system

Abstract

We provide the system. [Solution] Image acquisition method, An analysis means for analyzing images acquired by the image acquisition means and identifying food ingredient information, A generation means that generates cooking suggestions based on ingredient information obtained by the analysis means, A presentation means for presenting the generated cooking suggestions to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern busy lives, thinking about daily menus, maintaining a proper nutritional balance, and preventing food loss in the refrigerator are major challenges for housewives and single people in the family. Furthermore, it is also difficult for novice cooks and the elderly to learn correct cooking procedures. There is a need for support aimed at effectively solving these problems and reducing the burden on users regarding cooking.

Means for Solving the Problems

[0005] This invention provides a means for analyzing information about the contents of a refrigerator and ingredients acquired by a user using a camera-equipped earphone system, and generating cooking suggestions based on this information. Furthermore, it enables support for the user's cooking by monitoring the cooking process and providing real-time assistance information. In addition, it helps reduce food waste by making suggestions that take into account the expiration dates of ingredients.

[0006] The "image acquisition method" refers to a function that allows users to take pictures of the contents of a refrigerator or food items using a camera built into their earphones.

[0007] "Analysis means" refers to a function that performs a process of identifying and extracting food ingredient information using recognition technology based on acquired images.

[0008] The "generation method" refers to a function that creates cooking suggestions tailored to the user's health condition and dietary habits based on analyzed ingredient information.

[0009] A "presentation method" refers to a function that appropriately displays the generated cooking suggestions to the user and supports their decision-making.

[0010] The "monitoring method" is a function that continuously acquires video footage taken by the user while cooking, allowing for real-time monitoring of the situation.

[0011] The "support information generation means" is a function that analyzes video footage acquired by the monitoring means and generates information to support the next cooking step.

[0012] "Communication means" refers to a function that transmits generated cooking support information to the user via voice or visual means, thereby supporting the progress of cooking.

[0013] "Management measures" refer to a function that records the expiration dates of acquired ingredients and suggests prioritizing the use of ingredients based on those expiration dates. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a cooking support system consisting of a camera-equipped earphone worn by the user, a terminal that controls it, and a server connected to the cloud.

[0036] When a user takes a picture of the ingredients they will use for cooking or the contents of their refrigerator through the camera, the device sends the image to a server. The server receives this image, analyzes the type and quantity of ingredients, and compares it with a database. Based on this analysis, the server generates an appropriate recipe that takes into account the user's health condition and past eating history. The generated recipe is then presented to the user through the device.

[0037] For example, if a user takes a picture of the eggs, tomatoes, and spinach they have in their refrigerator, the server will suggest "tomato and spinach omelet" as the optimal recipe based on these ingredients.

[0038] Once cooking begins, the terminal continuously monitors the cooking progress using its camera and transmits the video to the server. The server analyzes this in real time and provides the user with specific cooking instructions as the process progresses. For example, the user can receive specific instructions via voice, such as "You should cook it a little longer" or "Add salt next."

[0039] Furthermore, this system manages the expiration dates of ingredients and automatically generates recipes that prioritize the use of ingredients with the nearest expiration date. This function helps reduce food waste.

[0040] As described above, the present invention enhances the efficiency of users' daily meal preparation, supporting the realization of a healthy diet and the reduction of food waste.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses the camera on their earphones to take pictures of food items in the refrigerator or flyers they are holding. The captured images are saved to the device.

[0044] Step 2:

[0045] The device sends the saved image to the server. The server receives the image and begins the analysis process.

[0046] Step 3:

[0047] The server uses a machine learning model to analyze the received images, identify ingredients and flyer information within the images, and extract the names of the ingredients, quantities, and special offer information.

[0048] Step 4:

[0049] The server compares the analysis results with the food ingredient database and builds a list of ingredients owned by the user. It also checks the expiration date information for each ingredient and updates it in the management database.

[0050] Step 5:

[0051] The server generates multiple recipes using a generation AI based on acquired ingredient information, user health data, and meal history. During this process, it prioritizes the use of ingredients with the nearest expiration date.

[0052] Step 6:

[0053] The generated recipe is sent to the device, which then suggests it to the user. The user reviews the recipe and selects the one they want to create.

[0054] Step 7:

[0055] The device starts cooking based on the recipe selected by the user. The terminal monitors the cooking process with a camera and transmits the video to the server in real time.

[0056] Step 8:

[0057] The server analyzes the transmitted video and determines the cooking process. Based on the situation, AI generates necessary operations and precautions, and sends specific cooking instructions to the terminal.

[0058] Step 9:

[0059] The terminal relays instructions from the server to the user via voice, supporting the user in ensuring they can cook correctly.

[0060] Step 10:

[0061] After a user finishes cooking, the server updates the ingredient list in its database and optimizes the next recipe suggestion by considering ingredient availability and expiration date information.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Maintaining a healthy diet and reducing food waste are crucial issues for modern households. However, in the midst of busy daily life, it is difficult to accurately assess the condition of ingredients and cook them appropriately. Furthermore, meal preparation does not automatically provide suggestions that take into account individual health conditions or past food choices, leading to a problem of food waste.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes a visual information acquisition means worn by the user, an analysis means for analyzing the visual data obtained by the visual information acquisition means and identifying item information, and a generation means for generating suggestion information based on the item information obtained by the analysis means. This enables the user to prepare efficient and healthy meals based on their individual health status and past selection history, thereby reducing food waste.

[0067] A "visual information acquisition means" is a device that, when worn by a user, acquires visual information from the site as digital data.

[0068] "Analysis means" refers to a function that processes acquired visual data and identifies the type and state of items contained within the image.

[0069] "Generation means" refers to a function that generates useful suggestions and instructions for the user based on analyzed item information.

[0070] "Adjustment means" refers to a function that optimizes the generated suggestion information according to the user's health status and past selection history.

[0071] "Presentation means" refers to a function for visually or audibly conveying generated or adjusted information to the user.

[0072] "Monitoring means" refers to devices and functions for continuously recording user actions and acquiring that data.

[0073] "Information generation means" refers to a function that analyzes data obtained by monitoring means and generates useful support information for the user.

[0074] A "means of communication" refers to a function for conveying generated support information to the user.

[0075] "Management measures" refer to functions that track the status of acquired items, such as their expiration dates, and propose the optimal usage method based on that information.

[0076] This invention relates to an information processing system for user operation. This system consists of a camera-equipped information acquisition device (hereinafter referred to as the earphone device) worn by the user, a terminal for processing data acquired from the earphone device, and a server connected to the cloud. The earphone device is designed to allow the user to easily acquire visual information during everyday activities.

[0077] The terminal receives image data from the earphone device and transmits it to the server via the internet. Specifically, the data is uploaded to the cloud via the HTTP protocol using Wi-Fi or mobile data communication. On the server side, advanced image recognition software such as TENSORFLOW® and PyTorch is used to analyze the image data and identify item information.

[0078] Based on this analysis information, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate recipes and necessary instructions for the user. For example, based on item information such as eggs, tomatoes, and spinach, it can present a recipe like "Tomato and Spinach Omelet."

[0079] Furthermore, to take into account the user's health information and past dietary history, the server can utilize a database system to optimize suggested recipes to individual needs. The generated suggestion information is presented to the user via a terminal and communicated clearly using text and speech synthesis software (e.g., Google® Text-to-Speech).

[0080] Another function is that the terminal continuously records the user's actions while cooking and sends the video to the server. The server analyzes this in real time and generates specific cooking instructions as the process progresses, providing instructions that the user can specifically follow, such as "You should bake it a little longer" or "Add salt next."

[0081] Regarding the ingredient management function, the server manages the expiration dates of each ingredient in a database, and automatically generates recipe suggestions that prioritize the use of ingredients as their expiration date approaches, thereby effectively reducing food waste. This process helps promote an efficient and healthy diet and contributes to the sustainable use of ingredients.

[0082] A concrete example of a prompt might be input such as, "Please tell me some recipes that use the ingredients in my refrigerator without wasting anything." Based on this prompt, the system can perform effective information processing and provide the user with the most suitable suggestions.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user wears a visual information acquisition device and takes pictures of items used for cooking and the inside of the refrigerator.

[0086] Input: Visual information is input via the camera.

[0087] Output: Image data is generated.

[0088] As a specific example, the user uses their earphone device to point the camera directly at an egg, a tomato, and spinach.

[0089] Step 2:

[0090] The device receives the captured image data and sends it to the server via the internet.

[0091] Input: Receive captured image data.

[0092] Output: An image file for sending to the server is generated.

[0093] Specifically, the device uses Wi-Fi to send image data to a server in the cloud via the HTTP protocol.

[0094] Step 3:

[0095] The server analyzes the image data to identify the item information.

[0096] Input: Image data received by the server.

[0097] Output: Information about the items in the image (e.g., identification of eggs, tomatoes, spinach) is generated.

[0098] In terms of specific operation, the server-side image recognition software (such as TensorFlow) processes the visual data using a neural network and outputs the results in text format.

[0099] Step 4:

[0100] The server generates suggested information using an AI model based on the analyzed item information.

[0101] Input: Item information data, user's health status, and past selection history.

[0102] Output: Suggested recipes and cooking instructions.

[0103] In terms of specific operations, the server references a database and an AI model (e.g., GPT-3) to generate recipes such as "tomato and spinach omelet."

[0104] Step 5:

[0105] The terminal presents the user with suggested information received from the server.

[0106] Input: Suggestion information from the server.

[0107] Output: Visual and auditory information presented to the user.

[0108] In terms of specific operations, the terminal displays the received information on its screen and uses speech synthesis software to give voice instructions to the user.

[0109] Step 6:

[0110] The terminal records user actions during cooking and sends that data to the server.

[0111] Input: Real-time video data during cooking.

[0112] Output: Video data sent to the server.

[0113] Specifically, the system uses the device's camera to capture images every few seconds and streams them to a server.

[0114] Step 7:

[0115] The server analyzes the cooking progress and provides additional cooking instructions to the user.

[0116] Input: Real-time cooking video data.

[0117] Output: Additional voice instructions based on the cooking status.

[0118] In terms of specific operations, the server uses deep learning to process the video data and generates specific instructions such as "bake it a little longer" or "add salt next."

[0119] Step 8:

[0120] The server manages the expiration dates of items and makes priority recommendations based on them.

[0121] Input: Information on managed items and their expiration dates.

[0122] Output: Suggestion information considering items nearing their expiration date.

[0123] Specifically, the server uses a database to access expiration date information and automatically generates recipes to reduce food waste.

[0124] (Application Example 1)

[0125] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0126] Users with limited cooking knowledge or those leading busy lives face challenges in using ingredients effectively and healthily, reducing food waste, and preparing meals smoothly. Technologies are needed to address this issue.

[0127] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0128] In this invention, the server includes an image acquisition means, an analysis means for analyzing the images acquired by the image acquisition means and identifying ingredient information, a generation means for generating cooking suggestions based on the ingredient information obtained by the analysis means, a presentation means for presenting the generated cooking suggestions to the user, and a voice transmission means for transmitting cooking support information to the user by voice. As a result, the user can obtain the information and instructions necessary for cooking in real time from both visual and auditory perspectives, enabling them to proceed with cooking efficiently.

[0129] "Image acquisition means" refers to devices and technologies for acquiring image data of food items and storage locations photographed by the user.

[0130] "Analysis means" refers to a system that executes processing and algorithms to identify food ingredient information from acquired image data.

[0131] "Generation means" refers to methods and techniques for creating appropriate cooking suggestions and recipes based on ingredient information obtained through analysis means.

[0132] "Presentation means" refers to devices and technologies for visually displaying generated cooking suggestions and recipes to the user.

[0133] "Voice communication means" refers to systems and technologies that use voice to convey generated cooking suggestions and support information to users.

[0134] "Monitoring means" refers to devices and technologies that continuously acquire video footage of a user cooking and allow for monitoring of the situation.

[0135] "Support information generation means" refers to a method that analyzes the cooking process obtained by monitoring means and generates instructions and advice necessary during the cooking process.

[0136] "Management measures" refer to devices and technologies for tracking the expiration dates of food ingredients and making priority usage suggestions based on that information.

[0137] To implement this invention, the user first wears earphones with a camera and uses a consumer robot or smartphone as a cooking support system. An image acquisition means captures images of the ingredients and the state of the refrigerator through this camera. This image data is transmitted to a cloud server via the terminal. Upon receiving the image data, the server uses an analysis means to identify the ingredients. The ingredients information includes not only the type and quantity, but also their arrangement in the refrigerator.

[0138] Next, the server uses a generation mechanism to generate appropriate cooking suggestions based on this ingredient information. This generation process takes into account the user's health information and past eating history. The generated cooking suggestions are then visualized on the user's smartphone or robot display via a presentation mechanism.

[0139] Once cooking begins, a monitoring device continuously records the user's cooking process and transmits the video to a server. The server analyzes the video and uses a support information generation device to create instructions based on the progress of the cooking. These instructions are then transmitted to the user in real time via a voice communication device. For example, specific instructions such as "cook a little longer" are conveyed by voice.

[0140] Furthermore, this system includes a management mechanism that tracks the expiration dates of food items in the refrigerator, suggesting that items nearing their expiration date be used first. This suggestion is also communicated to the user via voice, contributing to the reduction of food waste.

[0141] For example, if the prompt "I have taken pictures of the ingredients in my refrigerator with my camera. Please generate a healthy recipe using these ingredients" is input to the AI ​​model, a tomato and spinach omelet will be suggested. The user can then proceed with cooking based on this. In this way, by correctly analyzing the submitted images and providing the user with appropriate cooking support information, the user can lead an efficient and healthy diet.

[0142] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0143] Step 1:

[0144] The user takes pictures of the inside of the refrigerator and food items using a camera attached to their earphones. The input is image data acquired through the camera, and the device sends this data to a cloud server. At this time, the images are compressed and encrypted, and data conversion is performed to ensure stable communication.

[0145] Step 2:

[0146] The server processes the received image data using an analysis tool. The input is image data, and the analysis tool uses a machine learning algorithm to identify the type and quantity of ingredients. The output is the ingredient identification result, and this information is stored in a database and used to generate cooking suggestions. Specifically, an image recognition model analyzes the image of the ingredients and assigns labels accordingly.

[0147] Step 3:

[0148] The server uses ingredient information obtained through analysis to generate appropriate cooking suggestions via a generation mechanism. Inputs include ingredient information, user health data, and past meal history, while output is a cooking recipe suggested to the user. Data processing involves cross-referencing with a health information database to generate optimal cooking suggestions.

[0149] Step 4:

[0150] The terminal displays the generated cooking suggestions to the user through a presentation mechanism. The input is the generated cooking recipe, and the output is the recipe information displayed on the user interface. Specific actions include displaying ingredient lists and instructions on a smartphone or robot display.

[0151] Step 5:

[0152] When a user begins cooking, the monitoring system continuously acquires video footage of the cooking process. The input is the user's cooking video, which is transmitted to the server in real time by the terminal. The video data is optimized through data conversion for efficient processing.

[0153] Step 6:

[0154] The server analyzes the acquired cooking video and generates instructions using a support information generation system. The input is the cooking video, and the output is specific instructions corresponding to the progress of the cooking. A video analysis algorithm is used, and voice guidance is designed according to the progress.

[0155] Step 7:

[0156] The terminal communicates generated instructions to the user via voice transmission. The input is the generated instructions, and the output is the audio information that the user can hear. Specifically, speech synthesis technology is used to provide the user with accurate instructions.

[0157] Step 8:

[0158] The server uses management tools to track the expiration dates of food ingredients and provides priority usage suggestions based on those dates. Inputs are food ingredient storage status and expiration date data, while output is usage suggestions corresponding to the expiration date. Prioritization is determined by database queries and presented to the user.

[0159] Step 9:

[0160] The terminal uses voice communication to convey usage suggestions for reducing food waste to the user. The input is the usage suggestions generated by the management system, and the output is the voice that the user hears. The suggestions are converted into voice using a speech synthesis function and quickly conveyed to the user.

[0161] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0162] This invention is a system that utilizes an emotion engine installed in camera-equipped earphones worn by the user to support the user's cooking experience in a more personalized way. The emotion engine analyzes the user's facial expressions and voice tone to recognize emotions, and optimizes cooking suggestions and support based on this.

[0163] The user wears earphones and uses the camera to take pictures of the contents of the refrigerator and the food items, and the device sends these images to a server. The server analyzes the received images, extracts information about the food items, and compares it with a database.

[0164] In this system, an emotion engine monitors the user's voice and facial expressions to analyze the user's current emotional state. Based on this emotional information, the server generates cooking suggestions, customizing recipes according to the user's emotions. For example, if the user is feeling stressed, the system will suggest recipes that are expected to have a relaxing effect, providing emotion-responsive cooking suggestions.

[0165] During cooking, the device continuously transmits video using its camera, and the server analyzes the cooking progress based on the video and emotional information. The server generates necessary cooking support information in response to the user's emotional changes and provides specific instructions via voice. This process allows the user to have a comfortable cooking experience.

[0166] For example, if a user appears tired, the emotion engine analyzes this and suggests a nutritious recipe that helps with fatigue recovery. Also, if the server detects that the user is feeling anxious during cooking, it provides more detailed and specific instructions to help them proceed with cooking with peace of mind.

[0167] As described above, the present invention provides personalized cooking support that takes into account the user's emotions and health condition, making cooking more efficient and comfortable.

[0168] The following describes the processing flow.

[0169] Step 1:

[0170] The user uses the earphone's camera to photograph the food items inside the refrigerator. The captured image is saved to the device.

[0171] Step 2:

[0172] The device sends the saved image to the server. The server receives the image and begins the analysis process.

[0173] Step 3:

[0174] The server applies an image analysis algorithm to identify the type and quantity of ingredients and compares the ingredient information with a database.

[0175] Step 4:

[0176] The server uses an emotion engine to analyze the user's voice tone and facial expressions in real time via the camera and microphone, collecting emotional information.

[0177] Step 5:

[0178] Based on the acquired ingredient information and emotional information, the server generates a recipe that suits the user's emotional state. For example, if the user wants to relax, it will suggest a simple and delicious recipe.

[0179] Step 6:

[0180] The generated recipes are presented to the user via the device. The user reviews the suggested recipes and selects the one they wish to create.

[0181] Step 7:

[0182] The device begins cooking according to the recipe selected by the user. During cooking, the device continuously acquires video using its camera and sends it to the server.

[0183] Step 8:

[0184] The server analyzes video footage and continuously acquired emotional information to determine the progress of cooking in real time.

[0185] Step 9:

[0186] The server generates instructions according to the cooking process and provides specific cooking advice via voice through the terminal, taking into account the user's emotional state.

[0187] Step 10:

[0188] Once cooking is complete, the server records the ingredients used in a database and updates ingredient inventory and expiration date information to help with future recipe suggestions.

[0189] (Example 2)

[0190] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0191] In recent years, there has been a growing demand for personalized services that meet the diverse needs of users. However, conventional cooking support systems have merely provided ingredient information without considering the user's emotions. Therefore, there is a need to develop methods to reduce the anxiety and stress users feel while cooking and to provide a comfortable cooking experience.

[0192] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0193] In this invention, the server includes an image acquisition means, an analysis means, a generation means, an emotion analysis means, and a presentation means. This enables personalized cooking support that customizes cooking suggestions according to the user's emotions, providing the user with a sense of security and comfort.

[0194] "Image acquisition means" refers to a function that uses a hardware device owned by the user to acquire visual information of objects or scenes as digital data.

[0195] "Analysis means" refers to an algorithm or program for processing acquired digital image data and identifying useful information, specifically food ingredient information, from it.

[0196] The "generation means" refers to a function that automatically creates and provides appropriate cooking suggestions to the user based on the analyzed information.

[0197] "Emotional analysis methods" are technologies that analyze the tone of a user's voice and facial expressions obtained from audio and video to identify the emotional state of that person.

[0198] "Presentation means" refers to interfaces such as displays and audio output devices that convey the information and suggestions created by the system to the user in their final form.

[0199] "Monitoring means" refers to equipment or methods for continuously collecting video footage acquired by the user while cooking, in order to understand the current cooking status.

[0200] The "support information generation means" is a function that generates information to appropriately support the user based on data obtained by the monitoring means.

[0201] "Means of communication" refers to methods for efficiently conveying generated cooking support information to the user, primarily using audio and screen displays.

[0202] "Management methods" refer to the process of managing deterioration information based on acquired food ingredient information, utilizing that storage information, and suggesting the optimal timing for use.

[0203] This invention is a system that provides personalized support for a user's cooking activities through image acquisition means using a camera-equipped acoustic device worn by the user and data processing by a server.

[0204] The image acquisition method uses the built-in camera function of the user's audio device to photograph the contents of the refrigerator or the ingredients used for cooking. This image is then transmitted as digital data to the server by the terminal.

[0205] The server analyzes the received image data using image processing software to identify the food ingredient information contained within. A general image recognition library can be used as the analysis method. Based on the information obtained through the analysis, the server utilizes a generative AI model to optimize the user's ingredient selection and generates customized cooking suggestions.

[0206] Furthermore, the server analyzes the tone of voice and facial expressions acquired from the user's audio device and determines the user's emotional state using emotion analysis tools. This information is reflected in the generated cooking suggestions, so that the provided recipes and procedures are tailored to the user's emotions.

[0207] For example, if sentiment analysis indicates that the user is fatigued, the server can suggest a simple recipe using nutritious ingredients. An example of a prompt input to the generative AI model would be: "Based on the user's emotional state, suggest a dinner recipe for tonight. The user seems tired lately, so a dish that helps relieve fatigue would be good."

[0208] While the user is cooking, the device continuously transmits video to the server via its camera to monitor the progress of the cooking. Based on the user's emotional changes and the video information, the server guides the user through the cooking process and provides real-time cooking support. This allows the user to cook with peace of mind. This system aims to make cooking more comfortable and effective by providing services tailored to the user's experience and emotions.

[0209] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0210] Step 1:

[0211] The user wears a camera-equipped acoustic device and photographs the contents of a refrigerator or ingredients used for cooking. During this process, the user's device acquires image data and prepares to send it to the terminal. The input is digital image data, which is used for subsequent analysis.

[0212] Step 2:

[0213] The terminal transmits image data provided by the user to the server. This transmission process utilizes a communication protocol to ensure secure and rapid delivery of the data to the server. The input is the user's image data, and the output is the transmission of data to the server.

[0214] Step 3:

[0215] The server analyzes the received image data. It uses image recognition software to identify food ingredient information and extracts specific details. During this process, an image analysis algorithm is used to obtain an identifier for each food ingredient from the image data. The input is the image data sent to the server, and the output is food ingredient information.

[0216] Step 4:

[0217] The server uses a generative AI model to create cooking suggestions based on the analyzed ingredient information. Prompt statements are input to the generative AI model, generating recipes tailored to the user. For example, a prompt such as "Suggest a dinner recipe for tonight based on the user's emotional state" might be used. The input consists of ingredient information and prompt statements, while the output is a cooking suggestion.

[0218] Step 5:

[0219] Simultaneously, the user's facial expressions and voice tone are transmitted from their device to the server via emotion analysis to determine the user's emotional state. An emotion analysis algorithm is used to identify emotional information from the facial expression and voice data. The input is facial expression and voice data, and the output is emotional information.

[0220] Step 6:

[0221] The server customizes the generated cooking suggestions with emotional information and provides the user with an optimized recipe. Using a presentation method, the cooking suggestions are presented directly to the user via audio or visual means through the terminal. The input is the cooking suggestion and emotional information before customization, and the output is the customized recipe.

[0222] Step 7:

[0223] During cooking, the user uses a camera on their device to continuously transmit the progress of the cooking to the server. The server analyzes the received video data using monitoring and support information generation means to understand the progress of the cooking. The input is video data, and the output is cooking support information.

[0224] Step 8:

[0225] The server provides cooking assistance information to the user via voice or text, offering necessary instructions. This ensures a smooth and comfortable cooking process. The input is assistance information, and the output is voice or text instructions.

[0226] (Application Example 2)

[0227] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0228] In modern life, users seek increased efficiency and satisfaction during cooking. However, conventional cooking systems and methods often fail to adequately address users' emotional states and individual needs, resulting in a uniform cooking experience and potentially lower satisfaction. Furthermore, insufficient management of food storage conditions makes it difficult to effectively utilize ingredients, which also contributes to a decline in users' quality of life.

[0229] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0230] In this invention, the server includes an image acquisition means, an analysis means for identifying item information, a generation means for generating cooking suggestions based on the user's emotional state, and a management means for managing the shelf life of the items. This enables customized cooking suggestions and support according to the user's emotional state, improving satisfaction with the cooking experience and allowing for the effective use of the items.

[0231] "Image acquisition means" refers to a device installed in a user's equipment for collecting video information.

[0232] "Analysis means" refers to a device or program that processes acquired images and extracts useful data such as item information.

[0233] "Generation means" refers to a device or program that creates cooking suggestions tailored to the user's core state based on information obtained through analysis.

[0234] "Presentation means" refers to a device or interface for showing the generated cooking suggestions to the user.

[0235] A "monitoring device" is a device or program that continuously observes and records the tasks being performed by the user.

[0236] A "support information generation means" is a device or program that analyzes information obtained through monitoring and creates information to assist the user's work.

[0237] "Transmission means" refers to an audio output device or program for conveying generated support information to the user.

[0238] "Management means" refers to a device or program that tracks the retention period of acquired items and makes priority usage suggestions based on that information.

[0239] This system consists of camera-equipped earphones worn by the user, a cloud server, and a dedicated application. The user can use the earphone's camera to film the inside of a refrigerator or the cooking process. The earphones are equipped with sensors that detect the user's voice tone and facial expressions, and an emotion analysis engine analyzes this information to determine their emotions.

[0240] The server receives video transmitted from the image acquisition device and extracts item information using analysis software. At this stage, a machine learning model is used for food ingredient recognition, and the ingredients are identified by comparing them with a database. The analyzed item information and the user's emotional state are then used by a generation device to create appropriate cooking suggestions.

[0241] The generated cooking suggestions are guided to the user via audio through a presentation device and are customized to the user's emotional state. As the user proceeds with cooking, a monitoring device continuously acquires video, and a support information generation device generates real-time work support information. This allows for, for example, the provision of audio guides to make the recipe easier to understand.

[0242] The management method involves tracking expiration dates and optimizing suggestions to prioritize the use of ingredients nearing their expiration date.

[0243] For example, if a user feels fatigued while cooking, the server will recognize this through its emotion analysis engine and suggest a recipe using nutrients that are effective in relieving fatigue. If the cooking steps are deemed difficult, the server will assist the user by providing more detailed explanations through voice guidance.

[0244] The following is an example of a prompt message generated using a generative AI model.

[0245] "Based on the user's emotional analysis results from the emotion engine, suggest a relaxing dinner recipe. Also, display a list of the necessary ingredients."

[0246] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0247] Step 1:

[0248] The user wears earphones with a camera and films the inside of a refrigerator or while cooking. The input is video data from the camera, and the output is data transfer to a server. The user uses the earphones to record video in real time and prepares to send the video from the device to the server.

[0249] Step 2:

[0250] The server receives the transmitted video and extracts item information using image analysis software. The input is video data sent by the user, and the output is the analyzed item characteristic information. In this process, a machine learning model is used to identify the type and freshness of the food by comparing it with a database.

[0251] Step 3:

[0252] The server uses an emotion analysis engine to analyze the user's emotional state from the user's voice and video. The input is the user's voice tone and facial expression data, and the output is the analyzed emotional state. The server uses a voice analysis algorithm to evaluate the voice tone, and the emotion engine determines the user's emotion based on the results.

[0253] Step 4:

[0254] The server uses a generative AI model based on item information and emotional states to generate customized cooking suggestions via relevant prompts. The input is item information and emotional information, and the output is cooking suggestions. The generative AI model uses the prompts to infer and propose the most suitable recipe and cooking procedure for the user.

[0255] Step 5:

[0256] The terminal presents customized cooking suggestions to the user via voice through a presentation mechanism. The input is cooking suggestions generated on the server, and the output is voice guidance to the user. The terminal uses speech synthesis technology to convey the suggestions to the user.

[0257] Step 6:

[0258] During cooking, the device continuously acquires video and transmits it to the server. The input is video data from the device, and the output is data transmission to the server. The user keeps the earphones on and records the progress of the cooking.

[0259] Step 7:

[0260] The server analyzes video data and generates information to support the user's work using a support information generation mechanism. The input is video data of the cooking process, and the output is work support information. The server performs video analysis, monitors the progress, and generates necessary advice and instructions.

[0261] Step 8:

[0262] The terminal provides the user with specific instructions via voice based on the generated support information. The input is support information, and the output is voice-based user assistance. The terminal supports cooking by providing clear instructions to the user using a voice output device.

[0263] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0264] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0265] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0266] [Second Embodiment]

[0267] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0268] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0269] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0270] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0271] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0272] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0273] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0274] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0275] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0276] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0277] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0278] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0279] This invention relates to a cooking support system consisting of a camera-equipped earphone worn by the user, a terminal that controls it, and a server connected to the cloud.

[0280] When a user takes a picture of the ingredients they will use for cooking or the contents of their refrigerator through the camera, the device sends the image to a server. The server receives this image, analyzes the type and quantity of ingredients, and compares it with a database. Based on this analysis, the server generates an appropriate recipe that takes into account the user's health condition and past eating history. The generated recipe is then presented to the user through the device.

[0281] As a specific example, assume that the user takes pictures of the inventory of eggs, tomatoes, and spinach in the refrigerator with a camera. Then, based on these ingredients, the server proposes "tomato and spinach omelette" as the optimal recipe.

[0282] After cooking starts, the terminal continuously monitors the cooking status using the camera and transmits the video to the server. The server analyzes this in real time and provides the user with specific cooking instructions according to the progress. For example, the user can receive specific instructions such as "should be cooked a little longer" and "add salt next" by voice.

[0283] Furthermore, this system manages the expiration dates of ingredients and automatically generates recipes such that ingredients with approaching expiration dates are preferentially used. This function can reduce food waste.

[0284] As described above, the present invention improves the efficiency of the user's daily meal preparation, supports the realization of a healthy diet, and reduces food waste.

[0285] The processing flow will be described below.

[0286] Step 1:

[0287] The user uses the camera of the earphone to take pictures of the ingredients in the refrigerator and the leaflets in hand. The taken pictures are saved in the terminal.

[0288] Step 2:

[0289] The terminal transmits the saved pictures to the server. The server receives the pictures and starts the analysis process.

[0290] Step 3:

[0291] The server uses a machine learning model to analyze the received pictures, identify the ingredients and leaflet information in the pictures, and extract the ingredient names, quantities, and special sale information.

[0292] Step 4:

[0293] The server compares the analysis results with the food ingredient database and builds a list of ingredients owned by the user. It also checks the expiration date information for each ingredient and updates it in the management database.

[0294] Step 5:

[0295] The server generates multiple recipes using a generation AI based on acquired ingredient information, user health data, and meal history. During this process, it prioritizes the use of ingredients with the nearest expiration date.

[0296] Step 6:

[0297] The generated recipe is sent to the device, which then suggests it to the user. The user reviews the recipe and selects the one they want to create.

[0298] Step 7:

[0299] The device starts cooking based on the recipe selected by the user. The terminal monitors the cooking process with a camera and transmits the video to the server in real time.

[0300] Step 8:

[0301] The server analyzes the transmitted video and determines the cooking process. Based on the situation, AI generates necessary operations and precautions, and sends specific cooking instructions to the terminal.

[0302] Step 9:

[0303] The terminal relays instructions from the server to the user via voice, supporting the user in ensuring they can cook correctly.

[0304] Step 10:

[0305] After a user finishes cooking, the server updates the ingredient list in its database and optimizes the next recipe suggestion by considering ingredient availability and expiration date information.

[0306] (Example 1)

[0307] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0308] In modern households, maintaining a healthy diet and reducing food waste are important issues. However, it is difficult to accurately grasp the condition of food ingredients and perform appropriate cooking during daily busy schedules. Furthermore, in meal preparation, it is not possible to automatically obtain proposals considering individual health conditions and past selection histories, resulting in a problem that wasted food is likely to occur.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0310] In this invention, the server includes a visual information acquisition means worn by the user, an analysis means for analyzing the visual data obtained by the visual information acquisition means and identifying article information, and a generation means for generating proposal information based on the article information obtained by the analysis means. As a result, the user can efficiently and healthily prepare meals based on individual health conditions and past selection histories, and reduce food waste.

[0311] The "visual information acquisition means" is a device that acquires visual information on-site as digital data when worn by the user.

[0312] The "analysis means" is a function for processing the acquired visual data and identifying the types and conditions of articles included in the image.

[0313] The "generation means" is a function for generating useful proposals and instructions for the user based on the analyzed article information.

[0314] "Adjustment means" refers to a function that optimizes the generated suggestion information according to the user's health status and past selection history.

[0315] "Presentation means" refers to a function for visually or audibly conveying generated or adjusted information to the user.

[0316] "Monitoring means" refers to devices and functions for continuously recording user actions and acquiring that data.

[0317] "Information generation means" refers to a function that analyzes data obtained by monitoring means and generates useful support information for the user.

[0318] A "means of communication" refers to a function for conveying generated support information to the user.

[0319] "Management measures" refer to functions that track the status of acquired items, such as their expiration dates, and propose the optimal usage method based on that information.

[0320] This invention relates to an information processing system for user operation. This system consists of a camera-equipped information acquisition device (hereinafter referred to as the earphone device) worn by the user, a terminal for processing data acquired from the earphone device, and a server connected to the cloud. The earphone device is designed to allow the user to easily acquire visual information during everyday activities.

[0321] The terminal receives image data from the earphone device and transmits it to the server via the internet. Specifically, the data is uploaded to the cloud via the HTTP protocol using Wi-Fi or mobile data communication. On the server side, advanced image recognition software such as TensorFlow and PyTorch is used to analyze the image data and identify item information.

[0322] Based on this analysis information, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate recipes and necessary instructions for the user. For example, based on item information such as eggs, tomatoes, and spinach, it can suggest a recipe like "tomato and spinach omelet."

[0323] Furthermore, to take into account the user's health information and past dietary history, the server can utilize a database system to optimize suggested recipes to individual needs. The generated suggestion information is presented to the user via the terminal and communicated clearly using text and speech synthesis software (e.g., Google Text-to-Speech).

[0324] Another function is that the terminal continuously records the user's actions while cooking and sends the video to the server. The server analyzes this in real time and generates specific cooking instructions as the process progresses, providing instructions that the user can specifically follow, such as "You should bake it a little longer" or "Add salt next."

[0325] Regarding the ingredient management function, the server manages the expiration dates of each ingredient in a database, and automatically generates recipe suggestions that prioritize the use of ingredients as their expiration date approaches, thereby effectively reducing food waste. This process helps promote an efficient and healthy diet and contributes to the sustainable use of ingredients.

[0326] A concrete example of a prompt might be input such as, "Please tell me some recipes that use the ingredients in my refrigerator without wasting anything." Based on this prompt, the system can perform effective information processing and provide the user with the most suitable suggestions.

[0327] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0328] Step 1:

[0329] The user wears a visual information acquisition device and takes pictures of items used for cooking and the inside of the refrigerator.

[0330] Input: Visual information is input via the camera.

[0331] Output: Image data is generated.

[0332] As a specific example, the user uses their earphone device to point the camera directly at an egg, a tomato, and spinach.

[0333] Step 2:

[0334] The device receives the captured image data and sends it to the server via the internet.

[0335] Input: Receive captured image data.

[0336] Output: An image file for sending to the server is generated.

[0337] Specifically, the device uses Wi-Fi to send image data to a server in the cloud via the HTTP protocol.

[0338] Step 3:

[0339] The server analyzes the image data to identify the item information.

[0340] Input: Image data received by the server.

[0341] Output: Information about the items in the image (e.g., identification of eggs, tomatoes, spinach) is generated.

[0342] In terms of specific operation, the server-side image recognition software (such as TensorFlow) processes the visual data using a neural network and outputs the results in text format.

[0343] Step 4:

[0344] The server generates suggested information using an AI model based on the analyzed item information.

[0345] Input: Item information data, user's health status, and past selection history.

[0346] Output: Suggested recipes and cooking instructions.

[0347] In terms of specific operations, the server references a database and an AI model (e.g., GPT-3) to generate recipes such as "tomato and spinach omelet."

[0348] Step 5:

[0349] The terminal presents the user with suggested information received from the server.

[0350] Input: Suggestion information from the server.

[0351] Output: Visual and auditory information presented to the user.

[0352] In terms of specific operations, the terminal displays the received information on its screen and uses speech synthesis software to give voice instructions to the user.

[0353] Step 6:

[0354] The terminal records user actions during cooking and sends that data to the server.

[0355] Input: Real-time video data during cooking.

[0356] Output: Video data sent to the server.

[0357] Specifically, the system uses the device's camera to capture images every few seconds and streams them to a server.

[0358] Step 7:

[0359] The server analyzes the cooking progress and provides additional cooking instructions to the user.

[0360] Input: Real-time cooking video data.

[0361] Output: Additional voice instructions based on the cooking status.

[0362] In terms of specific operations, the server uses deep learning to process the video data and generates specific instructions such as "bake it a little longer" or "add salt next."

[0363] Step 8:

[0364] The server manages the expiration dates of items and makes priority recommendations based on them.

[0365] Input: Information on managed items and their expiration dates.

[0366] Output: Suggestion information considering items nearing their expiration date.

[0367] Specifically, the server uses a database to access expiration date information and automatically generates recipes to reduce food waste.

[0368] (Application Example 1)

[0369] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0370] Users with limited cooking knowledge or those leading busy lives face challenges in using ingredients effectively and healthily, reducing food waste, and preparing meals smoothly. Technologies are needed to address this issue.

[0371] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0372] In this invention, the server includes an image acquisition means, an analysis means for analyzing the images acquired by the image acquisition means and identifying ingredient information, a generation means for generating cooking suggestions based on the ingredient information obtained by the analysis means, a presentation means for presenting the generated cooking suggestions to the user, and a voice transmission means for transmitting cooking support information to the user by voice. As a result, the user can obtain the information and instructions necessary for cooking in real time from both visual and auditory perspectives, enabling them to proceed with cooking efficiently.

[0373] "Image acquisition means" refers to devices and technologies for acquiring image data of food items and storage locations photographed by the user.

[0374] "Analysis means" refers to a system that executes processing and algorithms to identify food ingredient information from acquired image data.

[0375] "Generation means" refers to methods and techniques for creating appropriate cooking suggestions and recipes based on ingredient information obtained through analysis means.

[0376] "Presentation means" refers to devices and technologies for visually displaying generated cooking suggestions and recipes to the user.

[0377] "Voice communication means" refers to systems and technologies that use voice to convey generated cooking suggestions and support information to users.

[0378] "Monitoring means" refers to devices and technologies that continuously acquire video footage of a user cooking and allow for monitoring of the situation.

[0379] "Support information generation means" refers to a method that analyzes the cooking process obtained by monitoring means and generates instructions and advice necessary during the cooking process.

[0380] "Management measures" refer to devices and technologies for tracking the expiration dates of food ingredients and making priority usage suggestions based on that information.

[0381] To implement this invention, the user first wears earphones with a camera and uses a consumer robot or smartphone as a cooking support system. An image acquisition means captures images of the ingredients and the state of the refrigerator through this camera. This image data is transmitted to a cloud server via the terminal. Upon receiving the image data, the server uses an analysis means to identify the ingredients. The ingredients information includes not only the type and quantity, but also their arrangement in the refrigerator.

[0382] Next, the server uses a generation mechanism to generate appropriate cooking suggestions based on this ingredient information. This generation process takes into account the user's health information and past eating history. The generated cooking suggestions are then visualized on the user's smartphone or robot display via a presentation mechanism.

[0383] Once cooking begins, a monitoring device continuously records the user's cooking process and transmits the video to a server. The server analyzes the video and uses a support information generation device to create instructions based on the progress of the cooking. These instructions are then transmitted to the user in real time via a voice communication device. For example, specific instructions such as "cook a little longer" are conveyed by voice.

[0384] Furthermore, this system includes a management mechanism that tracks the expiration dates of food items in the refrigerator, suggesting that items nearing their expiration date be used first. This suggestion is also communicated to the user via voice, contributing to the reduction of food waste.

[0385] For example, if the prompt "I have taken pictures of the ingredients in my refrigerator with my camera. Please generate a healthy recipe using these ingredients" is input to the AI ​​model, a tomato and spinach omelet will be suggested. The user can then proceed with cooking based on this. In this way, by correctly analyzing the submitted images and providing the user with appropriate cooking support information, the user can lead an efficient and healthy diet.

[0386] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0387] Step 1:

[0388] The user takes pictures of the inside of the refrigerator and food items using a camera attached to their earphones. The input is image data acquired through the camera, and the device sends this data to a cloud server. At this time, the images are compressed and encrypted, and data conversion is performed to ensure stable communication.

[0389] Step 2:

[0390] The server processes the received image data using an analysis tool. The input is image data, and the analysis tool uses a machine learning algorithm to identify the type and quantity of ingredients. The output is the ingredient identification result, and this information is stored in a database and used to generate cooking suggestions. Specifically, an image recognition model analyzes the image of the ingredients and assigns labels accordingly.

[0391] Step 3:

[0392] The server uses ingredient information obtained through analysis to generate appropriate cooking suggestions via a generation mechanism. Inputs include ingredient information, user health data, and past meal history, while output is a cooking recipe suggested to the user. Data processing involves cross-referencing with a health information database to generate optimal cooking suggestions.

[0393] Step 4:

[0394] The terminal displays the generated cooking suggestions to the user through a presentation mechanism. The input is the generated cooking recipe, and the output is the recipe information displayed on the user interface. Specific actions include displaying ingredient lists and instructions on a smartphone or robot display.

[0395] Step 5:

[0396] When a user begins cooking, the monitoring system continuously acquires video footage of the cooking process. The input is the user's cooking video, which is transmitted to the server in real time by the terminal. The video data is optimized through data conversion for efficient processing.

[0397] Step 6:

[0398] The server analyzes the acquired cooking video and generates instructions using a support information generation system. The input is the cooking video, and the output is specific instructions corresponding to the progress of the cooking. A video analysis algorithm is used, and voice guidance is designed according to the progress.

[0399] Step 7:

[0400] The terminal communicates generated instructions to the user via voice transmission. The input is the generated instructions, and the output is the audio information that the user can hear. Specifically, speech synthesis technology is used to provide the user with accurate instructions.

[0401] Step 8:

[0402] The server uses management tools to track the expiration dates of food ingredients and provides priority usage suggestions based on those dates. Inputs are food ingredient storage status and expiration date data, while output is usage suggestions corresponding to the expiration date. Prioritization is determined by database queries and presented to the user.

[0403] Step 9:

[0404] The terminal uses voice communication to convey usage suggestions for reducing food waste to the user. The input is the usage suggestions generated by the management system, and the output is the voice that the user hears. The suggestions are converted into voice using a speech synthesis function and quickly conveyed to the user.

[0405] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0406] This invention is a system that utilizes an emotion engine installed in camera-equipped earphones worn by the user to support the user's cooking experience in a more personalized way. The emotion engine analyzes the user's facial expressions and voice tone to recognize emotions, and optimizes cooking suggestions and support based on this.

[0407] The user wears earphones and uses the camera to take pictures of the contents of the refrigerator and the food items, and the device sends these images to a server. The server analyzes the received images, extracts information about the food items, and compares it with a database.

[0408] In this system, an emotion engine monitors the user's voice and facial expressions to analyze the user's current emotional state. Based on this emotional information, the server generates cooking suggestions, customizing recipes according to the user's emotions. For example, if the user is feeling stressed, the system will suggest recipes that are expected to have a relaxing effect, providing emotion-responsive cooking suggestions.

[0409] During cooking, the device continuously transmits video using its camera, and the server analyzes the cooking progress based on the video and emotional information. The server generates necessary cooking support information in response to the user's emotional changes and provides specific instructions via voice. This process allows the user to have a comfortable cooking experience.

[0410] For example, if a user appears tired, the emotion engine analyzes this and suggests a nutritious recipe that helps with fatigue recovery. Also, if the server detects that the user is feeling anxious during cooking, it provides more detailed and specific instructions to help them proceed with cooking with peace of mind.

[0411] As described above, the present invention provides personalized cooking support that takes into account the user's emotions and health condition, making cooking more efficient and comfortable.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] The user uses the earphone's camera to photograph the food items inside the refrigerator. The captured image is saved to the device.

[0415] Step 2:

[0416] The device sends the saved image to the server. The server receives the image and begins the analysis process.

[0417] Step 3:

[0418] The server applies an image analysis algorithm to identify the type and quantity of ingredients and compares the ingredient information with a database.

[0419] Step 4:

[0420] The server uses an emotion engine to analyze the user's voice tone and facial expressions in real time via the camera and microphone, collecting emotional information.

[0421] Step 5:

[0422] Based on the acquired ingredient information and emotional information, the server generates a recipe that suits the user's emotional state. For example, if the user wants to relax, it will suggest a simple and delicious recipe.

[0423] Step 6:

[0424] The generated recipes are presented to the user via the device. The user reviews the suggested recipes and selects the one they wish to create.

[0425] Step 7:

[0426] The device begins cooking according to the recipe selected by the user. During cooking, the device continuously acquires video using its camera and sends it to the server.

[0427] Step 8:

[0428] The server analyzes video footage and continuously acquired emotional information to determine the progress of cooking in real time.

[0429] Step 9:

[0430] The server generates instructions according to the cooking process and provides specific cooking advice via voice through the terminal, taking into account the user's emotional state.

[0431] Step 10:

[0432] Once cooking is complete, the server records the ingredients used in a database and updates ingredient inventory and expiration date information to help with future recipe suggestions.

[0433] (Example 2)

[0434] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0435] In recent years, there has been a growing demand for personalized services that meet the diverse needs of users. However, conventional cooking support systems have merely provided ingredient information without considering the user's emotions. Therefore, there is a need to develop methods to reduce the anxiety and stress users feel while cooking and to provide a comfortable cooking experience.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0437] In this invention, the server includes an image acquisition means, an analysis means, a generation means, an emotion analysis means, and a presentation means. This enables personalized cooking support that customizes cooking suggestions according to the user's emotions, providing the user with a sense of security and comfort.

[0438] "Image acquisition means" refers to a function that uses a hardware device owned by the user to acquire visual information of objects or scenes as digital data.

[0439] "Analysis means" refers to an algorithm or program for processing acquired digital image data and identifying useful information, specifically food ingredient information, from it.

[0440] The "generation means" refers to a function that automatically creates and provides appropriate cooking suggestions to the user based on the analyzed information.

[0441] "Emotional analysis methods" are technologies that analyze the tone of a user's voice and facial expressions obtained from audio and video to identify the emotional state of that person.

[0442] "Presentation means" refers to interfaces such as displays and audio output devices that convey the information and suggestions created by the system to the user in their final form.

[0443] "Monitoring means" refers to equipment or methods for continuously collecting video footage acquired by the user while cooking, in order to understand the current cooking status.

[0444] The "support information generation means" is a function that generates information to appropriately support the user based on data obtained by the monitoring means.

[0445] "Means of communication" refers to methods for efficiently conveying generated cooking support information to the user, primarily using audio and screen displays.

[0446] "Management methods" refer to the process of managing deterioration information based on acquired food ingredient information, utilizing that storage information, and suggesting the optimal timing for use.

[0447] This invention is a system that provides personalized support for a user's cooking activities through image acquisition means using a camera-equipped acoustic device worn by the user and data processing by a server.

[0448] The image acquisition method uses the built-in camera function of the user's audio device to photograph the contents of the refrigerator or the ingredients used for cooking. This image is then transmitted as digital data to the server by the terminal.

[0449] The server analyzes the received image data using image processing software to identify the food ingredient information contained within. A general image recognition library can be used as the analysis method. Based on the information obtained through the analysis, the server utilizes a generative AI model to optimize the user's ingredient selection and generates customized cooking suggestions.

[0450] Furthermore, the server analyzes the tone of voice and facial expressions acquired from the user's audio device and determines the user's emotional state using emotion analysis tools. This information is reflected in the generated cooking suggestions, so that the provided recipes and procedures are tailored to the user's emotions.

[0451] For example, if sentiment analysis indicates that the user is fatigued, the server can suggest a simple recipe using nutritious ingredients. An example of a prompt input to the generative AI model would be: "Based on the user's emotional state, suggest a dinner recipe for tonight. The user seems tired lately, so a dish that helps relieve fatigue would be good."

[0452] While the user is cooking, the device continuously transmits video to the server via its camera to monitor the progress of the cooking. Based on the user's emotional changes and the video information, the server guides the user through the cooking process and provides real-time cooking support. This allows the user to cook with peace of mind. This system aims to make cooking more comfortable and effective by providing services tailored to the user's experience and emotions.

[0453] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0454] Step 1:

[0455] The user wears a camera-equipped acoustic device and photographs the contents of a refrigerator or ingredients used for cooking. During this process, the user's device acquires image data and prepares to send it to the terminal. The input is digital image data, which is used for subsequent analysis.

[0456] Step 2:

[0457] The terminal transmits image data provided by the user to the server. This transmission process utilizes a communication protocol to ensure secure and rapid delivery of the data to the server. The input is the user's image data, and the output is the transmission of data to the server.

[0458] Step 3:

[0459] The server analyzes the received image data. It uses image recognition software to identify food ingredient information and extracts specific details. During this process, an image analysis algorithm is used to obtain an identifier for each food ingredient from the image data. The input is the image data sent to the server, and the output is food ingredient information.

[0460] Step 4:

[0461] The server uses a generative AI model to create cooking suggestions based on the analyzed ingredient information. Prompt statements are input to the generative AI model, generating recipes tailored to the user. For example, a prompt such as "Suggest a dinner recipe for tonight based on the user's emotional state" might be used. The input consists of ingredient information and prompt statements, while the output is a cooking suggestion.

[0462] Step 5:

[0463] Simultaneously, the user's facial expressions and voice tone are transmitted from their device to the server via emotion analysis to determine the user's emotional state. An emotion analysis algorithm is used to identify emotional information from the facial expression and voice data. The input is facial expression and voice data, and the output is emotional information.

[0464] Step 6:

[0465] The server customizes the generated cooking suggestions with emotional information and provides the user with an optimized recipe. Using a presentation method, the cooking suggestions are presented directly to the user via audio or visual means through the terminal. The input is the cooking suggestion and emotional information before customization, and the output is the customized recipe.

[0466] Step 7:

[0467] During cooking, the user uses a camera on their device to continuously transmit the progress of the cooking to the server. The server analyzes the received video data using monitoring and support information generation means to understand the progress of the cooking. The input is video data, and the output is cooking support information.

[0468] Step 8:

[0469] The server provides cooking assistance information to the user via voice or text, offering necessary instructions. This ensures a smooth and comfortable cooking process. The input is assistance information, and the output is voice or text instructions.

[0470] (Application Example 2)

[0471] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0472] In modern life, users seek increased efficiency and satisfaction during cooking. However, conventional cooking systems and methods often fail to adequately address users' emotional states and individual needs, resulting in a uniform cooking experience and potentially lower satisfaction. Furthermore, insufficient management of food storage conditions makes it difficult to effectively utilize ingredients, which also contributes to a decline in users' quality of life.

[0473] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0474] In this invention, the server includes an image acquisition means, an analysis means for identifying item information, a generation means for generating cooking suggestions based on the user's emotional state, and a management means for managing the shelf life of the items. This enables customized cooking suggestions and support according to the user's emotional state, improving satisfaction with the cooking experience and allowing for the effective use of the items.

[0475] "Image acquisition means" refers to a device installed in a user's equipment for collecting video information.

[0476] "Analysis means" refers to a device or program that processes acquired images and extracts useful data such as item information.

[0477] "Generation means" refers to a device or program that creates cooking suggestions tailored to the user's core state based on information obtained through analysis.

[0478] "Presentation means" refers to a device or interface for showing the generated cooking suggestions to the user.

[0479] A "monitoring device" is a device or program that continuously observes and records the tasks being performed by the user.

[0480] A "support information generation means" is a device or program that analyzes information obtained through monitoring and creates information to assist the user's work.

[0481] "Transmission means" refers to an audio output device or program for conveying generated support information to the user.

[0482] "Management means" refers to a device or program that tracks the retention period of acquired items and makes priority usage suggestions based on that information.

[0483] This system consists of camera-equipped earphones worn by the user, a cloud server, and a dedicated application. The user can use the earphone's camera to film the inside of a refrigerator or the cooking process. The earphones are equipped with sensors that detect the user's voice tone and facial expressions, and an emotion analysis engine analyzes this information to determine their emotions.

[0484] The server receives video transmitted from the image acquisition device and extracts item information using analysis software. At this stage, a machine learning model is used for food ingredient recognition, and the ingredients are identified by comparing them with a database. The analyzed item information and the user's emotional state are then used by a generation device to create appropriate cooking suggestions.

[0485] The generated cooking suggestions are guided to the user via audio through a presentation device and are customized to the user's emotional state. As the user proceeds with cooking, a monitoring device continuously acquires video, and a support information generation device generates real-time work support information. This allows for, for example, the provision of audio guides to make the recipe easier to understand.

[0486] The management method involves tracking expiration dates and optimizing suggestions to prioritize the use of ingredients nearing their expiration date.

[0487] For example, if a user feels fatigued while cooking, the server will recognize this through its emotion analysis engine and suggest a recipe using nutrients that are effective in relieving fatigue. If the cooking steps are deemed difficult, the server will assist the user by providing more detailed explanations through voice guidance.

[0488] The following is an example of a prompt message generated using a generative AI model.

[0489] "Based on the user's emotional analysis results from the emotion engine, suggest a relaxing dinner recipe. Also, display a list of the necessary ingredients."

[0490] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0491] Step 1:

[0492] The user wears earphones with a camera and films the inside of a refrigerator or while cooking. The input is video data from the camera, and the output is data transfer to a server. The user uses the earphones to record video in real time and prepares to send the video from the device to the server.

[0493] Step 2:

[0494] The server receives the transmitted video and extracts item information using image analysis software. The input is video data sent by the user, and the output is the analyzed item characteristic information. In this process, a machine learning model is used to identify the type and freshness of the food by comparing it with a database.

[0495] Step 3:

[0496] The server uses an emotion analysis engine to analyze the user's emotional state from the user's voice and video. The input is the user's voice tone and facial expression data, and the output is the analyzed emotional state. The server uses a voice analysis algorithm to evaluate the voice tone, and the emotion engine determines the user's emotion based on the results.

[0497] Step 4:

[0498] The server uses a generative AI model based on item information and emotional states to generate customized cooking suggestions via relevant prompts. The input is item information and emotional information, and the output is cooking suggestions. The generative AI model uses the prompts to infer and propose the most suitable recipe and cooking procedure for the user.

[0499] Step 5:

[0500] The terminal presents customized cooking suggestions to the user via voice through a presentation mechanism. The input is cooking suggestions generated on the server, and the output is voice guidance to the user. The terminal uses speech synthesis technology to convey the suggestions to the user.

[0501] Step 6:

[0502] During cooking, the device continuously acquires video and transmits it to the server. The input is video data from the device, and the output is data transmission to the server. The user keeps the earphones on and records the progress of the cooking.

[0503] Step 7:

[0504] The server analyzes video data and generates information to support the user's work using a support information generation mechanism. The input is video data of the cooking process, and the output is work support information. The server performs video analysis, monitors the progress, and generates necessary advice and instructions.

[0505] Step 8:

[0506] The terminal provides the user with specific instructions via voice based on the generated support information. The input is support information, and the output is voice-based user assistance. The terminal supports cooking by providing clear instructions to the user using a voice output device.

[0507] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0508] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0509] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0510] [Third Embodiment]

[0511] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0512] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0513] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0514] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0515] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0516] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0517] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0518] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0519] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0520] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0521] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0522] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0523] This invention relates to a cooking support system consisting of a camera-equipped earphone worn by the user, a terminal that controls it, and a server connected to the cloud.

[0524] When a user takes a picture of the ingredients they will use for cooking or the contents of their refrigerator through the camera, the device sends the image to a server. The server receives this image, analyzes the type and quantity of ingredients, and compares it with a database. Based on this analysis, the server generates an appropriate recipe that takes into account the user's health condition and past eating history. The generated recipe is then presented to the user through the device.

[0525] For example, if a user takes a picture of the eggs, tomatoes, and spinach they have in their refrigerator, the server will suggest "tomato and spinach omelet" as the optimal recipe based on these ingredients.

[0526] Once cooking begins, the terminal continuously monitors the cooking progress using its camera and transmits the video to the server. The server analyzes this in real time and provides the user with specific cooking instructions as the process progresses. For example, the user can receive specific instructions via voice, such as "You should cook it a little longer" or "Add salt next."

[0527] Furthermore, this system manages the expiration dates of ingredients and automatically generates recipes that prioritize the use of ingredients with the nearest expiration date. This function helps reduce food waste.

[0528] As described above, the present invention enhances the efficiency of users' daily meal preparation, supporting the realization of a healthy diet and the reduction of food waste.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The user uses the camera on their earphones to take pictures of food items in the refrigerator or flyers they are holding. The captured images are saved to the device.

[0532] Step 2:

[0533] The device sends the saved image to the server. The server receives the image and begins the analysis process.

[0534] Step 3:

[0535] The server uses a machine learning model to analyze the received images, identify ingredients and flyer information within the images, and extract the names of the ingredients, quantities, and special offer information.

[0536] Step 4:

[0537] The server compares the analysis results with the food ingredient database and builds a list of ingredients owned by the user. It also checks the expiration date information for each ingredient and updates it in the management database.

[0538] Step 5:

[0539] The server generates multiple recipes using a generation AI based on acquired ingredient information, user health data, and meal history. During this process, it prioritizes the use of ingredients with the nearest expiration date.

[0540] Step 6:

[0541] The generated recipe is sent to the device, which then suggests it to the user. The user reviews the recipe and selects the one they want to create.

[0542] Step 7:

[0543] The device starts cooking based on the recipe selected by the user. The terminal monitors the cooking process with a camera and transmits the video to the server in real time.

[0544] Step 8:

[0545] The server analyzes the transmitted video and determines the cooking process. Based on the situation, AI generates necessary operations and precautions, and sends specific cooking instructions to the terminal.

[0546] Step 9:

[0547] The terminal relays instructions from the server to the user via voice, supporting the user in ensuring they can cook correctly.

[0548] Step 10:

[0549] After a user finishes cooking, the server updates the ingredient list in its database and optimizes the next recipe suggestion by considering ingredient availability and expiration date information.

[0550] (Example 1)

[0551] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0552] Maintaining a healthy diet and reducing food waste are crucial issues for modern households. However, in the midst of busy daily life, it is difficult to accurately assess the condition of ingredients and cook them appropriately. Furthermore, meal preparation does not automatically provide suggestions that take into account individual health conditions or past food choices, leading to a problem of food waste.

[0553] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0554] In this invention, the server includes a visual information acquisition means worn by the user, an analysis means for analyzing the visual data obtained by the visual information acquisition means and identifying item information, and a generation means for generating suggestion information based on the item information obtained by the analysis means. This enables the user to prepare efficient and healthy meals based on their individual health status and past selection history, thereby reducing food waste.

[0555] A "visual information acquisition means" is a device that, when worn by a user, acquires visual information from the site as digital data.

[0556] "Analysis means" refers to a function that processes acquired visual data and identifies the type and state of items contained within the image.

[0557] "Generation means" refers to a function that generates useful suggestions and instructions for the user based on analyzed item information.

[0558] "Adjustment means" refers to a function that optimizes the generated suggestion information according to the user's health status and past selection history.

[0559] "Presentation means" refers to a function for visually or audibly conveying generated or adjusted information to the user.

[0560] "Monitoring means" refers to devices and functions for continuously recording user actions and acquiring that data.

[0561] "Information generation means" refers to a function that analyzes data obtained by monitoring means and generates useful support information for the user.

[0562] A "means of communication" refers to a function for conveying generated support information to the user.

[0563] "Management measures" refer to functions that track the status of acquired items, such as their expiration dates, and propose the optimal usage method based on that information.

[0564] This invention relates to an information processing system for user operation. This system consists of a camera-equipped information acquisition device (hereinafter referred to as the earphone device) worn by the user, a terminal for processing data acquired from the earphone device, and a server connected to the cloud. The earphone device is designed to allow the user to easily acquire visual information during everyday activities.

[0565] The terminal receives image data from the earphone device and transmits it to the server via the internet. Specifically, the data is uploaded to the cloud via the HTTP protocol using Wi-Fi or mobile data communication. On the server side, advanced image recognition software such as TensorFlow and PyTorch is used to analyze the image data and identify item information.

[0566] Based on this analysis information, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate recipes and necessary instructions for the user. For example, based on item information such as eggs, tomatoes, and spinach, it can suggest a recipe like "tomato and spinach omelet."

[0567] Furthermore, to take into account the user's health information and past dietary history, the server can utilize a database system to optimize suggested recipes to individual needs. The generated suggestion information is presented to the user via the terminal and communicated clearly using text and speech synthesis software (e.g., Google Text-to-Speech).

[0568] Another function is that the terminal continuously records the user's actions while cooking and sends the video to the server. The server analyzes this in real time and generates specific cooking instructions as the process progresses, providing instructions that the user can specifically follow, such as "You should bake it a little longer" or "Add salt next."

[0569] Regarding the ingredient management function, the server manages the expiration dates of each ingredient in a database, and automatically generates recipe suggestions that prioritize the use of ingredients as their expiration date approaches, thereby effectively reducing food waste. This process helps promote an efficient and healthy diet and contributes to the sustainable use of ingredients.

[0570] A concrete example of a prompt might be input such as, "Please tell me some recipes that use the ingredients in my refrigerator without wasting anything." Based on this prompt, the system can perform effective information processing and provide the user with the most suitable suggestions.

[0571] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0572] Step 1:

[0573] The user wears a visual information acquisition device and takes pictures of items used for cooking and the inside of the refrigerator.

[0574] Input: Visual information is input via the camera.

[0575] Output: Image data is generated.

[0576] As a specific example, the user uses their earphone device to point the camera directly at an egg, a tomato, and spinach.

[0577] Step 2:

[0578] The device receives the captured image data and sends it to the server via the internet.

[0579] Input: Receive captured image data.

[0580] Output: An image file for sending to the server is generated.

[0581] Specifically, the device uses Wi-Fi to send image data to a server in the cloud via the HTTP protocol.

[0582] Step 3:

[0583] The server analyzes the image data to identify the item information.

[0584] Input: Image data received by the server.

[0585] Output: Information about the items in the image (e.g., identification of eggs, tomatoes, spinach) is generated.

[0586] In terms of specific operation, the server-side image recognition software (such as TensorFlow) processes the visual data using a neural network and outputs the results in text format.

[0587] Step 4:

[0588] The server generates suggested information using an AI model based on the analyzed item information.

[0589] Input: Item information data, user's health status, and past selection history.

[0590] Output: Suggested recipes and cooking instructions.

[0591] In terms of specific operations, the server references a database and an AI model (e.g., GPT-3) to generate recipes such as "tomato and spinach omelet."

[0592] Step 5:

[0593] The terminal presents the user with suggested information received from the server.

[0594] Input: Suggestion information from the server.

[0595] Output: Visual and auditory information presented to the user.

[0596] In terms of specific operations, the terminal displays the received information on its screen and uses speech synthesis software to give voice instructions to the user.

[0597] Step 6:

[0598] The terminal records user actions during cooking and sends that data to the server.

[0599] Input: Real-time video data during cooking.

[0600] Output: Video data sent to the server.

[0601] Specifically, the system uses the device's camera to capture images every few seconds and streams them to a server.

[0602] Step 7:

[0603] The server analyzes the cooking progress and provides additional cooking instructions to the user.

[0604] Input: Real-time cooking video data.

[0605] Output: Additional voice instructions based on the cooking status.

[0606] In terms of specific operations, the server uses deep learning to process the video data and generates specific instructions such as "bake it a little longer" or "add salt next."

[0607] Step 8:

[0608] The server manages the expiration dates of items and makes priority recommendations based on them.

[0609] Input: Information on managed items and their expiration dates.

[0610] Output: Suggestion information considering items nearing their expiration date.

[0611] Specifically, the server uses a database to access expiration date information and automatically generates recipes to reduce food waste.

[0612] (Application Example 1)

[0613] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0614] Users with limited cooking knowledge or those leading busy lives face challenges in using ingredients effectively and healthily, reducing food waste, and preparing meals smoothly. Technologies are needed to address this issue.

[0615] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0616] In this invention, the server includes an image acquisition means, an analysis means for analyzing the images acquired by the image acquisition means and identifying ingredient information, a generation means for generating cooking suggestions based on the ingredient information obtained by the analysis means, a presentation means for presenting the generated cooking suggestions to the user, and a voice transmission means for transmitting cooking support information to the user by voice. As a result, the user can obtain the information and instructions necessary for cooking in real time from both visual and auditory perspectives, enabling them to proceed with cooking efficiently.

[0617] "Image acquisition means" refers to devices and technologies for acquiring image data of food items and storage locations photographed by the user.

[0618] "Analysis means" refers to a system that executes processing and algorithms to identify food ingredient information from acquired image data.

[0619] "Generation means" refers to methods and techniques for creating appropriate cooking suggestions and recipes based on ingredient information obtained through analysis means.

[0620] "Presentation means" refers to devices and technologies for visually displaying generated cooking suggestions and recipes to the user.

[0621] "Voice communication means" refers to systems and technologies that use voice to convey generated cooking suggestions and support information to users.

[0622] "Monitoring means" refers to devices and technologies that continuously acquire video footage of a user cooking and allow for monitoring of the situation.

[0623] "Support information generation means" refers to a method that analyzes the cooking process obtained by monitoring means and generates instructions and advice necessary during the cooking process.

[0624] "Management measures" refer to devices and technologies for tracking the expiration dates of food ingredients and making priority usage suggestions based on that information.

[0625] To implement this invention, the user first wears earphones with a camera and uses a consumer robot or smartphone as a cooking support system. An image acquisition means captures images of the ingredients and the state of the refrigerator through this camera. This image data is transmitted to a cloud server via the terminal. Upon receiving the image data, the server uses an analysis means to identify the ingredients. The ingredients information includes not only the type and quantity, but also their arrangement in the refrigerator.

[0626] Next, the server uses a generation mechanism to generate appropriate cooking suggestions based on this ingredient information. This generation process takes into account the user's health information and past eating history. The generated cooking suggestions are then visualized on the user's smartphone or robot display via a presentation mechanism.

[0627] Once cooking begins, a monitoring device continuously records the user's cooking process and transmits the video to a server. The server analyzes the video and uses a support information generation device to create instructions based on the progress of the cooking. These instructions are then transmitted to the user in real time via a voice communication device. For example, specific instructions such as "cook a little longer" are conveyed by voice.

[0628] Furthermore, this system includes a management mechanism that tracks the expiration dates of food items in the refrigerator, suggesting that items nearing their expiration date be used first. This suggestion is also communicated to the user via voice, contributing to the reduction of food waste.

[0629] For example, if the prompt "I have taken pictures of the ingredients in my refrigerator with my camera. Please generate a healthy recipe using these ingredients" is input to the AI ​​model, a tomato and spinach omelet will be suggested. The user can then proceed with cooking based on this. In this way, by correctly analyzing the submitted images and providing the user with appropriate cooking support information, the user can lead an efficient and healthy diet.

[0630] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0631] Step 1:

[0632] The user takes pictures of the inside of the refrigerator and food items using a camera attached to their earphones. The input is image data acquired through the camera, and the device sends this data to a cloud server. At this time, the images are compressed and encrypted, and data conversion is performed to ensure stable communication.

[0633] Step 2:

[0634] The server processes the received image data using an analysis tool. The input is image data, and the analysis tool uses a machine learning algorithm to identify the type and quantity of ingredients. The output is the ingredient identification result, and this information is stored in a database and used to generate cooking suggestions. Specifically, an image recognition model analyzes the image of the ingredients and assigns labels accordingly.

[0635] Step 3:

[0636] The server uses ingredient information obtained through analysis to generate appropriate cooking suggestions via a generation mechanism. Inputs include ingredient information, user health data, and past meal history, while output is a cooking recipe suggested to the user. Data processing involves cross-referencing with a health information database to generate optimal cooking suggestions.

[0637] Step 4:

[0638] The terminal displays the generated cooking suggestions to the user through a presentation mechanism. The input is the generated cooking recipe, and the output is the recipe information displayed on the user interface. Specific actions include displaying ingredient lists and instructions on a smartphone or robot display.

[0639] Step 5:

[0640] When a user begins cooking, the monitoring system continuously acquires video footage of the cooking process. The input is the user's cooking video, which is transmitted to the server in real time by the terminal. The video data is optimized through data conversion for efficient processing.

[0641] Step 6:

[0642] The server analyzes the acquired cooking video and generates instructions using a support information generation system. The input is the cooking video, and the output is specific instructions corresponding to the progress of the cooking. A video analysis algorithm is used, and voice guidance is designed according to the progress.

[0643] Step 7:

[0644] The terminal communicates generated instructions to the user via voice transmission. The input is the generated instructions, and the output is the audio information that the user can hear. Specifically, speech synthesis technology is used to provide the user with accurate instructions.

[0645] Step 8:

[0646] The server uses management tools to track the expiration dates of food ingredients and provides priority usage suggestions based on those dates. Inputs are food ingredient storage status and expiration date data, while output is usage suggestions corresponding to the expiration date. Prioritization is determined by database queries and presented to the user.

[0647] Step 9:

[0648] The terminal uses voice communication to convey usage suggestions for reducing food waste to the user. The input is the usage suggestions generated by the management system, and the output is the voice that the user hears. The suggestions are converted into voice using a speech synthesis function and quickly conveyed to the user.

[0649] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0650] This invention is a system that utilizes an emotion engine installed in camera-equipped earphones worn by the user to support the user's cooking experience in a more personalized way. The emotion engine analyzes the user's facial expressions and voice tone to recognize emotions, and optimizes cooking suggestions and support based on this.

[0651] The user wears earphones and uses the camera to take pictures of the contents of the refrigerator and the food items, and the device sends these images to a server. The server analyzes the received images, extracts information about the food items, and compares it with a database.

[0652] In this system, an emotion engine monitors the user's voice and facial expressions to analyze the user's current emotional state. Based on this emotional information, the server generates cooking suggestions, customizing recipes according to the user's emotions. For example, if the user is feeling stressed, the system will suggest recipes that are expected to have a relaxing effect, providing emotion-responsive cooking suggestions.

[0653] During cooking, the device continuously transmits video using its camera, and the server analyzes the cooking progress based on the video and emotional information. The server generates necessary cooking support information in response to the user's emotional changes and provides specific instructions via voice. This process allows the user to have a comfortable cooking experience.

[0654] For example, if a user appears tired, the emotion engine analyzes this and suggests a nutritious recipe that helps with fatigue recovery. Also, if the server detects that the user is feeling anxious during cooking, it provides more detailed and specific instructions to help them proceed with cooking with peace of mind.

[0655] As described above, the present invention provides personalized cooking support that takes into account the user's emotions and health condition, making cooking more efficient and comfortable.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The user uses the earphone's camera to photograph the food items inside the refrigerator. The captured image is saved to the device.

[0659] Step 2:

[0660] The device sends the saved image to the server. The server receives the image and begins the analysis process.

[0661] Step 3:

[0662] The server applies an image analysis algorithm to identify the type and quantity of ingredients and compares the ingredient information with a database.

[0663] Step 4:

[0664] The server uses an emotion engine to analyze the user's voice tone and facial expressions in real time via the camera and microphone, collecting emotional information.

[0665] Step 5:

[0666] Based on the acquired ingredient information and emotional information, the server generates a recipe that suits the user's emotional state. For example, if the user wants to relax, it will suggest a simple and delicious recipe.

[0667] Step 6:

[0668] The generated recipes are presented to the user via the device. The user reviews the suggested recipes and selects the one they wish to create.

[0669] Step 7:

[0670] The device begins cooking according to the recipe selected by the user. During cooking, the device continuously acquires video using its camera and sends it to the server.

[0671] Step 8:

[0672] The server analyzes video footage and continuously acquired emotional information to determine the progress of cooking in real time.

[0673] Step 9:

[0674] The server generates instructions according to the cooking process and provides specific cooking advice via voice through the terminal, taking into account the user's emotional state.

[0675] Step 10:

[0676] Once cooking is complete, the server records the ingredients used in a database and updates ingredient inventory and expiration date information to help with future recipe suggestions.

[0677] (Example 2)

[0678] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0679] In recent years, there has been a growing demand for personalized services that meet the diverse needs of users. However, conventional cooking support systems have merely provided ingredient information without considering the user's emotions. Therefore, there is a need to develop methods to reduce the anxiety and stress users feel while cooking and to provide a comfortable cooking experience.

[0680] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0681] In this invention, the server includes an image acquisition means, an analysis means, a generation means, an emotion analysis means, and a presentation means. This enables personalized cooking support that customizes cooking suggestions according to the user's emotions, providing the user with a sense of security and comfort.

[0682] "Image acquisition means" refers to a function that uses a hardware device owned by the user to acquire visual information of objects or scenes as digital data.

[0683] "Analysis means" refers to an algorithm or program for processing acquired digital image data and identifying useful information, specifically food ingredient information, from it.

[0684] The "generation means" refers to a function that automatically creates and provides appropriate cooking suggestions to the user based on the analyzed information.

[0685] "Emotional analysis methods" are technologies that analyze the tone of a user's voice and facial expressions obtained from audio and video to identify the emotional state of that person.

[0686] "Presentation means" refers to interfaces such as displays and audio output devices that convey the information and suggestions created by the system to the user in their final form.

[0687] "Monitoring means" refers to equipment or methods for continuously collecting video footage acquired by the user while cooking, in order to understand the current cooking status.

[0688] The "support information generation means" is a function that generates information to appropriately support the user based on data obtained by the monitoring means.

[0689] "Means of communication" refers to methods for efficiently conveying generated cooking support information to the user, primarily using audio and screen displays.

[0690] "Management methods" refer to the process of managing deterioration information based on acquired food ingredient information, utilizing that storage information, and suggesting the optimal timing for use.

[0691] This invention is a system that provides personalized support for a user's cooking activities through image acquisition means using a camera-equipped acoustic device worn by the user and data processing by a server.

[0692] The image acquisition method uses the built-in camera function of the user's audio device to photograph the contents of the refrigerator or the ingredients used for cooking. This image is then transmitted as digital data to the server by the terminal.

[0693] The server analyzes the received image data using image processing software to identify the food ingredient information contained within. A general image recognition library can be used as the analysis method. Based on the information obtained through the analysis, the server utilizes a generative AI model to optimize the user's ingredient selection and generates customized cooking suggestions.

[0694] Furthermore, the server analyzes the tone of voice and facial expressions acquired from the user's audio device and determines the user's emotional state using emotion analysis tools. This information is reflected in the generated cooking suggestions, so that the provided recipes and procedures are tailored to the user's emotions.

[0695] For example, if sentiment analysis indicates that the user is fatigued, the server can suggest a simple recipe using nutritious ingredients. An example of a prompt input to the generative AI model would be: "Based on the user's emotional state, suggest a dinner recipe for tonight. The user seems tired lately, so a dish that helps relieve fatigue would be good."

[0696] While the user is cooking, the device continuously transmits video to the server via its camera to monitor the progress of the cooking. Based on the user's emotional changes and the video information, the server guides the user through the cooking process and provides real-time cooking support. This allows the user to cook with peace of mind. This system aims to make cooking more comfortable and effective by providing services tailored to the user's experience and emotions.

[0697] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0698] Step 1:

[0699] The user wears a camera-equipped acoustic device and photographs the contents of a refrigerator or ingredients used for cooking. During this process, the user's device acquires image data and prepares to send it to the terminal. The input is digital image data, which is used for subsequent analysis.

[0700] Step 2:

[0701] The terminal transmits image data provided by the user to the server. This transmission process utilizes a communication protocol to ensure secure and rapid delivery of the data to the server. The input is the user's image data, and the output is the transmission of data to the server.

[0702] Step 3:

[0703] The server analyzes the received image data. It uses image recognition software to identify food ingredient information and extracts specific details. During this process, an image analysis algorithm is used to obtain an identifier for each food ingredient from the image data. The input is the image data sent to the server, and the output is food ingredient information.

[0704] Step 4:

[0705] The server uses a generative AI model to create cooking suggestions based on the analyzed ingredient information. Prompt statements are input to the generative AI model, generating recipes tailored to the user. For example, a prompt such as "Suggest a dinner recipe for tonight based on the user's emotional state" might be used. The input consists of ingredient information and prompt statements, while the output is a cooking suggestion.

[0706] Step 5:

[0707] Simultaneously, the user's facial expressions and voice tone are transmitted from their device to the server via emotion analysis to determine the user's emotional state. An emotion analysis algorithm is used to identify emotional information from the facial expression and voice data. The input is facial expression and voice data, and the output is emotional information.

[0708] Step 6:

[0709] The server customizes the generated cooking suggestions with emotional information and provides the user with an optimized recipe. Using a presentation method, the cooking suggestions are presented directly to the user via audio or visual means through the terminal. The input is the cooking suggestion and emotional information before customization, and the output is the customized recipe.

[0710] Step 7:

[0711] During cooking, the user uses a camera on their device to continuously transmit the progress of the cooking to the server. The server analyzes the received video data using monitoring and support information generation means to understand the progress of the cooking. The input is video data, and the output is cooking support information.

[0712] Step 8:

[0713] The server provides cooking assistance information to the user via voice or text, offering necessary instructions. This ensures a smooth and comfortable cooking process. The input is assistance information, and the output is voice or text instructions.

[0714] (Application Example 2)

[0715] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0716] In modern life, users seek increased efficiency and satisfaction during cooking. However, conventional cooking systems and methods often fail to adequately address users' emotional states and individual needs, resulting in a uniform cooking experience and potentially lower satisfaction. Furthermore, insufficient management of food storage conditions makes it difficult to effectively utilize ingredients, which also contributes to a decline in users' quality of life.

[0717] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0718] In this invention, the server includes an image acquisition means, an analysis means for identifying item information, a generation means for generating cooking suggestions based on the user's emotional state, and a management means for managing the shelf life of the items. This enables customized cooking suggestions and support according to the user's emotional state, improving satisfaction with the cooking experience and allowing for the effective use of the items.

[0719] "Image acquisition means" refers to a device installed in a user's equipment for collecting video information.

[0720] "Analysis means" refers to a device or program that processes acquired images and extracts useful data such as item information.

[0721] "Generation means" refers to a device or program that creates cooking suggestions tailored to the user's core state based on information obtained through analysis.

[0722] "Presentation means" refers to a device or interface for showing the generated cooking suggestions to the user.

[0723] A "monitoring device" is a device or program that continuously observes and records the tasks being performed by the user.

[0724] A "support information generation means" is a device or program that analyzes information obtained through monitoring and creates information to assist the user's work.

[0725] "Transmission means" refers to an audio output device or program for conveying generated support information to the user.

[0726] "Management means" refers to a device or program that tracks the retention period of acquired items and makes priority usage suggestions based on that information.

[0727] This system consists of camera-equipped earphones worn by the user, a cloud server, and a dedicated application. The user can use the earphone's camera to film the inside of a refrigerator or the cooking process. The earphones are equipped with sensors that detect the user's voice tone and facial expressions, and an emotion analysis engine analyzes this information to determine their emotions.

[0728] The server receives video transmitted from the image acquisition device and extracts item information using analysis software. At this stage, a machine learning model is used for food ingredient recognition, and the ingredients are identified by comparing them with a database. The analyzed item information and the user's emotional state are then used by a generation device to create appropriate cooking suggestions.

[0729] The generated cooking suggestions are guided to the user via audio through a presentation device and are customized to the user's emotional state. As the user proceeds with cooking, a monitoring device continuously acquires video, and a support information generation device generates real-time work support information. This allows for, for example, the provision of audio guides to make the recipe easier to understand.

[0730] The management method involves tracking expiration dates and optimizing suggestions to prioritize the use of ingredients nearing their expiration date.

[0731] For example, if a user feels fatigued while cooking, the server will recognize this through its emotion analysis engine and suggest a recipe using nutrients that are effective in relieving fatigue. If the cooking steps are deemed difficult, the server will assist the user by providing more detailed explanations through voice guidance.

[0732] The following is an example of a prompt message generated using a generative AI model.

[0733] "Based on the user's emotional analysis results from the emotion engine, suggest a relaxing dinner recipe. Also, display a list of the necessary ingredients."

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The user wears earphones with a camera and films the inside of a refrigerator or while cooking. The input is video data from the camera, and the output is data transfer to a server. The user uses the earphones to record video in real time and prepares to send the video from the device to the server.

[0737] Step 2:

[0738] The server receives the transmitted video and extracts item information using image analysis software. The input is video data sent by the user, and the output is the analyzed item characteristic information. In this process, a machine learning model is used to identify the type and freshness of the food by comparing it with a database.

[0739] Step 3:

[0740] The server uses an emotion analysis engine to analyze the user's emotional state from the user's voice and video. The input is the user's voice tone and facial expression data, and the output is the analyzed emotional state. The server uses a voice analysis algorithm to evaluate the voice tone, and the emotion engine determines the user's emotion based on the results.

[0741] Step 4:

[0742] The server uses a generative AI model based on item information and emotional states to generate customized cooking suggestions via relevant prompts. The input is item information and emotional information, and the output is cooking suggestions. The generative AI model uses the prompts to infer and propose the most suitable recipe and cooking procedure for the user.

[0743] Step 5:

[0744] The terminal presents customized cooking suggestions to the user via voice through a presentation mechanism. The input is cooking suggestions generated on the server, and the output is voice guidance to the user. The terminal uses speech synthesis technology to convey the suggestions to the user.

[0745] Step 6:

[0746] During cooking, the device continuously acquires video and transmits it to the server. The input is video data from the device, and the output is data transmission to the server. The user keeps the earphones on and records the progress of the cooking.

[0747] Step 7:

[0748] The server analyzes video data and generates information to support the user's work using a support information generation mechanism. The input is video data of the cooking process, and the output is work support information. The server performs video analysis, monitors the progress, and generates necessary advice and instructions.

[0749] Step 8:

[0750] The terminal provides the user with specific instructions via voice based on the generated support information. The input is support information, and the output is voice-based user assistance. The terminal supports cooking by providing clear instructions to the user using a voice output device.

[0751] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0752] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0753] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0754] [Fourth Embodiment]

[0755] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0756] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0757] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0758] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0759] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0760] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0761] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0762] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0763] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0764] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0765] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0766] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0767] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0768] This invention relates to a cooking support system consisting of a camera-equipped earphone worn by the user, a terminal that controls it, and a server connected to the cloud.

[0769] When a user takes a picture of the ingredients they will use for cooking or the contents of their refrigerator through the camera, the device sends the image to a server. The server receives this image, analyzes the type and quantity of ingredients, and compares it with a database. Based on this analysis, the server generates an appropriate recipe that takes into account the user's health condition and past eating history. The generated recipe is then presented to the user through the device.

[0770] For example, if a user takes a picture of the eggs, tomatoes, and spinach they have in their refrigerator, the server will suggest "tomato and spinach omelet" as the optimal recipe based on these ingredients.

[0771] Once cooking begins, the terminal continuously monitors the cooking progress using its camera and transmits the video to the server. The server analyzes this in real time and provides the user with specific cooking instructions as the process progresses. For example, the user can receive specific instructions via voice, such as "You should cook it a little longer" or "Add salt next."

[0772] Furthermore, this system manages the expiration dates of ingredients and automatically generates recipes that prioritize the use of ingredients with the nearest expiration date. This function helps reduce food waste.

[0773] As described above, the present invention enhances the efficiency of users' daily meal preparation, supporting the realization of a healthy diet and the reduction of food waste.

[0774] The following describes the processing flow.

[0775] Step 1:

[0776] The user uses the camera on their earphones to take pictures of food items in the refrigerator or flyers they are holding. The captured images are saved to the device.

[0777] Step 2:

[0778] The device sends the saved image to the server. The server receives the image and begins the analysis process.

[0779] Step 3:

[0780] The server uses a machine learning model to analyze the received images, identify ingredients and flyer information within the images, and extract the names of the ingredients, quantities, and special offer information.

[0781] Step 4:

[0782] The server compares the analysis results with the food ingredient database and builds a list of ingredients owned by the user. It also checks the expiration date information for each ingredient and updates it in the management database.

[0783] Step 5:

[0784] The server generates multiple recipes using a generation AI based on acquired ingredient information, user health data, and meal history. During this process, it prioritizes the use of ingredients with the nearest expiration date.

[0785] Step 6:

[0786] The generated recipe is sent to the device, which then suggests it to the user. The user reviews the recipe and selects the one they want to create.

[0787] Step 7:

[0788] The device starts cooking based on the recipe selected by the user. The terminal monitors the cooking process with a camera and transmits the video to the server in real time.

[0789] Step 8:

[0790] The server analyzes the transmitted video and determines the cooking process. Based on the situation, AI generates necessary operations and precautions, and sends specific cooking instructions to the terminal.

[0791] Step 9:

[0792] The terminal relays instructions from the server to the user via voice, supporting the user in ensuring they can cook correctly.

[0793] Step 10:

[0794] After a user finishes cooking, the server updates the ingredient list in its database and optimizes the next recipe suggestion by considering ingredient availability and expiration date information.

[0795] (Example 1)

[0796] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] Maintaining a healthy diet and reducing food waste are crucial issues for modern households. However, in the midst of busy daily life, it is difficult to accurately assess the condition of ingredients and cook them appropriately. Furthermore, meal preparation does not automatically provide suggestions that take into account individual health conditions or past food choices, leading to a problem of food waste.

[0798] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0799] In this invention, the server includes a visual information acquisition means worn by the user, an analysis means for analyzing the visual data obtained by the visual information acquisition means and identifying item information, and a generation means for generating suggestion information based on the item information obtained by the analysis means. This enables the user to prepare efficient and healthy meals based on their individual health status and past selection history, thereby reducing food waste.

[0800] A "visual information acquisition means" is a device that, when worn by a user, acquires visual information from the site as digital data.

[0801] "Analysis means" refers to a function that processes acquired visual data and identifies the type and state of items contained within the image.

[0802] "Generation means" refers to a function that generates useful suggestions and instructions for the user based on analyzed item information.

[0803] "Adjustment means" refers to a function that optimizes the generated suggestion information according to the user's health status and past selection history.

[0804] "Presentation means" refers to a function for visually or audibly conveying generated or adjusted information to the user.

[0805] "Monitoring means" refers to devices and functions for continuously recording user actions and acquiring that data.

[0806] "Information generation means" refers to a function that analyzes data obtained by monitoring means and generates useful support information for the user.

[0807] A "means of communication" refers to a function for conveying generated support information to the user.

[0808] "Management measures" refer to functions that track the status of acquired items, such as their expiration dates, and propose the optimal usage method based on that information.

[0809] This invention relates to an information processing system for user operation. This system consists of a camera-equipped information acquisition device (hereinafter referred to as the earphone device) worn by the user, a terminal for processing data acquired from the earphone device, and a server connected to the cloud. The earphone device is designed to allow the user to easily acquire visual information during everyday activities.

[0810] The terminal receives image data from the earphone device and transmits it to the server via the internet. Specifically, the data is uploaded to the cloud via the HTTP protocol using Wi-Fi or mobile data communication. On the server side, advanced image recognition software such as TensorFlow and PyTorch is used to analyze the image data and identify item information.

[0811] Based on this analysis information, the server uses a generative AI model (e.g., OpenAI's GPT-3) to generate recipes and necessary instructions for the user. For example, based on item information such as eggs, tomatoes, and spinach, it can suggest a recipe like "tomato and spinach omelet."

[0812] Furthermore, to take into account the user's health information and past dietary history, the server can utilize a database system to optimize suggested recipes to individual needs. The generated suggestion information is presented to the user via the terminal and communicated clearly using text and speech synthesis software (e.g., Google Text-to-Speech).

[0813] Another function is that the terminal continuously records the user's actions while cooking and sends the video to the server. The server analyzes this in real time and generates specific cooking instructions as the process progresses, providing instructions that the user can specifically follow, such as "You should bake it a little longer" or "Add salt next."

[0814] Regarding the ingredient management function, the server manages the expiration dates of each ingredient in a database, and automatically generates recipe suggestions that prioritize the use of ingredients as their expiration date approaches, thereby effectively reducing food waste. This process helps promote an efficient and healthy diet and contributes to the sustainable use of ingredients.

[0815] A concrete example of a prompt might be input such as, "Please tell me some recipes that use the ingredients in my refrigerator without wasting anything." Based on this prompt, the system can perform effective information processing and provide the user with the most suitable suggestions.

[0816] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0817] Step 1:

[0818] The user wears a visual information acquisition device and takes pictures of items used for cooking and the inside of the refrigerator.

[0819] Input: Visual information is input via the camera.

[0820] Output: Image data is generated.

[0821] As a specific example, the user uses their earphone device to point the camera directly at an egg, a tomato, and spinach.

[0822] Step 2:

[0823] The device receives the captured image data and sends it to the server via the internet.

[0824] Input: Receive captured image data.

[0825] Output: An image file for sending to the server is generated.

[0826] Specifically, the device uses Wi-Fi to send image data to a server in the cloud via the HTTP protocol.

[0827] Step 3:

[0828] The server analyzes the image data to identify the item information.

[0829] Input: Image data received by the server.

[0830] Output: Information about the items in the image (e.g., identification of eggs, tomatoes, spinach) is generated.

[0831] In terms of specific operation, the server-side image recognition software (such as TensorFlow) processes the visual data using a neural network and outputs the results in text format.

[0832] Step 4:

[0833] The server generates suggested information using an AI model based on the analyzed item information.

[0834] Input: Item information data, user's health status, and past selection history.

[0835] Output: Suggested recipes and cooking instructions.

[0836] In terms of specific operations, the server references a database and an AI model (e.g., GPT-3) to generate recipes such as "tomato and spinach omelet."

[0837] Step 5:

[0838] The terminal presents the user with suggested information received from the server.

[0839] Input: Suggestion information from the server.

[0840] Output: Visual and auditory information presented to the user.

[0841] In terms of specific operations, the terminal displays the received information on its screen and uses speech synthesis software to give voice instructions to the user.

[0842] Step 6:

[0843] The terminal records user actions during cooking and sends that data to the server.

[0844] Input: Real-time video data during cooking.

[0845] Output: Video data sent to the server.

[0846] Specifically, the system uses the device's camera to capture images every few seconds and streams them to a server.

[0847] Step 7:

[0848] The server analyzes the cooking progress and provides additional cooking instructions to the user.

[0849] Input: Real-time cooking video data.

[0850] Output: Additional voice instructions based on the cooking status.

[0851] In terms of specific operations, the server uses deep learning to process the video data and generates specific instructions such as "bake it a little longer" or "add salt next."

[0852] Step 8:

[0853] The server manages the expiration dates of items and makes priority recommendations based on them.

[0854] Input: Information on managed items and their expiration dates.

[0855] Output: Suggestion information considering items nearing their expiration date.

[0856] Specifically, the server uses a database to access expiration date information and automatically generates recipes to reduce food waste.

[0857] (Application Example 1)

[0858] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0859] Users with limited cooking knowledge or those leading busy lives face challenges in using ingredients effectively and healthily, reducing food waste, and preparing meals smoothly. Technologies are needed to address this issue.

[0860] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0861] In this invention, the server includes an image acquisition means, an analysis means for analyzing the images acquired by the image acquisition means and identifying ingredient information, a generation means for generating cooking suggestions based on the ingredient information obtained by the analysis means, a presentation means for presenting the generated cooking suggestions to the user, and a voice transmission means for transmitting cooking support information to the user by voice. As a result, the user can obtain the information and instructions necessary for cooking in real time from both visual and auditory perspectives, enabling them to proceed with cooking efficiently.

[0862] "Image acquisition means" refers to devices and technologies for acquiring image data of food items and storage locations photographed by the user.

[0863] "Analysis means" refers to a system that executes processing and algorithms to identify food ingredient information from acquired image data.

[0864] "Generation means" refers to methods and techniques for creating appropriate cooking suggestions and recipes based on ingredient information obtained through analysis means.

[0865] "Presentation means" refers to devices and technologies for visually displaying generated cooking suggestions and recipes to the user.

[0866] "Voice communication means" refers to systems and technologies that use voice to convey generated cooking suggestions and support information to users.

[0867] "Monitoring means" refers to devices and technologies that continuously acquire video footage of a user cooking and allow for monitoring of the situation.

[0868] "Support information generation means" refers to a method that analyzes the cooking process obtained by monitoring means and generates instructions and advice necessary during the cooking process.

[0869] "Management measures" refer to devices and technologies for tracking the expiration dates of food ingredients and making priority usage suggestions based on that information.

[0870] To implement this invention, the user first wears earphones with a camera and uses a consumer robot or smartphone as a cooking support system. An image acquisition means captures images of the ingredients and the state of the refrigerator through this camera. This image data is transmitted to a cloud server via the terminal. Upon receiving the image data, the server uses an analysis means to identify the ingredients. The ingredients information includes not only the type and quantity, but also their arrangement in the refrigerator.

[0871] Next, the server uses a generation mechanism to generate appropriate cooking suggestions based on this ingredient information. This generation process takes into account the user's health information and past eating history. The generated cooking suggestions are then visualized on the user's smartphone or robot display via a presentation mechanism.

[0872] Once cooking begins, a monitoring device continuously records the user's cooking process and transmits the video to a server. The server analyzes the video and uses a support information generation device to create instructions based on the progress of the cooking. These instructions are then transmitted to the user in real time via a voice communication device. For example, specific instructions such as "cook a little longer" are conveyed by voice.

[0873] Furthermore, this system includes a management mechanism that tracks the expiration dates of food items in the refrigerator, suggesting that items nearing their expiration date be used first. This suggestion is also communicated to the user via voice, contributing to the reduction of food waste.

[0874] For example, if the prompt "I have taken pictures of the ingredients in my refrigerator with my camera. Please generate a healthy recipe using these ingredients" is input to the AI ​​model, a tomato and spinach omelet will be suggested. The user can then proceed with cooking based on this. In this way, by correctly analyzing the submitted images and providing the user with appropriate cooking support information, the user can lead an efficient and healthy diet.

[0875] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0876] Step 1:

[0877] The user takes pictures of the inside of the refrigerator and food items using a camera attached to their earphones. The input is image data acquired through the camera, and the device sends this data to a cloud server. At this time, the images are compressed and encrypted, and data conversion is performed to ensure stable communication.

[0878] Step 2:

[0879] The server processes the received image data using an analysis tool. The input is image data, and the analysis tool uses a machine learning algorithm to identify the type and quantity of ingredients. The output is the ingredient identification result, and this information is stored in a database and used to generate cooking suggestions. Specifically, an image recognition model analyzes the image of the ingredients and assigns labels accordingly.

[0880] Step 3:

[0881] The server uses ingredient information obtained through analysis to generate appropriate cooking suggestions via a generation mechanism. Inputs include ingredient information, user health data, and past meal history, while output is a cooking recipe suggested to the user. Data processing involves cross-referencing with a health information database to generate optimal cooking suggestions.

[0882] Step 4:

[0883] The terminal displays the generated cooking suggestions to the user through a presentation mechanism. The input is the generated cooking recipe, and the output is the recipe information displayed on the user interface. Specific actions include displaying ingredient lists and instructions on a smartphone or robot display.

[0884] Step 5:

[0885] When a user begins cooking, the monitoring system continuously acquires video footage of the cooking process. The input is the user's cooking video, which is transmitted to the server in real time by the terminal. The video data is optimized through data conversion for efficient processing.

[0886] Step 6:

[0887] The server analyzes the acquired cooking video and generates instructions using a support information generation system. The input is the cooking video, and the output is specific instructions corresponding to the progress of the cooking. A video analysis algorithm is used, and voice guidance is designed according to the progress.

[0888] Step 7:

[0889] The terminal communicates generated instructions to the user via voice transmission. The input is the generated instructions, and the output is the audio information that the user can hear. Specifically, speech synthesis technology is used to provide the user with accurate instructions.

[0890] Step 8:

[0891] The server uses management tools to track the expiration dates of food ingredients and provides priority usage suggestions based on those dates. Inputs are food ingredient storage status and expiration date data, while output is usage suggestions corresponding to the expiration date. Prioritization is determined by database queries and presented to the user.

[0892] Step 9:

[0893] The terminal uses voice communication to convey usage suggestions for reducing food waste to the user. The input is the usage suggestions generated by the management system, and the output is the voice that the user hears. The suggestions are converted into voice using a speech synthesis function and quickly conveyed to the user.

[0894] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0895] This invention is a system that utilizes an emotion engine installed in camera-equipped earphones worn by the user to support the user's cooking experience in a more personalized way. The emotion engine analyzes the user's facial expressions and voice tone to recognize emotions, and optimizes cooking suggestions and support based on this.

[0896] The user wears earphones and uses the camera to take pictures of the contents of the refrigerator and the food items, and the device sends these images to a server. The server analyzes the received images, extracts information about the food items, and compares it with a database.

[0897] In this system, an emotion engine monitors the user's voice and facial expressions to analyze the user's current emotional state. Based on this emotional information, the server generates cooking suggestions, customizing recipes according to the user's emotions. For example, if the user is feeling stressed, the system will suggest recipes that are expected to have a relaxing effect, providing emotion-responsive cooking suggestions.

[0898] During cooking, the device continuously transmits video using its camera, and the server analyzes the cooking progress based on the video and emotional information. The server generates necessary cooking support information in response to the user's emotional changes and provides specific instructions via voice. This process allows the user to have a comfortable cooking experience.

[0899] For example, if a user appears tired, the emotion engine analyzes this and suggests a nutritious recipe that helps with fatigue recovery. Also, if the server detects that the user is feeling anxious during cooking, it provides more detailed and specific instructions to help them proceed with cooking with peace of mind.

[0900] As described above, the present invention provides personalized cooking support that takes into account the user's emotions and health condition, making cooking more efficient and comfortable.

[0901] The following describes the processing flow.

[0902] Step 1:

[0903] The user uses the earphone's camera to photograph the food items inside the refrigerator. The captured image is saved to the device.

[0904] Step 2:

[0905] The device sends the saved image to the server. The server receives the image and begins the analysis process.

[0906] Step 3:

[0907] The server applies an image analysis algorithm to identify the type and quantity of ingredients and compares the ingredient information with a database.

[0908] Step 4:

[0909] The server uses an emotion engine to analyze the user's voice tone and facial expressions in real time via the camera and microphone, collecting emotional information.

[0910] Step 5:

[0911] Based on the acquired ingredient information and emotional information, the server generates a recipe that suits the user's emotional state. For example, if the user wants to relax, it will suggest a simple and delicious recipe.

[0912] Step 6:

[0913] The generated recipes are presented to the user via the device. The user reviews the suggested recipes and selects the one they wish to create.

[0914] Step 7:

[0915] The device begins cooking according to the recipe selected by the user. During cooking, the device continuously acquires video using its camera and sends it to the server.

[0916] Step 8:

[0917] The server analyzes video footage and continuously acquired emotional information to determine the progress of cooking in real time.

[0918] Step 9:

[0919] The server generates instructions according to the cooking process and provides specific cooking advice via voice through the terminal, taking into account the user's emotional state.

[0920] Step 10:

[0921] Once cooking is complete, the server records the ingredients used in a database and updates ingredient inventory and expiration date information to help with future recipe suggestions.

[0922] (Example 2)

[0923] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0924] In recent years, there has been a growing demand for personalized services that meet the diverse needs of users. However, conventional cooking support systems have merely provided ingredient information without considering the user's emotions. Therefore, there is a need to develop methods to reduce the anxiety and stress users feel while cooking and to provide a comfortable cooking experience.

[0925] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0926] In this invention, the server includes an image acquisition means, an analysis means, a generation means, an emotion analysis means, and a presentation means. This enables personalized cooking support that customizes cooking suggestions according to the user's emotions, providing the user with a sense of security and comfort.

[0927] "Image acquisition means" refers to a function that uses a hardware device owned by the user to acquire visual information of objects or scenes as digital data.

[0928] "Analysis means" refers to an algorithm or program for processing acquired digital image data and identifying useful information, specifically food ingredient information, from it.

[0929] The "generation means" refers to a function that automatically creates and provides appropriate cooking suggestions to the user based on the analyzed information.

[0930] "Emotional analysis methods" are technologies that analyze the tone of a user's voice and facial expressions obtained from audio and video to identify the emotional state of that person.

[0931] "Presentation means" refers to interfaces such as displays and audio output devices that convey the information and suggestions created by the system to the user in their final form.

[0932] "Monitoring means" refers to equipment or methods for continuously collecting video footage acquired by the user while cooking, in order to understand the current cooking status.

[0933] The "support information generation means" is a function that generates information to appropriately support the user based on data obtained by the monitoring means.

[0934] "Means of communication" refers to methods for efficiently conveying generated cooking support information to the user, primarily using audio and screen displays.

[0935] "Management methods" refer to the process of managing deterioration information based on acquired food ingredient information, utilizing that storage information, and suggesting the optimal timing for use.

[0936] This invention is a system that provides personalized support for a user's cooking activities through image acquisition means using a camera-equipped acoustic device worn by the user and data processing by a server.

[0937] The image acquisition method uses the built-in camera function of the user's audio device to photograph the contents of the refrigerator or the ingredients used for cooking. This image is then transmitted as digital data to the server by the terminal.

[0938] The server analyzes the received image data using image processing software to identify the food ingredient information contained within. A general image recognition library can be used as the analysis method. Based on the information obtained through the analysis, the server utilizes a generative AI model to optimize the user's ingredient selection and generates customized cooking suggestions.

[0939] Furthermore, the server analyzes the tone of voice and facial expressions acquired from the user's audio device and determines the user's emotional state using emotion analysis tools. This information is reflected in the generated cooking suggestions, so that the provided recipes and procedures are tailored to the user's emotions.

[0940] For example, if sentiment analysis indicates that the user is fatigued, the server can suggest a simple recipe using nutritious ingredients. An example of a prompt input to the generative AI model would be: "Based on the user's emotional state, suggest a dinner recipe for tonight. The user seems tired lately, so a dish that helps relieve fatigue would be good."

[0941] While the user is cooking, the device continuously transmits video to the server via its camera to monitor the progress of the cooking. Based on the user's emotional changes and the video information, the server guides the user through the cooking process and provides real-time cooking support. This allows the user to cook with peace of mind. This system aims to make cooking more comfortable and effective by providing services tailored to the user's experience and emotions.

[0942] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0943] Step 1:

[0944] The user wears a camera-equipped acoustic device and photographs the contents of a refrigerator or ingredients used for cooking. During this process, the user's device acquires image data and prepares to send it to the terminal. The input is digital image data, which is used for subsequent analysis.

[0945] Step 2:

[0946] The terminal transmits image data provided by the user to the server. This transmission process utilizes a communication protocol to ensure secure and rapid delivery of the data to the server. The input is the user's image data, and the output is the transmission of data to the server.

[0947] Step 3:

[0948] The server analyzes the received image data. It uses image recognition software to identify food ingredient information and extracts specific details. During this process, an image analysis algorithm is used to obtain an identifier for each food ingredient from the image data. The input is the image data sent to the server, and the output is food ingredient information.

[0949] Step 4:

[0950] The server uses a generative AI model to create cooking suggestions based on the analyzed ingredient information. Prompt statements are input to the generative AI model, generating recipes tailored to the user. For example, a prompt such as "Suggest a dinner recipe for tonight based on the user's emotional state" might be used. The input consists of ingredient information and prompt statements, while the output is a cooking suggestion.

[0951] Step 5:

[0952] Simultaneously, the user's facial expressions and voice tone are transmitted from their device to the server via emotion analysis to determine the user's emotional state. An emotion analysis algorithm is used to identify emotional information from the facial expression and voice data. The input is facial expression and voice data, and the output is emotional information.

[0953] Step 6:

[0954] The server customizes the generated cooking suggestions with emotional information and provides the user with an optimized recipe. Using a presentation method, the cooking suggestions are presented directly to the user via audio or visual means through the terminal. The input is the cooking suggestion and emotional information before customization, and the output is the customized recipe.

[0955] Step 7:

[0956] During cooking, the user uses a camera on their device to continuously transmit the progress of the cooking to the server. The server analyzes the received video data using monitoring and support information generation means to understand the progress of the cooking. The input is video data, and the output is cooking support information.

[0957] Step 8:

[0958] The server provides cooking assistance information to the user via voice or text, offering necessary instructions. This ensures a smooth and comfortable cooking process. The input is assistance information, and the output is voice or text instructions.

[0959] (Application Example 2)

[0960] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0961] In modern life, users seek increased efficiency and satisfaction during cooking. However, conventional cooking systems and methods often fail to adequately address users' emotional states and individual needs, resulting in a uniform cooking experience and potentially lower satisfaction. Furthermore, insufficient management of food storage conditions makes it difficult to effectively utilize ingredients, which also contributes to a decline in users' quality of life.

[0962] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0963] In this invention, the server includes an image acquisition means, an analysis means for identifying item information, a generation means for generating cooking suggestions based on the user's emotional state, and a management means for managing the shelf life of the items. This enables customized cooking suggestions and support according to the user's emotional state, improving satisfaction with the cooking experience and allowing for the effective use of the items.

[0964] "Image acquisition means" refers to a device installed in a user's equipment for collecting video information.

[0965] "Analysis means" refers to a device or program that processes acquired images and extracts useful data such as item information.

[0966] "Generation means" refers to a device or program that creates cooking suggestions tailored to the user's core state based on information obtained through analysis.

[0967] "Presentation means" refers to a device or interface for showing the generated cooking suggestions to the user.

[0968] A "monitoring device" is a device or program that continuously observes and records the tasks being performed by the user.

[0969] A "support information generation means" is a device or program that analyzes information obtained through monitoring and creates information to assist the user's work.

[0970] "Transmission means" refers to an audio output device or program for conveying generated support information to the user.

[0971] "Management means" refers to a device or program that tracks the retention period of acquired items and makes priority usage suggestions based on that information.

[0972] This system consists of camera-equipped earphones worn by the user, a cloud server, and a dedicated application. The user can use the earphone's camera to film the inside of a refrigerator or the cooking process. The earphones are equipped with sensors that detect the user's voice tone and facial expressions, and an emotion analysis engine analyzes this information to determine their emotions.

[0973] The server receives video transmitted from the image acquisition device and extracts item information using analysis software. At this stage, a machine learning model is used for food ingredient recognition, and the ingredients are identified by comparing them with a database. The analyzed item information and the user's emotional state are then used by a generation device to create appropriate cooking suggestions.

[0974] The generated cooking suggestions are guided to the user via audio through a presentation device and are customized to the user's emotional state. As the user proceeds with cooking, a monitoring device continuously acquires video, and a support information generation device generates real-time work support information. This allows for, for example, the provision of audio guides to make the recipe easier to understand.

[0975] The management method involves tracking expiration dates and optimizing suggestions to prioritize the use of ingredients nearing their expiration date.

[0976] For example, if a user feels fatigued while cooking, the server will recognize this through its emotion analysis engine and suggest a recipe using nutrients that are effective in relieving fatigue. If the cooking steps are deemed difficult, the server will assist the user by providing more detailed explanations through voice guidance.

[0977] The following is an example of a prompt message generated using a generative AI model.

[0978] "Based on the user's emotional analysis results from the emotion engine, suggest a relaxing dinner recipe. Also, display a list of the necessary ingredients."

[0979] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0980] Step 1:

[0981] The user wears earphones with a camera and films the inside of a refrigerator or while cooking. The input is video data from the camera, and the output is data transfer to a server. The user uses the earphones to record video in real time and prepares to send the video from the device to the server.

[0982] Step 2:

[0983] The server receives the transmitted video and extracts item information using image analysis software. The input is video data sent by the user, and the output is the analyzed item characteristic information. In this process, a machine learning model is used to identify the type and freshness of the food by comparing it with a database.

[0984] Step 3:

[0985] The server uses an emotion analysis engine to analyze the user's emotional state from the user's voice and video. The input is the user's voice tone and facial expression data, and the output is the analyzed emotional state. The server uses a voice analysis algorithm to evaluate the voice tone, and the emotion engine determines the user's emotion based on the results.

[0986] Step 4:

[0987] The server uses a generative AI model based on item information and emotional states to generate customized cooking suggestions via relevant prompts. The input is item information and emotional information, and the output is cooking suggestions. The generative AI model uses the prompts to infer and propose the most suitable recipe and cooking procedure for the user.

[0988] Step 5:

[0989] The terminal presents customized cooking suggestions to the user via voice through a presentation mechanism. The input is cooking suggestions generated on the server, and the output is voice guidance to the user. The terminal uses speech synthesis technology to convey the suggestions to the user.

[0990] Step 6:

[0991] During cooking, the device continuously acquires video and transmits it to the server. The input is video data from the device, and the output is data transmission to the server. The user keeps the earphones on and records the progress of the cooking.

[0992] Step 7:

[0993] The server analyzes video data and generates information to support the user's work using a support information generation mechanism. The input is video data of the cooking process, and the output is work support information. The server performs video analysis, monitors the progress, and generates necessary advice and instructions.

[0994] Step 8:

[0995] The terminal provides the user with specific instructions via voice based on the generated support information. The input is support information, and the output is voice-based user assistance. The terminal supports cooking by providing clear instructions to the user using a voice output device.

[0996] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0997] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0998] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0999] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1000] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1001] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1002] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1003] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1004] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1005] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1006] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1007] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1008] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1009] 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.

[1010] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1011] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1012] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1013] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1014] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1015] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1016] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1017] The following is further disclosed regarding the embodiments described above.

[1018] (Claim 1)

[1019] Image acquisition method,

[1020] An analysis means for analyzing images acquired by the image acquisition means and identifying food ingredient information,

[1021] A generation means that generates cooking suggestions based on ingredient information obtained by the analysis means,

[1022] A presentation means for presenting the generated cooking suggestions to the user,

[1023] A system that includes this.

[1024] (Claim 2)

[1025] A monitoring means that allows the user to continuously acquire video footage of the cooking process,

[1026] A support information generation means that analyzes the video obtained by the monitoring means to generate cooking support information,

[1027] A means for transmitting the cooking support information to the user,

[1028] The system according to claim 1, including the following:

[1029] (Claim 3)

[1030] A management system that manages the expiration dates of acquired ingredients and provides priority usage suggestions based on those dates,

[1031] The system according to claim 1, including the following:

[1032] "Example 1"

[1033] (Claim 1)

[1034] A means of acquiring visual information worn by the user,

[1035] An analysis means for analyzing the visual data obtained by the visual information acquisition means and identifying the item information,

[1036] A generation means for generating proposed information based on the item information obtained by the analysis means,

[1037] An adjustment mechanism that adjusts suggested information considering the user's health status and past selection history,

[1038] A presentation means for presenting the generated proposal information to the user,

[1039] A system that includes this.

[1040] (Claim 2)

[1041] A monitoring means that allows the user to continuously acquire video footage of the operation,

[1042] Information generation means that analyzes motion video obtained by the monitoring means and generates support information,

[1043] A means of transmitting the support information to the user,

[1044] The system according to claim 1, including the following:

[1045] (Claim 3)

[1046] A management system for managing the expiration dates of acquired items and providing priority usage suggestions based on those expiration dates,

[1047] The system according to claim 1, including the following:

[1048] "Application Example 1"

[1049] (Claim 1)

[1050] Image acquisition method,

[1051] An analysis means for analyzing images acquired by the image acquisition means and identifying food ingredient information,

[1052] A generation means that generates cooking suggestions based on ingredient information obtained by the analysis means,

[1053] A presentation means for presenting the generated cooking suggestions to the user,

[1054] A voice communication method that conveys cooking support information to the user by voice,

[1055] A system that includes this.

[1056] (Claim 2)

[1057] A monitoring means that allows the user to continuously acquire video footage of the cooking process,

[1058] A support information generation means that analyzes the video obtained by the monitoring means to generate cooking support information,

[1059] A voice communication means for conveying the cooking support information to the user by voice,

[1060] The system according to claim 1, including the following:

[1061] (Claim 3)

[1062] A management system that manages the expiration dates of acquired ingredients and provides priority usage suggestions based on those dates,

[1063] A voice communication means for conveying the usage proposal to the user by voice,

[1064] The system according to claim 1, including the following:

[1065] "Example 2 of combining an emotion engine"

[1066] (Claim 1)

[1067] Image acquisition method,

[1068] An analysis means for analyzing images acquired by the image acquisition means and identifying food ingredient information,

[1069] A generation means that generates cooking suggestions based on ingredient information and user emotion information obtained by the analysis means,

[1070] An emotion analysis means that analyzes the user's facial expressions and voice tone to recognize emotions,

[1071] A presentation means that customizes and presents the generated cooking suggestions according to the user's emotions,

[1072] A system that includes this.

[1073] (Claim 2)

[1074] A monitoring means that allows the user to continuously acquire video footage of the cooking process,

[1075] A support information generation means that generates cooking support information by analyzing the video and user emotion information obtained by the monitoring means,

[1076] A means for conveying the cooking support information to the user by voice,

[1077] The system according to claim 1, including the following:

[1078] (Claim 3)

[1079] A management system for managing information on the deterioration of acquired food ingredients and for making priority usage suggestions based on deterioration information,

[1080] The system according to claim 1, including the following:

[1081] "Application example 2 when combining with an emotional engine"

[1082] (Claim 1)

[1083] Image acquisition method,

[1084] An analysis means for analyzing images acquired by the image acquisition means and identifying item information,

[1085] A generation means that generates cooking suggestions based on the item information obtained by the analysis means and the user's central state,

[1086] A presentation means that customizes and presents the generated cooking suggestions according to the user's central state,

[1087] A system that includes this.

[1088] (Claim 2)

[1089] A monitoring system that continuously acquires video footage of the user during work,

[1090] A support information generation means that analyzes the video obtained by the monitoring means to generate work support information,

[1091] A means for transmitting the work support information to the user by voice,

[1092] The system according to claim 1, including the following:

[1093] (Claim 3)

[1094] A management system for managing the retention period of acquired items and making priority usage suggestions based on the expiration date,

[1095] The system according to claim 1, including the following: [Explanation of symbols]

[1096] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Image acquisition method, An analysis means for analyzing images acquired by the image acquisition means and identifying food ingredient information, A generation means that generates cooking suggestions based on ingredient information obtained by the analysis means, A presentation means for presenting the generated cooking suggestions to the user, A system that includes this.

2. A monitoring means that allows the user to continuously acquire video footage of the cooking process, A support information generation means that analyzes the video obtained by the monitoring means to generate cooking support information, A means for transmitting the cooking support information to the user, The system according to claim 1, including the following:

3. A management system that manages the expiration dates of acquired ingredients and provides priority usage suggestions based on those dates, The system according to claim 1, including the following:

Images