System

Abstract

An object of a system according to an embodiment is to propose a recipe in accordance with a scene based on ingredients in a refrigerator.SOLUTION: A system includes a photographing part, an analysis part, a recipe proposal part, and a display part. The photographing unit photographs a foodstuff in the refrigerator. The analysis unit analyzes the photograph taken by the photograph taking unit. The recipe proposing unit proposes a recipe corresponding to the scene on the basis of the ingredient analyzed by the analyzing unit. The display unit displays the recipe proposed by the recipe proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to effectively utilize ingredients in the refrigerator and propose new recipes.

[0005] The system according to the embodiment aims to propose recipes suitable for various occasions based on the ingredients in the refrigerator. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, a recipe suggestion unit, and a display unit. The photographing unit photographs ingredients in the refrigerator. The analysis unit analyzes the photos taken by the photographing unit. The recipe suggestion unit suggests recipes appropriate for the scene based on the ingredients analyzed by the analysis unit. The display unit displays the recipes suggested by the recipe suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest recipes according to the situation based on the ingredients in the refrigerator. [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 recipe suggestion system according to the embodiment of the present invention is a system in which a user takes a photo of ingredients in a refrigerator, a generation AI analyzes the ingredients, and recommends recipes according to the situation. This allows the recipe suggestion system to help users effectively use the ingredients in their refrigerator and try new recipes.

[0029] A recipe suggestion system according to an embodiment includes a photography unit, an analysis unit, a recipe suggestion unit, and a display unit. The photography unit photographs ingredients in a refrigerator. For example, a user can open the refrigerator door and take a photo of the entire refrigerator, allowing them to see vegetables, meat, seasonings, and other ingredients at a glance. The photography unit can also take close-up photos of specific ingredients in the refrigerator. The analysis unit analyzes the photos taken by the photography unit. For example, the generation AI can identify tomatoes, lettuce, chicken, cheese, and other ingredients in the photo and determine the type and quantity of each ingredient. The analysis unit can also analyze the freshness and storage condition of the ingredients. The recipe suggestion unit proposes recipes tailored to specific occasions based on the ingredients analyzed by the analysis unit. For example, if a user requests to "try a new recipe," the generation AI proposes a recipe tailored to that request. The system also proposes recipes tailored to specific occasions (e.g., family dinner, a party with friends, a healthy lunch, etc.). The display unit displays the recipes proposed by the recipe suggestion unit. For example, the display unit displays the recipe's ingredients, cooking steps, cooking time, and other information. If additional ingredients are required for the recipe, that information is also provided. As a result, the recipe suggestion system according to the embodiment allows users to effectively use ingredients in the refrigerator and try new recipes. For example, users can use leftover ingredients in the refrigerator to make dinner for their family. They can also try new recipes at a party with friends. In addition, recipes for making healthy lunches are also suggested. This allows users to reduce food waste and increase the variety of their cooking.

[0030] When photographing ingredients, the photography unit automatically adjusts the camera to the optimal angle and lighting to capture the ingredients in the most visible state. For example, when a user photographs ingredients in a refrigerator, the photography unit automatically adjusts the camera to the optimal angle and positions the ingredients so that they do not overlap. For example, the camera moves up, down, left, and right to find the optimal viewpoint. Furthermore, when photographing, the camera automatically adjusts the lighting to brighten dark areas in the refrigerator. For example, LED lights can be used to make the ingredients clearly visible. Furthermore, the camera automatically adjusts the focus to capture even the finest details of the ingredients clearly. For example, the camera measures the distance to the ingredients and sets the optimal focus. This allows the ingredients to be photographed in the most visible state.

[0031] The photography unit simultaneously records the temperature and humidity information inside the refrigerator when taking a photo, allowing it to suggest recipes that take into account the freshness and storage conditions of ingredients. For example, the photography unit uses the temperature and humidity sensors inside the refrigerator when taking a photo to record the storage conditions of ingredients. For example, if the temperature is high, it suggests recipes that use ingredients early. It also evaluates the freshness of ingredients based on the temperature and humidity information and suggests recipes that prioritize ingredients that have lost their freshness. For example, if vegetables have lost their freshness, it suggests recipes for soups or stews. It also stores the temperature and humidity information inside the refrigerator in the cloud and compares it with past data to evaluate the storage conditions of ingredients. For example, it predicts the rate at which ingredients will deteriorate based on past data. This makes it possible to suggest recipes that take into account the freshness and storage conditions of ingredients.

[0032] When taking a picture of ingredients in the refrigerator, the photographing unit can improve the accuracy of ingredient recognition by having the user verbally speak the name of the ingredient using a voice recognition function. For example, when taking a picture of ingredients in the refrigerator, the photographing unit can have the user speak the name of the ingredient, and the voice recognition function identifies the ingredient. For example, when the user says "tomato," the camera focuses on the tomato. Also, using the voice recognition function, when the user speaks the name of an ingredient, the camera automatically adjusts the position of the ingredient. For example, when the user says "lettuce," the camera zooms in on the lettuce. Also, when taking a picture, the voice recognition function receives instructions from the user and automatically tags the name of the ingredient. For example, when the user says "chicken," the photo is tagged as chicken. This improves the accuracy of ingredient recognition.

[0033] The photography unit can automatically upload the photos it takes to the cloud and make them accessible from multiple devices, allowing all family members to share food ingredient information. For example, the photography unit can automatically upload the photos it takes to the cloud, allowing all family members to access them from their smartphones or tablets. For example, all family members can check the food ingredient information in the refrigerator in real time. In addition, an app can be developed that allows all family members to share food ingredient inventory status based on the photos uploaded to the cloud. For example, the app can record who used which ingredient. In addition, the photos taken can be saved in the cloud, allowing all family members to check the expiration date of ingredients. For example, the app can notify users when ingredients are close to their expiration date. This allows all family members to share food ingredient information.

[0034] When analyzing ingredients, the analysis unit simultaneously analyzes the nutritional value and allergen information of the ingredients, allowing it to suggest health-conscious recipes. For example, when the generation AI analyzes ingredients, the analysis unit calculates the nutritional value of the ingredients and suggests health-conscious recipes. For example, it suggests low-calorie and high-protein recipes. It also analyzes the allergen information of ingredients and suggests recipes suitable for users with allergies. For example, it suggests gluten-free and nut-free recipes. It also suggests recipes that meet specific health goals based on the nutritional value and allergen information. For example, it suggests recipes that are suitable for dieting or building muscle. This makes it possible to suggest health-conscious recipes.

[0035] The analysis unit can automatically calculate the shelf life and expiration dates of ingredients based on the analysis results of the ingredients, and suggest recipes that prioritize the use of ingredients with upcoming expiration dates. For example, the analysis unit uses a generation AI to analyze the shelf life and expiration dates of ingredients and suggest recipes that prioritize the use of ingredients with upcoming expiration dates. For example, it suggests recipes that use vegetables that are close to their expiration date. It can also predict the rate at which ingredients will deteriorate based on the shelf life and expiration dates of ingredients and suggest recipes that use ingredients early. For example, it suggests recipes that prioritize the use of ingredients that are difficult to store. It can also store shelf life and expiration dates in the cloud and compare them with past data to evaluate the storage status of ingredients. For example, it can predict the rate at which ingredients will deteriorate based on past data. This makes it possible to suggest recipes that take shelf life and expiration dates into consideration.

[0036] Based on the results of the ingredient analysis, the analysis unit can simultaneously obtain information on the origin and producer of the ingredients and suggest recipes that promote local production and consumption. For example, the analysis unit's generation AI analyzes information on the origin of ingredients and suggests recipes that use local ingredients. For example, it suggests recipes that use local vegetables and fruits. It also analyzes information on the producer of the ingredients and suggests recipes that use ingredients from reliable producers. For example, it suggests recipes that use ingredients grown using organic farming methods. It also suggests recipes that promote local production and consumption based on information on the origin and producer. For example, it suggests recipes that use local specialties. This makes it possible to suggest recipes that promote local production and consumption.

[0037] The analysis unit can evaluate the flavor compatibility of ingredient combinations based on the results of ingredient analysis and suggest recipes with the optimal flavor balance. For example, the analysis unit's generation AI analyzes the flavor characteristics of ingredients and suggests recipes with the optimal flavor balance. For example, it suggests recipes that take into account the balance of sweetness, sourness, saltiness, bitterness, and umami. It can also evaluate the flavor compatibility of ingredient combinations and suggest recipes using ingredients that go well together. For example, it can suggest a combination that goes well together, such as tomatoes and basil. It can also suggest recipes that suit the user's preferences based on the flavor balance. For example, it can learn the flavor balance that the user prefers and suggest recipes that match those preferences. This makes it possible to suggest recipes with the optimal flavor balance.

[0038] The recipe suggestion unit can learn the user's past cooking history and ratings, and suggest recipes optimized to the user's preferences. For example, the recipe suggestion unit uses a generation AI to learn the user's past cooking history and suggest new recipes based on recipes that the user has given high ratings. For example, it takes into account the ingredients and cooking methods that the user has previously preferred. It also suggests recipes optimized to the user's preferences based on the user's rating data. For example, it extracts the characteristics of recipes that the user has given high ratings, and generates new recipes based on these. It also learns the user's preferences based on the past cooking history and ratings, and suggests individually customized recipes. For example, it takes into account the user's preferred flavor balance and ingredient combinations. This makes it possible to suggest recipes optimized to the user's preferences.

[0039] When suggesting a recipe, the recipe suggestion unit takes cooking time and difficulty into consideration and can suggest recipes that suit the user's schedule and cooking skills. For example, the recipe suggestion unit uses a generation AI to consider the user's schedule and suggest recipes that can be cooked in a short time. For example, it suggests recipes that can be made in 30 minutes or less for dinner on a busy weekday. It also evaluates the user's cooking skills and suggests simple recipes for beginners and complex recipes for advanced cooks. For example, it suggests recipes with step-by-step guides for beginner cooks. It also suggests recipes that suit the user's schedule based on cooking time and difficulty. For example, it suggests special recipes that take more time to make on weekends. This makes it possible to suggest recipes that suit the user's schedule and cooking skills.

[0040] When suggesting a recipe, the recipe suggestion unit can suggest recipes that match the user's dietary goals. For example, the recipe suggestion unit uses a generation AI to analyze the user's dietary goals and suggest recipes that match those goals. For example, low-calorie recipes are suggested for a user who is on a diet. Recipes are also suggested that match the user's health goals. For example, high-protein recipes are suggested for a user who is aiming to build muscle. A system is also built that suggests recipes that match the user's dietary goals. For example, when you want to relax, recipes using herbal tea or ingredients with a relaxing effect are suggested. This makes it possible to suggest recipes that match the user's dietary goals.

[0041] When suggesting a recipe, the recipe suggestion unit can suggest ingredients and dishes that are appropriate for the season and weather, providing recipes that allow you to enjoy a sense of the season. For example, the recipe suggestion unit uses a generation AI to analyze the season and weather, and suggest recipes that use ingredients that are appropriate for that time of year. For example, in summer it would suggest cold or refreshing dishes. It would also suggest recipes that use seasonal specialties. For example, in autumn it would suggest recipes that use seasonal mushrooms or pumpkins. It would also build a system that suggests recipes that are appropriate for the weather. For example, on cold days it would suggest warm soups or stews. This makes it possible to suggest recipes that allow you to enjoy a sense of the season.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The recipe suggestion system can further include an audio guide unit. After the user selects a recipe, the audio guide unit guides the user through the cooking steps by voice. For example, the audio guide unit may announce the next step while the user is cooking with their hands. The audio guide unit can also provide a timer function during cooking. For example, it may count down the simmering time or baking time by voice. Furthermore, the audio guide unit can answer questions the user has while cooking in real time. For example, if the user asks, "What should I do next?", the audio guide unit will guide the user through the next step by voice. This allows the user to check the cooking steps without using their hands, improving cooking efficiency.

[0044] The photography unit can further include a barcode scanning function for ingredients. The barcode scanning function allows the user to scan the barcode of an ingredient and automatically obtain detailed information about the ingredient. For example, it obtains information such as the ingredient's name, expiration date, and nutritional value. The barcode scanning function can also be used for ingredient inventory management. For example, the scanned ingredient information can be saved in the cloud, allowing stock status to be checked in real time. Furthermore, the barcode scanning function can record ingredient purchase history and automatically generate a shopping list for the next purchase. This makes it easier for users to manage ingredients and reduce waste.

[0045] The photography unit can also be equipped with a 3D scanning function for food ingredients. The 3D scanning function scans food ingredients in three dimensions and analyzes their detailed shape and size. For example, the shape of vegetables and fruits can be saved as a 3D model. The 3D scanning function can also analyze the condition of food ingredients, such as scratches and discoloration. For example, if there is a scratch on the surface of an apple, this information can be recorded. Furthermore, the 3D scanning function can be used to optimize the placement of food ingredients. For example, it can suggest placements that will make efficient use of space in the refrigerator. This allows for a detailed understanding of the condition of food ingredients and suggests the best way to store them.

[0046] The photography unit can further include a voice recognition function for ingredients. The voice recognition function improves the accuracy of ingredient recognition by having the user speak the name of an ingredient. For example, if the user says "tomato," the camera focuses on the tomato. The voice recognition function also automatically adjusts the camera's position when the user speaks the name of an ingredient. For example, if the user says "lettuce," the camera zooms in on the lettuce. Furthermore, the voice recognition function can automatically tag the name of an ingredient in response to a user's instruction. For example, if the user says "chicken," the photo will be tagged as chicken. This improves the accuracy of ingredient recognition and simplifies user operation.

[0047] The photography unit can further include a temperature sensor for the ingredients. The temperature sensor measures the surface temperature of the ingredients and evaluates their storage conditions. For example, if the temperature inside the refrigerator is not appropriate, an alert is displayed. The temperature sensor can also be used to evaluate the freshness of ingredients. For example, if the temperature is high, a recipe for early consumption is suggested. The temperature sensor can also be used to predict the shelf life of ingredients. For example, the rate at which ingredients will deteriorate can be predicted based on past data. This allows for a detailed understanding of the storage conditions of ingredients and suggests optimal storage methods.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The photography unit takes a photo of the food in the refrigerator. For example, the user can open the refrigerator door and take a photo of the entire refrigerator, allowing them to see the vegetables, meat, seasonings, etc. at once. The photography unit can also take close-up photos of specific food items in the refrigerator. Step 2: The analysis unit analyzes the photos taken by the photography unit. For example, the generation AI identifies the tomatoes, lettuce, chicken, cheese, etc. in the photo and determines the type and quantity of each ingredient. The analysis unit can also analyze the freshness and storage conditions of the ingredients. Step 3: The recipe suggestion unit suggests recipes for specific occasions based on the ingredients analyzed by the analysis unit. For example, if a user requests to "try a new recipe," the generation AI will suggest recipes that meet that request. It also suggests recipes for specific occasions (e.g., family dinner, a party with friends, a healthy lunch, etc.). Step 4: The display unit displays the recipe suggested by the recipe suggestion unit, including the recipe ingredients, cooking steps, cooking time, etc. If there are any additional ingredients required for the recipe, that information is also provided.

[0050] (Example 2) The recipe suggestion system according to the embodiment of the present invention is a system in which a user takes a photo of ingredients in a refrigerator, a generation AI analyzes the ingredients, and recommends recipes according to the situation. This allows the recipe suggestion system to help users effectively use the ingredients in their refrigerator and try new recipes.

[0051] A recipe suggestion system according to an embodiment includes a photography unit, an analysis unit, a recipe suggestion unit, and a display unit. The photography unit photographs ingredients in a refrigerator. For example, a user can open the refrigerator door and take a photo of the entire refrigerator, allowing them to see vegetables, meat, seasonings, and other ingredients at a glance. The photography unit can also take close-up photos of specific ingredients in the refrigerator. The analysis unit analyzes the photos taken by the photography unit. For example, the generation AI can identify tomatoes, lettuce, chicken, cheese, and other ingredients in the photo and determine the type and quantity of each ingredient. The analysis unit can also analyze the freshness and storage condition of the ingredients. The recipe suggestion unit proposes recipes tailored to specific occasions based on the ingredients analyzed by the analysis unit. For example, if a user requests to "try a new recipe," the generation AI proposes a recipe tailored to that request. The system also proposes recipes tailored to specific occasions (e.g., family dinner, a party with friends, a healthy lunch, etc.). The display unit displays the recipes proposed by the recipe suggestion unit. For example, the display unit displays the recipe's ingredients, cooking steps, cooking time, and other information. If additional ingredients are required for the recipe, that information is also provided. As a result, the recipe suggestion system according to the embodiment allows users to effectively use ingredients in the refrigerator and try new recipes. For example, users can use leftover ingredients in the refrigerator to make dinner for their family. They can also try new recipes at a party with friends. In addition, recipes for making healthy lunches are also suggested. This allows users to reduce food waste and increase the variety of their cooking.

[0052] When photographing ingredients, the photography unit automatically adjusts the camera to the optimal angle and lighting to capture the ingredients in the most visible state. For example, when a user photographs ingredients in a refrigerator, the photography unit automatically adjusts the camera to the optimal angle and positions the ingredients so that they do not overlap. For example, the camera moves up, down, left, and right to find the optimal viewpoint. Furthermore, when photographing, the camera automatically adjusts the lighting to brighten dark areas in the refrigerator. For example, LED lights can be used to make the ingredients clearly visible. Furthermore, the camera automatically adjusts the focus to capture even the finest details of the ingredients clearly. For example, the camera measures the distance to the ingredients and sets the optimal focus. This allows the ingredients to be photographed in the most visible state.

[0053] The photography unit simultaneously records the temperature and humidity information inside the refrigerator when taking a photo, allowing it to suggest recipes that take into account the freshness and storage conditions of ingredients. For example, the photography unit uses the temperature and humidity sensors inside the refrigerator when taking a photo to record the storage conditions of ingredients. For example, if the temperature is high, it suggests recipes that use ingredients early. It also evaluates the freshness of ingredients based on the temperature and humidity information and suggests recipes that prioritize ingredients that have lost their freshness. For example, if vegetables have lost their freshness, it suggests recipes for soups or stews. It also stores the temperature and humidity information inside the refrigerator in the cloud and compares it with past data to evaluate the storage conditions of ingredients. For example, it predicts the rate at which ingredients will deteriorate based on past data. This makes it possible to suggest recipes that take into account the freshness and storage conditions of ingredients.

[0054] The photography unit can use the emotion estimation function to analyze the emotions the user is feeling when taking a photo and provide a photography assist function to bring out positive emotions. The photography unit, for example, analyzes the user's facial expression when taking a photo and estimates their emotions. For example, when the user is smiling, a positive message is displayed to make photography more enjoyable. The emotion estimation function can also be used to play relaxing music when the user is feeling stressed. For example, playing relaxing music while taking a photo can ease the user's mood. The photography assist function can also display an encouraging message when taking a photo to encourage the user to feel positive emotions. For example, it can display "It looks like you can make a great dish!". This can bring out positive emotions in the user.

[0055] When taking a picture of ingredients in the refrigerator, the photographing unit can improve the accuracy of ingredient recognition by having the user verbally speak the name of the ingredient using a voice recognition function. For example, when taking a picture of ingredients in the refrigerator, the photographing unit can have the user speak the name of the ingredient, and the voice recognition function identifies the ingredient. For example, when the user says "tomato," the camera focuses on the tomato. Also, using the voice recognition function, when the user speaks the name of an ingredient, the camera automatically adjusts the position of the ingredient. For example, when the user says "lettuce," the camera zooms in on the lettuce. Also, when taking a picture, the voice recognition function receives instructions from the user and automatically tags the name of the ingredient. For example, when the user says "chicken," the photo is tagged as chicken. This improves the accuracy of ingredient recognition.

[0056] The photography unit can automatically upload the photos it takes to the cloud and make them accessible from multiple devices, allowing all family members to share food ingredient information. For example, the photography unit can automatically upload the photos it takes to the cloud, allowing all family members to access them from their smartphones or tablets. For example, all family members can check the food ingredient information in the refrigerator in real time. In addition, an app can be developed that allows all family members to share food ingredient inventory status based on the photos uploaded to the cloud. For example, the app can record who used which ingredient. In addition, the photos taken can be saved in the cloud, allowing all family members to check the expiration date of ingredients. For example, the app can notify users when ingredients are close to their expiration date. This allows all family members to share food ingredient information.

[0057] The photography unit is equipped with an emotion estimation function and can provide relaxing music or guidance to reduce stress or anxiety felt by the user when taking a photo. For example, the photography unit analyzes the user's facial expression when taking a photo, and plays relaxing music if the user feels stressed or anxious. For example, classical music or natural sounds can be played. Furthermore, the emotion estimation function can be used to display a relaxation guide if the user feels anxious. For example, it can display "Take a deep breath and relax." Furthermore, the user's emotional state can be analyzed when taking a photo, and advice to reduce stress can be provided. For example, it can display "Try taking a slower photo." This can reduce the user's stress and anxiety.

[0058] When analyzing ingredients, the analysis unit simultaneously analyzes the nutritional value and allergen information of the ingredients, allowing it to suggest health-conscious recipes. For example, when the generation AI analyzes ingredients, the analysis unit calculates the nutritional value of the ingredients and suggests health-conscious recipes. For example, it suggests low-calorie and high-protein recipes. It also analyzes the allergen information of ingredients and suggests recipes suitable for users with allergies. For example, it suggests gluten-free and nut-free recipes. It also suggests recipes that meet specific health goals based on the nutritional value and allergen information. For example, it suggests recipes that are suitable for dieting or building muscle. This makes it possible to suggest health-conscious recipes.

[0059] The analysis unit can automatically calculate the shelf life and expiration dates of ingredients based on the analysis results of the ingredients, and suggest recipes that prioritize the use of ingredients with upcoming expiration dates. For example, the analysis unit uses a generation AI to analyze the shelf life and expiration dates of ingredients and suggest recipes that prioritize the use of ingredients with upcoming expiration dates. For example, it suggests recipes that use vegetables that are close to their expiration date. It can also predict the rate at which ingredients will deteriorate based on the shelf life and expiration dates of ingredients and suggest recipes that use ingredients early. For example, it suggests recipes that prioritize the use of ingredients that are difficult to store. It can also store shelf life and expiration dates in the cloud and compare them with past data to evaluate the storage status of ingredients. For example, it can predict the rate at which ingredients will deteriorate based on past data. This makes it possible to suggest recipes that take shelf life and expiration dates into consideration.

[0060] The analysis unit can use the emotion estimation function to learn the user's favorite ingredients and ingredients to avoid from the emotion data and propose individually customized recipes. The analysis unit, for example, uses the emotion estimation function to learn the user's favorite ingredients and propose recipes using those ingredients. For example, it proposes recipes that prioritize the use of the user's favorite ingredients. It can also learn the ingredients the user wants to avoid from the emotion data and propose recipes that do not use those ingredients. For example, it proposes recipes that exclude ingredients the user dislikes. It can also learn the user's preferences and ingredients to avoid based on the emotion data and propose individually customized recipes. For example, it proposes recipes based on the user's past preferences. This makes it possible to propose recipes that match the user's preferences.

[0061] Based on the results of the ingredient analysis, the analysis unit can simultaneously obtain information on the origin and producer of the ingredients and suggest recipes that promote local production and consumption. For example, the analysis unit's generation AI analyzes information on the origin of ingredients and suggests recipes that use local ingredients. For example, it suggests recipes that use local vegetables and fruits. It also analyzes information on the producer of the ingredients and suggests recipes that use ingredients from reliable producers. For example, it suggests recipes that use ingredients grown using organic farming methods. It also suggests recipes that promote local production and consumption based on information on the origin and producer. For example, it suggests recipes that use local specialties. This makes it possible to suggest recipes that promote local production and consumption.

[0062] The analysis unit can evaluate the flavor compatibility of ingredient combinations based on the results of ingredient analysis and suggest recipes with the optimal flavor balance. For example, the analysis unit's generation AI analyzes the flavor characteristics of ingredients and suggests recipes with the optimal flavor balance. For example, it suggests recipes that take into account the balance of sweetness, sourness, saltiness, bitterness, and umami. It can also evaluate the flavor compatibility of ingredient combinations and suggest recipes using ingredients that go well together. For example, it can suggest a combination that goes well together, such as tomatoes and basil. It can also suggest recipes that suit the user's preferences based on the flavor balance. For example, it can learn the flavor balance that the user prefers and suggest recipes that match those preferences. This makes it possible to suggest recipes with the optimal flavor balance.

[0063] The analysis unit can use the emotion estimation function to analyze the emotion a user has toward a specific ingredient and suggest alternative ingredients based on that emotion. The analysis unit, for example, uses the emotion estimation function to analyze the emotion a user has toward a specific ingredient and suggest alternative ingredients based on that emotion. For example, replacing an ingredient that the user dislikes with another ingredient. Also, based on the user's emotion data, the analysis unit evaluates the emotion toward a specific ingredient and suggests alternative ingredients based on that emotion. For example, replacing an ingredient to which the user is allergic with another ingredient. Also, based on the emotion estimation data, suggests alternative ingredients using ingredients that the user likes. For example, suggesting recipes using ingredients that the user likes. In this way, it is possible to suggest alternative ingredients based on the user's emotions.

[0064] The recipe suggestion unit can learn the user's past cooking history and ratings, and suggest recipes optimized to the user's preferences. For example, the recipe suggestion unit uses a generation AI to learn the user's past cooking history and suggest new recipes based on recipes that the user has given high ratings. For example, it takes into account the ingredients and cooking methods that the user has previously preferred. It also suggests recipes optimized to the user's preferences based on the user's rating data. For example, it extracts the characteristics of recipes that the user has given high ratings, and generates new recipes based on these. It also learns the user's preferences based on the past cooking history and ratings, and suggests individually customized recipes. For example, it takes into account the user's preferred flavor balance and ingredient combinations. This makes it possible to suggest recipes optimized to the user's preferences.

[0065] When suggesting a recipe, the recipe suggestion unit takes cooking time and difficulty into consideration and can suggest recipes that suit the user's schedule and cooking skills. For example, the recipe suggestion unit uses a generation AI to consider the user's schedule and suggest recipes that can be cooked in a short time. For example, it suggests recipes that can be made in 30 minutes or less for dinner on a busy weekday. It also evaluates the user's cooking skills and suggests simple recipes for beginners and complex recipes for advanced cooks. For example, it suggests recipes with step-by-step guides for beginner cooks. It also suggests recipes that suit the user's schedule based on cooking time and difficulty. For example, it suggests special recipes that take more time to make on weekends. This makes it possible to suggest recipes that suit the user's schedule and cooking skills.

[0066] The recipe suggestion unit uses the emotion estimation function to suggest recipes that match the user's current mood and physical condition, thereby improving meal satisfaction. For example, the recipe suggestion unit uses the emotion estimation function to analyze the user's current mood and suggest recipes that match that mood. For example, when the user wants to relax, it suggests easy and delicious recipes. It also analyzes the user's physical condition from emotion data and suggests recipes that match the user's physical condition. For example, when the user is tired, it suggests nutritious recipes. It also suggests recipes that are optimal for the user's mood and physical condition based on the emotion estimation data. For example, when the user is feeling stressed, it suggests recipes that use ingredients that have a relaxing effect. This makes it possible to suggest recipes that match the user's mood and physical condition.

[0067] When suggesting a recipe, the recipe suggestion unit can suggest recipes that match the user's dietary goals. For example, the recipe suggestion unit uses a generation AI to analyze the user's dietary goals and suggest recipes that match those goals. For example, low-calorie recipes are suggested for a user who is on a diet. Recipes are also suggested that match the user's health goals. For example, high-protein recipes are suggested for a user who is aiming to build muscle. A system is also built that suggests recipes that match the user's dietary goals. For example, when you want to relax, recipes using herbal tea or ingredients with a relaxing effect are suggested. This makes it possible to suggest recipes that match the user's dietary goals.

[0068] When suggesting a recipe, the recipe suggestion unit can suggest ingredients and dishes that are appropriate for the season and weather, providing recipes that allow you to enjoy a sense of the season. For example, the recipe suggestion unit uses a generation AI to analyze the season and weather, and suggest recipes that use ingredients that are appropriate for that time of year. For example, in summer it would suggest cold or refreshing dishes. It would also suggest recipes that use seasonal specialties. For example, in autumn it would suggest recipes that use seasonal mushrooms or pumpkins. It would also build a system that suggests recipes that are appropriate for the weather. For example, on cold days it would suggest warm soups or stews. This makes it possible to suggest recipes that allow you to enjoy a sense of the season.

[0069] The recipe suggestion unit can use the emotion estimation function to analyze the emotions a user feels in a specific scene and suggest recipes that are optimal for that scene based on those emotions. The recipe suggestion unit, for example, uses the emotion estimation function to analyze the emotions a user feels in a specific scene and suggest recipes based on those emotions. For example, if a user wants to relax during family dinner, an easy and delicious recipe is suggested. Furthermore, recipes that are optimal for specific scenes are suggested based on the user's emotion data. For example, if a user wants to enjoy a party with friends, dishes that are easy to share are suggested. Furthermore, a system is built that suggests recipes according to the scene based on the emotion estimation data. For example, if a user wants to make a healthy lunch, a nutritionally balanced recipe is suggested. This makes it possible to suggest recipes that are optimal for each scene.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The recipe suggestion system can further include an audio guide unit. After the user selects a recipe, the audio guide unit guides the user through the cooking steps by voice. For example, the audio guide unit may announce the next step while the user is cooking with their hands. The audio guide unit can also provide a timer function during cooking. For example, it may count down the simmering time or baking time by voice. Furthermore, the audio guide unit can answer questions the user has while cooking in real time. For example, if the user asks, "What should I do next?", the audio guide unit will guide the user through the next step by voice. This allows the user to check the cooking steps without using their hands, improving cooking efficiency.

[0072] The photography unit can further include a barcode scanning function for ingredients. The barcode scanning function allows the user to scan the barcode of an ingredient and automatically obtain detailed information about the ingredient. For example, it obtains information such as the ingredient's name, expiration date, and nutritional value. The barcode scanning function can also be used for ingredient inventory management. For example, the scanned ingredient information can be saved in the cloud, allowing stock status to be checked in real time. Furthermore, the barcode scanning function can record ingredient purchase history and automatically generate a shopping list for the next purchase. This makes it easier for users to manage ingredients and reduce waste.

[0073] The photography unit can also be equipped with a 3D scanning function for food ingredients. The 3D scanning function scans food ingredients in three dimensions and analyzes their detailed shape and size. For example, the shape of vegetables and fruits can be saved as a 3D model. The 3D scanning function can also analyze the condition of food ingredients, such as scratches and discoloration. For example, if there is a scratch on the surface of an apple, this information can be recorded. Furthermore, the 3D scanning function can be used to optimize the placement of food ingredients. For example, it can suggest placements that will make efficient use of space in the refrigerator. This allows for a detailed understanding of the condition of food ingredients and suggests the best way to store them.

[0074] The photography unit can further include a voice recognition function for ingredients. The voice recognition function improves the accuracy of ingredient recognition by having the user speak the name of an ingredient. For example, if the user says "tomato," the camera focuses on the tomato. The voice recognition function also automatically adjusts the camera's position when the user speaks the name of an ingredient. For example, if the user says "lettuce," the camera zooms in on the lettuce. Furthermore, the voice recognition function can automatically tag the name of an ingredient in response to a user's instruction. For example, if the user says "chicken," the photo will be tagged as chicken. This improves the accuracy of ingredient recognition and simplifies user operation.

[0075] The photography unit can further include a temperature sensor for the ingredients. The temperature sensor measures the surface temperature of the ingredients and evaluates their storage conditions. For example, if the temperature inside the refrigerator is not appropriate, an alert is displayed. The temperature sensor can also be used to evaluate the freshness of ingredients. For example, if the temperature is high, a recipe for early consumption is suggested. The temperature sensor can also be used to predict the shelf life of ingredients. For example, the rate at which ingredients will deteriorate can be predicted based on past data. This allows for a detailed understanding of the storage conditions of ingredients and suggests optimal storage methods.

[0076] The recipe suggestion unit can use the emotion estimation function to suggest recipes that match the user's current mood and physical condition. For example, when the user wants to relax, it can suggest simple and delicious recipes. Also, when the user is tired, it can suggest nutritious recipes. Furthermore, it can use the emotion estimation function to suggest recipes to reduce the user's stress. For example, it can suggest recipes that use ingredients that have a relaxing effect. This makes it possible to suggest recipes that match the user's mood and physical condition, thereby improving meal satisfaction.

[0077] The recipe suggestion unit can use the emotion estimation function to analyze the emotions a user feels in a specific scene and suggest recipes that are optimal for that scene based on those emotions. For example, if a user wants to relax during a family dinner, the unit can suggest easy and delicious recipes. Or, if a user wants to enjoy a party with friends, the unit can suggest dishes that are easy to share. Furthermore, the emotion estimation function can be used to build a system that analyzes the emotions a user feels in a specific scene and suggests recipes based on those emotions. For example, if a user wants to make a healthy lunch, the unit can suggest a recipe that is nutritionally balanced. This makes it possible to suggest recipes that are optimal for each scene.

[0078] The recipe suggestion unit can use the emotion estimation function to learn the user's past cooking history and ratings and suggest recipes optimized to the user's preferences. For example, it can suggest new recipes based on recipes that the user has given high ratings to in the past. It can also suggest recipes optimized to the user's preferences based on the user's rating data. Furthermore, it can use the emotion estimation function to learn the user's preferences and ingredients that the user wants to avoid and suggest individually customized recipes. For example, it can suggest recipes that prioritize the use of the user's favorite ingredients. This makes it possible to suggest recipes that match the user's preferences.

[0079] The recipe suggestion unit can use the emotion estimation function to analyze the user's emotions toward specific ingredients and suggest alternative ingredient suggestions based on those emotions. For example, it can replace an ingredient that the user dislikes with another ingredient. It can also evaluate the user's emotions toward a specific ingredient based on the user's emotion data and suggest alternative suggestions based on those emotions. Furthermore, it can use the emotion estimation function to suggest alternative suggestions using ingredients that the user likes. For example, it can suggest recipes using ingredients that the user likes. This makes it possible to suggest alternative ingredient suggestions based on the user's emotions.

[0080] The recipe suggestion unit uses the emotion estimation function to suggest recipes that match the user's current mood and physical condition, thereby improving meal satisfaction. For example, when the user wants to relax, it suggests simple and delicious recipes. Also, when the user is tired, it suggests nutritious recipes. Furthermore, it can also use the emotion estimation function to suggest recipes to reduce the user's stress. For example, it suggests recipes that use ingredients that have a relaxing effect. This makes it possible to suggest recipes that match the user's mood and physical condition, improving meal satisfaction.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The photography unit takes a photo of the food in the refrigerator. For example, the user can open the refrigerator door and take a photo of the entire refrigerator, allowing them to see the vegetables, meat, seasonings, etc. at once. The photography unit can also take close-up photos of specific food items in the refrigerator. Step 2: The analysis unit analyzes the photos taken by the photography unit. For example, the generation AI identifies the tomatoes, lettuce, chicken, cheese, etc. in the photo and determines the type and quantity of each ingredient. The analysis unit can also analyze the freshness and storage conditions of the ingredients. Step 3: The recipe suggestion unit suggests recipes for specific occasions based on the ingredients analyzed by the analysis unit. For example, if a user requests to "try a new recipe," the generation AI will suggest recipes that meet that request. It also suggests recipes for specific occasions (e.g., family dinner, a party with friends, a healthy lunch, etc.). Step 4: The display unit displays the recipe suggested by the recipe suggestion unit, including the recipe ingredients, cooking steps, cooking time, etc. If there are any additional ingredients required for the recipe, that information is also provided.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 photography club that takes photos of the ingredients in the refrigerator, an analysis unit that analyzes the photograph taken by the photography unit; a recipe suggestion unit that suggests recipes according to the scene based on the ingredients analyzed by the analysis unit; a display unit that displays the recipe suggested by the recipe suggestion unit. A system characterized by:

2. The photography unit is When photographing the food, the camera automatically adjusts the angle and lighting to ensure the food is photographed in the most visible condition.

2. The system of claim 1.

3. The photography unit is When taking a photo, the temperature and humidity information inside the refrigerator is also recorded, and recipe suggestions are made that take into account the freshness and storage conditions of the ingredients.

2. The system of claim 1.

4. The photography unit is Analyzes the emotions felt by the user when taking a photo and provides a shooting assistance function to bring out positive emotions 2. The system of claim 1.

5. The photography unit is When photographing the food ingredients in the refrigerator, the user verbally speaks the names of the food ingredients using a voice recognition function, thereby improving the recognition accuracy of the food ingredients.

2. The system of claim 1.

6. The photography unit is Photos taken are automatically uploaded to the cloud and can be accessed from multiple devices, allowing the whole family to share information about ingredients.

2. The system of claim 1.

Images