System

Abstract

An object of a system according to an embodiment is to propose an optimal menu based on health data of a user.SOLUTION: A system includes a data collection unit, an analysis unit, and a proposal unit. The data collecting unit collects heart rate, sleep information, medical history and basic information recorded by the wearable device. The analysis unit analyzes the data collected by the data collection unit. The proposal part proposes an optimum menu to the user on the basis of the data analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose optimal menus based on the user's health data, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu based on the user's health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects heart rate, sleep information, medical history, and basic information recorded by the wearable device. The analysis unit analyzes the data collected by the data collection unit. The suggestion unit suggests an optimal menu for the user based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal menu based on the user's health data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The menu suggestion system according to an embodiment of the present invention is a system that suggests optimal menus in a conversational format based on the heart rate and sleep information recorded by a wearable device, and the medical history and basic information recorded in HELPO. This allows the menu suggestion system to suggest optimal menus that are personalized based on the user's health condition and lifestyle habits.

[0029] A menu suggestion system according to an embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects heart rate data, sleep information, medical history data, and basic information recorded by a wearable device. For example, the data collection unit acquires heart rate data from the wearable device and estimates the user's exercise volume. The data collection unit can also collect sleep information and evaluate the user's sleep quality. The data collection unit can also collect medical history data and basic information recorded in HELPO and determine the user's health risks and nutritional needs. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes heart rate data and estimates the user's exercise volume. The analysis unit can also analyze sleep information and evaluate the user's sleep quality. The analysis unit can also analyze the medical history data and basic information and determine the user's health risks and nutritional needs. The suggestion unit suggests an optimal menu for the user based on the data analyzed by the analysis unit. For example, the suggestion unit can suggest a low-calorie, balanced diet to a user who wants to lose weight. In addition, the suggestion unit can suggest meals suitable for replenishing energy to a user who exercises a lot. Furthermore, the suggestion unit can suggest recipes that use specific ingredients to a user who likes a particular ingredient. In this way, the menu suggestion system according to the embodiment can suggest optimal menus based on the user's health condition and lifestyle habits.

[0030] The analysis unit can estimate the user's exercise volume from heart rate data and evaluate the user's sleep quality from sleep information. For example, the analysis unit analyzes heart rate and electrodermal activity data collected from a wearable device to estimate the user's stress level. For example, it calculates a stress score based on heart rate fluctuations and changes in electrodermal activity and suggests nutritional supplements based on that score. The analysis unit also takes into account heart rate fluctuation patterns and sleep quality when estimating stress levels. For example, if the heart rate is high and the sleep quality is low, it determines that stress is high and suggests foods with a relaxing effect. The analysis unit also analyzes data from the wearable device in real time and suggests nutritional supplements based on fluctuations in stress levels. For example, when stress levels increase, it suggests foods containing vitamin C and magnesium. This allows the system to suggest more appropriate meal plans by evaluating the user's exercise volume and sleep quality.

[0031] The suggestion unit can suggest a balanced diet with reduced calories to a user who wants to go on a diet, and a meal suitable for replenishing energy to a user who exercises a lot. The suggestion unit, for example, analyzes medical history data recorded in HELPO and extracts the user's medical history and allergy information. For example, it automatically filters out specific ingredients based on allergy information and excludes them from menus. The suggestion unit also lists ingredients that the user should avoid based on the medical history data and suggests menus based on that list. For example, it suggests ingredients with low sugar content to a user with diabetes. The suggestion unit also analyzes medical history data in real time and suggests ingredients according to the user's health condition. For example, it suggests ingredients with low salt content to a user with high blood pressure. This makes it possible to suggest the optimal meal according to the user's goals.

[0032] The suggestion unit collects feedback such as the user's impressions after actually trying the suggested menu and weight changes, and can improve the next suggestion based on that feedback. The suggestion unit estimates the user's emotional state based on data collected from a wearable device, for example. For example, it analyzes fluctuations in heart rate and electrodermal activity to evaluate stress and fatigue levels. The suggestion unit also uses an emotion estimation function to analyze the user's emotional state in real time and suggests ingredients that have a relaxing effect when stress or fatigue is high. For example, it suggests chamomile tea or dark chocolate. The suggestion unit also builds a system that suggests ingredients according to the user's emotional state based on the emotion estimation data. For example, when stress is high, it suggests herbs and spices that have a relaxing effect. This allows the next suggestion to be improved based on user feedback.

[0033] The suggestion unit can analyze the user's past conversation history and learn changes in preferences and new tastes. The suggestion unit, for example, builds a system that analyzes the user's past conversation history and learns changes in preferences and new tastes. For example, it detects changes in the user's tastes based on the content of past conversations. The suggestion unit also learns changes in the user's preferences in real time based on the conversation history and reflects them in the menu suggestions. For example, it suggests ingredients that the user has become interested in in recent conversations. The suggestion unit also develops a system that analyzes the user's past conversation history and learns new tastes. For example, it suggests ingredients that the user has become interested in based on the content of past conversations. This makes it possible to learn changes in the user's tastes and new tastes and suggest more appropriate menus.

[0034] The suggestion unit can analyze the user's dietary history and propose a long-term meal plan that takes nutritional balance into consideration. The suggestion unit, for example, builds a system that analyzes the user's dietary history and proposes a long-term meal plan that takes nutritional balance into consideration. For example, the suggestion unit evaluates nutrient intake status based on past meal content. The suggestion unit also analyzes the user's nutritional balance in real time based on the dietary history and proposes a long-term meal plan. For example, if a specific nutrient is lacking, ingredients containing that nutrient are suggested. The suggestion unit also develops a system that analyzes the user's dietary history and proposes a long-term meal plan that takes nutritional balance into consideration. For example, the suggestion unit evaluates the user's health condition based on past meal content and proposes an optimal meal plan. This makes it possible to propose a long-term meal plan that takes nutritional balance into consideration.

[0035] The suggestion unit is able to propose a menu that will satisfy everyone, taking into consideration the preferences and health conditions of the user's family or housemates. For example, the suggestion unit builds a system that proposes a menu that will satisfy everyone, taking into consideration the preferences and health conditions of the user's family and housemates. For example, it proposes a balanced menu based on the dietary history and health information of all family members. The suggestion unit also analyzes the preferences and health conditions of family members and housemates in real time to propose a menu that will satisfy everyone. For example, it proposes a menu that takes allergy information and nutritional balance into consideration. The suggestion unit is also able to develop a system that proposes a menu that will satisfy everyone, taking into consideration the preferences and health conditions of the user's family and housemates. For example, it proposes optimal ingredients and recipes based on the preferences and health conditions of all family members. This makes it possible to propose a menu that takes into consideration the preferences and health conditions of the user's family and housemates.

[0036] The suggestion unit can suggest special menus tailored to specific events or occasions. The suggestion unit, for example, builds a system that suggests special menus tailored to specific events or occasions. For example, it suggests special recipes tailored to events such as Christmas or birthdays. The suggestion unit also suggests menus tailored to the theme of the event or occasion. For example, it suggests dishes using pumpkins for Halloween, and desserts using chocolate for Valentine's Day. The suggestion unit also develops a system that suggests special menus tailored to specific events or occasions in real time. For example, it suggests special recipes tailored to seasonal events. This makes it possible to suggest special menus tailored to specific events or occasions.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0039] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0040] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0041] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0042] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0043] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0044] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0045] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0046] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0047] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

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

[0049] Step 1: The data collection unit collects the heart rate, sleep information, medical history, and basic information recorded by the wearable device. For example, the data collection unit obtains heart rate data from the wearable device and estimates the user's exercise volume. The data collection unit can also collect sleep information and evaluate the user's sleep quality. Furthermore, the data collection unit collects the medical history and basic information recorded in HELPO and determines the user's health risks and nutritional needs. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes heart rate data to estimate the user's exercise volume. The analysis unit can also analyze sleep information to evaluate the user's sleep quality. Furthermore, the analysis unit analyzes medical history and basic information to determine the user's health risks and nutritional needs. Step 3: The suggestion unit suggests an optimal menu for the user based on the data analyzed by the analysis unit. For example, the suggestion unit suggests a low-calorie, well-balanced meal to a user who wants to lose weight. The suggestion unit can also suggest a meal suitable for replenishing energy to a user who exercises a lot. Furthermore, the suggestion unit suggests recipes that use a particular ingredient to a user who likes that ingredient.

[0050] (Example 2) The menu suggestion system according to an embodiment of the present invention is a system that suggests optimal menus in a conversational format based on the heart rate and sleep information recorded by a wearable device, and the medical history and basic information recorded in HELPO. This allows the menu suggestion system to suggest optimal menus that are personalized based on the user's health condition and lifestyle habits.

[0051] A menu suggestion system according to an embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects heart rate data, sleep information, medical history data, and basic information recorded by a wearable device. For example, the data collection unit acquires heart rate data from the wearable device and estimates the user's exercise volume. The data collection unit can also collect sleep information and evaluate the user's sleep quality. The data collection unit can also collect medical history data and basic information recorded in HELPO and determine the user's health risks and nutritional needs. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes heart rate data and estimates the user's exercise volume. The analysis unit can also analyze sleep information and evaluate the user's sleep quality. The analysis unit can also analyze the medical history data and basic information and determine the user's health risks and nutritional needs. The suggestion unit suggests an optimal menu for the user based on the data analyzed by the analysis unit. For example, the suggestion unit can suggest a low-calorie, balanced diet to a user who wants to lose weight. In addition, the suggestion unit can suggest meals suitable for replenishing energy to a user who exercises a lot. Furthermore, the suggestion unit can suggest recipes that use specific ingredients to a user who likes a particular ingredient. In this way, the menu suggestion system according to the embodiment can suggest optimal menus based on the user's health condition and lifestyle habits.

[0052] The analysis unit can estimate the user's exercise volume from heart rate data and evaluate the user's sleep quality from sleep information. For example, the analysis unit analyzes heart rate and electrodermal activity data collected from a wearable device to estimate the user's stress level. For example, it calculates a stress score based on heart rate fluctuations and changes in electrodermal activity and suggests nutritional supplements based on that score. The analysis unit also takes into account heart rate fluctuation patterns and sleep quality when estimating stress levels. For example, if the heart rate is high and the sleep quality is low, it determines that stress is high and suggests foods with a relaxing effect. The analysis unit also analyzes data from the wearable device in real time and suggests nutritional supplements based on fluctuations in stress levels. For example, when stress levels increase, it suggests foods containing vitamin C and magnesium. This allows the system to suggest more appropriate meal plans by evaluating the user's exercise volume and sleep quality.

[0053] The suggestion unit can suggest a balanced diet with reduced calories to a user who wants to go on a diet, and a meal suitable for replenishing energy to a user who exercises a lot. The suggestion unit, for example, analyzes medical history data recorded in HELPO and extracts the user's medical history and allergy information. For example, it automatically filters out specific ingredients based on allergy information and excludes them from menus. The suggestion unit also lists ingredients that the user should avoid based on the medical history data and suggests menus based on that list. For example, it suggests ingredients with low sugar content to a user with diabetes. The suggestion unit also analyzes medical history data in real time and suggests ingredients according to the user's health condition. For example, it suggests ingredients with low salt content to a user with high blood pressure. This makes it possible to suggest the optimal meal according to the user's goals.

[0054] The suggestion unit collects feedback such as the user's impressions after actually trying the suggested menu and weight changes, and can improve the next suggestion based on that feedback. The suggestion unit estimates the user's emotional state based on data collected from a wearable device, for example. For example, it analyzes fluctuations in heart rate and electrodermal activity to evaluate stress and fatigue levels. The suggestion unit also uses an emotion estimation function to analyze the user's emotional state in real time and suggests ingredients that have a relaxing effect when stress or fatigue is high. For example, it suggests chamomile tea or dark chocolate. The suggestion unit also builds a system that suggests ingredients according to the user's emotional state based on the emotion estimation data. For example, when stress is high, it suggests herbs and spices that have a relaxing effect. This allows the next suggestion to be improved based on user feedback.

[0055] The suggestion unit can infer emotions from the user's tone of voice and speaking style, and suggest a menu according to that emotion. The suggestion unit, for example, analyzes the user's tone of voice and speaking style to build a system that infers emotions. For example, it calculates an emotion score based on the pitch, speed, and intonation of the voice, and suggests a menu according to that score. The suggestion unit also uses an emotion estimation function to analyze emotions in real time from the user's tone of voice and speaking style, and suggests a menu according to that emotion. For example, when the user is relaxed, it suggests ingredients that have a relaxing effect. The suggestion unit also develops a system that infers the user's emotional state based on the user's tone of voice and speaking style, and suggests ingredients according to that emotion. For example, when the user is excited, it suggests ingredients that are suitable for replenishing energy. This makes it possible to suggest the optimal menu according to the user's emotions.

[0056] The suggestion unit can analyze the user's past conversation history and learn changes in preferences and new tastes. The suggestion unit, for example, builds a system that analyzes the user's past conversation history and learns changes in preferences and new tastes. For example, it detects changes in the user's tastes based on the content of past conversations. The suggestion unit also learns changes in the user's preferences in real time based on the conversation history and reflects them in the menu suggestions. For example, it suggests ingredients that the user has become interested in in recent conversations. The suggestion unit also develops a system that analyzes the user's past conversation history and learns new tastes. For example, it suggests ingredients that the user has become interested in based on the content of past conversations. This makes it possible to learn changes in the user's tastes and new tastes and suggest more appropriate menus.

[0057] The suggestion unit can use the emotion estimation function to suggest foods that have a relaxing effect when the user is feeling stressed. The suggestion unit, for example, uses the emotion estimation function to build a system that suggests foods that have a relaxing effect when the user is feeling stressed. For example, the suggestion unit analyzes the user's tone of voice and facial expression to evaluate the stress level. The suggestion unit also suggests foods that have a relaxing effect when the user is feeling stressed. For example, it suggests chamomile tea or dark chocolate. The suggestion unit also develops a system that suggests foods that have a relaxing effect when the user is feeling stressed based on the emotion estimation data. For example, it suggests herbs and spices that have a relaxing effect depending on the user's emotional state. This makes it possible to suggest foods that have a relaxing effect when the user is feeling stressed.

[0058] The suggestion unit can analyze the user's dietary history and propose a long-term meal plan that takes nutritional balance into consideration. The suggestion unit, for example, builds a system that analyzes the user's dietary history and proposes a long-term meal plan that takes nutritional balance into consideration. For example, the suggestion unit evaluates nutrient intake status based on past meal content. The suggestion unit also analyzes the user's nutritional balance in real time based on the dietary history and proposes a long-term meal plan. For example, if a specific nutrient is lacking, ingredients containing that nutrient are suggested. The suggestion unit also develops a system that analyzes the user's dietary history and proposes a long-term meal plan that takes nutritional balance into consideration. For example, the suggestion unit evaluates the user's health condition based on past meal content and proposes an optimal meal plan. This makes it possible to propose a long-term meal plan that takes nutritional balance into consideration.

[0059] The suggestion unit uses the emotion estimation function to suggest ingredients according to the user's emotional state and provide a menu that will improve the user's mood. The suggestion unit, for example, uses the emotion estimation function to build a system that suggests ingredients according to the user's emotional state. For example, when the user is feeling stressed, ingredients that have a relaxing effect are suggested. The suggestion unit also analyzes the user's emotional state in real time and suggests a menu that will improve the user's mood. For example, when the user is tired, ingredients that are suitable for replenishing energy are suggested. The suggestion unit also develops a system that suggests ingredients according to the user's emotional state based on the emotion estimation data. For example, when the user is relaxed, herbs and spices that have a relaxing effect are suggested. This makes it possible to suggest ingredients according to the user's emotional state and provide a menu that will improve the user's mood.

[0060] The suggestion unit is able to propose a menu that will satisfy everyone, taking into consideration the preferences and health conditions of the user's family or housemates. For example, the suggestion unit builds a system that proposes a menu that will satisfy everyone, taking into consideration the preferences and health conditions of the user's family and housemates. For example, it proposes a balanced menu based on the dietary history and health information of all family members. The suggestion unit also analyzes the preferences and health conditions of family members and housemates in real time to propose a menu that will satisfy everyone. For example, it proposes a menu that takes allergy information and nutritional balance into consideration. The suggestion unit is also able to develop a system that proposes a menu that will satisfy everyone, taking into consideration the preferences and health conditions of the user's family and housemates. For example, it proposes optimal ingredients and recipes based on the preferences and health conditions of all family members. This makes it possible to propose a menu that takes into consideration the preferences and health conditions of the user's family and housemates.

[0061] The suggestion unit can suggest special menus tailored to specific events or occasions. The suggestion unit, for example, builds a system that suggests special menus tailored to specific events or occasions. For example, it suggests special recipes tailored to events such as Christmas or birthdays. The suggestion unit also suggests menus tailored to the theme of the event or occasion. For example, it suggests dishes using pumpkins for Halloween, and desserts using chocolate for Valentine's Day. The suggestion unit also develops a system that suggests special menus tailored to specific events or occasions in real time. For example, it suggests special recipes tailored to seasonal events. This makes it possible to suggest special menus tailored to specific events or occasions.

[0062] The suggestion unit can use the emotion estimation function to analyze the emotion a user has toward a specific ingredient and suggest ingredients that elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion a user has toward a specific ingredient. For example, it analyzes facial expressions and voice while eating to identify ingredients that elicit positive emotions. The suggestion unit also builds a system that suggests ingredients that elicit positive emotions based on the user's emotion data. For example, it suggests a menu based on ingredients that the user has previously expressed positive emotions about. The suggestion unit also analyzes the emotion estimation data in real time to suggest ingredients that correspond to the user's emotional state. For example, when the user is feeling stressed, it suggests ingredients that have a relaxing effect. In this way, it is possible to analyze the emotion a user has toward a specific ingredient and suggest ingredients that elicit positive emotions.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0065] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0066] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0067] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0068] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0069] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0070] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0071] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0072] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0073] The suggestion unit can take into account the user's dietary preferences and allergy information and suggest certain ingredients to avoid. For example, if the user has a nut allergy, ingredients containing nuts are automatically filtered and excluded from the menu. The suggestion unit can also preferentially suggest certain ingredients based on the user's dietary preferences. For example, if the user likes a particular vegetable, it can suggest recipes using that vegetable. Furthermore, the suggestion unit can analyze the user's allergy information in real time and suggest menus that address any newly discovered allergies. This makes it possible to suggest optimal menus that suit the user's health condition and preferences.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The data collection unit collects the heart rate, sleep information, medical history, and basic information recorded by the wearable device. For example, the data collection unit obtains heart rate data from the wearable device and estimates the user's exercise volume. The data collection unit can also collect sleep information and evaluate the user's sleep quality. Furthermore, the data collection unit collects the medical history and basic information recorded in HELPO and determines the user's health risks and nutritional needs. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes heart rate data to estimate the user's exercise volume. The analysis unit can also analyze sleep information to evaluate the user's sleep quality. Furthermore, the analysis unit analyzes medical history and basic information to determine the user's health risks and nutritional needs. Step 3: The suggestion unit suggests an optimal menu for the user based on the data analyzed by the analysis unit. For example, the suggestion unit suggests a low-calorie, well-balanced meal to a user who wants to lose weight. The suggestion unit can also suggest a meal suitable for replenishing energy to a user who exercises a lot. Furthermore, the suggestion unit suggests recipes that use a particular ingredient to a user who likes that ingredient.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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, in order to avoid confusion and to 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.

[0142] 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]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects heart rate, sleep information, medical history, and basic information recorded by the wearable device; an analysis unit that analyzes the data collected by the data collection unit; a suggestion unit that suggests an optimal menu to the user based on the data analyzed by the analysis unit. A system characterized by:

2. The analysis unit The amount of exercise of the user is estimated from the heart rate data, and the quality of sleep of the user is evaluated from the sleep information.

2. The system of claim 1.

3. The proposal unit The system proposes a low-calorie, well-balanced meal to the user who wants to lose weight, and proposes a meal suitable for energy replenishment to the user who exercises a lot.

2. The system of claim 1.

4. The proposal unit Analyze the user's dietary history and propose a long-term meal plan that takes nutritional balance into consideration.

2. The system of claim 1.

5. The proposal unit Estimating the user's emotion from the tone of voice or the way of speaking, and proposing the menu according to the emotion 2. The system of claim 1.

6. The proposal unit Suggesting foods that have a relaxing effect when the user is feeling stressed 2. The system of claim 1.

7. The proposal unit Providing a menu that improves mood by suggesting ingredients according to the emotional state of the user 2. The system of claim 1.

8. The proposal unit Analyzing the user's feelings toward specific ingredients and suggesting ingredients that elicit positive feelings 2. The system of claim 1.

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