system

Abstract

We provide the system. [Solution] A means of analyzing an image and obtaining the objects within the image as text data, A means for generating cooking procedures and dish composition based on acquired text data and information on cooking-related equipment, A means of providing the user with the generated cooking procedure and the composition of the dish, 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern life, the management of food ingredients in the home often places a time and mental burden. In addition, the accumulation of food waste has made food loss a major social problem. Furthermore, it is a difficult task for users to prepare a balanced meal based on the ingredients in the refrigerator without much effort. In response to these problems, there is an increasing need for a system that can simply and efficiently manage the contents of the refrigerator and utilize the ingredients to provide healthy and creative dishes.

Means for Solving the Problems

[0005] This invention provides a system that analyzes images of the inside of a refrigerator and obtains the items in the images as text data. It also includes a function to generate cooking procedures and meal compositions based on the acquired text data and information about cooking-related equipment. Furthermore, by providing the generated cooking procedures and meal compositions to the user, it enables efficient use of ingredients in the refrigerator, reducing waste and making it easy to prepare healthy meals. This reduces food waste in the home while simultaneously contributing to an improvement in the user's dietary habits.

[0006] An "image" is data that includes visual information captured by optical means.

[0007] "Analysis" refers to a series of processes that extract information and convert it into an easily understandable format.

[0008] "Articles" refers to individual or multiple items depicted in an image, and in this invention, it specifically refers to food and ingredients.

[0009] "Text data" refers to information represented in a human-readable string format.

[0010] "Cooking-related equipment" refers to the equipment and tools used when cooking, specifically stoves and ovens.

[0011] "Information" refers to data provided by users regarding cooking and the cooking environment, which is then used by the system.

[0012] A "procedure" refers to a series of steps or processes performed to achieve a specific objective.

[0013] "The composition of a dish" refers to the form and combination of ingredients in a meal that results from cooking.

[0014] "Provision" refers to presenting generated information or services to users in an accessible form.

[0015] "System" refers to a set consisting of multiple elements that operate in cooperation to achieve a specific purpose.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides an information processing system to improve food management at home and streamline cooking. This system uses image analysis technology and generative AI to extract information about ingredients in a refrigerator, and then generates and presents cooking recipes based on that information.

[0038] Specifically, the user takes pictures of the food in the refrigerator using a smartphone or other device. The device then sends this image data to the server. At this time, the user can also input kitchen specifications, such as the number of stovetops and ovens.

[0039] The server analyzes the received image using OCR (Optical Character Recognition) technology and extracts the items in the image as text data. During this process, it identifies the names and conditions of the food items, and obtains information about their quantity and quality as much as possible.

[0040] The server uses a generative AI model based on this transcribed information and the user's input of information about cooking equipment to generate cooking procedures and dish compositions. The generative AI can generate multiple recipes suitable for the user's situation, while also considering factors such as nutritional balance and cooking time constraints.

[0041] The generated recipe information is sent from the server to the terminal, and the terminal displays that information on its screen for the user to use. This allows the user to efficiently prepare a dish by following the suggested cooking procedure.

[0042] For example, if a user uses this system with tomatoes, eggs, and milk in their refrigerator, the server will generate recipes for dishes such as omelets and tomato soup based on this ingredient information and kitchen information, and will devise cooking procedures so that the user can use multiple stoves simultaneously.

[0043] Thus, this system is easy for users to use and can support both the reduction of food waste and a richer diet.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user activates the device and uses the camera function to take pictures of the food inside the refrigerator. The user also enters information about cooking appliances, such as the number of burners and ovens.

[0047] Step 2:

[0048] The device combines images captured by the device and information entered by the user into a single dataset, converts it to JSON format, and sends it to the server. A secure protocol (e.g., HTTPS) is used for transmission.

[0049] Step 3:

[0050] The server analyzes the data received from the terminal and sends the image data to the OCR API. The OCR analyzes the text information within the image and returns the names and quantities of ingredients as text data to the server.

[0051] Step 4:

[0052] The server creates a list of ingredients based on the text data received from the OCR, and also sends information about the user's cooking equipment to the AI ​​model.

[0053] Step 5:

[0054] The AI ​​model generates multiple recipes and instructions that consider nutritional balance and cooking efficiency, using the ingredient list and information on cooking equipment. The generated information is then sent back to the server.

[0055] Step 6:

[0056] The server receives the output from the generated AI model and sends the generated recipe information to the terminal. This information is again sent in JSON format.

[0057] Step 7:

[0058] The device analyzes the recipe information received from the server and displays it on the screen in a way that is easy for the user to understand. The user then begins cooking based on the displayed recipe.

[0059] (Example 1)

[0060] 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."

[0061] Managing ingredients efficiently and avoiding waste during cooking is a challenge for many households. In particular, keeping track of what's in the refrigerator and making the most of the ingredients available is difficult. Furthermore, there's a lack of systems that automatically provide optimal cooking procedures tailored to the user's specific cooking environment.

[0062] 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.

[0063] In this invention, the server includes means for analyzing an image to obtain the items in the image as text information and removing noise through preprocessing; means for generating multiple recipes that take nutritional balance and cooking time into consideration using a generation AI model based on the acquired text information and information on cooking-related devices entered by the user; and means for transmitting the generated multiple recipe information to a terminal and displaying it to the user. This allows the user to effectively manage the ingredients in their refrigerator, easily obtain the optimal cooking procedure based on that information, and cook without waste.

[0064] "Image analysis" is the process of processing image data and extracting useful information from it.

[0065] "Items" refers to physical items such as food and beverages that are present inside the refrigerator.

[0066] "Textual information" refers to string data obtained from images using OCR technology.

[0067] "Noise reduction" is the process of removing unnecessary information in image analysis to improve accuracy.

[0068] "Cooking-related equipment" refers to cooking appliances and facilities used in the kitchen, including, for example, stoves and ovens.

[0069] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate responses that meet user needs.

[0070] "Nutritional balance" refers to the fact that the prepared dish has a healthy distribution of nutrients.

[0071] "Cooking time" refers to the time required to complete a dish.

[0072] "Recipe information" refers to data about the steps and components of a dish created by a generative AI model.

[0073] "Device" refers to a device used by a user, and examples include smartphones and tablets.

[0074] This invention relates to an information processing system aimed at improving the efficiency of food management and cooking within the home. The main components are a user terminal, a central server, and a generative AI model.

[0075] Users take photos of food items inside their refrigerators using devices such as smartphones or tablets. The captured images are saved in JPEG or PNG format. This image data is then sent from the device to the server.

[0076] Upon receiving the image, the server first performs image preprocessing. This preprocessing includes removing noise and improving analysis accuracy. The server then uses optical character recognition (OCR) technology, leveraging libraries such as TENSORFLOW® and OpenCV, to extract textual information from the image. This textual information indicates the names, quantities, and conditions of the ingredients.

[0077] The server then collects information about cooking-related equipment entered by the user through the terminal. This includes information such as the number of available burners and whether or not there is an oven. This information is sent to the server in JSON format.

[0078] The server inputs the extracted text information and information about cooking equipment as prompts into the generative AI model. The generative AI model is built using, for example, natural language processing technology and can use algorithms such as GPT. For example, if the prompt is "I have tomatoes, eggs, and milk. Please generate recipes that can be used with a 3-burner stove and an oven," the AI ​​will generate multiple recipes. In this process, nutritional balance and cooking time will also be taken into consideration.

[0079] The generated recipe information is sent from the server to the terminal. The terminal analyzes the received information and displays it to the user in a visually easy-to-understand format. This allows the user to follow the suggested cooking procedure and effectively utilize the ingredients in their refrigerator to prepare a meal.

[0080] This system can reduce food waste and support a healthy and efficient diet.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user uses a device to take pictures of the food items inside the refrigerator. The captured images are saved in JPEG or PNG format. The input obtained from the user is image data that reflects the condition of the food items. The device sends this image data to the server. As output, the image data is passed to the server.

[0084] Step 2:

[0085] The server preprocesses the received image data. Preprocessing includes steps to remove noise from the image and improve the accuracy of the analysis. The input is the image data received by the server, and after preprocessing, clean image data with noise removed is output.

[0086] Step 3:

[0087] The server analyzes the pre-processed image data using OCR technology to extract text information from the image. At this stage, the server recognizes text such as labels and dates using libraries such as TensorFlow or OpenCV. The input is denoised image data, and the output is text information (such as the name, quantity, and condition of the ingredients).

[0088] Step 4:

[0089] The user inputs information about available cooking equipment through the terminal. This information includes, for example, the number of burners and whether or not there is an oven. The input is information about the user's own cooking equipment, and the terminal outputs this information by sending it to the server.

[0090] Step 5:

[0091] The server combines the analyzed textual information with the cooking equipment information provided by the user and inputs it as a prompt into the generating AI model. It generates prompt sentences such as, "I have tomatoes, eggs, and milk. Please generate a recipe that can be used with a 3-burner stove and an oven." Based on this, the server causes the AI ​​to generate a recipe that takes into account nutritional balance, cooking time, and other factors. The input consists of ingredient information and cooking equipment information, and multiple recipes are output by the AI.

[0092] Step 6:

[0093] The server sends the generated recipe information to the terminal. The input is recipe data generated by the AI, and the server outputs the recipe information sent to the terminal.

[0094] Step 7:

[0095] The terminal analyzes the received recipe information and displays it on the screen in a user-friendly format. The input is recipe information from the server, and the terminal outputs cooking instructions that are easy to understand visually. The user can then proceed with cooking based on this information.

[0096] (Application Example 1)

[0097] 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."

[0098] In modern life, reducing food waste and planning meals efficiently are crucial. Commercial facilities, in particular, are required to provide a satisfying cooking experience using ingredients purchased by customers. However, it is difficult for consumers to find appropriate recipes on the spot when purchasing ingredients. Therefore, there is a need for a system that effectively suggests cooking methods based on the purchased ingredients.

[0099] 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.

[0100] In this invention, the server includes means for analyzing visual information and obtaining items within the visual data as text data; means for generating cooking procedures and meal configurations based on the acquired text data and information on cooking-related equipment; and means for providing the generated cooking procedures and meal configurations to the user. This makes it possible to suggest appropriate recipes in real time at commercial facilities based on purchased ingredients.

[0101] "Analyzing visual information to obtain text data for items within visual data" refers to the process of recognizing the names and characteristics of items from video data obtained by imaging devices such as cameras, and converting that information into text format.

[0102] "Generating cooking procedures and meal compositions based on acquired text data and information on cooking-related equipment" refers to the process of constructing cooking procedures and menus based on ingredient information represented as text data and cooking equipment information entered by the user.

[0103] "Providing users with the generated cooking procedures and meal composition" refers to the act of displaying or transmitting information about the procedures and menus for the created dishes so that users can easily view them.

[0104] "Suggesting cooking methods based on items in visual data" refers to analyzing ingredients found in captured video data and suggesting new cooking ideas to users that utilize them.

[0105] To implement this invention, it is necessary to build a system that analyzes visual information using a user-owned terminal and a cloud server, and proposes cooking methods in real time based on the acquired data. This system mainly consists of the following hardware and software.

[0106] The user takes a picture of the purchased food items with their device (e.g., a smartphone). This device has a camera function, and the image data is immediately sent to a cloud server. On the server side, image processing software such as OpenCV or Tesseract OCR is used to extract the items in the image data as text data. This obtained text data is then input into a generative AI on the cloud, such as an AI model that incorporates ChatGPT (registered trademark).

[0107] The server uses this text data to generate cooking instructions and meal compositions based on the purchased ingredients. By using prompts that take into account the user's cooking environment and purchased ingredients, the generating AI model can provide specific and practical cooking suggestions. For example, a prompt such as, "Tomatoes, basil, and mozzarella cheese have been purchased. Please suggest a simple and delicious recipe using these ingredients," might be used.

[0108] Finally, the generated cooking instructions and dish composition are sent back to the user's device and displayed. This allows the user to quickly learn about various cooking methods based on the purchased ingredients, enabling them to cook efficiently.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user takes a picture of the purchased food items using a device such as a smartphone. The input is image data of the food items obtained through the device's camera. The output is this image data. The device sends this data to a cloud server.

[0112] Step 2:

[0113] The server analyzes the received image data using OpenCV or Tesseract OCR. This process extracts the names and characteristics of items within the visual data as text data. The input is the image data obtained in step 1, and the output is text data representing the names and characteristics of the items (food items).

[0114] Step 3:

[0115] The server uses a generation AI model based on the acquired text data to generate cooking methods. In this process, user information regarding cooking equipment is used as a prompt. The input consists of the text data obtained in step 2 and the user's cooking equipment information, while the output is data on the generated cooking procedure and dish composition. For example, data is generated based on a prompt such as, "Tomatoes, basil, and mozzarella cheese have been purchased. Please suggest a simple and delicious recipe using these ingredients."

[0116] Step 4:

[0117] The server sends the generated cooking procedure and ingredient data to the user's terminal. The input is the data generated in step 3, and the output is cooking procedure and ingredient information that can be displayed on the user's terminal. The user can receive this information as displayed content.

[0118] 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.

[0119] This invention relates to an information processing system that uses an emotion engine in a cooking support system to generate cooking procedures and dish compositions that correspond to the user's psychological state. This system integrates image analysis technology, generation AI, and emotion recognition technology to enable more personalized and efficient food management and cooking at home.

[0120] Specifically, the user takes pictures of the food in their refrigerator using a smartphone or other device. The device sends these pictures to a server and also collects information about the user's cooking equipment, such as the number of burners and ovens, as well as voice and facial expression data. This data is analyzed by an emotion engine to estimate the user's current emotional state.

[0121] The server uses OCR (Optical Character Recognition) technology to convert image data into text and create a list of ingredients. Furthermore, an AI model, using information on cooking equipment and emotional state data, generates recipes that match the user's emotional state while considering nutritional balance and cooking efficiency. In this process, for example, if the user is stressed, recipes containing ingredients that help alleviate stress will be prioritized.

[0122] The generated recipe information is sent from the server to the terminal and displayed in a format viewable by the user. Based on this recipe, the user can cook to reduce stress.

[0123] For example, if a user is feeling stressed because they have vegetables and cheese in their refrigerator, the server will generate a recipe for vegetable gratin using ingredients known to have a relaxing effect and provide it to the user. In this way, users can easily enjoy a meal that suits their current mood.

[0124] This invention enriches the home cooking experience, improves user satisfaction, and makes it possible to provide personalized meals while reducing food waste.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user takes photos of the food inside the refrigerator using a smartphone or dedicated device. In addition, they input information about cooking appliances (such as the number of burners and ovens), and voice and facial expression data are collected to recognize emotions.

[0128] Step 2:

[0129] The device sends captured image data, input information, and emotion-related data to the server. This data is organized in JSON format and transmitted via a secure protocol (e.g., HTTPS).

[0130] Step 3:

[0131] The server processes the received image data using an OCR API, analyzes the text information within the image, and obtains the ingredient list as text data.

[0132] Step 4:

[0133] The emotion engine analyzes the voice and facial expression data received by the server to estimate the user's current emotional state. This emotional state is categorized into categories such as stress, relaxation, and joy.

[0134] Step 5:

[0135] The server inputs a list of ingredients from OCR, information on cooking equipment, and emotional states estimated by the emotion engine into a generative AI model. The generative AI model considers this data to generate a cooking recipe and procedure that is appropriate for nutritional balance, cooking efficiency, and the emotional state.

[0136] Step 6:

[0137] The server receives recipe information generated from the AI ​​model and sends it to the device. This information is displayed on the device's UI so that the user can check it immediately.

[0138] Step 7:

[0139] The user checks the recipe displayed on their device and begins cooking. The presented recipe uses ingredients that match the user's mood and the cooking procedure is efficiently structured, resulting in a highly satisfying cooking experience.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] Conventional cooking support systems often lacked personalized suggestions tailored to the user's emotional state and individual cooking environment, resulting in a lower quality cooking experience. Furthermore, they failed to provide appropriate recipes based on the user's emotional state, hindering improvements in psychological satisfaction.

[0143] 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.

[0144] In this invention, the server includes means for analyzing an image and obtaining items within the image as text data; means for analyzing the obtained text data, information about cooking equipment, and the user's emotional state to generate cooking procedures and dish compositions that take nutritional balance and cooking efficiency into consideration; and means for providing the generated cooking procedures and dish compositions to the user. This makes it possible to propose personalized cooking procedures that are tailored to the individual cooking environment and the user's emotional state.

[0145] "Analyzing an image and obtaining the objects within it as text data" refers to the process of using image recognition technology to identify objects contained within an image and convert them into corresponding text data.

[0146] "Information regarding cooking equipment" refers to data on the number, types, and performance of cooking utensils and equipment available to the user.

[0147] "Analyzing the user's emotional state" refers to the process of estimating the user's emotions and psychological state based on data such as voice and facial expressions.

[0148] "Generating cooking procedures and dish compositions that consider nutritional balance and cooking efficiency" means determining appropriate and efficient cooking methods and dish compositions by considering the nutrients in the ingredients and the time and procedures required for cooking.

[0149] "Providing the generated cooking procedure and dish composition to the user" refers to the process of the server sending the recipe information it has created to the user's terminal in a format that the user can view and presenting it to the user.

[0150] This invention is an information processing system that provides cooking support tailored to the user's emotional state and cooking environment. This system is implemented using a terminal, a server, and a generative AI model. Specific embodiments are described below.

[0151] The user takes pictures of the food inside the refrigerator using a device such as a smartphone or tablet. The device sends the captured image data to a server and also collects information about the user's cooking equipment, as well as voice and facial expression data.

[0152] The server uses image analysis technology to perform optical character recognition (OCR) on received image data and generates text data for ingredients. Furthermore, the server utilizes emotion recognition technology to analyze voice and facial expression data transmitted from the terminal and estimate the user's current emotional state. This information enables more personalized cooking suggestions.

[0153] Next, the server uses a generative AI model to generate the optimal cooking recipe based on the ingredient list, information about cooking equipment, and the user's emotional state. The generated recipe takes nutritional balance and cooking efficiency into consideration, while also being tailored to the user's emotional state. For example, if the user is feeling stressed, a dish using ingredients with relaxation effects will be recommended.

[0154] Finally, the generated recipe information is sent from the server to the terminal and displayed to the user. The user can then cook based on the displayed recipe and enjoy a meal that suits their mood.

[0155] For example, if a user is feeling stressed because they have cabbage and cheese in their refrigerator, the server inputs a prompt into the AI ​​model such as, "Please suggest a stress-relieving recipe using cabbage and cheese." As a result, the server generates a recipe for vegetable gratin, which is said to have a relaxing effect, and provides it to the user. This system allows users to easily create dishes that suit their emotions and cooking environment at the time.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user takes pictures of the food in the refrigerator using the camera on their smartphone or tablet. The device sends the captured image data to the server. The input is image data including the food in the refrigerator, and the output is the data transferred to the server. Specifically, the camera app on the device captures the image and uploads that data to the server in real time.

[0159] Step 2:

[0160] The terminal collects information about the cooking equipment the user owns, as well as the user's voice and facial expression data. This information is transmitted to a server. The inputs are the number and type of cooking equipment, and the user's voice and facial expressions, while the output is the data transferred to the server. Specifically, the terminal's sensor functions are used to collect relevant data using voice recognition and facial recognition technology, and then transmitted to the server.

[0161] Step 3:

[0162] The server analyzes the received image data using OCR (Optical Character Recognition) technology and converts the food items in the image into text data. The input is image data, and the output is a text-based list of food items. Specifically, OCR software is used to analyze labels and text information in the image, and the results are registered in a database.

[0163] Step 4:

[0164] The server uses collected voice and facial expression data to apply emotion recognition technology and determine the user's emotional state. The input is voice and facial expression data, and the output is the estimated emotional state. Specifically, it analyzes the data using an emotion analysis algorithm and extracts emotional parameters such as stress and happiness.

[0165] Step 5:

[0166] The server uses a generative AI model to integrate a text-based list of ingredients, cooking equipment information, and emotional state to generate a cooking recipe tailored to the user. The input is the ingredient list, cooking equipment information, and emotional state, and the output is the generated cooking recipe. Specifically, the generative AI is given a prompt such as "Please suggest a stress-relieving recipe using cabbage and cheese," and the AI ​​outputs the optimal recipe.

[0167] Step 6:

[0168] The server sends the generated recipe information to the terminal, where it is displayed in an easy-to-read format for the user. The input is the generated cooking recipe, and the output is the display of the recipe on the terminal. Specifically, the server formats the recipe information and provides it to the user through the application.

[0169] Step 7:

[0170] The user cooks according to the provided recipe. The server sends additional advice and timers to the terminal as needed during cooking. The input is the user's cooking progress, and the output is timing instructions and hints. Specifically, the terminal monitors the cooking status in real time and supports the cooking process based on information from the server.

[0171] (Application Example 2)

[0172] 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 device 14 will be referred to as the "terminal."

[0173] In recent years, consumer needs have diversified, and restaurants in particular are demanding personalized services that cater to customers' psychological states and preferences. However, appropriately recognizing the emotional state of each individual customer and suggesting dishes based on that is a significant burden for businesses. There is a need for solutions to this challenge.

[0174] 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.

[0175] In this invention, the server includes means for analyzing an image and obtaining the objects within the image as text information; means for generating a cooking flow and dish structure based on the acquired text information and information on cooking-related equipment; and means for analyzing the user's emotional state and generating a cooking flow and dish structure corresponding to that state. This makes it possible to suggest personalized dishes that respond to the customer's emotions.

[0176] "Means for analyzing images and obtaining objects within those images as text information" refers to technologies that process captured image data using analysis techniques to convert objects and characters visible in the image into text-based information.

[0177] "Means for generating cooking procedures and dish structures based on acquired text information and information on cooking-related equipment" refers to a system that uses converted text data and information on cooking equipment owned by the user to derive efficient and optimal cooking procedures and specific arrangements for dishes.

[0178] "Means for analyzing the emotional state of users and generating a cooking flow and dish structure that corresponds to that state" refers to a system that analyzes the user's emotional state to determine their psychological state and adaptively construct a cooking plan in order to suggest dishes that match that state.

[0179] "Means for presenting the generated cooking process and dish structure to the user" refers to a method for showing the cooking example or dish design ultimately derived by the system in a way that is easy for the user to understand, via a terminal or other device.

[0180] In this invention, the user first initiates an experience at a target physical store using their smartphone. The user's device has an application with a camera function installed, which allows them to take images of the target object. The images taken by the camera are immediately sent to the server. The server uses image processing software and libraries such as OpenCV to analyze the object in the image and obtain the information as text.

[0181] Next, the analyzed sentiment data and acquired text information are used with a generative AI model to generate the optimal cooking flow and dish structure for the user. This process also takes into account the user's cooking-related equipment data and available ingredient information, making it possible to suggest menus tailored to individual needs.

[0182] Furthermore, information tailored to the user's emotional state and cooking progress is stored and analyzed on a cloud server and then pushed to the user's device in an appropriate format. Specifically, for example, if the user's emotional state is analyzed as "stressed," a special recipe using ingredients known to alleviate stress will be generated and displayed on the smartphone.

[0183] This allows users to easily select and prepare dishes that suit their emotional state, within a system that suggests dishes tailored to their individual cooking abilities and equipment. An example of a prompt message is shown below.

[0184] Example prompt: "The customer's emotion is set to 'tired.' Please suggest a relaxing meal menu using the available ingredients. Available ingredients are 'chicken, noodles, carrots, celery, and parsley.'"

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The user uses their device to take pictures of their face and food items inside the store. This provides image information as input data. The camera function of the user's device is used, and the images are immediately sent to the server.

[0188] Step 2:

[0189] The server first uses OpenCV to detect faces in an image and then analyzes the emotional state using the Emotion API. The input is a user's face image, and the output is emotional data. In parallel, it uses OCR technology to convert images of food ingredients into text data and outputs it as a list of ingredients.

[0190] Step 3:

[0191] The server uses acquired emotion data and ingredient lists as input data, and generates cooking suggestions appropriate to the emotions using a generative AI model. Information on the cooking equipment the user can use is also referenced. The output is a customized cooking recipe.

[0192] Step 4:

[0193] The server sends the generated cooking recipe to the user's terminal. The user receives the recipe information via the terminal and can cook based on the displayed information. Through this process, the recipe is provided as visual information as output.

[0194] 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.

[0195] 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 (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.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] This invention provides an information processing system to improve food management at home and streamline cooking. This system uses image analysis technology and generative AI to extract information about ingredients in a refrigerator, and then generates and presents cooking recipes based on that information.

[0211] Specifically, the user takes pictures of the food in the refrigerator using a smartphone or other device. The device then sends this image data to the server. At this time, the user can also input kitchen specifications, such as the number of stovetops and ovens.

[0212] The server analyzes the received image using OCR (Optical Character Recognition) technology and extracts the items in the image as text data. During this process, it identifies the names and conditions of the food items, and obtains information about their quantity and quality as much as possible.

[0213] The server uses a generative AI model based on this transcribed information and the user's input of information about cooking equipment to generate cooking procedures and dish compositions. The generative AI can generate multiple recipes suitable for the user's situation, while also considering factors such as nutritional balance and cooking time constraints.

[0214] The generated recipe information is sent from the server to the terminal, and the terminal displays that information on its screen for the user to use. This allows the user to efficiently prepare a dish by following the suggested cooking procedure.

[0215] For example, if a user uses this system with tomatoes, eggs, and milk in their refrigerator, the server will generate recipes for dishes such as omelets and tomato soup based on this ingredient information and kitchen information, and will devise cooking procedures so that the user can use multiple stoves simultaneously.

[0216] Thus, this system is easy for users to use and can support both the reduction of food waste and a richer diet.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The user activates the device and uses the camera function to take pictures of the food inside the refrigerator. The user also enters information about cooking appliances, such as the number of burners and ovens.

[0220] Step 2:

[0221] The device combines images captured by the device and information entered by the user into a single dataset, converts it to JSON format, and sends it to the server. A secure protocol (e.g., HTTPS) is used for transmission.

[0222] Step 3:

[0223] The server analyzes the data received from the terminal and sends the image data to the OCR API. The OCR analyzes the text information within the image and returns the names and quantities of ingredients as text data to the server.

[0224] Step 4:

[0225] The server creates a list of ingredients based on the text data received from the OCR, and also sends information about the user's cooking equipment to the AI ​​model.

[0226] Step 5:

[0227] The AI ​​model generates multiple recipes and instructions that consider nutritional balance and cooking efficiency, using the ingredient list and information on cooking equipment. The generated information is then sent back to the server.

[0228] Step 6:

[0229] The server receives the output from the generated AI model and sends the generated recipe information to the terminal. This information is again sent in JSON format.

[0230] Step 7:

[0231] The device analyzes the recipe information received from the server and displays it on the screen in a way that is easy for the user to understand. The user then begins cooking based on the displayed recipe.

[0232] (Example 1)

[0233] 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."

[0234] Managing ingredients efficiently and avoiding waste during cooking is a challenge for many households. In particular, keeping track of what's in the refrigerator and making the most of the ingredients available is difficult. Furthermore, there's a lack of systems that automatically provide optimal cooking procedures tailored to the user's specific cooking environment.

[0235] 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.

[0236] In this invention, the server includes means for analyzing an image to obtain the items in the image as text information and removing noise through preprocessing; means for generating multiple recipes that take nutritional balance and cooking time into consideration using a generation AI model based on the acquired text information and information on cooking-related devices entered by the user; and means for transmitting the generated multiple recipe information to a terminal and displaying it to the user. This allows the user to effectively manage the ingredients in their refrigerator, easily obtain the optimal cooking procedure based on that information, and cook without waste.

[0237] "Image analysis" is the process of processing image data and extracting useful information from it.

[0238] "Items" refers to physical items such as food and beverages that are present inside the refrigerator.

[0239] "Textual information" refers to string data obtained from images using OCR technology.

[0240] "Noise reduction" is the process of removing unnecessary information in image analysis to improve accuracy.

[0241] "Cooking-related equipment" refers to cooking appliances and facilities used in the kitchen, including, for example, stoves and ovens.

[0242] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate responses that meet user needs.

[0243] "Nutritional balance" refers to the fact that the prepared dish has a healthy distribution of nutrients.

[0244] "Cooking time" refers to the time required to complete a dish.

[0245] "Recipe information" refers to data about the steps and components of a dish created by a generative AI model.

[0246] "Device" refers to a device used by a user, and examples include smartphones and tablets.

[0247] This invention relates to an information processing system aimed at improving the efficiency of food management and cooking within the home. The main components are a user terminal, a central server, and a generative AI model.

[0248] Users take photos of food items inside their refrigerators using devices such as smartphones or tablets. The captured images are saved in JPEG or PNG format. This image data is then sent from the device to the server.

[0249] Upon receiving the image, the server first performs image preprocessing. This preprocessing includes removing noise and improving analysis accuracy. The server then uses optical character recognition (OCR) technology, leveraging libraries such as TensorFlow and OpenCV, to extract textual information from the image. This textual information indicates the names, quantities, and conditions of the ingredients.

[0250] The server then collects information about cooking-related equipment entered by the user through the terminal. This includes information such as the number of available burners and whether or not there is an oven. This information is sent to the server in JSON format.

[0251] The server inputs the extracted text information and information about cooking equipment as prompts into the generative AI model. The generative AI model is built using, for example, natural language processing technology and can use algorithms such as GPT. For example, if the prompt is "I have tomatoes, eggs, and milk. Please generate recipes that can be used with a 3-burner stove and an oven," the AI ​​will generate multiple recipes. In this process, nutritional balance and cooking time will also be taken into consideration.

[0252] The generated recipe information is sent from the server to the terminal. The terminal analyzes the received information and displays it to the user in a visually easy-to-understand format. This allows the user to follow the suggested cooking procedure and effectively utilize the ingredients in their refrigerator to prepare a meal.

[0253] This system can reduce food waste and support a healthy and efficient diet.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The user uses a device to take pictures of the food items inside the refrigerator. The captured images are saved in JPEG or PNG format. The input obtained from the user is image data that reflects the condition of the food items. The device sends this image data to the server. As output, the image data is passed to the server.

[0257] Step 2:

[0258] The server preprocesses the received image data. Preprocessing includes steps to remove noise from the image and improve the accuracy of the analysis. The input is the image data received by the server, and after preprocessing, clean image data with noise removed is output.

[0259] Step 3:

[0260] The server analyzes the pre-processed image data using OCR technology to extract text information from the image. At this stage, the server recognizes text such as labels and dates using libraries such as TensorFlow or OpenCV. The input is denoised image data, and the output is text information (such as the name, quantity, and condition of the ingredients).

[0261] Step 4:

[0262] The user inputs information about available cooking equipment through the terminal. This information includes, for example, the number of burners and whether or not there is an oven. The input is information about the user's own cooking equipment, and the terminal outputs this information by sending it to the server.

[0263] Step 5:

[0264] The server combines the analyzed textual information with the cooking equipment information provided by the user and inputs it as a prompt into the generating AI model. It generates prompt sentences such as, "I have tomatoes, eggs, and milk. Please generate a recipe that can be used with a 3-burner stove and an oven." Based on this, the server causes the AI ​​to generate a recipe that takes into account nutritional balance, cooking time, and other factors. The input consists of ingredient information and cooking equipment information, and multiple recipes are output by the AI.

[0265] Step 6:

[0266] The server sends the generated recipe information to the terminal. The input is recipe data generated by the AI, and the server outputs the recipe information sent to the terminal.

[0267] Step 7:

[0268] The terminal analyzes the received recipe information and displays it on the screen in a user-friendly format. The input is recipe information from the server, and the terminal outputs cooking instructions that are easy to understand visually. The user can then proceed with cooking based on this information.

[0269] (Application Example 1)

[0270] 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."

[0271] In modern life, reducing food waste and planning meals efficiently are crucial. Commercial facilities, in particular, are required to provide a satisfying cooking experience using ingredients purchased by customers. However, it is difficult for consumers to find appropriate recipes on the spot when purchasing ingredients. Therefore, there is a need for a system that effectively suggests cooking methods based on the purchased ingredients.

[0272] 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.

[0273] In this invention, the server includes means for analyzing visual information and obtaining items within the visual data as text data; means for generating cooking procedures and meal configurations based on the acquired text data and information on cooking-related equipment; and means for providing the generated cooking procedures and meal configurations to the user. This makes it possible to suggest appropriate recipes in real time at commercial facilities based on purchased ingredients.

[0274] "Analyzing visual information to obtain text data for items within visual data" refers to the process of recognizing the names and characteristics of items from video data obtained by imaging devices such as cameras, and converting that information into text format.

[0275] "Generating cooking procedures and meal compositions based on acquired text data and information on cooking-related equipment" refers to the process of constructing cooking procedures and menus based on ingredient information represented as text data and cooking equipment information entered by the user.

[0276] "Providing users with the generated cooking procedures and meal composition" refers to the act of displaying or transmitting information about the procedures and menus for the created dishes so that users can easily view them.

[0277] "Suggesting cooking methods based on items in visual data" refers to analyzing ingredients found in captured video data and suggesting new cooking ideas to users that utilize them.

[0278] To implement this invention, it is necessary to build a system that analyzes visual information using a user-owned terminal and a cloud server, and proposes cooking methods in real time based on the acquired data. This system mainly consists of the following hardware and software.

[0279] The user takes a picture of the purchased food items with their device (e.g., a smartphone). This device has a camera function, and the image data is immediately sent to a cloud server. On the server side, image processing software such as OpenCV or Tesseract OCR is used to extract the items from the image data as text data. This obtained text data is then input into a generative AI on the cloud, such as an AI model incorporating ChatGPT.

[0280] The server uses this text data to generate cooking instructions and meal compositions based on the purchased ingredients. By using prompts that take into account the user's cooking environment and purchased ingredients, the generating AI model can provide specific and practical cooking suggestions. For example, a prompt such as, "Tomatoes, basil, and mozzarella cheese have been purchased. Please suggest a simple and delicious recipe using these ingredients," might be used.

[0281] Finally, the generated cooking procedures and dish compositions are sent back to the user's terminal and displayed. This enables the user to immediately learn various cooking methods based on the purchased ingredients and cook efficiently.

[0282] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0283] Step 1:

[0284] The user takes a photo of the purchased ingredients using a terminal such as a smartphone. The input is the image data of the ingredients obtained through the terminal's camera. The output is this image data. The terminal sends this data to the cloud server.

[0285] Step 2:

[0286] The server analyzes the received image data using OpenCV or Tesseract OCR. In this process, the items in the visual data are obtained as character data. The input is the image data obtained in Step 1, and the output is the character data representing the names and characteristics of the items (ingredients).

[0287] Step 3:

[0288] The server utilizes the generated AI model based on the obtained character data to generate cooking methods. At this time, the user's cooking-related equipment information, etc., is used as a prompt. The input is the character data obtained in Step 2 and the user's cooking equipment information, and the output is the data of the generated cooking procedures and dish compositions. For example, data is generated based on a prompt such as "Tomato, basil, and mozzarella cheese have been purchased. Please propose a simple and delicious recipe using these ingredients."

[0289] Step 4:

[0290] The server sends the generated cooking procedure and ingredient data to the user's terminal. The input is the data generated in step 3, and the output is cooking procedure and ingredient information that can be displayed on the user's terminal. The user can receive this information as displayed content.

[0291] 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.

[0292] This invention relates to an information processing system that uses an emotion engine in a cooking support system to generate cooking procedures and dish compositions that correspond to the user's psychological state. This system integrates image analysis technology, generation AI, and emotion recognition technology to enable more personalized and efficient food management and cooking at home.

[0293] Specifically, the user takes pictures of the food in their refrigerator using a smartphone or other device. The device sends these pictures to a server and also collects information about the user's cooking equipment, such as the number of burners and ovens, as well as voice and facial expression data. This data is analyzed by an emotion engine to estimate the user's current emotional state.

[0294] The server uses OCR (Optical Character Recognition) technology to convert image data into text and create a list of ingredients. Furthermore, an AI model, using information on cooking equipment and emotional state data, generates recipes that match the user's emotional state while considering nutritional balance and cooking efficiency. In this process, for example, if the user is stressed, recipes containing ingredients that help alleviate stress will be prioritized.

[0295] The generated recipe information is sent from the server to the terminal and displayed in a format viewable by the user. Based on this recipe, the user can cook to reduce stress.

[0296] For example, if a user is feeling stressed because they have vegetables and cheese in their refrigerator, the server will generate a recipe for vegetable gratin using ingredients known to have a relaxing effect and provide it to the user. In this way, users can easily enjoy a meal that suits their current mood.

[0297] This invention enriches the home cooking experience, improves user satisfaction, and makes it possible to provide personalized meals while reducing food waste.

[0298] The following describes the processing flow.

[0299] Step 1:

[0300] The user takes photos of the food inside the refrigerator using a smartphone or dedicated device. In addition, they input information about cooking appliances (such as the number of burners and ovens), and voice and facial expression data are collected to recognize emotions.

[0301] Step 2:

[0302] The device sends captured image data, input information, and emotion-related data to the server. This data is organized in JSON format and transmitted via a secure protocol (e.g., HTTPS).

[0303] Step 3:

[0304] The server processes the received image data using an OCR API, analyzes the text information within the image, and obtains the ingredient list as text data.

[0305] Step 4:

[0306] The emotion engine analyzes the voice and facial expression data received by the server to estimate the user's current emotional state. This emotional state is categorized into categories such as stress, relaxation, and joy.

[0307] Step 5:

[0308] The server inputs the food list from OCR, the information of cooking-related equipment, and the emotional state estimated by the emotion engine into the generation AI model. The generation AI model generates a cooking recipe and cooking procedure suitable for the nutritional balance, cooking efficiency, and emotional state in consideration of these data.

[0309] Step 6:

[0310] The server receives the recipe information created from the generation AI model and transmits it to the terminal. This information is displayed on the UI of the terminal so that the user can immediately check it.

[0311] Step 7:

[0312] The user checks the recipe displayed on the terminal and starts cooking. Since the presented recipe uses ingredients suitable for the user's emotion and the cooking procedure is also efficiently organized, a highly satisfactory cooking experience is possible.

[0313] (Example 2)

[0314] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0315] In the conventional cooking support system, there are few personalized proposals according to the user's emotional state and individual cooking environment, and the quality of the cooking experience may decrease. In addition, there is a problem that an appropriate recipe based on the user's emotional state is not provided, and it is difficult to lead to an improvement in psychological satisfaction.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0317] In this invention, the server includes means for analyzing an image and obtaining items within the image as text data; means for analyzing the obtained text data, information about cooking equipment, and the user's emotional state to generate cooking procedures and dish compositions that take nutritional balance and cooking efficiency into consideration; and means for providing the generated cooking procedures and dish compositions to the user. This makes it possible to propose personalized cooking procedures that are tailored to the individual cooking environment and the user's emotional state.

[0318] "Analyzing an image and obtaining the objects within it as text data" refers to the process of using image recognition technology to identify objects contained within an image and convert them into corresponding text data.

[0319] "Information regarding cooking equipment" refers to data on the number, types, and performance of cooking utensils and equipment available to the user.

[0320] "Analyzing the user's emotional state" refers to the process of estimating the user's emotions and psychological state based on data such as voice and facial expressions.

[0321] "Generating cooking procedures and dish compositions that consider nutritional balance and cooking efficiency" means determining appropriate and efficient cooking methods and dish compositions by considering the nutrients in the ingredients and the time and procedures required for cooking.

[0322] "Providing the generated cooking procedure and dish composition to the user" refers to the process of the server sending the recipe information it has created to the user's terminal in a format that the user can view and presenting it to the user.

[0323] This invention is an information processing system that provides cooking support tailored to the user's emotional state and cooking environment. This system is implemented using a terminal, a server, and a generative AI model. Specific embodiments are described below.

[0324] The user takes pictures of the food inside the refrigerator using a device such as a smartphone or tablet. The device sends the captured image data to a server and also collects information about the user's cooking equipment, as well as voice and facial expression data.

[0325] The server uses image analysis technology to perform optical character recognition (OCR) on received image data and generates text data for ingredients. Furthermore, the server utilizes emotion recognition technology to analyze voice and facial expression data transmitted from the terminal and estimate the user's current emotional state. This information enables more personalized cooking suggestions.

[0326] Next, the server uses a generative AI model to generate the optimal cooking recipe based on the ingredient list, information about cooking equipment, and the user's emotional state. The generated recipe takes nutritional balance and cooking efficiency into consideration, while also being tailored to the user's emotional state. For example, if the user is feeling stressed, a dish using ingredients with relaxation effects will be recommended.

[0327] Finally, the generated recipe information is sent from the server to the terminal and displayed to the user. The user can then cook based on the displayed recipe and enjoy a meal that suits their mood.

[0328] For example, if a user is feeling stressed because they have cabbage and cheese in their refrigerator, the server inputs a prompt into the AI ​​model such as, "Please suggest a stress-relieving recipe using cabbage and cheese." As a result, the server generates a recipe for vegetable gratin, which is said to have a relaxing effect, and provides it to the user. This system allows users to easily create dishes that suit their emotions and cooking environment at the time.

[0329] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0330] Step 1:

[0331] The user takes pictures of the food in the refrigerator using the camera on their smartphone or tablet. The device sends the captured image data to the server. The input is image data including the food in the refrigerator, and the output is the data transferred to the server. Specifically, the camera app on the device captures the image and uploads that data to the server in real time.

[0332] Step 2:

[0333] The terminal collects information about the cooking equipment the user owns, as well as the user's voice and facial expression data. This information is transmitted to a server. The inputs are the number and type of cooking equipment, and the user's voice and facial expressions, while the output is the data transferred to the server. Specifically, the terminal's sensor functions are used to collect relevant data using voice recognition and facial recognition technology, and then transmitted to the server.

[0334] Step 3:

[0335] The server analyzes the received image data using OCR (Optical Character Recognition) technology and converts the food items in the image into text data. The input is image data, and the output is a text-based list of food items. Specifically, OCR software is used to analyze labels and text information in the image, and the results are registered in a database.

[0336] Step 4:

[0337] The server uses collected voice and facial expression data to apply emotion recognition technology and determine the user's emotional state. The input is voice and facial expression data, and the output is the estimated emotional state. Specifically, it analyzes the data using an emotion analysis algorithm and extracts emotional parameters such as stress and happiness.

[0338] Step 5:

[0339] The server uses a generative AI model to integrate a text-based list of ingredients, cooking equipment information, and emotional state to generate a cooking recipe tailored to the user. The input is the ingredient list, cooking equipment information, and emotional state, and the output is the generated cooking recipe. Specifically, the generative AI is given a prompt such as "Please suggest a stress-relieving recipe using cabbage and cheese," and the AI ​​outputs the optimal recipe.

[0340] Step 6:

[0341] The server sends the generated recipe information to the terminal, where it is displayed in an easy-to-read format for the user. The input is the generated cooking recipe, and the output is the display of the recipe on the terminal. Specifically, the server formats the recipe information and provides it to the user through the application.

[0342] Step 7:

[0343] The user cooks according to the provided recipe. The server sends additional advice and timers to the terminal as needed during cooking. The input is the user's cooking progress, and the output is timing instructions and hints. Specifically, the terminal monitors the cooking status in real time and supports the cooking process based on information from the server.

[0344] (Application Example 2)

[0345] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0346] In recent years, consumer needs have diversified, and restaurants in particular are demanding personalized services that cater to customers' psychological states and preferences. However, appropriately recognizing the emotional state of each individual customer and suggesting dishes based on that is a significant burden for businesses. There is a need for solutions to this challenge.

[0347] 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.

[0348] In this invention, the server includes means for analyzing an image and obtaining the objects within the image as text information; means for generating a cooking flow and dish structure based on the acquired text information and information on cooking-related equipment; and means for analyzing the user's emotional state and generating a cooking flow and dish structure corresponding to that state. This makes it possible to suggest personalized dishes that respond to the customer's emotions.

[0349] "Means for analyzing images and obtaining objects within those images as text information" refers to technologies that process captured image data using analysis techniques to convert objects and characters visible in the image into text-based information.

[0350] "Means for generating cooking procedures and dish structures based on acquired text information and information on cooking-related equipment" refers to a system that uses converted text data and information on cooking equipment owned by the user to derive efficient and optimal cooking procedures and specific arrangements for dishes.

[0351] "Means for analyzing the emotional state of users and generating a cooking flow and dish structure that corresponds to that state" refers to a system that analyzes the user's emotional state to determine their psychological state and adaptively construct a cooking plan in order to suggest dishes that match that state.

[0352] "Means for presenting the generated cooking process and dish structure to the user" refers to a method for showing the cooking example or dish design ultimately derived by the system in a way that is easy for the user to understand, via a terminal or other device.

[0353] In this invention, the user first initiates an experience at a target physical store using their smartphone. The user's device has an application with a camera function installed, which allows them to take images of the target object. The images taken by the camera are immediately sent to the server. The server uses image processing software and libraries such as OpenCV to analyze the object in the image and obtain the information as text.

[0354] Next, the analyzed sentiment data and acquired text information are used with a generative AI model to generate the optimal cooking flow and dish structure for the user. This process also takes into account the user's cooking-related equipment data and available ingredient information, making it possible to suggest menus tailored to individual needs.

[0355] Furthermore, information tailored to the user's emotional state and cooking progress is stored and analyzed on a cloud server and then pushed to the user's device in an appropriate format. Specifically, for example, if the user's emotional state is analyzed as "stressed," a special recipe using ingredients known to alleviate stress will be generated and displayed on the smartphone.

[0356] This allows users to easily select and prepare dishes that suit their emotional state, within a system that suggests dishes tailored to their individual cooking abilities and equipment. An example of a prompt message is shown below.

[0357] Example prompt: "The customer's emotion is set to 'tired.' Please suggest a relaxing meal menu using the available ingredients. Available ingredients are 'chicken, noodles, carrots, celery, and parsley.'"

[0358] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0359] Step 1:

[0360] The user uses their device to take pictures of their face and food items inside the store. This provides image information as input data. The camera function of the user's device is used, and the images are immediately sent to the server.

[0361] Step 2:

[0362] The server first uses OpenCV to detect faces in an image and then analyzes the emotional state using the Emotion API. The input is a user's face image, and the output is emotional data. In parallel, it uses OCR technology to convert images of food ingredients into text data and outputs it as a list of ingredients.

[0363] Step 3:

[0364] The server uses acquired emotion data and ingredient lists as input data, and generates cooking suggestions appropriate to the emotions using a generative AI model. Information on the cooking equipment the user can use is also referenced. The output is a customized cooking recipe.

[0365] Step 4:

[0366] The server sends the generated cooking recipe to the user's terminal. The user receives the recipe information via the terminal and can cook based on the displayed information. Through this process, the recipe is provided as visual information as output.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] [Third Embodiment]

[0371] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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".

[0383] This invention provides an information processing system to improve food management at home and streamline cooking. This system uses image analysis technology and generative AI to extract information about ingredients in a refrigerator, and then generates and presents cooking recipes based on that information.

[0384] Specifically, the user takes pictures of the food in the refrigerator using a smartphone or other device. The device then sends this image data to the server. At this time, the user can also input kitchen specifications, such as the number of stovetops and ovens.

[0385] The server analyzes the received image using OCR (Optical Character Recognition) technology and extracts the items in the image as text data. During this process, it identifies the names and conditions of the food items, and obtains information about their quantity and quality as much as possible.

[0386] The server uses a generative AI model based on this transcribed information and the user's input of information about cooking equipment to generate cooking procedures and dish compositions. The generative AI can generate multiple recipes suitable for the user's situation, while also considering factors such as nutritional balance and cooking time constraints.

[0387] The generated recipe information is sent from the server to the terminal, and the terminal displays that information on its screen for the user to use. This allows the user to efficiently prepare a dish by following the suggested cooking procedure.

[0388] For example, if a user uses this system with tomatoes, eggs, and milk in their refrigerator, the server will generate recipes for dishes such as omelets and tomato soup based on this ingredient information and kitchen information, and will devise cooking procedures so that the user can use multiple stoves simultaneously.

[0389] Thus, this system is easy for users to use and can support both the reduction of food waste and a richer diet.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The user activates the device and uses the camera function to take pictures of the food inside the refrigerator. The user also enters information about cooking appliances, such as the number of burners and ovens.

[0393] Step 2:

[0394] The device combines images captured by the device and information entered by the user into a single dataset, converts it to JSON format, and sends it to the server. A secure protocol (e.g., HTTPS) is used for transmission.

[0395] Step 3:

[0396] The server analyzes the data received from the terminal and sends the image data to the OCR API. The OCR analyzes the text information within the image and returns the names and quantities of ingredients as text data to the server.

[0397] Step 4:

[0398] The server creates a list of ingredients based on the text data received from the OCR, and also sends information about the user's cooking equipment to the AI ​​model.

[0399] Step 5:

[0400] The AI ​​model generates multiple recipes and instructions that consider nutritional balance and cooking efficiency, using the ingredient list and information on cooking equipment. The generated information is then sent back to the server.

[0401] Step 6:

[0402] The server receives the output from the generated AI model and sends the generated recipe information to the terminal. This information is again sent in JSON format.

[0403] Step 7:

[0404] The device analyzes the recipe information received from the server and displays it on the screen in a way that is easy for the user to understand. The user then begins cooking based on the displayed recipe.

[0405] (Example 1)

[0406] 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."

[0407] Managing ingredients efficiently and avoiding waste during cooking is a challenge for many households. In particular, keeping track of what's in the refrigerator and making the most of the ingredients available is difficult. Furthermore, there's a lack of systems that automatically provide optimal cooking procedures tailored to the user's specific cooking environment.

[0408] 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.

[0409] In this invention, the server includes means for analyzing an image to obtain the items in the image as text information and removing noise through preprocessing; means for generating multiple recipes that take nutritional balance and cooking time into consideration using a generation AI model based on the acquired text information and information on cooking-related devices entered by the user; and means for transmitting the generated multiple recipe information to a terminal and displaying it to the user. This allows the user to effectively manage the ingredients in their refrigerator, easily obtain the optimal cooking procedure based on that information, and cook without waste.

[0410] "Image analysis" is the process of processing image data and extracting useful information from it.

[0411] "Items" refers to physical items such as food and beverages that are present inside the refrigerator.

[0412] "Textual information" refers to string data obtained from images using OCR technology.

[0413] "Noise reduction" is the process of removing unnecessary information in image analysis to improve accuracy.

[0414] "Cooking-related equipment" refers to cooking appliances and facilities used in the kitchen, including, for example, stoves and ovens.

[0415] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate responses that meet user needs.

[0416] "Nutritional balance" refers to the fact that the prepared dish has a healthy distribution of nutrients.

[0417] "Cooking time" refers to the time required to complete a dish.

[0418] "Recipe information" refers to data about the steps and components of a dish created by a generative AI model.

[0419] "Device" refers to a device used by a user, and examples include smartphones and tablets.

[0420] This invention relates to an information processing system aimed at improving the efficiency of food management and cooking within the home. The main components are a user terminal, a central server, and a generative AI model.

[0421] Users take photos of food items inside their refrigerators using devices such as smartphones or tablets. The captured images are saved in JPEG or PNG format. This image data is then sent from the device to the server.

[0422] Upon receiving the image, the server first performs image preprocessing. This preprocessing includes removing noise and improving analysis accuracy. The server then uses optical character recognition (OCR) technology, leveraging libraries such as TensorFlow and OpenCV, to extract textual information from the image. This textual information indicates the names, quantities, and conditions of the ingredients.

[0423] The server then collects information about cooking-related equipment entered by the user through the terminal. This includes information such as the number of available burners and whether or not there is an oven. This information is sent to the server in JSON format.

[0424] The server inputs the extracted text information and information about cooking equipment as prompts into the generative AI model. The generative AI model is built using, for example, natural language processing technology and can use algorithms such as GPT. For example, if the prompt is "I have tomatoes, eggs, and milk. Please generate recipes that can be used with a 3-burner stove and an oven," the AI ​​will generate multiple recipes. In this process, nutritional balance and cooking time will also be taken into consideration.

[0425] The generated recipe information is sent from the server to the terminal. The terminal analyzes the received information and displays it to the user in a visually easy-to-understand format. This allows the user to follow the suggested cooking procedure and effectively utilize the ingredients in their refrigerator to prepare a meal.

[0426] This system can reduce food waste and support a healthy and efficient diet.

[0427] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0428] Step 1:

[0429] The user uses a device to take pictures of the food items inside the refrigerator. The captured images are saved in JPEG or PNG format. The input obtained from the user is image data that reflects the condition of the food items. The device sends this image data to the server. As output, the image data is passed to the server.

[0430] Step 2:

[0431] The server preprocesses the received image data. Preprocessing includes steps to remove noise from the image and improve the accuracy of the analysis. The input is the image data received by the server, and after preprocessing, clean image data with noise removed is output.

[0432] Step 3:

[0433] The server analyzes the pre-processed image data using OCR technology to extract text information from the image. At this stage, the server recognizes text such as labels and dates using libraries such as TensorFlow or OpenCV. The input is denoised image data, and the output is text information (such as the name, quantity, and condition of the ingredients).

[0434] Step 4:

[0435] The user inputs information about available cooking equipment through the terminal. This information includes, for example, the number of burners and whether or not there is an oven. The input is information about the user's own cooking equipment, and the terminal outputs this information by sending it to the server.

[0436] Step 5:

[0437] The server combines the analyzed textual information with the cooking equipment information provided by the user and inputs it as a prompt into the generating AI model. It generates prompt sentences such as, "I have tomatoes, eggs, and milk. Please generate a recipe that can be used with a 3-burner stove and an oven." Based on this, the server causes the AI ​​to generate a recipe that takes into account nutritional balance, cooking time, and other factors. The input consists of ingredient information and cooking equipment information, and multiple recipes are output by the AI.

[0438] Step 6:

[0439] The server sends the generated recipe information to the terminal. The input is recipe data generated by the AI, and the server outputs the recipe information sent to the terminal.

[0440] Step 7:

[0441] The terminal analyzes the received recipe information and displays it on the screen in a user-friendly format. The input is recipe information from the server, and the terminal outputs cooking instructions that are easy to understand visually. The user can then proceed with cooking based on this information.

[0442] (Application Example 1)

[0443] 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."

[0444] In modern life, reducing food waste and planning meals efficiently are crucial. Commercial facilities, in particular, are required to provide a satisfying cooking experience using ingredients purchased by customers. However, it is difficult for consumers to find appropriate recipes on the spot when purchasing ingredients. Therefore, there is a need for a system that effectively suggests cooking methods based on the purchased ingredients.

[0445] 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.

[0446] In this invention, the server includes means for analyzing visual information and obtaining items within the visual data as text data; means for generating cooking procedures and meal configurations based on the acquired text data and information on cooking-related equipment; and means for providing the generated cooking procedures and meal configurations to the user. This makes it possible to suggest appropriate recipes in real time at commercial facilities based on purchased ingredients.

[0447] "Analyzing visual information to obtain text data for items within visual data" refers to the process of recognizing the names and characteristics of items from video data obtained by imaging devices such as cameras, and converting that information into text format.

[0448] "Generating cooking procedures and meal compositions based on acquired text data and information on cooking-related equipment" refers to the process of constructing cooking procedures and menus based on ingredient information represented as text data and cooking equipment information entered by the user.

[0449] "Providing users with the generated cooking procedures and meal composition" refers to the act of displaying or transmitting information about the procedures and menus for the created dishes so that users can easily view them.

[0450] "Suggesting cooking methods based on items in visual data" refers to analyzing ingredients found in captured video data and suggesting new cooking ideas to users that utilize them.

[0451] To implement this invention, it is necessary to build a system that analyzes visual information using a user-owned terminal and a cloud server, and proposes cooking methods in real time based on the acquired data. This system mainly consists of the following hardware and software.

[0452] The user takes a picture of the purchased food items with their device (e.g., a smartphone). This device has a camera function, and the image data is immediately sent to a cloud server. On the server side, image processing software such as OpenCV or Tesseract OCR is used to extract the items from the image data as text data. This obtained text data is then input into a generative AI on the cloud, such as an AI model incorporating ChatGPT.

[0453] The server uses this text data to generate cooking instructions and meal compositions based on the purchased ingredients. By using prompts that take into account the user's cooking environment and purchased ingredients, the generating AI model can provide specific and practical cooking suggestions. For example, a prompt such as, "Tomatoes, basil, and mozzarella cheese have been purchased. Please suggest a simple and delicious recipe using these ingredients," might be used.

[0454] Finally, the generated cooking instructions and dish composition are sent back to the user's device and displayed. This allows the user to quickly learn about various cooking methods based on the purchased ingredients, enabling them to cook efficiently.

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The user takes a picture of the purchased food items using a device such as a smartphone. The input is image data of the food items obtained through the device's camera. The output is this image data. The device sends this data to a cloud server.

[0458] Step 2:

[0459] The server analyzes the received image data using OpenCV or Tesseract OCR. This process extracts the names and characteristics of items within the visual data as text data. The input is the image data obtained in step 1, and the output is text data representing the names and characteristics of the items (food items).

[0460] Step 3:

[0461] The server uses a generation AI model based on the acquired text data to generate cooking methods. In this process, user information regarding cooking equipment is used as a prompt. The input consists of the text data obtained in step 2 and the user's cooking equipment information, while the output is data on the generated cooking procedure and dish composition. For example, data is generated based on a prompt such as, "Tomatoes, basil, and mozzarella cheese have been purchased. Please suggest a simple and delicious recipe using these ingredients."

[0462] Step 4:

[0463] The server sends the generated cooking procedure and ingredient data to the user's terminal. The input is the data generated in step 3, and the output is cooking procedure and ingredient information that can be displayed on the user's terminal. The user can receive this information as displayed content.

[0464] 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.

[0465] This invention relates to an information processing system that uses an emotion engine in a cooking support system to generate cooking procedures and dish compositions that correspond to the user's psychological state. This system integrates image analysis technology, generation AI, and emotion recognition technology to enable more personalized and efficient food management and cooking at home.

[0466] Specifically, the user takes pictures of the food in their refrigerator using a smartphone or other device. The device sends these pictures to a server and also collects information about the user's cooking equipment, such as the number of burners and ovens, as well as voice and facial expression data. This data is analyzed by an emotion engine to estimate the user's current emotional state.

[0467] The server uses OCR (Optical Character Recognition) technology to convert image data into text and create a list of ingredients. Furthermore, an AI model, using information on cooking equipment and emotional state data, generates recipes that match the user's emotional state while considering nutritional balance and cooking efficiency. In this process, for example, if the user is stressed, recipes containing ingredients that help alleviate stress will be prioritized.

[0468] The generated recipe information is sent from the server to the terminal and displayed in a format viewable by the user. Based on this recipe, the user can cook to reduce stress.

[0469] For example, if a user is feeling stressed because they have vegetables and cheese in their refrigerator, the server will generate a recipe for vegetable gratin using ingredients known to have a relaxing effect and provide it to the user. In this way, users can easily enjoy a meal that suits their current mood.

[0470] This invention enriches the home cooking experience, improves user satisfaction, and makes it possible to provide personalized meals while reducing food waste.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The user takes photos of the food inside the refrigerator using a smartphone or dedicated device. In addition, they input information about cooking appliances (such as the number of burners and ovens), and voice and facial expression data are collected to recognize emotions.

[0474] Step 2:

[0475] The device sends captured image data, input information, and emotion-related data to the server. This data is organized in JSON format and transmitted via a secure protocol (e.g., HTTPS).

[0476] Step 3:

[0477] The server processes the received image data using an OCR API, analyzes the text information within the image, and obtains the ingredient list as text data.

[0478] Step 4:

[0479] The emotion engine analyzes the voice and facial expression data received by the server to estimate the user's current emotional state. This emotional state is categorized into categories such as stress, relaxation, and joy.

[0480] Step 5:

[0481] The server inputs a list of ingredients from OCR, information on cooking equipment, and emotional states estimated by the emotion engine into a generative AI model. The generative AI model considers this data to generate a cooking recipe and procedure that is appropriate for nutritional balance, cooking efficiency, and the emotional state.

[0482] Step 6:

[0483] The server receives recipe information generated from the AI ​​model and sends it to the device. This information is displayed on the device's UI so that the user can check it immediately.

[0484] Step 7:

[0485] The user checks the recipe displayed on their device and begins cooking. The presented recipe uses ingredients that match the user's mood and the cooking procedure is efficiently structured, resulting in a highly satisfying cooking experience.

[0486] (Example 2)

[0487] 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."

[0488] Conventional cooking support systems often lacked personalized suggestions tailored to the user's emotional state and individual cooking environment, resulting in a lower quality cooking experience. Furthermore, they failed to provide appropriate recipes based on the user's emotional state, hindering improvements in psychological satisfaction.

[0489] 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.

[0490] In this invention, the server includes means for analyzing an image and obtaining items within the image as text data; means for analyzing the obtained text data, information about cooking equipment, and the user's emotional state to generate cooking procedures and dish compositions that take nutritional balance and cooking efficiency into consideration; and means for providing the generated cooking procedures and dish compositions to the user. This makes it possible to propose personalized cooking procedures that are tailored to the individual cooking environment and the user's emotional state.

[0491] "Analyzing an image and obtaining the objects within it as text data" refers to the process of using image recognition technology to identify objects contained within an image and convert them into corresponding text data.

[0492] "Information regarding cooking equipment" refers to data on the number, types, and performance of cooking utensils and equipment available to the user.

[0493] "Analyzing the user's emotional state" refers to the process of estimating the user's emotions and psychological state based on data such as voice and facial expressions.

[0494] "Generating cooking procedures and dish compositions that consider nutritional balance and cooking efficiency" means determining appropriate and efficient cooking methods and dish compositions by considering the nutrients in the ingredients and the time and procedures required for cooking.

[0495] "Providing the generated cooking procedure and dish composition to the user" refers to the process of the server sending the recipe information it has created to the user's terminal in a format that the user can view and presenting it to the user.

[0496] This invention is an information processing system that provides cooking support tailored to the user's emotional state and cooking environment. This system is implemented using a terminal, a server, and a generative AI model. Specific embodiments are described below.

[0497] The user takes pictures of the food inside the refrigerator using a device such as a smartphone or tablet. The device sends the captured image data to a server and also collects information about the user's cooking equipment, as well as voice and facial expression data.

[0498] The server uses image analysis technology to perform optical character recognition (OCR) on received image data and generates text data for ingredients. Furthermore, the server utilizes emotion recognition technology to analyze voice and facial expression data transmitted from the terminal and estimate the user's current emotional state. This information enables more personalized cooking suggestions.

[0499] Next, the server uses a generative AI model to generate the optimal cooking recipe based on the ingredient list, information about cooking equipment, and the user's emotional state. The generated recipe takes nutritional balance and cooking efficiency into consideration, while also being tailored to the user's emotional state. For example, if the user is feeling stressed, a dish using ingredients with relaxation effects will be recommended.

[0500] Finally, the generated recipe information is sent from the server to the terminal and displayed to the user. The user can then cook based on the displayed recipe and enjoy a meal that suits their mood.

[0501] For example, if a user is feeling stressed because they have cabbage and cheese in their refrigerator, the server inputs a prompt into the AI ​​model such as, "Please suggest a stress-relieving recipe using cabbage and cheese." As a result, the server generates a recipe for vegetable gratin, which is said to have a relaxing effect, and provides it to the user. This system allows users to easily create dishes that suit their emotions and cooking environment at the time.

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The user takes pictures of the food in the refrigerator using the camera on their smartphone or tablet. The device sends the captured image data to the server. The input is image data including the food in the refrigerator, and the output is the data transferred to the server. Specifically, the camera app on the device captures the image and uploads that data to the server in real time.

[0505] Step 2:

[0506] The terminal collects information about the cooking equipment the user owns, as well as the user's voice and facial expression data. This information is transmitted to a server. The inputs are the number and type of cooking equipment, and the user's voice and facial expressions, while the output is the data transferred to the server. Specifically, the terminal's sensor functions are used to collect relevant data using voice recognition and facial recognition technology, and then transmitted to the server.

[0507] Step 3:

[0508] The server analyzes the received image data using OCR (Optical Character Recognition) technology and converts the food items in the image into text data. The input is image data, and the output is a text-based list of food items. Specifically, OCR software is used to analyze labels and text information in the image, and the results are registered in a database.

[0509] Step 4:

[0510] The server uses collected voice and facial expression data to apply emotion recognition technology and determine the user's emotional state. The input is voice and facial expression data, and the output is the estimated emotional state. Specifically, it analyzes the data using an emotion analysis algorithm and extracts emotional parameters such as stress and happiness.

[0511] Step 5:

[0512] The server uses a generative AI model to integrate a text-based list of ingredients, cooking equipment information, and emotional state to generate a cooking recipe tailored to the user. The input is the ingredient list, cooking equipment information, and emotional state, and the output is the generated cooking recipe. Specifically, the generative AI is given a prompt such as "Please suggest a stress-relieving recipe using cabbage and cheese," and the AI ​​outputs the optimal recipe.

[0513] Step 6:

[0514] The server sends the generated recipe information to the terminal, where it is displayed in an easy-to-read format for the user. The input is the generated cooking recipe, and the output is the display of the recipe on the terminal. Specifically, the server formats the recipe information and provides it to the user through the application.

[0515] Step 7:

[0516] The user cooks according to the provided recipe. The server sends additional advice and timers to the terminal as needed during cooking. The input is the user's cooking progress, and the output is timing instructions and hints. Specifically, the terminal monitors the cooking status in real time and supports the cooking process based on information from the server.

[0517] (Application Example 2)

[0518] 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."

[0519] In recent years, consumer needs have diversified, and restaurants in particular are demanding personalized services that cater to customers' psychological states and preferences. However, appropriately recognizing the emotional state of each individual customer and suggesting dishes based on that is a significant burden for businesses. There is a need for solutions to this challenge.

[0520] 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.

[0521] In this invention, the server includes means for analyzing an image and obtaining the objects within the image as text information; means for generating a cooking flow and dish structure based on the acquired text information and information on cooking-related equipment; and means for analyzing the user's emotional state and generating a cooking flow and dish structure corresponding to that state. This makes it possible to suggest personalized dishes that respond to the customer's emotions.

[0522] "Means for analyzing images and obtaining objects within those images as text information" refers to technologies that process captured image data using analysis techniques to convert objects and characters visible in the image into text-based information.

[0523] "Means for generating cooking procedures and dish structures based on acquired text information and information on cooking-related equipment" refers to a system that uses converted text data and information on cooking equipment owned by the user to derive efficient and optimal cooking procedures and specific arrangements for dishes.

[0524] "Means for analyzing the emotional state of users and generating a cooking flow and dish structure that corresponds to that state" refers to a system that analyzes the user's emotional state to determine their psychological state and adaptively construct a cooking plan in order to suggest dishes that match that state.

[0525] "Means for presenting the generated cooking process and dish structure to the user" refers to a method for showing the cooking example or dish design ultimately derived by the system in a way that is easy for the user to understand, via a terminal or other device.

[0526] In this invention, the user first initiates an experience at a target physical store using their smartphone. The user's device has an application with a camera function installed, which allows them to take images of the target object. The images taken by the camera are immediately sent to the server. The server uses image processing software and libraries such as OpenCV to analyze the object in the image and obtain the information as text.

[0527] Next, the analyzed sentiment data and acquired text information are used with a generative AI model to generate the optimal cooking flow and dish structure for the user. This process also takes into account the user's cooking-related equipment data and available ingredient information, making it possible to suggest menus tailored to individual needs.

[0528] Furthermore, information tailored to the user's emotional state and cooking progress is stored and analyzed on a cloud server and then pushed to the user's device in an appropriate format. Specifically, for example, if the user's emotional state is analyzed as "stressed," a special recipe using ingredients known to alleviate stress will be generated and displayed on the smartphone.

[0529] This allows users to easily select and prepare dishes that suit their emotional state, within a system that suggests dishes tailored to their individual cooking abilities and equipment. An example of a prompt message is shown below.

[0530] Example prompt: "The customer's emotion is set to 'tired.' Please suggest a relaxing meal menu using the available ingredients. Available ingredients are 'chicken, noodles, carrots, celery, and parsley.'"

[0531] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0532] Step 1:

[0533] The user uses their device to take pictures of their face and food items inside the store. This provides image information as input data. The camera function of the user's device is used, and the images are immediately sent to the server.

[0534] Step 2:

[0535] The server first uses OpenCV to detect faces in an image and then analyzes the emotional state using the Emotion API. The input is a user's face image, and the output is emotional data. In parallel, it uses OCR technology to convert images of food ingredients into text data and outputs it as a list of ingredients.

[0536] Step 3:

[0537] The server uses acquired emotion data and ingredient lists as input data, and generates cooking suggestions appropriate to the emotions using a generative AI model. Information on the cooking equipment the user can use is also referenced. The output is a customized cooking recipe.

[0538] Step 4:

[0539] The server sends the generated cooking recipe to the user's terminal. The user receives the recipe information via the terminal and can cook based on the displayed information. Through this process, the recipe is provided as visual information as output.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] [Fourth Embodiment]

[0544] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0545] 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.

[0546] 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).

[0547] 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.

[0548] 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.

[0549] 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).

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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".

[0557] This invention provides an information processing system to improve food management at home and streamline cooking. This system uses image analysis technology and generative AI to extract information about ingredients in a refrigerator, and then generates and presents cooking recipes based on that information.

[0558] Specifically, the user takes pictures of the food in the refrigerator using a smartphone or other device. The device then sends this image data to the server. At this time, the user can also input kitchen specifications, such as the number of stovetops and ovens.

[0559] The server analyzes the received image using OCR (Optical Character Recognition) technology and extracts the items in the image as text data. During this process, it identifies the names and conditions of the food items, and obtains information about their quantity and quality as much as possible.

[0560] The server uses a generative AI model based on this transcribed information and the user's input of information about cooking equipment to generate cooking procedures and dish compositions. The generative AI can generate multiple recipes suitable for the user's situation, while also considering factors such as nutritional balance and cooking time constraints.

[0561] The generated recipe information is sent from the server to the terminal, and the terminal displays that information on its screen for the user to use. This allows the user to efficiently prepare a dish by following the suggested cooking procedure.

[0562] For example, if a user uses this system with tomatoes, eggs, and milk in their refrigerator, the server will generate recipes for dishes such as omelets and tomato soup based on this ingredient information and kitchen information, and will devise cooking procedures so that the user can use multiple stoves simultaneously.

[0563] Thus, this system is easy for users to use and can support both the reduction of food waste and a richer diet.

[0564] The following describes the processing flow.

[0565] Step 1:

[0566] The user activates the device and uses the camera function to take pictures of the food inside the refrigerator. The user also enters information about cooking appliances, such as the number of burners and ovens.

[0567] Step 2:

[0568] The device combines images captured by the device and information entered by the user into a single dataset, converts it to JSON format, and sends it to the server. A secure protocol (e.g., HTTPS) is used for transmission.

[0569] Step 3:

[0570] The server analyzes the data received from the terminal and sends the image data to the OCR API. The OCR analyzes the text information within the image and returns the names and quantities of ingredients as text data to the server.

[0571] Step 4:

[0572] The server creates a list of ingredients based on the text data received from the OCR, and also sends information about the user's cooking equipment to the AI ​​model.

[0573] Step 5:

[0574] The AI ​​model generates multiple recipes and instructions that consider nutritional balance and cooking efficiency, using the ingredient list and information on cooking equipment. The generated information is then sent back to the server.

[0575] Step 6:

[0576] The server receives the output from the generated AI model and sends the generated recipe information to the terminal. This information is again sent in JSON format.

[0577] Step 7:

[0578] The device analyzes the recipe information received from the server and displays it on the screen in a way that is easy for the user to understand. The user then begins cooking based on the displayed recipe.

[0579] (Example 1)

[0580] 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".

[0581] Managing ingredients efficiently and avoiding waste during cooking is a challenge for many households. In particular, keeping track of what's in the refrigerator and making the most of the ingredients available is difficult. Furthermore, there's a lack of systems that automatically provide optimal cooking procedures tailored to the user's specific cooking environment.

[0582] 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.

[0583] In this invention, the server includes means for analyzing an image to obtain the items in the image as text information and removing noise through preprocessing; means for generating multiple recipes that take nutritional balance and cooking time into consideration using a generation AI model based on the acquired text information and information on cooking-related devices entered by the user; and means for transmitting the generated multiple recipe information to a terminal and displaying it to the user. This allows the user to effectively manage the ingredients in their refrigerator, easily obtain the optimal cooking procedure based on that information, and cook without waste.

[0584] "Image analysis" is the process of processing image data and extracting useful information from it.

[0585] "Items" refers to physical items such as food and beverages that are present inside the refrigerator.

[0586] "Textual information" refers to string data obtained from images using OCR technology.

[0587] "Noise reduction" is the process of removing unnecessary information in image analysis to improve accuracy.

[0588] "Cooking-related equipment" refers to cooking appliances and facilities used in the kitchen, including, for example, stoves and ovens.

[0589] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate responses that meet user needs.

[0590] "Nutritional balance" refers to the fact that the prepared dish has a healthy distribution of nutrients.

[0591] "Cooking time" refers to the time required to complete a dish.

[0592] "Recipe information" refers to data about the steps and components of a dish created by a generative AI model.

[0593] "Device" refers to a device used by a user, and examples include smartphones and tablets.

[0594] This invention relates to an information processing system aimed at improving the efficiency of food management and cooking within the home. The main components are a user terminal, a central server, and a generative AI model.

[0595] Users take photos of food items inside their refrigerators using devices such as smartphones or tablets. The captured images are saved in JPEG or PNG format. This image data is then sent from the device to the server.

[0596] Upon receiving the image, the server first performs image preprocessing. This preprocessing includes removing noise and improving analysis accuracy. The server then uses optical character recognition (OCR) technology, leveraging libraries such as TensorFlow and OpenCV, to extract textual information from the image. This textual information indicates the names, quantities, and conditions of the ingredients.

[0597] The server then collects information about cooking-related equipment entered by the user through the terminal. This includes information such as the number of available burners and whether or not there is an oven. This information is sent to the server in JSON format.

[0598] The server inputs the extracted text information and information about cooking equipment as prompts into the generative AI model. The generative AI model is built using, for example, natural language processing technology and can use algorithms such as GPT. For example, if the prompt is "I have tomatoes, eggs, and milk. Please generate recipes that can be used with a 3-burner stove and an oven," the AI ​​will generate multiple recipes. In this process, nutritional balance and cooking time will also be taken into consideration.

[0599] The generated recipe information is sent from the server to the terminal. The terminal analyzes the received information and displays it to the user in a visually easy-to-understand format. This allows the user to follow the suggested cooking procedure and effectively utilize the ingredients in their refrigerator to prepare a meal.

[0600] This system can reduce food waste and support a healthy and efficient diet.

[0601] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0602] Step 1:

[0603] The user uses a device to take pictures of the food items inside the refrigerator. The captured images are saved in JPEG or PNG format. The input obtained from the user is image data that reflects the condition of the food items. The device sends this image data to the server. As output, the image data is passed to the server.

[0604] Step 2:

[0605] The server preprocesses the received image data. Preprocessing includes steps to remove noise from the image and improve the accuracy of the analysis. The input is the image data received by the server, and after preprocessing, clean image data with noise removed is output.

[0606] Step 3:

[0607] The server analyzes the pre-processed image data using OCR technology to extract text information from the image. At this stage, the server recognizes text such as labels and dates using libraries such as TensorFlow or OpenCV. The input is denoised image data, and the output is text information (such as the name, quantity, and condition of the ingredients).

[0608] Step 4:

[0609] The user inputs information about available cooking equipment through the terminal. This information includes, for example, the number of burners and whether or not there is an oven. The input is information about the user's own cooking equipment, and the terminal outputs this information by sending it to the server.

[0610] Step 5:

[0611] The server combines the analyzed textual information with the cooking equipment information provided by the user and inputs it as a prompt into the generating AI model. It generates prompt sentences such as, "I have tomatoes, eggs, and milk. Please generate a recipe that can be used with a 3-burner stove and an oven." Based on this, the server causes the AI ​​to generate a recipe that takes into account nutritional balance, cooking time, and other factors. The input consists of ingredient information and cooking equipment information, and multiple recipes are output by the AI.

[0612] Step 6:

[0613] The server sends the generated recipe information to the terminal. The input is recipe data generated by the AI, and the server outputs the recipe information sent to the terminal.

[0614] Step 7:

[0615] The terminal analyzes the received recipe information and displays it on the screen in a user-friendly format. The input is recipe information from the server, and the terminal outputs cooking instructions that are easy to understand visually. The user can then proceed with cooking based on this information.

[0616] (Application Example 1)

[0617] 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".

[0618] In modern life, reducing food waste and planning meals efficiently are crucial. Commercial facilities, in particular, are required to provide a satisfying cooking experience using ingredients purchased by customers. However, it is difficult for consumers to find appropriate recipes on the spot when purchasing ingredients. Therefore, there is a need for a system that effectively suggests cooking methods based on the purchased ingredients.

[0619] 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.

[0620] In this invention, the server includes means for analyzing visual information and obtaining items within the visual data as text data; means for generating cooking procedures and meal configurations based on the acquired text data and information on cooking-related equipment; and means for providing the generated cooking procedures and meal configurations to the user. This makes it possible to suggest appropriate recipes in real time at commercial facilities based on purchased ingredients.

[0621] "Analyzing visual information to obtain text data for items within visual data" refers to the process of recognizing the names and characteristics of items from video data obtained by imaging devices such as cameras, and converting that information into text format.

[0622] "Generating cooking procedures and meal compositions based on acquired text data and information on cooking-related equipment" refers to the process of constructing cooking procedures and menus based on ingredient information represented as text data and cooking equipment information entered by the user.

[0623] "Providing users with the generated cooking procedures and meal composition" refers to the act of displaying or transmitting information about the procedures and menus for the created dishes so that users can easily view them.

[0624] "Suggesting cooking methods based on items in visual data" refers to analyzing ingredients found in captured video data and suggesting new cooking ideas to users that utilize them.

[0625] To implement this invention, it is necessary to build a system that analyzes visual information using a user-owned terminal and a cloud server, and proposes cooking methods in real time based on the acquired data. This system mainly consists of the following hardware and software.

[0626] The user takes a picture of the purchased food items with their device (e.g., a smartphone). This device has a camera function, and the image data is immediately sent to a cloud server. On the server side, image processing software such as OpenCV or Tesseract OCR is used to extract the items from the image data as text data. This obtained text data is then input into a generative AI on the cloud, such as an AI model incorporating ChatGPT.

[0627] The server uses this text data to generate cooking instructions and meal compositions based on the purchased ingredients. By using prompts that take into account the user's cooking environment and purchased ingredients, the generating AI model can provide specific and practical cooking suggestions. For example, a prompt such as, "Tomatoes, basil, and mozzarella cheese have been purchased. Please suggest a simple and delicious recipe using these ingredients," might be used.

[0628] Finally, the generated cooking instructions and dish composition are sent back to the user's device and displayed. This allows the user to quickly learn about various cooking methods based on the purchased ingredients, enabling them to cook efficiently.

[0629] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0630] Step 1:

[0631] The user takes a picture of the purchased food items using a device such as a smartphone. The input is image data of the food items obtained through the device's camera. The output is this image data. The device sends this data to a cloud server.

[0632] Step 2:

[0633] The server analyzes the received image data using OpenCV or Tesseract OCR. This process extracts the names and characteristics of items within the visual data as text data. The input is the image data obtained in step 1, and the output is text data representing the names and characteristics of the items (food items).

[0634] Step 3:

[0635] The server uses a generation AI model based on the acquired text data to generate cooking methods. In this process, user information regarding cooking equipment is used as a prompt. The input consists of the text data obtained in step 2 and the user's cooking equipment information, while the output is data on the generated cooking procedure and dish composition. For example, data is generated based on a prompt such as, "Tomatoes, basil, and mozzarella cheese have been purchased. Please suggest a simple and delicious recipe using these ingredients."

[0636] Step 4:

[0637] The server sends the generated cooking procedure and ingredient data to the user's terminal. The input is the data generated in step 3, and the output is cooking procedure and ingredient information that can be displayed on the user's terminal. The user can receive this information as displayed content.

[0638] 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.

[0639] This invention relates to an information processing system that uses an emotion engine in a cooking support system to generate cooking procedures and dish compositions that correspond to the user's psychological state. This system integrates image analysis technology, generation AI, and emotion recognition technology to enable more personalized and efficient food management and cooking at home.

[0640] Specifically, the user takes pictures of the food in their refrigerator using a smartphone or other device. The device sends these pictures to a server and also collects information about the user's cooking equipment, such as the number of burners and ovens, as well as voice and facial expression data. This data is analyzed by an emotion engine to estimate the user's current emotional state.

[0641] The server uses OCR (Optical Character Recognition) technology to convert image data into text and create a list of ingredients. Furthermore, an AI model, using information on cooking equipment and emotional state data, generates recipes that match the user's emotional state while considering nutritional balance and cooking efficiency. In this process, for example, if the user is stressed, recipes containing ingredients that help alleviate stress will be prioritized.

[0642] The generated recipe information is sent from the server to the terminal and displayed in a format viewable by the user. Based on this recipe, the user can cook to reduce stress.

[0643] For example, if a user is feeling stressed because they have vegetables and cheese in their refrigerator, the server will generate a recipe for vegetable gratin using ingredients known to have a relaxing effect and provide it to the user. In this way, users can easily enjoy a meal that suits their current mood.

[0644] This invention enriches the home cooking experience, improves user satisfaction, and makes it possible to provide personalized meals while reducing food waste.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The user takes photos of the food inside the refrigerator using a smartphone or dedicated device. In addition, they input information about cooking appliances (such as the number of burners and ovens), and voice and facial expression data are collected to recognize emotions.

[0648] Step 2:

[0649] The device sends captured image data, input information, and emotion-related data to the server. This data is organized in JSON format and transmitted via a secure protocol (e.g., HTTPS).

[0650] Step 3:

[0651] The server processes the received image data using an OCR API, analyzes the text information within the image, and obtains the ingredient list as text data.

[0652] Step 4:

[0653] The emotion engine analyzes the voice and facial expression data received by the server to estimate the user's current emotional state. This emotional state is categorized into categories such as stress, relaxation, and joy.

[0654] Step 5:

[0655] The server inputs a list of ingredients from OCR, information on cooking equipment, and emotional states estimated by the emotion engine into a generative AI model. The generative AI model considers this data to generate a cooking recipe and procedure that is appropriate for nutritional balance, cooking efficiency, and the emotional state.

[0656] Step 6:

[0657] The server receives recipe information generated from the AI ​​model and sends it to the device. This information is displayed on the device's UI so that the user can check it immediately.

[0658] Step 7:

[0659] The user checks the recipe displayed on their device and begins cooking. The presented recipe uses ingredients that match the user's mood and the cooking procedure is efficiently structured, resulting in a highly satisfying cooking experience.

[0660] (Example 2)

[0661] 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".

[0662] Conventional cooking support systems often lacked personalized suggestions tailored to the user's emotional state and individual cooking environment, resulting in a lower quality cooking experience. Furthermore, they failed to provide appropriate recipes based on the user's emotional state, hindering improvements in psychological satisfaction.

[0663] 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.

[0664] In this invention, the server includes means for analyzing an image and obtaining items within the image as text data; means for analyzing the obtained text data, information about cooking equipment, and the user's emotional state to generate cooking procedures and dish compositions that take nutritional balance and cooking efficiency into consideration; and means for providing the generated cooking procedures and dish compositions to the user. This makes it possible to propose personalized cooking procedures that are tailored to the individual cooking environment and the user's emotional state.

[0665] "Analyzing an image and obtaining the objects within it as text data" refers to the process of using image recognition technology to identify objects contained within an image and convert them into corresponding text data.

[0666] "Information regarding cooking equipment" refers to data on the number, types, and performance of cooking utensils and equipment available to the user.

[0667] "Analyzing the user's emotional state" refers to the process of estimating the user's emotions and psychological state based on data such as voice and facial expressions.

[0668] "Generating cooking procedures and dish compositions that consider nutritional balance and cooking efficiency" means determining appropriate and efficient cooking methods and dish compositions by considering the nutrients in the ingredients and the time and procedures required for cooking.

[0669] "Providing the generated cooking procedure and dish composition to the user" refers to the process of the server sending the recipe information it has created to the user's terminal in a format that the user can view and presenting it to the user.

[0670] This invention is an information processing system that provides cooking support tailored to the user's emotional state and cooking environment. This system is implemented using a terminal, a server, and a generative AI model. Specific embodiments are described below.

[0671] The user takes pictures of the food inside the refrigerator using a device such as a smartphone or tablet. The device sends the captured image data to a server and also collects information about the user's cooking equipment, as well as voice and facial expression data.

[0672] The server uses image analysis technology to perform optical character recognition (OCR) on received image data and generates text data for ingredients. Furthermore, the server utilizes emotion recognition technology to analyze voice and facial expression data transmitted from the terminal and estimate the user's current emotional state. This information enables more personalized cooking suggestions.

[0673] Next, the server uses a generative AI model to generate the optimal cooking recipe based on the ingredient list, information about cooking equipment, and the user's emotional state. The generated recipe takes nutritional balance and cooking efficiency into consideration, while also being tailored to the user's emotional state. For example, if the user is feeling stressed, a dish using ingredients with relaxation effects will be recommended.

[0674] Finally, the generated recipe information is sent from the server to the terminal and displayed to the user. The user can then cook based on the displayed recipe and enjoy a meal that suits their mood.

[0675] For example, if a user is feeling stressed because they have cabbage and cheese in their refrigerator, the server inputs a prompt into the AI ​​model such as, "Please suggest a stress-relieving recipe using cabbage and cheese." As a result, the server generates a recipe for vegetable gratin, which is said to have a relaxing effect, and provides it to the user. This system allows users to easily create dishes that suit their emotions and cooking environment at the time.

[0676] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0677] Step 1:

[0678] The user takes pictures of the food in the refrigerator using the camera on their smartphone or tablet. The device sends the captured image data to the server. The input is image data including the food in the refrigerator, and the output is the data transferred to the server. Specifically, the camera app on the device captures the image and uploads that data to the server in real time.

[0679] Step 2:

[0680] The terminal collects information about the cooking equipment the user owns, as well as the user's voice and facial expression data. This information is transmitted to a server. The inputs are the number and type of cooking equipment, and the user's voice and facial expressions, while the output is the data transferred to the server. Specifically, the terminal's sensor functions are used to collect relevant data using voice recognition and facial recognition technology, and then transmitted to the server.

[0681] Step 3:

[0682] The server analyzes the received image data using OCR (Optical Character Recognition) technology and converts the food items in the image into text data. The input is image data, and the output is a text-based list of food items. Specifically, OCR software is used to analyze labels and text information in the image, and the results are registered in a database.

[0683] Step 4:

[0684] The server uses collected voice and facial expression data to apply emotion recognition technology and determine the user's emotional state. The input is voice and facial expression data, and the output is the estimated emotional state. Specifically, it analyzes the data using an emotion analysis algorithm and extracts emotional parameters such as stress and happiness.

[0685] Step 5:

[0686] The server uses a generative AI model to integrate a text-based list of ingredients, cooking equipment information, and emotional state to generate a cooking recipe tailored to the user. The input is the ingredient list, cooking equipment information, and emotional state, and the output is the generated cooking recipe. Specifically, the generative AI is given a prompt such as "Please suggest a stress-relieving recipe using cabbage and cheese," and the AI ​​outputs the optimal recipe.

[0687] Step 6:

[0688] The server sends the generated recipe information to the terminal, where it is displayed in an easy-to-read format for the user. The input is the generated cooking recipe, and the output is the display of the recipe on the terminal. Specifically, the server formats the recipe information and provides it to the user through the application.

[0689] Step 7:

[0690] The user cooks according to the provided recipe. The server sends additional advice and timers to the terminal as needed during cooking. The input is the user's cooking progress, and the output is timing instructions and hints. Specifically, the terminal monitors the cooking status in real time and supports the cooking process based on information from the server.

[0691] (Application Example 2)

[0692] 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".

[0693] In recent years, consumer needs have diversified, and restaurants in particular are demanding personalized services that cater to customers' psychological states and preferences. However, appropriately recognizing the emotional state of each individual customer and suggesting dishes based on that is a significant burden for businesses. There is a need for solutions to this challenge.

[0694] 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.

[0695] In this invention, the server includes means for analyzing an image and obtaining the objects within the image as text information; means for generating a cooking flow and dish structure based on the acquired text information and information on cooking-related equipment; and means for analyzing the user's emotional state and generating a cooking flow and dish structure corresponding to that state. This makes it possible to suggest personalized dishes that respond to the customer's emotions.

[0696] "Means for analyzing images and obtaining objects within those images as text information" refers to technologies that process captured image data using analysis techniques to convert objects and characters visible in the image into text-based information.

[0697] "Means for generating cooking procedures and dish structures based on acquired text information and information on cooking-related equipment" refers to a system that uses converted text data and information on cooking equipment owned by the user to derive efficient and optimal cooking procedures and specific arrangements for dishes.

[0698] "Means for analyzing the emotional state of users and generating a cooking flow and dish structure that corresponds to that state" refers to a system that analyzes the user's emotional state to determine their psychological state and adaptively construct a cooking plan in order to suggest dishes that match that state.

[0699] "Means for presenting the generated cooking process and dish structure to the user" refers to a method for showing the cooking example or dish design ultimately derived by the system in a way that is easy for the user to understand, via a terminal or other device.

[0700] In this invention, the user first initiates an experience at a target physical store using their smartphone. The user's device has an application with a camera function installed, which allows them to take images of the target object. The images taken by the camera are immediately sent to the server. The server uses image processing software and libraries such as OpenCV to analyze the object in the image and obtain the information as text.

[0701] Next, the analyzed sentiment data and acquired text information are used with a generative AI model to generate the optimal cooking flow and dish structure for the user. This process also takes into account the user's cooking-related equipment data and available ingredient information, making it possible to suggest menus tailored to individual needs.

[0702] Furthermore, information tailored to the user's emotional state and cooking progress is stored and analyzed on a cloud server and then pushed to the user's device in an appropriate format. Specifically, for example, if the user's emotional state is analyzed as "stressed," a special recipe using ingredients known to alleviate stress will be generated and displayed on the smartphone.

[0703] This allows users to easily select and prepare dishes that suit their emotional state, within a system that suggests dishes tailored to their individual cooking abilities and equipment. An example of a prompt message is shown below.

[0704] Example prompt: "The customer's emotion is set to 'tired.' Please suggest a relaxing meal menu using the available ingredients. Available ingredients are 'chicken, noodles, carrots, celery, and parsley.'"

[0705] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0706] Step 1:

[0707] The user uses their device to take pictures of their face and food items inside the store. This provides image information as input data. The camera function of the user's device is used, and the images are immediately sent to the server.

[0708] Step 2:

[0709] The server first uses OpenCV to detect faces in an image and then analyzes the emotional state using the Emotion API. The input is a user's face image, and the output is emotional data. In parallel, it uses OCR technology to convert images of food ingredients into text data and outputs it as a list of ingredients.

[0710] Step 3:

[0711] The server uses acquired emotion data and ingredient lists as input data, and generates cooking suggestions appropriate to the emotions using a generative AI model. Information on the cooking equipment the user can use is also referenced. The output is a customized cooking recipe.

[0712] Step 4:

[0713] The server sends the generated cooking recipe to the user's terminal. The user receives the recipe information via the terminal and can cook based on the displayed information. Through this process, the recipe is provided as visual information as output.

[0714] 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.

[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 the specific processing may also be performed by the robot 414.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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."

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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 as being incorporated by reference.

[0735] The following is further disclosed regarding the embodiments described above.

[0736] (Claim 1)

[0737] [Methods for analyzing images and obtaining the objects within the images as text data,

[0738] [Means for generating cooking procedures and dish composition based on acquired text data and information on cooking-related equipment,

[0739] [Means for providing users with the generated cooking procedures and the composition of the dishes,

[0740] A system that includes this.

[0741] (Claim 2)

[0742] [The system according to claim 1, in which information about cooking-related equipment is entered by the user.

[0743] (Claim 3)

[0744] [The system according to claim 1, which generates cooking procedures and dish composition taking nutritional balance into consideration when the items in the image are ingredients.

[0745] "Example 1"

[0746] (Claim 1)

[0747] [Methods for analyzing an image to obtain textual information about objects within the image, and removing noise through preprocessing,

[0748] [A means of generating multiple recipes that take into account nutritional balance and cooking time, using a generation AI model based on acquired text information and information on cooking-related equipment entered by the user,

[0749] [Methods for sending generated recipe information to a terminal and displaying it to the user,

[0750] A system that includes this.

[0751] (Claim 2)

[0752] [The system according to claim 1, wherein the user inputs cooking conditions available for cooking into a terminal.

[0753] (Claim 3)

[0754] [The system according to claim 1, which uses character recognition technology to identify that items in an image are food ingredients, and then generates cooking procedures and a dish composition while considering nutritional balance.

[0755] "Application Example 1"

[0756] (Claim 1)

[0757] [Methods for analyzing visual information and obtaining items within visual data as text data,

[0758] [Means for generating cooking procedures and meal composition based on acquired text data and information on cooking-related equipment,

[0759] [Means of providing users with the generated cooking procedures and meal composition,

[0760] [Methods for proposing cooking methods based on items in visual data,

[0761] A system that includes this.

[0762] (Claim 2)

[0763] [The system according to claim 1, in which information about cooking-related equipment is entered by the user.

[0764] (Claim 3)

[0765] [The system according to claim 1, which, when an item in the visual data is an ingredient, generates a cooking plan and meal composition considering nutritional balance, and provides a cooking method that makes use of the purchased ingredients.

[0766] "Example 2 of combining an emotion engine"

[0767] (Claim 1)

[0768] [Methods for analyzing images and obtaining the objects within the images as text data,

[0769] [Means for analyzing acquired text data, information on cooking equipment, and the emotional state of users to generate cooking procedures and dish compositions that take nutritional balance and cooking efficiency into consideration,

[0770] [Means for providing users with the generated cooking procedures and the composition of the dishes,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] [The system according to claim 1, in which information regarding cooking equipment and data for emotion estimation are input by the user, and a cooking procedure is proposed according to the user's emotional state.

[0774] (Claim 3)

[0775] [The system according to claim 1, which generates cooking procedures and dish composition taking into account nutritional balance and the emotional state of the user when the items in the image are consumables.

[0776] "Application example 2 of combining emotional engines"

[0777] (Claim 1)

[0778] [Methods for analyzing images and obtaining objects within the images as text information,

[0779] [Means for generating the cooking flow and the structure of a dish based on acquired text information and information on cooking-related equipment,

[0780] [Means for analyzing the emotional state of the user and generating a cooking process and dish structure corresponding to that state,

[0781] [Means for presenting the generated cooking process and the structure of the dish to the user,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] [The system according to claim 1, in which information about cooking-related equipment is entered by the user.

[0785] (Claim 3)

[0786] [The system according to claim 1, which generates a cooking flow and dish structure considering nutritional balance when the object in the image is an ingredient, and further generates a cooking flow and dish structure that is tailored to the user's emotional state. [Explanation of Symbols]

[0787] 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 of analyzing an image and obtaining the objects within the image as text data, A means for generating cooking procedures and dish composition based on acquired text data and information on cooking-related equipment, A means of providing the user with the generated cooking procedure and the composition of the dish, A system that includes this.

2. The system according to claim 1, wherein information about cooking-related equipment is entered by the user.

3. The system according to claim 1, which generates cooking procedures and dish composition taking nutritional balance into consideration when the items in the image are ingredients.

Images