatigue and stress levels in real time. For example, it can detect accumulation of fatigue and stress through long-term observation. This makes it possible to infer the user's health condition from their body temperature.

[0032] The data collection unit can estimate the user's height and weight and infer their health condition. For example, the data collection unit uses a camera to analyze the user's emotions in real time and adjust the timing of data collection. For example, it collects data when the user is relaxed. It also uses an emotion estimation function to select a data collection method according to the user's emotional state. For example, it pauses data collection if the user is feeling stressed. It also continuously monitors the user's emotions and adjusts the data collection method according to changes in their emotional state. For example, it collects data when the user is feeling positive. This makes it possible to infer the user's health condition from their height and weight.

[0033] The health condition analysis unit can determine the possibility of fatigue or stress if the user's complexion is pale. The health condition analysis unit, for example, combines a camera and a microphone to collect the user's voice data. For example, it analyzes the tone of voice and speaking style to estimate the user's health condition. It also uses voice analysis technology to estimate the user's emotional state from the user's tone of voice and speaking style. For example, it analyzes the pitch and speed of the voice to identify signs of stress or fatigue. It also integrates the camera and voice data to comprehensively analyze the user's health condition. For example, it combines facial expressions and tone of voice to make more accurate health condition predictions. This makes it possible to determine the possibility of fatigue or stress from the user's complexion.

[0034] The health condition analysis unit can determine that a high body temperature indicates signs of fever. For example, the health condition analysis unit uses a camera to analyze the moisture content of the user's skin and estimate the user's health condition. For example, it detects the dryness of the skin and determines whether hydration is necessary. The camera also uses blood flow analysis technology to monitor the user's blood flow. For example, it analyzes the speed and pattern of blood flow to estimate the health of the circulatory system. The camera also simultaneously analyzes the user's skin moisture content and blood flow to assess the user's overall health condition. For example, it combines changes in the skin condition and blood flow to collect more detailed health condition data. This allows the user's body temperature to determine signs of fever.

[0035] The recommendation unit can suggest menus rich in vitamins and minerals if it determines that fatigue is building up. For example, the recommendation unit uses AI to collect the user's past menu selection history and analyze preferences and allergy information. For example, it can identify a tendency to avoid certain ingredients. Furthermore, using menu selection history analysis technology, the AI ​​can make recommendations that take into account the user's preferences and allergy information. For example, it can suggest new menus based on trends in past menu choices. Furthermore, the AI ​​can integrate the user's past menu selection history with health data and make recommendations that take into account preferences and allergy information. For example, it can suggest menus that suit the user's health condition. This makes it possible to suggest menus that suit the user's level of fatigue.

[0036] The recommendation unit can suggest light, easy-to-digest menus if there are signs of a fever. For example, the recommendation unit uses AI to collect seasonal and weather data and recommend menus based on that. For example, in summer, it would suggest cold dishes and hydrating menus. In addition, to suggest meals appropriate to the season and climate, AI analyzes past data and identifies popular seasonal menus. For example, it would suggest hot soups and hot pot dishes in winter. AI also analyzes real-time weather data and recommends menus appropriate for the weather of the day. For example, it would suggest hot drinks and comfort food on rainy days. This allows it to suggest menus that suit the user's fever.

[0037] The recommendation unit can select menus that take into account calories and nutritional balance according to physique and body type. For example, the recommendation unit uses AI to collect the health conditions and preferences of the user's friends and family and recommend the best menu for group meals based on that information. For example, it can suggest a balanced menu that everyone can enjoy. We can also build a system that recommends menus that take into account the health conditions and preferences of a group. For example, it can suggest menus that take allergy information and dietary restrictions into consideration. Furthermore, the AI ​​can integrate data on the user's friends and family and recommend menus based on the health conditions and preferences of the entire group. For example, it can suggest a diverse menu that will satisfy everyone. This makes it possible to select menus that suit the user's physique and body type.

[0038] The recommendation unit can improve the accuracy of recommendations based on user feedback. For example, the recommendation unit uses AI to collect a user's past menu selection history and analyze preferences and allergy information. For example, it can identify a tendency to avoid certain ingredients. Furthermore, using menu selection history analysis technology, the AI ​​can make recommendations that take into account the user's preferences and allergy information. For example, it can suggest new menus based on trends in past menu choices. Furthermore, the AI ​​can integrate a user's past menu selection history with health data and make recommendations that take into account preferences and allergy information. For example, it can suggest menus that suit a user's health condition. This allows the accuracy of recommendations to be improved based on user feedback.

[0039] The data collection unit can use a camera to analyze the user's movements and posture to estimate fatigue and stress levels. For example, the data collection unit uses a camera to analyze the user's movements and estimate fatigue levels. For example, it detects changes in walking speed and posture to identify signs of fatigue. It also uses posture analysis technology to estimate the user's stress level. For example, it analyzes the position of the shoulders and the degree of curvature of the back to detect signs of stress. The camera also continuously monitors the user's movements and records changes in fatigue and stress levels in real time. For example, it detects accumulation of fatigue and stress through long-term observation. This makes it possible to estimate fatigue and stress levels from the user's movements and posture.

[0040] In addition to collecting data using a camera, the data collection unit can collect the user's voice data and estimate the health condition from the tone of voice and speaking style. The data collection unit, for example, combines a camera and a microphone to collect the user's voice data. For example, it analyzes the tone of voice and speaking style to estimate the health condition. It also uses voice analysis technology to estimate the user's emotional state from the tone of voice and speaking style. For example, it analyzes the pitch and speed of the voice to identify signs of stress or fatigue. It also integrates the camera and voice data to comprehensively analyze the user's health condition. For example, it combines facial expressions and tone of voice to make more accurate predictions of the health condition. This makes it possible to estimate the health condition from the user's voice data.

[0041] The data collection unit can use a camera to analyze the moisture content of the user's skin and blood flow to collect more detailed health condition data. For example, the data collection unit uses a camera to analyze the moisture content of the user's skin and estimate the health condition. For example, it detects the dryness of the skin and determines whether hydration is necessary. The camera also uses blood flow analysis technology to monitor the user's blood flow. For example, it analyzes the speed and pattern of blood flow to estimate the health condition of the circulatory system. The camera also simultaneously analyzes the moisture content of the user's skin and blood flow to evaluate the overall health condition. For example, it combines changes in the skin condition and blood flow to collect more detailed health condition data. This allows detailed health condition data to be collected from the user's skin moisture content and blood flow.

[0042] The health status analysis unit can compare a user's past health data with current data and analyze long-term health trends. For example, AI collects the user's past health data and compares it with current data. For example, it analyzes past changes in body temperature and weight to identify long-term health trends. Furthermore, using health data trend analysis technology, AI monitors changes in the user's health status over the long term. For example, it predicts future health risks based on past data. Furthermore, AI integrates the user's past health data with current data to visualize long-term health trends. For example, it displays changes in health status using graphs and charts. This makes it possible to compare the user's past and current health data and analyze long-term health trends.

[0043] The health condition analysis unit analyzes the user's lifestyle data (e.g., sleep patterns and exercise amount) to improve the accuracy of health condition predictions. In the health condition analysis unit, for example, AI analyzes the user's sleep patterns and uses them to predict the health condition. For example, it monitors the quality and duration of sleep and identifies health risks. In addition, using exercise amount analysis technology, AI collects the user's exercise data and reflects it in the health condition prediction. For example, it analyzes the daily exercise amount and activity level. In addition, AI integrates the user's lifestyle data to improve the accuracy of health condition predictions. For example, it combines sleep patterns and exercise amount to make more accurate health predictions. In this way, it is possible to analyze the user's lifestyle data and improve the accuracy of health condition predictions.

[0044] The health condition analysis unit can analyze the user's dietary history and clarify the correlation between past dietary content and health status. For example, the health condition analysis unit uses AI to collect the user's past dietary history and analyze the correlation with health status. For example, it identifies the impact of specific ingredients and nutrients on health. Furthermore, using dietary history analysis technology, AI analyzes the user's dietary patterns and reflects this in predicting health status. For example, it analyzes meal frequency and balance. Furthermore, AI integrates the user's dietary history with health data and visualizes the impact of past dietary content on health. For example, it displays the correlation between diet and health using graphs and charts. This makes it possible to clarify the correlation between the user's past dietary content and health status.

[0045] The health condition analysis unit can analyze the user's genetic information and predict the health condition based on genetic factors. In the health condition analysis unit, for example, AI collects the user's genetic information and uses it to predict the health condition. For example, it identifies genetic risk factors and predicts health risks. Furthermore, using genetic information analysis technology, AI analyzes the user's genetic data and improves the accuracy of health condition predictions. For example, it identifies the risk of genetic diseases. Furthermore, AI integrates the user's genetic information and health data and makes health predictions based on genetic factors. For example, it analyzes the relationship between genetic risk and lifestyle habits. This makes it possible to predict the health condition based on the user's genetic information.

[0046] The recommendation unit can analyze a user's past menu selection history and make recommendations that take into account preferences and allergy information. For example, the recommendation unit uses AI to collect a user's past menu selection history and analyze preferences and allergy information. For example, it can identify a tendency to avoid certain ingredients. Furthermore, using menu selection history analysis technology, the AI ​​can make recommendations that take into account the user's preferences and allergy information. For example, it can suggest new menus based on trends in menu choices in the past. Furthermore, the AI ​​can integrate a user's past menu selection history with health data and make recommendations that take into account preferences and allergy information. For example, it can suggest menus that suit their health condition. This makes it possible to make recommendations that take into account the user's preferences and allergy information.

[0047] The recommendation unit can recommend menus according to the season and weather, and suggest meals that are suitable for the season and climate. For example, the recommendation unit uses AI to collect season and weather data and recommend menus based on that. For example, in summer, it would suggest cold dishes and hydrating menus. In addition, to suggest meals that are suitable for the season and climate, AI analyzes past data and identifies popular seasonal dishes. For example, it would suggest hot soups and hot pot dishes in winter. In addition, AI analyzes real-time weather data and recommends menus that are suitable for the weather of the day. For example, it would suggest hot drinks and comfort food on rainy days. This allows the recommendation unit to recommend menus according to the season and weather, and suggest meals that are suitable for the season and climate.

[0048] The recommendation unit can recommend the best menu for a group meal, taking into account the health conditions and preferences of the user's friends and family. For example, the recommendation unit uses AI to collect the health conditions and preferences of the user's friends and family and recommend the best menu for a group meal based on that information. For example, it can suggest a balanced menu that everyone can enjoy. We can also build a system that recommends menus that take into account the health conditions and preferences of a group. For example, it can suggest menus that take allergy information and dietary restrictions into consideration. Furthermore, the AI ​​can integrate data on the user's friends and family and recommend menus based on the health conditions and preferences of the entire group. For example, it can suggest a diverse menu that will satisfy everyone. This makes it possible to recommend the best menu for a group meal, taking into account the health conditions and preferences of the user's friends and family.

[0049] The recommendation unit can analyze restaurant information in the user's current location or travel destination and recommend local specialties and famous dishes. For example, the recommendation unit uses AI to collect restaurant information in the user's current location or travel destination and recommend local specialties and famous dishes. For example, it can suggest famous dishes at the travel destination. In order to recommend local specialties and famous dishes, the AI ​​also analyzes local restaurant data. For example, it can identify locally popular dishes. The AI ​​also integrates data on the user's current location and travel destination and recommends local specialties and famous dishes in real time. For example, it can suggest restaurants to visit during a trip. This allows the recommendation unit to analyze restaurant information in the user's current location or travel destination and recommend local specialties and famous dishes.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Recommendation systems can also collect a user's past exercise data and use it to predict their health condition. For example, they can link with an application that records the user's daily exercise volume to obtain exercise data. They can also use exercise data analysis technology to analyze the user's exercise patterns and reflect this in their health condition predictions. For example, if the user has been lacking exercise, they can suggest a menu to encourage exercise. They can also integrate exercise data and health data to evaluate overall health. For example, they can combine exercise volume and weight changes to collect more detailed health condition data. This makes it possible to utilize the user's exercise data to improve the accuracy of health condition predictions.

[0052] Recommendation systems can also collect users' sleep data and use it to predict their health status. For example, they can connect with the user's smartwatch or sleep tracker to collect sleep data. They can also use sleep data analysis technology to analyze the user's sleep patterns and reflect this in their health status predictions. For example, if a user has been experiencing a lack of sleep, they can suggest a relaxing activity. They can also integrate sleep data with health data to evaluate overall health status. For example, they can combine sleep quality with changes in body temperature to collect more detailed health status data. This makes it possible to utilize the user's sleep data to improve the accuracy of health status predictions.

[0053] Recommendation systems can also collect a user's dietary history and use it to predict their health condition. For example, they can link with a food recording application used by the user to obtain dietary data. Dietary data analysis technology can then be used to analyze the user's eating patterns and reflect this in health condition predictions. For example, if there is an imbalance in nutritional intake, a balanced menu can be suggested. Dietary data and health data can also be integrated to evaluate overall health. For example, dietary content and weight changes can be combined to collect more detailed health condition data. This makes it possible to utilize the user's dietary data to improve the accuracy of health condition predictions.

[0054] Recommendation systems can also collect users' genetic information and use it to predict their health status. For example, the system can analyze the genetic information provided by the user to identify genetic risk factors. It can also use genetic information analysis technology to analyze the user's genetic data and reflect this in health status predictions. For example, if a user has a genetic risk of high blood pressure, it can suggest low-salt menus. It can also integrate genetic information and health data to evaluate overall health status. For example, it can combine genetic risk with current health status to collect more detailed health status data. This makes it possible to utilize the user's genetic information to improve the accuracy of health status predictions.

[0055] Recommendation systems can also collect lifestyle data from users and use it to predict their health status. For example, they can link with a life log application used by the user to obtain lifestyle data. They can also use lifestyle data analysis technology to analyze the user's lifestyle patterns and reflect this in health status predictions. For example, if the user lives a stressful life, they can suggest a menu that helps them relax. They can also integrate lifestyle data and health data to evaluate their overall health status. For example, they can combine lifestyle data with changes in weight to collect more detailed health status data. This makes it possible to utilize the user's lifestyle data to improve the accuracy of health status predictions.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The camera detects the user's complexion, body temperature, height, and weight. For example, the camera scans the user's face and infers their health condition from their complexion. It also uses an infrared sensor to measure body temperature and estimate their height and weight. Step 2: The data collection unit collects data acquired by the camera, for example, image data or video data acquired by the camera, and stores it for analysis. Step 3: The health condition analysis unit analyzes the user's health condition based on the data collected by the data collection unit. For example, if the user's face is pale, it may be determined that the user is fatigued or stressed, and if the user's body temperature is high, it may be determined that the user has a fever. Step 4: The recommendation unit recommends restaurant menus based on the health status analyzed by the health status analysis unit. For example, if fatigue is detected, menus rich in vitamins and minerals will be suggested.

[0058] (Example 2) A recommendation system according to an embodiment of the present invention uses a camera to predict a user's complexion, body temperature, height, and weight, and recommends optimal menu items from restaurant menus based on that data. This allows the recommendation system to provide optimal restaurant menu items based on the user's health condition.

[0059] A recommendation system according to an embodiment includes a camera, a data collection unit, a health condition analysis unit, and a recommendation unit. The camera detects a user's complexion, body temperature, height, and weight. For example, the camera scans the user's face and estimates the user's health condition from the complexion. It also measures the user's body temperature using an infrared sensor to estimate the user's height and weight. The data collection unit collects data acquired by the camera. For example, it collects image data and video data acquired by the camera and stores them for analysis. The health condition analysis unit analyzes the user's health condition based on the data collected by the data collection unit. For example, a pale complexion may indicate fatigue or stress, and a high body temperature may indicate a fever. The recommendation unit recommends restaurant menu items based on the user's health condition analyzed by the health condition analysis unit. For example, if fatigue is determined to be present, a menu item rich in vitamins and minerals may be suggested. This allows the recommendation system according to an embodiment to recommend optimal restaurant menu items based on the user's health condition.

[0060] The data collection unit can use a camera to scan the user's facial color and infer their health condition. For example, the data collection unit uses a camera to scan the user's face and analyze changes in facial expression in real time. For example, it detects subtle changes in facial expression, such as smiling or furrowing the brow, and infers their emotional state. It also uses facial expression analysis technology to infer the user's emotional state in real time. For example, it analyzes eye movements and the degree to which the corners of the mouth are turned up to identify emotions such as joy or sadness. The camera also continuously monitors changes in the user's facial expression and records changes in their emotional state in real time. For example, it can detect signs of stress or fatigue through long-term observation. This makes it possible to infer the user's health condition from their facial color.

[0061] The data collection unit can measure the user's body temperature using an infrared sensor and infer their health condition. For example, the data collection unit uses a camera to analyze the user's movements and estimate their fatigue level. For example, it detects changes in walking speed and posture to identify signs of fatigue. It also uses posture analysis technology to estimate the user's stress level. For example, it analyzes the position of the shoulders and the degree of curvature of the back to detect signs of stress. The camera also continuously monitors the user's movements and records changes in fatigue and stress levels in real time. For example, it can detect accumulation of fatigue and stress through long-term observation. This makes it possible to infer the user's health condition from their body temperature.

[0062] The data collection unit can estimate the user's height and weight and infer their health condition. For example, the data collection unit uses a camera to analyze the user's emotions in real time and adjust the timing of data collection. For example, it collects data when the user is relaxed. It also uses an emotion estimation function to select a data collection method according to the user's emotional state. For example, it pauses data collection if the user is feeling stressed. It also continuously monitors the user's emotions and adjusts the data collection method according to changes in their emotional state. For example, it collects data when the user is feeling positive. This makes it possible to infer the user's health condition from their height and weight.

[0063] The health condition analysis unit can determine the possibility of fatigue or stress if the user's complexion is pale. The health condition analysis unit, for example, combines a camera and a microphone to collect the user's voice data. For example, it analyzes the tone of voice and speaking style to estimate the user's health condition. It also uses voice analysis technology to estimate the user's emotional state from the user's tone of voice and speaking style. For example, it analyzes the pitch and speed of the voice to identify signs of stress or fatigue. It also integrates the camera and voice data to comprehensively analyze the user's health condition. For example, it combines facial expressions and tone of voice to make more accurate health condition predictions. This makes it possible to determine the possibility of fatigue or stress from the user's complexion.

[0064] The health condition analysis unit can determine that a high body temperature indicates signs of fever. For example, the health condition analysis unit uses a camera to analyze the moisture content of the user's skin and estimate the user's health condition. For example, it detects the dryness of the skin and determines whether hydration is necessary. The camera also uses blood flow analysis technology to monitor the user's blood flow. For example, it analyzes the speed and pattern of blood flow to estimate the health of the circulatory system. The camera also simultaneously analyzes the user's skin moisture content and blood flow to assess the user's overall health condition. For example, it combines changes in the skin condition and blood flow to collect more detailed health condition data. This allows the user's body temperature to determine signs of fever.

[0065] The recommendation unit can suggest menus rich in vitamins and minerals if it determines that fatigue is building up. For example, the recommendation unit uses AI to collect the user's past menu selection history and analyze preferences and allergy information. For example, it can identify a tendency to avoid certain ingredients. Furthermore, using menu selection history analysis technology, the AI ​​can make recommendations that take into account the user's preferences and allergy information. For example, it can suggest new menus based on trends in past menu choices. Furthermore, the AI ​​can integrate the user's past menu selection history with health data and make recommendations that take into account preferences and allergy information. For example, it can suggest menus that suit the user's health condition. This makes it possible to suggest menus that suit the user's level of fatigue.

[0066] The recommendation unit can suggest light, easy-to-digest menus if there are signs of a fever. For example, the recommendation unit uses AI to collect seasonal and weather data and recommend menus based on that. For example, in summer, it would suggest cold dishes and hydrating menus. In addition, to suggest meals appropriate to the season and climate, AI analyzes past data and identifies popular seasonal menus. For example, it would suggest hot soups and hot pot dishes in winter. AI also analyzes real-time weather data and recommends menus appropriate for the weather of the day. For example, it would suggest hot drinks and comfort food on rainy days. This allows it to suggest menus that suit the user's fever.

[0067] The recommendation unit can select menus that take into account calories and nutritional balance according to physique and body type. For example, the recommendation unit uses AI to collect the health conditions and preferences of the user's friends and family and recommend the best menu for group meals based on that information. For example, it can suggest a balanced menu that everyone can enjoy. We can also build a system that recommends menus that take into account the health conditions and preferences of a group. For example, it can suggest menus that take allergy information and dietary restrictions into consideration. Furthermore, the AI ​​can integrate data on the user's friends and family and recommend menus based on the health conditions and preferences of the entire group. For example, it can suggest a diverse menu that will satisfy everyone. This makes it possible to select menus that suit the user's physique and body type.

[0068] The recommendation unit can improve the accuracy of recommendations based on user feedback. For example, the recommendation unit uses AI to collect a user's past menu selection history and analyze preferences and allergy information. For example, it can identify a tendency to avoid certain ingredients. Furthermore, using menu selection history analysis technology, the AI ​​can make recommendations that take into account the user's preferences and allergy information. For example, it can suggest new menus based on trends in past menu choices. Furthermore, the AI ​​can integrate a user's past menu selection history with health data and make recommendations that take into account preferences and allergy information. For example, it can suggest menus that suit a user's health condition. This allows the accuracy of recommendations to be improved based on user feedback.

[0069] The data collection unit can use a camera to analyze changes in the user's facial expression in real time and estimate the user's emotional state. For example, the data collection unit uses a camera to scan the user's face and analyze changes in facial expression in real time. For example, it detects subtle changes in facial expression, such as smiling or furrowing the brow, and estimates the user's emotional state. It also uses facial expression analysis technology to estimate the user's emotional state in real time. For example, it analyzes eye movements and the degree to which the corners of the mouth are turned up to identify emotions such as joy or sadness. The camera also continuously monitors changes in the user's facial expression and records changes in the user's emotional state in real time. For example, it can detect signs of stress or fatigue through long-term observation. This makes it possible to estimate the user's emotional state from changes in facial expression.

[0070] The data collection unit can use a camera to analyze the user's movements and posture to estimate fatigue and stress levels. For example, the data collection unit uses a camera to analyze the user's movements and estimate fatigue levels. For example, it detects changes in walking speed and posture to identify signs of fatigue. It also uses posture analysis technology to estimate the user's stress level. For example, it analyzes the position of the shoulders and the degree of curvature of the back to detect signs of stress. The camera also continuously monitors the user's movements and records changes in fatigue and stress levels in real time. For example, it detects accumulation of fatigue and stress through long-term observation. This makes it possible to estimate fatigue and stress levels from the user's movements and posture.

[0071] The data collection unit can use the emotion estimation function to analyze the emotion of the user when they stand in front of the camera and adjust the timing and method of data collection based on that emotion. For example, the data collection unit has the camera analyze the user's emotion in real time and adjust the timing of data collection. For example, it collects data when the user is relaxed. The emotion estimation function is also used to select a data collection method according to the user's emotional state. For example, it pauses data collection if the user is feeling stressed. The camera also continuously monitors the user's emotion and adjusts the method of data collection according to changes in the emotional state. For example, it collects data when the user is feeling positive emotions. This makes it possible to adjust the timing and method of data collection based on the user's emotion.

[0072] In addition to collecting data using a camera, the data collection unit can collect the user's voice data and estimate the health condition from the tone of voice and speaking style. The data collection unit, for example, combines a camera and a microphone to collect the user's voice data. For example, it analyzes the tone of voice and speaking style to estimate the health condition. It also uses voice analysis technology to estimate the user's emotional state from the tone of voice and speaking style. For example, it analyzes the pitch and speed of the voice to identify signs of stress or fatigue. It also integrates the camera and voice data to comprehensively analyze the user's health condition. For example, it combines facial expressions and tone of voice to make more accurate predictions of the health condition. This makes it possible to estimate the health condition from the user's voice data.

[0073] The data collection unit can use a camera to analyze the moisture content of the user's skin and blood flow to collect more detailed health condition data. For example, the data collection unit uses a camera to analyze the moisture content of the user's skin and estimate the health condition. For example, it detects the dryness of the skin and determines whether hydration is necessary. The camera also uses blood flow analysis technology to monitor the user's blood flow. For example, it analyzes the speed and pattern of blood flow to estimate the health condition of the circulatory system. The camera also simultaneously analyzes the moisture content of the user's skin and blood flow to evaluate the overall health condition. For example, it combines changes in the skin condition and blood flow to collect more detailed health condition data. This allows detailed health condition data to be collected from the user's skin moisture content and blood flow.

[0074] The data collection unit can use the emotion estimation function to analyze the emotions of a user when they stand in front of the camera and provide an interface for eliciting positive emotions. For example, the data collection unit allows the camera to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it displays a message that makes the user smile. The emotion estimation function is also used to customize the interface according to the user's emotional state. For example, it plays background music that helps the user relax. The camera also continuously monitors the user's emotions and dynamically adjusts the interface for eliciting positive emotions. For example, it changes the color or design of the interface according to the user's emotional state. This makes it possible to provide an interface that elicits positive emotions based on the user's emotions.

[0075] The health status analysis unit can compare a user's past health data with current data and analyze long-term health trends. For example, AI collects the user's past health data and compares it with current data. For example, it analyzes past changes in body temperature and weight to identify long-term health trends. Furthermore, using health data trend analysis technology, AI monitors changes in the user's health status over the long term. For example, it predicts future health risks based on past data. Furthermore, AI integrates the user's past health data with current data to visualize long-term health trends. For example, it displays changes in health status using graphs and charts. This makes it possible to compare the user's past and current health data and analyze long-term health trends.

[0076] The health condition analysis unit analyzes the user's lifestyle data (e.g., sleep patterns and exercise amount) to improve the accuracy of health condition predictions. In the health condition analysis unit, for example, AI analyzes the user's sleep patterns and uses them to predict the health condition. For example, it monitors the quality and duration of sleep and identifies health risks. In addition, using exercise amount analysis technology, AI collects the user's exercise data and reflects it in the health condition prediction. For example, it analyzes the daily exercise amount and activity level. In addition, AI integrates the user's lifestyle data to improve the accuracy of health condition predictions. For example, it combines sleep patterns and exercise amount to make more accurate health predictions. In this way, it is possible to analyze the user's lifestyle data and improve the accuracy of health condition predictions.

[0077] The health condition analysis unit can use the emotion estimation function to analyze the user's emotional state and clarify the relationship between emotions and health condition. The health condition analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, it analyzes facial expressions and voice and calculates an emotion score. It also integrates emotional data with health data to analyze the relationship between emotions and health condition. For example, it identifies the impact of stress and fatigue on health. In addition, AI continuously monitors the user's emotional state and analyzes the relationship between emotions and health condition over the long term. For example, it tracks the impact of changes in emotions on health. This makes it possible to clarify the relationship between the user's emotional state and health condition.

[0078] The health condition analysis unit can analyze the user's dietary history and clarify the correlation between past dietary content and health status. For example, the health condition analysis unit uses AI to collect the user's past dietary history and analyze the correlation with health status. For example, it identifies the impact of specific ingredients and nutrients on health. Furthermore, using dietary history analysis technology, AI analyzes the user's dietary patterns and reflects this in predicting health status. For example, it analyzes meal frequency and balance. Furthermore, AI integrates the user's dietary history with health data and visualizes the impact of past dietary content on health. For example, it displays the correlation between diet and health using graphs and charts. This makes it possible to clarify the correlation between the user's past dietary content and health status.

[0079] The health condition analysis unit can analyze the user's genetic information and predict the health condition based on genetic factors. In the health condition analysis unit, for example, AI collects the user's genetic information and uses it to predict the health condition. For example, it identifies genetic risk factors and predicts health risks. Furthermore, using genetic information analysis technology, AI analyzes the user's genetic data and improves the accuracy of health condition predictions. For example, it identifies the risk of genetic diseases. Furthermore, AI integrates the user's genetic information and health data and makes health predictions based on genetic factors. For example, it analyzes the relationship between genetic risk and lifestyle habits. This makes it possible to predict the health condition based on the user's genetic information.

[0080] The health condition analysis unit can use the emotion estimation function to analyze the user's emotional state and provide health advice based on the emotion. The health condition analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide health advice. For example, it can suggest relaxation methods when the user is feeling stressed. It can also integrate emotional data and health data to build a system that provides health advice based on emotions. For example, it can provide advice on diet and exercise according to the user's emotional state. It can also use AI to continuously monitor the user's emotional state and provide health advice based on the emotion. For example, it can dynamically adjust the content of the advice according to changes in emotions. This makes it possible to provide health advice based on the user's emotional state.

[0081] The recommendation unit can analyze a user's past menu selection history and make recommendations that take into account preferences and allergy information. For example, the recommendation unit uses AI to collect a user's past menu selection history and analyze preferences and allergy information. For example, it can identify a tendency to avoid certain ingredients. Furthermore, using menu selection history analysis technology, the AI ​​can make recommendations that take into account the user's preferences and allergy information. For example, it can suggest new menus based on trends in menu choices in the past. Furthermore, the AI ​​can integrate a user's past menu selection history with health data and make recommendations that take into account preferences and allergy information. For example, it can suggest menus that suit their health condition. This makes it possible to make recommendations that take into account the user's preferences and allergy information.

[0082] The recommendation unit can recommend menus according to the season and weather, and suggest meals that are suitable for the season and climate. For example, the recommendation unit uses AI to collect season and weather data and recommend menus based on that. For example, in summer, it would suggest cold dishes and hydrating menus. In addition, to suggest meals that are suitable for the season and climate, AI analyzes past data and identifies popular seasonal dishes. For example, it would suggest hot soups and hot pot dishes in winter. In addition, AI analyzes real-time weather data and recommends menus that are suitable for the weather of the day. For example, it would suggest hot drinks and comfort food on rainy days. This allows the recommendation unit to recommend menus according to the season and weather, and suggest meals that are suitable for the season and climate.

[0083] The recommendation unit uses the emotion estimation function to recommend menus based on the user's emotional state, thereby increasing emotional satisfaction. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and recommend menus based on those emotions. For example, if the user is feeling stressed, it can suggest a meal that will help them relax. A system is also built that recommends menus according to the user's emotional state. For example, it can suggest the user's favorite dishes to elicit positive emotions. Furthermore, AI continuously monitors the user's emotional state and dynamically adjusts menus that will increase emotional satisfaction. For example, it changes the menu according to changes in the user's emotions. This makes it possible to recommend menus based on the user's emotional state and increase emotional satisfaction.

[0084] The recommendation unit can recommend the best menu for a group meal, taking into account the health conditions and preferences of the user's friends and family. For example, the recommendation unit uses AI to collect the health conditions and preferences of the user's friends and family and recommend the best menu for a group meal based on that information. For example, it can suggest a balanced menu that everyone can enjoy. We can also build a system that recommends menus that take into account the health conditions and preferences of a group. For example, it can suggest menus that take allergy information and dietary restrictions into consideration. Furthermore, the AI ​​can integrate data on the user's friends and family and recommend menus based on the health conditions and preferences of the entire group. For example, it can suggest a diverse menu that will satisfy everyone. This makes it possible to recommend the best menu for a group meal, taking into account the health conditions and preferences of the user's friends and family.

[0085] The recommendation unit can analyze restaurant information in the user's current location or travel destination and recommend local specialties and famous dishes. For example, the recommendation unit uses AI to collect restaurant information in the user's current location or travel destination and recommend local specialties and famous dishes. For example, it can suggest famous dishes at the travel destination. In order to recommend local specialties and famous dishes, the AI ​​also analyzes local restaurant data. For example, it can identify locally popular dishes. The AI ​​also integrates data on the user's current location and travel destination and recommends local specialties and famous dishes in real time. For example, it can suggest restaurants to visit during a trip. This allows the recommendation unit to analyze restaurant information in the user's current location or travel destination and recommend local specialties and famous dishes.

[0086] The recommendation unit uses the emotion estimation function to recommend menus based on the user's emotional state, thereby increasing emotional satisfaction. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and recommend menus based on those emotions. For example, if the user is feeling stressed, it can suggest a meal that will help them relax. A system is also built that recommends menus according to the user's emotional state. For example, it can suggest the user's favorite dishes to elicit positive emotions. Furthermore, AI continuously monitors the user's emotional state and dynamically adjusts menus that will increase emotional satisfaction. For example, it changes the menu according to changes in the user's emotions. This makes it possible to recommend menus based on the user's emotional state and increase emotional satisfaction.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] Recommendation systems can also collect a user's past exercise data and use it to predict their health condition. For example, they can link with an application that records the user's daily exercise volume to obtain exercise data. They can also use exercise data analysis technology to analyze the user's exercise patterns and reflect this in their health condition predictions. For example, if the user has been lacking exercise, they can suggest a menu to encourage exercise. They can also integrate exercise data and health data to evaluate overall health. For example, they can combine exercise volume and weight changes to collect more detailed health condition data. This makes it possible to utilize the user's exercise data to improve the accuracy of health condition predictions.

[0089] Recommendation systems can also collect users' sleep data and use it to predict their health status. For example, they can connect with the user's smartwatch or sleep tracker to collect sleep data. They can also use sleep data analysis technology to analyze the user's sleep patterns and reflect this in their health status predictions. For example, if a user has been experiencing a lack of sleep, they can suggest a relaxing activity. They can also integrate sleep data with health data to evaluate overall health status. For example, they can combine sleep quality with changes in body temperature to collect more detailed health status data. This makes it possible to utilize the user's sleep data to improve the accuracy of health status predictions.

[0090] Recommendation systems can also collect a user's dietary history and use it to predict their health condition. For example, they can link with a food recording application used by the user to obtain dietary data. Dietary data analysis technology can then be used to analyze the user's eating patterns and reflect this in health condition predictions. For example, if there is an imbalance in nutritional intake, a balanced menu can be suggested. Dietary data and health data can also be integrated to evaluate overall health. For example, dietary content and weight changes can be combined to collect more detailed health condition data. This makes it possible to utilize the user's dietary data to improve the accuracy of health condition predictions.

[0091] Recommendation systems can also collect users' genetic information and use it to predict their health status. For example, the system can analyze the genetic information provided by the user to identify genetic risk factors. It can also use genetic information analysis technology to analyze the user's genetic data and reflect this in health status predictions. For example, if a user has a genetic risk of high blood pressure, it can suggest low-salt menus. It can also integrate genetic information and health data to evaluate overall health status. For example, it can combine genetic risk with current health status to collect more detailed health status data. This makes it possible to utilize the user's genetic information to improve the accuracy of health status predictions.

[0092] Recommendation systems can also collect lifestyle data from users and use it to predict their health status. For example, they can link with a life log application used by the user to obtain lifestyle data. They can also use lifestyle data analysis technology to analyze the user's lifestyle patterns and reflect this in health status predictions. For example, if the user lives a stressful life, they can suggest a menu that helps them relax. They can also integrate lifestyle data and health data to evaluate their overall health status. For example, they can combine lifestyle data with changes in weight to collect more detailed health status data. This makes it possible to utilize the user's lifestyle data to improve the accuracy of health status predictions.

[0093] Recommendation systems can also analyze a user's emotional state and provide health advice based on their emotions. For example, an emotion estimation function can be used to analyze a user's emotional state in real time and provide health advice. For example, if the user is feeling stressed, it can suggest relaxation methods. A system can also be built that integrates emotional data and health data to provide health advice based on emotions. For example, it can provide advice on diet and exercise according to the user's emotional state. AI can also continuously monitor a user's emotional state and provide health advice based on their emotions. For example, it can dynamically adjust the content of the advice according to changes in emotions. This makes it possible to provide health advice based on the user's emotional state.

[0094] Recommendation systems can also analyze a user's emotional state and recommend menus based on that emotion. For example, an emotion estimation function can be used to analyze a user's emotional state in real time and recommend menus based on that emotion. For example, if the user is feeling stressed, a relaxing meal can be suggested. We can also build a system that recommends menus according to the user's emotional state. For example, to elicit positive emotions, the system can suggest the user's favorite dishes. Furthermore, AI can continuously monitor the user's emotional state and dynamically adjust menus to increase emotional satisfaction. For example, the menu can be changed according to changes in the user's emotions. This allows us to recommend menus based on the user's emotional state and increase emotional satisfaction.

[0095] Recommendation systems can also analyze a user's emotional state and provide an interface based on the user's emotions. For example, an emotion estimation function can be used to analyze a user's emotional state in real time and provide an interface that elicits positive emotions. For example, a message that makes the user smile can be displayed. The emotion estimation function can also be used to customize the interface according to the user's emotional state. For example, background music that helps the user relax can be played. A camera can also continuously monitor the user's emotions and dynamically adjust the interface to elicit positive emotions. For example, the color or design of the interface can be changed depending on the user's emotional state. This makes it possible to provide an interface that elicits positive emotions based on the user's emotions.

[0096] Recommendation systems can also analyze users' emotional states and analyze health trends based on emotions. For example, they can use emotion estimation to analyze users' emotional states in real time and identify long-term health trends. They can also integrate emotional data with health data to analyze the relationship between emotions and health states. For example, they can identify the impact of stress and fatigue on health. AI can also continuously monitor users' emotional states and analyze the relationship between emotions and health states over the long term. For example, they can track the impact of emotional changes on health. This can clarify the relationship between a user's emotional state and health state.

[0097] Recommendation systems can also analyze a user's emotional state and provide health advice based on their emotions. For example, an emotion estimation function can be used to analyze a user's emotional state in real time and provide health advice. For example, if the user is feeling stressed, it can suggest relaxation methods. A system can also be built that integrates emotional data and health data to provide health advice based on emotions. For example, it can provide advice on diet and exercise according to the user's emotional state. AI can also continuously monitor a user's emotional state and provide health advice based on their emotions. For example, it can dynamically adjust the content of the advice according to changes in emotions. This makes it possible to provide health advice based on the user's emotional state.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The camera detects the user's complexion, body temperature, height, and weight. For example, the camera scans the user's face and infers their health condition from their complexion. It also uses an infrared sensor to measure body temperature and estimate their height and weight. Step 2: The data collection unit collects data acquired by the camera, for example, image data or video data acquired by the camera, and stores it for analysis. Step 3: The health condition analysis unit analyzes the user's health condition based on the data collected by the data collection unit. For example, if the user's face is pale, it may be determined that the user is fatigued or stressed, and if the user's body temperature is high, it may be determined that the user has a fever. Step 4: The recommendation unit recommends restaurant menus based on the health status analyzed by the health status analysis unit. For example, if fatigue is detected, menus rich in vitamins and minerals will be suggested.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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 camera and a data collection unit that collects data acquired by the camera; a health condition analysis unit that analyzes the health condition of the user based on the data collected by the data collection unit; a recommendation unit that recommends restaurant menus based on the health condition analyzed by the health condition analysis unit. A system characterized by:

2. The data collection unit The camera is used to scan the user's complexion and infer the health condition.

2. The system of claim 1.

3. The data collection unit Measure the user's body temperature using an infrared sensor to infer the health condition.

2. The system of claim 1.

4. The data collection unit Estimate the user's height and weight and infer their health status 2. The system of claim 1.

5. The health condition analysis unit If your face is pale, you may be feeling fatigued or stressed.

2. The system of claim 1.

Images