System

Abstract

An object of a system according to an embodiment is to propose an optimal menu on the basis of information of a user and efficiently utilize food ingredients.SOLUTION: A system according to an embodiment includes a customer information collection unit, an analysis unit, a proposal unit, a purchase-on-behalf unit, and a linking unit. The customer information collection unit collects information on time, budget, and preference that can be spent on cooking from the user. The analysis unit analyzes the information collected by the customer information collection unit. The proposal part proposes an optimum menu on the basis of the information analyzed by the analysis part. The purchase-on-behalf unit purchases the foodstuff based on the menu proposed by the proposal unit. The interworking unit interworks with the IoT refrigerator to utilize the remaining ingredients.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, planning dinner menus every day can be stressful, making it difficult to use ingredients efficiently.

[0005] The system according to the embodiment aims to propose optimal menus based on user information and to utilize ingredients efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a customer information collection unit, an analysis unit, a proposal unit, a purchasing agent unit, and an interlocking unit. The customer information collection unit collects information from the user, such as the time available for cooking, budget, and preferences. The analysis unit analyzes the information collected by the customer information collection unit. The proposal unit proposes an optimal menu based on the information analyzed by the analysis unit. The purchasing agent unit purchases ingredients based on the menu proposed by the proposal unit. The interlocking unit utilizes leftover ingredients in conjunction with an IoT refrigerator. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal menus based on user information and utilize ingredients efficiently. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The menu suggestion system according to an embodiment of the present invention is a system that suggests optimal menus based on user information, purchases ingredients on behalf of the user, and utilizes leftover ingredients. This reduces the burden on the user and enables efficient and economical meal preparation.

[0029] A menu suggestion system according to an embodiment includes a customer information collection unit, an analysis unit, a suggestion unit, a purchasing agent unit, and an interlocking unit. The customer information collection unit collects information from users, such as cooking time, budget, and preferences. For example, a user may input information such as, "I'd like a dinner that can be made in under 30 minutes today," "My budget is under 1,000 yen," and "I'd like a menu that my children will like." The analysis unit analyzes the information collected by the customer information collection unit. For example, a generation AI analyzes the user's input information and selects the optimal recipe. The suggestion unit suggests an optimal menu based on the information analyzed by the analysis unit. For example, the generation AI suggests specific menus such as "Teriyaki chicken and stir-fried vegetables that can be made in under 30 minutes" and "Pasta and salad that can be made for under 1,000 yen." The interlocking unit purchases ingredients based on the menu suggested by the suggestion unit. For example, the generation AI automatically generates a list of ingredients needed for the suggested menu and handles the purchasing process at an online supermarket. The interlocking unit interfaces with an IoT refrigerator to utilize leftover ingredients. For example, the menu suggestion system may suggest dishes such as "stir-fried vegetables using leftover ingredients" or "soup using leftover ingredients" based on the vegetables and meat remaining in the refrigerator. This reduces the burden on the user and allows for efficient and economical meal preparation. For example, a delicious dinner can be made in a short amount of time even on busy days, and food waste can be reduced by making effective use of leftover ingredients. Furthermore, the system can reduce the hassle of shopping by purchasing ingredients on behalf of the user through an online supermarket.

[0030] The customer information collection unit can analyze the user's past meal history and automatically update preference and allergy information. For example, the customer information collection unit analyzes the meal history previously entered by the user to identify frequently selected ingredients and dishes. This automatically updates the user's preferences and reflects them in the next menu proposal. The customer information collection unit also automatically excludes ingredients that may cause allergies based on the allergy information previously entered by the user. For example, it proposes recipes that do not contain nuts to a user with a nut allergy. The customer information collection unit also analyzes the user's past meal history and automatically updates preferences for specific ingredients and dishes. For example, it prioritizes the proposal of dishes that the user frequently chooses. This allows the system to automatically update preference and allergy information based on the user's past meal history, thereby proposing more appropriate menus.

[0031] The customer information collection unit can suggest nutritionally balanced menus based on the user's family composition and health condition. The customer information collection unit, for example, collects information about the user's family composition and suggests menus that take into account the nutritional balance of the entire family. For example, it suggests nutritious menus for children and low-salt menus for the elderly. The customer information collection unit also suggests menus that are fortified with specific nutrients based on the user's health condition information. For example, it suggests recipes using ingredients that are high in iron to a user who is iron deficient. The customer information collection unit also suggests balanced meals taking into account the user's family composition and health condition. For example, it suggests a menu that combines a main dish, side dish, and dessert to satisfy the entire family. In this way, it is possible to suggest nutritionally balanced menus by taking into account the user's family composition and health condition.

[0032] The customer information collection unit can analyze a user's SNS posts and suggest menus that reflect preferences and trends. For example, the customer information collection unit analyzes a user's SNS posts to understand recent food trends and preferences. For example, it suggests menus based on dishes and ingredients that the user frequently posts. The customer information collection unit also extracts the user's food preferences from the SNS posts and suggests menus that reflect them. For example, it analyzes food photos posted by the user and suggests similar recipes. The customer information collection unit also analyzes a user's SNS posts and suggests menus that match trends. For example, it suggests recipes that incorporate popular ingredients and dishes. In this way, by analyzing a user's SNS posts, it is possible to suggest menus that reflect preferences and trends.

[0033] The customer information collection unit can use the user's location information to suggest menus based on local specialties. The customer information collection unit, for example, uses the user's location information to suggest menus that utilize local specialties. For example, it suggests recipes that use fresh local vegetables or fish. The customer information collection unit also uses the location information to suggest seasonal menus that incorporate local specialties. For example, it suggests dishes that use seasonal ingredients. The customer information collection unit also uses the user's location information to suggest menus that utilize local specialties. For example, it suggests local traditional dishes or regional dishes. In this way, by using the user's location information, it is possible to suggest menus that utilize local specialties.

[0034] The suggestion unit can automatically suggest menus based on the season and weather. For example, the suggestion unit takes into account seasonal ingredients and suggests menus using seasonal ingredients. For example, it suggests cold noodles and salads in the summer and hot hotpot dishes in the winter. The suggestion unit also suggests menus based on temperature and humidity based on weather data. For example, it suggests refreshing dishes on hot days and hot soups on cold days. The suggestion unit also builds a system that automatically suggests menus based on the season and weather. For example, it updates menus as the seasons change. In this way, automatically suggesting menus based on the season and weather improves the user's satisfaction with their meal.

[0035] The suggestion unit can re-suggest popular menus based on the user's past ratings. For example, the suggestion unit re-suggests menus that the user has given high ratings to in the past. For example, it re-suggests dishes that the user has rated as "delicious." The suggestion unit also analyzes the user's past rating data and prioritizes suggesting popular menus. For example, it suggests dishes that the user has rated as "I would like to make again." The suggestion unit also builds a system that re-suggests popular menus based on the user's past ratings. For example, it automatically selects menus with high rating scores. In this way, by re-suggesting popular menus based on the user's past ratings, user satisfaction is improved.

[0036] The suggestion unit can suggest traditional dishes based on the user's cultural background. For example, the suggestion unit collects cultural background information about the user and suggests traditional dishes based on that culture. For example, it suggests Japanese food to a Japanese user and Italian food to an Italian user. The suggestion unit also takes cultural background into consideration and suggests traditional dishes suited to specific holidays. For example, it suggests roast chicken or turkey dishes for Christmas. The suggestion unit also builds a system that suggests traditional dishes based on the user's cultural background. For example, it registers traditional dishes for each region in a database and reflects them in the suggestions. In this way, it is possible to suggest traditional dishes by taking the user's cultural background into consideration.

[0037] The suggestion unit can also suggest breakfast and lunch menus taking into account the user's mealtimes. The suggestion unit, for example, collects the user's mealtime information and suggests breakfast and lunch menus. For example, it suggests breakfast menus that are easy to make for busy mornings. The suggestion unit also builds a system that automatically suggests breakfast and lunch menus taking into account mealtimes. For example, it suggests menus that match the time period specified by the user. The suggestion unit also suggests breakfast and lunch menus based on the user's mealtime information. For example, it suggests a nutritionally balanced menu for lunch. In this way, it is possible to suggest breakfast and lunch menus by taking into account the user's mealtimes.

[0038] The purchasing agent unit can analyze the user's purchasing history and automatically add ingredients that are purchased regularly to the list. The purchasing agent unit, for example, analyzes the user's past purchasing history and automatically adds ingredients that are purchased regularly to the list. For example, milk and bread that are purchased weekly are automatically added to the list. The purchasing agent unit also builds a system that automatically adds ingredients that the user frequently purchases to the list based on the purchasing history. For example, it adds vegetables and fruits that the user often purchases to the list. The purchasing agent unit also analyzes the user's purchasing history and automatically adds ingredients that are purchased regularly to the list. For example, it adds seasonings and preserved foods that the user purchases monthly to the list. In this way, by analyzing the user's purchasing history, ingredients that are purchased regularly can be automatically added to the list.

[0039] The purchasing agent unit monitors food price fluctuations in real time, allowing purchases to be made at the most cost-effective time. The purchasing agent unit, for example, builds a system that monitors food price fluctuations in real time and purchases at the most cost-effective time. For example, it automatically purchases when prices drop. The purchasing agent unit also suggests the optimal time to purchase to the user based on price fluctuation data. For example, it notifies the user when specific food ingredients will go on sale. The purchasing agent unit also monitors food price fluctuations in real time, allowing purchases to be made at the most cost-effective time. For example, it suggests buying in bulk when prices are stable. In this way, by monitoring food price fluctuations in real time, purchases can be made at the most cost-effective time.

[0040] The purchasing agent unit can provide an option to purchase ingredients directly from local farmers and markets. The purchasing agent unit, for example, builds a system that provides an option to purchase ingredients directly from local farmers and markets. For example, it allows users to purchase fresh local vegetables and fruits. The purchasing agent unit also works with local farmers and markets to provide an option that allows users to purchase directly. For example, it handles local specialties. The purchasing agent unit also provides an option to purchase ingredients directly from local farms and markets. For example, it provides a service that delivers fresh local ingredients on a regular basis. This allows users to obtain fresh ingredients by providing an option to purchase ingredients directly from local farms and markets.

[0041] The purchasing agent unit can suggest recipe variations based on the ingredients selected by the user. The purchasing agent unit, for example, builds a system that suggests multiple recipe variations based on the ingredients selected by the user. For example, different dishes using the same ingredients are suggested. The purchasing agent unit also suggests recipe variations based on the ingredients selected by the user. For example, multiple dishes using chicken are suggested. The purchasing agent unit also suggests recipe variations based on the ingredients selected by the user. For example, combinations of appetizers, main dishes, and desserts using the selected ingredients are suggested. This makes it possible to increase the enjoyment of meals by suggesting recipe variations based on the ingredients selected by the user.

[0042] The interlocking unit automatically manages the expiration dates of ingredients in the refrigerator and can suggest menus that prioritize using ingredients that are close to their expiration dates. The interlocking unit, for example, builds a system that automatically manages the expiration dates of ingredients in the refrigerator and suggests menus that prioritize using ingredients that are close to their expiration dates. For example, it suggests dishes that use ingredients that are close to their expiration dates. The interlocking unit also suggests menus that prioritize using ingredients that are close to their expiration dates based on the expiration date data of the ingredients. For example, it suggests soups that use vegetables that are close to their expiration dates. The interlocking unit also automatically manages the expiration dates of ingredients in the refrigerator and suggests menus that prioritize using ingredients that are close to their expiration dates. For example, it suggests stir-fries that use meat that is close to its expiration date. In this way, by automatically managing the expiration dates of ingredients in the refrigerator, it is possible to suggest menus that prioritize using ingredients that are close to their expiration dates.

[0043] The interlocking unit can analyze the nutritional value of ingredients in the refrigerator and suggest a balanced menu. The interlocking unit, for example, analyzes the nutritional value of ingredients in the refrigerator and builds a system that suggests a balanced menu. For example, it suggests dishes using ingredients rich in vitamins and minerals. The interlocking unit also suggests a balanced menu based on the nutritional value data of ingredients. For example, it suggests dishes with a good balance of protein and carbohydrates. The interlocking unit also analyzes the nutritional value of ingredients in the refrigerator and suggests a balanced menu. For example, it suggests salads and soups using nutritious ingredients. In this way, it is possible to suggest a balanced menu by analyzing the nutritional value of ingredients in the refrigerator.

[0044] The interlocking unit can provide advice on storage and cooking methods based on the ingredients in the refrigerator. The interlocking unit, for example, builds a system that advises on optimal storage methods based on the ingredients in the refrigerator. For example, it suggests storage methods for vegetables and fruits. The interlocking unit also advises on cooking methods according to the type of ingredient. For example, it suggests optimal cooking methods for meat and fish. The interlocking unit also provides advice on storage and cooking methods based on the ingredients in the refrigerator. For example, it suggests storage methods to keep ingredients fresh. In this way, by providing advice on storage and cooking methods based on the ingredients in the refrigerator, it is possible to reduce food waste.

[0045] The interlocking unit can suggest recipes for smoothies and juice drinks based on the ingredients in the refrigerator. The interlocking unit, for example, builds a system that suggests recipes for smoothies and juices based on the ingredients in the refrigerator. For example, it suggests smoothies using leftover fruits and vegetables. The interlocking unit also suggests drink recipes according to the type of ingredients. For example, it suggests juices rich in vitamins or smoothies with detoxifying effects. The interlocking unit also suggests recipes for smoothies and juices based on the ingredients in the refrigerator. For example, it suggests fresh juices using leftover fruits in the refrigerator. In this way, by suggesting recipes for drinks such as smoothies and juices based on the ingredients in the refrigerator, it is possible to reduce food waste.

[0046] The interlocking unit can analyze a user's ingredient consumption pattern and suggest a shopping list to minimize waste. The interlocking unit, for example, builds a system that analyzes a user's ingredient consumption pattern and suggests a shopping list to minimize waste. For example, frequently used ingredients are added to the list preferentially. The interlocking unit also suggests a shopping list to minimize waste based on the ingredient consumption pattern. For example, it suggests appropriate amounts based on past consumption data. The interlocking unit also analyzes a user's ingredient consumption pattern and suggests a shopping list to minimize waste. For example, it adds ingredients with an approaching expiration date to the list preferentially. In this way, a shopping list to minimize waste can be suggested by analyzing a user's ingredient consumption pattern.

[0047] The interlocking unit can suggest methods for storing and cooking ingredients, thereby extending the lifespan of ingredients. The interlocking unit, for example, suggests methods for storing ingredients and builds a system for extending the lifespan of ingredients. For example, it suggests the optimal method for storing vegetables and fruits. The interlocking unit also suggests cooking methods according to the type of ingredient, thereby extending the lifespan of ingredients. For example, it suggests the optimal cooking method for meat and fish. The interlocking unit also suggests methods for storing and cooking ingredients, thereby extending the lifespan of ingredients. For example, it suggests storage methods to keep ingredients fresh. In this way, by suggesting methods for storing and cooking ingredients, the lifespan of ingredients can be extended.

[0048] The interlocking unit can work with a local food bank to provide an option to donate surplus food ingredients. The interlocking unit, for example, builds a system that works with a local food bank to provide an option to donate surplus food ingredients. For example, it enables users to easily donate surplus food ingredients. The interlocking unit also works with a food bank to provide an option to donate surplus food ingredients. For example, it displays a list of food ingredients that can be donated and allows users to select. The interlocking unit also works with a local food bank to provide an option to donate surplus food ingredients. For example, it simplifies the donation procedure so that users can easily donate. In this way, by working with a local food bank, it is possible to provide an option to donate surplus food ingredients.

[0049] The interlocking unit can periodically suggest new recipes using leftover ingredients. The interlocking unit, for example, builds a system that periodically suggests new recipes using leftover ingredients. For example, it suggests new dishes based on leftover ingredients in the refrigerator. The interlocking unit also periodically suggests new recipes using leftover ingredients. For example, it suggests soups and stir-fries using leftover vegetables. The interlocking unit also periodically suggests new recipes using leftover ingredients. For example, it suggests desserts and snacks using leftover ingredients. In this way, by periodically suggesting new recipes using leftover ingredients, it is possible to reduce food waste.

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

[0051] The suggestion unit can suggest menus that do not use specific ingredients based on the user's dietary preferences and allergy information. For example, if the user has a nut allergy, it will suggest recipes that do not include nuts. The suggestion unit can also analyze the user's past meal history to identify frequently chosen ingredients and dishes and suggest menus based on them. For example, it can prioritize suggestions of dishes that the user frequently chooses. The suggestion unit also takes into account the user's family structure and health condition to suggest balanced meals. For example, it can suggest nutritious menus for children and low-salt menus for the elderly. This makes it possible to suggest more appropriate menus by taking into account the user's preferences, allergy information, family structure, and health condition.

[0052] The customer information collection unit can analyze a user's SNS posts and suggest menus that reflect preferences and trends. For example, it can analyze a user's SNS posts to understand recent food trends and preferences. For example, it can suggest menus based on the dishes and ingredients that the user frequently posts. The customer information collection unit can also extract the user's food preferences from the SNS posts and suggest menus that reflect them. For example, it can analyze food photos posted by the user and suggest similar recipes. The customer information collection unit can also analyze a user's SNS posts and suggest menus that match trends. For example, it can suggest recipes that incorporate popular ingredients and dishes. In this way, by analyzing a user's SNS posts, it can suggest menus that reflect preferences and trends.

[0053] The customer information collection unit can use the user's location information to suggest menus based on local specialties. For example, a menu that uses local specialties is suggested based on the user's location information. For example, recipes using fresh local vegetables or fish are suggested. The customer information collection unit also uses the location information to suggest seasonal menus that incorporate local specialties. For example, dishes using seasonal ingredients are suggested. The customer information collection unit also uses the user's location information to suggest menus that use local specialties. For example, local traditional dishes or regional dishes are suggested. In this way, by using the user's location information, menus that use local specialties can be suggested.

[0054] The customer information collection unit can suggest nutritionally balanced menus based on the user's family composition and health condition. For example, it collects information about the user's family composition and suggests menus that take into account the nutritional balance of the entire family. For example, it suggests nutritious menus for children and low-salt menus for the elderly. The customer information collection unit also suggests menus that are fortified with specific nutrients based on the user's health condition information. For example, it suggests recipes using ingredients that are high in iron to a user who is iron deficient. The customer information collection unit also suggests balanced meals taking into account the user's family composition and health condition. For example, it suggests a menu that combines a main dish, side dish, and dessert to satisfy the whole family. In this way, it is possible to suggest nutritionally balanced menus by taking into account the user's family composition and health condition.

[0055] The suggestion unit can automatically suggest menus based on the season and weather. For example, it takes into consideration seasonal ingredients and suggests menus using seasonal ingredients. For example, it suggests cold noodles and salads in the summer and hot hotpot dishes in the winter. The suggestion unit also suggests menus according to temperature and humidity based on weather data. For example, it suggests refreshing dishes on hot days and hot soups on cold days. The suggestion unit also builds a system that automatically suggests menus according to the season and weather. For example, it updates menus according to the change of seasons. In this way, automatically suggesting menus according to the season and weather improves the user's satisfaction with their meal.

[0056] The suggestion unit can re-suggest popular menus based on the user's past ratings. For example, it re-suggests menus that the user has given high ratings to in the past. For example, it re-suggests dishes that the user has rated as "delicious." The suggestion unit also analyzes the user's past rating data and prioritizes suggesting popular menus. For example, it suggests dishes that the user has rated as "I would like to make again." The suggestion unit also builds a system that re-suggests popular menus based on the user's past ratings. For example, it automatically selects menus with high rating scores. In this way, by re-suggesting popular menus based on the user's past ratings, user satisfaction is improved.

[0057] The suggestion unit can suggest traditional dishes based on the user's cultural background. For example, it collects information about the user's cultural background and suggests traditional dishes based on that culture. For example, it suggests Japanese food to a Japanese user and Italian food to an Italian user. The suggestion unit also takes cultural background into consideration and suggests traditional dishes for specific holidays. For example, it suggests roast chicken or turkey dishes for Christmas. The suggestion unit also builds a system that suggests traditional dishes based on the user's cultural background. For example, it registers traditional dishes for each region in a database and reflects them in the suggestions. This makes it possible to suggest traditional dishes by taking the user's cultural background into consideration.

[0058] The suggestion unit can also suggest breakfast and lunch menus by taking into account the user's mealtimes. For example, it collects information about the user's mealtimes and suggests breakfast and lunch menus. For example, it suggests breakfast menus that are easy to make for busy mornings. The suggestion unit also builds a system that automatically suggests breakfast and lunch menus by taking into account mealtimes. For example, it suggests menus that match the time period specified by the user. The suggestion unit also suggests breakfast and lunch menus based on the user's mealtime information. For example, it suggests a nutritionally balanced menu for lunch. In this way, it is possible to suggest breakfast and lunch menus by taking into account the user's mealtimes.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The customer information collection unit collects information from the user, such as the amount of time available for cooking, their budget, and their preferences. For example, the user enters information such as "Today I want a dinner that can be made in under 30 minutes," "My budget is under 1,000 yen," and "I want a menu that my children will like." Step 2: The analysis unit analyzes the information collected by the customer information collection unit. For example, the generation AI analyzes the user's input information and selects the optimal recipe. Step 3: The proposal section proposes the optimal menu based on the information analyzed by the analysis section. For example, the generation AI might suggest specific menu items such as "Teriyaki chicken and stir-fried vegetables that can be made in under 30 minutes" or "Pasta and salad that can be made for under 1,000 yen." Step 4: The purchasing agent purchases ingredients based on the menu proposed by the proposal agent. For example, the generation AI automatically generates a list of ingredients needed for the menu proposed by the proposal agent and handles the purchasing process at the online supermarket. Step 5: The linking unit works with the IoT refrigerator to utilize leftover ingredients. For example, based on the vegetables and meat left in the refrigerator, it will suggest dishes such as "stir-fried vegetables using leftover ingredients" or "soup using leftover ingredients."

[0061] (Example 2) The menu suggestion system according to an embodiment of the present invention is a system that suggests optimal menus based on user information, purchases ingredients on behalf of the user, and utilizes leftover ingredients. This reduces the burden on the user and enables efficient and economical meal preparation.

[0062] A menu suggestion system according to an embodiment includes a customer information collection unit, an analysis unit, a suggestion unit, a purchasing agent unit, and an interlocking unit. The customer information collection unit collects information from users, such as cooking time, budget, and preferences. For example, a user may input information such as, "I'd like a dinner that can be made in under 30 minutes today," "My budget is under 1,000 yen," and "I'd like a menu that my children will like." The analysis unit analyzes the information collected by the customer information collection unit. For example, a generation AI analyzes the user's input information and selects the optimal recipe. The suggestion unit suggests an optimal menu based on the information analyzed by the analysis unit. For example, the generation AI suggests specific menus such as "Teriyaki chicken and stir-fried vegetables that can be made in under 30 minutes" and "Pasta and salad that can be made for under 1,000 yen." The interlocking unit purchases ingredients based on the menu suggested by the suggestion unit. For example, the generation AI automatically generates a list of ingredients needed for the suggested menu and handles the purchasing process at an online supermarket. The interlocking unit interfaces with an IoT refrigerator to utilize leftover ingredients. For example, the menu suggestion system may suggest dishes such as "stir-fried vegetables using leftover ingredients" or "soup using leftover ingredients" based on the vegetables and meat remaining in the refrigerator. This reduces the burden on the user and allows for efficient and economical meal preparation. For example, a delicious dinner can be made in a short amount of time even on busy days, and food waste can be reduced by making effective use of leftover ingredients. Furthermore, the system can reduce the hassle of shopping by purchasing ingredients on behalf of the user through an online supermarket.

[0063] The customer information collection unit can analyze the user's past meal history and automatically update preference and allergy information. For example, the customer information collection unit analyzes the meal history previously entered by the user to identify frequently selected ingredients and dishes. This automatically updates the user's preferences and reflects them in the next menu proposal. The customer information collection unit also automatically excludes ingredients that may cause allergies based on the allergy information previously entered by the user. For example, it proposes recipes that do not contain nuts to a user with a nut allergy. The customer information collection unit also analyzes the user's past meal history and automatically updates preferences for specific ingredients and dishes. For example, it prioritizes the proposal of dishes that the user frequently chooses. This allows the system to automatically update preference and allergy information based on the user's past meal history, thereby proposing more appropriate menus.

[0064] The customer information collection unit can suggest nutritionally balanced menus based on the user's family composition and health condition. The customer information collection unit, for example, collects information about the user's family composition and suggests menus that take into account the nutritional balance of the entire family. For example, it suggests nutritious menus for children and low-salt menus for the elderly. The customer information collection unit also suggests menus that are fortified with specific nutrients based on the user's health condition information. For example, it suggests recipes using ingredients that are high in iron to a user who is iron deficient. The customer information collection unit also suggests balanced meals taking into account the user's family composition and health condition. For example, it suggests a menu that combines a main dish, side dish, and dessert to satisfy the entire family. In this way, it is possible to suggest nutritionally balanced menus by taking into account the user's family composition and health condition.

[0065] The customer information collection unit can use the emotion estimation function to suggest a menu based on the user's mood of the day. The customer information collection unit, for example, analyzes the user's facial expressions and voice when inputting information to estimate the user's mood of the day. For example, if the user is feeling stressed, the customer information collection unit suggests dishes that will help them relax. The customer information collection unit also suggests a menu that matches the user's mood of the day based on the user's emotion data. For example, if the user is feeling low, the customer information collection unit suggests dishes that will replenish energy. The customer information collection unit also uses the emotion estimation function to suggest a menu that matches the user's mood in real time. For example, if the user is feeling happy, the customer information collection unit suggests celebratory dishes. In this way, suggesting a menu that matches the user's mood of the day improves user satisfaction.

[0066] The customer information collection unit can analyze a user's SNS posts and suggest menus that reflect preferences and trends. For example, the customer information collection unit analyzes a user's SNS posts to understand recent food trends and preferences. For example, it suggests menus based on dishes and ingredients that the user frequently posts. The customer information collection unit also extracts the user's food preferences from the SNS posts and suggests menus that reflect them. For example, it analyzes food photos posted by the user and suggests similar recipes. The customer information collection unit also analyzes a user's SNS posts and suggests menus that match trends. For example, it suggests recipes that incorporate popular ingredients and dishes. In this way, by analyzing a user's SNS posts, it is possible to suggest menus that reflect preferences and trends.

[0067] The customer information collection unit can use the user's location information to suggest menus based on local specialties. The customer information collection unit, for example, uses the user's location information to suggest menus that utilize local specialties. For example, it suggests recipes that use fresh local vegetables or fish. The customer information collection unit also uses the location information to suggest seasonal menus that incorporate local specialties. For example, it suggests dishes that use seasonal ingredients. The customer information collection unit also uses the user's location information to suggest menus that utilize local specialties. For example, it suggests local traditional dishes or regional dishes. In this way, by using the user's location information, it is possible to suggest menus that utilize local specialties.

[0068] The customer information collection unit can use the emotion estimation function to analyze the user's emotions when entering data in real time and provide an interface for reducing stress. For example, the customer information collection unit analyzes the user's facial expressions and voice when entering data in real time and provides an interface that helps the user relax if the user is feeling stressed. For example, it displays calming music or an encouraging message. The customer information collection unit also uses the emotion estimation function to analyze the user's emotions when entering data and provides an interface for reducing stress. For example, it displays a guide to ensure smooth entry. The customer information collection unit also provides an interface for reducing stress based on the user's emotion data. For example, it employs colors and designs that help the user relax. In this way, the user's emotions when entering data are analyzed in real time and an interface for reducing stress is provided, thereby reducing the burden on the user.

[0069] The suggestion unit can automatically suggest menus based on the season and weather. For example, the suggestion unit takes into account seasonal ingredients and suggests menus using seasonal ingredients. For example, it suggests cold noodles and salads in the summer and hot hotpot dishes in the winter. The suggestion unit also suggests menus based on temperature and humidity based on weather data. For example, it suggests refreshing dishes on hot days and hot soups on cold days. The suggestion unit also builds a system that automatically suggests menus based on the season and weather. For example, it updates menus as the seasons change. In this way, automatically suggesting menus based on the season and weather improves the user's satisfaction with their meal.

[0070] The suggestion unit can re-suggest popular menus based on the user's past ratings. For example, the suggestion unit re-suggests menus that the user has given high ratings to in the past. For example, it re-suggests dishes that the user has rated as "delicious." The suggestion unit also analyzes the user's past rating data and prioritizes suggesting popular menus. For example, it suggests dishes that the user has rated as "I would like to make again." The suggestion unit also builds a system that re-suggests popular menus based on the user's past ratings. For example, it automatically selects menus with high rating scores. In this way, by re-suggesting popular menus based on the user's past ratings, user satisfaction is improved.

[0071] The suggestion unit can use the emotion estimation function to suggest "soothing menus" and "encouraging menus" that match the user's mood. For example, the suggestion unit uses the emotion estimation function to suggest menus that match the user's mood. For example, if the user is feeling stressed, it suggests dishes that will help them relax. The suggestion unit also suggests "soothing menus" and "encouraging menus" that match the user's mood of the day based on the user's emotion data. For example, it suggests dishes that will replenish energy when the user is feeling down. The suggestion unit also uses the emotion estimation function to suggest menus that match the user's mood in real time. For example, it suggests celebratory dishes when the user is feeling happy. In this way, suggesting menus that match the user's mood improves user satisfaction.

[0072] The suggestion unit can suggest traditional dishes based on the user's cultural background. For example, the suggestion unit collects cultural background information about the user and suggests traditional dishes based on that culture. For example, it suggests Japanese food to a Japanese user and Italian food to an Italian user. The suggestion unit also takes cultural background into consideration and suggests traditional dishes suited to specific holidays. For example, it suggests roast chicken or turkey dishes for Christmas. The suggestion unit also builds a system that suggests traditional dishes based on the user's cultural background. For example, it registers traditional dishes for each region in a database and reflects them in the suggestions. In this way, it is possible to suggest traditional dishes by taking the user's cultural background into consideration.

[0073] The suggestion unit can also suggest breakfast and lunch menus taking into account the user's mealtimes. The suggestion unit, for example, collects the user's mealtime information and suggests breakfast and lunch menus. For example, it suggests breakfast menus that are easy to make for busy mornings. The suggestion unit also builds a system that automatically suggests breakfast and lunch menus taking into account mealtimes. For example, it suggests menus that match the time period specified by the user. The suggestion unit also suggests breakfast and lunch menus based on the user's mealtime information. For example, it suggests a nutritionally balanced menu for lunch. In this way, it is possible to suggest breakfast and lunch menus by taking into account the user's mealtimes.

[0074] The suggestion unit can use the emotion estimation function to suggest a "family gathering menu" based on the emotions of all family members. For example, the suggestion unit uses the emotion estimation function to analyze the emotions of all family members and suggest a "family gathering menu" that will satisfy everyone. For example, it suggests dishes that will please all family members. The suggestion unit also suggests a "family gathering menu" that everyone can enjoy based on the emotional data of all family members. For example, it suggests dishes that will relax the whole family. The suggestion unit also uses the emotion estimation function to suggest a menu in real time that takes into account the emotions of all family members. For example, it suggests dishes that will energize the whole family. In this way, by taking into account the emotions of all family members, it is possible to suggest a "family gathering menu" that will satisfy everyone.

[0075] The purchasing agent unit can analyze the user's purchasing history and automatically add ingredients that are purchased regularly to the list. The purchasing agent unit, for example, analyzes the user's past purchasing history and automatically adds ingredients that are purchased regularly to the list. For example, milk and bread that are purchased weekly are automatically added to the list. The purchasing agent unit also builds a system that automatically adds ingredients that the user frequently purchases to the list based on the purchasing history. For example, it adds vegetables and fruits that the user often purchases to the list. The purchasing agent unit also analyzes the user's purchasing history and automatically adds ingredients that are purchased regularly to the list. For example, it adds seasonings and preserved foods that the user purchases monthly to the list. In this way, by analyzing the user's purchasing history, ingredients that are purchased regularly can be automatically added to the list.

[0076] The purchasing agent unit monitors food price fluctuations in real time, allowing purchases to be made at the most cost-effective time. The purchasing agent unit, for example, builds a system that monitors food price fluctuations in real time and purchases at the most cost-effective time. For example, it automatically purchases when prices drop. The purchasing agent unit also suggests the optimal time to purchase to the user based on price fluctuation data. For example, it notifies the user when specific food ingredients will go on sale. The purchasing agent unit also monitors food price fluctuations in real time, allowing purchases to be made at the most cost-effective time. For example, it suggests buying in bulk when prices are stable. In this way, by monitoring food price fluctuations in real time, purchases can be made at the most cost-effective time.

[0077] The purchasing agent unit can use the emotion estimation function to simplify the purchasing procedure so that the user does not feel stressed. For example, the purchasing agent unit uses the emotion estimation function to simplify the purchasing procedure if the user is feeling stressed. For example, it allows the purchasing procedure to be completed with one click. The purchasing agent unit also suggests a purchasing procedure to reduce stress based on the user's emotion data. For example, it reduces the number of steps in the purchasing procedure. The purchasing agent unit also uses the emotion estimation function to build a system that simplifies the purchasing procedure so that the user does not feel stressed. For example, it provides an interface that allows the user to relax. In this way, the emotion estimation function can be used to simplify the purchasing procedure so that the user does not feel stressed.

[0078] The purchasing agent unit can provide an option to purchase ingredients directly from local farmers and markets. The purchasing agent unit, for example, builds a system that provides an option to purchase ingredients directly from local farmers and markets. For example, it allows users to purchase fresh local vegetables and fruits. The purchasing agent unit also works with local farmers and markets to provide an option that allows users to purchase directly. For example, it handles local specialties. The purchasing agent unit also provides an option to purchase ingredients directly from local farms and markets. For example, it provides a service that delivers fresh local ingredients on a regular basis. This allows users to obtain fresh ingredients by providing an option to purchase ingredients directly from local farms and markets.

[0079] The purchasing agent unit can suggest recipe variations based on the ingredients selected by the user. The purchasing agent unit, for example, builds a system that suggests multiple recipe variations based on the ingredients selected by the user. For example, different dishes using the same ingredients are suggested. The purchasing agent unit also suggests recipe variations based on the ingredients selected by the user. For example, multiple dishes using chicken are suggested. The purchasing agent unit also suggests recipe variations based on the ingredients selected by the user. For example, combinations of appetizers, main dishes, and desserts using the selected ingredients are suggested. This makes it possible to increase the enjoyment of meals by suggesting recipe variations based on the ingredients selected by the user.

[0080] The purchasing agent unit uses the emotion estimation function to analyze the emotions of the user when making a purchase and can provide a positive purchasing experience. The purchasing agent unit, for example, uses the emotion estimation function to analyze the emotions of the user when making a purchase and builds a system that provides a positive purchasing experience. For example, it displays an encouraging message during the purchasing process. The purchasing agent unit also provides an interface for providing a positive purchasing experience based on the user's emotion data. For example, it plays relaxing music during the purchasing process. The purchasing agent unit also uses the emotion estimation function to analyze the emotions of the user when making a purchase and provide a positive purchasing experience. For example, it suggests products that match the user's preferences during the purchasing process. In this way, by using the emotion estimation function, the user's emotions when making a purchase can be analyzed and a positive purchasing experience can be provided.

[0081] The interlocking unit automatically manages the expiration dates of ingredients in the refrigerator and can suggest menus that prioritize using ingredients that are close to their expiration dates. The interlocking unit, for example, builds a system that automatically manages the expiration dates of ingredients in the refrigerator and suggests menus that prioritize using ingredients that are close to their expiration dates. For example, it suggests dishes that use ingredients that are close to their expiration dates. The interlocking unit also suggests menus that prioritize using ingredients that are close to their expiration dates based on the expiration date data of the ingredients. For example, it suggests soups that use vegetables that are close to their expiration dates. The interlocking unit also automatically manages the expiration dates of ingredients in the refrigerator and suggests menus that prioritize using ingredients that are close to their expiration dates. For example, it suggests stir-fries that use meat that is close to its expiration date. In this way, by automatically managing the expiration dates of ingredients in the refrigerator, it is possible to suggest menus that prioritize using ingredients that are close to their expiration dates.

[0082] The interlocking unit can analyze the nutritional value of ingredients in the refrigerator and suggest a balanced menu. The interlocking unit, for example, analyzes the nutritional value of ingredients in the refrigerator and builds a system that suggests a balanced menu. For example, it suggests dishes using ingredients rich in vitamins and minerals. The interlocking unit also suggests a balanced menu based on the nutritional value data of ingredients. For example, it suggests dishes with a good balance of protein and carbohydrates. The interlocking unit also analyzes the nutritional value of ingredients in the refrigerator and suggests a balanced menu. For example, it suggests salads and soups using nutritious ingredients. In this way, it is possible to suggest a balanced menu by analyzing the nutritional value of ingredients in the refrigerator.

[0083] The interlocking unit can use the emotion estimation function to analyze the emotion a user has when opening the refrigerator and make suggestions to reduce stress. The interlocking unit, for example, uses the emotion estimation function to build a system that analyzes the emotion a user has when opening the refrigerator and makes suggestions to reduce stress. For example, it suggests simple recipes using ingredients in the refrigerator. The interlocking unit also makes suggestions to reduce stress when opening the refrigerator based on the user's emotion data. For example, it suggests relaxing dishes using ingredients in the refrigerator. The interlocking unit also uses the emotion estimation function to analyze the emotion a user has when opening the refrigerator and make suggestions to reduce stress. For example, it suggests uplifting dishes using ingredients in the refrigerator. In this way, by using the emotion estimation function, it is possible to analyze the emotion a user has when opening the refrigerator and make suggestions to reduce stress.

[0084] The interlocking unit can provide advice on storage and cooking methods based on the ingredients in the refrigerator. The interlocking unit, for example, builds a system that advises on optimal storage methods based on the ingredients in the refrigerator. For example, it suggests storage methods for vegetables and fruits. The interlocking unit also advises on cooking methods according to the type of ingredient. For example, it suggests optimal cooking methods for meat and fish. The interlocking unit also provides advice on storage and cooking methods based on the ingredients in the refrigerator. For example, it suggests storage methods to keep ingredients fresh. In this way, by providing advice on storage and cooking methods based on the ingredients in the refrigerator, it is possible to reduce food waste.

[0085] The interlocking unit can suggest recipes for smoothies and juice drinks based on the ingredients in the refrigerator. The interlocking unit, for example, builds a system that suggests recipes for smoothies and juices based on the ingredients in the refrigerator. For example, it suggests smoothies using leftover fruits and vegetables. The interlocking unit also suggests drink recipes according to the type of ingredients. For example, it suggests juices rich in vitamins or smoothies with detoxifying effects. The interlocking unit also suggests recipes for smoothies and juices based on the ingredients in the refrigerator. For example, it suggests fresh juices using leftover fruits in the refrigerator. In this way, by suggesting recipes for drinks such as smoothies and juices based on the ingredients in the refrigerator, it is possible to reduce food waste.

[0086] The interlocking unit can use the emotion estimation function to analyze the emotion a user has when opening the refrigerator and make suggestions to elicit positive emotions. The interlocking unit, for example, uses the emotion estimation function to build a system that analyzes the emotion a user has when opening the refrigerator and makes suggestions to elicit positive emotions. For example, it can suggest fun dishes using ingredients in the refrigerator. The interlocking unit also makes suggestions to elicit positive emotions when opening the refrigerator based on the user's emotion data. For example, it can suggest celebratory dishes using ingredients in the refrigerator. The interlocking unit also uses the emotion estimation function to analyze the emotion a user has when opening the refrigerator and make suggestions to elicit positive emotions. For example, it can suggest uplifting dishes using ingredients in the refrigerator. In this way, by using the emotion estimation function, it is possible to analyze the emotion a user has when opening the refrigerator and make suggestions to elicit positive emotions.

[0087] The interlocking unit can analyze a user's ingredient consumption pattern and suggest a shopping list to minimize waste. The interlocking unit, for example, builds a system that analyzes a user's ingredient consumption pattern and suggests a shopping list to minimize waste. For example, frequently used ingredients are added to the list preferentially. The interlocking unit also suggests a shopping list to minimize waste based on the ingredient consumption pattern. For example, it suggests appropriate amounts based on past consumption data. The interlocking unit also analyzes a user's ingredient consumption pattern and suggests a shopping list to minimize waste. For example, it adds ingredients with an approaching expiration date to the list preferentially. In this way, a shopping list to minimize waste can be suggested by analyzing a user's ingredient consumption pattern.

[0088] The interlocking unit can suggest methods for storing and cooking ingredients, thereby extending the lifespan of ingredients. The interlocking unit, for example, suggests methods for storing ingredients and builds a system for extending the lifespan of ingredients. For example, it suggests the optimal method for storing vegetables and fruits. The interlocking unit also suggests cooking methods according to the type of ingredient, thereby extending the lifespan of ingredients. For example, it suggests the optimal cooking method for meat and fish. The interlocking unit also suggests methods for storing and cooking ingredients, thereby extending the lifespan of ingredients. For example, it suggests storage methods to keep ingredients fresh. In this way, by suggesting methods for storing and cooking ingredients, the lifespan of ingredients can be extended.

[0089] The interlocking unit can use the emotion estimation function to make suggestions to increase the user's satisfaction with reducing food waste. The interlocking unit, for example, uses the emotion estimation function to build a system that makes suggestions to increase the user's satisfaction with reducing food waste. For example, it visually displays the results of food waste reduction. The interlocking unit also makes suggestions to increase the user's satisfaction with reducing food waste based on the user's emotion data. For example, it displays the amount of ingredients reduced and the amount saved. The interlocking unit also uses the emotion estimation function to make suggestions to increase the user's satisfaction with reducing food waste. For example, it provides a function to share the results of food waste reduction. In this way, the emotion estimation function can be used to make suggestions to increase the user's satisfaction with reducing food waste.

[0090] The interlocking unit can work with a local food bank to provide an option to donate surplus food ingredients. The interlocking unit, for example, builds a system that works with a local food bank to provide an option to donate surplus food ingredients. For example, it enables users to easily donate surplus food ingredients. The interlocking unit also works with a food bank to provide an option to donate surplus food ingredients. For example, it displays a list of food ingredients that can be donated and allows users to select. The interlocking unit also works with a local food bank to provide an option to donate surplus food ingredients. For example, it simplifies the donation procedure so that users can easily donate. In this way, by working with a local food bank, it is possible to provide an option to donate surplus food ingredients.

[0091] The interlocking unit can periodically suggest new recipes using leftover ingredients. The interlocking unit, for example, builds a system that periodically suggests new recipes using leftover ingredients. For example, it suggests new dishes based on leftover ingredients in the refrigerator. The interlocking unit also periodically suggests new recipes using leftover ingredients. For example, it suggests soups and stir-fries using leftover vegetables. The interlocking unit also periodically suggests new recipes using leftover ingredients. For example, it suggests desserts and snacks using leftover ingredients. In this way, by periodically suggesting new recipes using leftover ingredients, it is possible to reduce food waste.

[0092] The linking unit can use the emotion estimation function to analyze the user's emotions about reducing food waste and provide positive feedback. The linking unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions about reducing food waste and provides positive feedback. For example, it displays a message praising the achievement of food waste reduction. The linking unit also provides positive feedback about food waste reduction based on the user's emotion data. For example, it displays the amount of ingredients reduced and the amount saved. The linking unit also uses the emotion estimation function to analyze the user's emotions about reducing food waste and provide positive feedback. For example, it provides a function to share the achievement of food waste reduction. In this way, by using the emotion estimation function, the user's emotions about reducing food waste can be analyzed and positive feedback can be provided.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The suggestion unit can suggest menus that do not use specific ingredients based on the user's dietary preferences and allergy information. For example, if the user has a nut allergy, it will suggest recipes that do not include nuts. The suggestion unit can also analyze the user's past meal history to identify frequently chosen ingredients and dishes and suggest menus based on them. For example, it can prioritize suggestions of dishes that the user frequently chooses. The suggestion unit also takes into account the user's family structure and health condition to suggest balanced meals. For example, it can suggest nutritious menus for children and low-salt menus for the elderly. This makes it possible to suggest more appropriate menus by taking into account the user's preferences, allergy information, family structure, and health condition.

[0095] The customer information collection unit can analyze a user's SNS posts and suggest menus that reflect preferences and trends. For example, it can analyze a user's SNS posts to understand recent food trends and preferences. For example, it can suggest menus based on the dishes and ingredients that the user frequently posts. The customer information collection unit can also extract the user's food preferences from the SNS posts and suggest menus that reflect them. For example, it can analyze food photos posted by the user and suggest similar recipes. The customer information collection unit can also analyze a user's SNS posts and suggest menus that match trends. For example, it can suggest recipes that incorporate popular ingredients and dishes. In this way, by analyzing a user's SNS posts, it can suggest menus that reflect preferences and trends.

[0096] The customer information collection unit can use the user's location information to suggest menus based on local specialties. For example, a menu that uses local specialties is suggested based on the user's location information. For example, recipes using fresh local vegetables or fish are suggested. The customer information collection unit also uses the location information to suggest seasonal menus that incorporate local specialties. For example, dishes using seasonal ingredients are suggested. The customer information collection unit also uses the user's location information to suggest menus that use local specialties. For example, local traditional dishes or regional dishes are suggested. In this way, by using the user's location information, menus that use local specialties can be suggested.

[0097] The customer information collection unit can use the emotion estimation function to suggest a menu based on the user's mood of the day. For example, it analyzes the user's facial expressions and voice when inputting information to estimate the user's mood of the day. For example, if the user is feeling stressed, it suggests dishes that will help them relax. The customer information collection unit also suggests a menu that matches the user's mood of the day based on the user's emotion data. For example, if the user is feeling low, it suggests dishes that will replenish energy. The customer information collection unit also uses the emotion estimation function to suggest a menu that matches the user's mood in real time. For example, if the user is feeling happy, it suggests celebratory dishes. In this way, suggesting a menu that matches the user's mood of the day improves user satisfaction.

[0098] The customer information collection unit can suggest nutritionally balanced menus based on the user's family composition and health condition. For example, it collects information about the user's family composition and suggests menus that take into account the nutritional balance of the entire family. For example, it suggests nutritious menus for children and low-salt menus for the elderly. The customer information collection unit also suggests menus that are fortified with specific nutrients based on the user's health condition information. For example, it suggests recipes using ingredients that are high in iron to a user who is iron deficient. The customer information collection unit also suggests balanced meals taking into account the user's family composition and health condition. For example, it suggests a menu that combines a main dish, side dish, and dessert to satisfy the whole family. In this way, it is possible to suggest nutritionally balanced menus by taking into account the user's family composition and health condition.

[0099] The suggestion unit can automatically suggest menus based on the season and weather. For example, it takes into consideration seasonal ingredients and suggests menus using seasonal ingredients. For example, it suggests cold noodles and salads in the summer and hot hotpot dishes in the winter. The suggestion unit also suggests menus according to temperature and humidity based on weather data. For example, it suggests refreshing dishes on hot days and hot soups on cold days. The suggestion unit also builds a system that automatically suggests menus according to the season and weather. For example, it updates menus according to the change of seasons. In this way, automatically suggesting menus according to the season and weather improves the user's satisfaction with their meal.

[0100] The suggestion unit can re-suggest popular menus based on the user's past ratings. For example, it re-suggests menus that the user has given high ratings to in the past. For example, it re-suggests dishes that the user has rated as "delicious." The suggestion unit also analyzes the user's past rating data and prioritizes suggesting popular menus. For example, it suggests dishes that the user has rated as "I would like to make again." The suggestion unit also builds a system that re-suggests popular menus based on the user's past ratings. For example, it automatically selects menus with high rating scores. In this way, by re-suggesting popular menus based on the user's past ratings, user satisfaction is improved.

[0101] The suggestion unit can use the emotion estimation function to suggest "soothing menus" and "encouraging menus" that match the user's mood. For example, the emotion estimation function is used to suggest menus that match the user's mood. For example, if the user is feeling stressed, it will suggest dishes that will help them relax. The suggestion unit also suggests "soothing menus" and "encouraging menus" that match the user's mood of the day based on the user's emotion data. For example, it will suggest dishes that will replenish energy when the user is feeling down. The suggestion unit also uses the emotion estimation function to suggest menus that match the user's mood in real time. For example, it will suggest celebratory dishes when the user is feeling happy. In this way, suggesting menus that match the user's mood improves user satisfaction.

[0102] The suggestion unit can suggest traditional dishes based on the user's cultural background. For example, it collects information about the user's cultural background and suggests traditional dishes based on that culture. For example, it suggests Japanese food to a Japanese user and Italian food to an Italian user. The suggestion unit also takes cultural background into consideration and suggests traditional dishes for specific holidays. For example, it suggests roast chicken or turkey dishes for Christmas. The suggestion unit also builds a system that suggests traditional dishes based on the user's cultural background. For example, it registers traditional dishes for each region in a database and reflects them in the suggestions. This makes it possible to suggest traditional dishes by taking the user's cultural background into consideration.

[0103] The suggestion unit can also suggest breakfast and lunch menus by taking into account the user's mealtimes. For example, it collects information about the user's mealtimes and suggests breakfast and lunch menus. For example, it suggests breakfast menus that are easy to make for busy mornings. The suggestion unit also builds a system that automatically suggests breakfast and lunch menus by taking into account mealtimes. For example, it suggests menus that match the time period specified by the user. The suggestion unit also suggests breakfast and lunch menus based on the user's mealtime information. For example, it suggests a nutritionally balanced menu for lunch. In this way, it is possible to suggest breakfast and lunch menus by taking into account the user's mealtimes.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The customer information collection unit collects information from the user, such as the amount of time available for cooking, their budget, and their preferences. For example, the user enters information such as "Today I want a dinner that can be made in under 30 minutes," "My budget is under 1,000 yen," and "I want a menu that my children will like." Step 2: The analysis unit analyzes the information collected by the customer information collection unit. For example, the generation AI analyzes the user's input information and selects the optimal recipe. Step 3: The proposal section proposes the optimal menu based on the information analyzed by the analysis section. For example, the generation AI might suggest specific menu items such as "Teriyaki chicken and stir-fried vegetables that can be made in under 30 minutes" or "Pasta and salad that can be made for under 1,000 yen." Step 4: The purchasing agent purchases ingredients based on the menu proposed by the proposal agent. For example, the generation AI automatically generates a list of ingredients needed for the menu proposed by the proposal agent and handles the purchasing process at the online supermarket. Step 5: The linking unit works with the IoT refrigerator to utilize leftover ingredients. For example, based on the vegetables and meat left in the refrigerator, it will suggest dishes such as "stir-fried vegetables using leftover ingredients" or "soup using leftover ingredients."

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a customer information collection unit that collects information from users about the time they can spend on cooking, their budget, and their preferences; an analysis unit that analyzes the information collected by the customer information collection unit; a suggestion unit that proposes an optimal menu based on the information analyzed by the analysis unit; a purchasing agent unit that purchases ingredients based on the menu proposed by the proposal unit; It also has a linking unit that works in conjunction with IoT refrigerators to utilize leftover ingredients. A system characterized by:

2. The customer information collection unit Analyzes the user's past dietary history and automatically updates preference and allergy information 2. The system of claim 1.

3. The customer information collection unit Suggests nutritionally balanced meals based on the user's family structure and health status 2. The system of claim 1.

4. The customer information collection unit Suggesting meals based on the user's mood that day 2. The system of claim 1.

5. The customer information collection unit Analyzes users' social media posts and suggests menus that reflect their preferences and trends 2. The system of claim 1.

6. The customer information collection unit Uses user location information to suggest menus based on local specialties 2. The system of claim 1.

Images