System

Abstract

The system according to an embodiment aims to provide personalized recipes based on the user's individual dietary preferences and nutritional needs.SOLUTION: A system includes a user information setting unit, a news aggregation unit, and a recipe generation unit. The user information setting unit sets food preferences, allergy information, nutritional needs, and news categories of the user. A news aggregate part collects and analyzes the latest news on the basis of the news category set by the user information setting part. The recipe generation unit generates a personalized recipe on the basis of the contents of the news collected and analyzed by the news aggregation unit and the information set by the user information setting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques fall short in providing personalized recipes based on a user's individual food preferences and nutritional needs, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized recipes based on the user's individual food preferences and nutritional needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information setting unit, a news aggregator unit, and a recipe generator unit. The user information setting unit sets the user's food preferences, allergy information, nutritional needs, and news categories. The news aggregator unit collects and analyzes the latest news based on the news categories set by the user information setting unit. The recipe generator generates personalized recipes based on the content of the news collected and analyzed by the news aggregator unit and the information set by the user information setting unit. [Effects of the Invention]

[0007] In accordance with an embodiment, the system can provide personalized recipes based on a user's individual food 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 cooking assistant app according to an embodiment of the present invention is a system in which a user sets their food preferences, allergy information, nutritional needs, and news categories, and a generation AI aggregates the latest news and generates personalized recipes based on the content, allowing the cooking assistant app to provide personalized recipes based on the user's preferences and needs.

[0029] A cooking assistant app according to an embodiment includes a user information setting unit, a news aggregation unit, and a recipe generation unit. The user information setting unit sets the user's food preferences, allergy information, nutritional needs, and news categories. For example, the user can input their favorite ingredients and cooking methods, ingredients to which they are allergic, necessary nutrients, and news categories of interest (environmental issues, health, economy, etc.). The news aggregation unit collects and analyzes the latest news based on the news categories set by the user information setting unit. For example, the generation AI collects news related to environmental issues and analyzes the content. The generation AI prepares to suggest optimal recipes to the user based on the news content. The recipe generation unit generates personalized recipes based on the news content collected and analyzed by the news aggregation unit and the information set by the user information setting unit. For example, the generation AI suggests recipes using sustainable ingredients based on news focusing on environmental issues. This allows the cooking assistant app to provide personalized recipes based on the user's preferences and needs.

[0030] The recipe generation unit can suggest recipes using sustainable ingredients from news that focuses on environmental issues. For example, the recipe generation unit analyzes news about environmental issues and suggests recipes using sustainable ingredients based on the content. For example, the generation AI generates recipes that use organic ingredients or locally produced ingredients. The generation AI can also suggest environmentally friendly cooking methods. For example, it can suggest energy-efficient cooking methods or cooking methods that minimize waste. This makes it possible to suggest recipes using sustainable ingredients to users who are interested in environmental issues.

[0031] The recipe generation unit can analyze the user's past meal history and automatically update their preferences and allergy information. For example, the recipe generation unit analyzes the meal history entered by the user in the past and identifies frequently selected ingredients and dishes. For example, the generation AI automatically updates the user's preferences based on the recipes and ingredients the user has previously selected and reflects them in the next recipe proposal. The generation AI can also automatically update the user's allergy information. For example, if the user reports a new allergy, the recipe proposals can be adjusted based on that information. This allows the user's preferences and allergy information to be automatically updated and reflected in the next recipe proposal.

[0032] The recipe generation unit can dynamically adjust nutritional needs by linking the user's health condition and fitness data. The recipe generation unit obtains health data from, for example, the user's fitness app or wearable device and dynamically adjusts nutritional needs. For example, if the user's exercise volume increases, the generation AI may suggest increasing protein intake. The generation AI can also adjust nutritional needs based on the user's health condition. For example, it may suggest appropriate ingredients and cooking methods based on the user's blood pressure and blood sugar levels. This makes it possible to dynamically adjust nutritional needs based on the user's health condition and fitness data.

[0033] The recipe generation unit can set the food preferences of family and friends and propose a shared meal plan. For example, a user inputs the food preferences of family and friends, and the recipe generation unit generates a shared meal plan. For example, the generation AI proposes recipes that the whole family can enjoy. The generation AI can also propose recipes for a dinner party with friends. For example, the generation AI considers friends' preferences and allergy information to propose a menu that everyone can enjoy. This makes it possible to propose a shared meal plan that matches the food preferences of family and friends.

[0034] The recipe generation unit can link the user's food preferences with other lifestyle data (e.g., sleep patterns and exercise habits). The recipe generation unit, for example, analyzes the user's sleep patterns and proposes a meal plan tailored to the quality of sleep. For example, the generation AI generates recipes using ingredients that promote good quality sleep. The generation AI can also adjust the meal plan based on the user's exercise habits. For example, the generation AI proposes recipes using ingredients that support post-exercise recovery. This makes it possible to adjust the user's food preferences based on their lifestyle data.

[0035] The news aggregator can evaluate the reliability of news and aggregate only highly reliable news. For example, the news aggregator develops an algorithm to evaluate the reliability of news sources and collects only highly reliable news. For example, the generation AI filters out news from less reliable sources. The generation AI can also evaluate the accuracy of the news content. For example, the generation AI checks whether the news content is consistent with other highly reliable sources. This makes it possible to aggregate only highly reliable news.

[0036] The news aggregator can analyze the content of news in detail and extract related ingredients and cooking methods. For example, the news aggregator can analyze the content of news using natural language processing technology and automatically extract related ingredients and cooking methods. For example, the generation AI extracts ingredients and cooking methods that appear in the news as keywords. The generation AI can also suggest new recipes based on the content of the news. For example, the generation AI can generate recipes using ingredients related to the news. This makes it possible to analyze the content of news in detail and extract related ingredients and cooking methods.

[0037] The news aggregation unit can also include social media trend information in the news aggregate. For example, the news aggregation unit adds social media trend information to the news aggregate to reflect the latest topics. For example, the generation AI collects trend information from Twitter and Instagram. The generation AI can also analyze the content of the news based on the social media trend information. For example, the generation AI prioritizes collecting news related to trend information. In this way, by including social media trend information, it is possible to aggregate news that reflects the latest topics.

[0038] The news aggregation unit can analyze news content from the perspectives of different cultures and regions and suggest a variety of recipes. For example, the news aggregation unit develops algorithms that analyze news content from the perspectives of different cultures and regions and suggest a variety of recipes. For example, the generative AI can reflect ingredients and cooking methods from different cultures and regions even in the same news. The generative AI can also suggest recipes that use ingredients from different cultures and regions. For example, the generative AI can generate recipes based on Asian culture and recipes based on European culture. This makes it possible to analyze news from the perspectives of different cultures and regions and suggest a variety of recipes.

[0039] The recipe generation unit can analyze the user's past recipe selection history and suggest recipes that match the user's preferences. For example, the recipe generation unit analyzes the user's past recipe selection history and identifies frequently selected ingredients and dishes. For example, the generation AI suggests recipes that match the user's preferences based on the recipes and ingredients the user has selected in the past. The generation AI can also automatically update the user's preferences. For example, if the user reports new preferences, the recipe suggestions can be adjusted based on that information. This makes it possible to suggest recipes that match the user's preferences based on the user's past recipe selection history.

[0040] The recipe generation unit can generate optimal recipes by taking into account the availability of ingredients depending on the season and region. For example, the recipe generation unit analyzes the availability of ingredients by season and suggests recipes that are optimal for that time of year. For example, the generation AI suggests recipes that use fresh vegetables in the spring. The generation AI can also suggest recipes that use ingredients that are local specialties. For example, the generation AI generates recipes that use local specialties. This makes it possible to generate optimal recipes based on the availability of ingredients depending on the season and region.

[0041] The recipe generation unit can generate recipes that reflect the ratings and feedback of other users. For example, the recipe generation unit collects ratings and feedback from other users and generates recipes based on that data. For example, the generation AI preferentially suggests highly rated recipes. The generation AI can also improve recipes based on user feedback. For example, the generation AI analyzes user comments and identifies areas for improvement in the recipe. This makes it possible to generate recipes that reflect the ratings and feedback of other users.

[0042] The recipe generation unit can generate recipes that incorporate elements of different cooking styles and cultures. For example, the recipe generation unit develops an algorithm that generates recipes that incorporate elements of different cooking styles and cultures. For example, the generation AI can suggest fusion dishes. The generation AI can also suggest recipes that combine ingredients and cooking methods from different cultures. For example, the generation AI can generate recipes that combine Italian and Japanese cuisine. This makes it possible to generate recipes that incorporate elements of different cooking styles and cultures.

[0043] Cooking Master Nanbu can analyze a user's actions while cooking using a camera and provide advice in real time. Cooking Master Nanbu, for example, builds a system that uses a camera to analyze a user's cooking actions and provide advice in real time. For example, the generation AI provides advice on how to use a knife and the progress of cooking. The generation AI can also analyze a user's actions and provide advice to improve cooking efficiency. For example, the generation AI suggests steps for the user to cook efficiently. This makes it possible to analyze a user's actions while cooking using a camera and provide advice in real time.

[0044] Cooking Master Nanbu can dynamically adjust the next step depending on the cooking progress. Cooking Master Nanbu, for example, builds a system that analyzes the cooking progress in real time and dynamically adjusts the next step. For example, the generation AI suggests the next step depending on the cooking time and temperature. The generation AI can also adjust the next step to match the user's cooking speed. For example, if the user is behind schedule, the generation AI suggests delaying the next step. This makes it possible to dynamically adjust the next step depending on the cooking progress.

[0045] Cooking Guide Nanbu can link cooking instructions to voice assistants and smart speakers. Cooking Guide Nanbu can, for example, link voice assistants and smart speakers to build a system where users can receive cooking instructions by voice. For example, the generation AI can give instructions on the next steps by voice. The generation AI can also answer user questions by voice. For example, if a user asks a question while cooking, the generation AI can provide appropriate advice in real time. In this way, by linking voice assistants and smart speakers, users can receive cooking instructions by voice.

[0046] Cooking Master Nanbu can combine cooking instructions with video tutorials and live streaming. For example, Cooking Master Nanbu will build a cooking instruction system that combines video tutorials to allow users to visually check the steps. For example, the generating AI will show each cooking step in a video. The generating AI can also provide cooking instructions in real time through live streaming. For example, the generating AI will provide a live Q&A session where users can ask questions in real time. This allows users to visually check the steps by combining video tutorials and live streaming.

[0047] The sharing function unit can analyze photos and comments of shared dishes and display a ranking of popular recipes. The sharing function unit, for example, analyzes photos and comments of shared dishes and builds a system that displays a ranking of popular recipes. For example, the generation AI creates a ranking based on the number of likes and comments. The generation AI can also adjust the ranking of recipes based on user ratings. For example, the generation AI displays highly rated recipes at the top. This makes it possible to analyze photos and comments of shared dishes and display a ranking of popular recipes.

[0048] The sharing function unit can add real-time chat and video calls to the sharing function, thereby promoting interaction between users. For example, the sharing function unit adds a real-time chat function to the sharing function, thereby building a system that allows users to instantly interact with each other. For example, the generation AI shares questions and opinions about recipes in real time. The generation AI can also add a video call function to enable users to interact face-to-face. For example, the generation AI provides one-on-one video calls and group video calls. This can promote interaction between users by adding real-time chat and video calls.

[0049] The sharing function unit can automatically generate recipes tailored to the user's preferences based on the shared information. The sharing function unit, for example, analyzes the shared information and builds a system that automatically generates recipes tailored to the user's preferences. For example, the generation AI suggests recipes based on the shared impressions and ratings. The generation AI can also customize recipes based on the user's preferences. For example, the generation AI adjusts ingredients and cooking methods to suit the user's preferences. This makes it possible to automatically generate recipes tailored to the user's preferences based on the shared information.

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

[0051] The recipe generator can analyze a user's past dietary history and automatically update preferences and allergy information. For example, the next recipe suggestion can be reflected based on the user's previously selected recipes and ingredients. Also, if the user reports a new allergy, the recipe suggestions can be adjusted based on that information. Furthermore, it is possible to link the user's health and fitness data and dynamically adjust nutritional needs. For example, if the user's exercise level increases, a suggestion can be made to increase protein intake. This makes it possible to provide personalized recipes based on the user's preferences and health status.

[0052] The recipe generation unit can set the food preferences of family and friends and propose a shared meal plan. For example, a user inputs the food preferences of family and friends and generates a shared meal plan. It can propose recipes that the whole family can enjoy. It can also propose recipes for a dinner party with friends. For example, it can propose a menu that everyone can enjoy, taking into account the preferences and allergy information of friends. This makes it possible to propose a shared meal plan that suits the food preferences of family and friends.

[0053] The recipe generation unit can link the user's food preferences with other lifestyle data (e.g., sleep patterns and exercise habits). For example, it can analyze the user's sleep patterns and propose a meal plan tailored to the quality of sleep. It can generate recipes using ingredients that promote good quality sleep. It can also adjust the meal plan based on the user's exercise habits. For example, it can propose recipes using ingredients that support post-exercise recovery. This makes it possible to adjust the user's food preferences based on their lifestyle data.

[0054] The news aggregation unit can evaluate the reliability of news and aggregate only highly reliable news. For example, it can develop an algorithm to evaluate the reliability of news sources and collect only highly reliable news. It can filter out news from less reliable sources. It can also evaluate the accuracy of news content. For example, it can check whether the news content is consistent with other highly reliable sources. This allows it to aggregate only highly reliable news.

[0055] The news aggregation unit can analyze the content of news in detail and extract related ingredients and cooking methods. For example, it can analyze the content of news using natural language processing technology and automatically extract related ingredients and cooking methods. It can extract ingredients and cooking methods that appear in the news as keywords. It can also suggest new recipes based on the content of the news. For example, it can generate recipes using ingredients related to the news. This makes it possible to analyze the content of news in detail and extract related ingredients and cooking methods.

[0056] The news aggregation unit can also include social media trend information in the news aggregate. For example, social media trend information can be added to the news aggregate to reflect the latest topics. Trend information from Twitter and Instagram can be collected. The news content can also be analyzed based on social media trend information. For example, news related to trend information can be collected preferentially. This makes it possible to aggregate news that reflects the latest topics by including social media trend information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The user information setting section sets the user's food preferences, allergy information, nutritional needs, and news categories. For example, the user can input their favorite ingredients and cooking methods, ingredients they are allergic to, nutrients they need, and news categories they are interested in (environmental issues, health, economy, etc.). Step 2: The news aggregation unit collects and analyzes the latest news based on the news categories set by the user information setting unit. For example, the generation AI collects news about environmental issues and analyzes their content. Step 3: The recipe generator generates personalized recipes based on the news content collected and analyzed by the news aggregator and the information set by the user information setting unit. For example, the generator AI might suggest recipes using sustainable ingredients based on news focusing on environmental issues.

[0059] (Example 2) The cooking assistant app according to an embodiment of the present invention is a system in which a user sets their food preferences, allergy information, nutritional needs, and news categories, and a generation AI aggregates the latest news and generates personalized recipes based on the content, allowing the cooking assistant app to provide personalized recipes based on the user's preferences and needs.

[0060] A cooking assistant app according to an embodiment includes a user information setting unit, a news aggregation unit, and a recipe generation unit. The user information setting unit sets the user's food preferences, allergy information, nutritional needs, and news categories. For example, the user can input their favorite ingredients and cooking methods, ingredients to which they are allergic, necessary nutrients, and news categories of interest (environmental issues, health, economy, etc.). The news aggregation unit collects and analyzes the latest news based on the news categories set by the user information setting unit. For example, the generation AI collects news related to environmental issues and analyzes the content. The generation AI prepares to suggest optimal recipes to the user based on the news content. The recipe generation unit generates personalized recipes based on the news content collected and analyzed by the news aggregation unit and the information set by the user information setting unit. For example, the generation AI suggests recipes using sustainable ingredients based on news focusing on environmental issues. This allows the cooking assistant app to provide personalized recipes based on the user's preferences and needs.

[0061] The recipe generation unit can suggest recipes using sustainable ingredients from news that focuses on environmental issues. For example, the recipe generation unit analyzes news about environmental issues and suggests recipes using sustainable ingredients based on the content. For example, the generation AI generates recipes that use organic ingredients or locally produced ingredients. The generation AI can also suggest environmentally friendly cooking methods. For example, it can suggest energy-efficient cooking methods or cooking methods that minimize waste. This makes it possible to suggest recipes using sustainable ingredients to users who are interested in environmental issues.

[0062] The recipe generation unit can analyze the user's past meal history and automatically update their preferences and allergy information. For example, the recipe generation unit analyzes the meal history entered by the user in the past and identifies frequently selected ingredients and dishes. For example, the generation AI automatically updates the user's preferences based on the recipes and ingredients the user has previously selected and reflects them in the next recipe proposal. The generation AI can also automatically update the user's allergy information. For example, if the user reports a new allergy, the recipe proposals can be adjusted based on that information. This allows the user's preferences and allergy information to be automatically updated and reflected in the next recipe proposal.

[0063] The recipe generation unit can dynamically adjust nutritional needs by linking the user's health condition and fitness data. The recipe generation unit obtains health data from, for example, the user's fitness app or wearable device and dynamically adjusts nutritional needs. For example, if the user's exercise volume increases, the generation AI may suggest increasing protein intake. The generation AI can also adjust nutritional needs based on the user's health condition. For example, it may suggest appropriate ingredients and cooking methods based on the user's blood pressure and blood sugar levels. This makes it possible to dynamically adjust nutritional needs based on the user's health condition and fitness data.

[0064] The recipe generation unit can use the emotion estimation function to suggest recipes that match the user's current mood. The recipe generation unit, for example, analyzes the user's facial expressions and voice tone to estimate the user's current mood. For example, if the user is tired, the generation AI can suggest a recipe that will help them relax. Also, if the user is feeling stressed, the generation AI can suggest recipes that use ingredients that will reduce stress. For example, the generation AI can suggest recipes that use herbs and spices that have a relaxing effect. This makes it possible to suggest recipes that match the user's current mood.

[0065] The recipe generation unit can set the food preferences of family and friends and propose a shared meal plan. For example, a user inputs the food preferences of family and friends, and the recipe generation unit generates a shared meal plan. For example, the generation AI proposes recipes that the whole family can enjoy. The generation AI can also propose recipes for a dinner party with friends. For example, the generation AI considers friends' preferences and allergy information to propose a menu that everyone can enjoy. This makes it possible to propose a shared meal plan that matches the food preferences of family and friends.

[0066] The recipe generation unit can link the user's food preferences with other lifestyle data (e.g., sleep patterns and exercise habits). The recipe generation unit, for example, analyzes the user's sleep patterns and proposes a meal plan tailored to the quality of sleep. For example, the generation AI generates recipes using ingredients that promote good quality sleep. The generation AI can also adjust the meal plan based on the user's exercise habits. For example, the generation AI proposes recipes using ingredients that support post-exercise recovery. This makes it possible to adjust the user's food preferences based on their lifestyle data.

[0067] The recipe generation unit can use the emotion estimation function to customize recipes based on the emotions a user feels about specific news. For example, the recipe generation unit analyzes the emotions a user feels when reading specific news and suggests recipes that match those emotions. For example, if the generation AI feels positive emotions about the news, it can suggest recipes that will further enhance those emotions. Also, if the user feels negative emotions about the news, the generation AI can suggest recipes that will alleviate those emotions. For example, the generation AI can suggest recipes that use ingredients that have a relaxing effect. This makes it possible to customize recipes based on the emotions a user feels about specific news.

[0068] The news aggregator can evaluate the reliability of news and aggregate only highly reliable news. For example, the news aggregator develops an algorithm to evaluate the reliability of news sources and collects only highly reliable news. For example, the generation AI filters out news from less reliable sources. The generation AI can also evaluate the accuracy of the news content. For example, the generation AI checks whether the news content is consistent with other highly reliable sources. This makes it possible to aggregate only highly reliable news.

[0069] The news aggregator can analyze the content of news in detail and extract related ingredients and cooking methods. For example, the news aggregator can analyze the content of news using natural language processing technology and automatically extract related ingredients and cooking methods. For example, the generation AI extracts ingredients and cooking methods that appear in the news as keywords. The generation AI can also suggest new recipes based on the content of the news. For example, the generation AI can generate recipes using ingredients related to the news. This makes it possible to analyze the content of news in detail and extract related ingredients and cooking methods.

[0070] The news aggregator unit can use the emotion estimation function to analyze the emotional impact of news and suggest recipes that match the user's emotions. For example, the news aggregator unit analyzes the content of news using the emotion estimation function and evaluates the emotional impact the news has on the user. For example, if the news evokes positive emotions, the generation AI can suggest recipes that match those emotions. Also, if the news evokes negative emotions, the generation AI can suggest recipes that will alleviate those emotions. For example, the generation AI can suggest recipes that use ingredients that have a relaxing effect. This makes it possible to analyze the emotional impact of news and suggest recipes that match the user's emotions.

[0071] The news aggregation unit can also include social media trend information in the news aggregate. For example, the news aggregation unit adds social media trend information to the news aggregate to reflect the latest topics. For example, the generation AI collects trend information from Twitter and Instagram. The generation AI can also analyze the content of the news based on the social media trend information. For example, the generation AI prioritizes collecting news related to trend information. In this way, by including social media trend information, it is possible to aggregate news that reflects the latest topics.

[0072] The news aggregation unit can analyze news content from the perspectives of different cultures and regions and suggest a variety of recipes. For example, the news aggregation unit develops algorithms that analyze news content from the perspectives of different cultures and regions and suggest a variety of recipes. For example, the generative AI can reflect ingredients and cooking methods from different cultures and regions even in the same news. The generative AI can also suggest recipes that use ingredients from different cultures and regions. For example, the generative AI can generate recipes based on Asian culture and recipes based on European culture. This makes it possible to analyze news from the perspectives of different cultures and regions and suggest a variety of recipes.

[0073] The news aggregator uses an emotion estimation function to analyze users' emotional reactions to the news in real time and reflect this in recipes. The news aggregator, for example, builds a system that analyzes users' emotional reactions to the news in real time and customizes recipes based on the results. For example, if the news evokes positive emotions, the generation AI will suggest a recipe that matches those emotions. Also, if the news evokes negative emotions, the generation AI can suggest a recipe that will alleviate those emotions. For example, the generation AI will suggest a recipe that uses ingredients that have a relaxing effect. This makes it possible to analyze users' emotional reactions to the news in real time and reflect this in recipes.

[0074] The recipe generation unit can analyze the user's past recipe selection history and suggest recipes that match the user's preferences. For example, the recipe generation unit analyzes the user's past recipe selection history and identifies frequently selected ingredients and dishes. For example, the generation AI suggests recipes that match the user's preferences based on the recipes and ingredients the user has selected in the past. The generation AI can also automatically update the user's preferences. For example, if the user reports new preferences, the recipe suggestions can be adjusted based on that information. This makes it possible to suggest recipes that match the user's preferences based on the user's past recipe selection history.

[0075] The recipe generation unit can generate optimal recipes by taking into account the availability of ingredients depending on the season and region. For example, the recipe generation unit analyzes the availability of ingredients by season and suggests recipes that are optimal for that time of year. For example, the generation AI suggests recipes that use fresh vegetables in the spring. The generation AI can also suggest recipes that use ingredients that are local specialties. For example, the generation AI generates recipes that use local specialties. This makes it possible to generate optimal recipes based on the availability of ingredients depending on the season and region.

[0076] The recipe generation unit can use the emotion estimation function to generate a recipe that is optimal for the user's current mood. The recipe generation unit, for example, analyzes the user's facial expressions and voice tone to estimate the user's current mood. For example, if the user is tired, the generation AI can suggest a recipe that will help them relax. Also, if the user is feeling stressed, the generation AI can suggest a recipe that uses ingredients that will reduce stress. For example, the generation AI can suggest a recipe that uses herbs and spices that have a relaxing effect. This makes it possible to generate a recipe that is optimal for the user's current mood.

[0077] The recipe generation unit can generate recipes that reflect the ratings and feedback of other users. For example, the recipe generation unit collects ratings and feedback from other users and generates recipes based on that data. For example, the generation AI preferentially suggests highly rated recipes. The generation AI can also improve recipes based on user feedback. For example, the generation AI analyzes user comments and identifies areas for improvement in the recipe. This makes it possible to generate recipes that reflect the ratings and feedback of other users.

[0078] The recipe generation unit can generate recipes that incorporate elements of different cooking styles and cultures. For example, the recipe generation unit develops an algorithm that generates recipes that incorporate elements of different cooking styles and cultures. For example, the generation AI can suggest fusion dishes. The generation AI can also suggest recipes that combine ingredients and cooking methods from different cultures. For example, the generation AI can generate recipes that combine Italian and Japanese cuisine. This makes it possible to generate recipes that incorporate elements of different cooking styles and cultures.

[0079] The recipe generation unit can use the emotion estimation function to customize recipes based on the emotions a user feels about specific news. For example, the recipe generation unit analyzes the emotions a user feels when reading specific news and suggests recipes that match those emotions. For example, if the generation AI feels positive emotions about the news, it can suggest recipes that will further enhance those emotions. Also, if the user feels negative emotions about the news, the generation AI can suggest recipes that will alleviate those emotions. For example, the generation AI can suggest recipes that use ingredients that have a relaxing effect. This makes it possible to customize recipes based on the emotions a user feels about specific news.

[0080] Cooking Master Nanbu can analyze a user's actions while cooking using a camera and provide advice in real time. Cooking Master Nanbu, for example, builds a system that uses a camera to analyze a user's cooking actions and provide advice in real time. For example, the generation AI provides advice on how to use a knife and the progress of cooking. The generation AI can also analyze a user's actions and provide advice to improve cooking efficiency. For example, the generation AI suggests steps for the user to cook efficiently. This makes it possible to analyze a user's actions while cooking using a camera and provide advice in real time.

[0081] Cooking Master Nanbu can dynamically adjust the next step depending on the cooking progress. Cooking Master Nanbu, for example, builds a system that analyzes the cooking progress in real time and dynamically adjusts the next step. For example, the generation AI suggests the next step depending on the cooking time and temperature. The generation AI can also adjust the next step to match the user's cooking speed. For example, if the user is behind schedule, the generation AI suggests delaying the next step. This makes it possible to dynamically adjust the next step depending on the cooking progress.

[0082] Cooking Master Nanbu can use its emotion estimation function to analyze a user's stress level and provide advice to help them relax. Cooking Master Nanbu, for example, analyzes a user's facial expressions and tone of voice to estimate their stress level. For example, if the user is feeling stressed, the generation AI can provide advice to help them relax. The generation AI can also adjust cooking procedures according to the user's stress level. For example, the generation AI can suggest simple cooking procedures to help the user relax. This allows the application to analyze a user's stress level and provide advice to help them relax.

[0083] Cooking Guide Nanbu can link cooking instructions to voice assistants and smart speakers. Cooking Guide Nanbu can, for example, link voice assistants and smart speakers to build a system where users can receive cooking instructions by voice. For example, the generation AI can give instructions on the next steps by voice. The generation AI can also answer user questions by voice. For example, if a user asks a question while cooking, the generation AI can provide appropriate advice in real time. In this way, by linking voice assistants and smart speakers, users can receive cooking instructions by voice.

[0084] Cooking Master Nanbu can combine cooking instructions with video tutorials and live streaming. For example, Cooking Master Nanbu will build a cooking instruction system that combines video tutorials to allow users to visually check the steps. For example, the generating AI will show each cooking step in a video. The generating AI can also provide cooking instructions in real time through live streaming. For example, the generating AI will provide a live Q&A session where users can ask questions in real time. This allows users to visually check the steps by combining video tutorials and live streaming.

[0085] Cooking Master Nanbu can use its emotion estimation function to provide encouragement and advice according to the user's emotions. Cooking Master Nanbu will build a system that analyzes the user's facial expressions and vocal tone to provide encouragement and advice according to their emotions. For example, if the user is feeling down, the generation AI will display an encouraging message. If the user is happy, the generation AI can also provide advice to further motivate them. For example, the generation AI will display positive messages or words of encouragement according to the user's emotions. This makes it possible to provide encouragement and advice according to the user's emotions.

[0086] The sharing function unit can analyze photos and comments of shared dishes and display a ranking of popular recipes. The sharing function unit, for example, analyzes photos and comments of shared dishes and builds a system that displays a ranking of popular recipes. For example, the generation AI creates a ranking based on the number of likes and comments. The generation AI can also adjust the ranking of recipes based on user ratings. For example, the generation AI displays highly rated recipes at the top. This makes it possible to analyze photos and comments of shared dishes and display a ranking of popular recipes.

[0087] The sharing function unit can use the emotion estimation function to analyze the emotional tone of shared impressions and emphasize positive feedback. The sharing function unit, for example, analyzes the emotional tone of shared impressions and builds a system that emphasizes positive feedback. For example, the generation AI prominently displays impressions with positive emotions. The generation AI can also filter impressions with negative emotions. For example, the generation AI preferentially displays impressions with positive emotions. This makes it possible to analyze the emotional tone of shared impressions and emphasize positive feedback.

[0088] The sharing function unit can add real-time chat and video calls to the sharing function, thereby promoting interaction between users. For example, the sharing function unit adds a real-time chat function to the sharing function, thereby building a system that allows users to instantly interact with each other. For example, the generation AI shares questions and opinions about recipes in real time. The generation AI can also add a video call function to enable users to interact face-to-face. For example, the generation AI provides one-on-one video calls and group video calls. This can promote interaction between users by adding real-time chat and video calls.

[0089] The sharing function unit can automatically generate recipes tailored to the user's preferences based on the shared information. The sharing function unit, for example, analyzes the shared information and builds a system that automatically generates recipes tailored to the user's preferences. For example, the generation AI suggests recipes based on the shared impressions and ratings. The generation AI can also customize recipes based on the user's preferences. For example, the generation AI adjusts ingredients and cooking methods to suit the user's preferences. This makes it possible to automatically generate recipes tailored to the user's preferences based on the shared information.

[0090] The sharing function unit can use the emotion estimation function to promote interactions within a community according to the user's emotions. The sharing function unit, for example, uses the emotion estimation function to build a system that promotes interactions within a community according to the user's emotions. For example, the generation AI matches users who have positive emotions with each other. The generation AI can also provide encouraging messages to users who have negative emotions. For example, the generation AI provides appropriate opportunities for interaction according to the user's emotions. In this way, the emotion estimation function can be used to promote interactions within a community according to the user's emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The cooking assistant app includes a user information setting section that sets the user's food preferences, allergy information, nutritional needs, and news categories; a news aggregation section that collects and analyzes news; and a recipe generation section that generates personalized recipes. It can also add an emotion estimation function that estimates the user's emotions and suggests recipes based on those emotions. For example, if the user is feeling stressed, it can suggest recipes using ingredients with a relaxing effect. Or, if the user is tired, it can suggest recipes to replenish energy. This allows it to provide personalized recipes tailored to the user's emotions.

[0093] The recipe generator can analyze a user's past dietary history and automatically update preferences and allergy information. For example, the next recipe suggestion can be reflected based on the user's previously selected recipes and ingredients. Also, if the user reports a new allergy, the recipe suggestions can be adjusted based on that information. Furthermore, it is possible to link the user's health and fitness data and dynamically adjust nutritional needs. For example, if the user's exercise level increases, a suggestion can be made to increase protein intake. This makes it possible to provide personalized recipes based on the user's preferences and health status.

[0094] The recipe generation unit can suggest recipes that match the user's current mood. For example, it can analyze the user's facial expressions and voice tone to estimate the user's current mood. If the user is tired, it can suggest recipes that will help them relax. Also, if the user is feeling stressed, it can suggest recipes that use ingredients that will reduce stress. For example, it can suggest recipes that use herbs and spices that have a relaxing effect. In this way, it is possible to suggest recipes that match the user's current mood.

[0095] The recipe generation unit can set the food preferences of family and friends and propose a shared meal plan. For example, a user inputs the food preferences of family and friends and generates a shared meal plan. It can propose recipes that the whole family can enjoy. It can also propose recipes for a dinner party with friends. For example, it can propose a menu that everyone can enjoy, taking into account the preferences and allergy information of friends. This makes it possible to propose a shared meal plan that suits the food preferences of family and friends.

[0096] The recipe generation unit can link the user's food preferences with other lifestyle data (e.g., sleep patterns and exercise habits). For example, it can analyze the user's sleep patterns and propose a meal plan tailored to the quality of sleep. It can generate recipes using ingredients that promote good quality sleep. It can also adjust the meal plan based on the user's exercise habits. For example, it can propose recipes using ingredients that support post-exercise recovery. This makes it possible to adjust the user's food preferences based on their lifestyle data.

[0097] The recipe generation unit can use the emotion estimation function to customize recipes based on the emotions a user feels about specific news. For example, it can analyze the emotions a user feels when reading specific news and suggest recipes that match those emotions. If the user feels positive emotions about the news, it can suggest recipes that will further enhance those emotions. Also, if the user feels negative emotions about the news, it can suggest recipes that will alleviate those emotions. For example, it can suggest recipes that use ingredients that have a relaxing effect. This makes it possible to customize recipes based on the emotions a user feels about specific news.

[0098] The news aggregation unit can evaluate the reliability of news and aggregate only highly reliable news. For example, it can develop an algorithm to evaluate the reliability of news sources and collect only highly reliable news. It can filter out news from less reliable sources. It can also evaluate the accuracy of news content. For example, it can check whether the news content is consistent with other highly reliable sources. This allows it to aggregate only highly reliable news.

[0099] The news aggregation unit can analyze the content of news in detail and extract related ingredients and cooking methods. For example, it can analyze the content of news using natural language processing technology and automatically extract related ingredients and cooking methods. It can extract ingredients and cooking methods that appear in the news as keywords. It can also suggest new recipes based on the content of the news. For example, it can generate recipes using ingredients related to the news. This makes it possible to analyze the content of news in detail and extract related ingredients and cooking methods.

[0100] The news aggregation unit can use the emotion estimation function to analyze the emotional impact of news and suggest recipes that match the user's emotions. For example, the emotion estimation function analyzes the content of the news and evaluates the emotional impact the news has on the user. If the news evokes positive emotions, it can suggest recipes that match those emotions. Also, if the news evokes negative emotions, it can suggest recipes that alleviate those emotions. For example, it can suggest recipes that use ingredients that have a relaxing effect. This makes it possible to analyze the emotional impact of news and suggest recipes that match the user's emotions.

[0101] The news aggregation unit can also include social media trend information in the news aggregate. For example, social media trend information can be added to the news aggregate to reflect the latest topics. Trend information from Twitter and Instagram can be collected. The news content can also be analyzed based on social media trend information. For example, news related to trend information can be collected preferentially. This makes it possible to aggregate news that reflects the latest topics by including social media trend information.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The user information setting section sets the user's food preferences, allergy information, nutritional needs, and news categories. For example, the user can input their favorite ingredients and cooking methods, ingredients they are allergic to, nutrients they need, and news categories they are interested in (environmental issues, health, economy, etc.). Step 2: The news aggregation unit collects and analyzes the latest news based on the news categories set by the user information setting unit. For example, the generation AI collects news about environmental issues and analyzes their content. Step 3: The recipe generator generates personalized recipes based on the news content collected and analyzed by the news aggregator and the information set by the user information setting unit. For example, the generator AI might suggest recipes using sustainable ingredients based on news focusing on environmental issues.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 user information setting unit for setting the user's food preferences, allergy information, nutritional needs, and news categories; a news aggregator that collects and analyzes the latest news based on the news categories set by the user information setting unit; a recipe generation unit that generates a personalized recipe based on the content of the news collected and analyzed by the news aggregation unit and the information set by the user information setting unit. A system characterized by:

2. The news aggregation unit Evaluate the reliability of the news and aggregate only the reliable news.

2. The system of claim 1.

3. The recipe generation unit Set your family and friends' food preferences and suggest shared meal plans 2. The system of claim 1.

4. The cuisine is Nanbu. The camera analyzes the user's actions while cooking and provides advice in real time.

2. The system of claim 1.

5. The recipe generation unit Suggesting the recipe that matches the user's current mood 2. The system of claim 1.

Images