system

Abstract

The system according to this embodiment aims to suggest the optimal menu by taking into account the contents of the refrigerator, information on nearby supermarkets, price information, and so on. [Solution] The system according to the embodiment comprises a refrigerator information acquisition unit, a supermarket information acquisition unit, a price information acquisition unit, an analysis unit, and a suggestion unit. The refrigerator information acquisition unit acquires the contents of the refrigerator. The supermarket information acquisition unit acquires information about nearby supermarkets and products. The price information acquisition unit acquires price information. The analysis unit analyzes the information acquired by the refrigerator information acquisition unit, the supermarket information acquisition unit, and the price information acquisition unit. The suggestion unit proposes the optimal menu based on the results analyzed by the analysis unit.

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, which includes 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 has not been fully carried out to propose an optimal menu considering the contents of the refrigerator, information on surrounding supermarkets, price information, etc., and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal menu considering the contents of the refrigerator, information on surrounding supermarkets, price information, etc.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a refrigerator information acquisition unit, a supermarket information acquisition unit, a price information acquisition unit, an analysis unit, and a suggestion unit. The refrigerator information acquisition unit acquires the contents of the refrigerator. The supermarket information acquisition unit acquires information about nearby supermarkets and products. The price information acquisition unit acquires price information. The analysis unit analyzes the information acquired by the refrigerator information acquisition unit, the supermarket information acquisition unit, and the price information acquisition unit. The suggestion unit proposes the optimal menu based on the results of the analysis performed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal menu by taking into account the contents of the refrigerator, information on nearby supermarkets, price information, and so on. [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, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 planner service according to the embodiment of the present invention is a system that proposes the optimal plan simply by asking, "What's for dinner tonight?" This system proposes the optimal menu by considering the following seven elements: It reduces waste by proposing a menu using the contents of the refrigerator. It considers recent menus to avoid making the same dishes repeatedly. It uses ingredients available at nearby supermarkets to save the trouble of going to buy special ingredients. It proposes a budget-friendly menu by avoiding expensive ingredients. It proposes a healthy menu by considering calories and nutritional balance. It proposes a highly satisfying menu by considering the user's preferences and disliked ingredients. It proposes an economically feasible menu by considering the household budget. This system proposes the optimal menu by considering all of these elements simultaneously simply by the user asking, "What's for dinner tonight?". As a result, even busy dual-income families can easily prepare healthy and economical meals. For example, the refrigerator information acquisition unit, which acquires the contents of the refrigerator, recognizes ingredients using cameras and sensors inside the refrigerator. The supermarket information acquisition unit, which acquires information on nearby supermarkets and products, acquires information from electronic flyers and online databases. The price information acquisition unit obtains the latest price information using online databases and APIs. The analysis unit, which analyzes this information, integrates and analyzes information from the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. The suggestion unit, which proposes the optimal menu, proposes the optimal menu based on the results of the analysis unit. This allows the user to analyze the contents of the refrigerator, supermarket information, and price information and propose the optimal menu. As a result, the planner service can propose the optimal menu simply by the user asking, "What's for dinner tonight?"

[0029] The planner service according to this embodiment includes a refrigerator information acquisition unit, a supermarket information acquisition unit, a price information acquisition unit, an analysis unit, and a proposal unit. The refrigerator information acquisition unit acquires the contents of the refrigerator. The refrigerator information acquisition unit recognizes food items using, for example, a camera or sensors inside the refrigerator. The refrigerator information acquisition unit can acquire the type, quantity, and storage condition of food items inside the refrigerator. The refrigerator information acquisition unit can, for example, take images of food items with a camera inside the refrigerator and identify the food items using image recognition technology. The refrigerator information acquisition unit can also measure the weight and temperature of food items using sensors inside the refrigerator. The supermarket information acquisition unit acquires information about nearby supermarkets and products. The supermarket information acquisition unit acquires information from, for example, electronic flyers or online databases. The supermarket information acquisition unit can acquire information such as the location of the supermarket, the type of product, and the price. The supermarket information acquisition unit can, for example, analyze data from electronic flyers to acquire special sale information. The supermarket information acquisition unit can also acquire product inventory information from online databases. The price information acquisition unit acquires price information. The price information acquisition unit obtains the latest price information, for example, using online databases or APIs. The price information acquisition unit can obtain information such as price fluctuations of products and price differences by region. For example, the price information acquisition unit obtains price information of products from online databases and analyzes price fluctuations. The price information acquisition unit can also obtain real-time price information using APIs. The analysis unit analyzes the information obtained by the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. For example, the analysis unit integrates and analyzes ingredients in the refrigerator, supermarket sale information, and price information. The analysis unit can analyze the information using data integration methods, analysis algorithms, etc. For example, the analysis unit preprocesses the data and applies analysis algorithms to generate information for proposing the optimal menu. The proposal unit proposes the optimal menu based on the results analyzed by the analysis unit. For example, the proposal unit proposes a menu based on criteria such as nutritional balance, cost performance, and cooking time. The proposal unit can also propose a highly satisfying menu by considering the user's preferences and disliked ingredients.The suggestion unit, for example, refers to the user's past meal history and suggests menus that suit their preferences. The suggestion unit can also suggest healthy menus considering the user's health condition and nutritional balance. As a result, the planner service according to this embodiment can analyze the contents of the refrigerator, supermarket information, and price information to suggest the optimal menu.

[0030] The refrigerator information acquisition unit acquires the contents of the refrigerator. For example, it uses cameras and sensors inside the refrigerator to recognize the food items. Specifically, a camera installed inside the refrigerator periodically takes images of the interior, and image recognition technology is used to identify the type and quantity of food items. The image recognition technology uses object detection algorithms and deep learning models to identify food items based on their shape, color, labels, etc. Sensors inside the refrigerator can also measure the weight and temperature of the food items. For example, a weight sensor is installed under the shelves where the food items are placed to detect changes in weight in real time. Temperature sensors measure the temperature in each area of ​​the refrigerator to monitor the food's storage condition. This allows the refrigerator information acquisition unit to accurately understand the type, quantity, and storage condition of the food items inside the refrigerator. Furthermore, the refrigerator information acquisition unit can transmit the acquired data to a cloud server and share the data with other systems. For example, information about the food items inside the refrigerator can be displayed in real time on the user's smartphone app, allowing the user to check the contents of the refrigerator even when away from home. This improves user convenience and reduces food waste.

[0031] The supermarket information acquisition unit acquires information about nearby supermarkets and products. For example, it obtains information from electronic flyers and online databases. Specifically, it periodically downloads electronic flyers from various supermarkets via the internet and analyzes information on special offers and new products. Electronic flyers contain detailed information such as product images, prices, and sale periods. By analyzing this information, the unit can provide users with the latest sale information. The supermarket information acquisition unit can also acquire product inventory information from online databases. For example, it can link with supermarket inventory management systems to obtain real-time information on product inventory status and expected arrival dates. This allows users to purchase specific products before they run out of stock. Furthermore, the supermarket information acquisition unit can also acquire supermarket location information. For example, it can use GPS data to identify the nearest supermarket to the user's current location and display it on a map. This allows users to efficiently purchase sale items at the nearest supermarket. The supermarket information acquisition unit integrates this information to propose the optimal shopping plan to the user.

[0032] The price information acquisition unit acquires price information. For example, it obtains the latest price information using online databases and APIs. Specifically, the price information acquisition unit collects data from multiple price comparison sites and online shopping sites on the internet and analyzes price fluctuations and price differences by region. For example, it can compare how much a particular product is sold for in different regions and stores and present the user with the lowest price. The price information acquisition unit can also acquire real-time price information using APIs. For example, it can use the API of an online shopping site to obtain the current price and inventory status of a product and provide the user with the latest information. Furthermore, the price information acquisition unit can accumulate historical price data and analyze price fluctuation trends. This allows it to predict when a particular product will decrease in price and suggest the optimal purchase timing to the user. The price information acquisition unit integrates this information and can provide the user with the most cost-effective shopping plan.

[0033] The analysis unit analyzes information acquired by the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. For example, the analysis unit integrates and analyzes information on ingredients in the refrigerator, supermarket sale information, and price information. Specifically, the analysis unit preprocesses the data and applies an analysis algorithm to generate information for suggesting the optimal menu. Data preprocessing includes data cleaning, normalization, and feature extraction. For example, it identifies ingredients nearing their expiration date from the refrigerator ingredient data and suggests menus that prioritize their use. It can also analyze supermarket sale information and suggest menus that utilize sale items. Furthermore, it analyzes price information and suggests menus with high cost performance. The analysis unit integrates this information to generate the optimal menu that takes into account the user's preferences and nutritional balance. For example, it considers the user's past eating history and health condition to suggest a nutritionally balanced menu. In addition, the analysis unit can use AI to analyze the data and learn the user's preferences and ingredient combinations to suggest menus that are more satisfying. This allows the analysis unit to suggest optimal menus to users, reduce food waste, and support a healthy diet.

[0034] The Proposal Department proposes optimal menus based on the results analyzed by the Analysis Department. The Proposal Department proposes menus based on criteria such as nutritional balance, cost-effectiveness, and cooking time. Specifically, the Proposal Department can propose highly satisfying menus by considering the user's preferences and disliked ingredients. For example, it can refer to the user's past eating history to propose menus that match their preferences. The Proposal Department can also propose healthy menus by considering the user's health condition and nutritional balance. For example, if a user is deficient in a particular nutrient, it can propose a menu using ingredients that supplement that nutrient. Furthermore, the Proposal Department can propose menus that suit the user's lifestyle by considering cooking time and difficulty. For example, it can propose menus that can be prepared quickly on busy weekdays and menus that require more time on weekends. The Proposal Department can also collect user feedback to continuously improve the accuracy and satisfaction of its suggestions. For example, users can evaluate the suggested menus, and the next suggestions can be improved based on that evaluation. This allows the Proposal Department to propose optimal menus to users and improve the quality of their eating habits.

[0035] The refrigerator information acquisition unit can recognize food items using cameras and sensors inside the refrigerator. For example, the refrigerator information acquisition unit can take images of food items with a camera inside the refrigerator and identify the food items using image recognition technology. The refrigerator information acquisition unit can also measure the weight and temperature of food items using sensors inside the refrigerator. For example, the refrigerator information acquisition unit inputs image data taken by a camera inside the refrigerator into a generating AI, which analyzes the image data to identify the food items. This allows the refrigerator to automatically recognize food items and acquire information. Some or all of the above-described processes in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0036] The supermarket information acquisition unit can acquire information from electronic flyers and online databases. For example, the supermarket information acquisition unit can analyze data from electronic flyers to acquire special sale information. The supermarket information acquisition unit can also acquire product inventory information from online databases. For example, the supermarket information acquisition unit can input data from electronic flyers into a generating AI, which then analyzes the data to acquire special sale information. This allows for the efficient acquisition of supermarket information and its use in menu suggestions. Some or all of the above-described processes in the supermarket information acquisition unit may be performed using AI, for example, or without AI.

[0037] The price information acquisition unit can obtain the latest price information using online databases or APIs. For example, the price information acquisition unit can obtain price information for goods from online databases and analyze price fluctuations. The price information acquisition unit can also obtain real-time price information using APIs. For example, the price information acquisition unit inputs the price information obtained from online databases into a generating AI, which then analyzes price fluctuations. This allows the system to obtain the latest price information and propose economical menus. Some or all of the above-described processes in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0038] The analysis unit can integrate and analyze information from the refrigerator information acquisition unit, the supermarket information acquisition unit, and the price information acquisition unit. For example, the analysis unit integrates and analyzes food items in the refrigerator, supermarket sale information, and price information. The analysis unit can analyze the information using data integration methods, analysis algorithms, etc. For example, the analysis unit preprocesses the data and applies analysis algorithms to generate information for suggesting the optimal menu. By integrating and analyzing each piece of information, it becomes possible to suggest menus with higher accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0039] The suggestion unit can propose the optimal menu based on the results of the analysis unit. The suggestion unit proposes menus based on criteria such as nutritional balance, cost performance, and cooking time. The suggestion unit can propose menus that are highly satisfying by taking into account the user's preferences and disliked ingredients. The suggestion unit can propose menus that match the user's preferences by referring to the user's past meal history. The suggestion unit can also propose healthy menus by taking into account the user's health condition and nutritional balance. This allows the suggestion unit to propose the optimal menu based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0040] The refrigerator information acquisition unit can select ingredients to acquire preferentially, taking into account the expiration dates of the ingredients in the refrigerator. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients that are nearing their expiration date to reduce waste. For example, the refrigerator information acquisition unit can automatically exclude ingredients that have passed their expiration date and remove them from acquisition. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients with shorter expiration dates, while delaying the acquisition of ingredients with longer expiration dates. By selecting ingredients while considering expiration dates, waste can be reduced and efficient menu suggestions can be made. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0041] The refrigerator information acquisition unit can analyze the nutritional value of the food in the refrigerator and acquire information to suggest healthy menus. For example, the refrigerator information acquisition unit can analyze the nutritional value of the food in the refrigerator and suggest a balanced menu. For example, if a particular nutrient is deficient, the refrigerator information acquisition unit can also prioritize acquiring food that supplements that nutrient. For example, the refrigerator information acquisition unit can acquire food containing necessary nutrients according to the user's health condition. This makes it possible to suggest healthy menus by analyzing nutritional value. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0042] The refrigerator information acquisition unit can select ingredients to acquire when acquiring ingredients from the refrigerator, taking into account the user's past food purchase history. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients that the user has frequently purchased in the past. The refrigerator information acquisition unit can also analyze the consumption patterns of ingredients that the user has purchased in the past and acquire the most suitable ingredients. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients that the user has particularly liked to use among the ingredients that the user has purchased in the past. This makes it easier to acquire ingredients that match the user's preferences by taking into account past purchase history. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0043] The refrigerator information acquisition unit can adjust the ingredients it acquires based on the user's family composition and dietary preferences when acquiring ingredients from the refrigerator. For example, the refrigerator information acquisition unit can acquire the necessary amount of ingredients according to the user's family composition. For example, the refrigerator information acquisition unit can also prioritize the acquisition of specific ingredients based on the dietary preferences of the user's family. For example, the refrigerator information acquisition unit can adjust the ingredients it acquires by taking into account the allergy information of the user's family. By adjusting the ingredients based on family composition and dietary preferences, it becomes possible to propose menus that are more satisfying. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0044] The supermarket information acquisition unit can prioritize acquiring supermarket sale information and collect information to propose economical meal plans. For example, the supermarket information acquisition unit can prioritize acquiring supermarket sale information and propose economical meal plans. For example, the supermarket information acquisition unit can also propose meal plans using the most economical ingredients based on sale information. For example, the supermarket information acquisition unit can acquire sale information in real time and propose meal plans based on the latest information. This makes it possible to propose economical meal plans by prioritizing the acquisition of sale information. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0045] The supermarket information acquisition unit can acquire supermarket inventory status in real time and ensure that the ingredients necessary for the suggested menu are available. For example, the supermarket information acquisition unit can acquire supermarket inventory status in real time and check whether the necessary ingredients are available. For example, the supermarket information acquisition unit can also ensure that the ingredients necessary for the suggested menu are available based on the inventory status. For example, the supermarket information acquisition unit can update the inventory status in real time and suggest menus based on the latest information. In this way, by acquiring inventory status in real time, it is possible to ensure that the necessary ingredients are available. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0046] The supermarket information acquisition unit can select the information to acquire when acquiring supermarket information, taking into account the user's past supermarket usage history. For example, the supermarket information acquisition unit may prioritize acquiring information on supermarkets that the user has frequently used in the past. The supermarket information acquisition unit can also acquire optimal supermarket information based on the user's past supermarket usage history. For example, the supermarket information acquisition unit may prioritize acquiring special sale information from supermarkets that the user has used in the past. This makes it easier to acquire optimal supermarket information for the user by taking past usage history into consideration. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0047] The supermarket information acquisition unit can prioritize acquiring information about the nearest supermarket by considering the user's geographical location when acquiring supermarket information. For example, the supermarket information acquisition unit prioritizes acquiring information about the nearest supermarket based on the user's current location. The supermarket information acquisition unit can also acquire optimal supermarket information based on the user's geographical location. For example, the supermarket information acquisition unit prioritizes acquiring sale information from the supermarket closest to the user's current location. This makes it easier to prioritize acquiring information about the nearest supermarket by considering geographical location. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0048] The price information acquisition unit can predict price fluctuations and collect information to propose economical menus in the future. For example, the price information acquisition unit can predict price fluctuations and collect information to propose economical menus in the future. For example, the price information acquisition unit can predict price fluctuations and propose menus using the most economical ingredients. For example, the price information acquisition unit can predict price fluctuations and predict the prices of ingredients that will be needed in the future. This makes it possible to propose economical menus in the future by predicting price fluctuations. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0049] The price information acquisition unit can compare price information by region and acquire information to suggest the most economical ingredients. For example, the price information acquisition unit can compare price information by region and acquire information to suggest the most economical ingredients. For example, the price information acquisition unit can also suggest a menu using the most economical ingredients based on price information by region. For example, the price information acquisition unit can acquire price information by region in real time and suggest a menu based on the latest information. In this way, by comparing price information by region, the most economical ingredients can be suggested. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0050] The price information acquisition unit can select the information to acquire when acquiring price information, taking into account the user's income and expenditure situation. For example, the price information acquisition unit can acquire the optimal price information based on the user's income and expenditure situation. For example, the price information acquisition unit can also acquire the most economical price information by taking into account the user's income and expenditure situation. For example, the price information acquisition unit can prioritize acquiring the most important price information based on the user's income and expenditure situation. This makes it possible to suggest economical menus by taking into account income and expenditure situation. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0051] The price information acquisition unit can adjust the information it acquires by considering the user's purchase history when acquiring price information. For example, the price information acquisition unit can acquire optimal price information based on the user's purchase history. For example, the price information acquisition unit can also acquire the most economical price information by considering the user's purchase history. For example, the price information acquisition unit prioritizes acquiring the most important price information based on the user's purchase history. This makes it easier to acquire the optimal price information for the user by considering the purchase history. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0052] The analysis unit can perform analysis to propose the optimal menu by integrating refrigerator information, supermarket information, and price information during the analysis process. For example, the analysis unit can integrate refrigerator information, supermarket information, and price information to propose the optimal menu. The analysis unit can also, for example, integrate each piece of information to propose the most economical and healthy menu. For example, the analysis unit can integrate each piece of information in real time and propose a menu based on the latest information. By integrating and analyzing each piece of information, it becomes possible to propose a more accurate menu. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0053] The analysis unit can perform analysis to propose more personalized menus by taking into account the user's past eating history. For example, the analysis unit proposes the optimal menu based on the user's past eating history. The analysis unit can also propose personalized menus by taking into account the user's past eating history. For example, the analysis unit analyzes the user's past eating history and proposes the menu that will provide the highest satisfaction. This makes it possible to propose more personalized menus by taking into account past eating history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0054] The analysis unit can perform analysis while considering the user's health status and nutritional balance. For example, the analysis unit can perform analysis to suggest an optimal menu based on the user's health status. The analysis unit can also perform analysis to suggest a healthy menu by considering the user's nutritional balance. For example, the analysis unit can perform analysis to suggest an optimal menu by considering the user's health status and nutritional balance in real time. This makes it possible to suggest healthier menus by considering health status and nutritional balance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0055] The analysis unit can adjust its analysis based on the user's family structure and dietary preferences. For example, the analysis unit can perform analysis to suggest the optimal menu based on the user's family structure. The analysis unit can also perform analysis to suggest the optimal menu based on the dietary preferences of the user's family. For example, the analysis unit can perform analysis to suggest the optimal menu considering the allergy information of the user's family. By adjusting the analysis based on family structure and dietary preferences, it becomes possible to suggest menus that are more satisfying. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0056] The suggestion unit can propose more personalized menus by considering the user's past eating history when making suggestions. For example, the suggestion unit can propose the optimal menu based on the user's past eating history. The suggestion unit can also propose personalized menus by considering the user's past eating history. For example, the suggestion unit can analyze the user's past eating history and propose the menu that will provide the highest level of satisfaction. This makes it possible to propose more personalized menus by considering past eating history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0057] The suggestion unit can propose healthy menus by considering the user's health condition and nutritional balance when making suggestions. For example, the suggestion unit can propose an optimal menu based on the user's health condition. The suggestion unit can also propose a healthy menu by considering the user's nutritional balance. For example, the suggestion unit can propose an optimal menu by considering the user's health condition and nutritional balance in real time. This makes it possible to propose healthier menus by considering health condition and nutritional balance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0058] The suggestion unit can adjust its suggestions based on the user's family structure and dietary preferences. For example, the suggestion unit can suggest the optimal menu based on the user's family structure. The suggestion unit can also suggest the optimal menu based on the dietary preferences of the user's family. For example, the suggestion unit can suggest the optimal menu taking into account the allergy information of the user's family. By adjusting the suggestions based on family structure and dietary preferences, it becomes possible to provide more satisfying menu suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0059] The suggestion unit can propose economical menus by considering the user's income and expenditure situation when making suggestions. For example, the suggestion unit proposes the optimal menu based on the user's income and expenditure situation. The suggestion unit can also propose economical menus by considering the user's income and expenditure situation. For example, the suggestion unit proposes the most economical menu based on the user's income and expenditure situation. This makes it possible to propose economical menus by considering income and expenditure situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The refrigerator information acquisition unit monitors the freshness of food inside the refrigerator in real time and can prioritize the acquisition of food that has lost its freshness. For example, it uses cameras and sensors inside the refrigerator to measure the freshness of food and identify food that has lost its freshness. In addition, when the refrigerator information acquisition unit acquires food that has lost its freshness, it can notify the user and encourage them to consume it as soon as possible. This reduces food waste and enables efficient menu suggestions.

[0062] The refrigerator information acquisition unit can acquire allergen information of food items inside the refrigerator and suggest menus suitable for users with allergies. For example, it can use cameras and sensors inside the refrigerator to identify allergen information of food items and prioritize the acquisition of ingredients suitable for users with allergies. In addition, the refrigerator information acquisition unit can also provide allergy warnings to the user when acquiring allergen information. This makes it possible to suggest safe menus for users with allergies.

[0063] The refrigerator information acquisition unit can suggest methods for storing food inside the refrigerator and provide advice on maintaining food freshness. For example, it can use cameras and sensors inside the refrigerator to monitor the storage status of food and notify the user of appropriate storage methods. Furthermore, when providing advice on storage methods, the refrigerator information acquisition unit can also consider the user's storage history to suggest the optimal storage method. This helps maintain food freshness and reduce waste.

[0064] The refrigerator information acquisition unit can analyze the nutritional value of the food inside the refrigerator and suggest menus tailored to the user's health condition. For example, it can use cameras and sensors inside the refrigerator to identify the nutritional value of the food and suggest nutritionally balanced menus that suit the user's health. Furthermore, when analyzing nutritional value, the refrigerator information acquisition unit can also consider the user's health history to suggest the optimal nutritional balance. This makes it possible to suggest menus that support the user's health.

[0065] The refrigerator information acquisition unit can analyze the consumption history of ingredients in the refrigerator and suggest menus tailored to the user's preferences. For example, it can use cameras and sensors inside the refrigerator to identify the consumption history of ingredients and prioritize acquiring ingredients that match the user's preferences. Furthermore, when analyzing consumption history, the refrigerator information acquisition unit can also consider the user's past eating history to suggest the most suitable menu. This makes it possible to suggest menus that match the user's preferences.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The refrigerator information acquisition unit acquires the contents of the refrigerator. For example, it uses cameras and sensors inside the refrigerator to recognize food items and acquires the type, quantity, and storage condition of the food items. It takes images of food items with the camera inside the refrigerator and identifies the food items using image recognition technology. It can also measure the weight and temperature of food items using sensors inside the refrigerator. Step 2: The supermarket information acquisition unit acquires information about nearby supermarkets and products. For example, it acquires information from electronic flyers and online databases to obtain information such as the location of supermarkets, types of products, and prices. It analyzes data from electronic flyers to obtain special sale information. It can also acquire product inventory information from online databases. Step 3: The price information acquisition unit acquires price information. For example, it uses online databases or APIs to acquire the latest price information and obtain information such as price fluctuations of goods and price differences by region. It acquires product price information from online databases and analyzes price fluctuations. It can also acquire real-time price information using APIs. Step 4: The analysis unit analyzes the information acquired by the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. For example, it integrates and analyzes the ingredients in the refrigerator, supermarket sale information, and price information, performs data preprocessing, and applies an analysis algorithm to generate information for suggesting the optimal menu. Step 5: The suggestion unit proposes the optimal menu based on the results analyzed by the analysis unit. For example, it proposes menus based on criteria such as nutritional balance, cost performance, and cooking time, and proposes highly satisfying menus that take into account the user's preferences and disliked ingredients. It also refers to the user's past eating history and proposes menus that match their preferences. Furthermore, it can propose healthy menus that take into account the user's health condition and nutritional balance.

[0068] (Example of form 2) The planner service according to the embodiment of the present invention is a system that proposes the optimal plan simply by asking, "What's for dinner tonight?" This system proposes the optimal menu by considering the following seven elements: It reduces waste by proposing a menu using the contents of the refrigerator. It considers recent menus to avoid making the same dishes repeatedly. It uses ingredients available at nearby supermarkets to save the trouble of going to buy special ingredients. It proposes a budget-friendly menu by avoiding expensive ingredients. It proposes a healthy menu by considering calories and nutritional balance. It proposes a highly satisfying menu by considering the user's preferences and disliked ingredients. It proposes an economically feasible menu by considering the household budget. This system proposes the optimal menu by considering all of these elements simultaneously simply by the user asking, "What's for dinner tonight?". As a result, even busy dual-income families can easily prepare healthy and economical meals. For example, the refrigerator information acquisition unit, which acquires the contents of the refrigerator, recognizes ingredients using cameras and sensors inside the refrigerator. The supermarket information acquisition unit, which acquires information on nearby supermarkets and products, acquires information from electronic flyers and online databases. The price information acquisition unit obtains the latest price information using online databases and APIs. The analysis unit, which analyzes this information, integrates and analyzes information from the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. The suggestion unit, which proposes the optimal menu, proposes the optimal menu based on the results of the analysis unit. This allows the user to analyze the contents of the refrigerator, supermarket information, and price information and propose the optimal menu. As a result, the planner service can propose the optimal menu simply by the user asking, "What's for dinner tonight?"

[0069] The planner service according to this embodiment includes a refrigerator information acquisition unit, a supermarket information acquisition unit, a price information acquisition unit, an analysis unit, and a proposal unit. The refrigerator information acquisition unit acquires the contents of the refrigerator. The refrigerator information acquisition unit recognizes food items using, for example, a camera or sensors inside the refrigerator. The refrigerator information acquisition unit can acquire the type, quantity, and storage condition of food items inside the refrigerator. The refrigerator information acquisition unit can, for example, take images of food items with a camera inside the refrigerator and identify the food items using image recognition technology. The refrigerator information acquisition unit can also measure the weight and temperature of food items using sensors inside the refrigerator. The supermarket information acquisition unit acquires information about nearby supermarkets and products. The supermarket information acquisition unit acquires information from, for example, electronic flyers or online databases. The supermarket information acquisition unit can acquire information such as the location of the supermarket, the type of product, and the price. The supermarket information acquisition unit can, for example, analyze data from electronic flyers to acquire special sale information. The supermarket information acquisition unit can also acquire product inventory information from online databases. The price information acquisition unit acquires price information. The price information acquisition unit obtains the latest price information, for example, using online databases or APIs. The price information acquisition unit can obtain information such as price fluctuations of products and price differences by region. For example, the price information acquisition unit obtains price information of products from online databases and analyzes price fluctuations. The price information acquisition unit can also obtain real-time price information using APIs. The analysis unit analyzes the information obtained by the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. For example, the analysis unit integrates and analyzes ingredients in the refrigerator, supermarket sale information, and price information. The analysis unit can analyze the information using data integration methods, analysis algorithms, etc. For example, the analysis unit preprocesses the data and applies analysis algorithms to generate information for proposing the optimal menu. The proposal unit proposes the optimal menu based on the results analyzed by the analysis unit. For example, the proposal unit proposes a menu based on criteria such as nutritional balance, cost performance, and cooking time. The proposal unit can also propose a highly satisfying menu by considering the user's preferences and disliked ingredients.The suggestion unit, for example, refers to the user's past meal history and suggests menus that suit their preferences. The suggestion unit can also suggest healthy menus considering the user's health condition and nutritional balance. As a result, the planner service according to this embodiment can analyze the contents of the refrigerator, supermarket information, and price information to suggest the optimal menu.

[0070] The refrigerator information acquisition unit acquires the contents of the refrigerator. For example, it uses cameras and sensors inside the refrigerator to recognize the food items. Specifically, a camera installed inside the refrigerator periodically takes images of the interior, and image recognition technology is used to identify the type and quantity of food items. The image recognition technology uses object detection algorithms and deep learning models to identify food items based on their shape, color, labels, etc. Sensors inside the refrigerator can also measure the weight and temperature of the food items. For example, a weight sensor is installed under the shelves where the food items are placed to detect changes in weight in real time. Temperature sensors measure the temperature in each area of ​​the refrigerator to monitor the food's storage condition. This allows the refrigerator information acquisition unit to accurately understand the type, quantity, and storage condition of the food items inside the refrigerator. Furthermore, the refrigerator information acquisition unit can transmit the acquired data to a cloud server and share the data with other systems. For example, information about the food items inside the refrigerator can be displayed in real time on the user's smartphone app, allowing the user to check the contents of the refrigerator even when away from home. This improves user convenience and reduces food waste.

[0071] The supermarket information acquisition unit acquires information about nearby supermarkets and products. For example, it obtains information from electronic flyers and online databases. Specifically, it periodically downloads electronic flyers from various supermarkets via the internet and analyzes information on special offers and new products. Electronic flyers contain detailed information such as product images, prices, and sale periods. By analyzing this information, the unit can provide users with the latest sale information. The supermarket information acquisition unit can also acquire product inventory information from online databases. For example, it can link with supermarket inventory management systems to obtain real-time information on product inventory status and expected arrival dates. This allows users to purchase specific products before they run out of stock. Furthermore, the supermarket information acquisition unit can also acquire supermarket location information. For example, it can use GPS data to identify the nearest supermarket to the user's current location and display it on a map. This allows users to efficiently purchase sale items at the nearest supermarket. The supermarket information acquisition unit integrates this information to propose the optimal shopping plan to the user.

[0072] The price information acquisition unit acquires price information. For example, it obtains the latest price information using online databases and APIs. Specifically, the price information acquisition unit collects data from multiple price comparison sites and online shopping sites on the internet and analyzes price fluctuations and price differences by region. For example, it can compare how much a particular product is sold for in different regions and stores and present the user with the lowest price. The price information acquisition unit can also acquire real-time price information using APIs. For example, it can use the API of an online shopping site to obtain the current price and inventory status of a product and provide the user with the latest information. Furthermore, the price information acquisition unit can accumulate historical price data and analyze price fluctuation trends. This allows it to predict when a particular product will decrease in price and suggest the optimal purchase timing to the user. The price information acquisition unit integrates this information and can provide the user with the most cost-effective shopping plan.

[0073] The analysis unit analyzes information acquired by the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. For example, the analysis unit integrates and analyzes information on ingredients in the refrigerator, supermarket sale information, and price information. Specifically, the analysis unit preprocesses the data and applies an analysis algorithm to generate information for suggesting the optimal menu. Data preprocessing includes data cleaning, normalization, and feature extraction. For example, it identifies ingredients nearing their expiration date from the refrigerator ingredient data and suggests menus that prioritize their use. It can also analyze supermarket sale information and suggest menus that utilize sale items. Furthermore, it analyzes price information and suggests menus with high cost performance. The analysis unit integrates this information to generate the optimal menu that takes into account the user's preferences and nutritional balance. For example, it considers the user's past eating history and health condition to suggest a nutritionally balanced menu. In addition, the analysis unit can use AI to analyze the data and learn the user's preferences and ingredient combinations to suggest menus that are more satisfying. This allows the analysis unit to suggest optimal menus to users, reduce food waste, and support a healthy diet.

[0074] The Proposal Department proposes optimal menus based on the results analyzed by the Analysis Department. The Proposal Department proposes menus based on criteria such as nutritional balance, cost-effectiveness, and cooking time. Specifically, the Proposal Department can propose highly satisfying menus by considering the user's preferences and disliked ingredients. For example, it can refer to the user's past eating history to propose menus that match their preferences. The Proposal Department can also propose healthy menus by considering the user's health condition and nutritional balance. For example, if a user is deficient in a particular nutrient, it can propose a menu using ingredients that supplement that nutrient. Furthermore, the Proposal Department can propose menus that suit the user's lifestyle by considering cooking time and difficulty. For example, it can propose menus that can be prepared quickly on busy weekdays and menus that require more time on weekends. The Proposal Department can also collect user feedback to continuously improve the accuracy and satisfaction of its suggestions. For example, users can evaluate the suggested menus, and the next suggestions can be improved based on that evaluation. This allows the Proposal Department to propose optimal menus to users and improve the quality of their eating habits.

[0075] The refrigerator information acquisition unit can recognize food items using cameras and sensors inside the refrigerator. For example, the refrigerator information acquisition unit can take images of food items with a camera inside the refrigerator and identify the food items using image recognition technology. The refrigerator information acquisition unit can also measure the weight and temperature of food items using sensors inside the refrigerator. For example, the refrigerator information acquisition unit inputs image data taken by a camera inside the refrigerator into a generating AI, which analyzes the image data to identify the food items. This allows the refrigerator to automatically recognize food items and acquire information. Some or all of the above-described processes in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0076] The supermarket information acquisition unit can acquire information from electronic flyers and online databases. For example, the supermarket information acquisition unit can analyze data from electronic flyers to acquire special sale information. The supermarket information acquisition unit can also acquire product inventory information from online databases. For example, the supermarket information acquisition unit can input data from electronic flyers into a generating AI, which then analyzes the data to acquire special sale information. This allows for the efficient acquisition of supermarket information and its use in menu suggestions. Some or all of the above-described processes in the supermarket information acquisition unit may be performed using AI, for example, or without AI.

[0077] The price information acquisition unit can obtain the latest price information using online databases or APIs. For example, the price information acquisition unit can obtain price information for goods from online databases and analyze price fluctuations. The price information acquisition unit can also obtain real-time price information using APIs. For example, the price information acquisition unit inputs the price information obtained from online databases into a generating AI, which then analyzes price fluctuations. This allows the system to obtain the latest price information and propose economical menus. Some or all of the above-described processes in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0078] The analysis unit can integrate and analyze information from the refrigerator information acquisition unit, the supermarket information acquisition unit, and the price information acquisition unit. For example, the analysis unit integrates and analyzes food items in the refrigerator, supermarket sale information, and price information. The analysis unit can analyze the information using data integration methods, analysis algorithms, etc. For example, the analysis unit preprocesses the data and applies analysis algorithms to generate information for suggesting the optimal menu. By integrating and analyzing each piece of information, it becomes possible to suggest menus with higher accuracy. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0079] The suggestion unit can propose the optimal menu based on the results of the analysis unit. The suggestion unit proposes menus based on criteria such as nutritional balance, cost performance, and cooking time. The suggestion unit can propose menus that are highly satisfying by taking into account the user's preferences and disliked ingredients. The suggestion unit can propose menus that match the user's preferences by referring to the user's past meal history. The suggestion unit can also propose healthy menus by taking into account the user's health condition and nutritional balance. This allows the suggestion unit to propose the optimal menu based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0080] The refrigerator information acquisition unit can estimate the user's emotions and adjust the timing of retrieving ingredients from the refrigerator based on the estimated emotions. For example, if the user is stressed, the refrigerator information acquisition unit can quickly retrieve ingredients from the refrigerator and expedite menu suggestions. For example, if the user is relaxed, the refrigerator information acquisition unit can slowly retrieve ingredients from the refrigerator and collect detailed information. For example, if the user is in a hurry, the refrigerator information acquisition unit can quickly retrieve only the main ingredients from the refrigerator and quickly provide menu suggestions. By adjusting the timing of retrieving ingredients from the refrigerator according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 refrigerator information acquisition unit may be performed using AI, for example, or without AI.

[0081] The refrigerator information acquisition unit can select ingredients to acquire preferentially, taking into account the expiration dates of the ingredients in the refrigerator. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients that are nearing their expiration date to reduce waste. For example, the refrigerator information acquisition unit can automatically exclude ingredients that have passed their expiration date and remove them from acquisition. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients with shorter expiration dates, while delaying the acquisition of ingredients with longer expiration dates. By selecting ingredients while considering expiration dates, waste can be reduced and efficient menu suggestions can be made. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0082] The refrigerator information acquisition unit can analyze the nutritional value of the food in the refrigerator and acquire information to suggest healthy menus. For example, the refrigerator information acquisition unit can analyze the nutritional value of the food in the refrigerator and suggest a balanced menu. For example, if a particular nutrient is deficient, the refrigerator information acquisition unit can also prioritize acquiring food that supplements that nutrient. For example, the refrigerator information acquisition unit can acquire food containing necessary nutrients according to the user's health condition. This makes it possible to suggest healthy menus by analyzing nutritional value. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0083] The refrigerator information acquisition unit can estimate the user's emotions and determine the priority of ingredients to acquire based on the estimated user emotions. For example, if the user is tired, the refrigerator information acquisition unit will prioritize acquiring ingredients that are easy to cook. For example, if the user is energetic, the refrigerator information acquisition unit may also prioritize acquiring ingredients needed for elaborate dishes. For example, if the user is health-conscious, the refrigerator information acquisition unit will prioritize acquiring highly nutritious ingredients. By determining the priority of ingredients according to the user's emotions, it becomes possible to suggest more appropriate menus. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0084] The refrigerator information acquisition unit can select ingredients to acquire when acquiring ingredients from the refrigerator, taking into account the user's past food purchase history. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients that the user has frequently purchased in the past. The refrigerator information acquisition unit can also analyze the consumption patterns of ingredients that the user has purchased in the past and acquire the most suitable ingredients. For example, the refrigerator information acquisition unit can prioritize acquiring ingredients that the user has particularly liked to use among the ingredients that the user has purchased in the past. This makes it easier to acquire ingredients that match the user's preferences by taking into account past purchase history. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0085] The refrigerator information acquisition unit can adjust the ingredients it acquires based on the user's family composition and dietary preferences when acquiring ingredients from the refrigerator. For example, the refrigerator information acquisition unit can acquire the necessary amount of ingredients according to the user's family composition. For example, the refrigerator information acquisition unit can also prioritize the acquisition of specific ingredients based on the dietary preferences of the user's family. For example, the refrigerator information acquisition unit can adjust the ingredients it acquires by taking into account the allergy information of the user's family. By adjusting the ingredients based on family composition and dietary preferences, it becomes possible to propose menus that are more satisfying. Some or all of the above processing in the refrigerator information acquisition unit may be performed using AI, for example, or without using AI.

[0086] The Super Information Acquisition Unit can estimate the user's emotions and adjust the timing of Super Information Acquisition based on the estimated user emotions. For example, if the user is in a hurry, the Super Information Acquisition Unit will acquire Super Information quickly and expedite menu suggestions. For example, if the user is relaxed, the Super Information Acquisition Unit can acquire Super Information slowly and collect detailed information. For example, if the user is stressed, the Super Information Acquisition Unit will acquire Super Information quickly and expedite menu suggestions. By adjusting the timing of Super Information Acquisition according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 Super Information Acquisition Unit may be performed using AI, for example, or without AI.

[0087] The supermarket information acquisition unit can prioritize acquiring supermarket sale information and collect information to propose economical meal plans. For example, the supermarket information acquisition unit can prioritize acquiring supermarket sale information and propose economical meal plans. For example, the supermarket information acquisition unit can also propose meal plans using the most economical ingredients based on sale information. For example, the supermarket information acquisition unit can acquire sale information in real time and propose meal plans based on the latest information. This makes it possible to propose economical meal plans by prioritizing the acquisition of sale information. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0088] The supermarket information acquisition unit can acquire supermarket inventory status in real time and ensure that the ingredients necessary for the suggested menu are available. For example, the supermarket information acquisition unit can acquire supermarket inventory status in real time and check whether the necessary ingredients are available. For example, the supermarket information acquisition unit can also ensure that the ingredients necessary for the suggested menu are available based on the inventory status. For example, the supermarket information acquisition unit can update the inventory status in real time and suggest menus based on the latest information. In this way, by acquiring inventory status in real time, it is possible to ensure that the necessary ingredients are available. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0089] The Super Information Acquisition Unit can estimate the user's emotions and determine the priority of Super Information to acquire based on the estimated user emotions. For example, if the user is in a hurry, the Super Information Acquisition Unit will prioritize acquiring sale information. For example, if the user is relaxed, the Super Information Acquisition Unit can also acquire detailed Super Information. For example, if the user is stressed, the Super Information Acquisition Unit will prioritize acquiring sale information. This allows for more appropriate menu suggestions by prioritizing Super Information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 Super Information Acquisition Unit may be performed using AI, for example, or without using AI.

[0090] The supermarket information acquisition unit can select the information to acquire when acquiring supermarket information, taking into account the user's past supermarket usage history. For example, the supermarket information acquisition unit may prioritize acquiring information on supermarkets that the user has frequently used in the past. The supermarket information acquisition unit can also acquire optimal supermarket information based on the user's past supermarket usage history. For example, the supermarket information acquisition unit may prioritize acquiring special sale information from supermarkets that the user has used in the past. This makes it easier to acquire optimal supermarket information for the user by taking past usage history into consideration. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0091] The supermarket information acquisition unit can prioritize acquiring information about the nearest supermarket by considering the user's geographical location when acquiring supermarket information. For example, the supermarket information acquisition unit prioritizes acquiring information about the nearest supermarket based on the user's current location. The supermarket information acquisition unit can also acquire optimal supermarket information based on the user's geographical location. For example, the supermarket information acquisition unit prioritizes acquiring sale information from the supermarket closest to the user's current location. This makes it easier to prioritize acquiring information about the nearest supermarket by considering geographical location. Some or all of the above processing in the supermarket information acquisition unit may be performed using AI, for example, or without using AI.

[0092] The price information acquisition unit can estimate the user's emotions and adjust the timing of price information acquisition based on the estimated emotions. For example, if the user is in a hurry, the price information acquisition unit can acquire price information quickly and expedite menu suggestions. For example, if the user is relaxed, the price information acquisition unit can acquire price information slowly and collect detailed information. For example, if the user is stressed, the price information acquisition unit can acquire price information quickly and expedite menu suggestions. By adjusting the timing of price information acquisition according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 price information acquisition unit may be performed using AI, for example, or without AI.

[0093] The price information acquisition unit can predict price fluctuations and collect information to propose economical menus in the future. For example, the price information acquisition unit can predict price fluctuations and collect information to propose economical menus in the future. For example, the price information acquisition unit can predict price fluctuations and propose menus using the most economical ingredients. For example, the price information acquisition unit can predict price fluctuations and predict the prices of ingredients that will be needed in the future. This makes it possible to propose economical menus in the future by predicting price fluctuations. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0094] The price information acquisition unit can compare price information by region and acquire information to suggest the most economical ingredients. For example, the price information acquisition unit can compare price information by region and acquire information to suggest the most economical ingredients. For example, the price information acquisition unit can also suggest a menu using the most economical ingredients based on price information by region. For example, the price information acquisition unit can acquire price information by region in real time and suggest a menu based on the latest information. In this way, by comparing price information by region, the most economical ingredients can be suggested. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0095] The price information acquisition unit can estimate the user's emotions and determine the priority of price information to acquire based on the estimated user emotions. For example, if the user is in a hurry, the price information acquisition unit will prioritize acquiring the most important price information. For example, if the user is relaxed, the price information acquisition unit can also acquire detailed price information. For example, if the user is stressed, the price information acquisition unit will prioritize acquiring the most important price information. This allows for more appropriate menu suggestions by prioritizing price information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 price information acquisition unit may be performed using AI, for example, or without using AI.

[0096] The price information acquisition unit can select the information to acquire when acquiring price information, taking into account the user's income and expenditure situation. For example, the price information acquisition unit can acquire the optimal price information based on the user's income and expenditure situation. For example, the price information acquisition unit can also acquire the most economical price information by taking into account the user's income and expenditure situation. For example, the price information acquisition unit can prioritize acquiring the most important price information based on the user's income and expenditure situation. This makes it possible to suggest economical menus by taking into account income and expenditure situation. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0097] The price information acquisition unit can adjust the information it acquires by considering the user's purchase history when acquiring price information. For example, the price information acquisition unit can acquire optimal price information based on the user's purchase history. For example, the price information acquisition unit can also acquire the most economical price information by considering the user's purchase history. For example, the price information acquisition unit prioritizes acquiring the most important price information based on the user's purchase history. This makes it easier to acquire the optimal price information for the user by considering the purchase history. Some or all of the above processing in the price information acquisition unit may be performed using AI, for example, or without using AI.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis and expedite menu suggestions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest the optimal menu. For example, if the user is stressed, the analysis unit can perform a rapid analysis and expedite menu suggestions. By adjusting the analysis method according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 analysis unit may be performed using AI, for example, or without AI.

[0099] The analysis unit can perform analysis to propose the optimal menu by integrating refrigerator information, supermarket information, and price information during the analysis process. For example, the analysis unit can integrate refrigerator information, supermarket information, and price information to propose the optimal menu. The analysis unit can also, for example, integrate each piece of information to propose the most economical and healthy menu. For example, the analysis unit can integrate each piece of information in real time and propose a menu based on the latest information. By integrating and analyzing each piece of information, it becomes possible to propose a more accurate menu. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0100] The analysis unit can perform analysis to propose more personalized menus by taking into account the user's past eating history. For example, the analysis unit proposes the optimal menu based on the user's past eating history. The analysis unit can also propose personalized menus by taking into account the user's past eating history. For example, the analysis unit analyzes the user's past eating history and proposes the menu that will provide the highest satisfaction. This makes it possible to propose more personalized menus by taking into account past eating history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit will prioritize analyzing the most important information. For example, if the user is relaxed, the analysis unit may prioritize analyzing detailed information. For example, if the user is stressed, the analysis unit will prioritize analyzing the most important information. This allows for more appropriate menu suggestions by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 analysis unit may be performed using AI, for example, or without using AI.

[0102] The analysis unit can perform analysis while considering the user's health status and nutritional balance. For example, the analysis unit can perform analysis to suggest an optimal menu based on the user's health status. The analysis unit can also perform analysis to suggest a healthy menu by considering the user's nutritional balance. For example, the analysis unit can perform analysis to suggest an optimal menu by considering the user's health status and nutritional balance in real time. This makes it possible to suggest healthier menus by considering health status and nutritional balance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0103] The analysis unit can adjust its analysis based on the user's family structure and dietary preferences. For example, the analysis unit can perform analysis to suggest the optimal menu based on the user's family structure. The analysis unit can also perform analysis to suggest the optimal menu based on the dietary preferences of the user's family. For example, the analysis unit can perform analysis to suggest the optimal menu considering the allergy information of the user's family. By adjusting the analysis based on family structure and dietary preferences, it becomes possible to suggest menus that are more satisfying. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0104] 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 in a hurry, the suggestion unit will provide concise and quick suggestions. If the user is relaxed, the suggestion unit may also provide detailed suggestions. If the user is stressed, the suggestion unit will provide concise and quick suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate menu suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI.

[0105] The suggestion unit can propose more personalized menus by considering the user's past eating history when making suggestions. For example, the suggestion unit can propose the optimal menu based on the user's past eating history. The suggestion unit can also propose personalized menus by considering the user's past eating history. For example, the suggestion unit can analyze the user's past eating history and propose the menu that will provide the highest level of satisfaction. This makes it possible to propose more personalized menus by considering past eating history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0106] The suggestion unit can propose healthy menus by considering the user's health condition and nutritional balance when making suggestions. For example, the suggestion unit can propose an optimal menu based on the user's health condition. The suggestion unit can also propose a healthy menu by considering the user's nutritional balance. For example, the suggestion unit can propose an optimal menu by considering the user's health condition and nutritional balance in real time. This makes it possible to propose healthier menus by considering health condition and nutritional balance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0107] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will prioritize the most important suggestions. For example, if the user is relaxed, the suggestion unit may also prioritize detailed suggestions. For example, if the user is stressed, the suggestion unit will prioritize the most important suggestions. This allows for more appropriate menu suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI.

[0108] The suggestion unit can adjust its suggestions based on the user's family structure and dietary preferences. For example, the suggestion unit can suggest the optimal menu based on the user's family structure. The suggestion unit can also suggest the optimal menu based on the dietary preferences of the user's family. For example, the suggestion unit can suggest the optimal menu taking into account the allergy information of the user's family. By adjusting the suggestions based on family structure and dietary preferences, it becomes possible to provide more satisfying menu suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0109] The suggestion unit can propose economical menus by considering the user's income and expenditure situation when making suggestions. For example, the suggestion unit proposes the optimal menu based on the user's income and expenditure situation. The suggestion unit can also propose economical menus by considering the user's income and expenditure situation. For example, the suggestion unit proposes the most economical menu based on the user's income and expenditure situation. This makes it possible to propose economical menus by considering income and expenditure situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The refrigerator information acquisition unit monitors the freshness of food inside the refrigerator in real time and can prioritize the acquisition of food that has lost its freshness. For example, it uses cameras and sensors inside the refrigerator to measure the freshness of food and identify food that has lost its freshness. In addition, when the refrigerator information acquisition unit acquires food that has lost its freshness, it can notify the user and encourage them to consume it as soon as possible. This reduces food waste and enables efficient menu suggestions.

[0112] The refrigerator information acquisition unit can acquire allergen information of food items inside the refrigerator and suggest menus suitable for users with allergies. For example, it can use cameras and sensors inside the refrigerator to identify allergen information of food items and prioritize the acquisition of ingredients suitable for users with allergies. In addition, the refrigerator information acquisition unit can also provide allergy warnings to the user when acquiring allergen information. This makes it possible to suggest safe menus for users with allergies.

[0113] The refrigerator information acquisition unit can suggest methods for storing food inside the refrigerator and provide advice on maintaining food freshness. For example, it can use cameras and sensors inside the refrigerator to monitor the storage status of food and notify the user of appropriate storage methods. Furthermore, when providing advice on storage methods, the refrigerator information acquisition unit can also consider the user's storage history to suggest the optimal storage method. This helps maintain food freshness and reduce waste.

[0114] The refrigerator information acquisition unit can analyze the nutritional value of the food inside the refrigerator and suggest menus tailored to the user's health condition. For example, it can use cameras and sensors inside the refrigerator to identify the nutritional value of the food and suggest nutritionally balanced menus that suit the user's health. Furthermore, when analyzing nutritional value, the refrigerator information acquisition unit can also consider the user's health history to suggest the optimal nutritional balance. This makes it possible to suggest menus that support the user's health.

[0115] The refrigerator information acquisition unit can analyze the consumption history of ingredients in the refrigerator and suggest menus tailored to the user's preferences. For example, it can use cameras and sensors inside the refrigerator to identify the consumption history of ingredients and prioritize acquiring ingredients that match the user's preferences. Furthermore, when analyzing consumption history, the refrigerator information acquisition unit can also consider the user's past eating history to suggest the most suitable menu. This makes it possible to suggest menus that match the user's preferences.

[0116] The refrigerator information acquisition unit can estimate the user's emotions and adjust the timing of retrieving ingredients from the refrigerator based on those emotions. For example, if the user is stressed, it can quickly retrieve ingredients from the refrigerator and expedite menu suggestions. If the user is relaxed, it can retrieve ingredients slowly and collect more detailed information. By adjusting the timing of ingredient retrieval according to the user's emotions, more appropriate menu suggestions become possible.

[0117] The refrigerator information acquisition unit can estimate the user's emotions and determine the priority of ingredients to acquire based on those emotions. For example, if the user is tired, it will prioritize ingredients that are easy to cook. If the user is energetic, it can prioritize ingredients needed for more elaborate dishes. By prioritizing ingredients according to the user's emotions, it becomes possible to suggest more appropriate menus.

[0118] The Super Information Acquisition Unit can estimate the user's emotions and adjust the timing of Super Information acquisition based on those emotions. For example, if the user is in a hurry, it can quickly acquire Super Information to expedite menu suggestions. If the user is relaxed, it can acquire Super Information slowly to gather more detailed information. By adjusting the timing of Super Information acquisition according to the user's emotions, it becomes possible to provide more appropriate menu suggestions.

[0119] The price information acquisition unit can estimate the user's emotions and adjust the timing of price information acquisition based on those emotions. For example, if the user is in a hurry, price information can be acquired quickly to expedite menu suggestions. If the user is relaxed, price information can be acquired slowly to gather more detailed information. By adjusting the timing of price information acquisition according to the user's emotions, more appropriate menu suggestions become possible.

[0120] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is in a hurry, it can provide concise and quick suggestions. If the user is relaxed, it can provide more detailed suggestions. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide more appropriate menu suggestions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The refrigerator information acquisition unit acquires the contents of the refrigerator. For example, it uses cameras and sensors inside the refrigerator to recognize food items and acquires the type, quantity, and storage condition of the food items. It takes images of food items with the camera inside the refrigerator and identifies the food items using image recognition technology. It can also measure the weight and temperature of food items using sensors inside the refrigerator. Step 2: The supermarket information acquisition unit acquires information about nearby supermarkets and products. For example, it acquires information from electronic flyers and online databases to obtain information such as the location of supermarkets, types of products, and prices. It analyzes data from electronic flyers to obtain special sale information. It can also acquire product inventory information from online databases. Step 3: The price information acquisition unit acquires price information. For example, it uses online databases or APIs to acquire the latest price information and obtain information such as price fluctuations of goods and price differences by region. It acquires product price information from online databases and analyzes price fluctuations. It can also acquire real-time price information using APIs. Step 4: The analysis unit analyzes the information acquired by the refrigerator information acquisition unit, supermarket information acquisition unit, and price information acquisition unit. For example, it integrates and analyzes the ingredients in the refrigerator, supermarket sale information, and price information, performs data preprocessing, and applies an analysis algorithm to generate information for suggesting the optimal menu. Step 5: The suggestion unit proposes the optimal menu based on the results analyzed by the analysis unit. For example, it proposes menus based on criteria such as nutritional balance, cost performance, and cooking time, and proposes highly satisfying menus that take into account the user's preferences and disliked ingredients. It also refers to the user's past eating history and proposes menus that match their preferences. Furthermore, it can propose healthy menus that take into account the user's health condition and nutritional balance.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the refrigerator information acquisition unit, supermarket information acquisition unit, price information acquisition unit, analysis unit, and suggestion unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the refrigerator information acquisition unit uses the camera 42 and sensors of the smart device 14 to recognize food items inside the refrigerator, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The supermarket information acquisition unit acquires information from electronic flyers and online databases via the communication I / F 44 of the smart device 14, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The price information acquisition unit acquires the latest price information from online databases and APIs via the communication I / F 44 of the smart device 14, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the refrigerator information, supermarket information, and price information. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal menu based on the analysis results. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0142] Each of the multiple elements described above, including the refrigerator information acquisition unit, supermarket information acquisition unit, price information acquisition unit, analysis unit, and suggestion unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the refrigerator information acquisition unit uses the camera 42 and sensors of the smart glasses 214 to recognize food items in the refrigerator, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The supermarket information acquisition unit acquires information from electronic flyers and online databases via the communication I / F 44 of the smart glasses 214, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The price information acquisition unit acquires the latest price information from online databases and APIs via the communication I / F 44 of the smart glasses 214, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the refrigerator information, supermarket information, and price information. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal menu based on the analysis results. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[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 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.

[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 (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).

[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] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the refrigerator information acquisition unit, supermarket information acquisition unit, price information acquisition unit, analysis unit, and suggestion unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the refrigerator information acquisition unit uses the camera 42 and sensors of the headset terminal 314 to recognize food items inside the refrigerator, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The supermarket information acquisition unit acquires information from electronic flyers and online databases via the communication I / F 44 of the headset terminal 314, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The price information acquisition unit acquires the latest price information from online databases and APIs via the communication I / F 44 of the headset terminal 314, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the refrigerator information, supermarket information, and price information. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal menu based on the analysis results. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the refrigerator information acquisition unit, supermarket information acquisition unit, price information acquisition unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the refrigerator information acquisition unit uses the camera 42 and sensors of the robot 414 to recognize food items inside the refrigerator, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The supermarket information acquisition unit acquires information from electronic flyers and online databases via the communication I / F 44 of the robot 414, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The price information acquisition unit acquires the latest price information from online databases and APIs via the communication I / F 44 of the robot 414, and this information is analyzed by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes the refrigerator information, supermarket information, and price information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal menu based on the analysis results. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A refrigerator information acquisition unit that acquires the contents of the refrigerator, The supermarket information acquisition unit acquires information on nearby supermarkets and products, Price information acquisition unit that acquires price information, An analysis unit that analyzes the information acquired by the refrigerator information acquisition unit, the supermarket information acquisition unit, and the price information acquisition unit, The system includes a proposal unit that suggests an optimal menu based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The refrigerator is equipped with a refrigerator information acquisition unit that uses cameras and sensors inside the refrigerator to recognize food items. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a super information acquisition unit that retrieves information from electronic flyers and online databases. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a price information acquisition unit that obtains the latest price information using online databases and APIs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The system integrates and analyzes information from the refrigerator information acquisition unit, the supermarket information acquisition unit, and the price information acquisition unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the results from the analysis unit, we propose the optimal menu. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned refrigerator information acquisition unit, The system estimates the user's emotions and adjusts the timing of retrieving food items from the refrigerator based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned refrigerator information acquisition unit, Considering the expiration dates of the ingredients in the refrigerator, select the ingredients to retrieve first. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned refrigerator information acquisition unit, We analyze the nutritional value of the ingredients in the refrigerator and obtain information to suggest healthy meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned refrigerator information acquisition unit, The system estimates the user's emotions and determines the priority of ingredients to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned refrigerator information acquisition unit, When retrieving food items from the refrigerator, the system selects the items to retrieve by considering the user's past food purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned refrigerator information acquisition unit, When retrieving food items from the refrigerator, the system adjusts the items retrieved based on the user's family structure and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned super information acquisition unit is It estimates the user's emotions and adjusts the timing of acquiring super data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned super information acquisition unit is Prioritize obtaining information on supermarket sales and gather data to propose economical meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned super information acquisition unit is We obtain real-time inventory information from supermarkets to ensure that the ingredients needed for the suggested menus are readily available. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned super information acquisition unit is It estimates the user's emotions and determines the priority of super-information to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned super information acquisition unit is When acquiring supermarket information, the system selects the information to acquire by considering the user's past supermarket usage history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned super information acquisition unit is When retrieving supermarket information, the system prioritizes retrieving information about the nearest supermarket, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The price information acquisition unit is, The system estimates the user's sentiment and adjusts the timing of price information acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The price information acquisition unit is, To predict price fluctuations and gather information to propose economical meal plans for the future. The system described in Appendix 1, characterized by the features described herein. (Note 21) The price information acquisition unit is, We collect information to compare price data by region and suggest the most economical food items. The system described in Appendix 1, characterized by the features described herein. (Note 22) The price information acquisition unit is, The system estimates the user's sentiment and determines the priority of price information to retrieve based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The price information acquisition unit is, When acquiring price information, the information to be acquired is selected considering the user's income and expenditure situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The price information acquisition unit is, When acquiring price information, the information acquired is adjusted considering the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During the analysis, we will integrate refrigerator information, supermarket information, and price information to propose the optimal menu. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During the analysis, we take into account the user's past eating history to suggest more personalized menus. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, During the analysis, the user's health status and nutritional balance are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During analysis, the analysis is adjusted based on the user's family structure and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 31) 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 32) The aforementioned proposal section is, When making suggestions, we take into account the user's past eating history to propose more personalized menus. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making suggestions, we propose healthy meal plans that take into account the user's health condition and nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making suggestions, we adjust them based on the user's family structure and dietary preferences. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making suggestions, we take into account the user's income and spending habits to propose economical meal plans. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 refrigerator information acquisition unit that acquires the contents of the refrigerator, The supermarket information acquisition unit acquires information on nearby supermarkets and products, Price information acquisition unit that acquires price information, An analysis unit that analyzes the information acquired by the refrigerator information acquisition unit, the supermarket information acquisition unit, and the price information acquisition unit, The system includes a proposal unit that suggests an optimal menu based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The refrigerator is equipped with a refrigerator information acquisition unit that uses cameras and sensors inside the refrigerator to recognize food items. The system according to feature 1.

3. It is equipped with a super information acquisition unit that retrieves information from electronic flyers and online databases. The system according to feature 1.

4. It includes a price information acquisition unit that obtains the latest price information using online databases and APIs. The system according to feature 1.

5. The aforementioned proposal section is, Based on the results from the aforementioned analysis unit, the optimal menu is proposed. The system according to feature 1.

6. The aforementioned refrigerator information acquisition unit, The system estimates the user's emotions and adjusts the timing of retrieving food items from the refrigerator based on those emotions. The system according to feature 1.

7. The aforementioned refrigerator information acquisition unit, Considering the expiration dates of the food items in the refrigerator, select the items to retrieve first. The system according to feature 1.

8. The aforementioned refrigerator information acquisition unit, We analyze the nutritional value of the ingredients in the refrigerator and obtain information to suggest healthy meal plans. The system according to feature 1.

9. The aforementioned refrigerator information acquisition unit, The system estimates the user's emotions and determines the priority of ingredients to acquire based on those estimated emotions. The system according to feature 1.

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