System

Abstract

An object of a system according to an embodiment is to propose a healthy snack on the basis of eating habits and preferences of a user.SOLUTION: The system according to the embodiment is provided with the eating habit analysis section and the snacking proposal section. The eating habit analysis unit collects and analyzes data on eating habits and preferences of the user. The snack proposing section proposes choices of healthy snacks based on the eating habit and the taste of the user collected and analyzed by the eating habit analysis section.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 suggest healthy snacks based on a user's eating habits and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest healthy snacks based on the user's eating habits and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes an eating habit analysis unit and a snack suggestion unit. The eating habit analysis unit collects and analyzes data related to a user's eating habits and preferences. The snack suggestion unit suggests healthy snack options based on the user's eating habits and preferences collected and analyzed by the eating habit analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest healthy snacks based on the user's eating habits and preferences. [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) A health support system according to an embodiment of the present invention is a system that prevents weight gain by suppressing snack eating when feeling a bit hungry. This system uses generative AI to suggest optimal meal plans and snack options for each individual user, supporting a healthy diet. As a result, the health support system analyzes the user's eating habits and suggests healthy snacks, thereby preventing weight gain.

[0029] A health support system according to an embodiment includes an eating habit analysis unit and a snack suggestion unit. The eating habit analysis unit collects and analyzes data related to a user's eating habits and preferences. For example, the eating habit analysis unit collects food records and questionnaire data to understand the user's usual dietary habits. The eating habit analysis unit can also collect the user's eating habit data through a smartphone app. The eating habit analysis unit then analyzes the collected data using a generation AI to understand the user's eating habits and preferences. For example, the generation AI analyzes the user's food records to identify the frequency and type of meals. The snack suggestion unit suggests healthy snack options based on the user's eating habits and preferences collected and analyzed by the eating habit analysis unit. For example, the snack suggestion unit suggests low-calorie, nutritious snacks, fruits, nuts, etc. The snack suggestion unit also suggests optimal snacks for the user using the generation AI. For example, the generation AI suggests snacks suitable for a specific time period based on the user's preferences. This allows the health support system to analyze the user's eating habits and suggest healthy snacks to prevent weight gain.

[0030] The eating habit analysis unit can analyze the user's exercise habits and sleep patterns in addition to the user's eating habit data to evaluate the user's overall health condition. For example, the eating habit analysis unit collects exercise data from an exercise app or wearable device in addition to the user's meal records, and analyzes the balance between diet and exercise. For example, it proposes an appropriate meal plan taking into account the number of steps taken daily and the amount of exercise time. The eating habit analysis unit also analyzes the user's sleep patterns and evaluates the quality and rhythm of sleep. For example, it evaluates the user's sleep quality based on data collected from a sleep app and suggests areas for improvement. This allows the user's overall health condition to be evaluated, and a more appropriate meal plan to be proposed.

[0031] The eating habit analysis unit analyzes photos of the user's meals and can understand eating habits from visual information as well. For example, the eating habit analysis unit allows the user to upload photos of their meals to the app and analyze the ingredients and types of dishes using image recognition technology. For example, it automatically calculates calories and nutrients from the photos. The eating habit analysis unit also evaluates the balance of meals based on visual information. For example, it analyzes the color and shape of ingredients from the photos to evaluate the nutritional balance. This allows for understanding eating habits from visual information as well, making it possible to propose more accurate meal plans.

[0032] The snack suggestion unit can suggest individually customized snacks by taking into account the user's allergy information and dietary restrictions. The snack suggestion unit, for example, collects the user's allergy information and builds a system that suggests safe snacks based on that information. For example, it suggests snacks that do not contain nuts to a user who has a nut allergy. The snack suggestion unit also suggests low-calorie and low-sugar snacks by taking into account the user's dietary restrictions. For example, it suggests low-sugar snacks to a user with diabetes. In this way, it is possible to suggest safe and appropriate snacks by taking into account the user's allergy information and dietary restrictions.

[0033] The snack suggestion unit can analyze the timing and frequency of a user's snacks and suggest optimal snack timing. The snack suggestion unit, for example, records the timing and frequency of a user's snacks and builds a system that suggests optimal snack timing based on that. For example, it analyzes the time periods when a user feels hungry and suggests snacks that are appropriate for those times. The snack suggestion unit also analyzes the user's snack frequency and suggests snacks at appropriate intervals. For example, suggesting three snacks a day prevents excessive snacking. In this way, by analyzing the timing and frequency of a user's snacks, it is possible to suggest optimal snack timing.

[0034] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0035] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The health support system may further include an environment analysis unit that analyzes the user's eating environment. The environment analysis unit may analyze, for example, the location and time of day where the user eats, as well as the surrounding sound and light conditions. For example, it may evaluate the difference between when the user eats in a quiet environment and when the user eats in a noisy environment. The environment analysis unit may also suggest an optimal eating environment based on the user's eating environment. For example, it may suggest playing relaxing music or appropriate lighting. This may optimize the user's eating environment, thereby supporting healthier eating habits.

[0038] The health support system may further include a quality evaluation unit that evaluates the quality of the user's diet. The quality evaluation unit evaluates, for example, the quality and freshness of the ingredients consumed by the user. For example, it analyzes the origin, cultivation method, and storage conditions of the ingredients. The quality evaluation unit may also analyze the ingredient labels of the foods purchased by the user and evaluate the presence or absence of additives and preservatives. This can support the user in selecting higher quality and healthier ingredients.

[0039] The health support system may further include a satisfaction assessment unit that assesses the user's satisfaction with the meal. The satisfaction assessment unit may collect, for example, the user's satisfaction after eating a meal through a questionnaire or feedback. For example, it may collect evaluations of the taste, quantity, and feeling of fullness of the meal. The satisfaction assessment unit may also analyze the user's satisfaction data and suggest improvements to the meal plan. This may increase the user's satisfaction and support continued healthy eating habits.

[0040] The health support system may further include a social factor evaluation unit that evaluates the social factors of the user's meals. The social factor evaluation unit may, for example, evaluate the user's interactions with family and friends when eating meals. For example, the social factor evaluation unit may analyze conversations during meals and the frequency of communal eating. The social factor evaluation unit may also suggest meal plans based on the user's social eating environment. For example, the social factor evaluation unit may suggest recipes that can be enjoyed with family or meal ideas that can be shared with friends. This may support the user's healthy eating habits by providing a meal plan that takes social factors into consideration.

[0041] The health support system may further include an economic factor evaluation unit that evaluates the economic factors of the user's meals. The economic factor evaluation unit may, for example, analyze the expenses the user spends on meals. For example, it may evaluate the cost of ingredients and the frequency of eating out. The economic factor evaluation unit may also suggest cost-effective meal plans based on the user's budget. For example, it may suggest recipes using inexpensive, nutritious ingredients and meal ideas that save money. This allows the system to support healthy eating habits by providing meal plans that take the user's financial situation into consideration.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The eating habit analysis unit collects and analyzes data on the user's eating habits and preferences. For example, it can collect food records and questionnaire data, and collect the user's eating habit data through a smartphone app. Furthermore, it uses the generation AI to analyze the collected data and understand the user's eating habits and preferences. The generation AI analyzes the user's food records and identifies the frequency and types of meals. Step 2: The snack suggestion unit suggests healthy snack options based on the user's eating habits and preferences collected and analyzed by the eating habits analysis unit. For example, it suggests low-calorie, nutritious snacks, fruits, nuts, etc. It also uses generative AI to suggest optimal snacks for the user and suggests snacks suitable for specific time periods.

[0044] (Example 2) A health support system according to an embodiment of the present invention is a system that prevents weight gain by suppressing snack eating when feeling a bit hungry. This system uses generative AI to suggest optimal meal plans and snack options for each individual user, supporting a healthy diet. As a result, the health support system analyzes the user's eating habits and suggests healthy snacks, thereby preventing weight gain.

[0045] A health support system according to an embodiment includes an eating habit analysis unit and a snack suggestion unit. The eating habit analysis unit collects and analyzes data related to a user's eating habits and preferences. For example, the eating habit analysis unit collects food records and questionnaire data to understand the user's usual dietary habits. The eating habit analysis unit can also collect the user's eating habit data through a smartphone app. The eating habit analysis unit then analyzes the collected data using a generation AI to understand the user's eating habits and preferences. For example, the generation AI analyzes the user's food records to identify the frequency and type of meals. The snack suggestion unit suggests healthy snack options based on the user's eating habits and preferences collected and analyzed by the eating habit analysis unit. For example, the snack suggestion unit suggests low-calorie, nutritious snacks, fruits, nuts, etc. The snack suggestion unit also suggests optimal snacks for the user using the generation AI. For example, the generation AI suggests snacks suitable for a specific time period based on the user's preferences. This allows the health support system to analyze the user's eating habits and suggest healthy snacks to prevent weight gain.

[0046] The eating habit analysis unit can analyze the user's exercise habits and sleep patterns in addition to the user's eating habit data to evaluate the user's overall health condition. For example, the eating habit analysis unit collects exercise data from an exercise app or wearable device in addition to the user's meal records, and analyzes the balance between diet and exercise. For example, it proposes an appropriate meal plan taking into account the number of steps taken daily and the amount of exercise time. The eating habit analysis unit also analyzes the user's sleep patterns and evaluates the quality and rhythm of sleep. For example, it evaluates the user's sleep quality based on data collected from a sleep app and suggests areas for improvement. This allows the user's overall health condition to be evaluated, and a more appropriate meal plan to be proposed.

[0047] The eating habit analysis unit analyzes photos of the user's meals and can understand eating habits from visual information as well. For example, the eating habit analysis unit allows the user to upload photos of their meals to the app and analyze the ingredients and types of dishes using image recognition technology. For example, it automatically calculates calories and nutrients from the photos. The eating habit analysis unit also evaluates the balance of meals based on visual information. For example, it analyzes the color and shape of ingredients from the photos to evaluate the nutritional balance. This allows for understanding eating habits from visual information as well, making it possible to propose more accurate meal plans.

[0048] The eating habit analysis unit uses the emotion estimation function to analyze the user's emotions when eating a meal and evaluate the impact of stress and emotional fluctuations on eating habits. The eating habit analysis unit, for example, analyzes the user's facial expressions and voice when eating a meal and builds a system to calculate an emotion score. For example, when stress is high, it suggests meals that have a relaxing effect. The eating habit analysis unit also uses the emotion estimation function to monitor the user's emotional fluctuations in real time. For example, it uses facial expression recognition technology to analyze the user's emotional fluctuations and reflect them in meal suggestions. This allows the system to suggest a more appropriate meal plan by evaluating the impact of emotional fluctuations on eating habits.

[0049] The snack suggestion unit can suggest individually customized snacks by taking into account the user's allergy information and dietary restrictions. The snack suggestion unit, for example, collects the user's allergy information and builds a system that suggests safe snacks based on that information. For example, it suggests snacks that do not contain nuts to a user who has a nut allergy. The snack suggestion unit also suggests low-calorie and low-sugar snacks by taking into account the user's dietary restrictions. For example, it suggests low-sugar snacks to a user with diabetes. In this way, it is possible to suggest safe and appropriate snacks by taking into account the user's allergy information and dietary restrictions.

[0050] The snack suggestion unit can analyze the timing and frequency of a user's snacks and suggest optimal snack timing. The snack suggestion unit, for example, records the timing and frequency of a user's snacks and builds a system that suggests optimal snack timing based on that. For example, it analyzes the time periods when a user feels hungry and suggests snacks that are appropriate for those times. The snack suggestion unit also analyzes the user's snack frequency and suggests snacks at appropriate intervals. For example, suggesting three snacks a day prevents excessive snacking. In this way, by analyzing the timing and frequency of a user's snacks, it is possible to suggest optimal snack timing.

[0051] The snack suggestion unit can use the emotion estimation function to monitor the emotions of a user when eating a snack in real time and suggest snacks that elicit positive emotions. The snack suggestion unit, for example, uses the emotion estimation function to build a system that monitors the emotions of a user when eating a snack in real time. For example, it collects emotion data when a user enjoys eating a snack. Furthermore, the snack suggestion unit suggests snacks that elicit positive emotions based on the user's emotion data. For example, it suggests snacks that allow the user to relax. In this way, it is possible to monitor the user's emotions in real time and suggest snacks that elicit positive emotions.

[0052] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0053] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0054] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The health support system may further include an environment analysis unit that analyzes the user's eating environment. The environment analysis unit may analyze, for example, the location and time of day where the user eats, as well as the surrounding sound and light conditions. For example, it may evaluate the difference between when the user eats in a quiet environment and when the user eats in a noisy environment. The environment analysis unit may also suggest an optimal eating environment based on the user's eating environment. For example, it may suggest playing relaxing music or appropriate lighting. This may optimize the user's eating environment, thereby supporting healthier eating habits.

[0057] The health support system may further include a quality evaluation unit that evaluates the quality of the user's diet. The quality evaluation unit evaluates, for example, the quality and freshness of the ingredients consumed by the user. For example, it analyzes the origin, cultivation method, and storage conditions of the ingredients. The quality evaluation unit may also analyze the ingredient labels of the foods purchased by the user and evaluate the presence or absence of additives and preservatives. This can support the user in selecting higher quality and healthier ingredients.

[0058] The health support system may further include a satisfaction assessment unit that assesses the user's satisfaction with the meal. The satisfaction assessment unit may collect, for example, the user's satisfaction after eating a meal through a questionnaire or feedback. For example, it may collect evaluations of the taste, quantity, and feeling of fullness of the meal. The satisfaction assessment unit may also analyze the user's satisfaction data and suggest improvements to the meal plan. This may increase the user's satisfaction and support continued healthy eating habits.

[0059] The health support system may further include a social factor evaluation unit that evaluates the social factors of the user's meals. The social factor evaluation unit may, for example, evaluate the user's interactions with family and friends when eating meals. For example, the social factor evaluation unit may analyze conversations during meals and the frequency of communal eating. The social factor evaluation unit may also suggest meal plans based on the user's social eating environment. For example, the social factor evaluation unit may suggest recipes that can be enjoyed with family or meal ideas that can be shared with friends. This may support the user's healthy eating habits by providing a meal plan that takes social factors into consideration.

[0060] The health support system may further include an economic factor evaluation unit that evaluates the economic factors of the user's meals. The economic factor evaluation unit may, for example, analyze the expenses the user spends on meals. For example, it may evaluate the cost of ingredients and the frequency of eating out. The economic factor evaluation unit may also suggest cost-effective meal plans based on the user's budget. For example, it may suggest recipes using inexpensive, nutritious ingredients and meal ideas that save money. This allows the system to support healthy eating habits by providing meal plans that take the user's financial situation into consideration.

[0061] The health support system may further include a timing suggestion unit that estimates the user's emotions and suggests meal times based on the estimated emotions. For example, when the user is feeling stressed, the timing suggestion unit suggests eating at a time when the user is able to relax. When the user is feeling positive, the timing suggestion unit suggests meal times that will help the user maintain those emotions. This makes it possible to support healthier eating habits by suggesting optimal meal times based on the user's emotions.

[0062] The health support system may further include a type suggestion unit that estimates the user's emotions and suggests types of meals based on the estimated emotions. For example, when the user is tired, the type suggestion unit suggests meals that will replenish energy. When the user wants to relax, the type suggestion unit suggests meals that use ingredients with a relaxing effect. In this way, the optimal type of meal can be suggested based on the user's emotions, thereby supporting healthier eating habits.

[0063] The health support system may further include a portion suggestion unit that estimates the user's emotions and suggests meal portions based on the estimated emotions. For example, when the user is feeling stressed, the portion suggestion unit suggests an appropriate amount of food to prevent overeating. Furthermore, when the user is feeling positive, the portion suggestion unit suggests an appropriate amount of food to maintain that emotion. This makes it possible to support healthier eating habits by suggesting optimal meal portions based on the user's emotions.

[0064] The health support system may further include a place suggestion unit that estimates the user's emotions and suggests places to eat based on the estimated emotions. For example, the place suggestion unit suggests a quiet place to eat when the user wants to relax. Also, the place suggestion unit suggests places to eat with friends or family when the user is in a sociable mood. In this way, the optimal place to eat based on the user's emotions can be suggested to support healthier eating habits.

[0065] The health support system may further include a frequency suggestion unit that estimates the user's emotions and suggests a meal frequency based on the estimated emotions. For example, the frequency suggestion unit suggests eating snacks frequently when the user is feeling stressed. Also, when the user is feeling positive, the frequency suggestion unit suggests eating at an appropriate frequency to maintain that emotion. This makes it possible to support healthier eating habits by suggesting an optimal meal frequency based on the user's emotions.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The eating habit analysis unit collects and analyzes data on the user's eating habits and preferences. For example, it can collect food records and questionnaire data, and collect the user's eating habit data through a smartphone app. Furthermore, it uses the generation AI to analyze the collected data and understand the user's eating habits and preferences. The generation AI analyzes the user's food records and identifies the frequency and types of meals. Step 2: The snack suggestion unit suggests healthy snack options based on the user's eating habits and preferences collected and analyzed by the eating habits analysis unit. For example, it suggests low-calorie, nutritious snacks, fruits, nuts, etc. It also uses generative AI to suggest optimal snacks for the user and suggests snacks suitable for specific time periods.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The 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.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 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.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0096] In the 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.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] 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.

[0109] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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.

[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 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.

[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 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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]

[0135] 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 dietary habit analysis unit that collects and analyzes data on the user's dietary habits and preferences; a snack suggestion unit that suggests healthy snack options based on the user's eating habits and preferences collected and analyzed by the eating habit analysis unit. A system characterized by:

2. The eating habit analysis unit In addition to the user's dietary habits, the system also analyzes their exercise habits and sleep patterns to assess their overall health.

2. The system of claim 1.

3. The snack suggestion unit Taking into account the user's allergy information and dietary restrictions, the app offers personalized snack suggestions 2. The system of claim 1.

4. The eating habit analysis unit Based on the user's eating habits, the app proposes meal plans according to the season and weather.

2. The system of claim 1.

5. The snack suggestion unit Based on the user's location information, nearby stores that offer healthy ingredients are suggested in real time.

2. The system of claim 1.

6. The eating habit analysis unit Using an emotion estimation function, the emotions of the user when eating are analyzed, and the effects of stress and emotional fluctuations on the eating habits are evaluated.

2. The system of claim 1.

7. The snack suggestion unit Using emotion estimation function, the system monitors the user's emotions in real time when eating snacks and suggests snacks that elicit positive emotions.

2. The system of claim 1.

8. The eating habit analysis unit Using emotion estimation function, the emotion of the user when recording meals is analyzed and feedback is provided to maintain motivation.

2. The system of claim 1.

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