system

Abstract

We provide the system. [Solution] A means for receiving image data captured by a user and analyzing the image data to identify item information, A means for converting voice instructions from the user into text data, and for analyzing the text data to understand the user's intent, A means for generating a process proposed based on the aforementioned item information and the user's intent, and providing the process to the user, A method for analyzing user voice input using artificial intelligence and outputting cooking instructions via voice in real time, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In daily cooking activities, there are problems such as it is difficult to minimize the time and waste of ingredients in considering a menu and maintain the nutritional balance of meals. Also, there are problems such as hands getting dirty when checking a recipe during cooking and not being able to immediately know the recipe of the dish to be made. Furthermore, in order to efficiently proceed with cooking within limited time, real-time cooking guidance is required. In addition, reducing food loss due to expiration is also an important issue.

Means for Solving the Problems

[0005] This invention provides a system that identifies food information by analyzing image data of the inside of a refrigerator taken by a user. Furthermore, it realizes a means of generating and providing appropriate cooking methods to the user by converting the user's voice instructions into text data and analyzing it. It also provides effective cooking support by analyzing real-time video during cooking and providing instructions to the user in accordance with the progress of cooking. In addition, it contributes to reducing food waste by suggesting recipes that prioritize the use of ingredients nearing their expiration date using expiration date information stored in a database. As a result, users can efficiently solve various cooking-related problems.

[0006] "Image data" refers to a digital representation of the visual information inside the refrigerator that the user has captured through a camera.

[0007] "Food information" refers to information about individual food items in the refrigerator, such as their names and expiration dates, which is identified by analyzing image data.

[0008] "Voice instructions" refers to various questions and instructions about cooking that users give via voice through AI earphones equipped with a camera.

[0009] "Text data" refers to data expressed in string format, obtained by converting voice commands using speech recognition technology.

[0010] A "cooking method" encompasses the procedures and necessary steps for preparing a dish using specific ingredients, based on ingredient information and the user's intent.

[0011] "Real-time cooking instructions" refers to specific steps and advice provided immediately based on the user's progress during cooking.

[0012] "Expiration date information" refers to data set for each individual food item, indicating the date by which the food item should be consumed appropriately. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a tagged 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.

[0017] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a tagged 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.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] As a specific embodiment of the present invention, a system is described in which the user uses AI earphones with a camera to support cooking. This system supports the user's daily cooking activities and promotes an efficient and healthy diet.

[0035] First, the user puts on AI earphones with a camera and activates the system by taking pictures of the food in the refrigerator. The device sends the captured image data to the server in real time. The server analyzes this image data and uses image recognition technology to identify the food in the refrigerator. This food information is registered in a database and used to provide future cooking suggestions to the user.

[0036] The user can then give voice commands into the earphones. For example, they might ask, "What should I make for dinner?" The device converts these voice commands into text data and sends it to the server. The server uses natural language processing technology to analyze this text and understand the user's intent.

[0037] The server determines the optimal cooking method based on the user's intentions and the ingredient information stored in the database. Specifically, it generates a recipe that utilizes available ingredients while also considering health balance, and sends it to the terminal. The terminal then uses speech synthesis technology to convey this information to the user and provide the necessary cooking instructions.

[0038] In addition, once the user starts cooking, the device continuously sends information about the cooking process to the server. The server analyzes this information and generates real-time instructions based on the progress of the cooking, providing the user with accurate cooking support.

[0039] For example, if the user has "chicken" and "cabbage" in the refrigerator, the system can suggest "stir-fried chicken and cabbage" in response to the user's inquiry, and provide instructions such as "cut the chicken into bite-sized pieces" and "add the cabbage and cook over medium heat."

[0040] In this way, the present invention comprehensively provides effective utilization of ingredients, recipe suggestions, and real-time support for cooking. As a result, users can easily achieve a balanced diet while saving the trouble of planning menus and preventing food waste.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device compresses the captured image data in real time and sends it to a server via wireless communication.

[0044] Step 2:

[0045] The server analyzes the received image data using an image recognition algorithm to identify the type and quantity of each ingredient present in the refrigerator. The identified ingredient information is stored in a database and prepared for future use in recipe suggestions.

[0046] Step 3:

[0047] The user gives voice instructions about cooking into the earphones. For example, the user's cooking requests might be included, such as "What would you like for dinner tonight?" The device converts this voice data into text data and sends it to the server.

[0048] Step 4:

[0049] The server analyzes text data using natural language processing technology to understand the user's questions and intentions. It then combines the analyzed information with pre-collected ingredient information and user preference data to select the optimal cooking method.

[0050] Step 5:

[0051] The server sends the selected cooking method to the terminal in text format. The terminal uses speech synthesis technology to provide the user with cooking suggestions via voice. If necessary, it also provides details such as ingredient preparation and cooking time.

[0052] Step 6:

[0053] As the user cooks, the device continuously films the cooking process with its camera and sends the video to the server. The server analyzes this video and generates specific cooking instructions based on the user's progress.

[0054] Step 7:

[0055] The server generates real-time cooking instructions and provides them to the user via the terminal. For example, it immediately guides the user on specific actions during cooking, such as "Reduce the heat a little more and simmer for 3 minutes."

[0056] Step 8:

[0057] The server analyzes expiration date information in the database and prioritizes suggesting cooking methods using ingredients that are nearing their expiration date. The terminal then provides these suggestions to the user via voice, contributing to the reduction of food waste.

[0058] (Example 1)

[0059] 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."

[0060] In modern life, cooking at home is crucial for maintaining an efficient and healthy diet. However, many people find it difficult to dedicate the time and effort to devising new dishes every day, and food waste is a challenge due to the inability to effectively manage and utilize ingredients. Furthermore, it is difficult to receive appropriate support as the cooking process progresses, and many people experience mistakes and inconveniences during the cooking process.

[0061] 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.

[0062] In this invention, the server includes means for receiving visual data acquired by the user using a camera device and analyzing the visual data to identify object information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; and means for generating a cooking method created based on the object information and the user's intent, and providing the cooking method to the user via voice output. This enables the user to utilize ingredients efficiently and healthily and receive cooking support in real time.

[0063] A "user" refers to an individual or group that operates or utilizes a system.

[0064] "Capturing device" refers to equipment used to acquire images or videos, and includes cameras and devices equipped with cameras.

[0065] "Visual data" refers to data of still images or moving images acquired by a camera device.

[0066] "Object information" refers to information about the type and characteristics of an object identified from visual data.

[0067] A "server" refers to a central computer system that processes data and manages storage over a network, and exchanges data with client terminals.

[0068] "Voice instructions" refer to voice commands or questions that users provide to the system.

[0069] "Text data" refers to data in text format obtained by converting voice commands.

[0070] "Cooking method" refers to information about the procedures and recipes for creating a dish using ingredients.

[0071] "Voice output" refers to a means of providing text data and other information to the user as audio.

[0072] "Dynamic video" refers to video data that changes in real time, and includes data that is captured and transmitted as video.

[0073] "Progress" refers to the current status of the user's activities or processes.

[0074] "Work instructions" refer to information that specifically guides the user on the next actions or procedures they should take.

[0075] "Expiration date information" refers to information about the deadline by which an object should be consumed appropriately.

[0076] A "storage device" refers to hardware or a medium used to store electronic data.

[0077] A "generative AI model" refers to a model that uses artificial intelligence technology to infer or generate from data.

[0078] A "prompt statement" refers to a text-based instruction given to a generative AI model for generating information.

[0079] This system utilizes innovative technology aimed at assisting with cooking, enabling users to achieve efficient and healthy eating habits through the cooking process. The specific implementation method is described below.

[0080] The user takes pictures inside a refrigerator using earphones equipped with a camera. This device features a high-performance camera and a microphone, which are used to acquire visual data and voice commands. The visual data captured by the user is transmitted from the device to a server in real time. The device uses Bluetooth or Wi-Fi for wireless communication.

[0081] Upon receiving visual data, the server uses image recognition technology to identify object information. This process utilizes common image recognition software, such as TENSORFLOW® or OpenCV. Subsequently, the voice commands spoken by the user into the earphones are converted into text using speech recognition technology, and the user's intent is analyzed using a generative AI model. Natural language processing technology is applied to this analysis to generate appropriate cooking methods tailored to the user's wishes and circumstances.

[0082] The cooking instructions generated by the server take into account available ingredients, health balance, and user preferences. The generated steps and recipes are output to the user via speech synthesis software. For example, it might say, "Let's make stir-fried chicken and cabbage. First, cut the chicken into bite-sized pieces."

[0083] After cooking begins, the user continuously sends dynamic video of the cooking process from their device to the server. The server analyzes this data and determines the progress in real time. Feedback is provided according to the user's cooking progress, such as instructions like, "Next, add the cabbage and sauté over medium heat."

[0084] For example, if a user has chicken and cabbage in the refrigerator for dinner one day, and asks "What should I make?", the system can respond and suggest an appropriate cooking method. An example of a prompt might be given to the generating AI model in the form of "Take a picture of the ingredients in the refrigerator and suggest a healthy dinner recipe."

[0085] As described above, this invention integrates multiple technologies to help users cook efficiently and effectively and make good use of ingredients.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The user takes a picture of the inside of the refrigerator using an earphone-type device with a camera function. This initializes the system's operation. The input is image data of the food items inside the refrigerator. This image is transferred to the terminal in real time, and the terminal then sends this data directly to the server.

[0089] Step 2:

[0090] The server analyzes the received image data using image analysis software. Specifically, it uses TensorFlow and OpenCV to identify and classify ingredients. The input is image data, and the output is analyzed object information (ingredient name and quantity). This data forms the basis for suggesting the optimal cooking method to the user.

[0091] Step 3:

[0092] The user gives voice commands into the microphone of their earphones. For example, they might say, "Tell me what I should make for dinner." The input is the user's voice.

[0093] Step 4:

[0094] The device converts voice commands into text data using speech recognition technology. It uses Google® Speech Recognition API, among others, to perform the speech-to-text conversion. The input is the user's voice, and the output is text data. This text is then sent to the server.

[0095] Step 5:

[0096] The server analyzes the transmitted text data using natural language processing techniques. Based on the analysis, it understands the user's intent and uses a generative AI model to determine the appropriate cooking method. The input is text data and previously identified object information, and the output is the generated cooking procedure.

[0097] Step 6:

[0098] The server generates cooking instructions, which the terminal then uses speech synthesis software to provide to the user via voice. Specific instructions, such as "We're going to make stir-fried chicken and cabbage. Cut the chicken into bite-sized pieces," are conveyed. The input is the cooking instructions, and the output is a voice message.

[0099] Step 7:

[0100] After the user starts cooking, the terminal continuously sends video of the cooking process to the server. The input is video data of the cooking process.

[0101] Step 8:

[0102] The server analyzes the received dynamic video and monitors the progress of cooking. If necessary, it generates real-time cooking instructions and provides them to the user via the terminal. For example, it might give specific instructions such as, "Next, sauté the cabbage over medium heat." The input is video data of the cooking process, and the output is real-time cooking instructions.

[0103] (Application Example 1)

[0104] 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."

[0105] Current home cooking support systems have limitations in the information provided to users and insufficient real-time cooking support. Furthermore, they may lead to food waste because users cannot effectively utilize the ingredients in their refrigerators. The purpose of this invention is to solve these problems and enable users to achieve a more efficient and healthy diet.

[0106] 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.

[0107] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify item information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; means for generating a process proposed based on the item information and the user's intent and providing the process to the user; and means for analyzing the user's voice input using artificial intelligence and outputting cooking instructions in voice in real time. This enables the user to utilize items efficiently and receive more accurate support.

[0108] A "user" is an individual or household that uses the system and manages and prepares goods.

[0109] "Item information" refers to information about food items and materials inside refrigerators and shelves, identified from image data analyzed by the system.

[0110] "Voice instructions" refer to voice data that users input into the system via voice, such as requests or questions related to cooking.

[0111] "Text data" refers to character information converted by the system based on voice commands, and is used for analysis.

[0112] A "process" refers to a series of processing methods that the system generates, including specific cooking steps and procedures that the user should perform.

[0113] "Artificial intelligence" refers to a technology that incorporates machine learning and natural language processing, used to analyze user voice input and generate optimal cooking procedures.

[0114] "Real-time" refers to a time concept that indicates the immediacy of providing immediate feedback and instructions in response to the user's cooking progress.

[0115] The specific system for implementing this invention is composed of a user, a terminal, and a server. The system of this invention uses a terminal, which is a consumer electronic device equipped with a camera function, in combination with a cloud-based server.

[0116] The terminal takes pictures of items inside the refrigerator or on kitchen shelves and sends the image data to the server. The server analyzes the images using image processing software and extracts information about specific items. To achieve high-quality image analysis, this process uses the open-source library OpenCV. The user can also give voice instructions related to cooking to the terminal, which converts this voice into text data and sends it to the server. Automatic speech recognition (ASR) and natural language processing (NLP) technologies are used for voice analysis.

[0117] The server analyzes this text data, understands the user's intent, and combines it with item information to generate appropriate steps. The specific cooking process uses an AI model. This AI model can suggest recipes that consider available ingredients while maintaining a healthy balance. The user receives this information and can begin cooking. During cooking, the device continuously records the user's actions live and sends them to the server. Based on this data, the server generates cooking instructions in real time and provides them to the user via speech synthesis through the device.

[0118] For example, if a user takes a picture of the ingredients in their refrigerator and requests a dinner suggestion, the server will recognize "chicken" and "cabbage" and suggest "stir-fried chicken and cabbage." During cooking, it will provide real-time voice instructions such as "cut the chicken" and "add the cabbage."

[0119] An example of a prompt message is, "Analyze the camera footage inside the refrigerator and generate a recipe suggestion for tonight's dinner."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The user uses the device's camera to photograph items inside the refrigerator. This generates image data, which the device then sends to the server. The input is the image data of the items, and the output is the data sent to the server. This step involves the acquisition and communication of image data.

[0123] Step 2:

[0124] The server analyzes the received image data using OpenCV to identify the object. The input is image data sent from the terminal, and the output is the analyzed object information. In this process, data processing and image recognition are performed by image processing algorithms.

[0125] Step 3:

[0126] The user inputs voice commands into the terminal, which converts the voice into text data and sends it to the server. The input is voice data from the user, and the output is text data sent to the server. In this step, speech recognition technology is used to analyze and convert the voice data.

[0127] Step 4:

[0128] The server analyzes text data using natural language processing techniques to understand the user's intent. The input is text data sent from the terminal, and the output is the analysis result that reflects the user's intent. Intent inference is performed by performing data calculations in this step.

[0129] Step 5:

[0130] The server generates the optimal process using a generative AI model based on item information and user intent. The input is item information and user intent, and the output is process data to be provided to the terminal. In this step, the AI ​​model is used to integrate the data and synthesize the process.

[0131] Step 6:

[0132] The generated process data is provided to the user via a terminal using speech synthesis technology. The input is process data from the server, and the output is an audio guide that the user can listen to. In this step, audio is generated and output based on the process data.

[0133] Step 7:

[0134] Once the user begins cooking, the terminal continuously sends progress updates to the server, which then generates cooking instructions in real time. The input is live data indicating the current cooking status, and the output is instruction data provided to the user during cooking. This process generates dynamic instructions based on the current state.

[0135] 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.

[0136] One embodiment of the present invention is to provide a system that comprehensively supports cooking activities while taking into account the user's emotional state. This system combines an AI earphone with a camera and an emotion engine to realize ingredient recognition, voice instruction analysis, real-time cooking support, and emotion-based recipe suggestions.

[0137] The process begins with the user wearing AI earphones with a camera and taking pictures of the food in their refrigerator. The device sends this image data to a server, which uses image recognition technology to identify the food items. At this stage, the emotion engine analyzes the user's voice and video at the time of shooting to recognize the user's emotional state.

[0138] Next, the user voice-questions about cooking into the earphones. The device converts this voice into text and sends it to the server. The server analyzes the received text data to understand the user's intent and, based on information from the emotion engine, generates cooking methods suitable for the user's mental state. For example, if the user is feeling stressed, it will suggest relaxing dishes and simple cooking instructions.

[0139] The cooking method determined by the server is sent to the terminal and communicated to the user using speech synthesis technology. This process also utilizes information from the emotion engine to adjust the tone of voice and provide user-friendly instructions. For example, if the user is tired, a gentle tone of voice will be used to provide a reminder.

[0140] Furthermore, once the user begins cooking, the device sends live video of the cooking process to the server. The server analyzes the video to understand the progress of the cooking. Based on this information, it generates and provides real-time cooking instructions tailored to the user's emotions.

[0141] For example, if the emotion engine determines that the user is "frustrated," it can suggest a "stress-relieving herbal tea and salmon recipe," and while the user is cooking, it can gently advise them, "Let's move on to the next step with a relaxed mind."

[0142] In this way, the system of the present invention, which combines an emotion engine, allows users to receive cooking assistance tailored to their emotional state, resulting in an efficient and comfortable cooking experience.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device sends this image data to a server in real time. In addition, the voice spoken by the user at this time is captured as input for the emotion engine.

[0146] Step 2:

[0147] The server analyzes the received image data using image recognition technology to identify the food items present in the refrigerator. Furthermore, an emotion engine analyzes the user's voice information to recognize the user's emotional state and reports the results to the server.

[0148] Step 3:

[0149] The user asks a cooking-related question via voice into the earphone. The device converts this voice data into text data and sends it to the server. The server analyzes the transmitted data to understand the user's intent.

[0150] Step 4:

[0151] The server combines ingredient information, user intent, and emotional state recognized by the emotion engine to generate an appropriate cooking method. If the user is feeling stressed, it will select a simple and relaxing dish.

[0152] Step 5:

[0153] The server generates a cooking method and sends it to the terminal. The terminal uses speech synthesis technology to provide the user with the cooking instructions in a voice-based manner, using a tone that is gentle or encouraging, depending on the user's emotional state.

[0154] Step 6:

[0155] Once the user begins cooking, the device continuously records the cooking process with its camera and sends the live video to the server. The server analyzes this video to understand the progress of the cooking.

[0156] Step 7:

[0157] The server generates real-time cooking instructions tailored to the user's situation, based on the cooking progress and information from the emotion engine. These instructions are delivered in a way that matches the user's emotions, for example, by saying, "Calm down, let's move on to the next step."

[0158] Step 8:

[0159] The server continuously adjusts and updates the content and tone of voice instructions during cooking based on the analysis results of the emotion engine. Through this process, users receive cooking assistance that matches their emotions, increasing their satisfaction.

[0160] (Example 2)

[0161] 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 will be referred to as the "terminal."

[0162] Conventional cooking support systems offer cooking suggestions without considering the user's emotional state, resulting in a uniform user experience that fails to contribute to stress reduction or increased satisfaction. Furthermore, providing cooking instructions that respond to the user's real-time emotions and circumstances is difficult, highlighting the need for more personalized cooking support.

[0163] 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.

[0164] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for analyzing the user's voice data and recognizing the user's emotional state; and means for converting the user's voice instructions into text data and analyzing the text data to understand the user's intentions. This makes it possible to provide personalized cooking methods in real time that are tailored to the user's emotional state and intentions.

[0165] "Image data" refers to data that digitally represents visual information captured by a user.

[0166] "Ingredient information" refers to information about the type and condition of ingredients used in cooking, which is analyzed from image data.

[0167] "Audio data" refers to digital data that represents the acoustic signals recorded from a user's speech.

[0168] "Emotional state" refers to the user's psychological state and emotions, and is the result of analysis from audio and video.

[0169] "Emotional analysis methods" refer to technologies and processes that analyze a user's voice data and facial expressions to identify their emotional state.

[0170] A "generative AI model" is an artificial intelligence technology that uses natural language processing and other techniques to generate the optimal cooking method according to the user's intentions.

[0171] "Cooking method" refers to the steps and methods used to prepare ingredients and complete a dish.

[0172] "Speech synthesis technology" is a technology that outputs text data as speech.

[0173] "Voice tone" refers to the characteristics of a voice, including its pitch, intensity, speed, and emotional nuances.

[0174] This invention provides a cooking support system that takes into account the user's emotional state, enabling the user to enjoy a comfortable and personalized cooking experience. This system functions by combining a terminal, a server, and emotion analysis means.

[0175] The user first puts on a digital earphone with a camera and takes pictures of the food inside the refrigerator. The earphone also captures audio, which is used by emotion analysis tools to evaluate the user's emotions. The image data and audio data sent from the earphone are transmitted to a server via the terminal. The image data is analyzed by software such as TensorFlow and OpenCV to identify the food items.

[0176] Next, the server uses speech recognition technology to convert the user's voice into text data and understand the user's intent. This process employs natural language processing technology, leveraging generative AI models (e.g., large-scale language models) to generate the most suitable cooking method for the user. Furthermore, this cooking method is personalized based on the user's emotional state. For example, a user experiencing stress might be presented with a cooking method using relaxing herbal tea.

[0177] The proposed cooking method is provided to the user as an audio guide via speech synthesis technology from the device. The voice tone is adjusted using emotion analysis technology to minimize the user's psychological burden.

[0178] As a concrete example of a prompt, a user could give instructions such as, "Tell me a relaxing dish I can make with the ingredients I have in my refrigerator." This allows the present invention to provide continuous, emotionally sensitive support to the user from before they start cooking until it is completed.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The user wears a digital earphone with a camera, takes pictures of the food inside the refrigerator, and uses voice input.

[0182] Input: Image data and audio data of food items photographed by the user.

[0183] Specific operation: Acquires image and audio data from the earphones and transfers it to the device.

[0184] Step 2:

[0185] The terminal sends the received image data and audio data to the server.

[0186] Input: Image data and audio data stored on the device.

[0187] Output: Image data and audio data of the ingredients sent to the server.

[0188] Specific operation: The terminal establishes communication with the server via the network and transfers data.

[0189] Step 3:

[0190] The server uses image recognition technology to analyze the received image data and identify the ingredients.

[0191] Input: Image data sent to the server.

[0192] Output: Ingredient information stored in the database.

[0193] Specific operation: The server uses TensorFlow to analyze images and identify ingredients such as "carrots" and "onions."

[0194] Step 4:

[0195] The server analyzes the voice data, recognizing the user's emotional state and understanding their intentions.

[0196] Input: Audio data sent to the server.

[0197] Output: User intent and emotional state information.

[0198] Specific operation: Speech recognition technology converts speech into text, and an emotion analysis algorithm detects emotional states such as "stress."

[0199] Step 5:

[0200] The server uses a generative AI model to generate cooking methods based on ingredient information, user intent, and emotional state.

[0201] Input: Ingredient information, user intent, emotional state information.

[0202] Output: Personalized cooking recipes.

[0203] Specific operation: The generative AI model constructs recipes such as "relaxing herbal tea and salmon."

[0204] Step 6:

[0205] The terminal receives cooking instructions from the server and communicates them to the user using speech synthesis technology.

[0206] Input: Cooking recipe sent from the server.

[0207] Output: Voice-guided cooking instructions to the user.

[0208] Specific actions: Using the device's speech synthesis function, it will convey cooking instructions in a gentle tone, such as "Next, let's cut the onions."

[0209] Step 7:

[0210] The user begins cooking, and the device sends the process to the server as live video.

[0211] Input: Live video data of cooking in progress.

[0212] Output: Cooking information provided to the server.

[0213] Specific operation: The device continuously captures video in real time and sends it to the server.

[0214] Step 8:

[0215] The server analyzes the video footage, monitors the cooking progress, and updates instructions in real time.

[0216] Input: Live video data.

[0217] Output: Updated cooking instructions.

[0218] Specific operation: The server detects the "salmon's browning" from the video, generates the appropriate cooking instruction "Flip it over," and sends it to the terminal.

[0219] (Application Example 2)

[0220] 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".

[0221] In modern life, cooking is an essential activity for maintaining health and family harmony. However, it is not easy to select the appropriate cooking method within a limited time while also considering the user's emotional state. In particular, conventional cooking support systems are insufficient when users are stressed or seeking satisfying meals with simple steps. Therefore, there is a need for technology that comprehensively supports cooking while taking the user's emotions into consideration.

[0222] 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.

[0223] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intentions; means for analyzing the user's voice data and video data to recognize the user's emotional state; means for generating a cooking method proposed based on the ingredient information and the user's intentions and providing the cooking method to the user; and means for generating a recipe proposed based on the emotional state and presenting the recipe to the user in an emotionally sensitive tone using speech synthesis technology. This enables comprehensive and efficient cooking support that takes the user's emotional state into consideration.

[0224] "Image data" refers to recorded information of still images or videos taken by the user, and is used for visually recognizing food ingredients and other items.

[0225] "Food ingredient information" refers to information about the type and condition of food identified by analyzing image data, and is used to suggest cooking methods.

[0226] "Voice instructions" refer to verbal instructions or questions given by the user to the system, which are then analyzed through technology.

[0227] "Text data" refers to character information obtained by converting audio information, such as voice instructions, and forms the basis for analysis and intent understanding.

[0228] "User intent" refers to the user's wishes and objectives, which are analyzed from voice instructions and text data, and which influence the generation of cooking methods.

[0229] "Cooking method" refers to the cooking procedures and processes generated based on specified ingredient information and the user's intentions.

[0230] "Emotional state" refers to the user's feelings and mood, which are recognized by analyzing the user's voice and video data, and which influence recipe suggestions.

[0231] A "recipe" is a plan that outlines the necessary ingredients and procedures for cooking, and is suggested to the user.

[0232] "Speech synthesis technology" is a technology that converts text data into speech output, and is used to generate speech when providing information to users.

[0233] The system for implementing this invention mainly consists of an AI-equipped earphone with a camera, an emotion engine, and a central server. When a user wears the AI ​​earphone with a camera and takes a picture of the food in the refrigerator, image data is generated. This image data is sent from the terminal to the server, which analyzes the food information using the image processing library OpenCV.

[0234] Furthermore, when the user issues a voice command into the earphones, the device uses a speech recognition library to convert the voice into text. This text data is then sent back to the server, where the server analyzes the user's intent. In addition, an emotion engine analyzes the user's voice and video data to recognize the user's emotional state. Simultaneously, a generative AI model suggests the optimal cooking method to provide recipe suggestions tailored to the emotional state.

[0235] The suggested cooking methods and recipes are communicated to the user using speech synthesis technology. The server adjusts the voice tone to match the user's emotional state, ensuring a comfortable learning experience. For example, if the user is feeling stressed, the server will suggest relaxing dishes and relatively simple cooking processes, guiding them in a gentle tone.

[0236] For example, it is possible to input prompt sentences into the AI ​​model such as, "Please create a system in which a robot analyzes the user's emotions and the ingredients in the refrigerator, suggests the best dish for the day, and provides gentle voice guidance while cooking."

[0237] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0238] Step 1:

[0239] The user uses AI earphones with a camera to photograph the food inside the refrigerator. The device sends this captured image data to a central server. In this step, image data is acquired as input and sent to the server for analysis. Specifically, the device uses its camera function to take high-resolution still images and uploads the data to the server via a wireless network.

[0240] Step 2:

[0241] The server uses the received image data to analyze the food information, utilizing image processing libraries such as OpenCV. The results of this analysis are recorded as text data. Image data is the input, and food information is generated as the output. Specifically, the server applies object recognition technology in the image to classify individual food items and extracts information on the names and quantities of the recognized food items.

[0242] Step 3:

[0243] The user gives cooking instructions and questions via voice into the AI ​​earphones. The device collects this voice data and converts it into text data using a speech recognition library. The input for this step is voice data, and the output is text data. More specifically, the device converts the voice signal into a digital format and then into text data that can be sent to the server.

[0244] Step 4:

[0245] The server analyzes text data to recognize the user's intent and requests. It also analyzes the user's voice and video data to understand their emotional state through an emotion engine. Inputs include text data and voice / video analysis data, while output is the user's intent and emotional state. Specifically, it utilizes natural language processing techniques to extract keywords from text data and uses an emotion recognition algorithm to identify the emotional state.

[0246] Step 5:

[0247] The server generates the optimal cooking method and recipe based on data on ingredient information, user intent, and emotional state. In this process, a generative AI model is used to create the suggested content. Inputs include ingredient information, user intent, and emotional state, while outputs are cooking methods and recipes. Specifically, the generative AI model integrates this information to construct a cooking procedure that is both appropriate for the user and emotionally satisfying.

[0248] Step 6:

[0249] The terminal uses speech synthesis technology to present cooking instructions sent from the server to the user via voice. The voice tone is adjusted to match the user's emotional state. The input is the cooking method and recipe, and the output is the voice instructions delivered to the user. Specifically, the terminal plays the synthesized voice with clear quality and provides navigation to the user at the appropriate time.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] [Second Embodiment]

[0254] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0255] 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.

[0256] 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).

[0257] 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.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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".

[0266] As a specific embodiment of the present invention, a system is described in which the user uses AI earphones with a camera to support cooking. This system supports the user's daily cooking activities and promotes an efficient and healthy diet.

[0267] First, the user puts on AI earphones with a camera and activates the system by taking pictures of the food in the refrigerator. The device sends the captured image data to the server in real time. The server analyzes this image data and uses image recognition technology to identify the food in the refrigerator. This food information is registered in a database and used to provide future cooking suggestions to the user.

[0268] The user can then give voice commands into the earphones. For example, they might ask, "What should I make for dinner?" The device converts these voice commands into text data and sends it to the server. The server uses natural language processing technology to analyze this text and understand the user's intent.

[0269] The server determines the optimal cooking method based on the user's intentions and the ingredient information stored in the database. Specifically, it generates a recipe that utilizes available ingredients while also considering health balance, and sends it to the terminal. The terminal then uses speech synthesis technology to convey this information to the user and provide the necessary cooking instructions.

[0270] In addition, once the user starts cooking, the device continuously sends information about the cooking process to the server. The server analyzes this information and generates real-time instructions based on the progress of the cooking, providing the user with accurate cooking support.

[0271] For example, if the user has "chicken" and "cabbage" in the refrigerator, the system can suggest "stir-fried chicken and cabbage" in response to the user's inquiry, and provide instructions such as "cut the chicken into bite-sized pieces" and "add the cabbage and cook over medium heat."

[0272] In this way, the present invention comprehensively provides effective utilization of ingredients, recipe suggestions, and real-time support for cooking. As a result, users can easily achieve a balanced diet while saving the trouble of planning menus and preventing food waste.

[0273] The following describes the processing flow.

[0274] Step 1:

[0275] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device compresses the captured image data in real time and sends it to a server via wireless communication.

[0276] Step 2:

[0277] The server analyzes the received image data using an image recognition algorithm to identify the types and quantities of each ingredient present in the refrigerator. The identified ingredient information is stored in a database in preparation for use in future cooking suggestions.

[0278] Step 3:

[0279] The user issues a voice instruction regarding cooking towards the earphone. For example, the user's cooking request is included in the form of "What's good for dinner tonight?" The terminal converts this voice data into text data and transmits it to the server.

[0280] Step 4:

[0281] The server analyzes the text data using natural language processing technology to understand the user's question and intention. By combining the analyzed information with the previously collected ingredient information and the user's preference data, an optimal cooking method is selected.

[0282] Step 5:

[0283] The server transmits the selected cooking method to the terminal in text format. The terminal uses voice synthesis technology to provide a cooking suggestion to the user in voice. At this time, details such as ingredient preparation and cooking time are also included in the guidance as necessary.

[0284] Step 6:

[0285] When the user executes the cooking, the terminal continuously captures the state of the cooking with a camera and continues to transmit the video to the server. The server analyzes this video and generates specific cooking instructions according to the user's progress.

[0286] Step 7:

[0287] The server generates real-time cooking instructions and provides them to the user via the terminal. For example, it immediately guides the user on specific actions during cooking, such as "Reduce the heat a little more and simmer for 3 minutes."

[0288] Step 8:

[0289] The server analyzes expiration date information in the database and prioritizes suggesting cooking methods using ingredients that are nearing their expiration date. The terminal then provides these suggestions to the user via voice, contributing to the reduction of food waste.

[0290] (Example 1)

[0291] 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 glasses 214 will be referred to as the "terminal."

[0292] In modern life, cooking at home is crucial for maintaining an efficient and healthy diet. However, many people find it difficult to dedicate the time and effort to devising new dishes every day, and food waste is a challenge due to the inability to effectively manage and utilize ingredients. Furthermore, it is difficult to receive appropriate support as the cooking process progresses, and many people experience mistakes and inconveniences during the cooking process.

[0293] 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.

[0294] In this invention, the server includes means for receiving visual data acquired by the user using a camera device and analyzing the visual data to identify object information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; and means for generating a cooking method created based on the object information and the user's intent, and providing the cooking method to the user via voice output. This enables the user to utilize ingredients efficiently and healthily and receive cooking support in real time.

[0295] A "user" refers to an individual or group that operates or utilizes a system.

[0296] "Capturing device" refers to equipment used to acquire images or videos, and includes cameras and devices equipped with cameras.

[0297] "Visual data" refers to data of still images or moving images acquired by a camera device.

[0298] "Object information" refers to information about the type and characteristics of an object identified from visual data.

[0299] A "server" refers to a central computer system that processes data and manages storage over a network, and exchanges data with client terminals.

[0300] "Voice instructions" refer to voice commands or questions that users provide to the system.

[0301] "Text data" refers to data in text format obtained by converting voice commands.

[0302] "Cooking method" refers to information about the procedures and recipes for creating a dish using ingredients.

[0303] "Voice output" refers to a means of providing text data and other information to the user as audio.

[0304] "Dynamic video" refers to video data that changes in real time, and includes data that is captured and transmitted as video.

[0305] "Progress" refers to the current status of the user's activities or processes.

[0306] "Work instructions" refer to information that specifically guides the user on the next actions or procedures they should take.

[0307] "Consumption deadline information" refers to information regarding the deadline by which an object should be appropriately consumed.

[0308] "Memory device" refers to hardware or media for storing electronic data.

[0309] "Generated AI model" refers to a model that infers or generates from data by applying artificial intelligence technology.

[0310] "Prompt sentence" refers to a text-based instruction input to a generated AI model for information generation.

[0311] This system uses innovative technology for cooking assistance and is a system that enables users to achieve efficient and healthy eating habits through cooking. The following describes specific implementation methods.

[0312] The user uses earphones with a camera-equipped shooting device to take pictures inside the refrigerator. This device has a high-performance camera and a voice reception microphone, and these are utilized to obtain visual data and voice instructions. The visual data taken by the user is transmitted from the terminal to the server in real time. Here, the terminal uses Bluetooth or Wi-Fi as the wireless communication technology.

[0313] When the server receives the visual data, it utilizes image recognition technology to identify object information. For this process, general image recognition software, such as TensorFlow or OpenCV, is used. Subsequently, the voice instructions issued by the user towards the earphones are converted into text through voice recognition technology, and the user's intention is analyzed using a generated AI model. Natural language processing technology is applied to this analysis to generate an appropriate cooking method according to the user's wishes and situation.

[0314] The cooking instructions generated by the server take into account available ingredients, health balance, and user preferences. The generated steps and recipes are output to the user via speech synthesis software. For example, it might say, "Let's make stir-fried chicken and cabbage. First, cut the chicken into bite-sized pieces."

[0315] After cooking begins, the user continuously sends dynamic video of the cooking process from their device to the server. The server analyzes this data and determines the progress in real time. Feedback is provided according to the user's cooking progress, such as instructions like, "Next, add the cabbage and sauté over medium heat."

[0316] For example, if a user has chicken and cabbage in the refrigerator for dinner one day, and asks "What should I make?", the system can respond and suggest an appropriate cooking method. An example of a prompt might be given to the generating AI model in the form of "Take a picture of the ingredients in the refrigerator and suggest a healthy dinner recipe."

[0317] As described above, this invention integrates multiple technologies to help users cook efficiently and effectively and make good use of ingredients.

[0318] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0319] Step 1:

[0320] The user takes a picture of the inside of the refrigerator using an earphone-type device with a camera function. This initializes the system's operation. The input is image data of the food items inside the refrigerator. This image is transferred to the terminal in real time, and the terminal then sends this data directly to the server.

[0321] Step 2:

[0322] The server analyzes the received image data using image analysis software. Specifically, it uses TensorFlow and OpenCV to identify and classify ingredients. The input is image data, and the output is analyzed object information (ingredient name and quantity). This data forms the basis for suggesting the optimal cooking method to the user.

[0323] Step 3:

[0324] The user gives voice commands into the microphone of their earphones. For example, they might say, "Tell me what I should make for dinner." The input is the user's voice.

[0325] Step 4:

[0326] The device converts voice commands into text data using speech recognition technology. It uses APIs such as Google's Speech Recognition API for this speech-to-text conversion. The input is the user's voice, and the output is text data. This text is then sent to the server.

[0327] Step 5:

[0328] The server analyzes the transmitted text data using natural language processing techniques. Based on the analysis, it understands the user's intent and uses a generative AI model to determine the appropriate cooking method. The input is text data and previously identified object information, and the output is the generated cooking procedure.

[0329] Step 6:

[0330] The server generates cooking instructions, which the terminal then uses speech synthesis software to provide to the user via voice. Specific instructions, such as "We're going to make stir-fried chicken and cabbage. Cut the chicken into bite-sized pieces," are conveyed. The input is the cooking instructions, and the output is a voice message.

[0331] Step 7:

[0332] After the user starts cooking, the terminal continuously sends video of the cooking process to the server. The input is video data of the cooking process.

[0333] Step 8:

[0334] The server analyzes the received dynamic video and monitors the progress of cooking. If necessary, it generates real-time cooking instructions and provides them to the user via the terminal. For example, it might give specific instructions such as, "Next, sauté the cabbage over medium heat." The input is video data of the cooking process, and the output is real-time cooking instructions.

[0335] (Application Example 1)

[0336] 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."

[0337] Current home cooking support systems have limitations in the information provided to users and insufficient real-time cooking support. Furthermore, they may lead to food waste because users cannot effectively utilize the ingredients in their refrigerators. The purpose of this invention is to solve these problems and enable users to achieve a more efficient and healthy diet.

[0338] 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.

[0339] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify item information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; means for generating a process proposed based on the item information and the user's intent and providing the process to the user; and means for analyzing the user's voice input using artificial intelligence and outputting cooking instructions in voice in real time. This enables the user to utilize items efficiently and receive more accurate support.

[0340] A "user" is an individual or household that uses the system and manages and prepares goods.

[0341] "Item information" refers to information about food items and materials inside refrigerators and shelves, identified from image data analyzed by the system.

[0342] "Voice instructions" refer to voice data that users input into the system via voice, such as requests or questions related to cooking.

[0343] "Text data" refers to character information converted by the system based on voice commands, and is used for analysis.

[0344] A "process" refers to a series of processing methods that the system generates, including specific cooking steps and procedures that the user should perform.

[0345] "Artificial intelligence" refers to a technology that incorporates machine learning and natural language processing, used to analyze user voice input and generate optimal cooking procedures.

[0346] "Real-time" refers to a time concept that indicates the immediacy of providing immediate feedback and instructions in response to the user's cooking progress.

[0347] The specific system for implementing this invention is composed of a user, a terminal, and a server. The system of this invention uses a terminal, which is a consumer electronic device equipped with a camera function, in combination with a cloud-based server.

[0348] The terminal takes pictures of items inside the refrigerator or on kitchen shelves and sends the image data to the server. The server analyzes the images using image processing software and extracts information about specific items. To achieve high-quality image analysis, this process uses the open-source library OpenCV. The user can also give voice instructions related to cooking to the terminal, which converts this voice into text data and sends it to the server. Automatic speech recognition (ASR) and natural language processing (NLP) technologies are used for voice analysis.

[0349] The server analyzes this text data, understands the user's intent, and combines it with item information to generate appropriate steps. The specific cooking process uses an AI model. This AI model can suggest recipes that consider available ingredients while maintaining a healthy balance. The user receives this information and can begin cooking. During cooking, the device continuously records the user's actions live and sends them to the server. Based on this data, the server generates cooking instructions in real time and provides them to the user via speech synthesis through the device.

[0350] For example, if a user takes a picture of the ingredients in their refrigerator and requests a dinner suggestion, the server will recognize "chicken" and "cabbage" and suggest "stir-fried chicken and cabbage." During cooking, it will provide real-time voice instructions such as "cut the chicken" and "add the cabbage."

[0351] An example of a prompt message is, "Analyze the camera footage inside the refrigerator and generate a recipe suggestion for tonight's dinner."

[0352] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0353] Step 1:

[0354] The user uses the device's camera to photograph items inside the refrigerator. This generates image data, which the device then sends to the server. The input is the image data of the items, and the output is the data sent to the server. This step involves the acquisition and communication of image data.

[0355] Step 2:

[0356] The server analyzes the received image data using OpenCV to identify the object. The input is image data sent from the terminal, and the output is the analyzed object information. In this process, data processing and image recognition are performed by image processing algorithms.

[0357] Step 3:

[0358] The user inputs voice commands into the terminal, which converts the voice into text data and sends it to the server. The input is voice data from the user, and the output is text data sent to the server. In this step, speech recognition technology is used to analyze and convert the voice data.

[0359] Step 4:

[0360] The server analyzes text data using natural language processing techniques to understand the user's intent. The input is text data sent from the terminal, and the output is the analysis result that reflects the user's intent. Intent inference is performed by performing data calculations in this step.

[0361] Step 5:

[0362] The server generates the optimal process using a generative AI model based on item information and user intent. The input is item information and user intent, and the output is process data to be provided to the terminal. In this step, the AI ​​model is used to integrate the data and synthesize the process.

[0363] Step 6:

[0364] The generated process data is provided to the user via a terminal using speech synthesis technology. The input is process data from the server, and the output is an audio guide that the user can listen to. In this step, audio is generated and output based on the process data.

[0365] Step 7:

[0366] Once the user begins cooking, the terminal continuously sends progress updates to the server, which then generates cooking instructions in real time. The input is live data indicating the current cooking status, and the output is instruction data provided to the user during cooking. This process generates dynamic instructions based on the current state.

[0367] 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.

[0368] One embodiment of the present invention is to provide a system that comprehensively supports cooking activities while taking into account the user's emotional state. This system combines an AI earphone with a camera and an emotion engine to realize ingredient recognition, voice instruction analysis, real-time cooking support, and emotion-based recipe suggestions.

[0369] The process begins with the user wearing AI earphones with a camera and taking pictures of the food in their refrigerator. The device sends this image data to a server, which uses image recognition technology to identify the food items. At this stage, the emotion engine analyzes the user's voice and video at the time of shooting to recognize the user's emotional state.

[0370] Next, the user voice-questions about cooking into the earphones. The device converts this voice into text and sends it to the server. The server analyzes the received text data to understand the user's intent and, based on information from the emotion engine, generates cooking methods suitable for the user's mental state. For example, if the user is feeling stressed, it will suggest relaxing dishes and simple cooking instructions.

[0371] The cooking method determined by the server is sent to the terminal and communicated to the user using speech synthesis technology. This process also utilizes information from the emotion engine to adjust the tone of voice and provide user-friendly instructions. For example, if the user is tired, a gentle tone of voice will be used to provide a reminder.

[0372] Furthermore, once the user begins cooking, the device sends live video of the cooking process to the server. The server analyzes the video to understand the progress of the cooking. Based on this information, it generates and provides real-time cooking instructions tailored to the user's emotions.

[0373] For example, if the emotion engine determines that the user is "frustrated," it can suggest a "stress-relieving herbal tea and salmon recipe," and while the user is cooking, it can gently advise them, "Let's move on to the next step with a relaxed mind."

[0374] In this way, the system of the present invention, which combines an emotion engine, allows users to receive cooking assistance tailored to their emotional state, resulting in an efficient and comfortable cooking experience.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device sends this image data to a server in real time. In addition, the voice spoken by the user at this time is captured as input for the emotion engine.

[0378] Step 2:

[0379] The server analyzes the received image data using image recognition technology to identify the food items present in the refrigerator. Furthermore, an emotion engine analyzes the user's voice information to recognize the user's emotional state and reports the results to the server.

[0380] Step 3:

[0381] The user asks a cooking-related question via voice into the earphone. The device converts this voice data into text data and sends it to the server. The server analyzes the transmitted data to understand the user's intent.

[0382] Step 4:

[0383] The server combines ingredient information, user intent, and emotional state recognized by the emotion engine to generate an appropriate cooking method. If the user is feeling stressed, it will select a simple and relaxing dish.

[0384] Step 5:

[0385] The server generates a cooking method and sends it to the terminal. The terminal uses speech synthesis technology to provide the user with the cooking instructions in a voice-based manner, using a tone that is gentle or encouraging, depending on the user's emotional state.

[0386] Step 6:

[0387] Once the user begins cooking, the device continuously records the cooking process with its camera and sends the live video to the server. The server analyzes this video to understand the progress of the cooking.

[0388] Step 7:

[0389] The server generates real-time cooking instructions tailored to the user's situation, based on the cooking progress and information from the emotion engine. These instructions are delivered in a way that matches the user's emotions, for example, by saying, "Calm down, let's move on to the next step."

[0390] Step 8:

[0391] The server continuously adjusts and updates the content and tone of voice instructions during cooking based on the analysis results of the emotion engine. Through this process, users receive cooking assistance that matches their emotions, increasing their satisfaction.

[0392] (Example 2)

[0393] 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".

[0394] Conventional cooking support systems offer cooking suggestions without considering the user's emotional state, resulting in a uniform user experience that fails to contribute to stress reduction or increased satisfaction. Furthermore, providing cooking instructions that respond to the user's real-time emotions and circumstances is difficult, highlighting the need for more personalized cooking support.

[0395] 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.

[0396] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for analyzing the user's voice data and recognizing the user's emotional state; and means for converting the user's voice instructions into text data and analyzing the text data to understand the user's intentions. This makes it possible to provide personalized cooking methods in real time that are tailored to the user's emotional state and intentions.

[0397] "Image data" refers to data that digitally represents visual information captured by a user.

[0398] "Ingredient information" refers to information about the type and condition of ingredients used in cooking, which is analyzed from image data.

[0399] "Audio data" refers to digital data that represents the acoustic signals recorded from a user's speech.

[0400] "Emotional state" refers to the user's psychological state and emotions, and is the result of analysis from audio and video.

[0401] "Emotional analysis methods" refer to technologies and processes that analyze a user's voice data and facial expressions to identify their emotional state.

[0402] A "generative AI model" is an artificial intelligence technology that uses natural language processing and other techniques to generate the optimal cooking method according to the user's intentions.

[0403] "Cooking method" refers to the steps and methods used to prepare ingredients and complete a dish.

[0404] "Speech synthesis technology" is a technology that outputs text data as speech.

[0405] "Voice tone" refers to the characteristics of a voice, including its pitch, intensity, speed, and emotional nuances.

[0406] This invention provides a cooking support system that takes into account the user's emotional state, enabling the user to enjoy a comfortable and personalized cooking experience. This system functions by combining a terminal, a server, and emotion analysis means.

[0407] The user first puts on a digital earphone with a camera and takes pictures of the food inside the refrigerator. The earphone also captures audio, which is used by emotion analysis tools to evaluate the user's emotions. The image data and audio data sent from the earphone are transmitted to a server via the terminal. The image data is analyzed by software such as TensorFlow and OpenCV to identify the food items.

[0408] Next, the server uses speech recognition technology to convert the user's voice into text data and understand the user's intent. This process employs natural language processing technology, leveraging generative AI models (e.g., large-scale language models) to generate the most suitable cooking method for the user. Furthermore, this cooking method is personalized based on the user's emotional state. For example, a user experiencing stress might be presented with a cooking method using relaxing herbal tea.

[0409] The proposed cooking method is provided to the user as an audio guide via speech synthesis technology from the device. The voice tone is adjusted using emotion analysis technology to minimize the user's psychological burden.

[0410] As a concrete example of a prompt, a user could give instructions such as, "Tell me a relaxing dish I can make with the ingredients I have in my refrigerator." This allows the present invention to provide continuous, emotionally sensitive support to the user from before they start cooking until it is completed.

[0411] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0412] Step 1:

[0413] The user wears a digital earphone with a camera, takes pictures of the food inside the refrigerator, and uses voice input.

[0414] Input: Image data and audio data of food items photographed by the user.

[0415] Specific operation: Acquires image and audio data from the earphones and transfers it to the device.

[0416] Step 2:

[0417] The terminal sends the received image data and audio data to the server.

[0418] Input: Image data and audio data stored on the device.

[0419] Output: Image data and audio data of the ingredients sent to the server.

[0420] Specific operation: The terminal establishes communication with the server via the network and transfers data.

[0421] Step 3:

[0422] The server uses image recognition technology to analyze the received image data and identify the ingredients.

[0423] Input: Image data sent to the server.

[0424] Output: Ingredient information stored in the database.

[0425] Specific operation: The server uses TensorFlow to analyze images and identify ingredients such as "carrots" and "onions."

[0426] Step 4:

[0427] The server analyzes the voice data, recognizing the user's emotional state and understanding their intentions.

[0428] Input: Audio data sent to the server.

[0429] Output: User intent and emotional state information.

[0430] Specific operation: Speech recognition technology converts speech into text, and an emotion analysis algorithm detects emotional states such as "stress."

[0431] Step 5:

[0432] The server uses a generative AI model to generate cooking methods based on ingredient information, user intent, and emotional state.

[0433] Input: Ingredient information, user intent, emotional state information.

[0434] Output: Personalized cooking recipes.

[0435] Specific operation: The generative AI model constructs recipes such as "relaxing herbal tea and salmon."

[0436] Step 6:

[0437] The terminal receives cooking instructions from the server and communicates them to the user using speech synthesis technology.

[0438] Input: Cooking recipe sent from the server.

[0439] Output: Voice-guided cooking instructions to the user.

[0440] Specific actions: Using the device's speech synthesis function, it will convey cooking instructions in a gentle tone, such as "Next, let's cut the onions."

[0441] Step 7:

[0442] The user begins cooking, and the device sends the process to the server as live video.

[0443] Input: Live video data of cooking in progress.

[0444] Output: Cooking information provided to the server.

[0445] Specific operation: The device continuously captures video in real time and sends it to the server.

[0446] Step 8:

[0447] The server analyzes the video footage, monitors the cooking progress, and updates instructions in real time.

[0448] Input: Live video data.

[0449] Output: Updated cooking instructions.

[0450] Specific operation: The server detects the "salmon's browning" from the video, generates the appropriate cooking instruction "Flip it over," and sends it to the terminal.

[0451] (Application Example 2)

[0452] 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 as the "terminal".

[0453] In modern life, cooking is an essential activity for maintaining health and family harmony. However, it is not easy to select the appropriate cooking method within a limited time while also considering the user's emotional state. In particular, conventional cooking support systems are insufficient when users are stressed or seeking satisfying meals with simple steps. Therefore, there is a need for technology that comprehensively supports cooking while taking the user's emotions into consideration.

[0454] 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.

[0455] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intentions; means for analyzing the user's voice data and video data to recognize the user's emotional state; means for generating a cooking method proposed based on the ingredient information and the user's intentions and providing the cooking method to the user; and means for generating a recipe proposed based on the emotional state and presenting the recipe to the user in an emotionally sensitive tone using speech synthesis technology. This enables comprehensive and efficient cooking support that takes the user's emotional state into consideration.

[0456] "Image data" refers to recorded information of still images or videos taken by the user, and is used for visually recognizing food ingredients and other items.

[0457] "Food ingredient information" refers to information about the type and condition of food identified by analyzing image data, and is used to suggest cooking methods.

[0458] "Voice instructions" refer to verbal instructions or questions given by the user to the system, which are then analyzed through technology.

[0459] "Text data" refers to character information obtained by converting audio information, such as voice instructions, and forms the basis for analysis and intent understanding.

[0460] "User intent" refers to the user's wishes and objectives, which are analyzed from voice instructions and text data, and which influence the generation of cooking methods.

[0461] "Cooking method" refers to the cooking procedures and processes generated based on specified ingredient information and the user's intentions.

[0462] "Emotional state" refers to the user's feelings and mood, which are recognized by analyzing the user's voice and video data, and which influence recipe suggestions.

[0463] A "recipe" is a plan that outlines the necessary ingredients and procedures for cooking, and is suggested to the user.

[0464] "Speech synthesis technology" is a technology that converts text data into speech output, and is used to generate speech when providing information to users.

[0465] The system for implementing this invention mainly consists of an AI-equipped earphone with a camera, an emotion engine, and a central server. When a user wears the AI ​​earphone with a camera and takes a picture of the food in the refrigerator, image data is generated. This image data is sent from the terminal to the server, which analyzes the food information using the image processing library OpenCV.

[0466] Furthermore, when the user issues a voice command into the earphones, the device uses a speech recognition library to convert the voice into text. This text data is then sent back to the server, where the server analyzes the user's intent. In addition, an emotion engine analyzes the user's voice and video data to recognize the user's emotional state. Simultaneously, a generative AI model suggests the optimal cooking method to provide recipe suggestions tailored to the emotional state.

[0467] The suggested cooking methods and recipes are communicated to the user using speech synthesis technology. The server adjusts the voice tone to match the user's emotional state, ensuring a comfortable learning experience. For example, if the user is feeling stressed, the server will suggest relaxing dishes and relatively simple cooking processes, guiding them in a gentle tone.

[0468] For example, it is possible to input prompt sentences into the AI ​​model such as, "Please create a system in which a robot analyzes the user's emotions and the ingredients in the refrigerator, suggests the best dish for the day, and provides gentle voice guidance while cooking."

[0469] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0470] Step 1:

[0471] The user uses AI earphones with a camera to photograph the food inside the refrigerator. The device sends this captured image data to a central server. In this step, image data is acquired as input and sent to the server for analysis. Specifically, the device uses its camera function to take high-resolution still images and uploads the data to the server via a wireless network.

[0472] Step 2:

[0473] The server uses the received image data to analyze the food information, utilizing image processing libraries such as OpenCV. The results of this analysis are recorded as text data. Image data is the input, and food information is generated as the output. Specifically, the server applies object recognition technology in the image to classify individual food items and extracts information on the names and quantities of the recognized food items.

[0474] Step 3:

[0475] The user gives cooking instructions and questions via voice into the AI ​​earphones. The device collects this voice data and converts it into text data using a speech recognition library. The input for this step is voice data, and the output is text data. More specifically, the device converts the voice signal into a digital format and then into text data that can be sent to the server.

[0476] Step 4:

[0477] The server analyzes text data to recognize the user's intent and requests. It also analyzes the user's voice and video data to understand their emotional state through an emotion engine. Inputs include text data and voice / video analysis data, while output is the user's intent and emotional state. Specifically, it utilizes natural language processing techniques to extract keywords from text data and uses an emotion recognition algorithm to identify the emotional state.

[0478] Step 5:

[0479] The server generates the optimal cooking method and recipe based on data on ingredient information, user intent, and emotional state. In this process, a generative AI model is used to create the suggested content. Inputs include ingredient information, user intent, and emotional state, while outputs are cooking methods and recipes. Specifically, the generative AI model integrates this information to construct a cooking procedure that is both appropriate for the user and emotionally satisfying.

[0480] Step 6:

[0481] The terminal uses speech synthesis technology to present cooking instructions sent from the server to the user via voice. The voice tone is adjusted to match the user's emotional state. The input is the cooking method and recipe, and the output is the voice instructions delivered to the user. Specifically, the terminal plays the synthesized voice with clear quality and provides navigation to the user at the appropriate time.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] [Third Embodiment]

[0486] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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".

[0498] As a specific embodiment of the present invention, a system is described in which the user uses AI earphones with a camera to support cooking. This system supports the user's daily cooking activities and promotes an efficient and healthy diet.

[0499] First, the user puts on AI earphones with a camera and activates the system by taking pictures of the food in the refrigerator. The device sends the captured image data to the server in real time. The server analyzes this image data and uses image recognition technology to identify the food in the refrigerator. This food information is registered in a database and used to provide future cooking suggestions to the user.

[0500] The user can then give voice commands into the earphones. For example, they might ask, "What should I make for dinner?" The device converts these voice commands into text data and sends it to the server. The server uses natural language processing technology to analyze this text and understand the user's intent.

[0501] The server determines the optimal cooking method based on the user's intentions and the ingredient information stored in the database. Specifically, it generates a recipe that utilizes available ingredients while also considering health balance, and sends it to the terminal. The terminal then uses speech synthesis technology to convey this information to the user and provide the necessary cooking instructions.

[0502] In addition, once the user starts cooking, the device continuously sends information about the cooking process to the server. The server analyzes this information and generates real-time instructions based on the progress of the cooking, providing the user with accurate cooking support.

[0503] For example, if the user has "chicken" and "cabbage" in the refrigerator, the system can suggest "stir-fried chicken and cabbage" in response to the user's inquiry, and provide instructions such as "cut the chicken into bite-sized pieces" and "add the cabbage and cook over medium heat."

[0504] In this way, the present invention comprehensively provides effective utilization of ingredients, recipe suggestions, and real-time support for cooking. As a result, users can easily achieve a balanced diet while saving the trouble of planning menus and preventing food waste.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device compresses the captured image data in real time and sends it to a server via wireless communication.

[0508] Step 2:

[0509] The server analyzes the received image data using an image recognition algorithm to identify the type and quantity of each ingredient present in the refrigerator. The identified ingredient information is stored in a database and prepared for future use in recipe suggestions.

[0510] Step 3:

[0511] The user gives voice instructions about cooking into the earphones. For example, the user's cooking requests might be included, such as "What would you like for dinner tonight?" The device converts this voice data into text data and sends it to the server.

[0512] Step 4:

[0513] The server analyzes text data using natural language processing technology to understand the user's questions and intentions. It then combines the analyzed information with pre-collected ingredient information and user preference data to select the optimal cooking method.

[0514] Step 5:

[0515] The server sends the selected cooking method to the terminal in text format. The terminal uses speech synthesis technology to provide the user with cooking suggestions via voice. If necessary, it also provides details such as ingredient preparation and cooking time.

[0516] Step 6:

[0517] As the user cooks, the device continuously films the cooking process with its camera and sends the video to the server. The server analyzes this video and generates specific cooking instructions based on the user's progress.

[0518] Step 7:

[0519] The server generates real-time cooking instructions and provides them to the user via the terminal. For example, it immediately guides the user on specific actions during cooking, such as "Reduce the heat a little more and simmer for 3 minutes."

[0520] Step 8:

[0521] The server analyzes expiration date information in the database and prioritizes suggesting cooking methods using ingredients that are nearing their expiration date. The terminal then provides these suggestions to the user via voice, contributing to the reduction of food waste.

[0522] (Example 1)

[0523] 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."

[0524] In modern life, cooking at home is crucial for maintaining an efficient and healthy diet. However, many people find it difficult to dedicate the time and effort to devising new dishes every day, and food waste is a challenge due to the inability to effectively manage and utilize ingredients. Furthermore, it is difficult to receive appropriate support as the cooking process progresses, and many people experience mistakes and inconveniences during the cooking process.

[0525] 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.

[0526] In this invention, the server includes means for receiving visual data acquired by the user using a camera device and analyzing the visual data to identify object information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; and means for generating a cooking method created based on the object information and the user's intent, and providing the cooking method to the user via voice output. This enables the user to utilize ingredients efficiently and healthily and receive cooking support in real time.

[0527] A "user" refers to an individual or group that operates or utilizes a system.

[0528] "Capturing device" refers to equipment used to acquire images or videos, and includes cameras and devices equipped with cameras.

[0529] "Visual data" refers to data of still images or moving images acquired by a camera device.

[0530] "Object information" refers to information about the type and characteristics of an object identified from visual data.

[0531] A "server" refers to a central computer system that processes data and manages storage over a network, and exchanges data with client terminals.

[0532] "Voice instructions" refer to voice commands or questions that users provide to the system.

[0533] "Text data" refers to data in text format obtained by converting voice commands.

[0534] "Cooking method" refers to information about the procedures and recipes for creating a dish using ingredients.

[0535] "Voice output" refers to a means of providing text data and other information to the user as audio.

[0536] "Dynamic video" refers to video data that changes in real time, and includes data that is captured and transmitted as video.

[0537] "Progress" refers to the current status of the user's activities or processes.

[0538] "Work instructions" refer to information that specifically guides the user on the next actions or procedures they should take.

[0539] "Expiration date information" refers to information about the deadline by which an object should be consumed appropriately.

[0540] A "storage device" refers to hardware or a medium used to store electronic data.

[0541] A "generative AI model" refers to a model that uses artificial intelligence technology to infer or generate from data.

[0542] A "prompt statement" refers to a text-based instruction given to a generative AI model for generating information.

[0543] This system utilizes innovative technology aimed at assisting with cooking, enabling users to achieve efficient and healthy eating habits through the cooking process. The specific implementation method is described below.

[0544] The user takes pictures inside a refrigerator using earphones equipped with a camera. This device features a high-performance camera and a microphone, which are used to acquire visual data and voice commands. The visual data captured by the user is transmitted from the device to a server in real time. The device uses Bluetooth or Wi-Fi for wireless communication.

[0545] Upon receiving visual data, the server uses image recognition technology to identify object information. This process employs common image recognition software, such as TensorFlow or OpenCV. Subsequently, the voice commands spoken by the user into the earphones are converted into text using speech recognition technology, and the user's intent is analyzed using a generative AI model. Natural language processing technology is applied to this analysis to generate appropriate cooking methods tailored to the user's wishes and circumstances.

[0546] The cooking instructions generated by the server take into account available ingredients, health balance, and user preferences. The generated steps and recipes are output to the user via speech synthesis software. For example, it might say, "Let's make stir-fried chicken and cabbage. First, cut the chicken into bite-sized pieces."

[0547] After cooking begins, the user continuously sends dynamic video of the cooking process from their device to the server. The server analyzes this data and determines the progress in real time. Feedback is provided according to the user's cooking progress, such as instructions like, "Next, add the cabbage and sauté over medium heat."

[0548] For example, if a user has chicken and cabbage in the refrigerator for dinner one day, and asks "What should I make?", the system can respond and suggest an appropriate cooking method. An example of a prompt might be given to the generating AI model in the form of "Take a picture of the ingredients in the refrigerator and suggest a healthy dinner recipe."

[0549] As described above, this invention integrates multiple technologies to help users cook efficiently and effectively and make good use of ingredients.

[0550] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0551] Step 1:

[0552] The user takes a picture of the inside of the refrigerator using an earphone-type device with a camera function. This initializes the system's operation. The input is image data of the food items inside the refrigerator. This image is transferred to the terminal in real time, and the terminal then sends this data directly to the server.

[0553] Step 2:

[0554] The server analyzes the received image data using image analysis software. Specifically, it uses TensorFlow and OpenCV to identify and classify ingredients. The input is image data, and the output is analyzed object information (ingredient name and quantity). This data forms the basis for suggesting the optimal cooking method to the user.

[0555] Step 3:

[0556] The user gives voice commands into the microphone of their earphones. For example, they might say, "Tell me what I should make for dinner." The input is the user's voice.

[0557] Step 4:

[0558] The device converts voice commands into text data using speech recognition technology. It uses APIs such as Google's Speech Recognition API for this speech-to-text conversion. The input is the user's voice, and the output is text data. This text is then sent to the server.

[0559] Step 5:

[0560] The server analyzes the transmitted text data using natural language processing techniques. Based on the analysis, it understands the user's intent and uses a generative AI model to determine the appropriate cooking method. The input is text data and previously identified object information, and the output is the generated cooking procedure.

[0561] Step 6:

[0562] The server generates cooking instructions, which the terminal then uses speech synthesis software to provide to the user via voice. Specific instructions, such as "We're going to make stir-fried chicken and cabbage. Cut the chicken into bite-sized pieces," are conveyed. The input is the cooking instructions, and the output is a voice message.

[0563] Step 7:

[0564] After the user starts cooking, the terminal continuously sends video of the cooking process to the server. The input is video data of the cooking process.

[0565] Step 8:

[0566] The server analyzes the received dynamic video and monitors the progress of cooking. If necessary, it generates real-time cooking instructions and provides them to the user via the terminal. For example, it might give specific instructions such as, "Next, sauté the cabbage over medium heat." The input is video data of the cooking process, and the output is real-time cooking instructions.

[0567] (Application Example 1)

[0568] 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."

[0569] Current home cooking support systems have limitations in the information provided to users and insufficient real-time cooking support. Furthermore, they may lead to food waste because users cannot effectively utilize the ingredients in their refrigerators. The purpose of this invention is to solve these problems and enable users to achieve a more efficient and healthy diet.

[0570] 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.

[0571] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify item information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; means for generating a process proposed based on the item information and the user's intent and providing the process to the user; and means for analyzing the user's voice input using artificial intelligence and outputting cooking instructions in voice in real time. This enables the user to utilize items efficiently and receive more accurate support.

[0572] A "user" is an individual or household that uses the system and manages and prepares goods.

[0573] "Item information" refers to information about food items and materials inside refrigerators and shelves, identified from image data analyzed by the system.

[0574] "Voice instructions" refer to voice data that users input into the system via voice, such as requests or questions related to cooking.

[0575] "Text data" refers to character information converted by the system based on voice commands, and is used for analysis.

[0576] A "process" refers to a series of processing methods that the system generates, including specific cooking steps and procedures that the user should perform.

[0577] "Artificial intelligence" refers to a technology that incorporates machine learning and natural language processing, used to analyze user voice input and generate optimal cooking procedures.

[0578] "Real-time" refers to a time concept that indicates the immediacy of providing immediate feedback and instructions in response to the user's cooking progress.

[0579] The specific system for implementing this invention is composed of a user, a terminal, and a server. The system of this invention uses a terminal, which is a consumer electronic device equipped with a camera function, in combination with a cloud-based server.

[0580] The terminal takes pictures of items inside the refrigerator or on kitchen shelves and sends the image data to the server. The server analyzes the images using image processing software and extracts information about specific items. To achieve high-quality image analysis, this process uses the open-source library OpenCV. The user can also give voice instructions related to cooking to the terminal, which converts this voice into text data and sends it to the server. Automatic speech recognition (ASR) and natural language processing (NLP) technologies are used for voice analysis.

[0581] The server analyzes this text data, understands the user's intent, and combines it with item information to generate appropriate steps. The specific cooking process uses an AI model. This AI model can suggest recipes that consider available ingredients while maintaining a healthy balance. The user receives this information and can begin cooking. During cooking, the device continuously records the user's actions live and sends them to the server. Based on this data, the server generates cooking instructions in real time and provides them to the user via speech synthesis through the device.

[0582] For example, if a user takes a picture of the ingredients in their refrigerator and requests a dinner suggestion, the server will recognize "chicken" and "cabbage" and suggest "stir-fried chicken and cabbage." During cooking, it will provide real-time voice instructions such as "cut the chicken" and "add the cabbage."

[0583] An example of a prompt message is, "Analyze the camera footage inside the refrigerator and generate a recipe suggestion for tonight's dinner."

[0584] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0585] Step 1:

[0586] The user uses the device's camera to photograph items inside the refrigerator. This generates image data, which the device then sends to the server. The input is the image data of the items, and the output is the data sent to the server. This step involves the acquisition and communication of image data.

[0587] Step 2:

[0588] The server analyzes the received image data using OpenCV to identify the object. The input is image data sent from the terminal, and the output is the analyzed object information. In this process, data processing and image recognition are performed by image processing algorithms.

[0589] Step 3:

[0590] The user inputs voice commands into the terminal, which converts the voice into text data and sends it to the server. The input is voice data from the user, and the output is text data sent to the server. In this step, speech recognition technology is used to analyze and convert the voice data.

[0591] Step 4:

[0592] The server analyzes text data using natural language processing techniques to understand the user's intent. The input is text data sent from the terminal, and the output is the analysis result that reflects the user's intent. Intent inference is performed by performing data calculations in this step.

[0593] Step 5:

[0594] The server generates the optimal process using a generative AI model based on item information and user intent. The input is item information and user intent, and the output is process data to be provided to the terminal. In this step, the AI ​​model is used to integrate the data and synthesize the process.

[0595] Step 6:

[0596] The generated process data is provided to the user via a terminal using speech synthesis technology. The input is process data from the server, and the output is an audio guide that the user can listen to. In this step, audio is generated and output based on the process data.

[0597] Step 7:

[0598] Once the user begins cooking, the terminal continuously sends progress updates to the server, which then generates cooking instructions in real time. The input is live data indicating the current cooking status, and the output is instruction data provided to the user during cooking. This process generates dynamic instructions based on the current state.

[0599] 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.

[0600] One embodiment of the present invention is to provide a system that comprehensively supports cooking activities while taking into account the user's emotional state. This system combines an AI earphone with a camera and an emotion engine to realize ingredient recognition, voice instruction analysis, real-time cooking support, and emotion-based recipe suggestions.

[0601] The process begins with the user wearing AI earphones with a camera and taking pictures of the food in their refrigerator. The device sends this image data to a server, which uses image recognition technology to identify the food items. At this stage, the emotion engine analyzes the user's voice and video at the time of shooting to recognize the user's emotional state.

[0602] Next, the user voice-questions about cooking into the earphones. The device converts this voice into text and sends it to the server. The server analyzes the received text data to understand the user's intent and, based on information from the emotion engine, generates cooking methods suitable for the user's mental state. For example, if the user is feeling stressed, it will suggest relaxing dishes and simple cooking instructions.

[0603] The cooking method determined by the server is sent to the terminal and communicated to the user using speech synthesis technology. This process also utilizes information from the emotion engine to adjust the tone of voice and provide user-friendly instructions. For example, if the user is tired, a gentle tone of voice will be used to provide a reminder.

[0604] Furthermore, once the user begins cooking, the device sends live video of the cooking process to the server. The server analyzes the video to understand the progress of the cooking. Based on this information, it generates and provides real-time cooking instructions tailored to the user's emotions.

[0605] For example, if the emotion engine determines that the user is "frustrated," it can suggest a "stress-relieving herbal tea and salmon recipe," and while the user is cooking, it can gently advise them, "Let's move on to the next step with a relaxed mind."

[0606] In this way, the system of the present invention, which combines an emotion engine, allows users to receive cooking assistance tailored to their emotional state, resulting in an efficient and comfortable cooking experience.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device sends this image data to a server in real time. In addition, the voice spoken by the user at this time is captured as input for the emotion engine.

[0610] Step 2:

[0611] The server analyzes the received image data using image recognition technology to identify the food items present in the refrigerator. Furthermore, an emotion engine analyzes the user's voice information to recognize the user's emotional state and reports the results to the server.

[0612] Step 3:

[0613] The user asks a cooking-related question via voice into the earphone. The device converts this voice data into text data and sends it to the server. The server analyzes the transmitted data to understand the user's intent.

[0614] Step 4:

[0615] The server combines ingredient information, user intent, and emotional state recognized by the emotion engine to generate an appropriate cooking method. If the user is feeling stressed, it will select a simple and relaxing dish.

[0616] Step 5:

[0617] The server generates a cooking method and sends it to the terminal. The terminal uses speech synthesis technology to provide the user with the cooking instructions in a voice-based manner, using a tone that is gentle or encouraging, depending on the user's emotional state.

[0618] Step 6:

[0619] Once the user begins cooking, the device continuously records the cooking process with its camera and sends the live video to the server. The server analyzes this video to understand the progress of the cooking.

[0620] Step 7:

[0621] The server generates real-time cooking instructions tailored to the user's situation, based on the cooking progress and information from the emotion engine. These instructions are delivered in a way that matches the user's emotions, for example, by saying, "Calm down, let's move on to the next step."

[0622] Step 8:

[0623] The server continuously adjusts and updates the content and tone of voice instructions during cooking based on the analysis results of the emotion engine. Through this process, users receive cooking assistance that matches their emotions, increasing their satisfaction.

[0624] (Example 2)

[0625] 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."

[0626] Conventional cooking support systems offer cooking suggestions without considering the user's emotional state, resulting in a uniform user experience that fails to contribute to stress reduction or increased satisfaction. Furthermore, providing cooking instructions that respond to the user's real-time emotions and circumstances is difficult, highlighting the need for more personalized cooking support.

[0627] 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.

[0628] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for analyzing the user's voice data and recognizing the user's emotional state; and means for converting the user's voice instructions into text data and analyzing the text data to understand the user's intentions. This makes it possible to provide personalized cooking methods in real time that are tailored to the user's emotional state and intentions.

[0629] "Image data" refers to data that digitally represents visual information captured by a user.

[0630] "Ingredient information" refers to information about the type and condition of ingredients used in cooking, which is analyzed from image data.

[0631] "Audio data" refers to digital data that represents the acoustic signals recorded from a user's speech.

[0632] "Emotional state" refers to the user's psychological state and emotions, and is the result of analysis from audio and video.

[0633] "Emotional analysis methods" refer to technologies and processes that analyze a user's voice data and facial expressions to identify their emotional state.

[0634] A "generative AI model" is an artificial intelligence technology that uses natural language processing and other techniques to generate the optimal cooking method according to the user's intentions.

[0635] "Cooking method" refers to the steps and methods used to prepare ingredients and complete a dish.

[0636] "Speech synthesis technology" is a technology that outputs text data as speech.

[0637] "Voice tone" refers to the characteristics of a voice, including its pitch, intensity, speed, and emotional nuances.

[0638] This invention provides a cooking support system that takes into account the user's emotional state, enabling the user to enjoy a comfortable and personalized cooking experience. This system functions by combining a terminal, a server, and emotion analysis means.

[0639] The user first puts on a digital earphone with a camera and takes pictures of the food inside the refrigerator. The earphone also captures audio, which is used by emotion analysis tools to evaluate the user's emotions. The image data and audio data sent from the earphone are transmitted to a server via the terminal. The image data is analyzed by software such as TensorFlow and OpenCV to identify the food items.

[0640] Next, the server uses speech recognition technology to convert the user's voice into text data and understand the user's intent. This process employs natural language processing technology, leveraging generative AI models (e.g., large-scale language models) to generate the most suitable cooking method for the user. Furthermore, this cooking method is personalized based on the user's emotional state. For example, a user experiencing stress might be presented with a cooking method using relaxing herbal tea.

[0641] The proposed cooking method is provided to the user as an audio guide via speech synthesis technology from the device. The voice tone is adjusted using emotion analysis technology to minimize the user's psychological burden.

[0642] As a concrete example of a prompt, a user could give instructions such as, "Tell me a relaxing dish I can make with the ingredients I have in my refrigerator." This allows the present invention to provide continuous, emotionally sensitive support to the user from before they start cooking until it is completed.

[0643] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0644] Step 1:

[0645] The user wears a digital earphone with a camera, takes pictures of the food inside the refrigerator, and uses voice input.

[0646] Input: Image data and audio data of food items photographed by the user.

[0647] Specific operation: Acquires image and audio data from the earphones and transfers it to the device.

[0648] Step 2:

[0649] The terminal sends the received image data and audio data to the server.

[0650] Input: Image data and audio data stored on the device.

[0651] Output: Image data and audio data of the ingredients sent to the server.

[0652] Specific operation: The terminal establishes communication with the server via the network and transfers data.

[0653] Step 3:

[0654] The server uses image recognition technology to analyze the received image data and identify the ingredients.

[0655] Input: Image data sent to the server.

[0656] Output: Ingredient information stored in the database.

[0657] Specific operation: The server uses TensorFlow to analyze images and identify ingredients such as "carrots" and "onions."

[0658] Step 4:

[0659] The server analyzes the voice data, recognizing the user's emotional state and understanding their intentions.

[0660] Input: Audio data sent to the server.

[0661] Output: User intent and emotional state information.

[0662] Specific operation: Speech recognition technology converts speech into text, and an emotion analysis algorithm detects emotional states such as "stress."

[0663] Step 5:

[0664] The server uses a generative AI model to generate cooking methods based on ingredient information, user intent, and emotional state.

[0665] Input: Ingredient information, user intent, emotional state information.

[0666] Output: Personalized cooking recipes.

[0667] Specific operation: The generative AI model constructs recipes such as "relaxing herbal tea and salmon."

[0668] Step 6:

[0669] The terminal receives cooking instructions from the server and communicates them to the user using speech synthesis technology.

[0670] Input: Cooking recipe sent from the server.

[0671] Output: Voice-guided cooking instructions to the user.

[0672] Specific actions: Using the device's speech synthesis function, it will convey cooking instructions in a gentle tone, such as "Next, let's cut the onions."

[0673] Step 7:

[0674] The user begins cooking, and the device sends the process to the server as live video.

[0675] Input: Live video data of cooking in progress.

[0676] Output: Cooking information provided to the server.

[0677] Specific operation: The device continuously captures video in real time and sends it to the server.

[0678] Step 8:

[0679] The server analyzes the video footage, monitors the cooking progress, and updates instructions in real time.

[0680] Input: Live video data.

[0681] Output: Updated cooking instructions.

[0682] Specific operation: The server detects the "salmon's browning" from the video, generates the appropriate cooking instruction "Flip it over," and sends it to the terminal.

[0683] (Application Example 2)

[0684] 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."

[0685] In modern life, cooking is an essential activity for maintaining health and family harmony. However, it is not easy to select the appropriate cooking method within a limited time while also considering the user's emotional state. In particular, conventional cooking support systems are insufficient when users are stressed or seeking satisfying meals with simple steps. Therefore, there is a need for technology that comprehensively supports cooking while taking the user's emotions into consideration.

[0686] 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.

[0687] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intentions; means for analyzing the user's voice data and video data to recognize the user's emotional state; means for generating a cooking method proposed based on the ingredient information and the user's intentions and providing the cooking method to the user; and means for generating a recipe proposed based on the emotional state and presenting the recipe to the user in an emotionally sensitive tone using speech synthesis technology. This enables comprehensive and efficient cooking support that takes the user's emotional state into consideration.

[0688] "Image data" refers to recorded information of still images or videos taken by the user, and is used for visually recognizing food ingredients and other items.

[0689] "Food ingredient information" refers to information about the type and condition of food identified by analyzing image data, and is used to suggest cooking methods.

[0690] "Voice instructions" refer to verbal instructions or questions given by the user to the system, which are then analyzed through technology.

[0691] "Text data" refers to character information obtained by converting audio information, such as voice instructions, and forms the basis for analysis and intent understanding.

[0692] "User intent" refers to the user's wishes and objectives, which are analyzed from voice instructions and text data, and which influence the generation of cooking methods.

[0693] "Cooking method" refers to the cooking procedures and processes generated based on specified ingredient information and the user's intentions.

[0694] "Emotional state" refers to the user's feelings and mood, which are recognized by analyzing the user's voice and video data, and which influence recipe suggestions.

[0695] A "recipe" is a plan that outlines the necessary ingredients and procedures for cooking, and is suggested to the user.

[0696] "Speech synthesis technology" is a technology that converts text data into speech output, and is used to generate speech when providing information to users.

[0697] The system for implementing this invention mainly consists of an AI-equipped earphone with a camera, an emotion engine, and a central server. When a user wears the AI ​​earphone with a camera and takes a picture of the food in the refrigerator, image data is generated. This image data is sent from the terminal to the server, which analyzes the food information using the image processing library OpenCV.

[0698] Furthermore, when the user issues a voice command into the earphones, the device uses a speech recognition library to convert the voice into text. This text data is then sent back to the server, where the server analyzes the user's intent. In addition, an emotion engine analyzes the user's voice and video data to recognize the user's emotional state. Simultaneously, a generative AI model suggests the optimal cooking method to provide recipe suggestions tailored to the emotional state.

[0699] The suggested cooking methods and recipes are communicated to the user using speech synthesis technology. The server adjusts the voice tone to match the user's emotional state, ensuring a comfortable learning experience. For example, if the user is feeling stressed, the server will suggest relaxing dishes and relatively simple cooking processes, guiding them in a gentle tone.

[0700] For example, it is possible to input prompt sentences into the AI ​​model such as, "Please create a system in which a robot analyzes the user's emotions and the ingredients in the refrigerator, suggests the best dish for the day, and provides gentle voice guidance while cooking."

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] The user uses AI earphones with a camera to photograph the food inside the refrigerator. The device sends this captured image data to a central server. In this step, image data is acquired as input and sent to the server for analysis. Specifically, the device uses its camera function to take high-resolution still images and uploads the data to the server via a wireless network.

[0704] Step 2:

[0705] The server uses the received image data to analyze the food information, utilizing image processing libraries such as OpenCV. The results of this analysis are recorded as text data. Image data is the input, and food information is generated as the output. Specifically, the server applies object recognition technology in the image to classify individual food items and extracts information on the names and quantities of the recognized food items.

[0706] Step 3:

[0707] The user gives cooking instructions and questions via voice into the AI ​​earphones. The device collects this voice data and converts it into text data using a speech recognition library. The input for this step is voice data, and the output is text data. More specifically, the device converts the voice signal into a digital format and then into text data that can be sent to the server.

[0708] Step 4:

[0709] The server analyzes text data to recognize the user's intent and requests. It also analyzes the user's voice and video data to understand their emotional state through an emotion engine. Inputs include text data and voice / video analysis data, while output is the user's intent and emotional state. Specifically, it utilizes natural language processing techniques to extract keywords from text data and uses an emotion recognition algorithm to identify the emotional state.

[0710] Step 5:

[0711] The server generates the optimal cooking method and recipe based on data on ingredient information, user intent, and emotional state. In this process, a generative AI model is used to create the suggested content. Inputs include ingredient information, user intent, and emotional state, while outputs are cooking methods and recipes. Specifically, the generative AI model integrates this information to construct a cooking procedure that is both appropriate for the user and emotionally satisfying.

[0712] Step 6:

[0713] The terminal uses speech synthesis technology to present cooking instructions sent from the server to the user via voice. The voice tone is adjusted to match the user's emotional state. The input is the cooking method and recipe, and the output is the voice instructions delivered to the user. Specifically, the terminal plays the synthesized voice with clear quality and provides navigation to the user at the appropriate time.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] [Fourth Embodiment]

[0718] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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).

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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".

[0731] As a specific embodiment of the present invention, a system is described in which the user uses AI earphones with a camera to support cooking. This system supports the user's daily cooking activities and promotes an efficient and healthy diet.

[0732] First, the user puts on AI earphones with a camera and activates the system by taking pictures of the food in the refrigerator. The device sends the captured image data to the server in real time. The server analyzes this image data and uses image recognition technology to identify the food in the refrigerator. This food information is registered in a database and used to provide future cooking suggestions to the user.

[0733] The user can then give voice commands into the earphones. For example, they might ask, "What should I make for dinner?" The device converts these voice commands into text data and sends it to the server. The server uses natural language processing technology to analyze this text and understand the user's intent.

[0734] The server determines the optimal cooking method based on the user's intentions and the ingredient information stored in the database. Specifically, it generates a recipe that utilizes available ingredients while also considering health balance, and sends it to the terminal. The terminal then uses speech synthesis technology to convey this information to the user and provide the necessary cooking instructions.

[0735] In addition, once the user starts cooking, the device continuously sends information about the cooking process to the server. The server analyzes this information and generates real-time instructions based on the progress of the cooking, providing the user with accurate cooking support.

[0736] For example, if the user has "chicken" and "cabbage" in the refrigerator, the system can suggest "stir-fried chicken and cabbage" in response to the user's inquiry, and provide instructions such as "cut the chicken into bite-sized pieces" and "add the cabbage and cook over medium heat."

[0737] In this way, the present invention comprehensively provides effective utilization of ingredients, recipe suggestions, and real-time support for cooking. As a result, users can easily achieve a balanced diet while saving the trouble of planning menus and preventing food waste.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device compresses the captured image data in real time and sends it to a server via wireless communication.

[0741] Step 2:

[0742] The server analyzes the received image data using an image recognition algorithm to identify the type and quantity of each ingredient present in the refrigerator. The identified ingredient information is stored in a database and prepared for future use in recipe suggestions.

[0743] Step 3:

[0744] The user gives voice instructions about cooking into the earphones. For example, the user's cooking requests might be included, such as "What would you like for dinner tonight?" The device converts this voice data into text data and sends it to the server.

[0745] Step 4:

[0746] The server analyzes text data using natural language processing technology to understand the user's questions and intentions. It then combines the analyzed information with pre-collected ingredient information and user preference data to select the optimal cooking method.

[0747] Step 5:

[0748] The server sends the selected cooking method to the terminal in text format. The terminal uses speech synthesis technology to provide the user with cooking suggestions via voice. If necessary, it also provides details such as ingredient preparation and cooking time.

[0749] Step 6:

[0750] As the user cooks, the device continuously films the cooking process with its camera and sends the video to the server. The server analyzes this video and generates specific cooking instructions based on the user's progress.

[0751] Step 7:

[0752] The server generates real-time cooking instructions and provides them to the user via the terminal. For example, it immediately guides the user on specific actions during cooking, such as "Reduce the heat a little more and simmer for 3 minutes."

[0753] Step 8:

[0754] The server analyzes expiration date information in the database and prioritizes suggesting cooking methods using ingredients that are nearing their expiration date. The terminal then provides these suggestions to the user via voice, contributing to the reduction of food waste.

[0755] (Example 1)

[0756] 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".

[0757] In modern life, cooking at home is crucial for maintaining an efficient and healthy diet. However, many people find it difficult to dedicate the time and effort to devising new dishes every day, and food waste is a challenge due to the inability to effectively manage and utilize ingredients. Furthermore, it is difficult to receive appropriate support as the cooking process progresses, and many people experience mistakes and inconveniences during the cooking process.

[0758] 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.

[0759] In this invention, the server includes means for receiving visual data acquired by the user using a camera device and analyzing the visual data to identify object information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; and means for generating a cooking method created based on the object information and the user's intent, and providing the cooking method to the user via voice output. This enables the user to utilize ingredients efficiently and healthily and receive cooking support in real time.

[0760] A "user" refers to an individual or group that operates or utilizes a system.

[0761] "Capturing device" refers to equipment used to acquire images or videos, and includes cameras and devices equipped with cameras.

[0762] "Visual data" refers to data of still images or moving images acquired by a camera device.

[0763] "Object information" refers to information about the type and characteristics of an object identified from visual data.

[0764] A "server" refers to a central computer system that processes data and manages storage over a network, and exchanges data with client terminals.

[0765] "Voice instructions" refer to voice commands or questions that users provide to the system.

[0766] "Text data" refers to data in text format obtained by converting voice commands.

[0767] "Cooking method" refers to information about the procedures and recipes for creating a dish using ingredients.

[0768] "Voice output" refers to a means of providing text data and other information to the user as audio.

[0769] "Dynamic video" refers to video data that changes in real time, and includes data that is captured and transmitted as video.

[0770] "Progress" refers to the current status of the user's activities or processes.

[0771] "Work instructions" refer to information that specifically guides the user on the next actions or procedures they should take.

[0772] "Expiration date information" refers to information about the deadline by which an object should be consumed appropriately.

[0773] A "storage device" refers to hardware or a medium used to store electronic data.

[0774] A "generative AI model" refers to a model that uses artificial intelligence technology to infer or generate from data.

[0775] A "prompt statement" refers to a text-based instruction given to a generative AI model for generating information.

[0776] This system utilizes innovative technology aimed at assisting with cooking, enabling users to achieve efficient and healthy eating habits through the cooking process. The specific implementation method is described below.

[0777] The user takes pictures inside a refrigerator using earphones equipped with a camera. This device features a high-performance camera and a microphone, which are used to acquire visual data and voice commands. The visual data captured by the user is transmitted from the device to a server in real time. The device uses Bluetooth or Wi-Fi for wireless communication.

[0778] Upon receiving visual data, the server uses image recognition technology to identify object information. This process employs common image recognition software, such as TensorFlow or OpenCV. Subsequently, the voice commands spoken by the user into the earphones are converted into text using speech recognition technology, and the user's intent is analyzed using a generative AI model. Natural language processing technology is applied to this analysis to generate appropriate cooking methods tailored to the user's wishes and circumstances.

[0779] The cooking instructions generated by the server take into account available ingredients, health balance, and user preferences. The generated steps and recipes are output to the user via speech synthesis software. For example, it might say, "Let's make stir-fried chicken and cabbage. First, cut the chicken into bite-sized pieces."

[0780] After cooking begins, the user continuously sends dynamic video of the cooking process from their device to the server. The server analyzes this data and determines the progress in real time. Feedback is provided according to the user's cooking progress, such as instructions like, "Next, add the cabbage and sauté over medium heat."

[0781] For example, if a user has chicken and cabbage in the refrigerator for dinner one day, and asks "What should I make?", the system can respond and suggest an appropriate cooking method. An example of a prompt might be given to the generating AI model in the form of "Take a picture of the ingredients in the refrigerator and suggest a healthy dinner recipe."

[0782] As described above, this invention integrates multiple technologies to help users cook efficiently and effectively and make good use of ingredients.

[0783] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0784] Step 1:

[0785] The user takes a picture of the inside of the refrigerator using an earphone-type device with a camera function. This initializes the system's operation. The input is image data of the food items inside the refrigerator. This image is transferred to the terminal in real time, and the terminal then sends this data directly to the server.

[0786] Step 2:

[0787] The server analyzes the received image data using image analysis software. Specifically, it uses TensorFlow and OpenCV to identify and classify ingredients. The input is image data, and the output is analyzed object information (ingredient name and quantity). This data forms the basis for suggesting the optimal cooking method to the user.

[0788] Step 3:

[0789] The user gives voice commands into the microphone of their earphones. For example, they might say, "Tell me what I should make for dinner." The input is the user's voice.

[0790] Step 4:

[0791] The device converts voice commands into text data using speech recognition technology. It uses APIs such as Google's Speech Recognition API for this speech-to-text conversion. The input is the user's voice, and the output is text data. This text is then sent to the server.

[0792] Step 5:

[0793] The server analyzes the transmitted text data using natural language processing techniques. Based on the analysis, it understands the user's intent and uses a generative AI model to determine the appropriate cooking method. The input is text data and previously identified object information, and the output is the generated cooking procedure.

[0794] Step 6:

[0795] The server generates cooking instructions, which the terminal then uses speech synthesis software to provide to the user via voice. Specific instructions, such as "We're going to make stir-fried chicken and cabbage. Cut the chicken into bite-sized pieces," are conveyed. The input is the cooking instructions, and the output is a voice message.

[0796] Step 7:

[0797] After the user starts cooking, the terminal continuously sends video of the cooking process to the server. The input is video data of the cooking process.

[0798] Step 8:

[0799] The server analyzes the received dynamic video and monitors the progress of cooking. If necessary, it generates real-time cooking instructions and provides them to the user via the terminal. For example, it might give specific instructions such as, "Next, sauté the cabbage over medium heat." The input is video data of the cooking process, and the output is real-time cooking instructions.

[0800] (Application Example 1)

[0801] 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".

[0802] Current home cooking support systems have limitations in the information provided to users and insufficient real-time cooking support. Furthermore, they may lead to food waste because users cannot effectively utilize the ingredients in their refrigerators. The purpose of this invention is to solve these problems and enable users to achieve a more efficient and healthy diet.

[0803] 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.

[0804] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify item information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intent; means for generating a process proposed based on the item information and the user's intent and providing the process to the user; and means for analyzing the user's voice input using artificial intelligence and outputting cooking instructions in voice in real time. This enables the user to utilize items efficiently and receive more accurate support.

[0805] A "user" is an individual or household that uses the system and manages and prepares goods.

[0806] "Item information" refers to information about food items and materials inside refrigerators and shelves, identified from image data analyzed by the system.

[0807] "Voice instructions" refer to voice data that users input into the system via voice, such as requests or questions related to cooking.

[0808] "Text data" refers to character information converted by the system based on voice commands, and is used for analysis.

[0809] A "process" refers to a series of processing methods that the system generates, including specific cooking steps and procedures that the user should perform.

[0810] "Artificial intelligence" refers to a technology that incorporates machine learning and natural language processing, used to analyze user voice input and generate optimal cooking procedures.

[0811] "Real-time" refers to a time concept that indicates the immediacy of providing immediate feedback and instructions in response to the user's cooking progress.

[0812] The specific system for implementing this invention is composed of a user, a terminal, and a server. The system of this invention uses a terminal, which is a consumer electronic device equipped with a camera function, in combination with a cloud-based server.

[0813] The terminal takes pictures of items inside the refrigerator or on kitchen shelves and sends the image data to the server. The server analyzes the images using image processing software and extracts information about specific items. To achieve high-quality image analysis, this process uses the open-source library OpenCV. The user can also give voice instructions related to cooking to the terminal, which converts this voice into text data and sends it to the server. Automatic speech recognition (ASR) and natural language processing (NLP) technologies are used for voice analysis.

[0814] The server analyzes this text data, understands the user's intent, and combines it with item information to generate appropriate steps. The specific cooking process uses an AI model. This AI model can suggest recipes that consider available ingredients while maintaining a healthy balance. The user receives this information and can begin cooking. During cooking, the device continuously records the user's actions live and sends them to the server. Based on this data, the server generates cooking instructions in real time and provides them to the user via speech synthesis through the device.

[0815] For example, if a user takes a picture of the ingredients in their refrigerator and requests a dinner suggestion, the server will recognize "chicken" and "cabbage" and suggest "stir-fried chicken and cabbage." During cooking, it will provide real-time voice instructions such as "cut the chicken" and "add the cabbage."

[0816] An example of a prompt message is, "Analyze the camera footage inside the refrigerator and generate a recipe suggestion for tonight's dinner."

[0817] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0818] Step 1:

[0819] The user uses the device's camera to photograph items inside the refrigerator. This generates image data, which the device then sends to the server. The input is the image data of the items, and the output is the data sent to the server. This step involves the acquisition and communication of image data.

[0820] Step 2:

[0821] The server analyzes the received image data using OpenCV to identify the object. The input is image data sent from the terminal, and the output is the analyzed object information. In this process, data processing and image recognition are performed by image processing algorithms.

[0822] Step 3:

[0823] The user inputs voice commands into the terminal, which converts the voice into text data and sends it to the server. The input is voice data from the user, and the output is text data sent to the server. In this step, speech recognition technology is used to analyze and convert the voice data.

[0824] Step 4:

[0825] The server analyzes text data using natural language processing techniques to understand the user's intent. The input is text data sent from the terminal, and the output is the analysis result that reflects the user's intent. Intent inference is performed by performing data calculations in this step.

[0826] Step 5:

[0827] The server generates the optimal process using a generative AI model based on item information and user intent. The input is item information and user intent, and the output is process data to be provided to the terminal. In this step, the AI ​​model is used to integrate the data and synthesize the process.

[0828] Step 6:

[0829] The generated process data is provided to the user via a terminal using speech synthesis technology. The input is process data from the server, and the output is an audio guide that the user can listen to. In this step, audio is generated and output based on the process data.

[0830] Step 7:

[0831] Once the user begins cooking, the terminal continuously sends progress updates to the server, which then generates cooking instructions in real time. The input is live data indicating the current cooking status, and the output is instruction data provided to the user during cooking. This process generates dynamic instructions based on the current state.

[0832] 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.

[0833] One embodiment of the present invention is to provide a system that comprehensively supports cooking activities while taking into account the user's emotional state. This system combines an AI earphone with a camera and an emotion engine to realize ingredient recognition, voice instruction analysis, real-time cooking support, and emotion-based recipe suggestions.

[0834] The process begins with the user wearing AI earphones with a camera and taking pictures of the food in their refrigerator. The device sends this image data to a server, which uses image recognition technology to identify the food items. At this stage, the emotion engine analyzes the user's voice and video at the time of shooting to recognize the user's emotional state.

[0835] Next, the user voice-questions about cooking into the earphones. The device converts this voice into text and sends it to the server. The server analyzes the received text data to understand the user's intent and, based on information from the emotion engine, generates cooking methods suitable for the user's mental state. For example, if the user is feeling stressed, it will suggest relaxing dishes and simple cooking instructions.

[0836] The cooking method determined by the server is sent to the terminal and communicated to the user using speech synthesis technology. This process also utilizes information from the emotion engine to adjust the tone of voice and provide user-friendly instructions. For example, if the user is tired, a gentle tone of voice will be used to provide a reminder.

[0837] Furthermore, once the user begins cooking, the device sends live video of the cooking process to the server. The server analyzes the video to understand the progress of the cooking. Based on this information, it generates and provides real-time cooking instructions tailored to the user's emotions.

[0838] For example, if the emotion engine determines that the user is "frustrated," it can suggest a "stress-relieving herbal tea and salmon recipe," and while the user is cooking, it can gently advise them, "Let's move on to the next step with a relaxed mind."

[0839] In this way, the system of the present invention, which combines an emotion engine, allows users to receive cooking assistance tailored to their emotional state, resulting in an efficient and comfortable cooking experience.

[0840] The following describes the processing flow.

[0841] Step 1:

[0842] The user wears AI earphones with a camera and takes pictures of the food inside the refrigerator. The device sends this image data to a server in real time. In addition, the voice spoken by the user at this time is captured as input for the emotion engine.

[0843] Step 2:

[0844] The server analyzes the received image data using image recognition technology to identify the food items present in the refrigerator. Furthermore, an emotion engine analyzes the user's voice information to recognize the user's emotional state and reports the results to the server.

[0845] Step 3:

[0846] The user asks a cooking-related question via voice into the earphone. The device converts this voice data into text data and sends it to the server. The server analyzes the transmitted data to understand the user's intent.

[0847] Step 4:

[0848] The server combines ingredient information, user intent, and emotional state recognized by the emotion engine to generate an appropriate cooking method. If the user is feeling stressed, it will select a simple and relaxing dish.

[0849] Step 5:

[0850] The server generates a cooking method and sends it to the terminal. The terminal uses speech synthesis technology to provide the user with the cooking instructions in a voice-based manner, using a tone that is gentle or encouraging, depending on the user's emotional state.

[0851] Step 6:

[0852] Once the user begins cooking, the device continuously records the cooking process with its camera and sends the live video to the server. The server analyzes this video to understand the progress of the cooking.

[0853] Step 7:

[0854] The server generates real-time cooking instructions tailored to the user's situation, based on the cooking progress and information from the emotion engine. These instructions are delivered in a way that matches the user's emotions, for example, by saying, "Calm down, let's move on to the next step."

[0855] Step 8:

[0856] The server continuously adjusts and updates the content and tone of voice instructions during cooking based on the analysis results of the emotion engine. Through this process, users receive cooking assistance that matches their emotions, increasing their satisfaction.

[0857] (Example 2)

[0858] 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".

[0859] Conventional cooking support systems offer cooking suggestions without considering the user's emotional state, resulting in a uniform user experience that fails to contribute to stress reduction or increased satisfaction. Furthermore, providing cooking instructions that respond to the user's real-time emotions and circumstances is difficult, highlighting the need for more personalized cooking support.

[0860] 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.

[0861] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for analyzing the user's voice data and recognizing the user's emotional state; and means for converting the user's voice instructions into text data and analyzing the text data to understand the user's intentions. This makes it possible to provide personalized cooking methods in real time that are tailored to the user's emotional state and intentions.

[0862] "Image data" refers to data that digitally represents visual information captured by a user.

[0863] "Ingredient information" refers to information about the type and condition of ingredients used in cooking, which is analyzed from image data.

[0864] "Audio data" refers to digital data that represents the acoustic signals recorded from a user's speech.

[0865] "Emotional state" refers to the user's psychological state and emotions, and is the result of analysis from audio and video.

[0866] "Emotional analysis methods" refer to technologies and processes that analyze a user's voice data and facial expressions to identify their emotional state.

[0867] A "generative AI model" is an artificial intelligence technology that uses natural language processing and other techniques to generate the optimal cooking method according to the user's intentions.

[0868] "Cooking method" refers to the steps and methods used to prepare ingredients and complete a dish.

[0869] "Speech synthesis technology" is a technology that outputs text data as speech.

[0870] "Voice tone" refers to the characteristics of a voice, including its pitch, intensity, speed, and emotional nuances.

[0871] This invention provides a cooking support system that takes into account the user's emotional state, enabling the user to enjoy a comfortable and personalized cooking experience. This system functions by combining a terminal, a server, and emotion analysis means.

[0872] The user first puts on a digital earphone with a camera and takes pictures of the food inside the refrigerator. The earphone also captures audio, which is used by emotion analysis tools to evaluate the user's emotions. The image data and audio data sent from the earphone are transmitted to a server via the terminal. The image data is analyzed by software such as TensorFlow and OpenCV to identify the food items.

[0873] Next, the server uses speech recognition technology to convert the user's voice into text data and understand the user's intent. This process employs natural language processing technology, leveraging generative AI models (e.g., large-scale language models) to generate the most suitable cooking method for the user. Furthermore, this cooking method is personalized based on the user's emotional state. For example, a user experiencing stress might be presented with a cooking method using relaxing herbal tea.

[0874] The proposed cooking method is provided to the user as an audio guide via speech synthesis technology from the device. The voice tone is adjusted using emotion analysis technology to minimize the user's psychological burden.

[0875] As a concrete example of a prompt, a user could give instructions such as, "Tell me a relaxing dish I can make with the ingredients I have in my refrigerator." This allows the present invention to provide continuous, emotionally sensitive support to the user from before they start cooking until it is completed.

[0876] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0877] Step 1:

[0878] The user wears a digital earphone with a camera, takes pictures of the food inside the refrigerator, and uses voice input.

[0879] Input: Image data and audio data of food items photographed by the user.

[0880] Specific operation: Acquires image and audio data from the earphones and transfers it to the device.

[0881] Step 2:

[0882] The terminal sends the received image data and audio data to the server.

[0883] Input: Image data and audio data stored on the device.

[0884] Output: Image data and audio data of the ingredients sent to the server.

[0885] Specific operation: The terminal establishes communication with the server via the network and transfers data.

[0886] Step 3:

[0887] The server uses image recognition technology to analyze the received image data and identify the ingredients.

[0888] Input: Image data sent to the server.

[0889] Output: Ingredient information stored in the database.

[0890] Specific operation: The server uses TensorFlow to analyze images and identify ingredients such as "carrots" and "onions."

[0891] Step 4:

[0892] The server analyzes the voice data, recognizing the user's emotional state and understanding their intentions.

[0893] Input: Audio data sent to the server.

[0894] Output: User intent and emotional state information.

[0895] Specific operation: Speech recognition technology converts speech into text, and an emotion analysis algorithm detects emotional states such as "stress."

[0896] Step 5:

[0897] The server uses a generative AI model to generate cooking methods based on ingredient information, user intent, and emotional state.

[0898] Input: Ingredient information, user intent, emotional state information.

[0899] Output: Personalized cooking recipes.

[0900] Specific operation: The generative AI model constructs recipes such as "relaxing herbal tea and salmon."

[0901] Step 6:

[0902] The terminal receives cooking instructions from the server and communicates them to the user using speech synthesis technology.

[0903] Input: Cooking recipe sent from the server.

[0904] Output: Voice-guided cooking instructions to the user.

[0905] Specific actions: Using the device's speech synthesis function, it will convey cooking instructions in a gentle tone, such as "Next, let's cut the onions."

[0906] Step 7:

[0907] The user begins cooking, and the device sends the process to the server as live video.

[0908] Input: Live video data of cooking in progress.

[0909] Output: Cooking information provided to the server.

[0910] Specific operation: The device continuously captures video in real time and sends it to the server.

[0911] Step 8:

[0912] The server analyzes the video footage, monitors the cooking progress, and updates instructions in real time.

[0913] Input: Live video data.

[0914] Output: Updated cooking instructions.

[0915] Specific operation: The server detects the "salmon's browning" from the video, generates the appropriate cooking instruction "Flip it over," and sends it to the terminal.

[0916] (Application Example 2)

[0917] 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".

[0918] In modern life, cooking is an essential activity for maintaining health and family harmony. However, it is not easy to select the appropriate cooking method within a limited time while also considering the user's emotional state. In particular, conventional cooking support systems are insufficient when users are stressed or seeking satisfying meals with simple steps. Therefore, there is a need for technology that comprehensively supports cooking while taking the user's emotions into consideration.

[0919] 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.

[0920] In this invention, the server includes means for receiving image data captured by the user and analyzing the image data to identify ingredient information; means for converting voice instructions from the user into text data and analyzing the text data to understand the user's intentions; means for analyzing the user's voice data and video data to recognize the user's emotional state; means for generating a cooking method proposed based on the ingredient information and the user's intentions and providing the cooking method to the user; and means for generating a recipe proposed based on the emotional state and presenting the recipe to the user in an emotionally sensitive tone using speech synthesis technology. This enables comprehensive and efficient cooking support that takes the user's emotional state into consideration.

[0921] "Image data" refers to recorded information of still images or videos taken by the user, and is used for visually recognizing food ingredients and other items.

[0922] "Food ingredient information" refers to information about the type and condition of food identified by analyzing image data, and is used to suggest cooking methods.

[0923] "Voice instructions" refer to verbal instructions or questions given by the user to the system, which are then analyzed through technology.

[0924] "Text data" refers to character information obtained by converting audio information, such as voice instructions, and forms the basis for analysis and intent understanding.

[0925] "User intent" refers to the user's wishes and objectives, which are analyzed from voice instructions and text data, and which influence the generation of cooking methods.

[0926] "Cooking method" refers to the cooking procedures and processes generated based on specified ingredient information and the user's intentions.

[0927] "Emotional state" refers to the user's feelings and mood, which are recognized by analyzing the user's voice and video data, and which influence recipe suggestions.

[0928] A "recipe" is a plan that outlines the necessary ingredients and procedures for cooking, and is suggested to the user.

[0929] "Speech synthesis technology" is a technology that converts text data into speech output, and is used to generate speech when providing information to users.

[0930] The system for implementing this invention mainly consists of an AI-equipped earphone with a camera, an emotion engine, and a central server. When a user wears the AI ​​earphone with a camera and takes a picture of the food in the refrigerator, image data is generated. This image data is sent from the terminal to the server, which analyzes the food information using the image processing library OpenCV.

[0931] Furthermore, when the user issues a voice command into the earphones, the device uses a speech recognition library to convert the voice into text. This text data is then sent back to the server, where the server analyzes the user's intent. In addition, an emotion engine analyzes the user's voice and video data to recognize the user's emotional state. Simultaneously, a generative AI model suggests the optimal cooking method to provide recipe suggestions tailored to the emotional state.

[0932] The suggested cooking methods and recipes are communicated to the user using speech synthesis technology. The server adjusts the voice tone to match the user's emotional state, ensuring a comfortable learning experience. For example, if the user is feeling stressed, the server will suggest relaxing dishes and relatively simple cooking processes, guiding them in a gentle tone.

[0933] For example, it is possible to input prompt sentences into the AI ​​model such as, "Please create a system in which a robot analyzes the user's emotions and the ingredients in the refrigerator, suggests the best dish for the day, and provides gentle voice guidance while cooking."

[0934] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0935] Step 1:

[0936] The user uses AI earphones with a camera to photograph the food inside the refrigerator. The device sends this captured image data to a central server. In this step, image data is acquired as input and sent to the server for analysis. Specifically, the device uses its camera function to take high-resolution still images and uploads the data to the server via a wireless network.

[0937] Step 2:

[0938] The server uses the received image data to analyze the food information, utilizing image processing libraries such as OpenCV. The results of this analysis are recorded as text data. Image data is the input, and food information is generated as the output. Specifically, the server applies object recognition technology in the image to classify individual food items and extracts information on the names and quantities of the recognized food items.

[0939] Step 3:

[0940] The user gives cooking instructions and questions via voice into the AI ​​earphones. The device collects this voice data and converts it into text data using a speech recognition library. The input for this step is voice data, and the output is text data. More specifically, the device converts the voice signal into a digital format and then into text data that can be sent to the server.

[0941] Step 4:

[0942] The server analyzes text data to recognize the user's intent and requests. It also analyzes the user's voice and video data to understand their emotional state through an emotion engine. Inputs include text data and voice / video analysis data, while output is the user's intent and emotional state. Specifically, it utilizes natural language processing techniques to extract keywords from text data and uses an emotion recognition algorithm to identify the emotional state.

[0943] Step 5:

[0944] The server generates the optimal cooking method and recipe based on data on ingredient information, user intent, and emotional state. In this process, a generative AI model is used to create the suggested content. Inputs include ingredient information, user intent, and emotional state, while outputs are cooking methods and recipes. Specifically, the generative AI model integrates this information to construct a cooking procedure that is both appropriate for the user and emotionally satisfying.

[0945] Step 6:

[0946] The terminal uses speech synthesis technology to present cooking instructions sent from the server to the user via voice. The voice tone is adjusted to match the user's emotional state. The input is the cooking method and recipe, and the output is the voice instructions delivered to the user. Specifically, the terminal plays the synthesized voice with clear quality and provides navigation to the user at the appropriate time.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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."

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] The following is further disclosed regarding the embodiments described above.

[0969] (Claim 1)

[0970] A means for receiving image data captured by a user and analyzing the image data to identify food ingredient information,

[0971] A means for converting voice instructions from the user into text data, and for analyzing the text data to understand the user's intent,

[0972] A means for generating a cooking method proposed based on the aforementioned ingredient information and the user's intent, and for providing the cooking method to the user,

[0973] A system that includes this.

[0974] (Claim 2)

[0975] The system according to claim 1, further comprising means for receiving live video of cooking transmitted by a user, analyzing the live video to understand the progress of cooking, generating real-time cooking instructions according to the progress, and providing the cooking instructions to the user.

[0976] (Claim 3)

[0977] The system according to claim 1, further comprising means for storing expiration date information of ingredients in a database and suggesting a cooking method that prioritizes the use of ingredients nearing their expiration date based on the expiration date information.

[0978] "Example 1"

[0979] (Claim 1)

[0980] A means for receiving visual data acquired by a user using a camera device, and for analyzing said visual data to identify object information,

[0981] A means for converting voice instructions from the user into text data, and for analyzing the text data to understand the user's intent,

[0982] A means for generating a cooking method based on the aforementioned object information and the user's intent, and providing the cooking method to the user through voice output,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, further comprising means for receiving dynamic video of cooking being performed transmitted by a user, monitoring the progress of the work by analyzing the dynamic video, generating real-time work instructions according to the progress, and providing the work instructions to the user.

[0986] (Claim 3)

[0987] The system according to claim 1, further comprising means for storing expiration date information of objects in a storage device and proposing a cooking method that prioritizes the use of objects nearing their expiration date based on said expiration date information.

[0988] "Application Example 1"

[0989] (Claim 1)

[0990] A means for receiving image data captured by a user and analyzing the image data to identify item information,

[0991] A means for converting voice instructions from the user into text data, and for analyzing the text data to understand the user's intent,

[0992] A means for generating a process proposed based on the aforementioned item information and the user's intent, and providing the process to the user,

[0993] A method for analyzing user voice input using artificial intelligence and outputting cooking instructions via voice in real time,

[0994] A system that includes this.

[0995] (Claim 2)

[0996] The system according to claim 1, further comprising means for receiving live video footage being processed and transmitted by a user, analyzing the live video footage to understand the progress, generating real-time instructions according to the progress, and providing the instructions to the user.

[0997] (Claim 3)

[0998] The system according to claim 1, further comprising means for storing information on the expiration dates of articles in a database and proposing a process for prioritizing the use of articles whose expiration dates are approaching based on the expiration date information.

[0999] "Example 2 of combining an emotion engine"

[1000] (Claim 1)

[1001] A means for receiving image data captured by a user and analyzing the image data to identify food ingredient information,

[1002] An emotion analysis method that analyzes user voice data and recognizes the user's emotional state,

[1003] A means for converting user voice commands into text data and analyzing the text data to understand the user's intent,

[1004] A means for generating a cooking method using an AI model based on the aforementioned ingredient information, user intent, and emotional state, and providing the cooking method to the user,

[1005] A system that includes this.

[1006] (Claim 2)

[1007] The system according to claim 1, further comprising means for understanding the progress of cooking by analyzing video data of cooking transmitted by the user, generating real-time cooking instructions according to the progress and the user's emotional state, and providing the cooking instructions to the user.

[1008] (Claim 3)

[1009] The system according to claim 1, further comprising means for providing cooking instructions by voice using speech synthesis technology and adjusting the voice tone according to the user's emotional state.

[1010] "Application example 2 when combining with an emotional engine"

[1011] (Claim 1)

[1012] A means for receiving image data captured by a user and analyzing the image data to identify food ingredient information,

[1013] A means for converting voice instructions from the user into text data, and for analyzing the text data to understand the user's intent,

[1014] A means for generating a cooking method proposed based on the aforementioned ingredient information and the user's intent, and for providing the cooking method to the user,

[1015] A means for analyzing user voice and video data to recognize the user's emotional state,

[1016] A means for generating a recipe based on the aforementioned emotional state and presenting the recipe to the user in an emotionally sensitive tone using speech synthesis technology,

[1017] A system that includes this.

[1018] (Claim 2)

[1019] The system according to claim 1, further comprising means for receiving live video of cooking transmitted by a user, analyzing the live video to understand the progress of cooking, generating real-time cooking instructions according to the progress, and providing the cooking instructions to the user in a voice tone that takes into account the emotional state.

[1020] (Claim 3)

[1021] The system according to claim 1, further comprising means for storing information on the shelf life of ingredients in a database and proposing a cooking method that prioritizes the use of ingredients nearing their expiration date based on the shelf life information. [Explanation of Symbols]

[1022] 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. A means for receiving image data captured by a user and analyzing the image data to identify item information, A means for converting voice instructions from the user into text data, and for analyzing the text data to understand the user's intent, A means for generating a process proposed based on the aforementioned item information and the user's intent, and providing the process to the user, A method for analyzing user voice input using artificial intelligence and outputting cooking instructions via voice in real time, A system that includes this.

2. The system according to claim 1, further comprising means for receiving live video footage being processed and transmitted by a user, analyzing the live video footage to understand the progress, generating real-time instructions according to the progress, and providing the instructions to the user.

3. The system according to claim 1, further comprising means for storing information on the expiration dates of articles in a database and proposing a process for prioritizing the use of articles whose expiration dates are approaching based on the expiration date information.

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