System

Abstract

An object of a system according to an embodiment is to grasp a lifestyle and an eating habit of a user and provide appropriate improvement advice.SOLUTION: A system according to an embodiment includes a lifestyle grasping unit, an improvement advice unit, a question answering unit, and a voice customization unit. The lifestyle grasping unit grasps the lifestyle and eating habits of the user through chats and questions with the user. The improvement advice unit provides improvement advice to the user on the basis of the lifestyle and eating habit grasped by the lifestyle grasping unit. The question answering unit provides an appropriate answer to the user's question. The voice customization unit can customize the gender, tone, wording, and conversation taste of the voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately grasped a user's lifestyle and dietary habits and provided appropriate advice for improving them, and there is room for improvement.

[0005] The system according to the embodiment aims to understand the lifestyle and dietary habits of a user and provide appropriate advice for improving them. [Means for solving the problem]

[0006] The system according to the embodiment includes a lifestyle habit identification unit, an improvement advice unit, a question and answer unit, and a voice customization unit. The lifestyle habit identification unit identifies a user's lifestyle and diet through casual conversations and questions with the user. The improvement advice unit provides improvement advice to the user based on the lifestyle and diet identified by the lifestyle habit identification unit. The question and answer unit provides appropriate answers to the user's questions. The voice customization unit enables customization of the gender, tone, wording, and conversational style of the voice. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the lifestyle and dietary habits of a user and provide appropriate advice for improving them. [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 health support system according to an embodiment of the present invention is a system that installs a voice dialogue generation AI as a home device, understands the user's lifestyle and dietary habits, and provides advice on how to improve them and answers to questions. This enables the health support system to support the user in improving their health and extending their healthy lifespan.

[0029] A health support system according to an embodiment includes a lifestyle habit identification unit, an improvement advice unit, a question and answer unit, and a voice customization unit. The lifestyle habit identification unit identifies a user's lifestyle and dietary habits through casual conversations and questions with the user. For example, the lifestyle habit identification unit asks the user, "What's your diet like lately?" and collects the user's answers. The lifestyle habit identification unit can also ask the user, "How often do you exercise?" to identify the user's exercise habits. The lifestyle habit identification unit can also analyze the user's answers and identify patterns of lifestyle and dietary habits. The improvement advice unit provides the user with improvement advice based on the lifestyle and dietary habits identified by the lifestyle habit identification unit. For example, the improvement advice unit can advise, "You should eat more vegetables." The improvement advice unit can also advise, "Try to exercise for 30 minutes every day." The improvement advice unit can also analyze data related to the user's lifestyle and dietary habits and generate optimal advice. The question and answering unit provides appropriate answers to the user's questions. For example, in response to the question, "What foods are rich in vitamin C?", the question answering unit answers, "Oranges and broccoli are rich in vitamin C." The question answering unit can also analyze the content of the user's question and provide appropriate information. The question answering unit can also analyze the user's past question history and provide relevant information. The voice customization unit can customize the gender, tone, phrasing, and conversational style of the voice. For example, if a user requests a "gentle female voice," the voice customization unit can set the voice according to the user's request. The voice customization unit can also customize the accent and intonation of the voice according to the user's preferences. The voice customization unit can also estimate the user's emotions and automatically adjust the voice tone according to the emotions. As a result, the health support system according to the embodiment can support the user's health promotion and healthy life expectancy. For example, the health support system can support the user's health by understanding the user's lifestyle and dietary habits and providing appropriate advice and answers.Furthermore, the voice customization function allows information to be provided in a format that is easy for the user to listen to.

[0030] The lifestyle habit identification unit works in conjunction with other health devices to collect data on the user's lifestyle and dietary habits, enabling more detailed information to be obtained. For example, the generation AI in the lifestyle habit identification unit works in conjunction with a smartwatch or fitness tracker to collect the user's exercise and sleep data. For example, it analyzes lifestyle habits based on the number of steps and heart rate. The lifestyle habit identification unit also integrates data acquired from health devices to obtain detailed information on the user's lifestyle and dietary habits. For example, it analyzes meal timing and calorie consumption. The lifestyle habit identification unit also works in conjunction with other health devices to collect data on the user's lifestyle and dietary habits in real time, and the generation AI provides advice based on that information. For example, it suggests what to eat after exercise. This allows for more detailed information on lifestyle and dietary habits to be obtained by working out in conjunction with other health devices.

[0031] The lifestyle habit identification unit can analyze the user's past conversation history and identify long-term lifestyle patterns. In the lifestyle habit identification unit, for example, the generation AI analyzes the user's past conversation history and identifies eating and exercise patterns. For example, it identifies a tendency to eat certain meals on weekends. In addition, the lifestyle habit identification unit tracks changes in the user's lifestyle based on past conversation data and identifies long-term patterns. For example, it analyzes seasonal fluctuations in exercise volume. In addition, the generation AI analyzes the user's past conversation history and identifies areas for improvement in lifestyle and eating habits. For example, it evaluates the effectiveness of past advice and reflects it in the next advice. In this way, it is possible to analyze past conversation history and identify long-term lifestyle patterns.

[0032] The lifestyle habit grasping unit dynamically changes the content of questions based on the user's living environment, allowing it to collect more appropriate information. In the lifestyle habit grasping unit, for example, the generation AI changes the content of questions according to the weather and season to collect information on the user's lifestyle and eating habits. For example, in cold seasons, it asks about hot meals. The lifestyle habit grasping unit also adjusts the content of questions based on the user's living environment (for example, how busy they are at work or their home situation) to collect more appropriate information. For example, it asks about simple meals during busy periods. The generation AI also detects changes in the user's living environment and generates questions accordingly. For example, it asks questions about their new living environment after moving. This allows it to dynamically change the content of questions based on the user's living environment, allowing it to collect more appropriate information.

[0033] The lifestyle habit assessment unit can simultaneously assess the lifestyle habits of family members and housemates, and evaluate the overall health status of the household. For example, the generation AI in the lifestyle habit assessment unit collects information on the lifestyle and eating habits of family members and housemates, and evaluates the overall health status of the household. For example, it assesses the dietary habits of each family member. The lifestyle habit assessment unit also integrates the lifestyle data of family members and housemates, and the generation AI analyzes the health status of the entire household. For example, it evaluates the amount of exercise and sleep time of each family member. The generation AI in the lifestyle habit assessment unit also collects data on the lifestyle habits of family members and housemates, and provides advice to improve the health status of the entire household. For example, it proposes an exercise plan for the entire family to work on. This allows the lifestyle habits of family members and housemates to be simultaneously assessed, and the overall health status of the household to be evaluated.

[0034] The improvement advice unit can refer to the latest research data on the user's lifestyle and dietary habits and provide advice based on scientific evidence. For example, the generation AI in the improvement advice unit refers to the latest research data and provides scientifically based advice on improving dietary habits. For example, it may suggest, "According to the latest research, XX is good for your health." The improvement advice unit also provides scientifically based exercise advice based on the latest research data on the user's lifestyle habits. For example, it may suggest, "According to the latest research, XX minutes of exercise is effective." The improvement advice unit also analyzes the latest research data and provides scientifically based advice on improving lifestyle habits to the user. For example, it may suggest, "According to the latest research, it is good to consume XX." This makes it possible to refer to the latest research data and provide scientifically based advice.

[0035] The improvement advice unit can propose an individually customized meal plan by taking into account the user's preferences and allergy information. In the improvement advice unit, for example, the generation AI proposes an individually customized meal plan based on the user's preferences and allergy information. For example, it provides recipes using ingredients that avoid allergies. In addition, the improvement advice unit proposes an individually customized meal plan by taking into account the user's dietary preferences and allergy information. For example, it proposes healthy menus using favorite ingredients. In addition, the improvement advice unit proposes an individually customized meal plan by analyzing the user's preferences and allergy information. For example, it proposes meals that suit the user's preferences while avoiding allergies. In this way, it is possible to propose an individually customized meal plan by taking into account the user's preferences and allergy information.

[0036] The improvement advice unit can provide advice at the optimal timing in accordance with the user's lifestyle rhythm. In the improvement advice unit, for example, the generation AI analyzes the user's lifestyle rhythm and provides advice to improve eating habits at the optimal timing. For example, it may suggest a healthy menu before a meal. In addition, the improvement advice unit provides exercise advice at the optimal timing based on the user's lifestyle rhythm. For example, it may suggest stretching before exercising. In addition, the improvement advice unit understands the user's lifestyle rhythm and provides advice to improve lifestyle habits at the optimal timing. For example, it may suggest relaxation methods before going to bed. In this way, advice can be provided at the optimal timing in accordance with the user's lifestyle rhythm.

[0037] The question answering unit can analyze a user's past question history and provide related information. For example, the generation AI analyzes a user's past question history and provides related information. For example, if a user has previously asked about vitamin C, new foods that are high in vitamin C are suggested. The question answering unit also provides information based on the user's past question history, where the generation AI provides information that matches the user's interests. For example, if a user has previously asked about exercise, a new exercise method is suggested. The question answering unit also analyzes a user's question history and provides related information. For example, if a user has previously asked about sleep, the generation AI suggests the latest method for improving sleep. This makes it possible to analyze a user's past question history and provide related information.

[0038] The question answering unit can analyze the content of a user's question and provide the most reliable information from multiple sources. For example, the generation AI analyzes the content of a user's question and provides the most reliable information from multiple sources. For example, the answer is based on information from a reliable medical site. The question answering unit can also analyze the content of a question and the generation AI selects and provides reliable information from multiple sources. For example, the answer is based on academic papers and expert opinions. The question answering unit can also analyze the content of a user's question and provide the most reliable information from multiple sources. For example, the answer is based on data from government agencies and public institutions. This allows the question answering unit to analyze the content of a user's question and provide the most reliable information from multiple sources.

[0039] The question answering unit can provide visual or audio answers to the user's questions. For example, the generation AI in the question answering unit provides visual answers to the user's questions. For example, it displays images of ingredients or videos of exercise methods. The generation AI in the question answering unit also provides audio answers to the questions. For example, it explains ingredients that are high in vitamin C through audio. The generation AI in the question answering unit also provides visual or audio answers to the user's questions. For example, it plays a video of healthy recipes. This allows the generation AI to provide visual or audio answers to the user's questions.

[0040] The question answering unit can suggest related videos and articles in response to a user's question. For example, the generation AI in the question answering unit suggests related videos in response to a user's question. For example, a video introducing cooking methods for ingredients that are rich in vitamin C is displayed. The generation AI in the question answering unit also suggests related articles in response to a question. For example, an article on the latest health research is provided. The generation AI in the question answering unit also suggests related videos and articles in response to a user's question. For example, a video or article on exercise methods is displayed. This allows related videos and articles to be suggested in response to a user's question.

[0041] The voice customization unit can customize the accent and intonation of the voice according to the user's preferences. In the voice customization unit, for example, the generation AI customizes the accent of the voice according to the user's preferences. For example, it sets the accent of the region preferred by the user. In addition, the voice customization unit customizes the intonation of the voice according to the user's preferences. For example, it sets the speaking rhythm preferred by the user. In addition, the voice customization unit customizes the accent and intonation of the voice according to the user's preferences. For example, it sets the speaking tone and pitch preferred by the user. In this way, the accent and intonation of the voice can be customized according to the user's preferences.

[0042] The voice customization unit can unify the user's voice settings in cooperation with other devices. For example, the generation AI in the voice customization unit can unify the user's voice settings with a smartphone or tablet to provide unified voice settings. For example, the same voice tone can be used across all devices. The voice customization unit can also unify the user's voice settings in cooperation with other devices. For example, the voice settings can be shared in cooperation with a smart speaker or an in-car system. The voice customization unit can also unify the user's voice settings in cooperation with other devices. For example, the voice gender and tone can be used across all devices. This allows the user's voice settings to be unified in cooperation with other devices.

[0043] The voice customization unit can automatically change the user's voice settings depending on the time of day and location. In the voice customization unit, for example, the generation AI automatically changes the user's voice settings depending on the time of day. For example, setting a cheerful tone in the morning and a calm tone in the evening. In addition, the voice customization unit automatically changes the user's voice settings depending on the location. For example, setting a relaxed tone at home and a formal tone at work. In addition, the voice customization unit automatically changes the user's voice settings depending on the time of day and location. For example, setting a cheerful tone when out and about and a calm tone at home. In this way, the user's voice settings can be automatically changed depending on the time of day and location.

[0044] The privacy protection unit can encrypt the user's data and prevent access by third parties. For example, the generation AI encrypts the user's data and prevents access by third parties. For example, a strong encryption algorithm is used when communicating data. The privacy protection unit also encrypts the user's data and the generation AI prevents unauthorized access by third parties. For example, encryption technology is used when storing data. The privacy protection unit also encrypts the user's data and prevents access by third parties. For example, an encryption protocol is used when sharing data. This allows the user's data to be encrypted and prevents access by third parties.

[0045] The privacy protection unit can analyze the user's privacy setting history and suggest optimal privacy settings. In the privacy protection unit, for example, the generation AI analyzes the user's privacy setting history and suggests optimal privacy settings. For example, suggestions are made based on settings that were preferred in the past. In addition, the privacy protection unit can analyze the user's privacy setting history and suggest privacy settings that suit the user's preferences. For example, suggestions are made based on settings that were selected in the past. In addition, the privacy protection unit can analyze the user's privacy setting history and suggest optimal privacy settings. For example, suggestions are made based on settings that were preferred in the past. In this way, the user's privacy setting history can be analyzed and optimal privacy settings can be suggested.

[0046] The privacy protection unit can unify the user's privacy settings in cooperation with other devices. For example, the generation AI in the privacy protection unit can unify the user's privacy settings with a smartphone or tablet to provide unified privacy settings. For example, the same privacy settings can be used on all devices. The privacy protection unit can also unify the user's privacy settings in cooperation with other devices by the generation AI. For example, the privacy settings can be shared in cooperation with a smart speaker or an in-car system. The privacy protection unit can also unify the user's privacy settings in cooperation with other devices by the generation AI. For example, the same privacy settings can be used on all devices. This allows the user's privacy settings to be unified in cooperation with other devices.

[0047] The privacy protection unit can automatically change the user's privacy settings depending on the time of day and location. In the privacy protection unit, for example, the generation AI automatically changes the user's privacy settings depending on the time of day. For example, the privacy settings are strengthened at night and relaxed during the day. In addition, the privacy protection unit automatically changes the user's privacy settings depending on the location. For example, the privacy settings are relaxed at home and strengthened in public places. In addition, the privacy protection unit automatically changes the user's privacy settings depending on the time of day and location. For example, the privacy settings are strengthened when out and relaxed at home. In this way, the user's privacy settings can be automatically changed depending on the time of day and location.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The health support system may further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit collects the user's sleep data and analyzes the user's sleep quality and patterns. For example, it may record the user's sleep time and the percentage of deep sleep and evaluate the quality of the sleep. The sleep analysis unit may also provide advice on improving sleep based on the user's sleep data. For example, it may provide advice such as "It's good to go to bed at the same time every night." The sleep analysis unit may also collect the user's sleep data in cooperation with other health devices and perform more detailed analysis. This can improve the user's sleep quality and support their overall health.

[0050] The health support system may further include a stress measurement unit that measures the user's stress level. The stress measurement unit collects biometric data such as the user's heart rate and skin electrical response to evaluate the stress level. For example, the stress level is measured based on heart rate fluctuations and skin electrical resistance. The stress measurement unit may also suggest relaxation methods based on the user's stress level. For example, the stress measurement unit may provide advice such as "Take deep breaths and relax." The stress measurement unit may also collect the user's stress data in cooperation with other health devices and perform more detailed analysis. This may support the user's stress management and improve their overall health.

[0051] The health support system may further include a motion analysis unit that analyzes the user's exercise performance. The motion analysis unit collects the user's exercise data and analyzes the quality and performance of the exercise. For example, it may record the user's running speed, distance, and calorie consumption, and evaluate the effectiveness of the exercise. The motion analysis unit may also provide advice on improving the exercise based on the user's exercise data. For example, it may provide advice such as, "You should increase your running pace a little." The motion analysis unit may also collect the user's exercise data in cooperation with other health devices and perform more detailed analysis. This can improve the user's exercise performance and support their overall health.

[0052] The health support system may further include a meal recording unit that automatically records the user's dietary information. The meal recording unit records the ingredients and dishes eaten by the user using photographs and voice input, and automatically analyzes the dietary information. For example, when the user takes a photo of their meal, the photo is analyzed to identify the ingredients and calories. The meal recording unit can also provide advice on improving the user's diet based on the user's dietary data. For example, the advice may be, "You should eat more vegetables." The meal recording unit can also collect the user's dietary data in cooperation with other health devices and perform more detailed analysis. This can improve the user's dietary habits and support their overall health.

[0053] The health support system can further include a fluid management unit that manages the user's fluid intake. The fluid management unit records the amount of fluid consumed by the user and supports appropriate fluid intake. For example, when the user inputs the amount of water consumed, the system manages the user's daily fluid intake based on that data. The fluid management unit can also provide fluid intake advice based on the user's fluid intake data. For example, the system may provide advice such as, "Drink a little more water." The fluid management unit can also collect the user's fluid intake data in cooperation with other health devices and perform more detailed analysis. This allows the user to appropriately manage their fluid intake and support their overall health.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The lifestyle habit identification unit identifies the user's lifestyle and dietary habits through casual conversations and questions with the user. For example, the lifestyle habit identification unit asks the user, "What is your diet like recently?" and collects the user's response. The lifestyle habit identification unit can also ask the user, "How often do you exercise?" to identify the user's exercise habits. The lifestyle habit identification unit can also analyze the user's response and identify patterns of lifestyle and dietary habits. Step 2: The improvement advice unit provides the user with improvement advice based on the lifestyle and dietary habits identified by the lifestyle habit identification unit. For example, the improvement advice unit may advise, "You should eat more vegetables." The improvement advice unit may also advise, "Try to exercise for 30 minutes every day." The improvement advice unit may also analyze data related to the user's lifestyle and dietary habits to generate optimal advice. Step 3: The question answering unit provides an appropriate answer to the user's question. For example, the question answering unit answers the question, "What foods are rich in vitamin C?" with, "Oranges and broccoli are rich in vitamin C." The question answering unit can also analyze the content of the user's question and provide appropriate information. The question answering unit can also analyze the user's past question history and provide related information. Step 4: The voice customization unit allows customization of the voice's gender, tone, phrasing, and conversational style. For example, if the user requests a "gentle female voice," the voice customization unit configures the voice settings to meet the user's needs. The voice customization unit can also customize the voice's accent and intonation according to the user's preferences. The voice customization unit can also estimate the user's emotions and automatically adjust the voice tone according to the emotions.

[0056] (Example 2) A health support system according to an embodiment of the present invention is a system that installs a voice dialogue generation AI as a home device, understands the user's lifestyle and dietary habits, and provides advice on how to improve them and answers to questions. This enables the health support system to support the user in improving their health and extending their healthy lifespan.

[0057] A health support system according to an embodiment includes a lifestyle habit identification unit, an improvement advice unit, a question and answer unit, and a voice customization unit. The lifestyle habit identification unit identifies a user's lifestyle and dietary habits through casual conversations and questions with the user. For example, the lifestyle habit identification unit asks the user, "What's your diet like lately?" and collects the user's answers. The lifestyle habit identification unit can also ask the user, "How often do you exercise?" to identify the user's exercise habits. The lifestyle habit identification unit can also analyze the user's answers and identify patterns of lifestyle and dietary habits. The improvement advice unit provides the user with improvement advice based on the lifestyle and dietary habits identified by the lifestyle habit identification unit. For example, the improvement advice unit can advise, "You should eat more vegetables." The improvement advice unit can also advise, "Try to exercise for 30 minutes every day." The improvement advice unit can also analyze data related to the user's lifestyle and dietary habits and generate optimal advice. The question and answering unit provides appropriate answers to the user's questions. For example, in response to the question, "What foods are rich in vitamin C?", the question answering unit answers, "Oranges and broccoli are rich in vitamin C." The question answering unit can also analyze the content of the user's question and provide appropriate information. The question answering unit can also analyze the user's past question history and provide relevant information. The voice customization unit can customize the gender, tone, phrasing, and conversational style of the voice. For example, if a user requests a "gentle female voice," the voice customization unit can set the voice according to the user's request. The voice customization unit can also customize the accent and intonation of the voice according to the user's preferences. The voice customization unit can also estimate the user's emotions and automatically adjust the voice tone according to the emotions. As a result, the health support system according to the embodiment can support the user's health promotion and healthy life expectancy. For example, the health support system can support the user's health by understanding the user's lifestyle and dietary habits and providing appropriate advice and answers.Furthermore, the voice customization function allows information to be provided in a format that is easy for the user to listen to.

[0058] The lifestyle habit assessment unit can estimate the user's emotions and track changes in lifestyle and dietary habits in real time based on changes in emotions. For example, the generation AI of the lifestyle habit assessment unit analyzes the user's voice tone and facial expressions to estimate changes in emotions in real time. For example, if the user is feeling stressed, the generation AI detects that emotion and tracks changes in lifestyle and dietary habits. The lifestyle habit assessment unit also predicts changes in lifestyle and dietary habits based on the user's emotional data. For example, if the user feels tired, the generation AI records the meal contents and exercise amount for that day and tracks changes. The lifestyle habit assessment unit also uses the emotion estimation function to generate questions in response to changes in the user's emotions and collect detailed information about the user's lifestyle and dietary habits. For example, the generation AI may ask about the user's diet when the user is relaxing. This allows changes in lifestyle and dietary habits to be tracked in real time based on changes in the user's emotions.

[0059] The lifestyle habit identification unit works in conjunction with other health devices to collect data on the user's lifestyle and dietary habits, enabling more detailed information to be obtained. For example, the generation AI in the lifestyle habit identification unit works in conjunction with a smartwatch or fitness tracker to collect the user's exercise and sleep data. For example, it analyzes lifestyle habits based on the number of steps and heart rate. The lifestyle habit identification unit also integrates data acquired from health devices to obtain detailed information on the user's lifestyle and dietary habits. For example, it analyzes meal timing and calorie consumption. The lifestyle habit identification unit also works in conjunction with other health devices to collect data on the user's lifestyle and dietary habits in real time, and the generation AI provides advice based on that information. For example, it suggests what to eat after exercise. This allows for more detailed information on lifestyle and dietary habits to be obtained by working out in conjunction with other health devices.

[0060] The lifestyle habit identification unit can analyze the user's past conversation history and identify long-term lifestyle patterns. In the lifestyle habit identification unit, for example, the generation AI analyzes the user's past conversation history and identifies eating and exercise patterns. For example, it identifies a tendency to eat certain meals on weekends. In addition, the lifestyle habit identification unit tracks changes in the user's lifestyle based on past conversation data and identifies long-term patterns. For example, it analyzes seasonal fluctuations in exercise volume. In addition, the generation AI analyzes the user's past conversation history and identifies areas for improvement in lifestyle and eating habits. For example, it evaluates the effectiveness of past advice and reflects it in the next advice. In this way, it is possible to analyze past conversation history and identify long-term lifestyle patterns.

[0061] The lifestyle habit grasping unit dynamically changes the content of questions based on the user's living environment, allowing it to collect more appropriate information. In the lifestyle habit grasping unit, for example, the generation AI changes the content of questions according to the weather and season to collect information on the user's lifestyle and eating habits. For example, in cold seasons, it asks about hot meals. The lifestyle habit grasping unit also adjusts the content of questions based on the user's living environment (for example, how busy they are at work or their home situation) to collect more appropriate information. For example, it asks about simple meals during busy periods. The generation AI also detects changes in the user's living environment and generates questions accordingly. For example, it asks questions about their new living environment after moving. This allows it to dynamically change the content of questions based on the user's living environment, allowing it to collect more appropriate information.

[0062] The lifestyle habit assessment unit can simultaneously assess the lifestyle habits of family members and housemates, and evaluate the overall health status of the household. For example, the generation AI in the lifestyle habit assessment unit collects information on the lifestyle and eating habits of family members and housemates, and evaluates the overall health status of the household. For example, it assesses the dietary habits of each family member. The lifestyle habit assessment unit also integrates the lifestyle data of family members and housemates, and the generation AI analyzes the health status of the entire household. For example, it evaluates the amount of exercise and sleep time of each family member. The generation AI in the lifestyle habit assessment unit also collects data on the lifestyle habits of family members and housemates, and provides advice to improve the health status of the entire household. For example, it proposes an exercise plan for the entire family to work on. This allows the lifestyle habits of family members and housemates to be simultaneously assessed, and the overall health status of the household to be evaluated.

[0063] The lifestyle habit grasping unit uses the emotion estimation function to ask questions when the user is relaxed, allowing more accurate information to be collected. For example, the generation AI of the lifestyle habit grasping unit estimates the user's emotions and asks questions about the user's lifestyle and diet when the user is relaxed. For example, asking about dietary habits when the user is relaxed. The lifestyle habit grasping unit also uses the emotion estimation function to ask questions when the user is not feeling stressed, allowing more accurate information to be collected. For example, asking about exercise habits when the user is relaxed. The lifestyle habit grasping unit also generates questions when the user is relaxed based on the user's emotion data, allowing more accurate information to be collected about the user's lifestyle and diet. For example, asking about meal frequency when the user is relaxed. This allows more accurate information to be collected by asking questions when the user is relaxed.

[0064] The improvement advice unit can estimate the user's emotions and provide advice with encouraging or comforting words according to the emotions. For example, the generation AI in the improvement advice unit estimates the user's emotions and provides advice with encouraging words according to the emotions. For example, when the user is feeling down, the improvement advice unit may encourage the user by saying, "You're doing well." The improvement advice unit also uses the emotion estimation function to provide advice with comforting words when the user is feeling stressed. For example, the improvement advice unit may gently advise the user, "Don't push yourself." The improvement advice unit also uses the generation AI to provide advice with appropriate words according to the emotions based on the user's emotional data. For example, when the user is relaxed, the improvement advice unit may encourage the user by saying, "Keep it up." This makes it possible to provide advice with encouraging or comforting words according to the user's emotions.

[0065] The improvement advice unit can refer to the latest research data on the user's lifestyle and dietary habits and provide advice based on scientific evidence. For example, the generation AI in the improvement advice unit refers to the latest research data and provides scientifically based advice on improving dietary habits. For example, it may suggest, "According to the latest research, XX is good for your health." The improvement advice unit also provides scientifically based exercise advice based on the latest research data on the user's lifestyle habits. For example, it may suggest, "According to the latest research, XX minutes of exercise is effective." The improvement advice unit also analyzes the latest research data and provides scientifically based advice on improving lifestyle habits to the user. For example, it may suggest, "According to the latest research, it is good to consume XX." This makes it possible to refer to the latest research data and provide scientifically based advice.

[0066] The improvement advice unit can propose an individually customized meal plan by taking into account the user's preferences and allergy information. In the improvement advice unit, for example, the generation AI proposes an individually customized meal plan based on the user's preferences and allergy information. For example, it provides recipes using ingredients that avoid allergies. In addition, the improvement advice unit proposes an individually customized meal plan by taking into account the user's dietary preferences and allergy information. For example, it proposes healthy menus using favorite ingredients. In addition, the improvement advice unit proposes an individually customized meal plan by analyzing the user's preferences and allergy information. For example, it proposes meals that suit the user's preferences while avoiding allergies. In this way, it is possible to propose an individually customized meal plan by taking into account the user's preferences and allergy information.

[0067] The improvement advice unit can provide advice at the optimal timing in accordance with the user's lifestyle rhythm. In the improvement advice unit, for example, the generation AI analyzes the user's lifestyle rhythm and provides advice to improve eating habits at the optimal timing. For example, it may suggest a healthy menu before a meal. In addition, the improvement advice unit provides exercise advice at the optimal timing based on the user's lifestyle rhythm. For example, it may suggest stretching before exercising. In addition, the improvement advice unit understands the user's lifestyle rhythm and provides advice to improve lifestyle habits at the optimal timing. For example, it may suggest relaxation methods before going to bed. In this way, advice can be provided at the optimal timing in accordance with the user's lifestyle rhythm.

[0068] The improvement advice unit can use the emotion estimation function to provide advice at the timing when the user is most likely to accept it. For example, the generation AI estimates the user's emotions and provides advice to improve dietary habits at the timing when the user is most likely to accept it. For example, the advice is provided when the user is relaxed. The improvement advice unit also uses the emotion estimation function to provide advice to improve lifestyle habits when the user is not feeling stressed. For example, it makes suggestions for exercise when the user is relaxed. The improvement advice unit also uses the generation AI to provide advice at the timing when the user is most likely to accept it based on the user's emotion data. For example, it makes suggestions for healthy meals when the user is relaxed. This allows the advice to be provided at the timing when the user is most likely to accept it.

[0069] The question answering unit can estimate the user's emotions and provide answers in a tone that corresponds to the emotions. For example, the generation AI in the question answering unit estimates the user's emotions and answers questions in a tone that corresponds to the emotions. For example, if the user is depressed, the answering unit answers in a gentle tone. Furthermore, the question answering unit uses the emotion estimation function to answer questions in a comforting tone when the user is feeling stressed. For example, the answering unit gently replies, "It's okay." Furthermore, the generation AI in the question answering unit answers questions in an appropriate tone that corresponds to the emotions based on the user's emotional data. For example, if the user is relaxed, the answering unit answers in a bright tone. This makes it possible to provide answers in a tone that corresponds to the user's emotions.

[0070] The question answering unit can analyze a user's past question history and provide related information. For example, the generation AI analyzes a user's past question history and provides related information. For example, if a user has previously asked about vitamin C, new foods that are high in vitamin C are suggested. The question answering unit also provides information based on the user's past question history, where the generation AI provides information that matches the user's interests. For example, if a user has previously asked about exercise, a new exercise method is suggested. The question answering unit also analyzes a user's question history and provides related information. For example, if a user has previously asked about sleep, the generation AI suggests the latest method for improving sleep. This makes it possible to analyze a user's past question history and provide related information.

[0071] The question answering unit can analyze the content of a user's question and provide the most reliable information from multiple sources. For example, the generation AI analyzes the content of a user's question and provides the most reliable information from multiple sources. For example, the answer is based on information from a reliable medical site. The question answering unit can also analyze the content of a question and the generation AI selects and provides reliable information from multiple sources. For example, the answer is based on academic papers and expert opinions. The question answering unit can also analyze the content of a user's question and provide the most reliable information from multiple sources. For example, the answer is based on data from government agencies and public institutions. This allows the question answering unit to analyze the content of a user's question and provide the most reliable information from multiple sources.

[0072] The question answering unit can provide visual or audio answers to the user's questions. For example, the generation AI in the question answering unit provides visual answers to the user's questions. For example, it displays images of ingredients or videos of exercise methods. The generation AI in the question answering unit also provides audio answers to the questions. For example, it explains ingredients that are high in vitamin C through audio. The generation AI in the question answering unit also provides visual or audio answers to the user's questions. For example, it plays a video of healthy recipes. This allows the generation AI to provide visual or audio answers to the user's questions.

[0073] The question answering unit can suggest related videos and articles in response to a user's question. For example, the generation AI in the question answering unit suggests related videos in response to a user's question. For example, a video introducing cooking methods for ingredients that are rich in vitamin C is displayed. The generation AI in the question answering unit also suggests related articles in response to a question. For example, an article on the latest health research is provided. The generation AI in the question answering unit also suggests related videos and articles in response to a user's question. For example, a video or article on exercise methods is displayed. This allows related videos and articles to be suggested in response to a user's question.

[0074] The question answering unit can use the emotion estimation function to provide answers in a format that is easiest for the user to understand. For example, the generation AI estimates the user's emotions and answers questions in a format that is easiest for the user to understand. For example, when the user is relaxed, it provides detailed explanations. The question answering unit also uses the emotion estimation function to provide concise answers when the user is feeling stressed. For example, it conveys the main points in short sentences. The question answering unit also uses the generation AI to answer questions in a format that is easiest for the user to understand based on the user's emotion data. For example, it provides detailed information when the user is concentrating. This makes it possible to provide answers in a format that is easiest for the user to understand.

[0075] The voice customization unit can estimate the user's emotions and automatically adjust the voice tone according to the emotions. For example, the generation AI in the voice customization unit estimates the user's emotions and automatically adjusts the voice tone according to the emotions. For example, when the user is depressed, the voice customization unit speaks in a gentle tone. Furthermore, the voice customization unit uses the emotion estimation function to speak in a comforting tone when the user is feeling stressed. For example, it speaks in a gentle tone, saying "It's okay." Furthermore, the generation AI in the voice customization unit automatically adjusts the voice tone appropriately according to the emotions based on the user's emotional data. For example, when the user is relaxed, the voice tone is spoken in a bright tone. This makes it possible to automatically adjust the voice tone according to the user's emotions.

[0076] The voice customization unit can customize the accent and intonation of the voice according to the user's preferences. In the voice customization unit, for example, the generation AI customizes the accent of the voice according to the user's preferences. For example, it sets the accent of the region preferred by the user. In addition, the voice customization unit customizes the intonation of the voice according to the user's preferences. For example, it sets the speaking rhythm preferred by the user. In addition, the voice customization unit customizes the accent and intonation of the voice according to the user's preferences. For example, it sets the speaking tone and pitch preferred by the user. In this way, the accent and intonation of the voice can be customized according to the user's preferences.

[0077] The voice customization unit can unify the user's voice settings in cooperation with other devices. For example, the generation AI in the voice customization unit can unify the user's voice settings with a smartphone or tablet to provide unified voice settings. For example, the same voice tone can be used across all devices. The voice customization unit can also unify the user's voice settings in cooperation with other devices. For example, the voice settings can be shared in cooperation with a smart speaker or an in-car system. The voice customization unit can also unify the user's voice settings in cooperation with other devices. For example, the voice gender and tone can be used across all devices. This allows the user's voice settings to be unified in cooperation with other devices.

[0078] The voice customization unit can automatically change the user's voice settings depending on the time of day and location. In the voice customization unit, for example, the generation AI automatically changes the user's voice settings depending on the time of day. For example, setting a cheerful tone in the morning and a calm tone in the evening. In addition, the voice customization unit automatically changes the user's voice settings depending on the location. For example, setting a relaxed tone at home and a formal tone at work. In addition, the voice customization unit automatically changes the user's voice settings depending on the time of day and location. For example, setting a cheerful tone when out and about and a calm tone at home. In this way, the user's voice settings can be automatically changed depending on the time of day and location.

[0079] The voice customization unit can use the emotion estimation function to suggest the most relaxing voice settings for the user. For example, the generation AI in the voice customization unit estimates the user's emotions and suggests the most relaxing voice settings. For example, it suggests settings based on the voice tone when the user is relaxed. The voice customization unit also uses the emotion estimation function to suggest voice settings that are relaxing when the user is feeling stressed. For example, it suggests a gentle or calm tone. The voice customization unit also uses the generation AI to suggest the most relaxing voice settings based on the user's emotional data. For example, it suggests settings based on the gender and tone of the voice when the user is relaxed. This makes it possible to suggest the most relaxing voice settings for the user.

[0080] The privacy protection unit can estimate the user's emotions and automatically adjust privacy settings according to the emotions. For example, the generation AI in the privacy protection unit estimates the user's emotions and automatically adjusts privacy settings according to the emotions. For example, it strengthens privacy settings when the user is feeling stressed. The privacy protection unit also uses the emotion estimation function to relax privacy settings when the user is relaxed. For example, it allows data sharing when the user is relaxed. The privacy protection unit also uses the generation AI in the privacy protection unit to automatically adjust appropriate privacy settings according to the emotions based on the user's emotional data. For example, it relaxes privacy settings when the user is calm. This makes it possible to automatically adjust privacy settings according to the user's emotions.

[0081] The privacy protection unit can encrypt the user's data and prevent access by third parties. For example, the generation AI encrypts the user's data and prevents access by third parties. For example, a strong encryption algorithm is used when communicating data. The privacy protection unit also encrypts the user's data and the generation AI prevents unauthorized access by third parties. For example, encryption technology is used when storing data. The privacy protection unit also encrypts the user's data and prevents access by third parties. For example, an encryption protocol is used when sharing data. This allows the user's data to be encrypted and prevents access by third parties.

[0082] The privacy protection unit can analyze the user's privacy setting history and suggest optimal privacy settings. In the privacy protection unit, for example, the generation AI analyzes the user's privacy setting history and suggests optimal privacy settings. For example, suggestions are made based on settings that were preferred in the past. In addition, the privacy protection unit can analyze the user's privacy setting history and suggest privacy settings that suit the user's preferences. For example, suggestions are made based on settings that were selected in the past. In addition, the privacy protection unit can analyze the user's privacy setting history and suggest optimal privacy settings. For example, suggestions are made based on settings that were preferred in the past. In this way, the user's privacy setting history can be analyzed and optimal privacy settings can be suggested.

[0083] The privacy protection unit can unify the user's privacy settings in cooperation with other devices. For example, the generation AI in the privacy protection unit can unify the user's privacy settings with a smartphone or tablet to provide unified privacy settings. For example, the same privacy settings can be used on all devices. The privacy protection unit can also unify the user's privacy settings in cooperation with other devices by the generation AI. For example, the privacy settings can be shared in cooperation with a smart speaker or an in-car system. The privacy protection unit can also unify the user's privacy settings in cooperation with other devices by the generation AI. For example, the same privacy settings can be used on all devices. This allows the user's privacy settings to be unified in cooperation with other devices.

[0084] The privacy protection unit can automatically change the user's privacy settings depending on the time of day and location. In the privacy protection unit, for example, the generation AI automatically changes the user's privacy settings depending on the time of day. For example, the privacy settings are strengthened at night and relaxed during the day. In addition, the privacy protection unit automatically changes the user's privacy settings depending on the location. For example, the privacy settings are relaxed at home and strengthened in public places. In addition, the privacy protection unit automatically changes the user's privacy settings depending on the time of day and location. For example, the privacy settings are strengthened when out and relaxed at home. In this way, the user's privacy settings can be automatically changed depending on the time of day and location.

[0085] The privacy protection unit can use the emotion estimation function to suggest privacy settings that make the user feel most secure. For example, the generation AI estimates the user's emotions and suggests privacy settings that make the user feel most secure. For example, the privacy settings are strengthened when the user is feeling stressed. The privacy protection unit also uses the emotion estimation function to relax privacy settings when the user is relaxed. For example, data sharing is permitted when the user is relaxed. The privacy protection unit also uses the generation AI to suggest privacy settings that make the user feel most secure based on the user's emotional data. For example, the privacy settings are relaxed when the user is calm. This makes it possible to suggest privacy settings that make the user feel most secure.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The health support system may further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit collects the user's sleep data and analyzes the user's sleep quality and patterns. For example, it may record the user's sleep time and the percentage of deep sleep and evaluate the quality of the sleep. The sleep analysis unit may also provide advice on improving sleep based on the user's sleep data. For example, it may provide advice such as "It's good to go to bed at the same time every night." The sleep analysis unit may also collect the user's sleep data in cooperation with other health devices and perform more detailed analysis. This can improve the user's sleep quality and support their overall health.

[0088] The health support system may further include a stress measurement unit that measures the user's stress level. The stress measurement unit collects biometric data such as the user's heart rate and skin electrical response to evaluate the stress level. For example, the stress level is measured based on heart rate fluctuations and skin electrical resistance. The stress measurement unit may also suggest relaxation methods based on the user's stress level. For example, the stress measurement unit may provide advice such as "Take deep breaths and relax." The stress measurement unit may also collect the user's stress data in cooperation with other health devices and perform more detailed analysis. This may support the user's stress management and improve their overall health.

[0089] The health support system may further include a motion analysis unit that analyzes the user's exercise performance. The motion analysis unit collects the user's exercise data and analyzes the quality and performance of the exercise. For example, it may record the user's running speed, distance, and calorie consumption, and evaluate the effectiveness of the exercise. The motion analysis unit may also provide advice on improving the exercise based on the user's exercise data. For example, it may provide advice such as, "You should increase your running pace a little." The motion analysis unit may also collect the user's exercise data in cooperation with other health devices and perform more detailed analysis. This can improve the user's exercise performance and support their overall health.

[0090] The health support system may further include a meal recording unit that automatically records the user's dietary information. The meal recording unit records the ingredients and dishes eaten by the user using photographs and voice input, and automatically analyzes the dietary information. For example, when the user takes a photo of their meal, the photo is analyzed to identify the ingredients and calories. The meal recording unit can also provide advice on improving the user's diet based on the user's dietary data. For example, the advice may be, "You should eat more vegetables." The meal recording unit can also collect the user's dietary data in cooperation with other health devices and perform more detailed analysis. This can improve the user's dietary habits and support their overall health.

[0091] The health support system can further include a fluid management unit that manages the user's fluid intake. The fluid management unit records the amount of fluid consumed by the user and supports appropriate fluid intake. For example, when the user inputs the amount of water consumed, the system manages the user's daily fluid intake based on that data. The fluid management unit can also provide fluid intake advice based on the user's fluid intake data. For example, the system may provide advice such as, "Drink a little more water." The fluid management unit can also collect the user's fluid intake data in cooperation with other health devices and perform more detailed analysis. This allows the user to appropriately manage their fluid intake and support their overall health.

[0092] The health support system may further include a relaxation suggestion unit that estimates the user's emotions and suggests relaxation methods according to the emotions. The relaxation suggestion unit estimates the user's emotions and suggests relaxation methods according to the emotions. For example, when the user is feeling stressed, the relaxation suggestion unit may give advice such as "Try doing yoga" when the user is feeling relaxed. The relaxation suggestion unit may also suggest relaxation methods according to the user's emotions based on the user's emotional data. For example, when the user is feeling relaxed, the relaxation suggestion unit may give advice such as "Try doing yoga." The relaxation suggestion unit may also collect the user's emotional data in cooperation with other health devices and perform more detailed analysis. This makes it possible to suggest relaxation methods according to the user's emotions and support their overall health.

[0093] The health support system may further include a music suggestion unit that estimates the user's emotions and suggests music that matches the emotions. The music suggestion unit estimates the user's emotions and suggests music that matches the emotions. For example, when the user is relaxed, the music suggestion unit may give advice such as "Try listening to relaxing music." The music suggestion unit may also suggest music that matches the user's emotions based on the user's emotional data. For example, when the user is feeling stressed, the music suggestion unit may give advice such as "Try listening to relaxing music." The music suggestion unit may also collect the user's emotional data in cooperation with other health devices and perform more detailed analysis. This allows the system to suggest music that matches the user's emotions and support their overall health.

[0094] The health support system may further include an exercise suggestion unit that estimates the user's emotions and proposes an exercise plan based on the user's emotions. The exercise suggestion unit estimates the user's emotions and proposes an exercise plan based on the user's emotions. For example, when the user is relaxed, the exercise suggestion unit may provide advice such as "Try some light stretching." The exercise suggestion unit may also propose an exercise plan based on the user's emotions based on the user's emotional data. For example, when the user is feeling stressed, the exercise suggestion unit may provide advice such as "Try some relaxing yoga." The exercise suggestion unit may also collect the user's emotional data in cooperation with other health devices and perform more detailed analysis. This allows the system to propose an exercise plan based on the user's emotions and support the user's overall health.

[0095] The health support system may further include a meal suggestion unit that estimates the user's emotions and proposes a meal plan based on the user's emotions. The meal suggestion unit estimates the user's emotions and proposes a meal plan based on the user's emotions. For example, when the user is relaxed, the meal suggestion unit may provide advice such as "Eat a meal that helps you relax." The meal suggestion unit may also propose a meal plan based on the user's emotions based on the user's emotional data. For example, when the user is feeling stressed, the meal suggestion unit may provide advice such as "Eat a meal that helps you relax." The meal suggestion unit may also collect the user's emotional data in cooperation with other health devices and perform more detailed analysis. This allows the system to propose a meal plan based on the user's emotions and support the user's overall health.

[0096] The health support system may further include a sleep environment suggestion unit that estimates the user's emotions and suggests a sleep environment according to the emotions. The sleep environment suggestion unit estimates the user's emotions and suggests a sleep environment according to the emotions. For example, when the user is relaxed, the sleep environment suggestion unit may provide advice such as "Use relaxing bedding." The sleep environment suggestion unit may also suggest a sleep environment according to the emotions based on the user's emotional data. For example, when the user is feeling stressed, the sleep environment suggestion unit may provide advice such as "Use relaxing bedding." The sleep environment suggestion unit may also collect the user's emotional data in cooperation with other health devices and perform more detailed analysis. This allows the system to suggest a sleep environment according to the user's emotions and support the user's overall health.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The lifestyle habit identification unit identifies the user's lifestyle and dietary habits through casual conversations and questions with the user. For example, the lifestyle habit identification unit asks the user, "What is your diet like recently?" and collects the user's response. The lifestyle habit identification unit can also ask the user, "How often do you exercise?" to identify the user's exercise habits. The lifestyle habit identification unit can also analyze the user's response and identify patterns of lifestyle and dietary habits. Step 2: The improvement advice unit provides the user with improvement advice based on the lifestyle and dietary habits identified by the lifestyle habit identification unit. For example, the improvement advice unit may advise, "You should eat more vegetables." The improvement advice unit may also advise, "Try to exercise for 30 minutes every day." The improvement advice unit may also analyze data related to the user's lifestyle and dietary habits to generate optimal advice. Step 3: The question answering unit provides an appropriate answer to the user's question. For example, the question answering unit answers the question, "What foods are rich in vitamin C?" with, "Oranges and broccoli are rich in vitamin C." The question answering unit can also analyze the content of the user's question and provide appropriate information. The question answering unit can also analyze the user's past question history and provide related information. Step 4: The voice customization unit allows customization of the voice's gender, tone, phrasing, and conversational style. For example, if the user requests a "gentle female voice," the voice customization unit configures the voice settings to meet the user's needs. The voice customization unit can also customize the voice's accent and intonation according to the user's preferences. The voice customization unit can also estimate the user's emotions and automatically adjust the voice tone according to the emotions.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0127] 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 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. Equipped with home devices equipped with generative AI, The generated AI is a lifestyle understanding unit that understands the lifestyle and eating habits of the user through casual conversations and questions with the user; an improvement advice unit that provides improvement advice to the user based on the lifestyle habits and the dietary habits grasped by the lifestyle habit grasping unit; a question answering unit that provides appropriate answers to the user's questions; A voice customization unit that allows the gender, tone, phrasing, and conversational style of the voice to be customized. A system characterized by:

2. The lifestyle habit grasping unit Estimating the user's emotions and tracking changes in the lifestyle and dietary habits in real time based on changes in the emotions.

2. The system of claim 1.

3. The lifestyle habit grasping unit In cooperation with other health devices, data on the user's lifestyle and dietary habits is collected to obtain more detailed information.

2. The system of claim 1.

4. The lifestyle habit grasping unit Analyzing the user's past conversation history to identify long-term patterns of the lifestyle habits 2. The system of claim 1.

5. The lifestyle habit grasping unit Dynamically change the content of questions based on the user's living environment to collect more appropriate information 2. The system of claim 1.

6. The lifestyle habit grasping unit At the same time, understand the lifestyle habits of family members and cohabitants to assess the overall health of the household.

2. The system of claim 1.

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