System

Abstract

An object of a system according to an embodiment is to provide an optimal meal plan based on preferences and nutritional needs of individual users.SOLUTION: A system according to an embodiment includes a preference comprehension unit, a record analysis unit, and a menu suggestion unit. The preference recognition unit recognizes preferences, restrictions, and nutritional needs of the user. The record analysis unit analyzes the meal record based on the information grasped by the preference grasping unit. The menu proposal unit proposes an optimal meal menu on the basis of the information analyzed by the record analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide meal plans based on individual user preferences and nutritional needs.

[0005] The system according to the embodiment aims to provide optimal meal plans based on individual user preferences and nutritional needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference ascertainment unit, a record analysis unit, and a menu suggestion unit. The preference ascertainment unit identifies the user's preferences, dietary restrictions, and nutritional needs. The record analysis unit analyzes the meal record based on the information ascertained by the preference ascertainment unit. The menu suggestion unit suggests an optimal meal menu based on the information analyzed by the record analysis unit. [Effects of the Invention]

[0007] An embodiment of the system can provide optimal meal plans based on individual user preferences and nutritional needs. [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 meal plan suggestion system according to the embodiment of the present invention is a system that identifies an individual's preferences, restrictions, and nutritional needs and proposes a personalized meal plan based on the identified preferences, restrictions, and nutritional needs. As a result, the meal plan suggestion system can provide a personalized meal plan for each individual.

[0029] The meal plan proposal system according to the embodiment includes a preference ascertainment unit, a record analysis unit, and a menu suggestion unit. The preference ascertainment unit identifies the user's preferences, restrictions, and nutritional needs. For example, the generation AI records information such as "I don't like dairy products" through dialogue with the user and proposes a dairy-free meal plan. The generation AI also considers restrictions such as "I have high blood pressure, so I want to limit my salt intake." The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI understands individual nutritional needs based on the prompts. The record analysis unit analyzes meal images taken by the user and records the meal contents. For example, it provides analysis results for images taken by the user, such as "This is salad and grilled chicken." This allows for an accurate understanding of the user's meal history. The menu suggestion unit customizes optimal meal menus based on the collected meal records, personal preferences, restrictions, and nutritional needs. For example, it suggests specific menus such as "low-carb cereal and yogurt for breakfast, a salad with plenty of vegetables for lunch, and a high-protein fish dish for dinner." The generation AI provides a balanced meal plan based on the user's health condition and goals, allowing the meal plan proposal system according to the embodiment to provide an individually personalized meal plan.

[0030] The preference understanding unit can suggest a dairy-free meal plan based on the user's input. For example, if the user inputs, "I don't like dairy products," the AI ​​will record this information and suggest a dairy-free meal plan. This allows the AI ​​to provide a meal plan that avoids specific ingredients.

[0031] The record analysis unit can analyze the food image taken by the user and record the meal contents, such as salad and grilled chicken. For example, the record analysis unit analyzes the food image taken by the user and provides the analysis result in the form of "This is salad and grilled chicken." This allows the meal contents to be accurately recorded.

[0032] The menu suggestion unit can suggest low-carbohydrate meal menus for diabetics and low-sodium meal menus for kidney disease patients according to the user's health condition. For example, the menu suggestion unit suggests low-carbohydrate menus for diabetic patients and low-sodium menus for kidney disease patients. This makes it possible to provide an appropriate meal menu according to the user's health condition.

[0033] The menu suggestion unit can adjust the next meal menu based on the user's feedback. For example, if the user provides feedback such as "This menu was delicious," the generation AI will adjust the next menu based on that information. This can increase user satisfaction.

[0034] The preference understanding unit analyzes the user's past dietary history and health checkup data to predict long-term nutritional needs such as calcium deficiency. For example, the preference understanding unit collects the user's past dietary history, and the generation AI analyzes that data. For example, it analyzes the dietary content of the past year to understand nutrient intake trends. This makes it possible to predict long-term nutritional needs and provide an appropriate meal plan.

[0035] The preference ascertainer can dynamically adjust nutritional needs based on the user's lifestyle habits and amount of exercise, as well as sleep time and stress level. For example, the preference ascertainer collects the user's lifestyle habits, and the generation AI analyzes the data. For example, the preference ascertainer adjusts nutritional needs by taking into account sleep time and stress level. This allows for dynamic adjustment of nutritional needs and the provision of an appropriate meal plan.

[0036] The preference understanding unit takes into account the preferences and restrictions of the user's family and housemates and can propose a meal plan that will satisfy everyone. For example, the preference understanding unit collects the preferences and restrictions of the user's family and housemates, and the generation AI proposes a meal plan based on that data. For example, it proposes a menu that everyone can eat. This makes it possible to provide a meal plan that will satisfy all family members and housemates.

[0037] The preference understanding unit can propose meal plans that incorporate seasonal and regional specialties. For example, the preference understanding unit collects seasonal specialties, and the generation AI proposes meal plans based on that data. For example, in spring, it proposes menus using fresh vegetables. This makes it possible to provide fresh meal plans that incorporate seasonal and regional specialties.

[0038] In addition to analyzing the image of the meal, the record analysis unit can automatically calculate the nutritional components of ingredients and provide detailed nutritional information. For example, the record analysis unit analyzes the image of the meal and builds a system that automatically calculates the nutritional components of ingredients. For example, the calorie and vitamin content can be displayed. This makes it possible to provide detailed nutritional information.

[0039] The record analysis unit can automatically estimate the amount and calories of food consumed and provide alerts to prevent overeating and malnutrition. The record analysis unit, for example, analyzes images of meals and builds a system that automatically estimates the amount and calories of food consumed. For example, an alert is displayed when there is a risk of overeating. This makes it possible to provide alerts to prevent overeating and malnutrition.

[0040] The record analysis unit can accept voice input and text input in addition to images of meals, allowing meal contents to be recorded from multiple angles. For example, the record analysis unit can build a system that accepts voice input and text input in addition to images of meals. For example, the user can describe the meal contents by voice. This allows meal contents to be recorded from multiple angles.

[0041] When analyzing images of meals, the record analysis unit can also consider the origin and production method of ingredients and propose eco-friendly meal plans. For example, the record analysis unit can analyze images of meals and build a system that considers the origin and production method of ingredients. For example, it can prioritize locally produced ingredients. This makes it possible to provide eco-friendly meal plans.

[0042] The menu suggestion unit can dynamically adjust the meal menu according to the user's health goals. For example, the menu suggestion unit collects the user's health goals, and the generation AI dynamically adjusts the meal menu based on that data. For example, if the user is aiming for weight loss, a low-calorie menu will be suggested. This makes it possible to provide a meal menu according to the user's health goals.

[0043] The menu suggestion unit can take into account the user's allergy information and medication status when proposing a meal menu. For example, the menu suggestion unit collects the user's allergy information, and the generation AI proposes meal menus based on that data. For example, if the user has a nut allergy, a menu that does not contain nuts is proposed. This makes it possible to provide a meal menu that takes into account the user's allergy information and medication status.

[0044] The menu suggestion unit can add recipe videos and cooking procedures to the user's meal menu, providing the enjoyment of cooking. The menu suggestion unit, for example, builds a system that adds recipe videos to the user's meal menu and provides the enjoyment of cooking. For example, it shows simple cooking procedures in video. This can provide the enjoyment of cooking.

[0045] The menu suggestion unit can take into account the user's budget and the availability of ingredients when proposing a meal menu. For example, the menu suggestion unit collects the user's budget, and the generation AI proposes a meal menu based on that data. For example, it proposes a menu that can be made on a low budget. This makes it possible to provide a realistic meal menu that takes into account the budget and the availability of ingredients.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The meal plan proposal system can further include a cultural analysis unit that takes into account the user's cultural background regarding meals. For example, if the user has dietary restrictions based on a specific culture or religion, that information can be collected and an appropriate meal plan can be proposed. For example, a menu that meets the needs of a user with halal or kosher dietary restrictions can be proposed. In addition, by incorporating traditional dishes from a specific region, a meal plan that takes into account the user's cultural background can be provided. This allows for a personalized meal plan to be provided that is tailored to the user's cultural background.

[0048] When analyzing the user's meal images, the recording and analysis unit can also evaluate the freshness and quality of ingredients. For example, it can estimate the freshness of ingredients from their color and shape and recommend meals using fresh ingredients. It can also evaluate the quality of ingredients and suggest menus using high-quality ingredients. It can also take into account the storage conditions of ingredients and suggest appropriate storage methods. This allows the user to enjoy meals using fresh, high-quality ingredients.

[0049] When analyzing the user's meal images, the record analysis unit can propose eco-friendly meal plans by taking into consideration the origin and production method of ingredients. For example, it can prioritize locally produced ingredients to reduce the environmental impact of transportation. It can also provide environmentally friendly meal plans by recommending ingredients from organic farming and sustainable fishing. It can also provide information on the producers of ingredients to help users understand the background of the ingredients. This makes it possible to provide eco-friendly meal plans.

[0050] When analyzing a user's meal image, the record / analysis unit can automatically calculate the nutritional content of ingredients and provide detailed nutritional information. For example, it can analyze the meal image and display the calorie, vitamin, and mineral content. It can also evaluate the nutritional balance of ingredients and make suggestions for supplementing necessary nutrients. It can also suggest menus fortified with specific nutrients based on the user's health condition and goals. This helps the user choose healthy meals.

[0051] The menu suggestion unit can add recipe videos and cooking instructions to the user's meal menu to make cooking more enjoyable. For example, it can show simple cooking steps in a video for a menu selected by the user. It can also improve cooking skills by explaining tips and tricks for specific dishes. It can also suggest ideas and techniques to help the user enjoy cooking. This helps the user prepare healthy meals while enjoying cooking.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The preference understanding unit understands the user's preferences, restrictions, and nutritional needs. For example, through dialogue with the user, the generation AI records information such as "I don't like dairy products" and proposes a dairy-free meal plan. It also takes into account restrictions such as "I have high blood pressure, so I want to limit my salt intake." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI understands individual nutritional needs based on the prompt. Step 2: The record analysis unit analyzes the food images taken by the user and records the meal contents. For example, it provides the analysis results in the form of "This is salad and grilled chicken" for the image taken by the user. This allows the user's meal history to be accurately understood. Step 3: The menu suggestion module customizes the optimal meal plan based on the collected food records, personal preferences, restrictions, and nutritional needs. For example, it might suggest a specific menu such as "low-sugar cereal and yogurt for breakfast, a salad with plenty of vegetables for lunch, and a high-protein fish dish for dinner." The generative AI provides a balanced meal plan based on the user's health status and goals.

[0054] (Example 2) The meal plan suggestion system according to the embodiment of the present invention is a system that identifies an individual's preferences, restrictions, and nutritional needs and proposes a personalized meal plan based on the identified preferences, restrictions, and nutritional needs. As a result, the meal plan suggestion system can provide a personalized meal plan for each individual.

[0055] The meal plan proposal system according to the embodiment includes a preference ascertainment unit, a record analysis unit, and a menu suggestion unit. The preference ascertainment unit identifies the user's preferences, restrictions, and nutritional needs. For example, the generation AI records information such as "I don't like dairy products" through dialogue with the user and proposes a dairy-free meal plan. The generation AI also considers restrictions such as "I have high blood pressure, so I want to limit my salt intake." The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI understands individual nutritional needs based on the prompts. The record analysis unit analyzes meal images taken by the user and records the meal contents. For example, it provides analysis results for images taken by the user, such as "This is salad and grilled chicken." This allows for an accurate understanding of the user's meal history. The menu suggestion unit customizes optimal meal menus based on the collected meal records, personal preferences, restrictions, and nutritional needs. For example, it suggests specific menus such as "low-carb cereal and yogurt for breakfast, a salad with plenty of vegetables for lunch, and a high-protein fish dish for dinner." The generation AI provides a balanced meal plan based on the user's health condition and goals, allowing the meal plan proposal system according to the embodiment to provide an individually personalized meal plan.

[0056] The preference understanding unit can suggest a dairy-free meal plan based on the user's input. For example, if the user inputs, "I don't like dairy products," the AI ​​will record this information and suggest a dairy-free meal plan. This allows the AI ​​to provide a meal plan that avoids specific ingredients.

[0057] The record analysis unit can analyze the food image taken by the user and record the meal contents, such as salad and grilled chicken. For example, the record analysis unit analyzes the food image taken by the user and provides the analysis result in the form of "This is salad and grilled chicken." This allows the meal contents to be accurately recorded.

[0058] The menu suggestion unit can suggest low-carbohydrate meal menus for diabetics and low-sodium meal menus for kidney disease patients according to the user's health condition. For example, the menu suggestion unit suggests low-carbohydrate menus for diabetic patients and low-sodium menus for kidney disease patients. This makes it possible to provide an appropriate meal menu according to the user's health condition.

[0059] The menu suggestion unit can adjust the next meal menu based on the user's feedback. For example, if the user provides feedback such as "This menu was delicious," the generation AI will adjust the next menu based on that information. This can increase user satisfaction.

[0060] The preference understanding unit analyzes the user's past dietary history and health checkup data to predict long-term nutritional needs such as calcium deficiency. For example, the preference understanding unit collects the user's past dietary history, and the generation AI analyzes that data. For example, it analyzes the dietary content of the past year to understand nutrient intake trends. This makes it possible to predict long-term nutritional needs and provide an appropriate meal plan.

[0061] The preference ascertainer can dynamically adjust nutritional needs based on the user's lifestyle habits and amount of exercise, as well as sleep time and stress level. For example, the preference ascertainer collects the user's lifestyle habits, and the generation AI analyzes the data. For example, the preference ascertainer adjusts nutritional needs by taking into account sleep time and stress level. This allows for dynamic adjustment of nutritional needs and the provision of an appropriate meal plan.

[0062] The preference understanding unit can use the emotion estimation function to analyze the user's emotions about food and suggest ingredients and menus that elicit positive emotions. For example, the preference understanding unit can use the emotion estimation function to analyze the user's emotions about food in real time and identify ingredients that elicit positive emotions. For example, it can suggest menus that include many of the user's favorite ingredients. This makes it possible to suggest ingredients and menus that elicit positive emotions.

[0063] The preference understanding unit takes into account the preferences and restrictions of the user's family and housemates and can propose a meal plan that will satisfy everyone. For example, the preference understanding unit collects the preferences and restrictions of the user's family and housemates, and the generation AI proposes a meal plan based on that data. For example, it proposes a menu that everyone can eat. This makes it possible to provide a meal plan that will satisfy all family members and housemates.

[0064] The preference understanding unit can propose meal plans that incorporate seasonal and regional specialties. For example, the preference understanding unit collects seasonal specialties, and the generation AI proposes meal plans based on that data. For example, in spring, it proposes menus using fresh vegetables. This makes it possible to provide fresh meal plans that incorporate seasonal and regional specialties.

[0065] The preference understanding unit can use the emotion estimation function to analyze the emotion the user has toward a specific ingredient and make suggestions to avoid ingredients associated with negative emotions. The preference understanding unit, for example, uses the emotion estimation function to analyze the negative emotions the user has toward a specific ingredient. For example, it can suggest a menu that avoids ingredients that the user dislikes. This makes it possible to provide a meal plan that avoids ingredients associated with negative emotions.

[0066] In addition to analyzing the image of the meal, the record analysis unit can automatically calculate the nutritional components of ingredients and provide detailed nutritional information. For example, the record analysis unit analyzes the image of the meal and builds a system that automatically calculates the nutritional components of ingredients. For example, the calorie and vitamin content can be displayed. This makes it possible to provide detailed nutritional information.

[0067] The record analysis unit can automatically estimate the amount and calories of food consumed and provide alerts to prevent overeating and malnutrition. The record analysis unit, for example, analyzes images of meals and builds a system that automatically estimates the amount and calories of food consumed. For example, an alert is displayed when there is a risk of overeating. This makes it possible to provide alerts to prevent overeating and malnutrition.

[0068] The record analysis unit can use the emotion estimation function to analyze the facial expression of the user while eating and evaluate the satisfaction level with the meal. For example, the record analysis unit uses the emotion estimation function to analyze the facial expression of the user while eating and build a system to evaluate the satisfaction level with the meal. For example, the satisfaction level is evaluated based on the frequency of smiling. This makes it possible to evaluate the satisfaction level with the meal.

[0069] The record analysis unit can accept voice input and text input in addition to images of meals, allowing meal contents to be recorded from multiple angles. For example, the record analysis unit can build a system that accepts voice input and text input in addition to images of meals. For example, the user can describe the meal contents by voice. This allows meal contents to be recorded from multiple angles.

[0070] When analyzing images of meals, the record analysis unit can also consider the origin and production method of ingredients and propose eco-friendly meal plans. For example, the record analysis unit can analyze images of meals and build a system that considers the origin and production method of ingredients. For example, it can prioritize locally produced ingredients. This makes it possible to provide eco-friendly meal plans.

[0071] The record analysis unit uses the emotion estimation function to monitor the user's emotions during a meal in real time and can make suggestions to improve the quality of the meal. The record analysis unit, for example, uses the emotion estimation function to build a system that monitors the user's emotions during a meal in real time. For example, it analyzes facial expressions during a meal and calculates an emotion score. This makes it possible to make suggestions to improve the quality of the meal.

[0072] The menu suggestion unit can dynamically adjust the meal menu according to the user's health goals. For example, the menu suggestion unit collects the user's health goals, and the generation AI dynamically adjusts the meal menu based on that data. For example, if the user is aiming for weight loss, a low-calorie menu will be suggested. This makes it possible to provide a meal menu according to the user's health goals.

[0073] The menu suggestion unit can take into account the user's allergy information and medication status when proposing a meal menu. For example, the menu suggestion unit collects the user's allergy information, and the generation AI proposes meal menus based on that data. For example, if the user has a nut allergy, a menu that does not contain nuts is proposed. This makes it possible to provide a meal menu that takes into account the user's allergy information and medication status.

[0074] The menu suggestion unit uses the emotion estimation function to suggest a meal menu that will most satisfy the user, maximizing the enjoyment of the meal. The menu suggestion unit, for example, uses the emotion estimation function to build a system that suggests a meal menu that will most satisfy the user. For example, the menu is suggested based on positive emotion data about past meals. This makes it possible to provide a meal menu that will most satisfy the user.

[0075] The menu suggestion unit can add recipe videos and cooking procedures to the user's meal menu, providing the enjoyment of cooking. The menu suggestion unit, for example, builds a system that adds recipe videos to the user's meal menu and provides the enjoyment of cooking. For example, it shows simple cooking procedures in video. This can provide the enjoyment of cooking.

[0076] The menu suggestion unit can take into account the user's budget and the availability of ingredients when proposing a meal menu. For example, the menu suggestion unit collects the user's budget, and the generation AI proposes a meal menu based on that data. For example, it proposes a menu that can be made on a low budget. This makes it possible to provide a realistic meal menu that takes into account the budget and the availability of ingredients.

[0077] The menu suggestion unit can use the emotion estimation function to analyze the user's emotions about food and suggest menus that elicit positive emotions. For example, the menu suggestion unit uses the emotion estimation function to build a system that analyzes the user's emotions about food and suggests menus that elicit positive emotions. For example, it re-suggests menus that have been highly rated in the past. This makes it possible to provide menus that elicit positive emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The meal plan proposal system can further include a cultural analysis unit that takes into account the user's cultural background regarding meals. For example, if the user has dietary restrictions based on a specific culture or religion, that information can be collected and an appropriate meal plan can be proposed. For example, a menu that meets the needs of a user with halal or kosher dietary restrictions can be proposed. In addition, by incorporating traditional dishes from a specific region, a meal plan that takes into account the user's cultural background can be provided. This allows for a personalized meal plan to be provided that is tailored to the user's cultural background.

[0080] The preference understanding unit can estimate the user's feelings about food and suggest ingredients and menus that will bring out specific emotions. For example, if the user is feeling stressed, a menu containing ingredients that have a relaxing effect can be suggested. If the user wants to feel energized, a menu containing ingredients that will increase energy can be suggested. Furthermore, if the user has special emotions regarding a particular event or anniversary, a special menu that matches that emotion can be suggested. This makes it possible to provide a meal plan that matches the user's emotions.

[0081] When analyzing the user's meal images, the recording and analysis unit can also evaluate the freshness and quality of ingredients. For example, it can estimate the freshness of ingredients from their color and shape and recommend meals using fresh ingredients. It can also evaluate the quality of ingredients and suggest menus using high-quality ingredients. It can also take into account the storage conditions of ingredients and suggest appropriate storage methods. This allows the user to enjoy meals using fresh, high-quality ingredients.

[0082] The menu suggestion unit can estimate the user's emotions and suggest a dining environment that will elicit a specific emotion. For example, if the user feels like relaxing, the unit can suggest a quiet dining environment. If the user is in a sociable mood, the unit can suggest an environment where the user can enjoy a meal with friends or family. Furthermore, if the user prefers specific music or lighting, the unit can customize the dining environment based on that information. This makes it possible to provide the optimal dining environment according to the user's emotions.

[0083] The preference understanding unit can estimate the user's feelings about food and make suggestions that avoid ingredients that evoke negative feelings. For example, if the user has an aversion to a particular ingredient, the unit can suggest a menu that does not include that ingredient. Also, by avoiding ingredients that have previously caused negative feelings, the user's dining experience can be improved. Furthermore, if the user has an allergy to a particular ingredient, the unit can suggest a safe menu that takes that information into account. This makes it possible to provide a meal plan that takes the user's feelings into consideration.

[0084] When analyzing the user's meal images, the record analysis unit can propose eco-friendly meal plans by taking into consideration the origin and production method of ingredients. For example, it can prioritize locally produced ingredients to reduce the environmental impact of transportation. It can also provide environmentally friendly meal plans by recommending ingredients from organic farming and sustainable fishing. It can also provide information on the producers of ingredients to help users understand the background of the ingredients. This makes it possible to provide eco-friendly meal plans.

[0085] The menu suggestion unit can estimate the user's emotions and suggest a meal menu that will elicit a specific emotion. For example, if the user wants to feel happy, a menu containing chocolate and berries can be suggested. If the user wants to relax, a menu containing herbal tea and chamomile can be suggested. If the user wants to increase their energy, a menu containing nuts and bananas can be suggested. This makes it possible to provide a meal menu that matches the user's emotions.

[0086] The preference understanding unit can estimate the user's feelings about food and suggest ingredients and menus that will elicit positive emotions. For example, if the user has positive emotions about a particular ingredient, the unit can suggest a menu that contains a lot of that ingredient. In addition, by re-suggesting ingredients that have previously elicited positive emotions, the unit can increase user satisfaction. Furthermore, if the user has special emotions regarding a particular event or anniversary, the unit can suggest a special menu that matches those emotions. This makes it possible to provide a meal plan that matches the user's emotions.

[0087] When analyzing a user's meal image, the record / analysis unit can automatically calculate the nutritional content of ingredients and provide detailed nutritional information. For example, it can analyze the meal image and display the calorie, vitamin, and mineral content. It can also evaluate the nutritional balance of ingredients and make suggestions for supplementing necessary nutrients. It can also suggest menus fortified with specific nutrients based on the user's health condition and goals. This helps the user choose healthy meals.

[0088] The menu suggestion unit can add recipe videos and cooking instructions to the user's meal menu to make cooking more enjoyable. For example, it can show simple cooking steps in a video for a menu selected by the user. It can also improve cooking skills by explaining tips and tricks for specific dishes. It can also suggest ideas and techniques to help the user enjoy cooking. This helps the user prepare healthy meals while enjoying cooking.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The preference understanding unit understands the user's preferences, restrictions, and nutritional needs. For example, through dialogue with the user, the generation AI records information such as "I don't like dairy products" and proposes a dairy-free meal plan. It also takes into account restrictions such as "I have high blood pressure, so I want to limit my salt intake." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI understands individual nutritional needs based on the prompt. Step 2: The record analysis unit analyzes the food images taken by the user and records the meal contents. For example, it provides the analysis results in the form of "This is salad and grilled chicken" for the image taken by the user. This allows the user's meal history to be accurately understood. Step 3: The menu suggestion module customizes the optimal meal plan based on the collected food records, personal preferences, restrictions, and nutritional needs. For example, it might suggest a specific menu such as "low-sugar cereal and yogurt for breakfast, a salad with plenty of vegetables for lunch, and a high-protein fish dish for dinner." The generative AI provides a balanced meal plan based on the user's health status and goals.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[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 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.

[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 (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).

[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] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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 preference understanding unit that understands the preferences, restrictions, and nutritional needs of a user; a record analysis unit that analyzes the meal record based on the information grasped by the preference grasping unit; a menu suggestion unit that suggests an optimal meal menu based on the information analyzed by the recording and analysis unit. A system characterized by:

2. The record analysis unit It analyzes photos of meals taken by users and records the contents of meals such as salad and grilled chicken.

2. The system of claim 1.

3. The menu suggestion unit Adjust your next meal menu based on user feedback 2. The system of claim 1.

4. The preference understanding unit Propose meal plans incorporating seasonal and local specialties 2. The system of claim 1.

5. The record analysis unit In addition to analyzing food images, it automatically calculates the nutritional content of ingredients and provides detailed nutritional information.

2. The system of claim 1.

6. The menu suggestion unit Dynamically adjust meal menus based on user health goals 2. The system of claim 1.

7. The preference understanding unit Analyzes the user's feelings about food and suggests ingredients and menus that elicit positive emotions 2. The system of claim 1.

8. The menu suggestion unit Suggesting the most satisfying meal menu for users, maximizing the enjoyment of meals 2. The system of claim 1.

Images