system

Abstract

The system according to this embodiment aims to provide a meal plan that is optimal for the user's individual dietary needs. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a learning unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a meal plan based on the analysis results obtained by the analysis unit. The learning unit learns user feedback based on the meal plan proposed by the proposal unit and improves the proposal.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide a diet plan optimized for an individual user's diet, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a diet plan optimized for an individual user's diet.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a learning unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a meal plan based on the analysis results obtained by the analysis unit. The learning unit learns user feedback based on the meal plan proposed by the proposal unit and improves the proposal. [Effects of the Invention]

[0007] The system according to this embodiment can provide a meal plan that is optimal for the user's individual dietary needs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The kitchen concierge system according to an embodiment of the present invention is a comprehensive meal management application that optimizes the eating habits of each individual user by utilizing the latest AI technology. This kitchen concierge system analyzes in detail the user's preferences, allergy information, health goals, and even daily changes in physical condition, and proposes a personalized meal plan. For example, it suggests appropriate ingredients for users with specific dietary restrictions and menus tailored to users with health goals. Furthermore, it considers ingredients available at home and seasonal ingredients, contributing to the reduction of food waste. The kitchen concierge system goes beyond simply suggesting recipes, considering the nutritional value of ingredients, cooking time, and difficulty level, providing a feasible meal plan that suits the user's lifestyle. It also learns the user's eating history and changes in physical condition, continuously improving the accuracy of its suggestions. For example, if a user likes a particular ingredient, it prioritizes suggesting recipes that include that ingredient. In this way, the kitchen concierge system provides meal plans tailored to the user's preferences and health condition, supporting the realization of a healthy diet and improving the quality of life through meals. It also contributes to the reduction of food waste and supports a sustainable diet. This allows the kitchen concierge system to optimize users' eating habits and support the realization of a healthy diet and an improvement in their quality of life.

[0029] The kitchen concierge system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a learning unit. The collection unit collects user information. User information includes, but is not limited to, examples of meal history, health status, and preferences. The collection unit collects, for example, information entered by the user into the app. The collection unit can also acquire data from the user's health devices. For example, the collection unit collects data such as heart rate and steps from a smartwatch or fitness tracker. Furthermore, the collection unit can automatically record the user's meal history. For example, the collection unit takes photos for the user to record meals, recognizes ingredients from the photos, and stores them in a database. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the user's meal history and evaluates nutritional balance. The analysis unit can also evaluate the user's health status and generate data to suggest an appropriate meal plan. For example, the analysis unit analyzes the user's blood glucose data and suggests a meal plan suitable for blood glucose management. The suggestion unit proposes a meal plan based on the analysis results obtained by the analysis unit. The suggestions are based on, for example, nutritional balance and calorie calculations, but are not limited to these examples. For example, the suggestion unit proposes menus that match the user's health goals. The suggestion unit can also suggest appropriate ingredients for users with specific dietary restrictions. For example, the suggestion unit can suggest allergen-free ingredients for users with allergies. The learning unit learns from user feedback based on the meal plans proposed by the suggestion unit and improves the suggestions. Learning is performed, for example, using machine learning algorithms, but is not limited to these examples. For example, the learning unit learns the user's eating history and changes in physical condition to continuously improve the accuracy of the suggestions. The learning unit can also learn the user's preferences and tastes and propose more individually customized meal plans. For example, if the learning unit knows that a user likes a particular ingredient, it will prioritize suggesting recipes that include that ingredient.As a result, the kitchen concierge system according to this embodiment can provide individually customized meal plans by collecting, analyzing, suggesting, and learning user information.

[0030] The data collection unit collects user information. This information includes, but is not limited to, examples such as meal history, health status, and preferences. For example, the data collection unit collects information entered by the user into the app. The data collection unit can also acquire data from the user's health devices. For example, the data collection unit collects data such as heart rate and steps from smartwatches and fitness trackers. Furthermore, the data collection unit can automatically record the user's meal history. For example, the data collection unit takes photos of meals for the user to record, recognizes the ingredients from the photos, and stores them in a database. Specifically, the data collection unit collects detailed information such as meal content, calorie intake, and meal times entered by the user into the app. This allows for an accurate understanding of the user's eating patterns and preferences. The data collection unit also collects data acquired from the user's health devices in real time and continuously monitors the user's health status. For example, it can evaluate the user's stress level and sleep quality based on heart rate data and sleep data acquired from smartwatches. Furthermore, the data collection unit has technology to analyze photos taken by the user to record meals and automatically recognize the type and quantity of ingredients. This allows users to record their meal history without any hassle. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze a user's dietary history and evaluate nutritional balance. It can also generate data to assess a user's health status and suggest appropriate meal plans. For example, it can analyze a user's blood glucose data and suggest a meal plan suitable for blood glucose management. Specifically, the analysis unit analyzes the user's dietary history in detail to evaluate the balance of nutrients consumed and calorie intake. This allows it to identify which nutrients the user is consuming in excess or insufficiently. Furthermore, the analysis unit comprehensively evaluates the user's health status based on data obtained from the user's health devices. For example, it can analyze heart rate, steps, and sleep data to evaluate the user's exercise level, stress level, and sleep quality. In addition, the analysis unit can use machine learning algorithms to extract patterns from the user's dietary history and health data to predict future health risks. For example, based on past data, it can assess the user's risk of developing a specific disease and suggest a meal plan to reduce that risk. Furthermore, the analysis unit can generate individually customized meal plans by taking into account the user's preferences and dietary tastes. This allows the analysis unit to provide optimal meal plans tailored to the user's health condition and preferences, supporting the user's health maintenance and improvement.

[0032] The suggestion unit proposes meal plans based on the analysis results obtained by the analysis unit. Suggestions are based on, for example, nutritional balance and calorie calculations, but are not limited to these examples. For instance, the suggestion unit proposes menus tailored to the user's health goals. It can also suggest appropriate ingredients for users with specific dietary restrictions. For example, it suggests allergen-free ingredients for users with allergies. Specifically, the suggestion unit generates individually customized meal plans considering the user's health condition, preferences, and dietary restrictions. For example, if a user is aiming to lose weight, it can suggest a low-calorie, nutritionally balanced menu. If a user is aiming to build muscle, it can suggest a high-protein menu with appropriate calories. Furthermore, the suggestion unit considers the user's allergy information and suggests allergen-free ingredients and recipes. For example, if a user has a nut allergy, it can suggest recipes using nut-free alternative ingredients. The suggestion unit can also prioritize suggesting ingredients and dishes the user prefers based on their eating history and preferences. This allows users to enjoy meal plans tailored to their tastes. The suggestion unit notifies the user of the proposed meal plan and provides support to help them easily implement it. For example, the system provides detailed explanations of the suggested menu recipes and cooking methods, as well as a list of necessary ingredients. Furthermore, the system can collect feedback from users after they have followed the suggested meal plan, allowing for continuous improvement of the accuracy and effectiveness of the suggestions. This enables the system to support users in maintaining and improving their health and to provide individually customized meal plans.

[0033] The learning unit learns from user feedback based on the meal plans proposed by the suggestion unit and improves the suggestions. Learning is performed using, for example, machine learning algorithms, but is not limited to such examples. For example, the learning unit learns the user's eating history and changes in physical condition to continuously improve the accuracy of suggestions. The learning unit can also learn the user's preferences and tastes and propose more individually customized meal plans. Specifically, the learning unit collects feedback after the user has followed the suggested meal plan and improves the suggestions based on that data. For example, by having the user actually cook the suggested menu and record their impressions and changes in physical condition after eating, the learning unit can analyze this data and reflect it in future suggestions. The learning unit also continuously learns the user's eating history and health data to support long-term health management. For example, it can track fluctuations in the user's weight, blood pressure, and blood sugar levels and adjust the meal plan based on that. Furthermore, the learning unit learns the user's preferences and food tastes and proposes more individually customized meal plans. For example, if the user likes a particular ingredient, it can prioritize suggesting recipes that include that ingredient. Furthermore, if a user has a preference for a particular dish, the system can suggest recipes related to that dish. This allows the learning department to provide meal plans tailored to the user's preferences and tastes, thereby improving user satisfaction. The learning department centrally manages this data and can collaborate with other systems and departments as needed. For example, the learning department can collaborate with the data collection and analysis departments to continuously improve its suggestions based on the latest data. The learning department can also continuously improve the accuracy and effectiveness of its suggestions based on user feedback. In this way, the learning department can support users in maintaining and improving their health and provide individually customized meal plans.

[0034] The suggestion unit can suggest appropriate ingredients to users with specific dietary restrictions. For example, it can suggest allergen-free ingredients to users with allergies. For instance, it can generate a list of allergen-free ingredients based on allergy information and suggest appropriate ingredients from that list. The suggestion unit can also suggest low-carbohydrate ingredients to users with diabetes. For example, it can generate a list of low-carbohydrate ingredients based on diabetes information and suggest appropriate ingredients from that list. The suggestion unit can also suggest ingredients rich in plant-based protein to vegetarian users. For example, it can generate a list of plant-based protein-rich ingredients based on vegetarian information and suggest appropriate ingredients from that list. In this way, by suggesting appropriate ingredients to users with specific dietary restrictions, the system can provide meal plans that accommodate the user's dietary restrictions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the suggestion unit can input allergy information into a generation AI and have the generation AI generate a list of allergen-free ingredients.

[0035] The suggestion unit can propose menus tailored to users who have health goals. For example, the suggestion unit can propose low-calorie menus to users aiming for weight loss. For instance, based on the weight loss goal, the suggestion unit generates a list of low-calorie menus and proposes an appropriate menu from among them. The suggestion unit can also propose high-protein menus to users aiming for muscle building. For example, based on the muscle building goal, the suggestion unit generates a list of high-protein menus and proposes an appropriate menu from among them. The suggestion unit can also propose low-carbohydrate menus to users aiming for blood glucose management. For example, based on the blood glucose management goal, the suggestion unit generates a list of low-carbohydrate menus and proposes an appropriate menu from among them. In this way, by proposing menus tailored to users who have health goals, it is possible to provide meal plans that correspond to the user's health goals. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the suggestion unit can input health goal information into a generation AI and have the generation AI generate a menu list tailored to the health goals.

[0036] The suggestion department can propose meal plans that take into account ingredients available at home and seasonal ingredients. For example, the suggestion department can propose meal plans that minimize food waste based on ingredients available at home. For example, the suggestion department can propose recipes that utilize available ingredients based on refrigerator inventory management information. The suggestion department can also propose meal plans that utilize seasonal ingredients based on seasonal ingredients. For example, the suggestion department can propose recipes that utilize seasonal ingredients based on seasonal ingredient lists. The suggestion department can also propose meal plans that utilize local ingredients based on local specialty products. For example, the suggestion department can propose recipes that utilize local specialty products based on a list of local specialty products. In this way, by taking into account ingredients available at home and seasonal ingredients, it is possible to contribute to reducing food waste. Some or all of the above processing in the suggestion department may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion department can input refrigerator inventory management information into a generation AI and have the generation AI generate recipes that utilize available ingredients.

[0037] The suggestion unit can propose meal plans considering the nutritional value of ingredients, cooking time, and difficulty level. For example, the suggestion unit can propose a balanced meal plan based on nutritional value. For example, the suggestion unit can propose a balanced recipe based on nutritional information such as calories, vitamins, and minerals. The suggestion unit can also propose meal plans that can be prepared in a short time based on cooking time. For example, the suggestion unit can propose a recipe that can be prepared in a short time based on cooking procedures and the time spent using cooking equipment. The suggestion unit can also propose meal plans for beginners based on the difficulty level of cooking. For example, the suggestion unit can propose a simple recipe for beginners based on information on the difficulty level of cooking. In this way, by considering the nutritional value of ingredients, cooking time, and difficulty level, it is possible to provide feasible meal plans that are tailored to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the suggestion unit can input nutritional information into a generation AI and have the generation AI generate a balanced recipe.

[0038] The learning unit can learn the user's eating history and changes in their physical condition, and continuously improve the accuracy of its suggestions. For example, the learning unit can learn the user's preferences and tastes based on their eating history. For example, the learning unit can identify ingredients and dishes that the user frequently eats and improve its suggestions accordingly. The learning unit can also improve the accuracy of its suggestions based on changes in the user's physical condition. For example, the learning unit can learn changes in the user's weight and blood pressure and adjust its suggestions accordingly. The learning unit can also improve the accuracy of its suggestions based on user feedback. For example, the learning unit can learn the user's evaluations and comments on suggested meal plans and improve its suggestions accordingly. In this way, by learning the user's eating history and changes in their physical condition, the accuracy of its suggestions can be continuously improved. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's eating history data into a generative AI and have the generative AI learn the user's preferences and tastes.

[0039] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the ingredients the user has previously enjoyed eating. For example, the data collection unit can analyze the user's eating history data, generate a list of favorite ingredients, and collect information based on that list. The data collection unit can also filter information to be avoided based on ingredients the user has previously avoided. For example, the data collection unit can analyze the user's eating history data, generate a list of avoided ingredients, and filter information based on that list. The data collection unit can also find specific patterns in the user's past eating history and collect information based on them. For example, the data collection unit can analyze the user's eating history data, extract specific patterns, and collect information based on those patterns. This allows the optimal information collection method to be selected by analyzing the user's past eating history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's eating history data into a generative AI and have the generative AI select the optimal information collection method.

[0040] The data collection unit can filter information based on the user's current health status and lifestyle. For example, the data collection unit can collect appropriate food information based on the user's current health status. For example, the data collection unit can collect food information suitable for blood pressure management based on the user's blood pressure data. The data collection unit can also filter relevant meal plans based on the user's lifestyle. For example, the data collection unit can filter meal plans suitable after exercise based on the user's exercise habit data. The data collection unit can also collect optimal information based on the user's health goals. For example, the data collection unit can collect low-calorie food information based on the user's weight loss goal. By filtering based on the user's current health status and lifestyle, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's health status data into a generative AI and have the generative AI collect appropriate food information.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of nearby food information based on the user's current location. For example, the data collection unit can collect nearby food information based on the user's GPS data. The data collection unit can also collect region-specific food information based on the user's geographical location. For example, the data collection unit can collect region-specific food information based on the user's address information. The data collection unit can also collect information on local markets and supermarkets based on the user's location. For example, the data collection unit can collect information on local markets and supermarkets based on the user's location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's GPS data into a generative AI and have the generative AI collect nearby food information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the user's social media posts and collect information on ingredients of interest. For example, the data collection unit can analyze the user's social media post data, generate a list of ingredients of interest, and collect information based on that list. The data collection unit can also collect relevant ingredient information based on information from accounts that the user follows. For example, the data collection unit can analyze the post data of accounts that the user follows and collect relevant ingredient information. The data collection unit can also analyze the user's social media activity history and collect relevant meal plans. For example, the data collection unit can analyze the user's social media activity history data and collect relevant meal plans. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media post data into a generative AI and have the generative AI generate a list of ingredients of interest.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit will perform a detailed analysis on highly important information. For instance, it will perform a detailed analysis of important data regarding the user's health status. The analysis unit can also perform a concise analysis on less important information. For example, it will perform a concise analysis of data regarding the user's preferences. The analysis unit can also perform an analysis with a moderate level of detail on information of moderate importance. For example, it will analyze data regarding the user's dietary history with a moderate level of detail. By adjusting the level of detail of the analysis based on the importance of the collected information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance data of the collected information into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a nutritional analysis algorithm to information about nutritional value. For instance, the analysis unit can perform a detailed nutritional evaluation by applying a nutritional analysis algorithm based on nutritional value data. The analysis unit can also apply a time efficiency analysis algorithm to information about cooking time. For example, the analysis unit can optimize cooking time by applying a time efficiency analysis algorithm based on cooking time data. The analysis unit can also apply a difficulty analysis algorithm to information about the difficulty of ingredients. For example, the analysis unit evaluates the difficulty of cooking by applying a difficulty analysis algorithm based on the difficulty of ingredients data. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input nutritional value data into a generative AI and have the generative AI execute the application of the nutritional analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the timing of information collection. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may prioritize the analysis of the most recent information based on the information collection timing data. The analysis unit can also postpone the analysis of older information. For example, the analysis unit may postpone the analysis of older information based on the information collection timing data. The analysis unit can also prioritize the analysis of information collected during a specific period. For example, the analysis unit may prioritize the analysis of information collected during a specific period based on the information collection timing data. By determining the priority of analysis based on the timing of information collection, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the information collection timing data into a generative AI and have the generative AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of information with high relevance. For example, the analysis unit can prioritize the analysis of information with high relevance based on the information relevance data. The analysis unit can also postpone the analysis of information with low relevance. For example, the analysis unit can postpone the analysis of information with low relevance based on the information relevance data. The analysis unit can also moderately analyze information with moderate relevance. For example, the analysis unit can moderately analyze information with moderate relevance based on the information relevance data. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input information relevance data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0047] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the meal plan. For example, the suggestion unit will provide detailed suggestions for high-importance meal plans. For instance, it might suggest a detailed meal plan based on important data regarding the user's health status. The suggestion unit can also provide concise suggestions for low-importance meal plans. For example, it might suggest a concise meal plan based on data regarding the user's preferences. Furthermore, the suggestion unit can provide suggestions with a moderate level of detail for meal plans of moderate importance. For example, it might suggest a meal plan with a moderate level of detail based on data regarding the user's eating history. By adjusting the level of detail in suggestions based on the importance of the meal plan, the suggestion unit can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input meal plan importance data into a generative AI and have the generative AI adjust the level of detail in the suggestions.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the meal plan when making a suggestion. For example, for meal plans related to nutritional value, the suggestion unit can apply a nutrition suggestion algorithm. For example, the suggestion unit can apply a nutrition suggestion algorithm based on nutritional value data to perform a detailed nutritional evaluation. The suggestion unit can also apply a time efficiency suggestion algorithm to meal plans related to cooking time. For example, the suggestion unit can apply a time efficiency suggestion algorithm based on cooking time data to optimize cooking time. The suggestion unit can also apply a difficulty suggestion algorithm to meal plans related to the difficulty of ingredients. For example, the suggestion unit can apply a difficulty suggestion algorithm based on the difficulty of ingredients to evaluate the difficulty of cooking. In this way, by applying different suggestion algorithms depending on the category of the meal plan, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input nutritional value data into a generative AI and have the generative AI execute the application of the nutrition suggestion algorithm.

[0049] The proposal department can determine the priority of proposals based on the submission timing of meal plans. For example, the proposal department may prioritize the most recent meal plans. For example, the proposal department may prioritize the most recent meal plans based on the submission timing data of meal plans. The proposal department may also postpone proposals for older meal plans. For example, the proposal department may postpone proposals for older meal plans based on the submission timing data of meal plans. The proposal department may also prioritize proposals for meal plans submitted within a specific period. For example, the proposal department may prioritize proposals for meal plans submitted within a specific period based on the submission timing data of meal plans. By determining the priority of proposals based on the submission timing of meal plans, more appropriate proposals can be provided. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission timing data of meal plans into a generative AI and have the generative AI perform the determination of proposal priorities.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the meal plans. For example, the suggestion unit can prioritize suggesting meal plans with high relevance. For example, the suggestion unit can prioritize suggesting meal plans with high relevance based on the relevance data of the meal plans. The suggestion unit can also postpone suggesting meal plans with low relevance. For example, the suggestion unit can postpone suggesting meal plans with low relevance based on the relevance data of the meal plans. The suggestion unit can also moderately suggest meal plans with moderate relevance. For example, the suggestion unit can moderately suggest meal plans with moderate relevance based on the relevance data of the meal plans. By adjusting the order of suggestions based on the relevance of the meal plans, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the relevance data of the meal plans into a generative AI and have the generative AI perform the adjustment of the suggestion order.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also analyze past learning data and improve the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can build a feedback loop to improve the accuracy of the learning algorithm based on past learning data. This allows for more accurate learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0052] The learning unit can weight the training data based on the submission date of the meal history during training. For example, the learning unit can give a higher weight to the most recent meal history. For example, the learning unit can give a higher weight to the most recent meal history based on the submission date data of the meal history. The learning unit can also give a lower weight to older meal history. For example, the learning unit can give a lower weight to older meal history based on the submission date data of the meal history. The learning unit can also give an appropriate weight to meal history submitted within a specific period. For example, the learning unit can give an appropriate weight to meal history submitted within a specific period based on the submission date data of the meal history. This allows for more appropriate training by weighting the training data based on the submission date of the meal history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the meal history submission date data into a generative AI and have the generative AI perform the weighting of the training data.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can collect information based on the user's meal history, taking into account the expiration dates of ingredients. For example, the unit can prioritize collecting ingredients that are nearing their expiration date, thereby reducing food waste. It can also prioritize collecting ingredients with longer expiration dates. Furthermore, the unit can automatically exclude ingredients that have passed their expiration date. This allows for the collection of more appropriate information by considering the expiration dates of ingredients.

[0055] The suggestion function can make recommendations based on the user's eating history, taking into account the combination of ingredients. For example, it can prioritize suggesting combinations of ingredients that the user has enjoyed eating in the past. It can also suggest new ingredient combinations while considering nutritional balance. Furthermore, it can suggest safe ingredient combinations based on the user's allergy information. In this way, by considering the combination of ingredients, it can provide more appropriate recommendations.

[0056] The data collection unit can collect information based on the user's dietary history, taking into account the nutritional value of the ingredients. For example, the unit can prioritize collecting ingredients with high nutritional value. Furthermore, if a user is deficient in a particular nutrient, the unit can prioritize collecting ingredients containing that nutrient. In addition, the unit can collect ingredients with appropriate nutritional value according to the user's health goals. This allows for the collection of more relevant information by considering the nutritional value of the ingredients.

[0057] The analysis department can perform analyses based on the user's meal history, taking into account the price of ingredients. For example, the analysis department can prioritize analyzing ingredients with high cost performance. It can also select and analyze ingredients that fit within a budget. Furthermore, the analysis department can analyze the optimal ingredients while considering seasonal price fluctuations. This allows for the provision of more accurate analysis results by taking ingredient prices into account.

[0058] The suggestion department can make suggestions based on the user's eating history, taking into account how to store the ingredients. For example, the suggestion department can prioritize suggesting ingredients that require refrigeration. It can also postpone suggesting ingredients that can be stored at room temperature. Furthermore, it can suggest ingredients that can be frozen. By considering how to store the ingredients, the department can provide more appropriate suggestions.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects user information. This information includes meal history, health status, and preferences. The data collection unit collects information entered by the user into the app, as well as data from smartwatches and fitness trackers. It also automatically records the user's meal history by taking photos of meals, recognizing the ingredients from the photos, and saving them to a database. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes the user's meal history and evaluates nutritional balance. It also evaluates the user's health status and generates data to suggest an appropriate meal plan. For example, it analyzes the user's blood glucose data and suggests a meal plan suitable for blood glucose management. Step 3: The proposal department proposes a meal plan based on the analysis results obtained by the analysis department. The proposal is based on nutritional balance and calorie calculations. For example, it proposes menus that match the user's health goals. It also proposes appropriate ingredients for users with specific dietary restrictions. For example, it proposes allergen-free ingredients for users with allergies. Step 4: The learning unit learns from user feedback based on the meal plan proposed by the suggestion unit and improves the suggestions. Learning is performed using machine learning algorithms. For example, it learns the user's eating history and changes in physical condition to continuously improve the accuracy of the suggestions. It also learns the user's preferences and tastes to propose more individually customized meal plans. For example, if the user likes a particular ingredient, it will prioritize suggesting recipes that include that ingredient.

[0061] (Example of form 2) The kitchen concierge system according to an embodiment of the present invention is a comprehensive meal management application that optimizes the eating habits of each individual user by utilizing the latest AI technology. This kitchen concierge system analyzes in detail the user's preferences, allergy information, health goals, and even daily changes in physical condition, and proposes a personalized meal plan. For example, it suggests appropriate ingredients for users with specific dietary restrictions and menus tailored to users with health goals. Furthermore, it considers ingredients available at home and seasonal ingredients, contributing to the reduction of food waste. The kitchen concierge system goes beyond simply suggesting recipes, considering the nutritional value of ingredients, cooking time, and difficulty level, providing a feasible meal plan that suits the user's lifestyle. It also learns the user's eating history and changes in physical condition, continuously improving the accuracy of its suggestions. For example, if a user likes a particular ingredient, it prioritizes suggesting recipes that include that ingredient. In this way, the kitchen concierge system provides meal plans tailored to the user's preferences and health condition, supporting the realization of a healthy diet and improving the quality of life through meals. It also contributes to the reduction of food waste and supports a sustainable diet. This allows the kitchen concierge system to optimize users' eating habits and support the realization of a healthy diet and an improvement in their quality of life.

[0062] The kitchen concierge system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a learning unit. The collection unit collects user information. User information includes, but is not limited to, examples of meal history, health status, and preferences. The collection unit collects, for example, information entered by the user into the app. The collection unit can also acquire data from the user's health devices. For example, the collection unit collects data such as heart rate and steps from a smartwatch or fitness tracker. Furthermore, the collection unit can automatically record the user's meal history. For example, the collection unit takes photos for the user to record meals, recognizes ingredients from the photos, and stores them in a database. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the user's meal history and evaluates nutritional balance. The analysis unit can also evaluate the user's health status and generate data to suggest an appropriate meal plan. For example, the analysis unit analyzes the user's blood glucose data and suggests a meal plan suitable for blood glucose management. The suggestion unit proposes a meal plan based on the analysis results obtained by the analysis unit. The suggestions are based on, for example, nutritional balance and calorie calculations, but are not limited to these examples. For example, the suggestion unit proposes menus that match the user's health goals. The suggestion unit can also suggest appropriate ingredients for users with specific dietary restrictions. For example, the suggestion unit can suggest allergen-free ingredients for users with allergies. The learning unit learns from user feedback based on the meal plans proposed by the suggestion unit and improves the suggestions. Learning is performed, for example, using machine learning algorithms, but is not limited to these examples. For example, the learning unit learns the user's eating history and changes in physical condition to continuously improve the accuracy of the suggestions. The learning unit can also learn the user's preferences and tastes and propose more individually customized meal plans. For example, if the learning unit knows that a user likes a particular ingredient, it will prioritize suggesting recipes that include that ingredient.As a result, the kitchen concierge system according to this embodiment can provide individually customized meal plans by collecting, analyzing, suggesting, and learning user information.

[0063] The data collection unit collects user information. This information includes, but is not limited to, examples such as meal history, health status, and preferences. For example, the data collection unit collects information entered by the user into the app. The data collection unit can also acquire data from the user's health devices. For example, the data collection unit collects data such as heart rate and steps from smartwatches and fitness trackers. Furthermore, the data collection unit can automatically record the user's meal history. For example, the data collection unit takes photos of meals for the user to record, recognizes the ingredients from the photos, and stores them in a database. Specifically, the data collection unit collects detailed information such as meal content, calorie intake, and meal times entered by the user into the app. This allows for an accurate understanding of the user's eating patterns and preferences. The data collection unit also collects data acquired from the user's health devices in real time and continuously monitors the user's health status. For example, it can evaluate the user's stress level and sleep quality based on heart rate data and sleep data acquired from smartwatches. Furthermore, the data collection unit has technology to analyze photos taken by the user to record meals and automatically recognize the type and quantity of ingredients. This allows users to record their meal history without any hassle. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0064] The analysis unit analyzes the information collected by the data collection unit. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze a user's dietary history and evaluate nutritional balance. It can also generate data to assess a user's health status and suggest appropriate meal plans. For example, it can analyze a user's blood glucose data and suggest a meal plan suitable for blood glucose management. Specifically, the analysis unit analyzes the user's dietary history in detail to evaluate the balance of nutrients consumed and calorie intake. This allows it to identify which nutrients the user is consuming in excess or insufficiently. Furthermore, the analysis unit comprehensively evaluates the user's health status based on data obtained from the user's health devices. For example, it can analyze heart rate, steps, and sleep data to evaluate the user's exercise level, stress level, and sleep quality. In addition, the analysis unit can use machine learning algorithms to extract patterns from the user's dietary history and health data to predict future health risks. For example, based on past data, it can assess the user's risk of developing a specific disease and suggest a meal plan to reduce that risk. Furthermore, the analysis unit can generate individually customized meal plans by taking into account the user's preferences and dietary tastes. This allows the analysis unit to provide optimal meal plans tailored to the user's health condition and preferences, supporting the user's health maintenance and improvement.

[0065] The suggestion unit proposes meal plans based on the analysis results obtained by the analysis unit. Suggestions are based on, for example, nutritional balance and calorie calculations, but are not limited to these examples. For instance, the suggestion unit proposes menus tailored to the user's health goals. It can also suggest appropriate ingredients for users with specific dietary restrictions. For example, it suggests allergen-free ingredients for users with allergies. Specifically, the suggestion unit generates individually customized meal plans considering the user's health condition, preferences, and dietary restrictions. For example, if a user is aiming to lose weight, it can suggest a low-calorie, nutritionally balanced menu. If a user is aiming to build muscle, it can suggest a high-protein menu with appropriate calories. Furthermore, the suggestion unit considers the user's allergy information and suggests allergen-free ingredients and recipes. For example, if a user has a nut allergy, it can suggest recipes using nut-free alternative ingredients. The suggestion unit can also prioritize suggesting ingredients and dishes the user prefers based on their eating history and preferences. This allows users to enjoy meal plans tailored to their tastes. The suggestion unit notifies the user of the proposed meal plan and provides support to help them easily implement it. For example, the system provides detailed explanations of the suggested menu recipes and cooking methods, as well as a list of necessary ingredients. Furthermore, the system can collect feedback from users after they have followed the suggested meal plan, allowing for continuous improvement of the accuracy and effectiveness of the suggestions. This enables the system to support users in maintaining and improving their health and to provide individually customized meal plans.

[0066] The learning unit learns from user feedback based on the meal plans proposed by the suggestion unit and improves the suggestions. Learning is performed using, for example, machine learning algorithms, but is not limited to such examples. For example, the learning unit learns the user's eating history and changes in physical condition to continuously improve the accuracy of suggestions. The learning unit can also learn the user's preferences and tastes and propose more individually customized meal plans. Specifically, the learning unit collects feedback after the user has followed the suggested meal plan and improves the suggestions based on that data. For example, by having the user actually cook the suggested menu and record their impressions and changes in physical condition after eating, the learning unit can analyze this data and reflect it in future suggestions. The learning unit also continuously learns the user's eating history and health data to support long-term health management. For example, it can track fluctuations in the user's weight, blood pressure, and blood sugar levels and adjust the meal plan based on that. Furthermore, the learning unit learns the user's preferences and food tastes and proposes more individually customized meal plans. For example, if the user likes a particular ingredient, it can prioritize suggesting recipes that include that ingredient. Furthermore, if a user has a preference for a particular dish, the system can suggest recipes related to that dish. This allows the learning department to provide meal plans tailored to the user's preferences and tastes, thereby improving user satisfaction. The learning department centrally manages this data and can collaborate with other systems and departments as needed. For example, the learning department can collaborate with the data collection and analysis departments to continuously improve its suggestions based on the latest data. The learning department can also continuously improve the accuracy and effectiveness of its suggestions based on user feedback. In this way, the learning department can support users in maintaining and improving their health and provide individually customized meal plans.

[0067] The suggestion unit can suggest appropriate ingredients to users with specific dietary restrictions. For example, it can suggest allergen-free ingredients to users with allergies. For instance, it can generate a list of allergen-free ingredients based on allergy information and suggest appropriate ingredients from that list. The suggestion unit can also suggest low-carbohydrate ingredients to users with diabetes. For example, it can generate a list of low-carbohydrate ingredients based on diabetes information and suggest appropriate ingredients from that list. The suggestion unit can also suggest ingredients rich in plant-based protein to vegetarian users. For example, it can generate a list of plant-based protein-rich ingredients based on vegetarian information and suggest appropriate ingredients from that list. In this way, by suggesting appropriate ingredients to users with specific dietary restrictions, the system can provide meal plans that accommodate the user's dietary restrictions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the suggestion unit can input allergy information into a generation AI and have the generation AI generate a list of allergen-free ingredients.

[0068] The suggestion unit can propose menus tailored to users who have health goals. For example, the suggestion unit can propose low-calorie menus to users aiming for weight loss. For instance, based on the weight loss goal, the suggestion unit generates a list of low-calorie menus and proposes an appropriate menu from among them. The suggestion unit can also propose high-protein menus to users aiming for muscle building. For example, based on the muscle building goal, the suggestion unit generates a list of high-protein menus and proposes an appropriate menu from among them. The suggestion unit can also propose low-carbohydrate menus to users aiming for blood glucose management. For example, based on the blood glucose management goal, the suggestion unit generates a list of low-carbohydrate menus and proposes an appropriate menu from among them. In this way, by proposing menus tailored to users who have health goals, it is possible to provide meal plans that correspond to the user's health goals. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the suggestion unit can input health goal information into a generation AI and have the generation AI generate a menu list tailored to the health goals.

[0069] The suggestion department can propose meal plans that take into account ingredients available at home and seasonal ingredients. For example, the suggestion department can propose meal plans that minimize food waste based on ingredients available at home. For example, the suggestion department can propose recipes that utilize available ingredients based on refrigerator inventory management information. The suggestion department can also propose meal plans that utilize seasonal ingredients based on seasonal ingredients. For example, the suggestion department can propose recipes that utilize seasonal ingredients based on seasonal ingredient lists. The suggestion department can also propose meal plans that utilize local ingredients based on local specialty products. For example, the suggestion department can propose recipes that utilize local specialty products based on a list of local specialty products. In this way, by taking into account ingredients available at home and seasonal ingredients, it is possible to contribute to reducing food waste. Some or all of the above processing in the suggestion department may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion department can input refrigerator inventory management information into a generation AI and have the generation AI generate recipes that utilize available ingredients.

[0070] The suggestion unit can propose meal plans considering the nutritional value of ingredients, cooking time, and difficulty level. For example, the suggestion unit can propose a balanced meal plan based on nutritional value. For example, the suggestion unit can propose a balanced recipe based on nutritional information such as calories, vitamins, and minerals. The suggestion unit can also propose meal plans that can be prepared in a short time based on cooking time. For example, the suggestion unit can propose a recipe that can be prepared in a short time based on cooking procedures and the time spent using cooking equipment. The suggestion unit can also propose meal plans for beginners based on the difficulty level of cooking. For example, the suggestion unit can propose a simple recipe for beginners based on information on the difficulty level of cooking. In this way, by considering the nutritional value of ingredients, cooking time, and difficulty level, it is possible to provide feasible meal plans that are tailored to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the suggestion unit can input nutritional information into a generation AI and have the generation AI generate a balanced recipe.

[0071] The learning unit can learn the user's eating history and changes in their physical condition, and continuously improve the accuracy of its suggestions. For example, the learning unit can learn the user's preferences and tastes based on their eating history. For example, the learning unit can identify ingredients and dishes that the user frequently eats and improve its suggestions accordingly. The learning unit can also improve the accuracy of its suggestions based on changes in the user's physical condition. For example, the learning unit can learn changes in the user's weight and blood pressure and adjust its suggestions accordingly. The learning unit can also improve the accuracy of its suggestions based on user feedback. For example, the learning unit can learn the user's evaluations and comments on suggested meal plans and improve its suggestions accordingly. In this way, by learning the user's eating history and changes in their physical condition, the accuracy of its suggestions can be continuously improved. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's eating history data into a generative AI and have the generative AI learn the user's preferences and tastes.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during a relaxed period. For example, the data collection unit will capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit will collect information during a relaxed period. The data collection unit can also collect information during the user's free time if they are busy. For example, the data collection unit will record the user's voice and estimate their emotions using voice analysis technology. The data collection unit will collect information during a relaxed period. The data collection unit can also collect information in real time if the user is relaxed. For example, the data collection unit will collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The data collection unit will collect information in real time if the user is relaxed. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0073] The data collection unit can analyze the user's past eating history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the ingredients the user has previously enjoyed eating. For example, the data collection unit can analyze the user's eating history data, generate a list of favorite ingredients, and collect information based on that list. The data collection unit can also filter information to be avoided based on ingredients the user has previously avoided. For example, the data collection unit can analyze the user's eating history data, generate a list of avoided ingredients, and filter information based on that list. The data collection unit can also find specific patterns in the user's past eating history and collect information based on them. For example, the data collection unit can analyze the user's eating history data, extract specific patterns, and collect information based on those patterns. This allows the optimal information collection method to be selected by analyzing the user's past eating history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's eating history data into a generative AI and have the generative AI select the optimal information collection method.

[0074] The data collection unit can filter information based on the user's current health status and lifestyle. For example, the data collection unit can collect appropriate food information based on the user's current health status. For example, the data collection unit can collect food information suitable for blood pressure management based on the user's blood pressure data. The data collection unit can also filter relevant meal plans based on the user's lifestyle. For example, the data collection unit can filter meal plans suitable after exercise based on the user's exercise habit data. The data collection unit can also collect optimal information based on the user's health goals. For example, the data collection unit can collect low-calorie food information based on the user's weight loss goal. By filtering based on the user's current health status and lifestyle, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's health status data into a generative AI and have the generative AI collect appropriate food information.

[0075] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is stressed, the unit will prioritize collecting information on foods with relaxing effects. For instance, the unit might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The unit will then prioritize collecting information on foods with relaxing effects. Similarly, if the user is tired, the unit can prioritize collecting information on foods suitable for energy replenishment. For example, the unit might record the user's voice and estimate their emotions using voice analysis technology. The unit will then prioritize collecting information on foods suitable for energy replenishment. Furthermore, if the user is healthy, the unit can prioritize collecting information on balanced foods. For example, the unit might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The unit will then prioritize collecting information on balanced foods. This allows for the collection of more appropriate information by prioritizing the information to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of nearby food information based on the user's current location. For example, the data collection unit can collect nearby food information based on the user's GPS data. The data collection unit can also collect region-specific food information based on the user's geographical location. For example, the data collection unit can collect region-specific food information based on the user's address information. The data collection unit can also collect information on local markets and supermarkets based on the user's location. For example, the data collection unit can collect information on local markets and supermarkets based on the user's location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's GPS data into a generative AI and have the generative AI collect nearby food information.

[0077] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can analyze the user's social media posts and collect information on ingredients of interest. For example, the data collection unit can analyze the user's social media post data, generate a list of ingredients of interest, and collect information based on that list. The data collection unit can also collect relevant ingredient information based on information from accounts that the user follows. For example, the data collection unit can analyze the post data of accounts that the user follows and collect relevant ingredient information. The data collection unit can also analyze the user's social media activity history and collect relevant meal plans. For example, the data collection unit can analyze the user's social media activity history data and collect relevant meal plans. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media post data into a generative AI and have the generative AI generate a list of ingredients of interest.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For instance, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit provides detailed analysis results for relaxed users. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit provides concise analysis results for users in a hurry. The analysis unit can also provide visually easy-to-understand analysis results if the user is stressed. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The analysis unit provides visually easy-to-understand analysis results for stressed users. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit will perform a detailed analysis on highly important information. For instance, it will perform a detailed analysis of important data regarding the user's health status. The analysis unit can also perform a concise analysis on less important information. For example, it will perform a concise analysis of data regarding the user's preferences. The analysis unit can also perform an analysis with a moderate level of detail on information of moderate importance. For example, it will analyze data regarding the user's dietary history with a moderate level of detail. By adjusting the level of detail of the analysis based on the importance of the collected information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance data of the collected information into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a nutritional analysis algorithm to information about nutritional value. For instance, the analysis unit can perform a detailed nutritional evaluation by applying a nutritional analysis algorithm based on nutritional value data. The analysis unit can also apply a time efficiency analysis algorithm to information about cooking time. For example, the analysis unit can optimize cooking time by applying a time efficiency analysis algorithm based on cooking time data. The analysis unit can also apply a difficulty analysis algorithm to information about the difficulty of ingredients. For example, the analysis unit evaluates the difficulty of cooking by applying a difficulty analysis algorithm based on the difficulty of ingredients data. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input nutritional value data into a generative AI and have the generative AI execute the application of the nutritional analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can provide a short analysis result to a user in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can provide a detailed analysis result to a relaxed user. The analysis unit can also provide a concise and easy-to-understand analysis result if the user is stressed. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. The analysis unit can provide a concise and easy-to-understand analysis result to a stressed user. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0082] The analysis unit can determine the priority of analysis based on the timing of information collection. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may prioritize the analysis of the most recent information based on the information collection timing data. The analysis unit can also postpone the analysis of older information. For example, the analysis unit may postpone the analysis of older information based on the information collection timing data. The analysis unit can also prioritize the analysis of information collected during a specific period. For example, the analysis unit may prioritize the analysis of information collected during a specific period based on the information collection timing data. By determining the priority of analysis based on the timing of information collection, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the information collection timing data into a generative AI and have the generative AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of information with high relevance. For example, the analysis unit can prioritize the analysis of information with high relevance based on the information relevance data. The analysis unit can also postpone the analysis of information with low relevance. For example, the analysis unit can postpone the analysis of information with low relevance based on the information relevance data. The analysis unit can also moderately analyze information with moderate relevance. For example, the analysis unit can moderately analyze information with moderate relevance based on the information relevance data. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input information relevance data into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For instance, it might capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit then provides detailed suggestions to the relaxed user. The suggestion unit can also provide concise suggestions if the user is in a hurry. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The suggestion unit then provides concise suggestions to the hurried user. Furthermore, if the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The suggestion unit then provides visually easy-to-understand suggestions to the stressed user. This allows the system to provide more appropriate suggestions by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0085] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the meal plan. For example, the suggestion unit will provide detailed suggestions for high-importance meal plans. For instance, it might suggest a detailed meal plan based on important data regarding the user's health status. The suggestion unit can also provide concise suggestions for low-importance meal plans. For example, it might suggest a concise meal plan based on data regarding the user's preferences. Furthermore, the suggestion unit can provide suggestions with a moderate level of detail for meal plans of moderate importance. For example, it might suggest a meal plan with a moderate level of detail based on data regarding the user's eating history. By adjusting the level of detail in suggestions based on the importance of the meal plan, the suggestion unit can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input meal plan importance data into a generative AI and have the generative AI adjust the level of detail in the suggestions.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of the meal plan when making a suggestion. For example, for meal plans related to nutritional value, the suggestion unit can apply a nutrition suggestion algorithm. For example, the suggestion unit can apply a nutrition suggestion algorithm based on nutritional value data to perform a detailed nutritional evaluation. The suggestion unit can also apply a time efficiency suggestion algorithm to meal plans related to cooking time. For example, the suggestion unit can apply a time efficiency suggestion algorithm based on cooking time data to optimize cooking time. The suggestion unit can also apply a difficulty suggestion algorithm to meal plans related to the difficulty of ingredients. For example, the suggestion unit can apply a difficulty suggestion algorithm based on the difficulty of ingredients to evaluate the difficulty of cooking. In this way, by applying different suggestion algorithms depending on the category of the meal plan, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input nutritional value data into a generative AI and have the generative AI execute the application of the nutrition suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short suggestion. For example, the suggestion unit will capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit will then provide a short suggestion to the user who is in a hurry. The suggestion unit can also provide a detailed suggestion if the user is relaxed. For example, the suggestion unit will record the user's voice and estimate the emotion using voice analysis technology. The suggestion unit will then provide a detailed suggestion to the relaxed user. The suggestion unit can also provide a concise and easy-to-understand suggestion if the user is stressed. For example, the suggestion unit will collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. The suggestion unit will then provide a concise and easy-to-understand suggestion to the stressed user. By adjusting the length of the suggestion based on the user's emotions, it is possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The proposal department can determine the priority of proposals based on the submission timing of meal plans. For example, the proposal department may prioritize the most recent meal plans. For example, the proposal department may prioritize the most recent meal plans based on the submission timing data of meal plans. The proposal department may also postpone proposals for older meal plans. For example, the proposal department may postpone proposals for older meal plans based on the submission timing data of meal plans. The proposal department may also prioritize proposals for meal plans submitted within a specific period. For example, the proposal department may prioritize proposals for meal plans submitted within a specific period based on the submission timing data of meal plans. By determining the priority of proposals based on the submission timing of meal plans, more appropriate proposals can be provided. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the submission timing data of meal plans into a generative AI and have the generative AI perform the determination of proposal priorities.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of the meal plans. For example, the suggestion unit can prioritize suggesting meal plans with high relevance. For example, the suggestion unit can prioritize suggesting meal plans with high relevance based on the relevance data of the meal plans. The suggestion unit can also postpone suggesting meal plans with low relevance. For example, the suggestion unit can postpone suggesting meal plans with low relevance based on the relevance data of the meal plans. The suggestion unit can also moderately suggest meal plans with moderate relevance. For example, the suggestion unit can moderately suggest meal plans with moderate relevance based on the relevance data of the meal plans. By adjusting the order of suggestions based on the relevance of the meal plans, more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the suggestion unit can input the relevance data of the meal plans into a generative AI and have the generative AI perform the adjustment of the suggestion order.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select detailed training data. For instance, the learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The learning unit will then select detailed training data for the relaxed user. The learning unit can also select concise training data if the user is in a hurry. For example, the learning unit can record the user's voice and estimate their emotions using voice analysis technology. The learning unit will then select concise training data for the hurried user. Furthermore, if the user is stressed, the learning unit can select visually easy-to-understand training data. For example, the learning unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The learning unit will then select visually easy-to-understand training data for the stressed user. This allows for more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also analyze past learning data and improve the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can build a feedback loop to improve the accuracy of the learning algorithm based on past learning data. This allows for more accurate learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0092] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated emotions. For example, the learning unit learns frequently when the user is relaxed. For instance, the learning unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. The learning unit learns frequently for relaxed users. The learning unit can also reduce the frequency of learning when the user is in a hurry. For example, the learning unit records the user's voice and estimates their emotions using voice analysis technology. The learning unit reduces the frequency of learning for users in a hurry. The learning unit can also learn at a moderate frequency when the user is stressed. For example, the learning unit collects the user's biometric data (heart rate and skin electrical activity) with sensors and estimates their emotions using an emotion estimation algorithm. The learning unit learns at a moderate frequency for stressed users. This allows for more appropriate learning by adjusting the frequency of learning based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The learning unit can weight the training data based on the submission date of the meal history during training. For example, the learning unit can give a higher weight to the most recent meal history. For example, the learning unit can give a higher weight to the most recent meal history based on the submission date data of the meal history. The learning unit can also give a lower weight to older meal history. For example, the learning unit can give a lower weight to older meal history based on the submission date data of the meal history. The learning unit can also give an appropriate weight to meal history submitted within a specific period. For example, the learning unit can give an appropriate weight to meal history submitted within a specific period based on the submission date data of the meal history. This allows for more appropriate training by weighting the training data based on the submission date of the meal history. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the meal history submission date data into a generative AI and have the generative AI perform the weighting of the training data.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can offer a detailed meal plan. If the user is stressed, the suggestion function can offer concise and easy-to-understand suggestions. If the user is in a hurry, the suggestion function can offer a meal plan that can be completed in a short time. By adjusting the timing of suggestions based on the user's emotions, the system can provide more appropriate suggestions.

[0096] The data collection unit can collect information based on the user's meal history, taking into account the expiration dates of ingredients. For example, the unit can prioritize collecting ingredients that are nearing their expiration date, thereby reducing food waste. It can also prioritize collecting ingredients with longer expiration dates. Furthermore, the unit can automatically exclude ingredients that have passed their expiration date. This allows for the collection of more appropriate information by considering the expiration dates of ingredients.

[0097] The analysis unit can estimate the user's emotions and prioritize analyses based on those emotions. For example, if the user is relaxed, the analysis unit can prioritize detailed analyses. If the user is stressed, the analysis unit can prioritize concise analyses. Also, if the user is in a hurry, the analysis unit can prioritize analyses that can be completed quickly. By prioritizing analyses based on the user's emotions, more appropriate analysis results can be provided.

[0098] The suggestion function can make recommendations based on the user's eating history, taking into account the combination of ingredients. For example, it can prioritize suggesting combinations of ingredients that the user has enjoyed eating in the past. It can also suggest new ingredient combinations while considering nutritional balance. Furthermore, it can suggest safe ingredient combinations based on the user's allergy information. In this way, by considering the combination of ingredients, it can provide more appropriate recommendations.

[0099] The learning unit can estimate the user's emotions and adjust the learning content based on those emotions. For example, if the user is relaxed, the learning unit can provide detailed learning content. If the user is stressed, the learning unit can provide concise learning content. Also, if the user is in a hurry, the learning unit can provide content that can be learned in a short amount of time. In this way, by adjusting the learning content based on the user's emotions, more appropriate learning can be achieved.

[0100] The data collection unit can collect information based on the user's dietary history, taking into account the nutritional value of the ingredients. For example, the unit can prioritize collecting ingredients with high nutritional value. Furthermore, if a user is deficient in a particular nutrient, the unit can prioritize collecting ingredients containing that nutrient. In addition, the unit can collect ingredients with appropriate nutritional value according to the user's health goals. This allows for the collection of more relevant information by considering the nutritional value of the ingredients.

[0101] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can suggest a new recipe. If the user is stressed, the suggestion function can suggest a simple and easy-to-make recipe. Also, if the user is in a hurry, the suggestion function can suggest a recipe that can be prepared in a short amount of time. In this way, by adjusting the content of suggestions based on the user's emotions, it can provide more appropriate suggestions.

[0102] The analysis department can perform analyses based on the user's meal history, taking into account the price of ingredients. For example, the analysis department can prioritize analyzing ingredients with high cost performance. It can also select and analyze ingredients that fit within a budget. Furthermore, the analysis department can analyze the optimal ingredients while considering seasonal price fluctuations. This allows for the provision of more accurate analysis results by taking ingredient prices into account.

[0103] The learning unit can estimate the user's emotions and adjust the timing of learning based on those emotions. For example, if the user is relaxed, the learning unit can provide detailed learning. If the user is stressed, the learning unit can provide concise learning. Also, if the user is in a hurry, the learning unit can provide content that can be learned in a short amount of time. In this way, by adjusting the timing of learning based on the user's emotions, more appropriate learning can be achieved.

[0104] The suggestion department can make suggestions based on the user's eating history, taking into account how to store the ingredients. For example, the suggestion department can prioritize suggesting ingredients that require refrigeration. It can also postpone suggesting ingredients that can be stored at room temperature. Furthermore, it can suggest ingredients that can be frozen. By considering how to store the ingredients, the department can provide more appropriate suggestions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data collection unit collects user information. This information includes meal history, health status, and preferences. The data collection unit collects information entered by the user into the app, as well as data from smartwatches and fitness trackers. It also automatically records the user's meal history by taking photos of meals, recognizing the ingredients from the photos, and saving them to a database. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, it analyzes the user's meal history and evaluates nutritional balance. It also evaluates the user's health status and generates data to suggest an appropriate meal plan. For example, it analyzes the user's blood glucose data and suggests a meal plan suitable for blood glucose management. Step 3: The proposal department proposes a meal plan based on the analysis results obtained by the analysis department. The proposal is based on nutritional balance and calorie calculations. For example, it proposes menus that match the user's health goals. It also proposes appropriate ingredients for users with specific dietary restrictions. For example, it proposes allergen-free ingredients for users with allergies. Step 4: The learning unit learns from user feedback based on the meal plan proposed by the suggestion unit and improves the suggestions. Learning is performed using machine learning algorithms. For example, it learns the user's eating history and changes in physical condition to continuously improve the accuracy of the suggestions. It also learns the user's preferences and tastes to propose more individually customized meal plans. For example, if the user likes a particular ingredient, it will prioritize suggesting recipes that include that ingredient.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a meal plan based on the analysis results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and improves the accuracy of the proposals by learning user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a meal plan based on the analysis results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and improves the accuracy of the proposals by learning user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a meal plan based on the analysis results. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and improves the accuracy of the proposals by learning user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a meal plan based on the analysis results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and improves the accuracy of the proposals by learning user feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a meal plan, The system includes a learning unit that learns user feedback based on the meal plan proposed by the aforementioned proposal unit and improves the proposal. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Suggesting appropriate ingredients for users with specific dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose menus tailored to the health goals of users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose meal plans that take into account ingredients you have at home and seasonal ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose meal plans that take into account the nutritional value of ingredients, cooking time, and difficulty level. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system learns the user's eating history and changes in their physical condition to continuously improve the accuracy of its recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past meal history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the meal plan is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, During training, the training data is weighted based on when the meal history was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a meal plan, The system includes a learning unit that learns user feedback based on the meal plan proposed by the aforementioned proposal unit and improves the proposal. A system characterized by the following features.

2. The aforementioned proposal section is, Suggesting appropriate ingredients for users with specific dietary restrictions. The system according to feature 1.

3. The aforementioned proposal section is, We propose menus tailored to the health goals of users. The system according to feature 1.

4. The aforementioned proposal section is, We propose meal plans that take into account ingredients you have at home and seasonal ingredients. The system according to feature 1.

5. The aforementioned proposal section is, We propose meal plans that take into account the nutritional value of ingredients, cooking time, and difficulty level. The system according to feature 1.

6. The aforementioned learning unit, The system learns the user's eating history and changes in their physical condition to continuously improve the accuracy of its suggestions. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past meal history and select the optimal method for collecting information. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current health status and lifestyle. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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