System

Abstract

An object of a system according to an embodiment is to efficiently utilize the contents of a refrigerator and propose an optimal cooking recipe and cooking order.SOLUTION: A system according to an embodiment includes an image analyzer, a text generator, a food ingredient list generator, a recipe suggester, and a cooking order suggester. The image analyzer analyzes an image of the contents of the refrigerator. The text generator converts the image of the contents of the refrigerator analyzed by the image analyzer into text data. The food ingredient list generation unit generates a food ingredient list based on the text data generated by the text generation unit. The recipe proposing unit proposes a cooking recipe based on the ingredient list generated by the ingredient-list generating unit. The cooking order proposal unit proposes a cooking order based on the cooking 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 is difficult to efficiently use the contents of a refrigerator when cooking, which can result in food waste.

[0005] The system according to the embodiment aims to efficiently utilize the contents of a refrigerator and propose optimal cooking recipes and cooking sequences. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a text conversion unit, an ingredient list generation unit, a recipe suggestion unit, and a cooking order suggestion unit. The image analysis unit analyzes images of the contents of the refrigerator. The text conversion unit converts the images of the contents of the refrigerator analyzed by the image analysis unit into text data. The ingredient list generation unit generates an ingredient list based on the text data generated by the text conversion unit. The recipe suggestion unit suggests a cooking recipe based on the ingredient list generated by the ingredient list generation unit. The cooking order suggestion unit suggests a cooking order based on the cooking recipe suggested by the recipe suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently utilize the contents of the refrigerator and suggest optimal cooking recipes and cooking sequences. [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 cooking recipe suggestion system according to an embodiment of the present invention uses an API to convert images of the contents of a refrigerator into text, and a generation AI automatically suggests cooking recipes and an efficient cooking order, taking into account the number of stoves and ovens in the kitchen. This allows users to enjoy creative cooking without hassle and reduces food waste.

[0029] A cooking recipe suggestion system according to an embodiment includes an image analysis unit, a text conversion unit, an ingredient list generation unit, a recipe suggestion unit, and a cooking sequence suggestion unit. The image analysis unit analyzes images of the contents of a refrigerator. For example, the image analysis unit may identify ingredients in the refrigerator using object detection technology. The image analysis unit may also classify the types of ingredients using deep learning technology. The image analysis unit may also evaluate the condition of ingredients using an image classification algorithm. The text conversion unit converts the images of the contents of the refrigerator analyzed by the image analysis unit into text data. For example, the text conversion unit may extract character information from the images using OCR technology. The text conversion unit may also convert the names and quantities of ingredients into text data using natural language processing technology. The text conversion unit may also output the information in the images as structured data. The ingredient list generation unit generates an ingredient list based on the text data generated by the text conversion unit. For example, the ingredient list generation unit may analyze the text data to list the names and quantities of ingredients. The ingredient list generation unit may also categorize ingredients and add them to the list. The ingredient list generation unit can also generate the list taking into account the storage conditions of ingredients. The recipe suggestion unit suggests cooking recipes based on the ingredient list generated by the ingredient list generation unit. For example, the recipe suggestion unit generates recipes that combine ingredients using a generation AI (e.g., a text generation AI or a multimodal generation AI). The recipe suggestion unit can also suggest recipes taking into account nutritional balance. The recipe suggestion unit can also suggest efficient recipes taking into account cooking time. The cooking order suggestion unit suggests a cooking order based on the cooking recipe suggested by the recipe suggestion unit. For example, the cooking order suggestion unit optimizes the order of cooking steps before suggesting a cooking order. The cooking order suggestion unit can also suggest a cooking order taking into account cooking timing. The cooking order suggestion unit can also suggest a cooking order taking into account cooking temperature management. As a result, the cooking recipe suggestion system according to the embodiment allows users to enjoy creative cooking without much effort and reduces ingredient waste. For example, users can make effective use of leftover ingredients in the refrigerator and easily prepare nutritionally balanced dishes.In addition, by suggesting efficient cooking procedures, cooking time can be reduced and work in the kitchen can proceed smoothly.

[0030] The image analysis unit can automatically determine the freshness or expiration date of ingredients and include it in the text data. For example, the image analysis unit uses image analysis technology to determine the freshness of ingredients in a refrigerator. For example, it analyzes changes in the color and shape of vegetables to evaluate freshness. It displays a warning for ingredients with low freshness. To automatically determine expiration dates, the image analysis unit also uses OCR technology to read the dates printed on ingredient packaging and includes them in the text data. For example, it automatically extracts the expiration date of milk. The image analysis unit also combines image analysis and machine learning to determine the freshness and expiration date of ingredients. For example, it learns the deterioration patterns of ingredients based on past data and predicts freshness. This allows users to use fresh ingredients by automatically determining the freshness and expiration date of ingredients.

[0031] The image analysis unit can analyze the shape or color of ingredients to evaluate their quality and condition. For example, the image analysis unit uses image analysis technology to analyze the shape and color of ingredients and evaluate their quality. For example, it analyzes the color and shape of a tomato to determine whether it is ripe. The image analysis unit also combines image analysis and machine learning to evaluate the condition of ingredients. For example, it learns the deterioration pattern of ingredients based on past data and predicts whether they are spoiled. The image analysis unit also combines multiple image analysis algorithms to evaluate the quality of ingredients. For example, it combines color analysis and shape analysis to evaluate the ripeness and degree of spoilage of ingredients. This allows users to select the most suitable ingredients by evaluating the quality and condition of ingredients.

[0032] The text generation unit can recreate the arrangement of ingredients in the refrigerator as a 3D model, allowing the user to visually check it. The text generation unit recreates the arrangement of ingredients in the refrigerator as a 3D model, for example, using image analysis technology. For example, it acquires the positional information of each ingredient and arranges them in 3D space. The text generation unit also uses the 3D model to build a system that allows the user to visually check the ingredients in the refrigerator. For example, it displays the 3D model on a smartphone app. The text generation unit also updates the 3D model in real time, keeping the arrangement of ingredients in the refrigerator always up to date. For example, newly added ingredients are instantly reflected in the 3D model. This allows the user to visually check the arrangement of ingredients in the refrigerator, making it easier to manage ingredients.

[0033] The text conversion unit can link the image analysis results with other smart home appliances to automate cooking. The text conversion unit, for example, links the image analysis results with a smart oven to automate cooking. For example, the oven automatically starts cooking at the appropriate temperature and time. The text conversion unit also links with a smart stove to automate cooking procedures based on the image analysis results. For example, the stove automatically adjusts the heat and proceeds with cooking. The text conversion unit also links with other smart home appliances to build a system that automates the entire cooking process based on the image analysis results. For example, a smart refrigerator, oven, and stove work together to cook. In this way, by linking the image analysis results with other smart home appliances, cooking can be automated.

[0034] The text generator can 3D scan the user's kitchen environment and automatically recognize the placement of stoves and ovens. For example, the text generator can use 3D scanning technology to scan the user's kitchen environment and automatically recognize the placement of stoves and ovens. For example, it can scan the entire kitchen and obtain the location information of each piece of equipment. The text generator can then generate a 3D model of the kitchen based on the scan data and visually display the placement of stoves and ovens. For example, it can display the 3D model on a smartphone app. The text generator can also update the 3D scan data in real time to keep the kitchen environment up to date with changes. For example, newly installed equipment can be instantly reflected in the 3D model. This allows the user to 3D scan the user's kitchen environment and automatically recognize the placement of stoves and ovens, thereby optimizing cooking procedures.

[0035] The text conversion unit can record the usage history of the stove or oven and learn the optimal cooking procedure. The text conversion unit, for example, records the usage history of the stove or oven and builds a system that learns the optimal cooking procedure. For example, it suggests the optimal cooking procedure based on past usage data. The text conversion unit also analyzes the usage history and aims to improve the efficiency of cooking procedures. For example, it prioritizes suggesting frequently used procedures. The text conversion unit also learns the user's cooking style based on the usage history data and suggests cooking procedures accordingly. For example, it suggests procedures that emphasize time saving. In this way, by recording the usage history of the stove or oven and learning the optimal cooking procedure, it is possible to improve the efficiency of cooking.

[0036] The text generation unit can propose cooking procedures based on other kitchen equipment. For example, the text generation unit builds a system that proposes cooking procedures that take other kitchen equipment (e.g., a microwave oven or rice cooker) into consideration. For example, it proposes a procedure for using a microwave. The text generation unit also records the usage history of other equipment and proposes optimal cooking procedures based on that. For example, it proposes cooking procedures based on the usage history of a rice cooker. The text generation unit also builds a system that automates cooking procedures in cooperation with other equipment. For example, it proposes a procedure for a microwave oven to automatically start cooking. This makes it possible to propose cooking procedures that take other kitchen equipment into consideration, thereby making cooking more efficient.

[0037] The text conversion unit can learn based on the user's cooking style and suggest recipes that match it. For example, the text conversion unit learns the user's cooking style (e.g., emphasis on time saving, emphasis on health) and builds a system that suggests recipes that match it. For example, it suggests recipes that emphasize time saving. The text conversion unit also analyzes past cooking history to learn the cooking style. For example, it suggests recipes that emphasize health. The text conversion unit also builds a system that customizes recipes according to the user's cooking style. For example, it suggests recipes that match the user's preferences. In this way, by learning the user's cooking style and suggesting recipes that match it, it becomes possible to cook according to the user's preferences.

[0038] The ingredient list generation unit can automatically add nutritional information or allergen information for each ingredient to the ingredient list. The ingredient list generation unit, for example, builds a system that automatically adds nutritional information for each ingredient to the ingredient list. For example, calorie and vitamin content information is added to the list. The ingredient list generation unit also analyzes ingredient information for ingredients to automatically add allergen information. For example, allergen information for nuts and dairy products is added to the list. The ingredient list generation unit also builds a system that customizes the ingredient list based on nutritional information or allergen information. For example, ingredients containing specific nutrients are preferentially added to the list. This allows the user to select healthy meals by automatically adding nutritional information and allergen information for each ingredient to the ingredient list.

[0039] The ingredient list generation unit can ensure traceability by including information on the origin or producer of the ingredients. The ingredient list generation unit, for example, builds a system that automatically adds origin information for each ingredient to the ingredient list. For example, the ingredient list generation unit adds the origin of a vegetable or the name of the producer to the list. The ingredient list generation unit also analyzes ingredient supply chain data to include producer information. For example, information on farms and factories is added to the list. The ingredient list generation unit also builds a system that adds ingredient history information to the list to ensure traceability. For example, the harvest date and shipping date are added to the list. In this way, by including information on the origin and producer of the ingredients, traceability is ensured and users can use ingredients with peace of mind.

[0040] The ingredient list generation unit can share the ingredient list with other users and promote recipe exchange in the community. The ingredient list generation unit, for example, builds a platform for sharing ingredient lists with other users. For example, the ingredient list is shared in an online community to promote recipe exchange. The ingredient list generation unit also builds a system that receives recipe suggestions from other users based on the shared ingredient list. For example, the ingredient list generation unit proposes recipes based on the shared list. The ingredient list generation unit also adds a comment function and a rating function to the ingredient list to promote recipe exchange in the community. For example, other users can leave comments on the list and rate it. This allows the ingredient list to be shared with other users and promotes recipe exchange in the community, deepening interactions between users.

[0041] The ingredient list generation unit can provide an automatic ordering function on an online shopping site based on the ingredient list. The ingredient list generation unit, for example, builds a system that provides the automatic ordering function on an online shopping site based on the ingredient list. For example, it automatically orders the necessary ingredients based on the list. The ingredient list generation unit also builds a system that links with the online shopping site to provide the automatic ordering function. For example, it sends order information using an API. The ingredient list generation unit also builds a system that selects the optimal online shopping site based on the ingredient list and performs automatic ordering. For example, it selects the optimal site taking price and delivery time into consideration. In this way, by providing the automatic ordering function on the online shopping site based on the ingredient list, users can purchase the necessary ingredients without hassle.

[0042] The recipe suggestion unit can reflect the user's past cooking history or preferences. For example, the recipe suggestion unit builds a system that reflects the user's past cooking history in recipes suggested by the generation AI. For example, it suggests recipes based on dishes made in the past. The recipe suggestion unit also builds a system that learns the user's preferences and suggests recipes based on them. For example, it suggests recipes using ingredients that the user prefers. The recipe suggestion unit also builds a system that customizes recipes based on the user's past cooking history and preferences. For example, it suggests cooking procedures that suit the user's preferences. In this way, by reflecting the user's past cooking history and preferences, it becomes possible to suggest recipes that suit the user's preferences.

[0043] The recipe suggestion unit can take into account the seasonality or in-season of ingredients when suggesting recipes. For example, the recipe suggestion unit builds a system in which the generation AI takes into account the seasonality or in-season of ingredients when suggesting recipes. For example, it proposes recipes using seasonal ingredients. The recipe suggestion unit also builds a database of ingredients to take into account the seasonality or in-season, and the generation AI references that data. For example, it creates a list of ingredients by season. The recipe suggestion unit also builds a system that predicts the in-season of ingredients based on past data in order to suggest recipes that take into account the seasonality or in-season. For example, it predicts the in-season of ingredients based on past weather data. This makes it possible to propose recipes using seasonal ingredients by taking into account the seasonality or in-season of ingredients when suggesting recipes.

[0044] The recipe suggestion unit can share the proposed recipe with other users and improve it based on feedback. For example, the recipe suggestion unit builds a platform for sharing the proposed recipe with other users. For example, the recipe is shared in an online community and feedback is received. The recipe suggestion unit also builds a system for improving the recipe based on feedback on the shared recipe. For example, the recipe is adjusted to reflect user opinions. The recipe suggestion unit also adds a comment function and a rating function to the recipe to encourage feedback in the community. For example, other users leave comments on the recipe and rate it. This allows the proposed recipe to be shared with other users and improved based on feedback, thereby improving the quality of the recipe.

[0045] The recipe suggestion unit can also suggest ingredient substitutes or ways to arrange ingredients when proposing a recipe. For example, the recipe suggestion unit builds a system in which the generation AI simultaneously suggests ingredient substitutes and ways to arrange ingredients when proposing a recipe. For example, it suggests a substitute if a specific ingredient is not available. The recipe suggestion unit also builds a database of ingredients to suggest substitutes and ways to arrange ingredients, and the generation AI references that data. For example, it creates a list of substitutes. The recipe suggestion unit also builds a system that predicts ingredient substitutes based on past data to suggest substitutes and ways to arrange ingredients. For example, it predicts substitutes based on past recipe data. This allows the system to simultaneously suggest ingredient substitutes and ways to arrange ingredients when proposing a recipe, thereby expanding the user's options.

[0046] The cooking sequence suggestion unit can include detailed timing or temperature control for each step when suggesting cooking steps. For example, the cooking sequence suggestion unit builds a system that includes detailed timing for each step when suggesting cooking steps. For example, the cooking sequence suggestion unit proposes cooking steps by specifying specific times. The cooking sequence suggestion unit also builds a system that proposes cooking steps that include temperature control. For example, the cooking sequence suggestion unit proposes cooking steps by specifying the oven temperature or the stove's heat power. The cooking sequence suggestion unit also builds a system that customizes cooking steps based on detailed timing and temperature control for each step. For example, the cooking sequence suggestion unit proposes cooking steps that suit the user's preferences. In this way, by including detailed timing and temperature control for each step when suggesting cooking steps, the user can cook accurately.

[0047] The cooking sequence suggestion unit can customize cooking procedures based on the user's cooking skill level. For example, the cooking sequence suggestion unit builds a system that customizes cooking procedures according to the user's cooking skill level. For example, it proposes simple procedures for beginners. The cooking sequence suggestion unit also builds a system that learns the cooking skill level and proposes cooking procedures accordingly. For example, it evaluates the skill level based on past cooking history. The cooking sequence suggestion unit also builds a system that adjusts cooking procedures in real time according to the cooking skill level. For example, it makes the procedures more complex as the skill level improves. In this way, by customizing cooking procedures according to the user's cooking skill level, the user can cook in a way that suits their own skills.

[0048] The cooking sequence suggestion unit can share cooking procedures with other users and improve them based on feedback. For example, the cooking sequence suggestion unit builds a platform for sharing cooking procedures with other users. For example, cooking procedures are shared in an online community and feedback is received. The cooking sequence suggestion unit also builds a system for improving the procedures based on feedback on the shared cooking procedures. For example, the procedures are adjusted to reflect user opinions. The cooking sequence suggestion unit also adds a comment function and a rating function to the cooking procedures to encourage feedback in the community. For example, other users can leave comments on the procedures and rate them. This allows cooking procedures to be shared with other users and improved based on feedback, thereby improving the quality of the cooking procedures.

[0049] The cooking sequence suggestion unit can provide cooking steps in video or animation format to make them visually easier to understand. The cooking sequence suggestion unit, for example, builds a system that provides cooking steps in video format. For example, each step is explained using a video to make it visually easier to understand. The cooking sequence suggestion unit also builds a system that provides cooking steps in animation format. For example, each step is displayed using an animation to make it visually easier to understand. The cooking sequence suggestion unit also builds a system that supports the user when actually cooking based on the cooking steps provided in video or animation format. For example, the user can proceed with cooking while watching the video. In this way, providing cooking steps in video or animation format makes it visually easier for the user to understand.

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

[0051] The recipe suggestion system can suggest recipes taking into account a user's ingredient preferences and allergy information. For example, if a user is allergic to a particular ingredient, it will prioritize suggesting recipes that do not contain that ingredient. It can also learn the user's preferences based on the user's past recipe selection history and suggest recipes that contain many of the user's favorite ingredients. Furthermore, if a user wants to consume more of a particular nutrient, it can suggest recipes that use ingredients that contain a lot of that nutrient. This allows users to easily find recipes that suit their health condition and preferences.

[0052] The recipe suggestion system can suggest recipes according to the user's cooking skill level. For example, it can lower the hurdle of cooking by suggesting simple recipes for beginners. It can also suggest recipes requiring more advanced techniques to experienced users. It can also learn the user's skill level and gradually increase the difficulty of the suggested recipes as the user's skill improves. This allows users to enjoy recipes according to their own skill level and experience the joy of cooking.

[0053] The cooking recipe suggestion system can suggest recipes taking into account the expiration dates of ingredients used by the user. For example, it can reduce food waste by suggesting recipes that prioritize ingredients that are close to their expiration date. It can also display a warning for ingredients that have passed their expiration date, encouraging the user to avoid using them. Furthermore, when suggesting a recipe that uses ingredients that are close to their expiration date, it can also suggest multiple recipes that use up the ingredients. This allows the user to use ingredients efficiently without wasting them.

[0054] The cooking recipe suggestion system can suggest recipes taking into account the storage conditions of ingredients used by the user. For example, it can evaluate the freshness of ingredients in the refrigerator and suggest recipes that prioritize fresh ingredients. It can also display a warning for ingredients that are not very fresh, encouraging the user to use them sooner. Furthermore, it is possible to obtain optimal cooking results by adjusting the cooking method according to the storage conditions of ingredients. This allows users to cook efficiently while maintaining the freshness of ingredients.

[0055] The recipe suggestion system can suggest recipes taking into account the nutritional information of ingredients used by the user. For example, it can support the user's health by suggesting recipes using ingredients rich in specific nutrients. It can also suggest balanced meals by taking into account the user's nutritional balance. Furthermore, it can customize recipes rich in specific nutrients according to the user's health condition and goals. This allows the user to find the best recipes for their own health condition.

[0056] The cooking recipe suggestion system can 3D scan the user's kitchen environment and automatically recognize the placement of stoves and ovens. For example, it can scan the entire kitchen and obtain the location information of each piece of equipment. It can also generate a 3D model of the kitchen based on the scan data and visually display the placement of stoves and ovens. It can also update the 3D scan data in real time to keep it up to date with changes in the kitchen environment. This allows users to accurately understand their kitchen environment and plan efficient cooking procedures.

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

[0058] Step 1: The image analysis unit analyzes images of the refrigerator contents, for example, using object detection technology to identify the ingredients in the refrigerator, deep learning technology to classify the type of ingredients, and image classification algorithms to evaluate the condition of the ingredients. Step 2: The text conversion unit converts the image of the refrigerator contents analyzed by the image analysis unit into text data. For example, it uses OCR technology to extract character information from the image, and natural language processing technology to convert the names and quantities of ingredients into text data, outputting the information in the image as structured data. Step 3: The ingredient list generator generates an ingredient list based on the text data generated by the text converter. For example, it analyzes the text data to list the names and quantities of ingredients, categorizes the ingredients and adds them to the list, and generates the list taking into account the storage conditions of the ingredients. Step 4: The recipe suggestion unit suggests cooking recipes based on the ingredient list generated by the ingredient list generation unit. For example, it uses generation AI to generate recipes that combine ingredients, suggests recipes taking into account nutritional balance, and suggests efficient recipes taking into account cooking time. Step 5: The cooking sequence suggestion unit suggests a cooking sequence based on the cooking recipe suggested by the recipe suggestion unit. For example, the cooking sequence suggestion unit may suggest an optimized order of cooking steps, suggest a cooking sequence taking into consideration the timing of cooking, and suggest a cooking sequence taking into consideration temperature control.

[0059] (Example 2) The cooking recipe suggestion system according to an embodiment of the present invention uses an API to convert images of the contents of a refrigerator into text, and a generation AI automatically suggests cooking recipes and an efficient cooking order, taking into account the number of stoves and ovens in the kitchen. This allows users to enjoy creative cooking without hassle and reduces food waste.

[0060] A cooking recipe suggestion system according to an embodiment includes an image analysis unit, a text conversion unit, an ingredient list generation unit, a recipe suggestion unit, and a cooking sequence suggestion unit. The image analysis unit analyzes images of the contents of a refrigerator. For example, the image analysis unit may identify ingredients in the refrigerator using object detection technology. The image analysis unit may also classify the types of ingredients using deep learning technology. The image analysis unit may also evaluate the condition of ingredients using an image classification algorithm. The text conversion unit converts the images of the contents of the refrigerator analyzed by the image analysis unit into text data. For example, the text conversion unit may extract character information from the images using OCR technology. The text conversion unit may also convert the names and quantities of ingredients into text data using natural language processing technology. The text conversion unit may also output the information in the images as structured data. The ingredient list generation unit generates an ingredient list based on the text data generated by the text conversion unit. For example, the ingredient list generation unit may analyze the text data to list the names and quantities of ingredients. The ingredient list generation unit may also categorize ingredients and add them to the list. The ingredient list generation unit can also generate the list taking into account the storage conditions of ingredients. The recipe suggestion unit suggests cooking recipes based on the ingredient list generated by the ingredient list generation unit. For example, the recipe suggestion unit generates recipes that combine ingredients using a generation AI (e.g., a text generation AI or a multimodal generation AI). The recipe suggestion unit can also suggest recipes taking into account nutritional balance. The recipe suggestion unit can also suggest efficient recipes taking into account cooking time. The cooking order suggestion unit suggests a cooking order based on the cooking recipe suggested by the recipe suggestion unit. For example, the cooking order suggestion unit optimizes the order of cooking steps before suggesting a cooking order. The cooking order suggestion unit can also suggest a cooking order taking into account cooking timing. The cooking order suggestion unit can also suggest a cooking order taking into account cooking temperature management. As a result, the cooking recipe suggestion system according to the embodiment allows users to enjoy creative cooking without much effort and reduces ingredient waste. For example, users can make effective use of leftover ingredients in the refrigerator and easily prepare nutritionally balanced dishes.In addition, by suggesting efficient cooking procedures, cooking time can be reduced and work in the kitchen can proceed smoothly.

[0061] The image analysis unit can automatically determine the freshness or expiration date of ingredients and include it in the text data. For example, the image analysis unit uses image analysis technology to determine the freshness of ingredients in a refrigerator. For example, it analyzes changes in the color and shape of vegetables to evaluate freshness. It displays a warning for ingredients with low freshness. To automatically determine expiration dates, the image analysis unit also uses OCR technology to read the dates printed on ingredient packaging and includes them in the text data. For example, it automatically extracts the expiration date of milk. The image analysis unit also combines image analysis and machine learning to determine the freshness and expiration date of ingredients. For example, it learns the deterioration patterns of ingredients based on past data and predicts freshness. This allows users to use fresh ingredients by automatically determining the freshness and expiration date of ingredients.

[0062] The image analysis unit can analyze the shape or color of ingredients to evaluate their quality and condition. For example, the image analysis unit uses image analysis technology to analyze the shape and color of ingredients and evaluate their quality. For example, it analyzes the color and shape of a tomato to determine whether it is ripe. The image analysis unit also combines image analysis and machine learning to evaluate the condition of ingredients. For example, it learns the deterioration pattern of ingredients based on past data and predicts whether they are spoiled. The image analysis unit also combines multiple image analysis algorithms to evaluate the quality of ingredients. For example, it combines color analysis and shape analysis to evaluate the ripeness and degree of spoilage of ingredients. This allows users to select the most suitable ingredients by evaluating the quality and condition of ingredients.

[0063] The image analysis unit can use the emotion estimation function to estimate the emotion a user feels when looking at the contents of the refrigerator and determine the priority of ingredients based on that emotion. The image analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when looking at the contents of the refrigerator. For example, it reads the user's facial expression with a camera and calculates an emotion score. The image analysis unit also builds a system that determines the priority of ingredients based on the user's emotion. For example, it prioritizes ingredients that evoke positive emotions. The image analysis unit also learns the frequency of ingredient use and preferences based on the emotion estimation data and suggests a shopping list for the next time. For example, it adds ingredients that the user prefers to the list. In this way, by determining the priority of ingredients based on the user's emotion, cooking can be done according to the user's preferences.

[0064] The text generation unit can recreate the arrangement of ingredients in the refrigerator as a 3D model, allowing the user to visually check it. The text generation unit recreates the arrangement of ingredients in the refrigerator as a 3D model, for example, using image analysis technology. For example, it acquires the positional information of each ingredient and arranges them in 3D space. The text generation unit also uses the 3D model to build a system that allows the user to visually check the ingredients in the refrigerator. For example, it displays the 3D model on a smartphone app. The text generation unit also updates the 3D model in real time, keeping the arrangement of ingredients in the refrigerator always up to date. For example, newly added ingredients are instantly reflected in the 3D model. This allows the user to visually check the arrangement of ingredients in the refrigerator, making it easier to manage ingredients.

[0065] The text conversion unit can link the image analysis results with other smart home appliances to automate cooking. The text conversion unit, for example, links the image analysis results with a smart oven to automate cooking. For example, the oven automatically starts cooking at the appropriate temperature and time. The text conversion unit also links with a smart stove to automate cooking procedures based on the image analysis results. For example, the stove automatically adjusts the heat and proceeds with cooking. The text conversion unit also links with other smart home appliances to build a system that automates the entire cooking process based on the image analysis results. For example, a smart refrigerator, oven, and stove work together to cook. In this way, by linking the image analysis results with other smart home appliances, cooking can be automated.

[0066] The text conversion unit can use the emotion estimation function to learn the frequency of use or preferences of ingredients based on the user's emotions and suggest the next shopping list. For example, the text conversion unit uses the emotion estimation function to analyze the user's emotions when looking at the contents of the refrigerator and learn the frequency of use and preferences of ingredients. For example, it prioritizes learning about ingredients associated with positive emotions. The text conversion unit also builds a system that suggests the next shopping list based on the learned data. For example, it adds ingredients that the user likes to the list. The text conversion unit also updates the user's preferences and frequency of use in real time based on the emotion estimation data and always suggests the latest shopping list. For example, it adds new favorite ingredients to the list. In this way, by suggesting the next shopping list based on the user's emotions, shopping that suits the user's preferences becomes possible.

[0067] The text generator can 3D scan the user's kitchen environment and automatically recognize the placement of stoves and ovens. For example, the text generator can use 3D scanning technology to scan the user's kitchen environment and automatically recognize the placement of stoves and ovens. For example, it can scan the entire kitchen and obtain the location information of each piece of equipment. The text generator can then generate a 3D model of the kitchen based on the scan data and visually display the placement of stoves and ovens. For example, it can display the 3D model on a smartphone app. The text generator can also update the 3D scan data in real time to keep the kitchen environment up to date with changes. For example, newly installed equipment can be instantly reflected in the 3D model. This allows the user to 3D scan the user's kitchen environment and automatically recognize the placement of stoves and ovens, thereby optimizing cooking procedures.

[0068] The text conversion unit can record the usage history of the stove or oven and learn the optimal cooking procedure. The text conversion unit, for example, records the usage history of the stove or oven and builds a system that learns the optimal cooking procedure. For example, it suggests the optimal cooking procedure based on past usage data. The text conversion unit also analyzes the usage history and aims to improve the efficiency of cooking procedures. For example, it prioritizes suggesting frequently used procedures. The text conversion unit also learns the user's cooking style based on the usage history data and suggests cooking procedures accordingly. For example, it suggests procedures that emphasize time saving. In this way, by recording the usage history of the stove or oven and learning the optimal cooking procedure, it is possible to improve the efficiency of cooking.

[0069] The text conversion unit can use the emotion estimation function to evaluate the stress or satisfaction felt by the user while cooking and optimize cooking procedures based on the evaluation. The text conversion unit, for example, uses the emotion estimation function to analyze the stress and satisfaction felt by the user while cooking in real time. For example, it reads the user's facial expression with a camera and calculates an emotion score. The text conversion unit also builds a system that optimizes cooking procedures based on the emotion data. For example, it prioritizes proposing procedures that cause less stress. The text conversion unit also learns the user's cooking style based on the emotion estimation data and proposes cooking procedures accordingly. For example, it prioritizes proposing procedures that cause higher satisfaction. In this way, the user's cooking experience is improved by evaluating the stress and satisfaction felt by the user while cooking and optimizing cooking procedures based on the evaluation.

[0070] The text generation unit can propose cooking procedures based on other kitchen equipment. For example, the text generation unit builds a system that proposes cooking procedures that take other kitchen equipment (e.g., a microwave oven or rice cooker) into consideration. For example, it proposes a procedure for using a microwave. The text generation unit also records the usage history of other equipment and proposes optimal cooking procedures based on that. For example, it proposes cooking procedures based on the usage history of a rice cooker. The text generation unit also builds a system that automates cooking procedures in cooperation with other equipment. For example, it proposes a procedure for a microwave oven to automatically start cooking. This makes it possible to propose cooking procedures that take other kitchen equipment into consideration, thereby making cooking more efficient.

[0071] The text conversion unit can learn based on the user's cooking style and suggest recipes that match it. For example, the text conversion unit learns the user's cooking style (e.g., emphasis on time saving, emphasis on health) and builds a system that suggests recipes that match it. For example, it suggests recipes that emphasize time saving. The text conversion unit also analyzes past cooking history to learn the cooking style. For example, it suggests recipes that emphasize health. The text conversion unit also builds a system that customizes recipes according to the user's cooking style. For example, it suggests recipes that match the user's preferences. In this way, by learning the user's cooking style and suggesting recipes that match it, it becomes possible to cook according to the user's preferences.

[0072] The text generation unit can use the emotion estimation function to monitor the emotions felt by the user while cooking in real time and dynamically adjust cooking procedures. The text generation unit, for example, uses the emotion estimation function to build a system that monitors the emotions felt by the user while cooking in real time. For example, a camera is used to read the user's facial expression and calculate an emotion score. The text generation unit also builds a system that dynamically adjusts cooking procedures based on the emotion data. For example, it prioritizes proposing procedures that cause less stress. The text generation unit also learns the user's cooking style based on the emotion estimation data and dynamically adjusts cooking procedures accordingly. For example, it prioritizes proposing procedures that result in high satisfaction. In this way, the user's cooking experience is improved by monitoring the emotions felt by the user while cooking in real time and dynamically adjusting cooking procedures.

[0073] The ingredient list generation unit can automatically add nutritional information or allergen information for each ingredient to the ingredient list. The ingredient list generation unit, for example, builds a system that automatically adds nutritional information for each ingredient to the ingredient list. For example, calorie and vitamin content information is added to the list. The ingredient list generation unit also analyzes ingredient information for ingredients to automatically add allergen information. For example, allergen information for nuts and dairy products is added to the list. The ingredient list generation unit also builds a system that customizes the ingredient list based on nutritional information or allergen information. For example, ingredients containing specific nutrients are preferentially added to the list. This allows the user to select healthy meals by automatically adding nutritional information and allergen information for each ingredient to the ingredient list.

[0074] The ingredient list generation unit can ensure traceability by including information on the origin or producer of the ingredients. The ingredient list generation unit, for example, builds a system that automatically adds origin information for each ingredient to the ingredient list. For example, the ingredient list generation unit adds the origin of a vegetable or the name of the producer to the list. The ingredient list generation unit also analyzes ingredient supply chain data to include producer information. For example, information on farms and factories is added to the list. The ingredient list generation unit also builds a system that adds ingredient history information to the list to ensure traceability. For example, the harvest date and shipping date are added to the list. In this way, by including information on the origin and producer of the ingredients, traceability is ensured and users can use ingredients with peace of mind.

[0075] The ingredient list generation unit can use the emotion estimation function to learn which ingredients a user likes or avoids and reflect this in the list. The ingredient list generation unit, for example, uses the emotion estimation function to build a system that learns which ingredients a user likes or avoids. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The ingredient list generation unit also builds a system that customizes the ingredient list based on the learned data. For example, it adds ingredients that the user likes to the list and removes ingredients that the user wants to avoid. The ingredient list generation unit also updates the user's preferences and frequency of use in real time based on the emotion estimation data, providing an always-updated ingredient list. For example, it adds new favorite ingredients to the list. In this way, the ingredient list generation unit learns which ingredients a user likes or avoids and reflects this in the list, enabling the user to select ingredients according to their preferences.

[0076] The ingredient list generation unit can share the ingredient list with other users and promote recipe exchange in the community. The ingredient list generation unit, for example, builds a platform for sharing ingredient lists with other users. For example, the ingredient list is shared in an online community to promote recipe exchange. The ingredient list generation unit also builds a system that receives recipe suggestions from other users based on the shared ingredient list. For example, the ingredient list generation unit proposes recipes based on the shared list. The ingredient list generation unit also adds a comment function and a rating function to the ingredient list to promote recipe exchange in the community. For example, other users can leave comments on the list and rate it. This allows the ingredient list to be shared with other users and promotes recipe exchange in the community, deepening interactions between users.

[0077] The ingredient list generation unit can provide an automatic ordering function on an online shopping site based on the ingredient list. The ingredient list generation unit, for example, builds a system that provides the automatic ordering function on an online shopping site based on the ingredient list. For example, it automatically orders the necessary ingredients based on the list. The ingredient list generation unit also builds a system that links with the online shopping site to provide the automatic ordering function. For example, it sends order information using an API. The ingredient list generation unit also builds a system that selects the optimal online shopping site based on the ingredient list and performs automatic ordering. For example, it selects the optimal site taking price and delivery time into consideration. In this way, by providing the automatic ordering function on the online shopping site based on the ingredient list, users can purchase the necessary ingredients without hassle.

[0078] The ingredient list generation unit can optimize the display order of the ingredient list based on the emotion expressed by the user when viewing the ingredient list using the emotion estimation function. The ingredient list generation unit, for example, builds a system that uses the emotion estimation function to analyze the emotion expressed by the user when viewing the ingredient list. For example, a camera is used to read the user's facial expression and calculate an emotion score. The ingredient list generation unit also builds a system that optimizes the display order of the ingredient list based on the emotion data. For example, ingredients associated with positive emotions are preferentially displayed. The ingredient list generation unit also updates the user's preferences and frequency of use in real time based on the emotion estimation data to always provide an optimal display order. For example, newly preferred ingredients are displayed at the top of the list. This allows the user to select ingredients according to their preferences by optimizing the display order of the list based on the emotion expressed by the user when viewing the ingredient list.

[0079] The recipe suggestion unit can reflect the user's past cooking history or preferences. For example, the recipe suggestion unit builds a system that reflects the user's past cooking history in recipes suggested by the generation AI. For example, it suggests recipes based on dishes made in the past. The recipe suggestion unit also builds a system that learns the user's preferences and suggests recipes based on them. For example, it suggests recipes using ingredients that the user prefers. The recipe suggestion unit also builds a system that customizes recipes based on the user's past cooking history and preferences. For example, it suggests cooking procedures that suit the user's preferences. In this way, by reflecting the user's past cooking history and preferences, it becomes possible to suggest recipes that suit the user's preferences.

[0080] The recipe suggestion unit can take into account the seasonality or in-season of ingredients when suggesting recipes. For example, the recipe suggestion unit builds a system in which the generation AI takes into account the seasonality or in-season of ingredients when suggesting recipes. For example, it proposes recipes using seasonal ingredients. The recipe suggestion unit also builds a database of ingredients to take into account the seasonality or in-season, and the generation AI references that data. For example, it creates a list of ingredients by season. The recipe suggestion unit also builds a system that predicts the in-season of ingredients based on past data in order to suggest recipes that take into account the seasonality or in-season. For example, it predicts the in-season of ingredients based on past weather data. This makes it possible to propose recipes using seasonal ingredients by taking into account the seasonality or in-season of ingredients when suggesting recipes.

[0081] The recipe suggestion unit can use the emotion estimation function to evaluate the user's anticipation or excitement for a proposed recipe and adjust the recipe based on that. The recipe suggestion unit, for example, uses the emotion estimation function to build a system that analyzes the user's anticipation and excitement for a proposed recipe in real time. For example, it reads the user's facial expression with a camera and calculates an emotion score. The recipe suggestion unit also builds a system that adjusts recipes based on the emotion data. For example, it prioritizes suggesting recipes with high anticipation or excitement. The recipe suggestion unit also updates the user's preferences and expectations in real time based on the emotion estimation data to always provide optimal recipes. For example, it suggests new favorite recipes. In this way, by evaluating the user's anticipation and excitement for a proposed recipe and adjusting the recipe based on that, user satisfaction is improved.

[0082] The recipe suggestion unit can share the proposed recipe with other users and improve it based on feedback. For example, the recipe suggestion unit builds a platform for sharing the proposed recipe with other users. For example, the recipe is shared in an online community and feedback is received. The recipe suggestion unit also builds a system for improving the recipe based on feedback on the shared recipe. For example, the recipe is adjusted to reflect user opinions. The recipe suggestion unit also adds a comment function and a rating function to the recipe to encourage feedback in the community. For example, other users leave comments on the recipe and rate it. This allows the proposed recipe to be shared with other users and improved based on feedback, thereby improving the quality of the recipe.

[0083] The recipe suggestion unit can also suggest ingredient substitutes or ways to arrange ingredients when proposing a recipe. For example, the recipe suggestion unit builds a system in which the generation AI simultaneously suggests ingredient substitutes and ways to arrange ingredients when proposing a recipe. For example, it suggests a substitute if a specific ingredient is not available. The recipe suggestion unit also builds a database of ingredients to suggest substitutes and ways to arrange ingredients, and the generation AI references that data. For example, it creates a list of substitutes. The recipe suggestion unit also builds a system that predicts ingredient substitutes based on past data to suggest substitutes and ways to arrange ingredients. For example, it predicts substitutes based on past recipe data. This allows the system to simultaneously suggest ingredient substitutes and ways to arrange ingredients when proposing a recipe, thereby expanding the user's options.

[0084] The recipe suggestion unit can use the emotion estimation function to adjust the difficulty or cooking time of a recipe based on the emotion the user feels toward the proposed recipe. The recipe suggestion unit, for example, uses the emotion estimation function to build a system that analyzes the emotion the user feels toward the proposed recipe in real time. For example, it reads the user's facial expression with a camera and calculates an emotion score. The recipe suggestion unit also builds a system that adjusts the difficulty or cooking time of a recipe based on the emotion data. For example, it prioritizes suggesting recipes that are less stressful. The recipe suggestion unit also updates the user's preferences and expectations in real time based on the emotion estimation data to always provide optimal recipes. For example, it suggests a new favorite recipe. In this way, the difficulty or cooking time of a recipe can be adjusted based on the user's emotion toward the proposed recipe, thereby improving user satisfaction.

[0085] The cooking sequence suggestion unit can include detailed timing or temperature control for each step when suggesting cooking steps. For example, the cooking sequence suggestion unit builds a system that includes detailed timing for each step when suggesting cooking steps. For example, the cooking sequence suggestion unit proposes cooking steps by specifying specific times. The cooking sequence suggestion unit also builds a system that proposes cooking steps that include temperature control. For example, the cooking sequence suggestion unit proposes cooking steps by specifying the oven temperature or the stove's heat power. The cooking sequence suggestion unit also builds a system that customizes cooking steps based on detailed timing and temperature control for each step. For example, the cooking sequence suggestion unit proposes cooking steps that suit the user's preferences. In this way, by including detailed timing and temperature control for each step when suggesting cooking steps, the user can cook accurately.

[0086] The cooking sequence suggestion unit can customize cooking procedures based on the user's cooking skill level. For example, the cooking sequence suggestion unit builds a system that customizes cooking procedures according to the user's cooking skill level. For example, it proposes simple procedures for beginners. The cooking sequence suggestion unit also builds a system that learns the cooking skill level and proposes cooking procedures accordingly. For example, it evaluates the skill level based on past cooking history. The cooking sequence suggestion unit also builds a system that adjusts cooking procedures in real time according to the cooking skill level. For example, it makes the procedures more complex as the skill level improves. In this way, by customizing cooking procedures according to the user's cooking skill level, the user can cook in a way that suits their own skills.

[0087] The cooking order suggestion unit can use the emotion estimation function to monitor the stress or satisfaction felt by the user while cooking in real time and dynamically adjust the cooking procedures. The cooking order suggestion unit, for example, uses the emotion estimation function to build a system that monitors the stress and satisfaction felt by the user while cooking in real time. For example, it reads the user's facial expression with a camera and calculates an emotion score. The cooking order suggestion unit also builds a system that dynamically adjusts cooking procedures based on the emotion data. For example, it prioritizes proposing procedures that cause less stress. The cooking order suggestion unit also learns the user's cooking style based on the emotion estimation data and dynamically adjusts cooking procedures accordingly. For example, it prioritizes proposing procedures that cause higher satisfaction. In this way, the user's cooking experience is improved by monitoring the stress and satisfaction felt by the user while cooking in real time and dynamically adjusting the cooking procedures.

[0088] The cooking sequence suggestion unit can share cooking procedures with other users and improve them based on feedback. For example, the cooking sequence suggestion unit builds a platform for sharing cooking procedures with other users. For example, cooking procedures are shared in an online community and feedback is received. The cooking sequence suggestion unit also builds a system for improving the procedures based on feedback on the shared cooking procedures. For example, the procedures are adjusted to reflect user opinions. The cooking sequence suggestion unit also adds a comment function and a rating function to the cooking procedures to encourage feedback in the community. For example, other users can leave comments on the procedures and rate them. This allows cooking procedures to be shared with other users and improved based on feedback, thereby improving the quality of the cooking procedures.

[0089] The cooking sequence suggestion unit can provide cooking steps in video or animation format to make them visually easier to understand. The cooking sequence suggestion unit, for example, builds a system that provides cooking steps in video format. For example, each step is explained using a video to make it visually easier to understand. The cooking sequence suggestion unit also builds a system that provides cooking steps in animation format. For example, each step is displayed using an animation to make it visually easier to understand. The cooking sequence suggestion unit also builds a system that supports the user when actually cooking based on the cooking steps provided in video or animation format. For example, the user can proceed with cooking while watching the video. In this way, providing cooking steps in video or animation format makes it visually easier for the user to understand.

[0090] The cooking order suggestion unit can use the emotion estimation function to optimize the display order or content of the cooking steps based on the emotion the user felt when viewing the cooking steps. For example, the cooking order suggestion unit uses the emotion estimation function to build a system that analyzes the emotion the user felt when viewing the cooking steps in real time. For example, it reads the user's facial expression with a camera and calculates an emotion score. The cooking order suggestion unit also builds a system that optimizes the display order and content of the cooking steps based on the emotion data. For example, it prioritizes displaying steps that evoke positive emotions. The cooking order suggestion unit also updates the user's preferences and expectations in real time based on the emotion estimation data to always provide optimal cooking steps. For example, it suggests new preferred steps. This improves the user's cooking experience by optimizing the display order and content of the steps based on the emotion the user felt when viewing the cooking steps.

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

[0092] The recipe suggestion system can suggest recipes taking into account a user's ingredient preferences and allergy information. For example, if a user is allergic to a particular ingredient, it will prioritize suggesting recipes that do not contain that ingredient. It can also learn the user's preferences based on the user's past recipe selection history and suggest recipes that contain many of the user's favorite ingredients. Furthermore, if a user wants to consume more of a particular nutrient, it can suggest recipes that use ingredients that contain a lot of that nutrient. This allows users to easily find recipes that suit their health condition and preferences.

[0093] The recipe suggestion system can suggest recipes according to the user's cooking skill level. For example, it can lower the hurdle of cooking by suggesting simple recipes for beginners. It can also suggest recipes requiring more advanced techniques to experienced users. It can also learn the user's skill level and gradually increase the difficulty of the suggested recipes as the user's skill improves. This allows users to enjoy recipes according to their own skill level and experience the joy of cooking.

[0094] The cooking recipe suggestion system can estimate a user's emotions and suggest recipes based on those emotions. For example, if a user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. Or, if a user wants to cheer up, it can suggest recipes using ingredients that will replenish energy. Furthermore, by suggesting recipes that take into account cooking time and effort according to the user's emotions, it is possible to provide a cooking experience that matches the user's mood. This allows the user to find the best recipe that suits their emotions.

[0095] The cooking recipe suggestion system can suggest recipes taking into account the expiration dates of ingredients used by the user. For example, it can reduce food waste by suggesting recipes that prioritize ingredients that are close to their expiration date. It can also display a warning for ingredients that have passed their expiration date, encouraging the user to avoid using them. Furthermore, when suggesting a recipe that uses ingredients that are close to their expiration date, it can also suggest multiple recipes that use up the ingredients. This allows the user to use ingredients efficiently without wasting them.

[0096] The cooking recipe suggestion system can suggest recipes taking into account the storage conditions of ingredients used by the user. For example, it can evaluate the freshness of ingredients in the refrigerator and suggest recipes that prioritize fresh ingredients. It can also display a warning for ingredients that are not very fresh, encouraging the user to use them sooner. Furthermore, it is possible to obtain optimal cooking results by adjusting the cooking method according to the storage conditions of ingredients. This allows users to cook efficiently while maintaining the freshness of ingredients.

[0097] The cooking recipe recommendation system can estimate a user's emotions and optimize cooking procedures based on those emotions. For example, if a user is feeling stressed, it can suggest simple and hassle-free cooking procedures. Also, if a user is relaxed, it can suggest cooking procedures that can be enjoyed over time. Furthermore, it is possible to improve the user's cooking experience by adjusting the order and timing of cooking procedures according to the user's emotions. This allows the user to find the optimal cooking procedures that suit their own emotions.

[0098] The recipe suggestion system can suggest recipes taking into account the nutritional information of ingredients used by the user. For example, it can support the user's health by suggesting recipes using ingredients rich in specific nutrients. It can also suggest balanced meals by taking into account the user's nutritional balance. Furthermore, it can customize recipes rich in specific nutrients according to the user's health condition and goals. This allows the user to find the best recipes for their own health condition.

[0099] The recipe recommendation system can estimate a user's emotions and prioritize ingredients based on those emotions. For example, it can suggest recipes that prioritize ingredients that the user has positive emotions about. It can also suggest recipes that avoid ingredients that the user has negative emotions about. It can also learn the frequency of ingredient use and preferences based on the user's emotions and suggest the next shopping list. This allows users to find recipes that use the best ingredients according to their emotions.

[0100] The cooking recipe suggestion system can 3D scan the user's kitchen environment and automatically recognize the placement of stoves and ovens. For example, it can scan the entire kitchen and obtain the location information of each piece of equipment. It can also generate a 3D model of the kitchen based on the scan data and visually display the placement of stoves and ovens. It can also update the 3D scan data in real time to keep it up to date with changes in the kitchen environment. This allows users to accurately understand their kitchen environment and plan efficient cooking procedures.

[0101] The cooking recipe suggestion system can estimate a user's emotions and adjust the difficulty and cooking time of the recipe based on those emotions. For example, if a user is feeling stressed, it can suggest recipes that are easy to make and can be made quickly. Also, if a user is relaxed, it can suggest recipes that can be enjoyed over time. Furthermore, by adjusting the recipe steps and ingredients according to the user's emotions, it is possible to improve the user's cooking experience. This allows the user to find the best recipe that suits their emotions.

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

[0103] Step 1: The image analysis unit analyzes images of the refrigerator contents, for example, using object detection technology to identify the ingredients in the refrigerator, deep learning technology to classify the type of ingredients, and image classification algorithms to evaluate the condition of the ingredients. Step 2: The text conversion unit converts the image of the refrigerator contents analyzed by the image analysis unit into text data. For example, it uses OCR technology to extract character information from the image, and natural language processing technology to convert the names and quantities of ingredients into text data, outputting the information in the image as structured data. Step 3: The ingredient list generator generates an ingredient list based on the text data generated by the text converter. For example, it analyzes the text data to list the names and quantities of ingredients, categorizes the ingredients and adds them to the list, and generates the list taking into account the storage conditions of the ingredients. Step 4: The recipe suggestion unit suggests cooking recipes based on the ingredient list generated by the ingredient list generation unit. For example, it uses generation AI to generate recipes that combine ingredients, suggests recipes taking into account nutritional balance, and suggests efficient recipes taking into account cooking time. Step 5: The cooking sequence suggestion unit suggests a cooking sequence based on the cooking recipe suggested by the recipe suggestion unit. For example, the cooking sequence suggestion unit may suggest an optimized order of cooking steps, suggest a cooking sequence taking into consideration the timing of cooking, and suggest a cooking sequence taking into consideration temperature control.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0132] 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.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0148] 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.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image analysis unit that analyzes images of the contents of the refrigerator; a text conversion unit that converts the image of the contents of the refrigerator analyzed by the image analysis unit into text data; an ingredient list generation unit that generates an ingredient list based on the text data generated by the text conversion unit; a recipe suggestion unit that suggests cooking recipes based on the ingredient list generated by the ingredient list generation unit; a cooking sequence suggestion unit that suggests a cooking sequence based on the cooking recipes suggested by the recipe suggestion unit. A system characterized by:

2. The image analysis unit Automatically determine the freshness or expiration date of ingredients and include it in the text data 2. The system of claim 1.

3. The image analysis unit Analyzing the shape or color of food ingredients to evaluate their quality and condition 2. The system of claim 1.

4. The image analysis unit Estimate the user's emotions when looking at the contents of the refrigerator and prioritize ingredients based on those emotions.

2. The system of claim 1.

5. The text conversion unit The arrangement of ingredients in the refrigerator is reproduced as a 3D model, allowing users to visually check it.

2. The system of claim 1.

6. The text conversion unit Image analysis results will be linked to other smart home appliances to automate cooking.

2. The system of claim 1.

7. The text conversion unit Learns the frequency of use or preferences of ingredients based on the user's emotions and suggests the next shopping list 2. The system of claim 1.

8. The text conversion unit The system 3D scans the user's kitchen environment and automatically recognizes the placement of stoves and ovens.

2. The system of claim 1.

Images