System

Abstract

A system is provided.SOLUTION: A system comprising a camera means for a user to photograph a raw material name and a nutrient composition display of a food package, a means for converting image data photographed by the camera means into text data using a character recognition technology, an AI analysis means for analyzing the text data and generating an optimal diet proposal, an exercise plan, and a health risk notification based on a health condition of each user, and a means for presenting the generated advice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, the selection of processed foods and daily health management are becoming increasingly important. There is a particular demand for personalized meal suggestions and exercise plans tailored to individual health conditions and food preferences. However, current systems require users to manually check and input the ingredient names and nutritional information printed on food packages, which is time-consuming and often requires specialized knowledge. As a result, users often lack sufficient support when choosing the right foods and managing their health. [Means for solving the problem]

[0005] To address the above-mentioned challenges, the present invention provides the following system. It includes a camera means for a user to photograph the ingredient names and nutritional information on food packages, and a means for converting the image data photographed by the camera means into text data using character recognition technology. It also includes an AI analysis means for analyzing the text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition. It also includes a means for presenting the generated advice to the user. It also includes a means for referencing an individual profile of the user, including their health condition, allergy information, and past dietary history, and a means for linking data with other healthcare services and wearable devices, thereby achieving more comprehensive and personalized health management support.

[0006] "User" refers to an individual or organization that uses this system.

[0007] "Camera means" refers to a camera device that a user uses to take a picture of a food package.

[0008] "Image data" refers to an image of a food package taken by a camera means.

[0009] "Character recognition technology" refers to the technology of extracting text information from image data, specifically optical character recognition (OCR) technology.

[0010] "Text data" refers to character information converted from image data using character recognition technology.

[0011] "AI analysis means" refers to means that use analysis algorithms and machine learning models to generate optimal dietary suggestions, exercise plans, and health risk notifications for users based on text data.

[0012] "User's health condition" refers to health-related information such as the user's current physical condition, past health history, and allergy information.

[0013] "Meal suggestions" refers to providing recommended food combinations and menus to help users lead a healthy diet.

[0014] "Exercise plan" refers to a plan that indicates the type and frequency of exercise recommended for improving a user's health or achieving a specific health goal.

[0015] "Health risk notification" refers to a notification that detects and alerts the user to potential health risks associated with consuming food.

[0016] "Individual profile" refers to individual information such as the user's health condition, allergy information, and past dietary history.

[0017] "Healthcare services" refers to systems and platforms that provide services such as health management and disease prevention.

[0018] A "wearable device" is a device that can be worn by the user and can collect health and exercise data.

[0019] "Fusion" refers to different devices and services sharing data and interacting with each other.

[0020] "System" refers to a set of equipment and software that allows users to photograph food packaging and provide analysis and advice. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The present invention relates to a system that allows users to take a photo of the names of ingredients and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes a camera, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[0043] Program Overview

[0044] Reading package information

[0045] The user launches the smartphone application and takes a photo of the food package with the camera. The device then converts the captured image data into text using character recognition technology. This OCR process extracts the names of ingredients and nutritional information.

[0046] Data transmission and analysis

[0047] The converted text data is sent from the device to a server, which analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[0048] Generating and presenting personalized advice

[0049] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. Users can check this information through the app and use it to manage their daily health.

[0050] Integration with other healthcare services and wearable devices

[0051] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[0052] Specific examples

[0053] Food packaging photography and analysis

[0054] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0055] Data transmission and AI analysis

[0056] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions (e.g., high blood pressure risk) and dietary history previously entered by the user into the app. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[0057] Providing advice and guidelines

[0058] The generated advice is sent to the device and displayed to the user on the app. The user can then follow the advice and, for example, select additional low-salt foods, taking into account the overall balance of their diet, and make purchasing decisions.

[0059] Collaboration with wearable devices

[0060] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[0061] As described above, the present invention aims to enable users to easily and effectively manage their own health, and provides specific means and a system for achieving this.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[0065] Step 2:

[0066] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[0067] Step 3:

[0068] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[0069] Step 4:

[0070] The device sends the converted text data to the server, which then sends the JSON formatted data via the API using an HTTP request.

[0071] Step 5:

[0072] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[0073] Step 6:

[0074] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[0075] Step 7:

[0076] The server retrieves the user's individual profile (health status, allergy information, past dietary history, etc.) from a database.

[0077] Step 8:

[0078] The server generates meal suggestions, exercise plans, and health risk notifications based on the user's individual profile and analytical data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[0079] Step 9:

[0080] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[0081] Step 10:

[0082] The device displays the advice received to the user, visually displaying the advice in the app's UI and providing specific suggestions and risk notifications.

[0083] Step 11:

[0084] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[0085] Step 12:

[0086] The device synchronizes the user's latest dietary data and calorie intake information with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[0087] Example 1

[0088] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0089] There is a demand for a system that can easily digitize the ingredient names and nutritional information printed on food packages, and provide optimal meal suggestions, exercise plans, and health risk notifications based on the user's health condition and past dietary history. Furthermore, it is desirable for data to be easily linked with other healthcare services and wearable devices in order to more effectively manage the user's health. However, it has been difficult to meet all of these requirements with conventional methods.

[0090] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0091] In this invention, the server includes an imaging device that allows a user to photograph the ingredient names and nutritional information on food packages; a means for converting the image data captured by the imaging device into text data using character recognition technology; a means for analyzing the converted text data using AI analysis and comparing it with nutritional information and past diet history in a database; a means for generating optimal diet suggestions, exercise plans, and health risk notifications based on the user's individual health status; a means for presenting the generated advice to the user; and a means for linking data with other healthcare services and wearable devices. This allows for fast and accurate digitization of information from food packages, enabling the provision of personalized, specific, and effective health management advice to users. Furthermore, data linking with different healthcare services and wearable devices enables comprehensive health management.

[0092] "Imaging means" refers to a device for photographing the ingredient names and nutritional information on food packaging.

[0093] "Character recognition technology" refers to technology that analyzes image data captured by an imaging device and converts it into text data.

[0094] "Text data" refers to character information extracted from image data using character recognition technology.

[0095] "AI analysis means" refers to a function that includes artificial intelligence technology for analyzing the converted text data and comparing it with nutritional information and past dietary history in a database.

[0096] A "database" refers to a system that systematically stores information such as nutritional information and past dietary history and manages it in an accessible form.

[0097] "Dietary Suggestion" refers to advice that provides specific information to recommend optimal food choices to a user.

[0098] An "exercise plan" refers to an appropriate exercise schedule and activity content suggested based on the user's health condition and goals.

[0099] "Health risk notification" refers to notifications that warn users of potential health risks based on their health status and the foods they have consumed.

[0100] The "means for presenting advice" refers to a method or tool for displaying the generated advice to the user and encouraging appropriate action.

[0101] "Healthcare services" refers to all services related to health management, including, for example, medical institutions and fitness programs.

[0102] A "wearable device" refers to an electronic device that can be worn by a user and that can measure and record health and activity data.

[0103] A "generative AI model" refers to an artificial intelligence algorithm or system that generates appropriate outputs given inputs.

[0104] A "prompt" refers to a specific instruction or question-style sentence that is input into a generative AI model.

[0105] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes an imaging means, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[0106] Hardware and Software Use

[0107] The user launches the smartphone application and takes a picture of the food package using the imaging device (smartphone camera). The captured image data is converted into text data using the device's internal character recognition technology (OCR). The converted text data is then sent from the device to the server.

[0108] On the server side, the text data is analyzed using AI analysis tools and compared with nutritional information in the database and the user's past dietary history. Based on the user's health condition and individual profile, meal suggestions, exercise plans, and health risk notifications are generated. The generated advice is then sent back to the device and presented to the user.

[0109] Collaboration with other healthcare services and wearable devices is also an important factor. The system automatically synchronizes the user's exercise data and calorie intake information with the wearable device, supporting comprehensive health management.

[0110] Specific examples

[0111] A user picks up a sandwich they are about to purchase at a convenience store and launches an application on their smartphone. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0112] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions the user has previously entered into the app (e.g., whether they are at risk of high blood pressure) and their past dietary history. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[0113] The generated advice is displayed on the user's smartphone. The user follows this advice, for example, by selecting additional low-salt foods and making purchase decisions while taking into account the overall balance of their diet.

[0114] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[0115] Prompt Sentence Examples

[0116] By feeding the following prompts into the generative AI model, more specific and personalized advice can be given to the user:

[0117] "The food consumed was juice, with 20.0g of carbohydrates, 15mg of vitamin C, and 100kcal of calories. What dietary suggestions should be made for this food to a user at high risk of diabetes?"

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1: Launching the Application

[0120] explanation

[0121] The user taps on the smartphone to launch the application.

[0122] input

[0123] The user taps the application icon on their smartphone.

[0124] output

[0125] The application will launch and the home screen will be displayed.

[0126] Specific actions

[0127] A user's finger touches an application icon on the smartphone screen and taps the icon to launch the application.

[0128] Step 2: Photograph the packaging

[0129] explanation

[0130] The user takes a photo of the food package using the app's camera function.

[0131] input

[0132] The user selects the application's camera function and takes a photo of the food package using their smartphone.

[0133] output

[0134] An image of the food packaging is saved to the device.

[0135] Specific actions

[0136] The user selects the camera function button, points the smartphone camera at the food package, and taps the shutter button.

[0137] Step 3: Recognize text using OCR

[0138] explanation

[0139] The device processes the captured image data using OCR technology and converts it into text data.

[0140] input

[0141] Image data of food packaging.

[0142] output

[0143] Text data of ingredient names and nutritional information.

[0144] Specific actions

[0145] The device's internal OCR engine analyzes the image data and converts the recognized character information into text. For example, text data such as "Carbohydrates 15.0g, Salt 0.5g, Calories 200kcal" is generated.

[0146] Step 4: Sending data

[0147] explanation

[0148] The terminal transmits the converted text data to the server.

[0149] input

[0150] The converted text data.

[0151] output

[0152] The text data is sent to the server.

[0153] Specific actions

[0154] The device uses Wi-Fi or mobile data to send the text data extracted by OCR to the server.

[0155] Step 5: Analyze the data

[0156] explanation

[0157] The server analyzes the received data and compares it with the user profile and nutritional information in the database.

[0158] input

[0159] Text data and user profile data received by the server.

[0160] output

[0161] Generate optimal dietary suggestions, exercise plans, and health risk notifications.

[0162] Specific actions

[0163] The server's AI analysis tools compare the text data with a database and generate meal suggestions and exercise plans based on the user's health status.

[0164] Step 6: Generate personalized advice

[0165] explanation

[0166] The server generates individual advice and sends it to the terminal.

[0167] input

[0168] Analytics and user profile data.

[0169] output

[0170] The generated personalized advice is sent to the terminal.

[0171] Specific actions

[0172] The server generates advice and sends the data to the user's device. For example, the advice generated might be, "This food is high in salt, so eat it with low-salt foods."

[0173] Step 7: Providing advice

[0174] explanation

[0175] The terminal presents the received advice to the user.

[0176] input

[0177] Personalized advice received by the device.

[0178] output

[0179] Advice is displayed to the user within the application.

[0180] Specific actions

[0181] The device displays the advice received from the server within the application. When the user opens the app, the advice is displayed in a pop-up format.

[0182] Step 8: Integrate with wearable devices

[0183] explanation

[0184] The device syncs the food data to the wearable device.

[0185] input

[0186] User's dietary data.

[0187] output

[0188] Calorie intake and nutritional balance data synced to wearable devices.

[0189] Specific actions

[0190] The device transmits the user's meal data via Bluetooth or Wi-Fi to the wearable device, which updates the data in real time.

[0191] (Application example 1)

[0192] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0193] Conventional food delivery services often fail to provide users with sufficient information about the nutritional content and ingredients of the food they provide, which can lead to health risks and inappropriate dietary choices. Furthermore, the inability to provide real-time dietary suggestions based on individual health conditions and allergy information makes it difficult for users to manage their health.

[0194] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0195] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, an AI analysis means for analyzing the text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition, a means for presenting the generated advice to the user, and a means for photographing the nutritional components and ingredient names of the food the user is about to order with the camera and providing advice and health risk notifications. This allows users to select appropriate meals based on their individual health condition when ordering food, making health management easier.

[0196] "User" refers to the person who photographs and orders food packages.

[0197] "Food packaging" refers to packaging that lists the names of food ingredients and nutritional information.

[0198] "Ingredient name" refers to the name of the ingredient that makes up the food.

[0199] "Nutritional information labeling" indicates nutritional information such as calories, protein, fat, carbohydrates, vitamins, and minerals contained in food.

[0200] The term "camera means" refers to a device for capturing image data.

[0201] "Character recognition technology" refers to technology for converting image data into text data.

[0202] "Text data" refers to character information obtained by character recognition technology.

[0203] "AI analysis means" refers to a device or program for analyzing data using artificial intelligence.

[0204] "Advice" refers to guidance or suggestions provided to a user, such as dietary suggestions, exercise plans, and health risk notifications.

[0205] "Healthcare services" refers to various services aimed at health management.

[0206] A "wearable device" is a wearable electronic device that monitors health status and performs other functions.

[0207] "Food delivery" refers to a service that delivers ordered food to users.

[0208] "Means of providing advice and health risk notifications" refers to a function that provides health information to users based on the nutritional content and ingredient names of the photographed food.

[0209] This system photographs and analyzes the names of ingredients and nutritional information on food packages, and provides users with appropriate dietary suggestions and health risk notifications. This system mainly consists of the following main components:

[0210] Main Components

[0211] 1. Camera Means

[0212] This is a means for users to take pictures of food packages using a camera such as a smartphone.

[0213] 2. Character recognition technology

[0214] This is a technology used to convert captured image data into text data. Specifically, Optical Character Recognition (OCR) technology is used.

[0215] 3. AI analysis means

[0216] This is an AI technology that analyzes the converted text data and generates optimal diet suggestions, exercise plans, and health risk notifications based on the user's health status.

[0217] 4. How to access your user profile

[0218] It is a means of referencing a user's individual profile, including their health status, allergy information, and past dietary history.

[0219] 5. Collaboration Methods

[0220] It is a means of linking data with other healthcare services and wearable devices.

[0221] 6. How to present generated advice

[0222] This is a means for presenting the generated advice to the user. The user is notified via an application on a smartphone or the like.

[0223] System program processing

[0224] The processing flow in this system is explained in natural language.

[0225] Camera photography and character recognition

[0226] Users use their smartphone camera to take a photo of the ingredients and nutritional information on food packages. The image data is then converted into text using OCR technology. The software used includes Python, PIL (Python Imaging Library), and pytesseract.

[0227] Data transmission and AI analysis

[0228] The converted text data is sent to a server via a smartphone application. Inside the server, this text data is analyzed using AI analysis tools. Specifically, optimal meal suggestions and health risk notifications are generated based on the user's health condition and dietary history.

[0229] Personalized advice and displays

[0230] The advice generated by the server is sent in real time to a smartphone application and displayed to the user, allowing the user to easily select appropriate dietary choices and exercise plans.

[0231] Collaboration with wearable devices

[0232] Additionally, the generated dietary data and exercise plans are automatically synchronized with other healthcare services and wearable devices.

[0233] Specific examples

[0234] When a user uses a food delivery service, they take a photo of the food package they are ordering with their smartphone. The captured image is converted into text data using OCR technology, and that data is sent to a server. The server then uses AI analysis to generate personalized health advice based on the user's health condition and past dietary history. For example, a message such as "This item is high in calories, so we recommend choosing a low-calorie option for your next meal" is displayed.

[0235] Prompt Sentence Examples

[0236] "I would like to develop an application that allows users to take a photo of the nutritional information on food packaging, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. Please write the following program in Python. The input is an image of the food package, and the nutritional information is extracted using OCR, sent to the server for analysis, and the generated advice is presented to the user."

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is the image captured by the camera. This image data becomes the first input to the system. The specific operation is for the user to turn on the smartphone, open the camera, focus on the food package, and press the shutter button.

[0240] Step 2:

[0241] The device converts the captured image data into text data using optical character recognition (OCR). The input is the captured image data, and the output is text data including the names of ingredients and nutritional information. The specific software used here is Python, the PIL library, and pytesseract.

[0242] Step 3:

[0243] The device sends the converted text data to the server. The input is the text data obtained by the OCR process, and the output is the text data sent to the server. The specific operation is that the smartphone application generates an HTTP request and POSTs the text data to the server.

[0244] Step 4:

[0245] The server analyzes the received text data using AI analysis methods. The input is the text data sent to the server, and the output is advice data including optimal dietary suggestions and health risk notifications for the user. This analysis uses machine learning models and database matching.

[0246] Step 5:

[0247] The server sends the generated advice data to the terminal. The input is the advice data generated by AI analysis, and the output is the advice message sent to the smartphone. The specific operation is that the server returns the advice data to the terminal as an HTTP response.

[0248] Step 6:

[0249] The terminal notifies the user of the received advice data. The input is the advice data sent from the server, and the output is an advice message displayed to the user. The specific operation is that the smartphone application displays the advice message on the user interface.

[0250] Step 7:

[0251] The device automatically synchronizes the generated dietary data and exercise plan with wearable devices and other healthcare services. The input is advice data sent from the server, and the output is data synchronized with the wearable device or healthcare service. Specifically, the smartphone transmits the data to other devices or services via Bluetooth or an internet connection.

[0252] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0253] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides users with appropriate meal suggestions, exercise plans, and health risk notifications. It also combines an emotion engine that recognizes the user's emotions to provide even more personalized advice.

[0254] Program Overview

[0255] Reading and parsing package information

[0256] The user launches the smartphone application and takes a picture of the food package using the camera. The device then converts the captured image data into text using optical character recognition (OCR). This OCR process extracts information such as the names of ingredients and nutritional information.

[0257] Use of emotion engine

[0258] When a user operates the app, an emotion engine works to recognize the user's emotions in real time through sensors such as the camera and microphone. The emotion engine analyzes facial expressions, tone of voice, and choice of words to determine the user's current emotional state.

[0259] Data transmission and AI analysis

[0260] The converted text data and the user's emotional state data are sent from the device to a server. The server analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[0261] Generating and presenting personalized advice

[0262] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. The emotion engine also takes into account the user's emotional state, providing more appropriate advice. For example, if the user is feeling stressed, the system can suggest foods with a relaxing effect and an exercise plan to help them relax.

[0263] Integration with other healthcare services and wearable devices

[0264] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[0265] Specific examples

[0266] Food packaging photography and analysis

[0267] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0268] Emotion engine operation and data collection

[0269] When a user uses the app, the emotion engine analyzes the user's facial expressions and tone of voice to recognize when the user is feeling stressed. This emotion data is collected in real time and sent to the server along with the text data.

[0270] Data transmission and AI analysis

[0271] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[0272] Providing advice and guidelines

[0273] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[0274] Collaboration with wearable devices

[0275] The device can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[0276] The present invention aims to enable users to manage their own health simply and effectively. As a specific means for achieving this, the invention provides more personalized health management support by combining an emotion engine.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[0280] Step 2:

[0281] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[0282] Step 3:

[0283] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[0284] Step 4:

[0285] The device sends text data to the server. The data is sent in JSON format via an API using an HTTP request.

[0286] Step 5:

[0287] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[0288] Step 6:

[0289] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[0290] Step 7:

[0291] The emotion engine captures the user's facial expressions and voice data through the smartphone's camera and microphone, and analyzes the user's emotions in real time. For example, it analyzes changes in facial expressions and tone of voice to identify the user's emotional state.

[0292] Step 8:

[0293] The extracted user emotion data is sent from the terminal to the server together with the text data.

[0294] Step 9:

[0295] The server analyzes the user's emotional data and compares it with the user's individual profile stored in a database, taking into account the user's health condition, allergy information, and past dietary history.

[0296] Step 10:

[0297] The server generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual profile, text data, and emotional data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[0298] Step 11:

[0299] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[0300] Step 12:

[0301] The device presents the received advice to the user. The advice is visually displayed in the app's UI, providing specific suggestions and risk notifications. If the user is under stress, additional suggestions for relaxing foods and exercise plans are also provided.

[0302] Step 13:

[0303] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[0304] Step 14:

[0305] The device synchronizes the user's latest dietary data and emotional state with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[0306] Example 2

[0307] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] In today's world, many people lead busy lives that make it difficult to find time to manage their health. This has led to a demand for systems that efficiently provide appropriate advice on food choices and exercise plans. However, conventional systems have struggled to provide personalized advice that fully considers the user's emotions and individual health status. In particular, there has been a lack of dietary suggestions and exercise plans that take into account health status and stress, which fluctuate depending on emotions.

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

[0310] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information label on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, a means for transmitting the text data converted by the character recognition technology and the user's emotional data to the server, an AI analysis means for analyzing the text data and emotional data received by the server and comparing them with nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications, and a means for presenting the generated advice to the user, thereby enabling personalized advice that takes into account the user's emotional state and individual health profile.

[0311] "User" refers to an individual who uses this system to photograph food packages and obtain advice.

[0312] "Camera means" refers to a device for capturing image data using the camera function installed on a smartphone, tablet, etc.

[0313] "Character recognition technology" refers to the process of converting characters in image data into text data using OCR (optical character recognition) technology.

[0314] "Text data" refers to digital string information extracted from image data using character recognition technology.

[0315] "Server" refers to the computer system that analyzes the received data and generates and provides advice to the user.

[0316] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[0317] "AI analysis means" refers to artificial intelligence technology that uses information in a database and past historical data to generate optimal advice.

[0318] "Meal Suggestion" refers to recommended food and meal combinations based on the user's nutritional balance and health status.

[0319] "Exercise Plan" refers to an exercise or fitness plan created based on a user's health status and individual goals.

[0320] "Health risk notification" refers to information that notifies users of potential health risks based on their current health condition and dietary habits.

[0321] An "emotion analysis engine" refers to software that analyzes a user's facial expressions and voice data to determine their emotional state.

[0322] "Personalized advice" refers to individualized advice provided based on the individual user's health status, emotions, dietary history, etc.

[0323] "Data integration means" refers to technology for synchronizing and sharing data with other healthcare services and wearable devices.

[0324] "Wearable device" refers to an electronic device worn on the user's body that measures and records health and exercise data.

[0325] The present invention is a system that allows users to photograph the ingredient names and nutritional information on food packages, analyzes the data, and provides the user with appropriate dietary suggestions, exercise plans, and health risk notifications. Furthermore, by combining this with an emotion analysis engine that recognizes the user's emotions, the system provides even more personalized advice. Specific examples of hardware and software for implementing the present invention use a smartphone camera, OCR technology, AI analysis on a server, and an emotion analysis engine.

[0326] User interaction and data acquisition

[0327] A user launches a smartphone application and uses the camera function to take a picture of a food package. For example, if a user wants to buy a sandwich at a convenience store, they take a picture of the sandwich package. The smartphone used in this case can be any smartphone with a commonly available camera function.

[0328] Image analysis and data conversion

[0329] The device's built-in OCR technology converts the captured image data into text data. For example, nutritional information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted using OCR technology. This process uses an OCR service such as Google® Cloud Vision API.

[0330] Emotion analysis

[0331] When a user operates the app, the emotion analysis engine works through sensors such as the smartphone's camera and microphone. The emotion analysis engine analyzes the user's facial expressions and tone of voice to recognize their emotional state in real time. For example, it can identify whether the user is smiling or stressed.

[0332] Data transmission and analysis

[0333] The device sends the text data and emotion data converted using OCR technology to the server. The server receives this data and uses AI analysis tools to compare it with standard nutritional information and past dietary history in a database. The server then uses the Python TENSORFLOW (registered trademark) library to perform data analysis using an AI model.

[0334] Generating and presenting personalized advice

[0335] The advice generated by the server is sent to the device and presented to the user through the app. For example, the server might create specific advice such as, "This sandwich is high in salt, so we recommend combining it with low-salt foods. We also recommend herbal tea, which helps relieve stress."

[0336] Collaboration with wearable devices

[0337] The device collects the latest dietary data and emotional state and syncs it to a wearable device via Bluetooth or Wi-Fi, allowing users to closely monitor their health.

[0338] Prompt Sentence Examples

[0339] An example of a prompt to be input to the generative AI model might be, "Analyze the nutritional information on a food package photographed by the user and generate personalized advice based on the user's emotions and health profile."

[0340] As described above, the present invention is a system that provides personalized advice that takes into account the user's emotional state and individual health profile, and supports health management.

[0341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0342] Step 1:

[0343] The user launches the smartphone application and takes a photo of the food package.

[0344] Input: Food packaging image

[0345] Output: Captured image data

[0346] The user uses the smartphone camera to take a picture of the food packaging so that the ingredients and nutritional information are clearly visible. For example, the user takes a picture of the packaging of a sandwich they are about to purchase at a convenience store.

[0347] Step 2:

[0348] The device converts the captured image data into text data using OCR technology.

[0349] Input: Photographed image data

[0350] Output: Converted text data

[0351] The device uses OCR technology and services such as Google Cloud Vision API to recognize and extract text from image data. For example, text data such as "Salt 2.0g, Carbohydrate 15.0g, Calories 250kcal" can be extracted.

[0352] Step 3:

[0353] The device's emotion analysis engine analyzes the user's emotions.

[0354] Input: User's facial expression data and voice data

[0355] Output: Parsed emotion data

[0356] The emotion analysis engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time to recognize the user's emotional state, for example, determining whether the user is smiling or feeling stressed.

[0357] Step 4:

[0358] The terminal transmits the text data and the emotion data to the server.

[0359] Input: Converted text data and emotion data

[0360] Output: Data sent to the server

[0361] The device uses the HTTPS protocol to securely transmit text data and emotion data to the server.

[0362] Step 5:

[0363] The data received by the server is analyzed using AI.

[0364] Input: Transmitted text data and emotion data

[0365] Output: Analysis results of dietary suggestions, exercise plans, and health risk notifications

[0366] The server uses Python's TensorFlow library to run AI models that compare the data with standard nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications.

[0367] Step 6:

[0368] The server transmits the generated advice to the terminal and presents it to the user.

[0369] Input: Advice generated by AI analysis

[0370] Output: Advisory message displayed on the user's terminal

[0371] The server sends the generated advice to the device, which then displays it on the app's user interface. For example, the device might display a specific message like, "This sandwich is high in salt, so we recommend pairing it with a low-salt food. We also recommend herbal tea, which is effective for relieving stress."

[0372] Step 7:

[0373] The terminal synchronizes the collected data with the wearable device.

[0374] Input: Latest dietary and emotional state data

[0375] Output: Data synced to the wearable device

[0376] The terminal automatically synchronizes the latest data to the wearable device via Bluetooth or Wi-Fi, allowing the wearable device to reflect the user's latest dietary and emotional state, enabling detailed health management.

[0377] Through these steps, users can easily and effectively manage their own health. The system also works with an emotion analysis engine to provide personalized advice based on the user's emotional state, providing even more effective support.

[0378] (Application example 2)

[0379] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0380] In modern society, poor eating habits and poor stress management are having a negative impact on health. In particular, it is difficult to consider one's own health and emotional state when choosing food, making it difficult to provide appropriate dietary recommendations and health management. For this reason, there is a need for a system that allows users to easily understand the nutritional information of foods and receive dietary recommendations based on their own health and emotional state.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0382] In this invention, the server includes a photographing means for a user to photograph the ingredient names and nutritional information on a food package, a conversion means for converting the image data photographed by the photographing means into text data using character recognition technology, an emotion recognition means for recognizing the user's emotional state, an AI analysis means for analyzing the text data and the emotional state data and generating optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual health condition, and a presentation means for presenting the generated advice to the user. This enables the user to select meals that take their own health condition and emotions into consideration, enabling comprehensive health management.

[0383] "Photographing means" is a general term for devices or modules that allow users to photograph the ingredient names and nutritional information on food packages. This includes cameras and the camera function on smartphones.

[0384] "Conversion means" refers to a process or device that converts image data obtained by the imaging means into text data using character recognition technology. Specifically, this includes OCR (Optical Character Recognition) technology.

[0385] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice to recognize their emotional state. Examples of this include facial recognition technology and voice analysis technology.

[0386] "AI analysis means" refers to an artificial intelligence algorithm or system that analyzes text data and emotional state data and generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition.

[0387] The "presentation means" refers to a device or interface for displaying the generated advice to the user, including the display and notification functions of a smartphone.

[0388] "Health status" refers to information that indicates the user's physical and mental condition, and includes medical data and self-reported information.

[0389] "Meal Suggestions" are content that provides optimal foods and meal plans based on the user's health and emotional state.

[0390] An "exercise plan" refers to appropriate exercise content or programs suggested based on the user's health and emotional state.

[0391] A "health risk notification" is an alert or notification that notifies a user of a risk regarding their health condition, such as a suggestion to avoid excessive salt intake.

[0392] "Emotional state" is information that indicates the psychological state of the user, and includes data that is primarily acquired through facial expression recognition and voice analysis.

[0393] MODE FOR CARRYING OUT THE INVENTION

[0394] This invention is a system that supports users in choosing their own meals and managing their health. The system analyzes the user's photographed data using character recognition technology and provides personalized advice combined with the user's emotional state.

[0395] Hardware and software used

[0396] Hardware

[0397] 1. Smartphone: Used by users to take photos of food packaging.

[0398] 2. Camera: Use the built-in camera on your smartphone.

[0399] 3. Server: A remote server used for data analysis and advice generation.

[0400] 4. Wearable device (optional): Used to synchronize user exercise data and calorie intake information.

[0401] software

[0402] 1. OCR software: Extract text data from images using software such as Tesseract OCR.

[0403] 2. Emotion recognition software: Analyzes user emotions using libraries such as DeepFace.

[0404] 3. AI analysis system: Algorithms for generating dietary suggestions and health risk notifications.

[0405] What the system does

[0406] Filming method

[0407] Users use their smartphone camera to take a photo of the ingredient list and nutritional information on a food package, and the image is saved in the smartphone's internal storage.

[0408] Conversion Method

[0409] The captured image is converted into text data using OCR technology (e.g., Tesseract OCR) within the smartphone, which contains important information such as nutritional information and ingredient names.

[0410] emotion recognition means

[0411] While the user is interacting with the application, the smartphone's camera and microphone detect the user's facial expressions and voice, and emotion recognition software (e.g., DeepFace) is used to analyze the user's emotional state. This data is collected in real time.

[0412] AI analysis means

[0413] The device sends text data and emotional state data to a server, which analyzes the received data and generates optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual profile (e.g., health status and dietary history). The algorithms used here include standard machine learning and deep learning models.

[0414] Presentation means

[0415] The generated advice is sent from the server to the smartphone and displayed to the user, and may include, for example, reasons why certain foods should be avoided, suggestions for alternative foods, or relaxing exercise plans.

[0416] Specific examples

[0417] Food packaging photography and analysis

[0418] A user picks up a sandwich they are planning to buy at a convenience store, launches the app on their smartphone, and uses the camera to take a picture of the package. This image data is then converted into text data using OCR technology within the app. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0419] Emotion engine operation and data collection

[0420] When a user uses the app, emotion recognition software analyzes their facial expressions and tone of voice to determine if they are feeling stressed. This emotional data is collected in real time and sent to a server along with text data.

[0421] Data transmission and AI analysis

[0422] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[0423] Providing advice and guidelines

[0424] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[0425] Collaboration with wearable devices

[0426] The terminal can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[0427] Example prompts for generative AI models

[0428] When a user scans a food package, the app uses OCR technology to extract nutritional information. It then uses an emotion recognition engine to analyze the user's emotional state. This data is sent to a server, which generates optimal meal suggestions and exercise plans for the user. By providing advice based on the user's emotional state, the app supports personalized health management.

[0429] This allows users to receive optimal food suggestions based on their individual health condition and emotions, making it possible to achieve comprehensive health management.

[0430] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0431] Step 1:

[0432] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is an image file. This image is saved in the application's internal storage. Specifically, the user activates the camera function within the application, points the camera at the food package, and presses the capture button.

[0433] Step 2:

[0434] The device converts the captured image data into text data using optical character recognition (OCR). OCR software (e.g., Tesseract OCR) is used to extract nutritional information and ingredient names from the input image. The input is image data, and the output is text data. Specifically, the OCR software is launched and the conversion process is carried out.

[0435] Step 3:

[0436] The device uses the smartphone's camera and microphone to detect the user's facial expressions and voice, and analyzes their emotional state using emotion recognition software (e.g., DeepFace). The input is an image of the user's facial expression and voice data, and the output is emotional state data. Specifically, the device takes a photo of the user's face with the camera and records their voice with the microphone, and then analyzes the data.

[0437] Step 4:

[0438] The terminal sends text data and emotional state data to the server. The input is the text data and emotional state data, and the output is the data sent to the server. Specifically, the application establishes communication with the server and sends an HTTP request.

[0439] Step 5:

[0440] The server analyzes the received text data and emotional state data and compares it with standard nutritional information in a database and the user's individual profile to generate optimal meal suggestions, exercise plans, and health risk notifications. The input is the text data and emotional state data, and the output is the generated advice. Specifically, it uses machine learning algorithms to analyze the data and generate personalized advice.

[0441] Step 6:

[0442] The server sends the generated advice to the terminal, and the terminal presents the advice to the user. The input is the advice data sent from the server, and the output is the advice displayed to the user. Specifically, it displays a message on the application's UI.

[0443] Step 7:

[0444] After the user selects food based on the advice, the terminal synchronizes the latest dietary data and emotional state with the wearable device. The input is dietary data and emotional state data, and the output is data synchronized to the wearable device. Specifically, the data is synchronized using a communication method such as Bluetooth.

[0445] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0446] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0447] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0448] [Second embodiment]

[0449] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0450] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0451] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0453] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0455] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0456] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0457] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0458] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0459] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0460] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0461] The present invention relates to a system that allows users to take a photo of the names of ingredients and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes a camera, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[0462] Program Overview

[0463] Reading package information

[0464] The user launches the smartphone application and takes a photo of the food package with the camera. The device then converts the captured image data into text using character recognition technology. This OCR process extracts the names of ingredients and nutritional information.

[0465] Data transmission and analysis

[0466] The converted text data is sent from the device to a server, which analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[0467] Generating and presenting personalized advice

[0468] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. Users can check this information through the app and use it to manage their daily health.

[0469] Integration with other healthcare services and wearable devices

[0470] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[0471] Specific examples

[0472] Food packaging photography and analysis

[0473] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0474] Data transmission and AI analysis

[0475] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions (e.g., high blood pressure risk) and dietary history previously entered by the user into the app. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[0476] Providing advice and guidelines

[0477] The generated advice is sent to the device and displayed to the user on the app. The user can then follow the advice and, for example, select additional low-salt foods, taking into account the overall balance of their diet, and make purchasing decisions.

[0478] Collaboration with wearable devices

[0479] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[0480] As described above, the present invention aims to enable users to easily and effectively manage their own health, and provides specific means and a system for achieving this.

[0481] The processing flow will be explained below.

[0482] Step 1:

[0483] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[0484] Step 2:

[0485] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[0486] Step 3:

[0487] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[0488] Step 4:

[0489] The device sends the converted text data to the server, which then sends the JSON formatted data via the API using an HTTP request.

[0490] Step 5:

[0491] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[0492] Step 6:

[0493] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[0494] Step 7:

[0495] The server retrieves the user's individual profile (health status, allergy information, past dietary history, etc.) from a database.

[0496] Step 8:

[0497] The server generates meal suggestions, exercise plans, and health risk notifications based on the user's individual profile and analytical data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[0498] Step 9:

[0499] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[0500] Step 10:

[0501] The device displays the advice received to the user, visually displaying the advice in the app's UI and providing specific suggestions and risk notifications.

[0502] Step 11:

[0503] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[0504] Step 12:

[0505] The device synchronizes the user's latest dietary data and calorie intake information with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[0506] Example 1

[0507] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0508] There is a demand for a system that can easily digitize the ingredient names and nutritional information printed on food packages, and provide optimal meal suggestions, exercise plans, and health risk notifications based on the user's health condition and past dietary history. Furthermore, it is desirable for data to be easily linked with other healthcare services and wearable devices in order to more effectively manage the user's health. However, it has been difficult to meet all of these requirements with conventional methods.

[0509] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0510] In this invention, the server includes an imaging device that allows a user to photograph the ingredient names and nutritional information on food packages; a means for converting the image data captured by the imaging device into text data using character recognition technology; a means for analyzing the converted text data using AI analysis and comparing it with nutritional information and past diet history in a database; a means for generating optimal diet suggestions, exercise plans, and health risk notifications based on the user's individual health status; a means for presenting the generated advice to the user; and a means for linking data with other healthcare services and wearable devices. This allows for fast and accurate digitization of information from food packages, enabling the provision of personalized, specific, and effective health management advice to users. Furthermore, data linking with different healthcare services and wearable devices enables comprehensive health management.

[0511] "Imaging means" refers to a device for photographing the ingredient names and nutritional information on food packaging.

[0512] "Character recognition technology" refers to technology that analyzes image data captured by an imaging device and converts it into text data.

[0513] "Text data" refers to character information extracted from image data using character recognition technology.

[0514] "AI analysis means" refers to a function that includes artificial intelligence technology for analyzing the converted text data and comparing it with nutritional information and past dietary history in a database.

[0515] A "database" refers to a system that systematically stores information such as nutritional information and past dietary history and manages it in an accessible form.

[0516] "Dietary Suggestion" refers to advice that provides specific information to recommend optimal food choices to a user.

[0517] An "exercise plan" refers to an appropriate exercise schedule and activity content suggested based on the user's health condition and goals.

[0518] "Health risk notification" refers to notifications that warn users of potential health risks based on their health status and the foods they have consumed.

[0519] The "means for presenting advice" refers to a method or tool for displaying the generated advice to the user and encouraging appropriate action.

[0520] "Healthcare services" refers to all services related to health management, including, for example, medical institutions and fitness programs.

[0521] A "wearable device" refers to an electronic device that can be worn by a user and that can measure and record health and activity data.

[0522] A "generative AI model" refers to an artificial intelligence algorithm or system that generates appropriate outputs given inputs.

[0523] A "prompt" refers to a specific instruction or question-style sentence that is input into a generative AI model.

[0524] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes an imaging means, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[0525] Hardware and Software Use

[0526] The user launches the smartphone application and takes a picture of the food package using the imaging device (smartphone camera). The captured image data is converted into text data using the device's internal character recognition technology (OCR). The converted text data is then sent from the device to the server.

[0527] On the server side, the text data is analyzed using AI analysis tools and compared with nutritional information in the database and the user's past dietary history. Based on the user's health condition and individual profile, meal suggestions, exercise plans, and health risk notifications are generated. The generated advice is then sent back to the device and presented to the user.

[0528] Collaboration with other healthcare services and wearable devices is also an important factor. The system automatically synchronizes the user's exercise data and calorie intake information with the wearable device, supporting comprehensive health management.

[0529] Specific examples

[0530] A user picks up a sandwich they are about to purchase at a convenience store and launches an application on their smartphone. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0531] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions the user has previously entered into the app (e.g., whether they are at risk of high blood pressure) and their past dietary history. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[0532] The generated advice is displayed on the user's smartphone. The user follows this advice, for example, by selecting additional low-salt foods and making purchase decisions while taking into account the overall balance of their diet.

[0533] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[0534] Prompt Sentence Examples

[0535] By feeding the following prompts into the generative AI model, more specific and personalized advice can be given to the user:

[0536] "The food consumed was juice, with 20.0g of carbohydrates, 15mg of vitamin C, and 100kcal of calories. What dietary suggestions should be made for this food to a user at high risk of diabetes?"

[0537] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0538] Step 1: Launching the Application

[0539] explanation

[0540] The user taps on the smartphone to launch the application.

[0541] input

[0542] The user taps the application icon on their smartphone.

[0543] output

[0544] The application will launch and the home screen will be displayed.

[0545] Specific actions

[0546] A user's finger touches an application icon on the smartphone screen and taps the icon to launch the application.

[0547] Step 2: Photograph the packaging

[0548] explanation

[0549] The user takes a photo of the food package using the app's camera function.

[0550] input

[0551] The user selects the application's camera function and takes a photo of the food package using their smartphone.

[0552] output

[0553] An image of the food packaging is saved to the device.

[0554] Specific actions

[0555] The user selects the camera function button, points the smartphone camera at the food package, and taps the shutter button.

[0556] Step 3: Recognize text using OCR

[0557] explanation

[0558] The device processes the captured image data using OCR technology and converts it into text data.

[0559] input

[0560] Image data of food packaging.

[0561] output

[0562] Text data of ingredient names and nutritional information.

[0563] Specific actions

[0564] The device's internal OCR engine analyzes the image data and converts the recognized character information into text. For example, text data such as "Carbohydrates 15.0g, Salt 0.5g, Calories 200kcal" is generated.

[0565] Step 4: Sending data

[0566] explanation

[0567] The terminal transmits the converted text data to the server.

[0568] input

[0569] The converted text data.

[0570] output

[0571] The text data is sent to the server.

[0572] Specific actions

[0573] The device uses Wi-Fi or mobile data to send the text data extracted by OCR to the server.

[0574] Step 5: Analyze the data

[0575] explanation

[0576] The server analyzes the received data and compares it with the user profile and nutritional information in the database.

[0577] input

[0578] Text data and user profile data received by the server.

[0579] output

[0580] Generate optimal dietary suggestions, exercise plans, and health risk notifications.

[0581] Specific actions

[0582] The server's AI analysis tools compare the text data with a database and generate meal suggestions and exercise plans based on the user's health status.

[0583] Step 6: Generate personalized advice

[0584] explanation

[0585] The server generates individual advice and sends it to the terminal.

[0586] input

[0587] Analytics and user profile data.

[0588] output

[0589] The generated personalized advice is sent to the terminal.

[0590] Specific actions

[0591] The server generates advice and sends the data to the user's device. For example, the advice generated might be, "This food is high in salt, so eat it with low-salt foods."

[0592] Step 7: Providing advice

[0593] explanation

[0594] The terminal presents the received advice to the user.

[0595] input

[0596] Personalized advice received by the device.

[0597] output

[0598] Advice is displayed to the user within the application.

[0599] Specific actions

[0600] The device displays the advice received from the server within the application. When the user opens the app, the advice is displayed in a pop-up format.

[0601] Step 8: Integrate with wearable devices

[0602] explanation

[0603] The device syncs the food data to the wearable device.

[0604] input

[0605] User's dietary data.

[0606] output

[0607] Calorie intake and nutritional balance data synced to wearable devices.

[0608] Specific actions

[0609] The device transmits the user's meal data via Bluetooth or Wi-Fi to the wearable device, which updates the data in real time.

[0610] (Application example 1)

[0611] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0612] Conventional food delivery services often fail to provide users with sufficient information about the nutritional content and ingredients of the food they provide, which can lead to health risks and inappropriate dietary choices. Furthermore, the inability to provide real-time dietary suggestions based on individual health conditions and allergy information makes it difficult for users to manage their health.

[0613] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0614] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, an AI analysis means for analyzing the text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition, a means for presenting the generated advice to the user, and a means for photographing the nutritional components and ingredient names of the food the user is about to order with the camera and providing advice and health risk notifications. This allows users to select appropriate meals based on their individual health condition when ordering food, making health management easier.

[0615] "User" refers to the person who photographs and orders food packages.

[0616] "Food packaging" refers to packaging that lists the names of food ingredients and nutritional information.

[0617] "Ingredient name" refers to the name of the ingredient that makes up the food.

[0618] "Nutritional information labeling" indicates nutritional information such as calories, protein, fat, carbohydrates, vitamins, and minerals contained in food.

[0619] The term "camera means" refers to a device for capturing image data.

[0620] "Character recognition technology" refers to technology for converting image data into text data.

[0621] "Text data" refers to character information obtained by character recognition technology.

[0622] "AI analysis means" refers to a device or program for analyzing data using artificial intelligence.

[0623] "Advice" refers to guidance or suggestions provided to a user, such as dietary suggestions, exercise plans, and health risk notifications.

[0624] "Healthcare services" refers to various services aimed at health management.

[0625] A "wearable device" is a wearable electronic device that monitors health status and performs other functions.

[0626] "Food delivery" refers to a service that delivers ordered food to users.

[0627] "Means of providing advice and health risk notifications" refers to a function that provides health information to users based on the nutritional content and ingredient names of the photographed food.

[0628] This system photographs and analyzes the names of ingredients and nutritional information on food packages, and provides users with appropriate dietary suggestions and health risk notifications. This system mainly consists of the following main components:

[0629] Main Components

[0630] 1. Camera Means

[0631] This is a means for users to take pictures of food packages using a camera such as a smartphone.

[0632] 2. Character recognition technology

[0633] This is a technology used to convert captured image data into text data. Specifically, Optical Character Recognition (OCR) technology is used.

[0634] 3. AI analysis means

[0635] This is an AI technology that analyzes the converted text data and generates optimal diet suggestions, exercise plans, and health risk notifications based on the user's health status.

[0636] 4. How to access your user profile

[0637] It is a means of referencing a user's individual profile, including their health status, allergy information, and past dietary history.

[0638] 5. Collaboration Methods

[0639] It is a means of linking data with other healthcare services and wearable devices.

[0640] 6. How to present generated advice

[0641] This is a means for presenting the generated advice to the user. The user is notified via an application on a smartphone or the like.

[0642] System program processing

[0643] The processing flow in this system is explained in natural language.

[0644] Camera photography and character recognition

[0645] Users use their smartphone camera to take a photo of the ingredients and nutritional information on food packages. The image data is then converted into text using OCR technology. The software used includes Python, PIL (Python Imaging Library), and pytesseract.

[0646] Data transmission and AI analysis

[0647] The converted text data is sent to a server via a smartphone application. Inside the server, this text data is analyzed using AI analysis tools. Specifically, optimal meal suggestions and health risk notifications are generated based on the user's health condition and dietary history.

[0648] Personalized advice and displays

[0649] The advice generated by the server is sent in real time to a smartphone application and displayed to the user, allowing the user to easily select appropriate dietary choices and exercise plans.

[0650] Collaboration with wearable devices

[0651] Additionally, the generated dietary data and exercise plans are automatically synchronized with other healthcare services and wearable devices.

[0652] Specific examples

[0653] When a user uses a food delivery service, they take a photo of the food package they are ordering with their smartphone. The captured image is converted into text data using OCR technology, and that data is sent to a server. The server then uses AI analysis to generate personalized health advice based on the user's health condition and past dietary history. For example, a message such as "This item is high in calories, so we recommend choosing a low-calorie option for your next meal" is displayed.

[0654] Prompt Sentence Examples

[0655] "I would like to develop an application that allows users to take a photo of the nutritional information on food packaging, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. Please write the following program in Python. The input is an image of the food package, and the nutritional information is extracted using OCR, sent to the server for analysis, and the generated advice is presented to the user."

[0656] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0657] Step 1:

[0658] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is the image captured by the camera. This image data becomes the first input to the system. The specific operation is for the user to turn on the smartphone, open the camera, focus on the food package, and press the shutter button.

[0659] Step 2:

[0660] The device converts the captured image data into text data using optical character recognition (OCR). The input is the captured image data, and the output is text data including the names of ingredients and nutritional information. The specific software used here is Python, the PIL library, and pytesseract.

[0661] Step 3:

[0662] The device sends the converted text data to the server. The input is the text data obtained by the OCR process, and the output is the text data sent to the server. The specific operation is that the smartphone application generates an HTTP request and POSTs the text data to the server.

[0663] Step 4:

[0664] The server analyzes the received text data using AI analysis methods. The input is the text data sent to the server, and the output is advice data including optimal dietary suggestions and health risk notifications for the user. This analysis uses machine learning models and database matching.

[0665] Step 5:

[0666] The server sends the generated advice data to the terminal. The input is the advice data generated by AI analysis, and the output is the advice message sent to the smartphone. The specific operation is that the server returns the advice data to the terminal as an HTTP response.

[0667] Step 6:

[0668] The terminal notifies the user of the received advice data. The input is the advice data sent from the server, and the output is an advice message displayed to the user. The specific operation is that the smartphone application displays the advice message on the user interface.

[0669] Step 7:

[0670] The device automatically synchronizes the generated dietary data and exercise plan with wearable devices and other healthcare services. The input is advice data sent from the server, and the output is data synchronized with the wearable device or healthcare service. Specifically, the smartphone transmits the data to other devices or services via Bluetooth or an internet connection.

[0671] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0672] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides users with appropriate meal suggestions, exercise plans, and health risk notifications. It also combines an emotion engine that recognizes the user's emotions to provide even more personalized advice.

[0673] Program Overview

[0674] Reading and parsing package information

[0675] The user launches the smartphone application and takes a picture of the food package using the camera. The device then converts the captured image data into text using optical character recognition (OCR). This OCR process extracts information such as the names of ingredients and nutritional information.

[0676] Use of emotion engine

[0677] When a user operates the app, an emotion engine works to recognize the user's emotions in real time through sensors such as the camera and microphone. The emotion engine analyzes facial expressions, tone of voice, and choice of words to determine the user's current emotional state.

[0678] Data transmission and AI analysis

[0679] The converted text data and the user's emotional state data are sent from the device to a server. The server analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[0680] Generating and presenting personalized advice

[0681] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. The emotion engine also takes into account the user's emotional state, providing more appropriate advice. For example, if the user is feeling stressed, the system can suggest foods with a relaxing effect and an exercise plan to help them relax.

[0682] Integration with other healthcare services and wearable devices

[0683] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[0684] Specific examples

[0685] Food packaging photography and analysis

[0686] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0687] Emotion engine operation and data collection

[0688] When a user uses the app, the emotion engine analyzes the user's facial expressions and tone of voice to recognize when the user is feeling stressed. This emotion data is collected in real time and sent to the server along with the text data.

[0689] Data transmission and AI analysis

[0690] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[0691] Providing advice and guidelines

[0692] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[0693] Collaboration with wearable devices

[0694] The device can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[0695] The present invention aims to enable users to manage their own health simply and effectively. As a specific means for achieving this, the invention provides more personalized health management support by combining an emotion engine.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[0699] Step 2:

[0700] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[0701] Step 3:

[0702] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[0703] Step 4:

[0704] The device sends text data to the server. The data is sent in JSON format via an API using an HTTP request.

[0705] Step 5:

[0706] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[0707] Step 6:

[0708] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[0709] Step 7:

[0710] The emotion engine captures the user's facial expressions and voice data through the smartphone's camera and microphone, and analyzes the user's emotions in real time. For example, it analyzes changes in facial expressions and tone of voice to identify the user's emotional state.

[0711] Step 8:

[0712] The extracted user emotion data is sent from the terminal to the server together with the text data.

[0713] Step 9:

[0714] The server analyzes the user's emotional data and compares it with the user's individual profile stored in a database, taking into account the user's health condition, allergy information, and past dietary history.

[0715] Step 10:

[0716] The server generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual profile, text data, and emotional data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[0717] Step 11:

[0718] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[0719] Step 12:

[0720] The device presents the received advice to the user. The advice is visually displayed in the app's UI, providing specific suggestions and risk notifications. If the user is under stress, additional suggestions for relaxing foods and exercise plans are also provided.

[0721] Step 13:

[0722] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[0723] Step 14:

[0724] The device synchronizes the user's latest dietary data and emotional state with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[0725] Example 2

[0726] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0727] In today's world, many people lead busy lives that make it difficult to find time to manage their health. This has led to a demand for systems that efficiently provide appropriate advice on food choices and exercise plans. However, conventional systems have struggled to provide personalized advice that fully considers the user's emotions and individual health status. In particular, there has been a lack of dietary suggestions and exercise plans that take into account health status and stress, which fluctuate depending on emotions.

[0728] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0729] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information label on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, a means for transmitting the text data converted by the character recognition technology and the user's emotional data to the server, an AI analysis means for analyzing the text data and emotional data received by the server and comparing them with nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications, and a means for presenting the generated advice to the user, thereby enabling personalized advice that takes into account the user's emotional state and individual health profile.

[0730] "User" refers to an individual who uses this system to photograph food packages and obtain advice.

[0731] "Camera means" refers to a device for capturing image data using the camera function installed on a smartphone, tablet, etc.

[0732] "Character recognition technology" refers to the process of converting characters in image data into text data using OCR (optical character recognition) technology.

[0733] "Text data" refers to digital string information extracted from image data using character recognition technology.

[0734] "Server" refers to the computer system that analyzes the received data and generates and provides advice to the user.

[0735] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[0736] "AI analysis means" refers to artificial intelligence technology that uses information in a database and past historical data to generate optimal advice.

[0737] "Meal Suggestion" refers to recommended food and meal combinations based on the user's nutritional balance and health status.

[0738] "Exercise Plan" refers to an exercise or fitness plan created based on a user's health status and individual goals.

[0739] "Health risk notification" refers to information that notifies users of potential health risks based on their current health condition and dietary habits.

[0740] An "emotion analysis engine" refers to software that analyzes a user's facial expressions and voice data to determine their emotional state.

[0741] "Personalized advice" refers to individualized advice provided based on the individual user's health status, emotions, dietary history, etc.

[0742] "Data integration means" refers to technology for synchronizing and sharing data with other healthcare services and wearable devices.

[0743] "Wearable device" refers to an electronic device worn on the user's body that measures and records health and exercise data.

[0744] The present invention is a system that allows users to photograph the ingredient names and nutritional information on food packages, analyzes the data, and provides the user with appropriate dietary suggestions, exercise plans, and health risk notifications. Furthermore, by combining this with an emotion analysis engine that recognizes the user's emotions, the system provides even more personalized advice. Specific examples of hardware and software for implementing the present invention use a smartphone camera, OCR technology, AI analysis on a server, and an emotion analysis engine.

[0745] User interaction and data acquisition

[0746] A user launches a smartphone application and uses the camera function to take a picture of a food package. For example, if a user wants to buy a sandwich at a convenience store, they take a picture of the sandwich package. The smartphone used in this case can be any smartphone with a commonly available camera function.

[0747] Image analysis and data conversion

[0748] The device's built-in OCR technology converts the captured image data into text data. For example, nutritional information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted using OCR technology. This process uses an OCR service such as Google Cloud Vision API.

[0749] Emotion analysis

[0750] When a user operates the app, the emotion analysis engine works through sensors such as the smartphone's camera and microphone. The emotion analysis engine analyzes the user's facial expressions and tone of voice to recognize their emotional state in real time. For example, it can identify whether the user is smiling or stressed.

[0751] Data transmission and analysis

[0752] The device sends the text data and emotion data converted using OCR technology to a server. The server receives this data and uses AI analysis methods to compare it with standard nutritional information and past dietary history in a database. The server then uses Python's TensorFlow library to perform data analysis using an AI model.

[0753] Generating and presenting personalized advice

[0754] The advice generated by the server is sent to the device and presented to the user through the app. For example, the server might create specific advice such as, "This sandwich is high in salt, so we recommend combining it with low-salt foods. We also recommend herbal tea, which helps relieve stress."

[0755] Collaboration with wearable devices

[0756] The device collects the latest dietary data and emotional state and syncs it to a wearable device via Bluetooth or Wi-Fi, allowing users to closely monitor their health.

[0757] Prompt Sentence Examples

[0758] An example of a prompt to be input to the generative AI model might be, "Analyze the nutritional information on a food package photographed by the user and generate personalized advice based on the user's emotions and health profile."

[0759] As described above, the present invention is a system that provides personalized advice that takes into account the user's emotional state and individual health profile, and supports health management.

[0760] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0761] Step 1:

[0762] The user launches the smartphone application and takes a photo of the food package.

[0763] Input: Food packaging image

[0764] Output: Captured image data

[0765] The user uses the smartphone camera to take a picture of the food packaging so that the ingredients and nutritional information are clearly visible. For example, the user takes a picture of the packaging of a sandwich they are about to purchase at a convenience store.

[0766] Step 2:

[0767] The device converts the captured image data into text data using OCR technology.

[0768] Input: Photographed image data

[0769] Output: Converted text data

[0770] The device uses OCR technology and services such as Google Cloud Vision API to recognize and extract text from image data. For example, text data such as "Salt 2.0g, Carbohydrate 15.0g, Calories 250kcal" can be extracted.

[0771] Step 3:

[0772] The device's emotion analysis engine analyzes the user's emotions.

[0773] Input: User's facial expression data and voice data

[0774] Output: Parsed emotion data

[0775] The emotion analysis engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time to recognize the user's emotional state, for example, determining whether the user is smiling or feeling stressed.

[0776] Step 4:

[0777] The terminal transmits the text data and the emotion data to the server.

[0778] Input: Converted text data and emotion data

[0779] Output: Data sent to the server

[0780] The device uses the HTTPS protocol to securely transmit text data and emotion data to the server.

[0781] Step 5:

[0782] The data received by the server is analyzed using AI.

[0783] Input: Transmitted text data and emotion data

[0784] Output: Analysis results of dietary suggestions, exercise plans, and health risk notifications

[0785] The server uses Python's TensorFlow library to run AI models that compare the data with standard nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications.

[0786] Step 6:

[0787] The server transmits the generated advice to the terminal and presents it to the user.

[0788] Input: Advice generated by AI analysis

[0789] Output: Advisory message displayed on the user's terminal

[0790] The server sends the generated advice to the device, which then displays it on the app's user interface. For example, the device might display a specific message like, "This sandwich is high in salt, so we recommend pairing it with a low-salt food. We also recommend herbal tea, which is effective for relieving stress."

[0791] Step 7:

[0792] The terminal synchronizes the collected data with the wearable device.

[0793] Input: Latest dietary and emotional state data

[0794] Output: Data synced to the wearable device

[0795] The terminal automatically synchronizes the latest data to the wearable device via Bluetooth or Wi-Fi, allowing the wearable device to reflect the user's latest dietary and emotional state, enabling detailed health management.

[0796] Through these steps, users can easily and effectively manage their own health. The system also works with an emotion analysis engine to provide personalized advice based on the user's emotional state, providing even more effective support.

[0797] (Application example 2)

[0798] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0799] In modern society, poor eating habits and poor stress management are having a negative impact on health. In particular, it is difficult to consider one's own health and emotional state when choosing food, making it difficult to provide appropriate dietary recommendations and health management. For this reason, there is a need for a system that allows users to easily understand the nutritional information of foods and receive dietary recommendations based on their own health and emotional state.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0801] In this invention, the server includes a photographing means for a user to photograph the ingredient names and nutritional information on a food package, a conversion means for converting the image data photographed by the photographing means into text data using character recognition technology, an emotion recognition means for recognizing the user's emotional state, an AI analysis means for analyzing the text data and the emotional state data and generating optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual health condition, and a presentation means for presenting the generated advice to the user. This enables the user to select meals that take their own health condition and emotions into consideration, enabling comprehensive health management.

[0802] "Photographing means" is a general term for devices or modules that allow users to photograph the ingredient names and nutritional information on food packages. This includes cameras and the camera function on smartphones.

[0803] "Conversion means" refers to a process or device that converts image data obtained by the imaging means into text data using character recognition technology. Specifically, this includes OCR (Optical Character Recognition) technology.

[0804] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice to recognize their emotional state. Examples of this include facial recognition technology and voice analysis technology.

[0805] "AI analysis means" refers to an artificial intelligence algorithm or system that analyzes text data and emotional state data and generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition.

[0806] The "presentation means" refers to a device or interface for displaying the generated advice to the user, including the display and notification functions of a smartphone.

[0807] "Health status" refers to information that indicates the user's physical and mental condition, and includes medical data and self-reported information.

[0808] "Meal Suggestions" are content that provides optimal foods and meal plans based on the user's health and emotional state.

[0809] An "exercise plan" refers to appropriate exercise content or programs suggested based on the user's health and emotional state.

[0810] A "health risk notification" is an alert or notification that notifies a user of a risk regarding their health condition, such as a suggestion to avoid excessive salt intake.

[0811] "Emotional state" is information that indicates the psychological state of the user, and includes data that is primarily acquired through facial expression recognition and voice analysis.

[0812] MODE FOR CARRYING OUT THE INVENTION

[0813] This invention is a system that supports users in choosing their own meals and managing their health. The system analyzes the user's photographed data using character recognition technology and provides personalized advice combined with the user's emotional state.

[0814] Hardware and software used

[0815] Hardware

[0816] 1. Smartphone: Used by users to take photos of food packaging.

[0817] 2. Camera: Use the built-in camera on your smartphone.

[0818] 3. Server: A remote server used for data analysis and advice generation.

[0819] 4. Wearable device (optional): Used to synchronize user exercise data and calorie intake information.

[0820] software

[0821] 1. OCR software: Extract text data from images using software such as Tesseract OCR.

[0822] 2. Emotion recognition software: Analyzes user emotions using libraries such as DeepFace.

[0823] 3. AI analysis system: Algorithms for generating dietary suggestions and health risk notifications.

[0824] What the system does

[0825] Filming method

[0826] Users use their smartphone camera to take a photo of the ingredient list and nutritional information on a food package, and the image is saved in the smartphone's internal storage.

[0827] Conversion Method

[0828] The captured image is converted into text data using OCR technology (e.g., Tesseract OCR) within the smartphone, which contains important information such as nutritional information and ingredient names.

[0829] emotion recognition means

[0830] While the user is interacting with the application, the smartphone's camera and microphone detect the user's facial expressions and voice, and emotion recognition software (e.g., DeepFace) is used to analyze the user's emotional state. This data is collected in real time.

[0831] AI analysis means

[0832] The device sends text data and emotional state data to a server, which analyzes the received data and generates optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual profile (e.g., health status and dietary history). The algorithms used here include standard machine learning and deep learning models.

[0833] Presentation means

[0834] The generated advice is sent from the server to the smartphone and displayed to the user, and may include, for example, reasons why certain foods should be avoided, suggestions for alternative foods, or relaxing exercise plans.

[0835] Specific examples

[0836] Food packaging photography and analysis

[0837] A user picks up a sandwich they are planning to buy at a convenience store, launches the app on their smartphone, and uses the camera to take a picture of the package. This image data is then converted into text data using OCR technology within the app. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0838] Emotion engine operation and data collection

[0839] When a user uses the app, emotion recognition software analyzes their facial expressions and tone of voice to determine if they are feeling stressed. This emotional data is collected in real time and sent to a server along with text data.

[0840] Data transmission and AI analysis

[0841] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[0842] Providing advice and guidelines

[0843] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[0844] Collaboration with wearable devices

[0845] The terminal can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[0846] Example prompts for generative AI models

[0847] When a user scans a food package, the app uses OCR technology to extract nutritional information. It then uses an emotion recognition engine to analyze the user's emotional state. This data is sent to a server, which generates optimal meal suggestions and exercise plans for the user. By providing advice based on the user's emotional state, the app supports personalized health management.

[0848] This allows users to receive optimal food suggestions based on their individual health condition and emotions, making it possible to achieve comprehensive health management.

[0849] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0850] Step 1:

[0851] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is an image file. This image is saved in the application's internal storage. Specifically, the user activates the camera function within the application, points the camera at the food package, and presses the capture button.

[0852] Step 2:

[0853] The device converts the captured image data into text data using optical character recognition (OCR). OCR software (e.g., Tesseract OCR) is used to extract nutritional information and ingredient names from the input image. The input is image data, and the output is text data. Specifically, the OCR software is launched and the conversion process is carried out.

[0854] Step 3:

[0855] The device uses the smartphone's camera and microphone to detect the user's facial expressions and voice, and analyzes their emotional state using emotion recognition software (e.g., DeepFace). The input is an image of the user's facial expression and voice data, and the output is emotional state data. Specifically, the device takes a photo of the user's face with the camera and records their voice with the microphone, and then analyzes the data.

[0856] Step 4:

[0857] The terminal sends text data and emotional state data to the server. The input is the text data and emotional state data, and the output is the data sent to the server. Specifically, the application establishes communication with the server and sends an HTTP request.

[0858] Step 5:

[0859] The server analyzes the received text data and emotional state data and compares it with standard nutritional information in a database and the user's individual profile to generate optimal meal suggestions, exercise plans, and health risk notifications. The input is the text data and emotional state data, and the output is the generated advice. Specifically, it uses machine learning algorithms to analyze the data and generate personalized advice.

[0860] Step 6:

[0861] The server sends the generated advice to the terminal, and the terminal presents the advice to the user. The input is the advice data sent from the server, and the output is the advice displayed to the user. Specifically, it displays a message on the application's UI.

[0862] Step 7:

[0863] After the user selects food based on the advice, the terminal synchronizes the latest dietary data and emotional state with the wearable device. The input is dietary data and emotional state data, and the output is data synchronized to the wearable device. Specifically, the data is synchronized using a communication method such as Bluetooth.

[0864] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0865] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0866] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0867] [Third embodiment]

[0868] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0869] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0870] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0871] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0872] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0873] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0874] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0875] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0876] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0877] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0878] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0879] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0880] The present invention relates to a system that allows users to take a photo of the names of ingredients and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes a camera, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[0881] Program Overview

[0882] Reading package information

[0883] The user launches the smartphone application and takes a photo of the food package with the camera. The device then converts the captured image data into text using character recognition technology. This OCR process extracts the names of ingredients and nutritional information.

[0884] Data transmission and analysis

[0885] The converted text data is sent from the device to a server, which analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[0886] Generating and presenting personalized advice

[0887] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. Users can check this information through the app and use it to manage their daily health.

[0888] Integration with other healthcare services and wearable devices

[0889] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[0890] Specific examples

[0891] Food packaging photography and analysis

[0892] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0893] Data transmission and AI analysis

[0894] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions (e.g., high blood pressure risk) and dietary history previously entered by the user into the app. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[0895] Providing advice and guidelines

[0896] The generated advice is sent to the device and displayed to the user on the app. The user can then follow the advice and, for example, select additional low-salt foods, taking into account the overall balance of their diet, and make purchasing decisions.

[0897] Collaboration with wearable devices

[0898] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[0899] As described above, the present invention aims to enable users to easily and effectively manage their own health, and provides specific means and a system for achieving this.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[0903] Step 2:

[0904] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[0905] Step 3:

[0906] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[0907] Step 4:

[0908] The device sends the converted text data to the server, which then sends the JSON formatted data via the API using an HTTP request.

[0909] Step 5:

[0910] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[0911] Step 6:

[0912] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[0913] Step 7:

[0914] The server retrieves the user's individual profile (health status, allergy information, past dietary history, etc.) from a database.

[0915] Step 8:

[0916] The server generates meal suggestions, exercise plans, and health risk notifications based on the user's individual profile and analytical data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[0917] Step 9:

[0918] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[0919] Step 10:

[0920] The device displays the advice received to the user, visually displaying the advice in the app's UI and providing specific suggestions and risk notifications.

[0921] Step 11:

[0922] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[0923] Step 12:

[0924] The device synchronizes the user's latest dietary data and calorie intake information with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[0925] Example 1

[0926] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0927] There is a demand for a system that can easily digitize the ingredient names and nutritional information printed on food packages, and provide optimal meal suggestions, exercise plans, and health risk notifications based on the user's health condition and past dietary history. Furthermore, it is desirable for data to be easily linked with other healthcare services and wearable devices in order to more effectively manage the user's health. However, it has been difficult to meet all of these requirements with conventional methods.

[0928] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0929] In this invention, the server includes an imaging device that allows a user to photograph the ingredient names and nutritional information on food packages; a means for converting the image data captured by the imaging device into text data using character recognition technology; a means for analyzing the converted text data using AI analysis and comparing it with nutritional information and past diet history in a database; a means for generating optimal diet suggestions, exercise plans, and health risk notifications based on the user's individual health status; a means for presenting the generated advice to the user; and a means for linking data with other healthcare services and wearable devices. This allows for fast and accurate digitization of information from food packages, enabling the provision of personalized, specific, and effective health management advice to users. Furthermore, data linking with different healthcare services and wearable devices enables comprehensive health management.

[0930] "Imaging means" refers to a device for photographing the ingredient names and nutritional information on food packaging.

[0931] "Character recognition technology" refers to technology that analyzes image data captured by an imaging device and converts it into text data.

[0932] "Text data" refers to character information extracted from image data using character recognition technology.

[0933] "AI analysis means" refers to a function that includes artificial intelligence technology for analyzing the converted text data and comparing it with nutritional information and past dietary history in a database.

[0934] A "database" refers to a system that systematically stores information such as nutritional information and past dietary history and manages it in an accessible form.

[0935] "Dietary Suggestion" refers to advice that provides specific information to recommend optimal food choices to a user.

[0936] An "exercise plan" refers to an appropriate exercise schedule and activity content suggested based on the user's health condition and goals.

[0937] "Health risk notification" refers to notifications that warn users of potential health risks based on their health status and the foods they have consumed.

[0938] The "means for presenting advice" refers to a method or tool for displaying the generated advice to the user and encouraging appropriate action.

[0939] "Healthcare services" refers to all services related to health management, including, for example, medical institutions and fitness programs.

[0940] A "wearable device" refers to an electronic device that can be worn by a user and that can measure and record health and activity data.

[0941] A "generative AI model" refers to an artificial intelligence algorithm or system that generates appropriate outputs given inputs.

[0942] A "prompt" refers to a specific instruction or question-style sentence that is input into a generative AI model.

[0943] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes an imaging means, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[0944] Hardware and Software Use

[0945] The user launches the smartphone application and takes a picture of the food package using the imaging device (smartphone camera). The captured image data is converted into text data using the device's internal character recognition technology (OCR). The converted text data is then sent from the device to the server.

[0946] On the server side, the text data is analyzed using AI analysis tools and compared with nutritional information in the database and the user's past dietary history. Based on the user's health condition and individual profile, meal suggestions, exercise plans, and health risk notifications are generated. The generated advice is then sent back to the device and presented to the user.

[0947] Collaboration with other healthcare services and wearable devices is also an important factor. The system automatically synchronizes the user's exercise data and calorie intake information with the wearable device, supporting comprehensive health management.

[0948] Specific examples

[0949] A user picks up a sandwich they are about to purchase at a convenience store and launches an application on their smartphone. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[0950] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions the user has previously entered into the app (e.g., whether they are at risk of high blood pressure) and their past dietary history. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[0951] The generated advice is displayed on the user's smartphone. The user follows this advice, for example, by selecting additional low-salt foods and making purchase decisions while taking into account the overall balance of their diet.

[0952] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[0953] Prompt Sentence Examples

[0954] By feeding the following prompts into the generative AI model, more specific and personalized advice can be given to the user:

[0955] "The food consumed was juice, with 20.0g of carbohydrates, 15mg of vitamin C, and 100kcal of calories. What dietary suggestions should be made for this food to a user at high risk of diabetes?"

[0956] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0957] Step 1: Launching the Application

[0958] explanation

[0959] The user taps on the smartphone to launch the application.

[0960] input

[0961] The user taps the application icon on their smartphone.

[0962] output

[0963] The application will launch and the home screen will be displayed.

[0964] Specific actions

[0965] A user's finger touches an application icon on the smartphone screen and taps the icon to launch the application.

[0966] Step 2: Photograph the packaging

[0967] explanation

[0968] The user takes a photo of the food package using the app's camera function.

[0969] input

[0970] The user selects the application's camera function and takes a photo of the food package using their smartphone.

[0971] output

[0972] An image of the food packaging is saved to the device.

[0973] Specific actions

[0974] The user selects the camera function button, points the smartphone camera at the food package, and taps the shutter button.

[0975] Step 3: Recognize text using OCR

[0976] explanation

[0977] The device processes the captured image data using OCR technology and converts it into text data.

[0978] input

[0979] Image data of food packaging.

[0980] output

[0981] Text data of ingredient names and nutritional information.

[0982] Specific actions

[0983] The device's internal OCR engine analyzes the image data and converts the recognized character information into text. For example, text data such as "Carbohydrates 15.0g, Salt 0.5g, Calories 200kcal" is generated.

[0984] Step 4: Sending data

[0985] explanation

[0986] The terminal transmits the converted text data to the server.

[0987] input

[0988] The converted text data.

[0989] output

[0990] The text data is sent to the server.

[0991] Specific actions

[0992] The device uses Wi-Fi or mobile data to send the text data extracted by OCR to the server.

[0993] Step 5: Analyze the data

[0994] explanation

[0995] The server analyzes the received data and compares it with the user profile and nutritional information in the database.

[0996] input

[0997] Text data and user profile data received by the server.

[0998] output

[0999] Generate optimal dietary suggestions, exercise plans, and health risk notifications.

[1000] Specific actions

[1001] The server's AI analysis tools compare the text data with a database and generate meal suggestions and exercise plans based on the user's health status.

[1002] Step 6: Generate personalized advice

[1003] explanation

[1004] The server generates individual advice and sends it to the terminal.

[1005] input

[1006] Analytics and user profile data.

[1007] output

[1008] The generated personalized advice is sent to the terminal.

[1009] Specific actions

[1010] The server generates advice and sends the data to the user's device. For example, the advice generated might be, "This food is high in salt, so eat it with low-salt foods."

[1011] Step 7: Providing advice

[1012] explanation

[1013] The terminal presents the received advice to the user.

[1014] input

[1015] Personalized advice received by the device.

[1016] output

[1017] Advice is displayed to the user within the application.

[1018] Specific actions

[1019] The device displays the advice received from the server within the application. When the user opens the app, the advice is displayed in a pop-up format.

[1020] Step 8: Integrate with wearable devices

[1021] explanation

[1022] The device syncs the food data to the wearable device.

[1023] input

[1024] User's dietary data.

[1025] output

[1026] Calorie intake and nutritional balance data synced to wearable devices.

[1027] Specific actions

[1028] The device transmits the user's meal data via Bluetooth or Wi-Fi to the wearable device, which updates the data in real time.

[1029] (Application example 1)

[1030] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1031] Conventional food delivery services often fail to provide users with sufficient information about the nutritional content and ingredients of the food they provide, which can lead to health risks and inappropriate dietary choices. Furthermore, the inability to provide real-time dietary suggestions based on individual health conditions and allergy information makes it difficult for users to manage their health.

[1032] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1033] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, an AI analysis means for analyzing the text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition, a means for presenting the generated advice to the user, and a means for photographing the nutritional components and ingredient names of the food the user is about to order with the camera and providing advice and health risk notifications. This allows users to select appropriate meals based on their individual health condition when ordering food, making health management easier.

[1034] "User" refers to the person who photographs and orders food packages.

[1035] "Food packaging" refers to packaging that lists the names of food ingredients and nutritional information.

[1036] "Ingredient name" refers to the name of the ingredient that makes up the food.

[1037] "Nutritional information labeling" indicates nutritional information such as calories, protein, fat, carbohydrates, vitamins, and minerals contained in food.

[1038] The term "camera means" refers to a device for capturing image data.

[1039] "Character recognition technology" refers to technology for converting image data into text data.

[1040] "Text data" refers to character information obtained by character recognition technology.

[1041] "AI analysis means" refers to a device or program for analyzing data using artificial intelligence.

[1042] "Advice" refers to guidance or suggestions provided to a user, such as dietary suggestions, exercise plans, and health risk notifications.

[1043] "Healthcare services" refers to various services aimed at health management.

[1044] A "wearable device" is a wearable electronic device that monitors health status and performs other functions.

[1045] "Food delivery" refers to a service that delivers ordered food to users.

[1046] "Means of providing advice and health risk notifications" refers to a function that provides health information to users based on the nutritional content and ingredient names of the photographed food.

[1047] This system photographs and analyzes the names of ingredients and nutritional information on food packages, and provides users with appropriate dietary suggestions and health risk notifications. This system mainly consists of the following main components:

[1048] Main Components

[1049] 1. Camera Means

[1050] This is a means for users to take pictures of food packages using a camera such as a smartphone.

[1051] 2. Character recognition technology

[1052] This is a technology used to convert captured image data into text data. Specifically, Optical Character Recognition (OCR) technology is used.

[1053] 3. AI analysis means

[1054] This is an AI technology that analyzes the converted text data and generates optimal diet suggestions, exercise plans, and health risk notifications based on the user's health status.

[1055] 4. How to access your user profile

[1056] It is a means of referencing a user's individual profile, including their health status, allergy information, and past dietary history.

[1057] 5. Collaboration Methods

[1058] It is a means of linking data with other healthcare services and wearable devices.

[1059] 6. How to present generated advice

[1060] This is a means for presenting the generated advice to the user. The user is notified via an application on a smartphone or the like.

[1061] System program processing

[1062] The processing flow in this system is explained in natural language.

[1063] Camera photography and character recognition

[1064] Users use their smartphone camera to take a photo of the ingredients and nutritional information on food packages. The image data is then converted into text using OCR technology. The software used includes Python, PIL (Python Imaging Library), and pytesseract.

[1065] Data transmission and AI analysis

[1066] The converted text data is sent to a server via a smartphone application. Inside the server, this text data is analyzed using AI analysis tools. Specifically, optimal meal suggestions and health risk notifications are generated based on the user's health condition and dietary history.

[1067] Personalized advice and displays

[1068] The advice generated by the server is sent in real time to a smartphone application and displayed to the user, allowing the user to easily select appropriate dietary choices and exercise plans.

[1069] Collaboration with wearable devices

[1070] Additionally, the generated dietary data and exercise plans are automatically synchronized with other healthcare services and wearable devices.

[1071] Specific examples

[1072] When a user uses a food delivery service, they take a photo of the food package they are ordering with their smartphone. The captured image is converted into text data using OCR technology, and that data is sent to a server. The server then uses AI analysis to generate personalized health advice based on the user's health condition and past dietary history. For example, a message such as "This item is high in calories, so we recommend choosing a low-calorie option for your next meal" is displayed.

[1073] Prompt Sentence Examples

[1074] "I would like to develop an application that allows users to take a photo of the nutritional information on food packaging, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. Please write the following program in Python. The input is an image of the food package, and the nutritional information is extracted using OCR, sent to the server for analysis, and the generated advice is presented to the user."

[1075] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1076] Step 1:

[1077] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is the image captured by the camera. This image data becomes the first input to the system. The specific operation is for the user to turn on the smartphone, open the camera, focus on the food package, and press the shutter button.

[1078] Step 2:

[1079] The device converts the captured image data into text data using optical character recognition (OCR). The input is the captured image data, and the output is text data including the names of ingredients and nutritional information. The specific software used here is Python, the PIL library, and pytesseract.

[1080] Step 3:

[1081] The device sends the converted text data to the server. The input is the text data obtained by the OCR process, and the output is the text data sent to the server. The specific operation is that the smartphone application generates an HTTP request and POSTs the text data to the server.

[1082] Step 4:

[1083] The server analyzes the received text data using AI analysis methods. The input is the text data sent to the server, and the output is advice data including optimal dietary suggestions and health risk notifications for the user. This analysis uses machine learning models and database matching.

[1084] Step 5:

[1085] The server sends the generated advice data to the terminal. The input is the advice data generated by AI analysis, and the output is the advice message sent to the smartphone. The specific operation is that the server returns the advice data to the terminal as an HTTP response.

[1086] Step 6:

[1087] The terminal notifies the user of the received advice data. The input is the advice data sent from the server, and the output is an advice message displayed to the user. The specific operation is that the smartphone application displays the advice message on the user interface.

[1088] Step 7:

[1089] The device automatically synchronizes the generated dietary data and exercise plan with wearable devices and other healthcare services. The input is advice data sent from the server, and the output is data synchronized with the wearable device or healthcare service. Specifically, the smartphone transmits the data to other devices or services via Bluetooth or an internet connection.

[1090] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1091] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides users with appropriate meal suggestions, exercise plans, and health risk notifications. It also combines an emotion engine that recognizes the user's emotions to provide even more personalized advice.

[1092] Program Overview

[1093] Reading and parsing package information

[1094] The user launches the smartphone application and takes a picture of the food package using the camera. The device then converts the captured image data into text using optical character recognition (OCR). This OCR process extracts information such as the names of ingredients and nutritional information.

[1095] Use of emotion engine

[1096] When a user operates the app, an emotion engine works to recognize the user's emotions in real time through sensors such as the camera and microphone. The emotion engine analyzes facial expressions, tone of voice, and choice of words to determine the user's current emotional state.

[1097] Data transmission and AI analysis

[1098] The converted text data and the user's emotional state data are sent from the device to a server. The server analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[1099] Generating and presenting personalized advice

[1100] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. The emotion engine also takes into account the user's emotional state, providing more appropriate advice. For example, if the user is feeling stressed, the system can suggest foods with a relaxing effect and an exercise plan to help them relax.

[1101] Integration with other healthcare services and wearable devices

[1102] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[1103] Specific examples

[1104] Food packaging photography and analysis

[1105] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[1106] Emotion engine operation and data collection

[1107] When a user uses the app, the emotion engine analyzes the user's facial expressions and tone of voice to recognize when the user is feeling stressed. This emotion data is collected in real time and sent to the server along with the text data.

[1108] Data transmission and AI analysis

[1109] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[1110] Providing advice and guidelines

[1111] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[1112] Collaboration with wearable devices

[1113] The device can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[1114] The present invention aims to enable users to manage their own health simply and effectively. As a specific means for achieving this, the invention provides more personalized health management support by combining an emotion engine.

[1115] The processing flow will be explained below.

[1116] Step 1:

[1117] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[1118] Step 2:

[1119] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[1120] Step 3:

[1121] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[1122] Step 4:

[1123] The device sends text data to the server. The data is sent in JSON format via an API using an HTTP request.

[1124] Step 5:

[1125] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[1126] Step 6:

[1127] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[1128] Step 7:

[1129] The emotion engine captures the user's facial expressions and voice data through the smartphone's camera and microphone, and analyzes the user's emotions in real time. For example, it analyzes changes in facial expressions and tone of voice to identify the user's emotional state.

[1130] Step 8:

[1131] The extracted user emotion data is sent from the terminal to the server together with the text data.

[1132] Step 9:

[1133] The server analyzes the user's emotional data and compares it with the user's individual profile stored in a database, taking into account the user's health condition, allergy information, and past dietary history.

[1134] Step 10:

[1135] The server generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual profile, text data, and emotional data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[1136] Step 11:

[1137] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[1138] Step 12:

[1139] The device presents the received advice to the user. The advice is visually displayed in the app's UI, providing specific suggestions and risk notifications. If the user is under stress, additional suggestions for relaxing foods and exercise plans are also provided.

[1140] Step 13:

[1141] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[1142] Step 14:

[1143] The device synchronizes the user's latest dietary data and emotional state with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[1144] Example 2

[1145] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1146] In today's world, many people lead busy lives that make it difficult to find time to manage their health. This has led to a demand for systems that efficiently provide appropriate advice on food choices and exercise plans. However, conventional systems have struggled to provide personalized advice that fully considers the user's emotions and individual health status. In particular, there has been a lack of dietary suggestions and exercise plans that take into account health status and stress, which fluctuate depending on emotions.

[1147] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1148] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information label on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, a means for transmitting the text data converted by the character recognition technology and the user's emotional data to the server, an AI analysis means for analyzing the text data and emotional data received by the server and comparing them with nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications, and a means for presenting the generated advice to the user, thereby enabling personalized advice that takes into account the user's emotional state and individual health profile.

[1149] "User" refers to an individual who uses this system to photograph food packages and obtain advice.

[1150] "Camera means" refers to a device for capturing image data using the camera function installed on a smartphone, tablet, etc.

[1151] "Character recognition technology" refers to the process of converting characters in image data into text data using OCR (optical character recognition) technology.

[1152] "Text data" refers to digital string information extracted from image data using character recognition technology.

[1153] "Server" refers to the computer system that analyzes the received data and generates and provides advice to the user.

[1154] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[1155] "AI analysis means" refers to artificial intelligence technology that uses information in a database and past historical data to generate optimal advice.

[1156] "Meal Suggestion" refers to recommended food and meal combinations based on the user's nutritional balance and health status.

[1157] "Exercise Plan" refers to an exercise or fitness plan created based on a user's health status and individual goals.

[1158] "Health risk notification" refers to information that notifies users of potential health risks based on their current health condition and dietary habits.

[1159] An "emotion analysis engine" refers to software that analyzes a user's facial expressions and voice data to determine their emotional state.

[1160] "Personalized advice" refers to individualized advice provided based on the individual user's health status, emotions, dietary history, etc.

[1161] "Data integration means" refers to technology for synchronizing and sharing data with other healthcare services and wearable devices.

[1162] "Wearable device" refers to an electronic device worn on the user's body that measures and records health and exercise data.

[1163] The present invention is a system that allows users to photograph the ingredient names and nutritional information on food packages, analyzes the data, and provides the user with appropriate dietary suggestions, exercise plans, and health risk notifications. Furthermore, by combining this with an emotion analysis engine that recognizes the user's emotions, the system provides even more personalized advice. Specific examples of hardware and software for implementing the present invention use a smartphone camera, OCR technology, AI analysis on a server, and an emotion analysis engine.

[1164] User interaction and data acquisition

[1165] A user launches a smartphone application and uses the camera function to take a picture of a food package. For example, if a user wants to buy a sandwich at a convenience store, they take a picture of the sandwich package. The smartphone used in this case can be any smartphone with a commonly available camera function.

[1166] Image analysis and data conversion

[1167] The device's built-in OCR technology converts the captured image data into text data. For example, nutritional information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted using OCR technology. This process uses an OCR service such as Google Cloud Vision API.

[1168] Emotion analysis

[1169] When a user operates the app, the emotion analysis engine works through sensors such as the smartphone's camera and microphone. The emotion analysis engine analyzes the user's facial expressions and tone of voice to recognize their emotional state in real time. For example, it can identify whether the user is smiling or stressed.

[1170] Data transmission and analysis

[1171] The device sends the text data and emotion data converted using OCR technology to a server. The server receives this data and uses AI analysis methods to compare it with standard nutritional information and past dietary history in a database. The server then uses Python's TensorFlow library to perform data analysis using an AI model.

[1172] Generating and presenting personalized advice

[1173] The advice generated by the server is sent to the device and presented to the user through the app. For example, the server might create specific advice such as, "This sandwich is high in salt, so we recommend combining it with low-salt foods. We also recommend herbal tea, which helps relieve stress."

[1174] Collaboration with wearable devices

[1175] The device collects the latest dietary data and emotional state and syncs it to a wearable device via Bluetooth or Wi-Fi, allowing users to closely monitor their health.

[1176] Prompt Sentence Examples

[1177] An example of a prompt to be input to the generative AI model might be, "Analyze the nutritional information on a food package photographed by the user and generate personalized advice based on the user's emotions and health profile."

[1178] As described above, the present invention is a system that provides personalized advice that takes into account the user's emotional state and individual health profile, and supports health management.

[1179] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1180] Step 1:

[1181] The user launches the smartphone application and takes a photo of the food package.

[1182] Input: Food packaging image

[1183] Output: Captured image data

[1184] The user uses the smartphone camera to take a picture of the food packaging so that the ingredients and nutritional information are clearly visible. For example, the user takes a picture of the packaging of a sandwich they are about to purchase at a convenience store.

[1185] Step 2:

[1186] The device converts the captured image data into text data using OCR technology.

[1187] Input: Photographed image data

[1188] Output: Converted text data

[1189] The device uses OCR technology and services such as Google Cloud Vision API to recognize and extract text from image data. For example, text data such as "Salt 2.0g, Carbohydrate 15.0g, Calories 250kcal" can be extracted.

[1190] Step 3:

[1191] The device's emotion analysis engine analyzes the user's emotions.

[1192] Input: User's facial expression data and voice data

[1193] Output: Parsed emotion data

[1194] The emotion analysis engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time to recognize the user's emotional state, for example, determining whether the user is smiling or feeling stressed.

[1195] Step 4:

[1196] The terminal transmits the text data and the emotion data to the server.

[1197] Input: Converted text data and emotion data

[1198] Output: Data sent to the server

[1199] The device uses the HTTPS protocol to securely transmit text data and emotion data to the server.

[1200] Step 5:

[1201] The data received by the server is analyzed using AI.

[1202] Input: Transmitted text data and emotion data

[1203] Output: Analysis results of dietary suggestions, exercise plans, and health risk notifications

[1204] The server uses Python's TensorFlow library to run AI models that compare the data with standard nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications.

[1205] Step 6:

[1206] The server transmits the generated advice to the terminal and presents it to the user.

[1207] Input: Advice generated by AI analysis

[1208] Output: Advisory message displayed on the user's terminal

[1209] The server sends the generated advice to the device, which then displays it on the app's user interface. For example, the device might display a specific message like, "This sandwich is high in salt, so we recommend pairing it with a low-salt food. We also recommend herbal tea, which is effective for relieving stress."

[1210] Step 7:

[1211] The terminal synchronizes the collected data with the wearable device.

[1212] Input: Latest dietary and emotional state data

[1213] Output: Data synced to the wearable device

[1214] The terminal automatically synchronizes the latest data to the wearable device via Bluetooth or Wi-Fi, allowing the wearable device to reflect the user's latest dietary and emotional state, enabling detailed health management.

[1215] Through these steps, users can easily and effectively manage their own health. The system also works with an emotion analysis engine to provide personalized advice based on the user's emotional state, providing even more effective support.

[1216] (Application example 2)

[1217] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1218] In modern society, poor eating habits and poor stress management are having a negative impact on health. In particular, it is difficult to consider one's own health and emotional state when choosing food, making it difficult to provide appropriate dietary recommendations and health management. For this reason, there is a need for a system that allows users to easily understand the nutritional information of foods and receive dietary recommendations based on their own health and emotional state.

[1219] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1220] In this invention, the server includes a photographing means for a user to photograph the ingredient names and nutritional information on a food package, a conversion means for converting the image data photographed by the photographing means into text data using character recognition technology, an emotion recognition means for recognizing the user's emotional state, an AI analysis means for analyzing the text data and the emotional state data and generating optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual health condition, and a presentation means for presenting the generated advice to the user. This enables the user to select meals that take their own health condition and emotions into consideration, enabling comprehensive health management.

[1221] "Photographing means" is a general term for devices or modules that allow users to photograph the ingredient names and nutritional information on food packages. This includes cameras and the camera function on smartphones.

[1222] "Conversion means" refers to a process or device that converts image data obtained by the imaging means into text data using character recognition technology. Specifically, this includes OCR (Optical Character Recognition) technology.

[1223] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice to recognize their emotional state. Examples of this include facial recognition technology and voice analysis technology.

[1224] "AI analysis means" refers to an artificial intelligence algorithm or system that analyzes text data and emotional state data and generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition.

[1225] The "presentation means" refers to a device or interface for displaying the generated advice to the user, including the display and notification functions of a smartphone.

[1226] "Health status" refers to information that indicates the user's physical and mental condition, and includes medical data and self-reported information.

[1227] "Meal Suggestions" are content that provides optimal foods and meal plans based on the user's health and emotional state.

[1228] An "exercise plan" refers to appropriate exercise content or programs suggested based on the user's health and emotional state.

[1229] A "health risk notification" is an alert or notification that notifies a user of a risk regarding their health condition, such as a suggestion to avoid excessive salt intake.

[1230] "Emotional state" is information that indicates the psychological state of the user, and includes data that is primarily acquired through facial expression recognition and voice analysis.

[1231] MODE FOR CARRYING OUT THE INVENTION

[1232] This invention is a system that supports users in choosing their own meals and managing their health. The system analyzes the user's photographed data using character recognition technology and provides personalized advice combined with the user's emotional state.

[1233] Hardware and software used

[1234] Hardware

[1235] 1. Smartphone: Used by users to take photos of food packaging.

[1236] 2. Camera: Use the built-in camera on your smartphone.

[1237] 3. Server: A remote server used for data analysis and advice generation.

[1238] 4. Wearable device (optional): Used to synchronize user exercise data and calorie intake information.

[1239] software

[1240] 1. OCR software: Extract text data from images using software such as Tesseract OCR.

[1241] 2. Emotion recognition software: Analyzes user emotions using libraries such as DeepFace.

[1242] 3. AI analysis system: Algorithms for generating dietary suggestions and health risk notifications.

[1243] What the system does

[1244] Filming method

[1245] Users use their smartphone camera to take a photo of the ingredient list and nutritional information on a food package, and the image is saved in the smartphone's internal storage.

[1246] Conversion Method

[1247] The captured image is converted into text data using OCR technology (e.g., Tesseract OCR) within the smartphone, which contains important information such as nutritional information and ingredient names.

[1248] emotion recognition means

[1249] While the user is interacting with the application, the smartphone's camera and microphone detect the user's facial expressions and voice, and emotion recognition software (e.g., DeepFace) is used to analyze the user's emotional state. This data is collected in real time.

[1250] AI analysis means

[1251] The device sends text data and emotional state data to a server, which analyzes the received data and generates optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual profile (e.g., health status and dietary history). The algorithms used here include standard machine learning and deep learning models.

[1252] Presentation means

[1253] The generated advice is sent from the server to the smartphone and displayed to the user, and may include, for example, reasons why certain foods should be avoided, suggestions for alternative foods, or relaxing exercise plans.

[1254] Specific examples

[1255] Food packaging photography and analysis

[1256] A user picks up a sandwich they are planning to buy at a convenience store, launches the app on their smartphone, and uses the camera to take a picture of the package. This image data is then converted into text data using OCR technology within the app. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[1257] Emotion engine operation and data collection

[1258] When a user uses the app, emotion recognition software analyzes their facial expressions and tone of voice to determine if they are feeling stressed. This emotional data is collected in real time and sent to a server along with text data.

[1259] Data transmission and AI analysis

[1260] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[1261] Providing advice and guidelines

[1262] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[1263] Collaboration with wearable devices

[1264] The terminal can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[1265] Example prompts for generative AI models

[1266] When a user scans a food package, the app uses OCR technology to extract nutritional information. It then uses an emotion recognition engine to analyze the user's emotional state. This data is sent to a server, which generates optimal meal suggestions and exercise plans for the user. By providing advice based on the user's emotional state, the app supports personalized health management.

[1267] This allows users to receive optimal food suggestions based on their individual health condition and emotions, making it possible to achieve comprehensive health management.

[1268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1269] Step 1:

[1270] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is an image file. This image is saved in the application's internal storage. Specifically, the user activates the camera function within the application, points the camera at the food package, and presses the capture button.

[1271] Step 2:

[1272] The device converts the captured image data into text data using optical character recognition (OCR). OCR software (e.g., Tesseract OCR) is used to extract nutritional information and ingredient names from the input image. The input is image data, and the output is text data. Specifically, the OCR software is launched and the conversion process is carried out.

[1273] Step 3:

[1274] The device uses the smartphone's camera and microphone to detect the user's facial expressions and voice, and analyzes their emotional state using emotion recognition software (e.g., DeepFace). The input is an image of the user's facial expression and voice data, and the output is emotional state data. Specifically, the device takes a photo of the user's face with the camera and records their voice with the microphone, and then analyzes the data.

[1275] Step 4:

[1276] The terminal sends text data and emotional state data to the server. The input is the text data and emotional state data, and the output is the data sent to the server. Specifically, the application establishes communication with the server and sends an HTTP request.

[1277] Step 5:

[1278] The server analyzes the received text data and emotional state data and compares it with standard nutritional information in a database and the user's individual profile to generate optimal meal suggestions, exercise plans, and health risk notifications. The input is the text data and emotional state data, and the output is the generated advice. Specifically, it uses machine learning algorithms to analyze the data and generate personalized advice.

[1279] Step 6:

[1280] The server sends the generated advice to the terminal, and the terminal presents the advice to the user. The input is the advice data sent from the server, and the output is the advice displayed to the user. Specifically, it displays a message on the application's UI.

[1281] Step 7:

[1282] After the user selects food based on the advice, the terminal synchronizes the latest dietary data and emotional state with the wearable device. The input is dietary data and emotional state data, and the output is data synchronized to the wearable device. Specifically, the data is synchronized using a communication method such as Bluetooth.

[1283] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1284] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1285] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1286] [Fourth embodiment]

[1287] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1288] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1289] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1290] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1291] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1292] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1293] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1294] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1295] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1296] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1297] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1298] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1299] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1300] The present invention relates to a system that allows users to take a photo of the names of ingredients and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes a camera, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[1301] Program Overview

[1302] Reading package information

[1303] The user launches the smartphone application and takes a photo of the food package with the camera. The device then converts the captured image data into text using character recognition technology. This OCR process extracts the names of ingredients and nutritional information.

[1304] Data transmission and analysis

[1305] The converted text data is sent from the device to a server, which analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[1306] Generating and presenting personalized advice

[1307] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. Users can check this information through the app and use it to manage their daily health.

[1308] Integration with other healthcare services and wearable devices

[1309] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[1310] Specific examples

[1311] Food packaging photography and analysis

[1312] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[1313] Data transmission and AI analysis

[1314] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions (e.g., high blood pressure risk) and dietary history previously entered by the user into the app. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[1315] Providing advice and guidelines

[1316] The generated advice is sent to the device and displayed to the user on the app. The user can then follow the advice and, for example, select additional low-salt foods, taking into account the overall balance of their diet, and make purchasing decisions.

[1317] Collaboration with wearable devices

[1318] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[1319] As described above, the present invention aims to enable users to easily and effectively manage their own health, and provides specific means and a system for achieving this.

[1320] The processing flow will be explained below.

[1321] Step 1:

[1322] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[1323] Step 2:

[1324] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[1325] Step 3:

[1326] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[1327] Step 4:

[1328] The device sends the converted text data to the server, which then sends the JSON formatted data via the API using an HTTP request.

[1329] Step 5:

[1330] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[1331] Step 6:

[1332] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[1333] Step 7:

[1334] The server retrieves the user's individual profile (health status, allergy information, past dietary history, etc.) from a database.

[1335] Step 8:

[1336] The server generates meal suggestions, exercise plans, and health risk notifications based on the user's individual profile and analytical data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[1337] Step 9:

[1338] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[1339] Step 10:

[1340] The device displays the advice received to the user, visually displaying the advice in the app's UI and providing specific suggestions and risk notifications.

[1341] Step 11:

[1342] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[1343] Step 12:

[1344] The device synchronizes the user's latest dietary data and calorie intake information with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[1345] Example 1

[1346] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1347] There is a demand for a system that can easily digitize the ingredient names and nutritional information printed on food packages, and provide optimal meal suggestions, exercise plans, and health risk notifications based on the user's health condition and past dietary history. Furthermore, it is desirable for data to be easily linked with other healthcare services and wearable devices in order to more effectively manage the user's health. However, it has been difficult to meet all of these requirements with conventional methods.

[1348] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1349] In this invention, the server includes an imaging device that allows a user to photograph the ingredient names and nutritional information on food packages; a means for converting the image data captured by the imaging device into text data using character recognition technology; a means for analyzing the converted text data using AI analysis and comparing it with nutritional information and past diet history in a database; a means for generating optimal diet suggestions, exercise plans, and health risk notifications based on the user's individual health status; a means for presenting the generated advice to the user; and a means for linking data with other healthcare services and wearable devices. This allows for fast and accurate digitization of information from food packages, enabling the provision of personalized, specific, and effective health management advice to users. Furthermore, data linking with different healthcare services and wearable devices enables comprehensive health management.

[1350] "Imaging means" refers to a device for photographing the ingredient names and nutritional information on food packaging.

[1351] "Character recognition technology" refers to technology that analyzes image data captured by an imaging device and converts it into text data.

[1352] "Text data" refers to character information extracted from image data using character recognition technology.

[1353] "AI analysis means" refers to a function that includes artificial intelligence technology for analyzing the converted text data and comparing it with nutritional information and past dietary history in a database.

[1354] A "database" refers to a system that systematically stores information such as nutritional information and past dietary history and manages it in an accessible form.

[1355] "Dietary Suggestion" refers to advice that provides specific information to recommend optimal food choices to a user.

[1356] An "exercise plan" refers to an appropriate exercise schedule and activity content suggested based on the user's health condition and goals.

[1357] "Health risk notification" refers to notifications that warn users of potential health risks based on their health status and the foods they have consumed.

[1358] The "means for presenting advice" refers to a method or tool for displaying the generated advice to the user and encouraging appropriate action.

[1359] "Healthcare services" refers to all services related to health management, including, for example, medical institutions and fitness programs.

[1360] A "wearable device" refers to an electronic device that can be worn by a user and that can measure and record health and activity data.

[1361] A "generative AI model" refers to an artificial intelligence algorithm or system that generates appropriate outputs given inputs.

[1362] A "prompt" refers to a specific instruction or question-style sentence that is input into a generative AI model.

[1363] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. This system includes an imaging means, optical character recognition (OCR), an AI analysis means, a user profile reference means, and a means for linking with other healthcare services and wearable devices.

[1364] Hardware and Software Use

[1365] The user launches the smartphone application and takes a picture of the food package using the imaging device (smartphone camera). The captured image data is converted into text data using the device's internal character recognition technology (OCR). The converted text data is then sent from the device to the server.

[1366] On the server side, the text data is analyzed using AI analysis tools and compared with nutritional information in the database and the user's past dietary history. Based on the user's health condition and individual profile, meal suggestions, exercise plans, and health risk notifications are generated. The generated advice is then sent back to the device and presented to the user.

[1367] Collaboration with other healthcare services and wearable devices is also an important factor. The system automatically synchronizes the user's exercise data and calorie intake information with the wearable device, supporting comprehensive health management.

[1368] Specific examples

[1369] A user picks up a sandwich they are about to purchase at a convenience store and launches an application on their smartphone. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[1370] The device sends this text data to a server, which analyzes it and compares it with standard nutritional information stored in a database. The server then generates optimal advice based on the health conditions the user has previously entered into the app (e.g., whether they are at risk of high blood pressure) and their past dietary history. The resulting advice might be, "This sandwich is high in salt, so we recommend combining it with low-salt foods."

[1371] The generated advice is displayed on the user's smartphone. The user follows this advice, for example, by selecting additional low-salt foods and making purchase decisions while taking into account the overall balance of their diet.

[1372] The device can then sync the user's dietary data to a wearable device, which updates calorie intake and nutritional balance data in real time, allowing users to continuously monitor their health.

[1373] Prompt Sentence Examples

[1374] By feeding the following prompts into the generative AI model, more specific and personalized advice can be given to the user:

[1375] "The food consumed was juice, with 20.0g of carbohydrates, 15mg of vitamin C, and 100kcal of calories. What dietary suggestions should be made for this food to a user at high risk of diabetes?"

[1376] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1377] Step 1: Launching the Application

[1378] explanation

[1379] The user taps on the smartphone to launch the application.

[1380] input

[1381] The user taps the application icon on their smartphone.

[1382] output

[1383] The application will launch and the home screen will be displayed.

[1384] Specific actions

[1385] A user's finger touches an application icon on the smartphone screen and taps the icon to launch the application.

[1386] Step 2: Photograph the packaging

[1387] explanation

[1388] The user takes a photo of the food package using the app's camera function.

[1389] input

[1390] The user selects the application's camera function and takes a photo of the food package using their smartphone.

[1391] output

[1392] An image of the food packaging is saved to the device.

[1393] Specific actions

[1394] The user selects the camera function button, points the smartphone camera at the food package, and taps the shutter button.

[1395] Step 3: Recognize text using OCR

[1396] explanation

[1397] The device processes the captured image data using OCR technology and converts it into text data.

[1398] input

[1399] Image data of food packaging.

[1400] output

[1401] Text data of ingredient names and nutritional information.

[1402] Specific actions

[1403] The device's internal OCR engine analyzes the image data and converts the recognized character information into text. For example, text data such as "Carbohydrates 15.0g, Salt 0.5g, Calories 200kcal" is generated.

[1404] Step 4: Sending data

[1405] explanation

[1406] The terminal transmits the converted text data to the server.

[1407] input

[1408] The converted text data.

[1409] output

[1410] The text data is sent to the server.

[1411] Specific actions

[1412] The device uses Wi-Fi or mobile data to send the text data extracted by OCR to the server.

[1413] Step 5: Analyze the data

[1414] explanation

[1415] The server analyzes the received data and compares it with the user profile and nutritional information in the database.

[1416] input

[1417] Text data and user profile data received by the server.

[1418] output

[1419] Generate optimal dietary suggestions, exercise plans, and health risk notifications.

[1420] Specific actions

[1421] The server's AI analysis tools compare the text data with a database and generate meal suggestions and exercise plans based on the user's health status.

[1422] Step 6: Generate personalized advice

[1423] explanation

[1424] The server generates individual advice and sends it to the terminal.

[1425] input

[1426] Analytics and user profile data.

[1427] output

[1428] The generated personalized advice is sent to the terminal.

[1429] Specific actions

[1430] The server generates advice and sends the data to the user's device. For example, the advice generated might be, "This food is high in salt, so eat it with low-salt foods."

[1431] Step 7: Providing advice

[1432] explanation

[1433] The terminal presents the received advice to the user.

[1434] input

[1435] Personalized advice received by the device.

[1436] output

[1437] Advice is displayed to the user within the application.

[1438] Specific actions

[1439] The device displays the advice received from the server within the application. When the user opens the app, the advice is displayed in a pop-up format.

[1440] Step 8: Integrate with wearable devices

[1441] explanation

[1442] The device syncs the food data to the wearable device.

[1443] input

[1444] User's dietary data.

[1445] output

[1446] Calorie intake and nutritional balance data synced to wearable devices.

[1447] Specific actions

[1448] The device transmits the user's meal data via Bluetooth or Wi-Fi to the wearable device, which updates the data in real time.

[1449] (Application example 1)

[1450] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1451] Conventional food delivery services often fail to provide users with sufficient information about the nutritional content and ingredients of the food they provide, which can lead to health risks and inappropriate dietary choices. Furthermore, the inability to provide real-time dietary suggestions based on individual health conditions and allergy information makes it difficult for users to manage their health.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1453] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, an AI analysis means for analyzing the text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition, a means for presenting the generated advice to the user, and a means for photographing the nutritional components and ingredient names of the food the user is about to order with the camera and providing advice and health risk notifications. This allows users to select appropriate meals based on their individual health condition when ordering food, making health management easier.

[1454] "User" refers to the person who photographs and orders food packages.

[1455] "Food packaging" refers to packaging that lists the names of food ingredients and nutritional information.

[1456] "Ingredient name" refers to the name of the ingredient that makes up the food.

[1457] "Nutritional information labeling" indicates nutritional information such as calories, protein, fat, carbohydrates, vitamins, and minerals contained in food.

[1458] The term "camera means" refers to a device for capturing image data.

[1459] "Character recognition technology" refers to technology for converting image data into text data.

[1460] "Text data" refers to character information obtained by character recognition technology.

[1461] "AI analysis means" refers to a device or program for analyzing data using artificial intelligence.

[1462] "Advice" refers to guidance or suggestions provided to a user, such as dietary suggestions, exercise plans, and health risk notifications.

[1463] "Healthcare services" refers to various services aimed at health management.

[1464] A "wearable device" is a wearable electronic device that monitors health status and performs other functions.

[1465] "Food delivery" refers to a service that delivers ordered food to users.

[1466] "Means of providing advice and health risk notifications" refers to a function that provides health information to users based on the nutritional content and ingredient names of the photographed food.

[1467] This system photographs and analyzes the names of ingredients and nutritional information on food packages, and provides users with appropriate dietary suggestions and health risk notifications. This system mainly consists of the following main components:

[1468] Main Components

[1469] 1. Camera Means

[1470] This is a means for users to take pictures of food packages using a camera such as a smartphone.

[1471] 2. Character recognition technology

[1472] This is a technology used to convert captured image data into text data. Specifically, Optical Character Recognition (OCR) technology is used.

[1473] 3. AI analysis means

[1474] This is an AI technology that analyzes the converted text data and generates optimal diet suggestions, exercise plans, and health risk notifications based on the user's health status.

[1475] 4. How to access your user profile

[1476] It is a means of referencing a user's individual profile, including their health status, allergy information, and past dietary history.

[1477] 5. Collaboration Methods

[1478] It is a means of linking data with other healthcare services and wearable devices.

[1479] 6. How to present generated advice

[1480] This is a means for presenting the generated advice to the user. The user is notified via an application on a smartphone or the like.

[1481] System program processing

[1482] The processing flow in this system is explained in natural language.

[1483] Camera photography and character recognition

[1484] Users use their smartphone camera to take a photo of the ingredients and nutritional information on food packages. The image data is then converted into text using OCR technology. The software used includes Python, PIL (Python Imaging Library), and pytesseract.

[1485] Data transmission and AI analysis

[1486] The converted text data is sent to a server via a smartphone application. Inside the server, this text data is analyzed using AI analysis tools. Specifically, optimal meal suggestions and health risk notifications are generated based on the user's health condition and dietary history.

[1487] Personalized advice and displays

[1488] The advice generated by the server is sent in real time to a smartphone application and displayed to the user, allowing the user to easily select appropriate dietary choices and exercise plans.

[1489] Collaboration with wearable devices

[1490] Additionally, the generated dietary data and exercise plans are automatically synchronized with other healthcare services and wearable devices.

[1491] Specific examples

[1492] When a user uses a food delivery service, they take a photo of the food package they are ordering with their smartphone. The captured image is converted into text data using OCR technology, and that data is sent to a server. The server then uses AI analysis to generate personalized health advice based on the user's health condition and past dietary history. For example, a message such as "This item is high in calories, so we recommend choosing a low-calorie option for your next meal" is displayed.

[1493] Prompt Sentence Examples

[1494] "I would like to develop an application that allows users to take a photo of the nutritional information on food packaging, analyzes the data, and provides appropriate meal suggestions, exercise plans, and health risk notifications to the user. Please write the following program in Python. The input is an image of the food package, and the nutritional information is extracted using OCR, sent to the server for analysis, and the generated advice is presented to the user."

[1495] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1496] Step 1:

[1497] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is the image captured by the camera. This image data becomes the first input to the system. The specific operation is for the user to turn on the smartphone, open the camera, focus on the food package, and press the shutter button.

[1498] Step 2:

[1499] The device converts the captured image data into text data using optical character recognition (OCR). The input is the captured image data, and the output is text data including the names of ingredients and nutritional information. The specific software used here is Python, the PIL library, and pytesseract.

[1500] Step 3:

[1501] The device sends the converted text data to the server. The input is the text data obtained by the OCR process, and the output is the text data sent to the server. The specific operation is that the smartphone application generates an HTTP request and POSTs the text data to the server.

[1502] Step 4:

[1503] The server analyzes the received text data using AI analysis methods. The input is the text data sent to the server, and the output is advice data including optimal dietary suggestions and health risk notifications for the user. This analysis uses machine learning models and database matching.

[1504] Step 5:

[1505] The server sends the generated advice data to the terminal. The input is the advice data generated by AI analysis, and the output is the advice message sent to the smartphone. The specific operation is that the server returns the advice data to the terminal as an HTTP response.

[1506] Step 6:

[1507] The terminal notifies the user of the received advice data. The input is the advice data sent from the server, and the output is an advice message displayed to the user. The specific operation is that the smartphone application displays the advice message on the user interface.

[1508] Step 7:

[1509] The device automatically synchronizes the generated dietary data and exercise plan with wearable devices and other healthcare services. The input is advice data sent from the server, and the output is data synchronized with the wearable device or healthcare service. Specifically, the smartphone transmits the data to other devices or services via Bluetooth or an internet connection.

[1510] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1511] The present invention relates to a system that allows users to take a photo of the ingredient names and nutritional information on food packages, analyzes the data, and provides users with appropriate meal suggestions, exercise plans, and health risk notifications. It also combines an emotion engine that recognizes the user's emotions to provide even more personalized advice.

[1512] Program Overview

[1513] Reading and parsing package information

[1514] The user launches the smartphone application and takes a picture of the food package using the camera. The device then converts the captured image data into text using optical character recognition (OCR). This OCR process extracts information such as the names of ingredients and nutritional information.

[1515] Use of emotion engine

[1516] When a user operates the app, an emotion engine works to recognize the user's emotions in real time through sensors such as the camera and microphone. The emotion engine analyzes facial expressions, tone of voice, and choice of words to determine the user's current emotional state.

[1517] Data transmission and AI analysis

[1518] The converted text data and the user's emotional state data are sent from the device to a server. The server analyzes the received data and compares it with nutritional information and past dietary history stored in a database. It also references the user's individual profile to generate data such as meal suggestions, exercise plans, and health risk notifications.

[1519] Generating and presenting personalized advice

[1520] The advice generated by the server is sent to the device and presented to the user. This advice includes specific suggestions on which foods to choose or avoid, as well as appropriate exercise plans. The emotion engine also takes into account the user's emotional state, providing more appropriate advice. For example, if the user is feeling stressed, the system can suggest foods with a relaxing effect and an exercise plan to help them relax.

[1521] Integration with other healthcare services and wearable devices

[1522] The system can also be integrated with other healthcare services and wearable devices. For example, a user's exercise data and calorie intake information can be automatically synchronized with the wearable device, enabling comprehensive health management.

[1523] Specific examples

[1524] Food packaging photography and analysis

[1525] A user picks up a sandwich they are about to purchase at a convenience store and launches the smartphone app. They use the camera to take a picture of the ingredients and nutritional information on the package. This image data is converted into text data using OCR technology on the device. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[1526] Emotion engine operation and data collection

[1527] When a user uses the app, the emotion engine analyzes the user's facial expressions and tone of voice to recognize when the user is feeling stressed. This emotion data is collected in real time and sent to the server along with the text data.

[1528] Data transmission and AI analysis

[1529] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[1530] Providing advice and guidelines

[1531] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[1532] Collaboration with wearable devices

[1533] The device can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[1534] The present invention aims to enable users to manage their own health simply and effectively. As a specific means for achieving this, the invention provides more personalized health management support by combining an emotion engine.

[1535] The processing flow will be explained below.

[1536] Step 1:

[1537] The user launches the smartphone application and taps the "New Scan" button on the home screen.

[1538] Step 2:

[1539] The user points the camera at the ingredient list or nutritional information on a food package, then presses the camera button to capture the image.

[1540] Step 3:

[1541] Image data captured by the device is converted into text data using OCR (Optical Character Recognition) technology. The OCR engine extracts text information from the image and stores it as JSON format data.

[1542] Step 4:

[1543] The device sends text data to the server. The data is sent in JSON format via an API using an HTTP request.

[1544] Step 5:

[1545] The server analyzes the received text data, parses the data, and extracts information about nutritional components and ingredients.

[1546] Step 6:

[1547] The server checks the extracted information against a database, compares it with standard data on nutritional components and ingredients, and makes any necessary corrections or additions.

[1548] Step 7:

[1549] The emotion engine captures the user's facial expressions and voice data through the smartphone's camera and microphone, and analyzes the user's emotions in real time. For example, it analyzes changes in facial expressions and tone of voice to identify the user's emotional state.

[1550] Step 8:

[1551] The extracted user emotion data is sent from the terminal to the server together with the text data.

[1552] Step 9:

[1553] The server analyzes the user's emotional data and compares it with the user's individual profile stored in a database, taking into account the user's health condition, allergy information, and past dietary history.

[1554] Step 10:

[1555] The server generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual profile, text data, and emotional data, using AI analysis methods to apply specialized rule-based algorithms and machine learning models.

[1556] Step 11:

[1557] The server generates advice and sends it to the device in JSON format as an HTTP response using the API.

[1558] Step 12:

[1559] The device presents the received advice to the user. The advice is visually displayed in the app's UI, providing specific suggestions and risk notifications. If the user is under stress, additional suggestions for relaxing foods and exercise plans are also provided.

[1560] Step 13:

[1561] The user follows the advice and chooses an action, for example, choosing different foods based on the advice or implementing a recommended exercise plan.

[1562] Step 14:

[1563] The device synchronizes the user's latest dietary data and emotional state with other healthcare services and wearable devices, transmitting the data via Bluetooth or an internet connection.

[1564] Example 2

[1565] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1566] In today's world, many people lead busy lives that make it difficult to find time to manage their health. This has led to a demand for systems that efficiently provide appropriate advice on food choices and exercise plans. However, conventional systems have struggled to provide personalized advice that fully considers the user's emotions and individual health status. In particular, there has been a lack of dietary suggestions and exercise plans that take into account health status and stress, which fluctuate depending on emotions.

[1567] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1568] In this invention, the server includes a camera means for a user to photograph the ingredient names and nutritional information label on a food package, a means for converting the image data photographed by the camera means into text data using character recognition technology, a means for transmitting the text data converted by the character recognition technology and the user's emotional data to the server, an AI analysis means for analyzing the text data and emotional data received by the server and comparing them with nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications, and a means for presenting the generated advice to the user, thereby enabling personalized advice that takes into account the user's emotional state and individual health profile.

[1569] "User" refers to an individual who uses this system to photograph food packages and obtain advice.

[1570] "Camera means" refers to a device for capturing image data using the camera function installed on a smartphone, tablet, etc.

[1571] "Character recognition technology" refers to the process of converting characters in image data into text data using OCR (optical character recognition) technology.

[1572] "Text data" refers to digital string information extracted from image data using character recognition technology.

[1573] "Server" refers to the computer system that analyzes the received data and generates and provides advice to the user.

[1574] "Emotional data" refers to data that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[1575] "AI analysis means" refers to artificial intelligence technology that uses information in a database and past historical data to generate optimal advice.

[1576] "Meal Suggestion" refers to recommended food and meal combinations based on the user's nutritional balance and health status.

[1577] "Exercise Plan" refers to an exercise or fitness plan created based on a user's health status and individual goals.

[1578] "Health risk notification" refers to information that notifies users of potential health risks based on their current health condition and dietary habits.

[1579] An "emotion analysis engine" refers to software that analyzes a user's facial expressions and voice data to determine their emotional state.

[1580] "Personalized advice" refers to individualized advice provided based on the individual user's health status, emotions, dietary history, etc.

[1581] "Data integration means" refers to technology for synchronizing and sharing data with other healthcare services and wearable devices.

[1582] "Wearable device" refers to an electronic device worn on the user's body that measures and records health and exercise data.

[1583] The present invention is a system that allows users to photograph the ingredient names and nutritional information on food packages, analyzes the data, and provides the user with appropriate dietary suggestions, exercise plans, and health risk notifications. Furthermore, by combining this with an emotion analysis engine that recognizes the user's emotions, the system provides even more personalized advice. Specific examples of hardware and software for implementing the present invention use a smartphone camera, OCR technology, AI analysis on a server, and an emotion analysis engine.

[1584] User interaction and data acquisition

[1585] A user launches a smartphone application and uses the camera function to take a picture of a food package. For example, if a user wants to buy a sandwich at a convenience store, they take a picture of the sandwich package. The smartphone used in this case can be any smartphone with a commonly available camera function.

[1586] Image analysis and data conversion

[1587] The device's built-in OCR technology converts the captured image data into text data. For example, nutritional information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted using OCR technology. This process uses an OCR service such as Google Cloud Vision API.

[1588] Emotion analysis

[1589] When a user operates the app, the emotion analysis engine works through sensors such as the smartphone's camera and microphone. The emotion analysis engine analyzes the user's facial expressions and tone of voice to recognize their emotional state in real time. For example, it can identify whether the user is smiling or stressed.

[1590] Data transmission and analysis

[1591] The device sends the text data and emotion data converted using OCR technology to a server. The server receives this data and uses AI analysis methods to compare it with standard nutritional information and past dietary history in a database. The server then uses Python's TensorFlow library to perform data analysis using an AI model.

[1592] Generating and presenting personalized advice

[1593] The advice generated by the server is sent to the device and presented to the user through the app. For example, the server might create specific advice such as, "This sandwich is high in salt, so we recommend combining it with low-salt foods. We also recommend herbal tea, which helps relieve stress."

[1594] Collaboration with wearable devices

[1595] The device collects the latest dietary data and emotional state and syncs it to a wearable device via Bluetooth or Wi-Fi, allowing users to closely monitor their health.

[1596] Prompt Sentence Examples

[1597] An example of a prompt to be input to the generative AI model might be, "Analyze the nutritional information on a food package photographed by the user and generate personalized advice based on the user's emotions and health profile."

[1598] As described above, the present invention is a system that provides personalized advice that takes into account the user's emotional state and individual health profile, and supports health management.

[1599] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1600] Step 1:

[1601] The user launches the smartphone application and takes a photo of the food package.

[1602] Input: Food packaging image

[1603] Output: Captured image data

[1604] The user uses the smartphone camera to take a picture of the food packaging so that the ingredients and nutritional information are clearly visible. For example, the user takes a picture of the packaging of a sandwich they are about to purchase at a convenience store.

[1605] Step 2:

[1606] The device converts the captured image data into text data using OCR technology.

[1607] Input: Photographed image data

[1608] Output: Converted text data

[1609] The device uses OCR technology and services such as Google Cloud Vision API to recognize and extract text from image data. For example, text data such as "Salt 2.0g, Carbohydrate 15.0g, Calories 250kcal" can be extracted.

[1610] Step 3:

[1611] The device's emotion analysis engine analyzes the user's emotions.

[1612] Input: User's facial expression data and voice data

[1613] Output: Parsed emotion data

[1614] The emotion analysis engine uses the smartphone's camera and microphone to analyze the user's facial expressions and tone of voice in real time to recognize the user's emotional state, for example, determining whether the user is smiling or feeling stressed.

[1615] Step 4:

[1616] The terminal transmits the text data and the emotion data to the server.

[1617] Input: Converted text data and emotion data

[1618] Output: Data sent to the server

[1619] The device uses the HTTPS protocol to securely transmit text data and emotion data to the server.

[1620] Step 5:

[1621] The data received by the server is analyzed using AI.

[1622] Input: Transmitted text data and emotion data

[1623] Output: Analysis results of dietary suggestions, exercise plans, and health risk notifications

[1624] The server uses Python's TensorFlow library to run AI models that compare the data with standard nutritional information in a database and past dietary history to generate optimal meal suggestions, exercise plans, and health risk notifications.

[1625] Step 6:

[1626] The server transmits the generated advice to the terminal and presents it to the user.

[1627] Input: Advice generated by AI analysis

[1628] Output: Advisory message displayed on the user's terminal

[1629] The server sends the generated advice to the device, which then displays it on the app's user interface. For example, the device might display a specific message like, "This sandwich is high in salt, so we recommend pairing it with a low-salt food. We also recommend herbal tea, which is effective for relieving stress."

[1630] Step 7:

[1631] The terminal synchronizes the collected data with the wearable device.

[1632] Input: Latest dietary and emotional state data

[1633] Output: Data synced to the wearable device

[1634] The terminal automatically synchronizes the latest data to the wearable device via Bluetooth or Wi-Fi, allowing the wearable device to reflect the user's latest dietary and emotional state, enabling detailed health management.

[1635] Through these steps, users can easily and effectively manage their own health. The system also works with an emotion analysis engine to provide personalized advice based on the user's emotional state, providing even more effective support.

[1636] (Application example 2)

[1637] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1638] In modern society, poor eating habits and poor stress management are having a negative impact on health. In particular, it is difficult to consider one's own health and emotional state when choosing food, making it difficult to provide appropriate dietary recommendations and health management. For this reason, there is a need for a system that allows users to easily understand the nutritional information of foods and receive dietary recommendations based on their own health and emotional state.

[1639] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1640] In this invention, the server includes a photographing means for a user to photograph the ingredient names and nutritional information on a food package, a conversion means for converting the image data photographed by the photographing means into text data using character recognition technology, an emotion recognition means for recognizing the user's emotional state, an AI analysis means for analyzing the text data and the emotional state data and generating optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual health condition, and a presentation means for presenting the generated advice to the user. This enables the user to select meals that take their own health condition and emotions into consideration, enabling comprehensive health management.

[1641] "Photographing means" is a general term for devices or modules that allow users to photograph the ingredient names and nutritional information on food packages. This includes cameras and the camera function on smartphones.

[1642] "Conversion means" refers to a process or device that converts image data obtained by the imaging means into text data using character recognition technology. Specifically, this includes OCR (Optical Character Recognition) technology.

[1643] "Emotion recognition means" refers to technology or devices that analyze a user's facial expressions and voice to recognize their emotional state. Examples of this include facial recognition technology and voice analysis technology.

[1644] "AI analysis means" refers to an artificial intelligence algorithm or system that analyzes text data and emotional state data and generates optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition.

[1645] The "presentation means" refers to a device or interface for displaying the generated advice to the user, including the display and notification functions of a smartphone.

[1646] "Health status" refers to information that indicates the user's physical and mental condition, and includes medical data and self-reported information.

[1647] "Meal Suggestions" are content that provides optimal foods and meal plans based on the user's health and emotional state.

[1648] An "exercise plan" refers to appropriate exercise content or programs suggested based on the user's health and emotional state.

[1649] A "health risk notification" is an alert or notification that notifies a user of a risk regarding their health condition, such as a suggestion to avoid excessive salt intake.

[1650] "Emotional state" is information that indicates the psychological state of the user, and includes data that is primarily acquired through facial expression recognition and voice analysis.

[1651] MODE FOR CARRYING OUT THE INVENTION

[1652] This invention is a system that supports users in choosing their own meals and managing their health. The system analyzes the user's photographed data using character recognition technology and provides personalized advice combined with the user's emotional state.

[1653] Hardware and software used

[1654] Hardware

[1655] 1. Smartphone: Used by users to take photos of food packaging.

[1656] 2. Camera: Use the built-in camera on your smartphone.

[1657] 3. Server: A remote server used for data analysis and advice generation.

[1658] 4. Wearable device (optional): Used to synchronize user exercise data and calorie intake information.

[1659] software

[1660] 1. OCR software: Extract text data from images using software such as Tesseract OCR.

[1661] 2. Emotion recognition software: Analyzes user emotions using libraries such as DeepFace.

[1662] 3. AI analysis system: Algorithms for generating dietary suggestions and health risk notifications.

[1663] What the system does

[1664] Filming method

[1665] Users use their smartphone camera to take a photo of the ingredient list and nutritional information on a food package, and the image is saved in the smartphone's internal storage.

[1666] Conversion Method

[1667] The captured image is converted into text data using OCR technology (e.g., Tesseract OCR) within the smartphone, which contains important information such as nutritional information and ingredient names.

[1668] emotion recognition means

[1669] While the user is interacting with the application, the smartphone's camera and microphone detect the user's facial expressions and voice, and emotion recognition software (e.g., DeepFace) is used to analyze the user's emotional state. This data is collected in real time.

[1670] AI analysis means

[1671] The device sends text data and emotional state data to a server, which analyzes the received data and generates optimal meal suggestions, exercise plans, and health risk notifications based on the user's individual profile (e.g., health status and dietary history). The algorithms used here include standard machine learning and deep learning models.

[1672] Presentation means

[1673] The generated advice is sent from the server to the smartphone and displayed to the user, and may include, for example, reasons why certain foods should be avoided, suggestions for alternative foods, or relaxing exercise plans.

[1674] Specific examples

[1675] Food packaging photography and analysis

[1676] A user picks up a sandwich they are planning to buy at a convenience store, launches the app on their smartphone, and uses the camera to take a picture of the package. This image data is then converted into text data using OCR technology within the app. For example, information such as "salt 2.0g, carbohydrates 15.0g, calories 250kcal" is extracted.

[1677] Emotion engine operation and data collection

[1678] When a user uses the app, emotion recognition software analyzes their facial expressions and tone of voice to determine if they are feeling stressed. This emotional data is collected in real time and sent to a server along with text data.

[1679] Data transmission and AI analysis

[1680] The device sends text data and emotional data to a server, which analyzes the nutritional data and compares it with standard nutritional information in a database. The server also references the user's health condition and dietary history, which they have previously entered into the app, to generate optimal advice that also takes into account their emotional state.

[1681] Providing advice and guidelines

[1682] The generated advice is sent to the device and displayed to the user on the app. For example, specific advice such as "This sandwich is high in salt, so we recommend pairing it with a low-salt food. It would also be a good idea to purchase some herbal tea for relaxation" is presented. The user can follow this advice to make appropriate food choices.

[1683] Collaboration with wearable devices

[1684] The terminal can then sync the user's latest dietary data and emotional state to the wearable device, allowing the user to monitor their health in more detail.

[1685] Example prompts for generative AI models

[1686] When a user scans a food package, the app uses OCR technology to extract nutritional information. It then uses an emotion recognition engine to analyze the user's emotional state. This data is sent to a server, which generates optimal meal suggestions and exercise plans for the user. By providing advice based on the user's emotional state, the app supports personalized health management.

[1687] This allows users to receive optimal food suggestions based on their individual health condition and emotions, making it possible to achieve comprehensive health management.

[1688] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1689] Step 1:

[1690] The user uses the smartphone camera to take a photo of the ingredient list and nutritional information on a food package. The input is image data of the food package, and the output is an image file. This image is saved in the application's internal storage. Specifically, the user activates the camera function within the application, points the camera at the food package, and presses the capture button.

[1691] Step 2:

[1692] The device converts the captured image data into text data using optical character recognition (OCR). OCR software (e.g., Tesseract OCR) is used to extract nutritional information and ingredient names from the input image. The input is image data, and the output is text data. Specifically, the OCR software is launched and the conversion process is carried out.

[1693] Step 3:

[1694] The device uses the smartphone's camera and microphone to detect the user's facial expressions and voice, and analyzes their emotional state using emotion recognition software (e.g., DeepFace). The input is an image of the user's facial expression and voice data, and the output is emotional state data. Specifically, the device takes a photo of the user's face with the camera and records their voice with the microphone, and then analyzes the data.

[1695] Step 4:

[1696] The terminal sends text data and emotional state data to the server. The input is the text data and emotional state data, and the output is the data sent to the server. Specifically, the application establishes communication with the server and sends an HTTP request.

[1697] Step 5:

[1698] The server analyzes the received text data and emotional state data and compares it with standard nutritional information in a database and the user's individual profile to generate optimal meal suggestions, exercise plans, and health risk notifications. The input is the text data and emotional state data, and the output is the generated advice. Specifically, it uses machine learning algorithms to analyze the data and generate personalized advice.

[1699] Step 6:

[1700] The server sends the generated advice to the terminal, and the terminal presents the advice to the user. The input is the advice data sent from the server, and the output is the advice displayed to the user. Specifically, it displays a message on the application's UI.

[1701] Step 7:

[1702] After the user selects food based on the advice, the terminal synchronizes the latest dietary data and emotional state with the wearable device. The input is dietary data and emotional state data, and the output is data synchronized to the wearable device. Specifically, the data is synchronized using a communication method such as Bluetooth.

[1703] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1704] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1705] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1706] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1707] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1708] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1709] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1710] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1711] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1712] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1713] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1714] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1715] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1716] 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.

[1717] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1718] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1719] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1720] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1721] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1722] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1723] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1724] The following is further disclosed regarding the above embodiment.

[1725] (Claim 1)

[1726] a camera means for a user to photograph the names of ingredients and nutritional information on a food package;

[1727] means for converting image data captured by a camera means into text data using character recognition technology;

[1728] An AI analysis means for analyzing text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition;

[1729] means for presenting the generated advice to a user;

[1730] A system including:

[1731] (Claim 2)

[1732] 10. The system of claim 1, further comprising means for referencing an individual profile of the user, such as health status, allergy information, and past dietary history.

[1733] (Claim 3)

[1734] The system of claim 1, further comprising means for linking data with other healthcare services and wearable devices.

[1735] "Example 1"

[1736] (Claim 1)

[1737] An imaging means for a user to photograph the names of ingredients and nutritional information on a food package;

[1738] means for converting image data captured by the imaging means into text data using character recognition technology;

[1739] A method for analyzing the converted text data using AI analysis tools and comparing it with nutritional information and past dietary history in a database;

[1740] A means for generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health status;

[1741] means for presenting the generated advice to a user;

[1742] A means to link data with other healthcare services and wearable devices,

[1743] A system including:

[1744] (Claim 2)

[1745] 10. The system of claim 1, further comprising means for referencing an individual profile of the user, such as health status, allergy information, and past dietary history.

[1746] (Claim 3)

[1747] 10. The system of claim 1, further comprising means for inputting the generated advice as a prompt sentence into a generative AI model to obtain more detailed personalized advice.

[1748] "Application Example 1"

[1749] (Claim 1)

[1750] a camera means for a user to photograph the names of ingredients and nutritional information on a food package;

[1751] means for converting image data captured by a camera means into text data using character recognition technology;

[1752] An AI analysis means for analyzing text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition;

[1753] means for presenting the generated advice to a user;

[1754] A means to take a photo of the nutritional information and ingredients of the food the user is about to order and provide advice and health risk notifications;

[1755] A system including:

[1756] (Claim 2)

[1757] 10. The system of claim 1, further comprising means for referencing an individual profile of the user, such as health status, allergy information, and past dietary history.

[1758] (Claim 3)

[1759] The system of claim 1, further comprising means for linking data with other healthcare services and wearable devices.

[1760] "Example 2: Combining Emotion Engines"

[1761] (Claim 1)

[1762] a camera means for a user to photograph the names of ingredients and nutritional information on a food package;

[1763] means for converting image data captured by a camera means into text data using character recognition technology;

[1764] means for transmitting the text data converted by the character recognition technology and the user's emotion data to a server;

[1765] An AI analysis means that analyzes the text data and emotion data received by the server and compares it with nutritional information and past dietary history in the database to generate optimal meal suggestions, exercise plans, and health risk notifications;

[1766] means for presenting the generated advice to a user;

[1767] A system including:

[1768] (Claim 2)

[1769] 10. The system of claim 1, further comprising: a sentiment analysis engine for recognizing a user's sentiment; and means for analyzing the user's sentiment in real time.

[1770] (Claim 3)

[1771] The system according to claim 1, further comprising means for generating personalized advice by referring to an individual profile of the user, such as health conditions, allergy information, and past dietary history.

[1772] (Claim 4)

[1773] The system of claim 1, further comprising means for linking data with other healthcare services and wearable devices to comprehensively manage the user's health condition.

[1774] "Application example 2 when combining emotion engines"

[1775] (Claim 1)

[1776] a photographing means for a user to photograph the names of ingredients and nutritional information on a food package;

[1777] a conversion means for converting image data captured by the imaging means into text data using character recognition technology;

[1778] emotion recognition means for recognizing an emotional state of a user;

[1779] An AI analysis means for analyzing the text data and emotional state data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition;

[1780] a presentation means for presenting the generated advice to a user;

[1781] A system including:

[1782] (Claim 2)

[1783] 10. The system of claim 1, further comprising means for referencing an individual profile of the user, such as health status, allergy information, and past dietary history.

[1784] (Claim 3)

[1785] The system of claim 1, further comprising means for linking data with other healthcare services and wearable devices. [Explanation of symbols]

[1786] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a camera means for a user to photograph the names of ingredients and nutritional information on a food package; means for converting image data captured by a camera means into text data using character recognition technology; An AI analysis means for analyzing text data and generating optimal dietary suggestions, exercise plans, and health risk notifications based on the user's individual health condition; means for presenting the generated advice to a user; A system including:

2. The system according to claim 1, further comprising means for referencing an individual profile of the user, such as health status, allergy information, and past dietary history.

3. The system according to claim 1 , further comprising means for linking data with other healthcare services and wearable devices.

Images