system

Abstract

We provide the system. [Solution] A means of inputting the user's personal information and past meal records, A means for generating meal recipes using a predictive model based on the aforementioned personal information and meal records, A method for identifying intake amounts and estimating calories from captured images of meals, A means of providing an exercise plan based on user information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Diabetic patients need to appropriately control their blood glucose levels in daily life, but there is a problem that it is difficult for individuals to manage their diet and carry out appropriate exercise plans. In addition, since this may increase the risk of complications, a simple and highly accurate support system is required.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system that takes the user's personal information and past meal records as input and generates an optimal meal recipe using a predictive model based on this information. It also identifies intake amounts from captured meal images and accurately estimates calories. Furthermore, it supports lifestyle improvements by providing personalized exercise plans based on the user's detailed information. In this way, it provides scientifically-based and effective support for the prevention and management of lifestyle-related diseases.

[0006] "User personal information" refers to information necessary for the system to understand the user's basic attributes, including age, height, weight, and exercise history.

[0007] "Past meal records" refer to the content of meals a user has eaten in the past and related data, which the system uses to analyze their eating habits.

[0008] A "predictive model" refers to a mathematical or statistical method used to predict future events based on past data.

[0009] A "meal recipe" is a detailed set of instructions on how to prepare a dish that has been adjusted to meet specific nutritional requirements or dietary needs.

[0010] "Captured meal images" refer to images of meals taken by users using camera devices, and serve as the information source for the system to analyze the contents of the meal.

[0011] "Intake" refers to the actual quantity of food consumed in a particular meal and is an important indicator for nutritional management and maintaining health.

[0012] "Estimating calories" means calculating the amount of energy contained in a particular meal or food and expressing it as a numerical value.

[0013] An "exercise plan" is a plan that includes the type, intensity, and frequency of exercise suggested to users to efficiently improve their physical fitness and maintain their health. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is implemented as a system to efficiently support lifestyle improvements for diabetic patients. The system mainly consists of user terminals, a server, and a network connecting them.

[0036] First, users log in to the system using their own devices and enter basic information and recent health status. This includes age, height, weight, past exercise history, diet, and recent blood glucose levels. Users can also take pictures of their daily meals and send the meal records to the server.

[0037] Next, user information received through the device is sent to the server. The server stores the received information in a database and uses an AI model to evaluate diet management and exercise plans. The AI ​​model analyzes past data entered by the user and similar cases to generate personalized meal recipes. The server sends these meal recipes to the device, and the user can check the recommended meals through the app.

[0038] The server also analyzes the received meal images and calculates calories from the captured food. By using image recognition technology to return information about the contents and quantity of the meal, as well as the corresponding calorie information, users can easily manage their own diet.

[0039] Furthermore, regarding the exercise plan, the server creates an optimal exercise plan based on the user's age, exercise history, and daily diet. Specific types of exercise, frequency, and duration are suggested and provided to the user via the device.

[0040] As a concrete example, suppose a 40-year-old male user takes a picture of his breakfast and sends it to the server. The server analyzes the image, recognizes that the meal consists of toast, eggs, and coffee, and estimates the calorie content to be approximately 350 calories. Furthermore, based on this information, the server suggests a salad and chicken for lunch and generates a recipe for dinner that includes vegetable soup. At the same time, it recommends a 30-minute walk as exercise for the day, thus comprehensively supporting the user's health management.

[0041] In this way, this system realizes a concrete embodiment that utilizes AI technology to support users' health management.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users log in to the app using their device and enter personal information such as age, height, weight, exercise history, past meals, and recent blood glucose levels. The device collects this data and prepares it for transmission to the server.

[0045] Step 2:

[0046] The terminal collects information entered by the user into data packets and sends them to the server. The server receives this data and stores it in its database.

[0047] Step 3:

[0048] The user takes a photo of their meal and sends the image from their device to the server. The server receives the image and uses image recognition technology to analyze the ingredients and quantities.

[0049] Step 4:

[0050] The server estimates the calorie content of the meal based on the image analysis results. The estimated calorie information is then sent back to the user's device, allowing the user to check their calorie intake.

[0051] Step 5:

[0052] The server uses an AI model to analyze the user's personal information and past meal data, and generates meal recipes that are effective for managing blood sugar levels. This suggestion is sent to the user's device, and the user reviews the recommended meal plan.

[0053] Step 6:

[0054] The server uses an AI model to calculate the optimal exercise plan based on the user's age, exercise history, and daily meal information. The created exercise plan is then sent to the device.

[0055] Step 7:

[0056] Users can manage their diabetes by reviewing suggested meal recipes and exercise plans through their devices and incorporating them into their daily lives.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] There is a need to efficiently and individually provide appropriate health management and lifestyle improvement support for patients with diabetes, a lifestyle-related disease. Currently, inputting information and managing daily meals and exercise is cumbersome, and there is a lack of specific guidance tailored to individual lifestyles. As a result, users find it difficult to manage their health properly on their own and struggle to sustain an effective improvement plan.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for inputting the user's basic information and health status, means for transmitting the data of the basic information and health status to the server, means for generating personalized meal recipes using a generation AI model with a server that provides a service for accumulating and analyzing diverse data, means for analyzing meal images, detecting ingested nutrients using image recognition technology and calculating calories, and means for providing an exercise plan based on the user's basic information, exercise history and daily meal content. As a result, users can receive optimal meal and exercise plans tailored to their individual health status, enabling them to efficiently and continuously improve their lifestyle habits.

[0062] "Users" refers to all individuals who use the system and who input and receive information for the purpose of managing their health status and improving their lifestyle.

[0063] "Basic information" refers to personal data necessary for managing the user's health, such as age, height, and weight.

[0064] "Health status" refers to information about the user's physical condition, such as exercise records, dietary information, and blood sugar levels, which they input into the system.

[0065] A "server" refers to a computer device connected via a network for receiving and analyzing data.

[0066] A "generative AI model" refers to an artificial intelligence system that uses machine learning to generate appropriate output (e.g., meal recipes or exercise plans) based on input data.

[0067] "Meal images" refer to photographic data that users use to visually record the meals they have eaten and send to the system for analysis.

[0068] "Image recognition technology" refers to technologies that include computer vision techniques for analyzing input images and understanding their content.

[0069] "Calories" are a numerical representation of the amount of energy contained in food, and are an important indicator in dietary management.

[0070] An "exercise plan" refers to a proposal document that includes the content, frequency, and intensity of exercises planned based on the user's health condition.

[0071] This invention is a system that efficiently supports individualized lifestyle improvements for diabetic patients. The system mainly consists of a user terminal, a server that receives and processes data, and a network that connects each device. A specific embodiment of the system is shown below.

[0072] Users input basic information and health data using a dedicated application on their own devices. Basic information includes age, height, and weight, while health data includes recent exercise history, diet, and blood sugar levels. This information is transmitted from the device to the server via the network.

[0073] The server uses Python and SQL to store and manage received user data in a database. It also uses a generative AI model to analyze user data and generate personalized meal recipes. The generative AI model is implemented using either the machine learning framework TENSORFLOW® or PyTorch. The AI ​​model suggests optimal meal options based on the user's past data and similar cases.

[0074] Furthermore, users take photos of their daily meals with their device's camera and send the images to the server. The server analyzes these images using image recognition technology and calculates the contents and calories of the meal using OpenCV and Google® Cloud Vision API. Based on this information, users can manage their diet in detail.

[0075] Furthermore, the server creates a personalized exercise plan based on the user's information. It determines the type, frequency, and intensity of exercise, taking into account exercise history, age, and daily diet, and sends this information to the user's device. This allows users to utilize it for daily health management.

[0076] As a concrete example, if a 40-year-old male user takes a picture of his breakfast and sends the data to the server, the server analyzes the image, recognizes that it is a meal consisting of toast, eggs, and coffee, and estimates the calorie content to be 350 calories. Based on this, it suggests a salad and chicken for lunch, vegetable soup for dinner, and recommends a 30-minute walk as exercise for the day.

[0077] Example of a prompt:

[0078] "We have a 40-year-old user whose recent diet consists of toast, eggs, and coffee. Please use AI to generate recommended recipes for lunch and dinner for this user. Also, please suggest a suitable exercise plan."

[0079] In this way, this system uses AI technology to support users in improving their lifestyle habits and provides them with optimal health management.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users open a dedicated application on their own devices and log in. At this time, they enter basic information such as age, height, weight, past exercise history, diet, and recent blood glucose levels into a form. The entered information is temporarily stored on the device. Specifically, the process involves the user entering data into the form and pressing the "Submit" button, after which the information is compiled on the device.

[0083] Step 2:

[0084] The terminal transmits collected user information to the server via the network. The data is securely encrypted using the HTTPS protocol. Specifically, a background process encrypts the data and then sends it to the server. The input is user health information, and the output is the arrival of the data at the server.

[0085] Step 3:

[0086] The server saves the received user information to the database. This database operation is performed using Python and SQL. Specifically, the server executes SQL INSERT statements to store the user information in the appropriate table. The input contains user data, and the output is the information stored in the database.

[0087] Step 4:

[0088] The user takes a picture of their meal and sends the image data to the server via their device. Specifically, they use the "Photo Shooting" function within the app and upload the captured image to the server using the "Send Image" button. The input is the photographed meal image, and the output is the image sent to the server.

[0089] Step 5:

[0090] The server analyzes the received image data using image recognition technology. It performs image analysis using OpenCV and the Google Cloud Vision API to identify the contents of the meal and calculate the calories. Specifically, an AI model runs a process to calculate calories based on the analysis results. The input is the received image, and the output is the calorie information from the analysis results.

[0091] Step 6:

[0092] The server uses a generative AI model to analyze accumulated user data and similar cases to generate personalized meal recipes. This model is built using TensorFlow and PyTorch. The AI ​​model creates individually optimized recipes from the data and sends them from the server to the terminal. The input is user data, and the output is a recommended meal recipe.

[0093] Step 7:

[0094] The server generates an exercise plan, determining the optimal type, frequency, and intensity of exercise based on the user's basic information and exercise history. The generated exercise plan is sent to the terminal, where the user can use it for daily health management. Specifically, an algorithm calculates the plan individually, packages it as data, and sends it. The input is the user's health information and dietary details, and the output is the suggested exercise plan.

[0095] Step 8:

[0096] The device displays meal recipes and exercise plans received from the server to the user, who then takes specific actions to improve their lifestyle based on this information. This information is conveyed to the user through the app's interface, and options are also provided to help the user manage their health. The input is data sent from the server, and the output is the presentation of information to the user.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In modern times, managing diabetes, a lifestyle-related disease, is considered crucial, but many patients struggle with effective dietary management and exercise planning. Furthermore, choosing appropriate foods and maintaining consistent health management are burdensome for patients. There is a need for a system that develops personalized plans and proposes them in an easily implementable format.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for inputting biometric data and dietary history, means for generating a nutrition plan using a predictive algorithm, means for identifying nutrients from meal images and calculating calories, and means for presenting food ingredients in cooperation with an online shopping site. This provides individually optimized meal and exercise plans, enabling users to manage their health by easily purchasing the suggested food ingredients.

[0102] "Biometric data" refers to information about an individual's body, including their age, height, weight, and health status.

[0103] "Dietary history" refers to the contents of meals consumed by the user in the past and the records thereof.

[0104] A "predictive algorithm" is a computational method used to generate optimal nutrition plans and exercise plans based on input data.

[0105] A "nutrition plan" is a suggestion of recommended meals based on the user's health condition and lifestyle.

[0106] "Meal images" refer to photographic data of meals taken by users.

[0107] "Identifying nutrients" means using image recognition technology to identify the ingredients contained in a food image and recognize their nutritional components.

[0108] "Calculating calories" means determining the total energy content of a meal based on the quantity and type of food items identified.

[0109] A "physical activity plan" is a proposal for individualized exercise content and frequency, formulated based on the user's data.

[0110] "Integrating with e-commerce sites" means transmitting information so that users can obtain food ingredients suggested based on their nutrition plan through external e-commerce platforms.

[0111] "Food ingredients" refer to the raw materials and ingredients necessary to implement a recommended nutrition plan.

[0112] This invention relates to the implementation of a system aimed at comprehensive health management for patients with lifestyle-related diseases, such as diabetes. The main components are user terminals, a server, and a network connecting them.

[0113] First, users log in to the system using their own devices and enter their biometric data and dietary history. The entered information is sent to a server via the network and stored in a database. The server processes this data and generates a personalized nutrition plan using predictive algorithms. The software used in this process includes AI models such as TensorFlow and PyTorch.

[0114] Furthermore, users take photos of their daily meals with their smartphones and send these images to the server. The server uses image recognition technology to identify the nutrients in the meal and calculate the calories. For this purpose, the server can utilize libraries such as Scikit-learn and OpenCV.

[0115] Once the analysis of each meal is complete, the server develops and proposes an optimal physical activity plan based on this analysis. The proposed nutrition plan also includes a function that links to online shopping sites to suggest relevant food ingredients. This function allows users to conveniently purchase the suggested food ingredients.

[0116] As a concrete example, consider a scenario where a user sends a picture of their breakfast to a server. The server recognizes the meal consists of toast, eggs, and coffee, and then creates a suitable lunch and dinner plan. Links are then provided to purchase the suggested food items through an online shopping site. A possible prompt for the generative AI model might be: "A user's meal image has been sent to the server. Identify the specific foods that make up this meal and calculate the calories."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] Users access the system via a login screen using their device and enter biometric data and past meal history. The entered data is compiled on the device and sent to the server via the network. Examples of entered data include the user's age, weight, and meal content for the past week. The server stores the received data in a database.

[0120] Step 2:

[0121] Users take photos of their daily meals using their smartphones and upload the images to a server. These images are processed on the device and converted into a format that clearly identifies the contents of the meal. The server receives these images and uses image recognition technology to identify the nutrients contained in the meal. Specifically, OpenCV is used as the image processing library, and the output includes the names of the ingredients and an estimated calorie amount.

[0122] Step 3:

[0123] The server executes a predictive algorithm based on the biometric data saved in Step 1 and the dietary information identified in Step 2. Using TensorFlow, it analyzes the various input data and generates an individualized nutrition plan. The output provides a recommended daily meal menu and its nutritional balance.

[0124] Step 4:

[0125] The server creates an optimal physical activity plan based on the generated nutrition plan. Using Scikit-learn, it determines exercise intensity and frequency, taking into account the user's age and exercise history. The output of this step is a specific exercise plan proposed to the user.

[0126] Step 5:

[0127] The server displays food ingredients corresponding to the recommended nutrition plan in conjunction with online shopping sites. This allows users to easily purchase the necessary ingredients. The server accesses the online shopping site's database via an API, and the output provides available product information and purchase links.

[0128] Step 6:

[0129] The user reviews the meal and exercise plans presented through the device and, if necessary, purchases food ingredients from the suggested online shopping site. The operation is performed via the device's GUI, and the actual purchase process is completed by redirection to an external online shopping site. Output includes a purchase completion notification and a display of the healthcare action plan.

[0130] This series of processes allows users to manage their health more efficiently and easily. Examples of prompts for the generating AI model include: "New user data and meal images have been received. Please generate a personalized nutrition plan based on them."

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention is a system for effectively managing the lifestyle habits of diabetic patients, and by combining it with an emotion engine, it enables responses tailored to the user's emotional state. The system consists of a user's terminal, a server, and a network connection.

[0133] Users first log in to the system using their device and enter information such as age, height, weight, exercise history, past meals, and blood glucose levels. Users also take pictures of their daily meals and send this information from their device to the server. Furthermore, users can express their emotional state through voice input and facial recognition.

[0134] The server receives the user's basic information and entered meal images and stores them in a database. Based on the received data, an AI model is used to create meal management and exercise plans. This AI model uses a predictive model to generate optimal meal recipes tailored to each individual user. The generated meal recipes are provided to the user via the terminal. At this stage, an emotion engine analyzes the user's emotional state and adjusts the meal recipes and exercise plans accordingly.

[0135] The emotion engine identifies the user's emotional state from their voice and facial expressions, thereby determining their stress level and motivation. For example, if the server detects that the user is under high stress, it will suggest meals containing stress-relieving foods and recommend relaxing exercise plans (such as yoga or stretching).

[0136] For example, if a user says "I'm tired" in a voice that indicates high stress, the emotion engine analyzes the voice and determines that the user is experiencing high stress. The server takes this analysis into account and suggests a light meal and an exercise plan to help reduce stress. Furthermore, if the user sends a picture of their meal with a smile, they are considered highly motivated, and the system can suggest a more challenging exercise routine.

[0137] Thus, the present invention realizes a system that not only aims to manage diabetes but also supports the user's psychological state.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user logs into the system via a terminal and enters information such as age, height, weight, past exercise history, and recent meals and blood sugar levels. The terminal collects this information and prepares it for transmission.

[0141] Step 2:

[0142] The user takes a picture of their meal using their device and sends the image along with emotional data such as audio and facial expressions to the server. The device then packages this data and sends it to the server.

[0143] Step 3:

[0144] The server stores personal information, food images, and emotional data received from the terminal in a database. An image recognition algorithm is used to analyze the food content and estimate calorie intake.

[0145] Step 4:

[0146] The server uses an emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the analysis results, it understands the user's stress level and motivation.

[0147] Step 5:

[0148] The server uses an AI model to analyze each user's personal information and past eating data to generate optimal meal recipes. It creates meal recipes and exercise plans that take into account the results of sentiment analysis.

[0149] Step 6:

[0150] The server sends generated meal recipes, estimated calories, and an exercise plan based on emotions to the user's device. The user can then review this information via their device and use it to improve their lifestyle.

[0151] Step 7:

[0152] Based on the information received, users adjust their daily lifestyle habits. For example, they manage their health by incorporating stress-reducing foods and exercise.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In managing the lifestyle habits of diabetic patients, there is a need to achieve integrated and personalized health management that takes into account not only individual physical information and past consumption history, but also the user's psychological state. While conventional technologies could provide plans based on consumption records and personal attribute information, they lacked specific suggestions that reflected the user's psychological state, which led to decreased user motivation and a lack of continuity in health management.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for inputting user attribute information and historical consumption information, means for generating a meal plan using a predictive algorithm based on the attribute information and consumption information, and means for identifying the user's psychological state through emotion analysis and adjusting the meal plan and activity plan accordingly. This enables comprehensive health management based on both the user's physical attributes and psychological state.

[0158] "User attribute information" is a general term for basic physical information used to identify and characterize an individual, such as the user's age, height, and weight.

[0159] "Historical consumption information" refers to a record of meals consumed by a user in the past, including details such as the type, timing, and quantity of food eaten.

[0160] A "predictive algorithm" refers to mathematical methods and models used to calculate and generate future trends and optimal plans based on input data.

[0161] "Captured consumption images" refer to photographic data of meals taken by users, and are the subject of image analysis.

[0162] "Nutritional value" is a numerical indicator that shows the amount of energy and specific nutrients that food provides to the human body.

[0163] An "activity plan" is a plan of exercise and training designed to promote the user's physical activity and maintain their health.

[0164] "Emotional analysis" refers to the technology and process of identifying a user's emotional state from audio and images and analyzing their psychological characteristics.

[0165] "Psychological state" refers to an internal state related to the user's psychology, such as emotions, stress, and motivation.

[0166] In an embodiment of this invention, the system mainly consists of a user terminal, a server, and a network. The user accesses the system using the terminal and inputs their basic physical information (age, height, weight, etc.) and past meal records. The terminal uses its built-in camera and microphone to acquire images of meals and voice data of the user, and transmits this to the server.

[0167] The server receives this data and stores it in a high-performance database. Based on the information stored in the database, a predictive algorithm is used to generate personalized meal and activity plans for each individual user. This predictive algorithm uses a generative AI model, enabling personalized suggestions that meet the user's expectations. In addition, an emotion analysis module analyzes voice and images to understand the user's psychological state and adjust the suggested plans accordingly. This system achieves more effective health management by taking into account the user's stress and motivation.

[0168] Specifically, the system has a function that allows users to leave voice comments reflecting their emotional state, such as "I'm very tired today," and then suggests relaxing meals and activity plans such as yoga or stretching. In this way, it provides comprehensive feedback based on individual physical information and psychological state.

[0169] An example of a prompt would be, "Generate a meal and exercise plan to suggest when the emotion engine determines that the user is in a high-stress state." The server would then return personalized health suggestions accordingly.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user enters their personal attribute information (age, height, weight, etc.) and past consumption information into the terminal. The terminal packages this information as structured data and sends it to the server. At this stage, data integrity and format checks are performed.

[0173] Step 2:

[0174] The server receives user attribute and consumption information sent from the terminal and stores it in the database. Here, it performs processing to ensure data consistency by integrating it with historical data. Furthermore, it integrates historical and new data and processes it into a format suitable for analysis.

[0175] Step 3:

[0176] Users take pictures of their meals and upload them to their devices. They also use voice input to express their emotional state. The devices compress the image data and send it to the server along with the voice data. The image data is preprocessed and converted into a format suitable for analysis.

[0177] Step 4:

[0178] The server analyzes the received image data to identify the content of consumption and nutritional value. It utilizes a generative AI model to perform highly accurate image analysis and stores the identification results in a database. This allows for detailed identification of meal contents, which can then be used to plan future meals.

[0179] Step 5:

[0180] The server uses voice data to perform sentiment analysis. The sentiment analysis module analyzes the tone and content of the voice to identify the user's psychological state. Based on this, it estimates the emotional state and adds it to the user's profile.

[0181] Step 6:

[0182] The server uses a generative AI model to generate meal and activity plans based on the user's attribute information, consumption history, and current emotional state. A predictive algorithm analyzes the generated data and suggests the optimal plan.

[0183] Step 7:

[0184] The generated plan is sent from the server to the terminal and provided to the user. The terminal presents the plan through an intuitive and easy-to-use user interface. This plan includes meal and activity suggestions to support personalized health management for the user.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Lifestyle management for diabetic patients tends to be limited to general dietary management and exercise planning, but appropriate responses that take into account the psychological state and emotional fluctuations of individual patients are required. However, current systems have difficulty properly analyzing users' emotions and providing personalized guidance and support based on that analysis. Therefore, there is a need to develop a system that can provide more nuanced support in diabetes management.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for inputting the user's personal information and past meal records, means for generating dietary guidelines using a predictive model, means for identifying intake amounts and estimating energy amounts from captured meal images, and means for adjusting dietary guidelines and exercise plans according to the user's emotional state. This enables appropriate lifestyle management according to the user's emotional state.

[0190] "Personal information" refers to information about an individual user's profile, such as their age, height, weight, exercise history, and blood glucose level information.

[0191] A "meal record" is data that records the details of meals a user has eaten in the past, and may include the type and quantity of food consumed.

[0192] A "predictive model" is a computational method that includes algorithms and AI used to generate optimal dietary guidelines and exercise plans based on input data.

[0193] "Meal images" are image data taken to visually record the contents of meals consumed by users.

[0194] "Intake" refers to the amount of each food item included in a meal and serves as the basic data for calculating calories and nutrients.

[0195] "Energy amount" refers to the total amount of calories obtained from ingested food, and is a numerical value that serves as an energy source for human activity and metabolism.

[0196] An "exercise plan" is a program that specifically outlines exercises suitable for the user's health condition and lifestyle, including the type, duration, and frequency of exercise.

[0197] "Emotional state" refers to the psychological state of the user, and includes the type and intensity of emotions detected from voice, facial expressions, etc.

[0198] "Emotion recognition means" refers to a method or device for identifying a user's emotional state using voice analysis and facial expression analysis technology.

[0199] "Adjustment measures" refer to functions and methods for modifying previously presented dietary guidelines and exercise plans according to the individual circumstances and emotional state of the user.

[0200] The system for realizing this invention consists of a user terminal, a server, and a network connecting them. The user terminal is a device such as smart glasses or a smartphone, through which it can input images of meals and voice, and express its emotional state to the system. The server receives this data and performs the necessary processing for analysis.

[0201] The server analyzes the received image data using computer vision technology (e.g., OpenCV) to analyze the meal content and estimate the amount of food consumed and the amount of energy. For audio data, it uses speech recognition technology (e.g., Google Speech-to-Text API) to estimate the emotional state and uses emotion recognition tools to identify the emotional state from the audio and facial expressions.

[0202] Subsequently, the server uses a predictive model (e.g., TensorFlow) to generate dietary guidelines based on the user's personal information and past meal records. These guidelines are then adjusted to take into account the user's emotional state and are also used to provide exercise plans. The AI ​​model enables the provision of lifestyle management optimized for individual emotions.

[0203] For example, if a user wears smart glasses and sends an image of themselves smiling after breakfast to the system, the system can determine from that smile that the user is highly motivated and suggest a more active exercise plan than usual. Such specific responses can be obtained through a prompt example from the generative AI model: "If the user was smiling after breakfast this morning, what kind of exercise suggestion should be made based on that emotional state?"

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user operates the device to enter personal information and past meal records. This includes age, height, weight, exercise history, and meal details. The entered data is collected on the device and sent to the server.

[0207] Step 2:

[0208] The user sends images of their meals and voice messages to the system using a device. The image data records the meals the user has consumed, and the voice messages indicate their emotional state. This data is transmitted to the server using a secure protocol.

[0209] Step 3:

[0210] The server analyzes the received meal images. Using computer vision technology, it recognizes the contents of the meal from the image and calculates the intake and energy content. The input is a meal image, and the output is data on calories and nutrients.

[0211] Step 4:

[0212] The server analyzes voice messages to identify emotional states. Using a speech recognition API, it estimates emotions from voice tone and patterns using an emotion recognition model. The input is voice data, and the output is data indicating the user's emotional state.

[0213] Step 5:

[0214] The server uses a predictive model to generate dietary guidelines based on the user's personal information and meal records. The generating AI model creates guidelines tailored to the user's health condition and preferences, and data processing yields individually optimized recipes.

[0215] Step 6:

[0216] The server adjusts dietary guidelines based on emotional state and generates exercise plans that reflect individual needs. For example, if a high-stress state is detected, it recommends relaxing exercises. This enables more personalized health management.

[0217] Step 7:

[0218] The device displays adjusted dietary guidelines and exercise plans sent from the server to the user visually or audibly. Based on this, the user can improve their daily lifestyle and manage their health.

[0219] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] This invention is implemented as a system to efficiently support lifestyle improvements for diabetic patients. The system mainly consists of user terminals, a server, and a network connecting them.

[0236] First, users log in to the system using their own devices and enter basic information and recent health status. This includes age, height, weight, past exercise history, diet, and recent blood glucose levels. Users can also take pictures of their daily meals and send the meal records to the server.

[0237] Next, user information received through the device is sent to the server. The server stores the received information in a database and uses an AI model to evaluate diet management and exercise plans. The AI ​​model analyzes past data entered by the user and similar cases to generate personalized meal recipes. The server sends these meal recipes to the device, and the user can check the recommended meals through the app.

[0238] The server also analyzes the received meal images and calculates calories from the captured food. By using image recognition technology to return information about the contents and quantity of the meal, as well as the corresponding calorie information, users can easily manage their own diet.

[0239] Furthermore, regarding the exercise plan, the server creates an optimal exercise plan based on the user's age, exercise history, and daily diet. Specific types of exercise, frequency, and duration are suggested and provided to the user via the device.

[0240] As a concrete example, suppose a 40-year-old male user takes a picture of his breakfast and sends it to the server. The server analyzes the image, recognizes that the meal consists of toast, eggs, and coffee, and estimates the calorie content to be approximately 350 calories. Furthermore, based on this information, the server suggests a salad and chicken for lunch and generates a recipe for dinner that includes vegetable soup. At the same time, it recommends a 30-minute walk as exercise for the day, thus comprehensively supporting the user's health management.

[0241] In this way, this system realizes a concrete embodiment that utilizes AI technology to support users' health management.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users log in to the app using their device and enter personal information such as age, height, weight, exercise history, past meals, and recent blood glucose levels. The device collects this data and prepares it for transmission to the server.

[0245] Step 2:

[0246] The terminal collects information entered by the user into data packets and sends them to the server. The server receives this data and stores it in its database.

[0247] Step 3:

[0248] The user takes a photo of their meal and sends the image from their device to the server. The server receives the image and uses image recognition technology to analyze the ingredients and quantities.

[0249] Step 4:

[0250] The server estimates the calorie content of the meal based on the image analysis results. The estimated calorie information is then sent back to the user's device, allowing the user to check their calorie intake.

[0251] Step 5:

[0252] The server uses an AI model to analyze the user's personal information and past meal data, and generates meal recipes that are effective for managing blood sugar levels. This suggestion is sent to the user's device, and the user reviews the recommended meal plan.

[0253] Step 6:

[0254] The server uses an AI model to calculate the optimal exercise plan based on the user's age, exercise history, and daily meal information. The created exercise plan is then sent to the device.

[0255] Step 7:

[0256] Users can manage their diabetes by reviewing suggested meal recipes and exercise plans through their devices and incorporating them into their daily lives.

[0257] (Example 1)

[0258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0259] There is a need to efficiently and individually provide appropriate health management and lifestyle improvement support for patients with diabetes, a lifestyle-related disease. Currently, inputting information and managing daily meals and exercise is cumbersome, and there is a lack of specific guidance tailored to individual lifestyles. As a result, users find it difficult to manage their health properly on their own and struggle to sustain an effective improvement plan.

[0260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0261] In this invention, the server includes means for inputting the user's basic information and health status, means for transmitting the data of the basic information and health status to the server, means for generating personalized meal recipes using a generation AI model with a server that provides a service for accumulating and analyzing diverse data, means for analyzing meal images, detecting ingested nutrients using image recognition technology and calculating calories, and means for providing an exercise plan based on the user's basic information, exercise history and daily meal content. As a result, users can receive optimal meal and exercise plans tailored to their individual health status, enabling them to efficiently and continuously improve their lifestyle habits.

[0262] "Users" refers to all individuals who use the system and who input and receive information for the purpose of managing their health status and improving their lifestyle.

[0263] "Basic information" refers to personal data necessary for managing the user's health, such as age, height, and weight.

[0264] "Health status" refers to information about the user's physical condition, such as exercise records, dietary information, and blood sugar levels, which they input into the system.

[0265] A "server" refers to a computer device connected via a network for receiving and analyzing data.

[0266] A "generative AI model" refers to an artificial intelligence system that uses machine learning to generate appropriate output (e.g., meal recipes or exercise plans) based on input data.

[0267] "Meal images" refer to photographic data that users use to visually record the meals they have eaten and send to the system for analysis.

[0268] "Image recognition technology" refers to technologies that include computer vision techniques for analyzing input images and understanding their content.

[0269] "Calories" are a numerical representation of the amount of energy contained in food, and are an important indicator in dietary management.

[0270] An "exercise plan" refers to a proposal document that includes the content, frequency, and intensity of exercises planned based on the user's health condition.

[0271] This invention is a system that efficiently supports individualized lifestyle improvements for diabetic patients. The system mainly consists of a user terminal, a server that receives and processes data, and a network that connects each device. A specific embodiment of the system is shown below.

[0272] Users input basic information and health data using a dedicated application on their own devices. Basic information includes age, height, and weight, while health data includes recent exercise history, diet, and blood sugar levels. This information is transmitted from the device to the server via the network.

[0273] The server uses Python and SQL to store and manage received user data in a database. It also uses a generative AI model to analyze user data and generate personalized meal recipes. The generative AI model is implemented using machine learning frameworks such as TensorFlow or PyTorch. The AI ​​model suggests optimal meal options based on the user's past data and similar cases.

[0274] Furthermore, users take photos of their daily meals with their device's camera and send the images to a server. The server analyzes these images using image recognition technology and calculates the contents and calories of the meal using OpenCV and the Google Cloud Vision API. Based on this information, users can meticulously manage their own diet.

[0275] Furthermore, the server creates a personalized exercise plan based on the user's information. It determines the type, frequency, and intensity of exercise, taking into account exercise history, age, and daily diet, and sends this information to the user's device. This allows users to utilize it for daily health management.

[0276] As a concrete example, if a 40-year-old male user takes a picture of his breakfast and sends the data to the server, the server analyzes the image, recognizes that it is a meal consisting of toast, eggs, and coffee, and estimates the calorie content to be 350 calories. Based on this, it suggests a salad and chicken for lunch, vegetable soup for dinner, and recommends a 30-minute walk as exercise for the day.

[0277] Example of a prompt:

[0278] "A 40-year-old user. Their recent meals are toast, eggs, and coffee. For this user, please generate recommended lunch and dinner recipes using AI. Also, please propose an appropriate exercise plan."

[0279] In this way, this system uses AI technology to support the improvement of users' living habits and provides optimal health management for users.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user opens and logs in to a dedicated application using their terminal. At this time, basic information such as age, height, weight, past exercise performance, diet content, and recent blood sugar levels is entered into a form. The entered information is temporarily stored on the terminal. As a specific operation, it is a process where the user enters data into the form and presses the "Send" button, and the information is summarized on the terminal.

[0283] Step 2:

[0284] The terminal sends the collected user information to the server through the network. For the transmission, the HTTPS protocol is used to securely encrypt and send the data. As a specific operation, the data is encrypted in a background process, and communication to send it to the server occurs. The input is the user's health information, and the output is the arrival of the data at the server.

[0285] Step 3:

[0286] The server saves the received user information in the database. At this time, database operations are performed using Python and SQL. As a specific operation, the server executes an SQL INSERT statement and stores the user information in an appropriate table. The input includes user data, and the output is that the information is stored in the database.

[0287] Step 4:

[0288] The user takes a picture of their meal and sends the image data to the server via their device. Specifically, they use the "Photo Shooting" function within the app and upload the captured image to the server using the "Send Image" button. The input is the photographed meal image, and the output is the image sent to the server.

[0289] Step 5:

[0290] The server analyzes the received image data using image recognition technology. It performs image analysis using OpenCV and the Google Cloud Vision API to identify the contents of the meal and calculate the calories. Specifically, an AI model runs a process to calculate calories based on the analysis results. The input is the received image, and the output is the calorie information from the analysis results.

[0291] Step 6:

[0292] The server uses a generative AI model to analyze accumulated user data and similar cases to generate personalized meal recipes. This model is built using TensorFlow and PyTorch. The AI ​​model creates individually optimized recipes from the data and sends them from the server to the terminal. The input is user data, and the output is a recommended meal recipe.

[0293] Step 7:

[0294] The server generates an exercise plan, determining the optimal type, frequency, and intensity of exercise based on the user's basic information and exercise history. The generated exercise plan is sent to the terminal, where the user can use it for daily health management. Specifically, an algorithm calculates the plan individually, packages it as data, and sends it. The input is the user's health information and dietary details, and the output is the suggested exercise plan.

[0295] Step 8:

[0296] The device displays meal recipes and exercise plans received from the server to the user, who then takes specific actions to improve their lifestyle based on this information. This information is conveyed to the user through the app's interface, and options are also provided to help the user manage their health. The input is data sent from the server, and the output is the presentation of information to the user.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] In modern times, managing diabetes, a lifestyle-related disease, is considered crucial, but many patients struggle with effective dietary management and exercise planning. Furthermore, choosing appropriate foods and maintaining consistent health management are burdensome for patients. There is a need for a system that develops personalized plans and proposes them in an easily implementable format.

[0300] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0301] In this invention, the server includes means for inputting biometric data and dietary history, means for generating a nutrition plan using a predictive algorithm, means for identifying nutrients from meal images and calculating calories, and means for presenting food ingredients in cooperation with an online shopping site. This provides individually optimized meal and exercise plans, enabling users to manage their health by easily purchasing the suggested food ingredients.

[0302] "Biometric data" refers to information about an individual's body, including their age, height, weight, and health status.

[0303] "Dietary history" refers to the contents of meals consumed by the user in the past and the records thereof.

[0304] A "prediction algorithm" is a computational method used to generate an optimal nutrition plan and exercise program based on the input data.

[0305] A "nutrition plan" is a proposal for the recommended diet content based on the user's health condition and lifestyle.

[0306] A "meal image" is photo data taken by the user of their own meal.

[0307] "Identifying nutrients" means using image recognition technology to identify the ingredients contained in a meal image and recognize their nutritional components.

[0308] "Calculating calories" means calculating the total energy amount of a meal based on the amount and type of recognized ingredients.

[0309] A "physical activity plan" is a proposal for individual exercise content and its implementation frequency formulated based on the user's data.

[0310] "Collaborating with an e-commerce site" means transmitting information so that users can obtain the food materials proposed based on the nutrition plan through an external e-commerce trading platform.

[0311] "Food materials" are the raw materials and ingredients necessary to realize the recommended nutrition plan.

[0312] This invention is related to the implementation of a system for comprehensive health management of patients with lifestyle diseases such as diabetes. The main components are a user terminal, a server, and a network connecting them.

[0313] First, users log in to the system using their own devices and enter their biometric data and dietary history. The entered information is sent to a server via the network and stored in a database. The server processes this data and generates a personalized nutrition plan using predictive algorithms. The software used in this process includes AI models such as TensorFlow and PyTorch.

[0314] Furthermore, users take photos of their daily meals with their smartphones and send these images to the server. The server uses image recognition technology to identify the nutrients in the meal and calculate the calories. For this purpose, the server can utilize libraries such as Scikit-learn and OpenCV.

[0315] Once the analysis of each meal is complete, the server develops and proposes an optimal physical activity plan based on this analysis. The proposed nutrition plan also includes a function that links to online shopping sites to suggest relevant food ingredients. This function allows users to conveniently purchase the suggested food ingredients.

[0316] As a concrete example, consider a scenario where a user sends a picture of their breakfast to a server. The server recognizes the meal consists of toast, eggs, and coffee, and then creates a suitable lunch and dinner plan. Links are then provided to purchase the suggested food items through an online shopping site. A possible prompt for the generative AI model might be: "A user's meal image has been sent to the server. Identify the specific foods that make up this meal and calculate the calories."

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] Users access the system via a login screen using their device and enter biometric data and past meal history. The entered data is compiled on the device and sent to the server via the network. Examples of entered data include the user's age, weight, and meal content for the past week. The server stores the received data in a database.

[0320] Step 2:

[0321] Users take photos of their daily meals using their smartphones and upload the images to a server. These images are processed on the device and converted into a format that clearly identifies the contents of the meal. The server receives these images and uses image recognition technology to identify the nutrients contained in the meal. Specifically, OpenCV is used as the image processing library, and the output includes the names of the ingredients and an estimated calorie amount.

[0322] Step 3:

[0323] The server executes a predictive algorithm based on the biometric data saved in Step 1 and the dietary information identified in Step 2. Using TensorFlow, it analyzes the various input data and generates an individualized nutrition plan. The output provides a recommended daily meal menu and its nutritional balance.

[0324] Step 4:

[0325] The server creates an optimal physical activity plan based on the generated nutrition plan. Using Scikit-learn, it determines exercise intensity and frequency, taking into account the user's age and exercise history. The output of this step is a specific exercise plan proposed to the user.

[0326] Step 5:

[0327] The server displays food ingredients corresponding to the recommended nutrition plan in conjunction with online shopping sites. This allows users to easily purchase the necessary ingredients. The server accesses the online shopping site's database via an API, and the output provides available product information and purchase links.

[0328] Step 6:

[0329] The user reviews the meal and exercise plans presented through the device and, if necessary, purchases food ingredients from the suggested online shopping site. The operation is performed via the device's GUI, and the actual purchase process is completed by redirection to an external online shopping site. Output includes a purchase completion notification and a display of the healthcare action plan.

[0330] This series of processes allows users to manage their health more efficiently and easily. Examples of prompts for the generating AI model include: "New user data and meal images have been received. Please generate a personalized nutrition plan based on them."

[0331] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0332] This invention is a system for effectively managing the lifestyle habits of diabetic patients, and by combining it with an emotion engine, it enables responses tailored to the user's emotional state. The system consists of a user's terminal, a server, and a network connection.

[0333] Users first log in to the system using their device and enter information such as age, height, weight, exercise history, past meals, and blood glucose levels. Users also take pictures of their daily meals and send this information from their device to the server. Furthermore, users can express their emotional state through voice input and facial recognition.

[0334] The server receives the user's basic information and entered meal images and stores them in a database. Based on the received data, an AI model is used to create meal management and exercise plans. This AI model uses a predictive model to generate optimal meal recipes tailored to each individual user. The generated meal recipes are provided to the user via the terminal. At this stage, an emotion engine analyzes the user's emotional state and adjusts the meal recipes and exercise plans accordingly.

[0335] The emotion engine identifies the user's emotional state from their voice and facial expressions, thereby determining their stress level and motivation. For example, if the server detects that the user is under high stress, it will suggest meals containing stress-relieving foods and recommend relaxing exercise plans (such as yoga or stretching).

[0336] For example, if a user says "I'm tired" in a voice that indicates high stress, the emotion engine analyzes the voice and determines that the user is experiencing high stress. The server takes this analysis into account and suggests a light meal and an exercise plan to help reduce stress. Furthermore, if the user sends a picture of their meal with a smile, they are considered highly motivated, and the system can suggest a more challenging exercise routine.

[0337] Thus, the present invention realizes a system that not only aims to manage diabetes but also supports the user's psychological state.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The user logs into the system via a terminal and enters information such as age, height, weight, past exercise history, and recent meals and blood sugar levels. The terminal collects this information and prepares it for transmission.

[0341] Step 2:

[0342] The user takes a picture of their meal using their device and sends the image along with emotional data such as audio and facial expressions to the server. The device then packages this data and sends it to the server.

[0343] Step 3:

[0344] The server stores personal information, food images, and emotional data received from the terminal in a database. An image recognition algorithm is used to analyze the food content and estimate calorie intake.

[0345] Step 4:

[0346] The server uses an emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the analysis results, it understands the user's stress level and motivation.

[0347] Step 5:

[0348] The server uses an AI model to analyze each user's personal information and past eating data to generate optimal meal recipes. It creates meal recipes and exercise plans that take into account the results of sentiment analysis.

[0349] Step 6:

[0350] The server sends generated meal recipes, estimated calories, and an exercise plan based on emotions to the user's device. The user can then review this information via their device and use it to improve their lifestyle.

[0351] Step 7:

[0352] Based on the information received, users adjust their daily lifestyle habits. For example, they manage their health by incorporating stress-reducing foods and exercise.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] In managing the lifestyle habits of diabetic patients, there is a need to achieve integrated and personalized health management that takes into account not only individual physical information and past consumption history, but also the user's psychological state. While conventional technologies could provide plans based on consumption records and personal attribute information, they lacked specific suggestions that reflected the user's psychological state, which led to decreased user motivation and a lack of continuity in health management.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes means for inputting user attribute information and historical consumption information, means for generating a meal plan using a predictive algorithm based on the attribute information and consumption information, and means for identifying the user's psychological state through emotion analysis and adjusting the meal plan and activity plan accordingly. This enables comprehensive health management based on both the user's physical attributes and psychological state.

[0358] "User attribute information" is a general term for basic physical information used to identify and characterize an individual, such as the user's age, height, and weight.

[0359] "Historical consumption information" refers to a record of meals consumed by a user in the past, including details such as the type, timing, and quantity of food eaten.

[0360] A "predictive algorithm" refers to mathematical methods and models used to calculate and generate future trends and optimal plans based on input data.

[0361] "Captured consumption images" refer to photographic data of meals taken by users, and are the subject of image analysis.

[0362] "Nutritional value" is a numerical indicator that shows the amount of energy and specific nutrients that food provides to the human body.

[0363] An "activity plan" is a plan of exercise and training designed to promote the user's physical activity and maintain their health.

[0364] "Emotional analysis" refers to the technology and process of identifying a user's emotional state from audio and images and analyzing their psychological characteristics.

[0365] "Psychological state" refers to an internal state related to the user's psychology, such as emotions, stress, and motivation.

[0366] In an embodiment of this invention, the system mainly consists of a user terminal, a server, and a network. The user accesses the system using the terminal and inputs their basic physical information (age, height, weight, etc.) and past meal records. The terminal uses its built-in camera and microphone to acquire images of meals and voice data of the user, and transmits this to the server.

[0367] The server receives this data and stores it in a high-performance database. Based on the information stored in the database, a predictive algorithm is used to generate personalized meal and activity plans for each individual user. This predictive algorithm uses a generative AI model, enabling personalized suggestions that meet the user's expectations. In addition, an emotion analysis module analyzes voice and images to understand the user's psychological state and adjust the suggested plans accordingly. This system achieves more effective health management by taking into account the user's stress and motivation.

[0368] Specifically, the system has a function that allows users to leave voice comments reflecting their emotional state, such as "I'm very tired today," and then suggests relaxing meals and activity plans such as yoga or stretching. In this way, it provides comprehensive feedback based on individual physical information and psychological state.

[0369] An example of a prompt would be, "Generate a meal and exercise plan to suggest when the emotion engine determines that the user is in a high-stress state." The server would then return personalized health suggestions accordingly.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The user enters their personal attribute information (age, height, weight, etc.) and past consumption information into the terminal. The terminal packages this information as structured data and sends it to the server. At this stage, data integrity and format checks are performed.

[0373] Step 2:

[0374] The server receives user attribute and consumption information sent from the terminal and stores it in the database. Here, it performs processing to ensure data consistency by integrating it with historical data. Furthermore, it integrates historical and new data and processes it into a format suitable for analysis.

[0375] Step 3:

[0376] Users take pictures of their meals and upload them to their devices. They also use voice input to express their emotional state. The devices compress the image data and send it to the server along with the voice data. The image data is preprocessed and converted into a format suitable for analysis.

[0377] Step 4:

[0378] The server analyzes the received image data to identify the content of consumption and nutritional value. It utilizes a generative AI model to perform highly accurate image analysis and stores the identification results in a database. This allows for detailed identification of meal contents, which can then be used to plan future meals.

[0379] Step 5:

[0380] The server uses voice data to perform sentiment analysis. The sentiment analysis module analyzes the tone and content of the voice to identify the user's psychological state. Based on this, it estimates the emotional state and adds it to the user's profile.

[0381] Step 6:

[0382] The server uses a generative AI model to generate meal and activity plans based on the user's attribute information, consumption history, and current emotional state. A predictive algorithm analyzes the generated data and suggests the optimal plan.

[0383] Step 7:

[0384] The generated plan is sent from the server to the terminal and provided to the user. The terminal presents the plan through an intuitive and easy-to-use user interface. This plan includes meal and activity suggestions to support personalized health management for the user.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] Lifestyle management for diabetic patients tends to be limited to general dietary management and exercise planning, but appropriate responses that take into account the psychological state and emotional fluctuations of individual patients are required. However, current systems have difficulty properly analyzing users' emotions and providing personalized guidance and support based on that analysis. Therefore, there is a need to develop a system that can provide more nuanced support in diabetes management.

[0388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0389] In this invention, the server includes means for inputting the user's personal information and past meal records, means for generating dietary guidelines using a predictive model, means for identifying intake amounts and estimating energy amounts from captured meal images, and means for adjusting dietary guidelines and exercise plans according to the user's emotional state. This enables appropriate lifestyle management according to the user's emotional state.

[0390] "Personal information" refers to information about an individual user's profile, such as their age, height, weight, exercise history, and blood glucose level information.

[0391] A "meal record" is data that records the details of meals a user has eaten in the past, and may include the type and quantity of food consumed.

[0392] A "predictive model" is a computational method that includes algorithms and AI used to generate optimal dietary guidelines and exercise plans based on input data.

[0393] "Meal images" are image data taken to visually record the contents of meals consumed by users.

[0394] "Intake" refers to the amount of each food item included in a meal and serves as the basic data for calculating calories and nutrients.

[0395] "Energy amount" refers to the total amount of calories obtained from ingested food, and is a numerical value that serves as an energy source for human activity and metabolism.

[0396] An "exercise plan" is a program that specifically outlines exercises suitable for the user's health condition and lifestyle, including the type, duration, and frequency of exercise.

[0397] "Emotional state" refers to the psychological state of the user, and includes the type and intensity of emotions detected from voice, facial expressions, etc.

[0398] "Emotion recognition means" refers to a method or device for identifying a user's emotional state using voice analysis and facial expression analysis technology.

[0399] "Adjustment measures" refer to functions and methods for modifying previously presented dietary guidelines and exercise plans according to the individual circumstances and emotional state of the user.

[0400] The system for realizing this invention consists of a user terminal, a server, and a network connecting them. The user terminal is a device such as smart glasses or a smartphone, through which it can input images of meals and voice, and express its emotional state to the system. The server receives this data and performs the necessary processing for analysis.

[0401] The server analyzes the received image data using computer vision technology (e.g., OpenCV) to analyze the meal content and estimate the amount of food consumed and the amount of energy. For audio data, it uses speech recognition technology (e.g., Google Speech-to-Text API) to estimate the emotional state and uses emotion recognition tools to identify the emotional state from the audio and facial expressions.

[0402] Subsequently, the server uses a predictive model (e.g., TensorFlow) to generate dietary guidelines based on the user's personal information and past meal records. These guidelines are then adjusted to take into account the user's emotional state and are also used to provide exercise plans. The AI ​​model enables the provision of lifestyle management optimized for individual emotions.

[0403] For example, if a user wears smart glasses and sends an image of themselves smiling after breakfast to the system, the system can determine from that smile that the user is highly motivated and suggest a more active exercise plan than usual. Such specific responses can be obtained through a prompt example from the generative AI model: "If the user was smiling after breakfast this morning, what kind of exercise suggestion should be made based on that emotional state?"

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user operates the device to enter personal information and past meal records. This includes age, height, weight, exercise history, and meal details. The entered data is collected on the device and sent to the server.

[0407] Step 2:

[0408] The user sends images of their meals and voice messages to the system using a device. The image data records the meals the user has consumed, and the voice messages indicate their emotional state. This data is transmitted to the server using a secure protocol.

[0409] Step 3:

[0410] The server analyzes the received meal images. Using computer vision technology, it recognizes the contents of the meal from the image and calculates the intake and energy content. The input is a meal image, and the output is data on calories and nutrients.

[0411] Step 4:

[0412] The server analyzes voice messages to identify emotional states. Using a speech recognition API, it estimates emotions from voice tone and patterns using an emotion recognition model. The input is voice data, and the output is data indicating the user's emotional state.

[0413] Step 5:

[0414] The server uses a predictive model to generate dietary guidelines based on the user's personal information and meal records. The generating AI model creates guidelines tailored to the user's health condition and preferences, and data processing yields individually optimized recipes.

[0415] Step 6:

[0416] The server adjusts dietary guidelines based on emotional state and generates exercise plans that reflect individual needs. For example, if a high-stress state is detected, it recommends relaxing exercises. This enables more personalized health management.

[0417] Step 7:

[0418] The device displays adjusted dietary guidelines and exercise plans sent from the server to the user visually or audibly. Based on this, the user can improve their daily lifestyle and manage their health.

[0419] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0420] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0425] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0426] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0427] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0428] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0429] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0430] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0431] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0432] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0433] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0434] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0435] This invention is implemented as a system to efficiently support lifestyle improvements for diabetic patients. The system mainly consists of user terminals, a server, and a network connecting them.

[0436] First, users log in to the system using their own devices and enter basic information and recent health status. This includes age, height, weight, past exercise history, diet, and recent blood glucose levels. Users can also take pictures of their daily meals and send the meal records to the server.

[0437] Next, user information received through the device is sent to the server. The server stores the received information in a database and uses an AI model to evaluate diet management and exercise plans. The AI ​​model analyzes past data entered by the user and similar cases to generate personalized meal recipes. The server sends these meal recipes to the device, and the user can check the recommended meals through the app.

[0438] The server also analyzes the received meal images and calculates calories from the captured food. By using image recognition technology to return information about the contents and quantity of the meal, as well as the corresponding calorie information, users can easily manage their own diet.

[0439] Furthermore, regarding the exercise plan, the server creates an optimal exercise plan based on the user's age, exercise history, and daily diet. Specific types of exercise, frequency, and duration are suggested and provided to the user via the device.

[0440] As a concrete example, suppose a 40-year-old male user takes a picture of his breakfast and sends it to the server. The server analyzes the image, recognizes that the meal consists of toast, eggs, and coffee, and estimates the calorie content to be approximately 350 calories. Furthermore, based on this information, the server suggests a salad and chicken for lunch and generates a recipe for dinner that includes vegetable soup. At the same time, it recommends a 30-minute walk as exercise for the day, thus comprehensively supporting the user's health management.

[0441] In this way, this system realizes a concrete embodiment that utilizes AI technology to support users' health management.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] Users log in to the app using their device and enter personal information such as age, height, weight, exercise history, past meals, and recent blood glucose levels. The device collects this data and prepares it for transmission to the server.

[0445] Step 2:

[0446] The terminal collects information entered by the user into data packets and sends them to the server. The server receives this data and stores it in its database.

[0447] Step 3:

[0448] The user takes a photo of their meal and sends the image from their device to the server. The server receives the image and uses image recognition technology to analyze the ingredients and quantities.

[0449] Step 4:

[0450] The server estimates the calorie content of the meal based on the image analysis results. The estimated calorie information is then sent back to the user's device, allowing the user to check their calorie intake.

[0451] Step 5:

[0452] The server uses an AI model to analyze the user's personal information and past meal data, and generates meal recipes that are effective for managing blood sugar levels. This suggestion is sent to the user's device, and the user reviews the recommended meal plan.

[0453] Step 6:

[0454] The server uses an AI model to calculate the optimal exercise plan based on the user's age, exercise history, and daily meal information. The created exercise plan is then sent to the device.

[0455] Step 7:

[0456] Users can manage their diabetes by reviewing suggested meal recipes and exercise plans through their devices and incorporating them into their daily lives.

[0457] (Example 1)

[0458] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] There is a need to efficiently and individually provide appropriate health management and lifestyle improvement support for patients with diabetes, a lifestyle-related disease. Currently, inputting information and managing daily meals and exercise is cumbersome, and there is a lack of specific guidance tailored to individual lifestyles. As a result, users find it difficult to manage their health properly on their own and struggle to sustain an effective improvement plan.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes means for inputting the user's basic information and health status, means for transmitting the data of the basic information and health status to the server, means for generating personalized meal recipes using a generation AI model with a server that provides a service for accumulating and analyzing diverse data, means for analyzing meal images, detecting ingested nutrients using image recognition technology and calculating calories, and means for providing an exercise plan based on the user's basic information, exercise history and daily meal content. As a result, users can receive optimal meal and exercise plans tailored to their individual health status, enabling them to efficiently and continuously improve their lifestyle habits.

[0462] "Users" refers to all individuals who use the system and who input and receive information for the purpose of managing their health status and improving their lifestyle.

[0463] "Basic information" refers to personal data necessary for managing the user's health, such as age, height, and weight.

[0464] "Health status" refers to information about the user's physical condition, such as exercise records, dietary information, and blood sugar levels, which they input into the system.

[0465] A "server" refers to a computer device connected via a network for receiving and analyzing data.

[0466] A "generative AI model" refers to an artificial intelligence system that uses machine learning to generate appropriate output (e.g., meal recipes or exercise plans) based on input data.

[0467] "Meal images" refer to photographic data that users use to visually record the meals they have eaten and send to the system for analysis.

[0468] "Image recognition technology" refers to technologies that include computer vision techniques for analyzing input images and understanding their content.

[0469] "Calories" are a numerical representation of the amount of energy contained in food, and are an important indicator in dietary management.

[0470] An "exercise plan" refers to a proposal document that includes the content, frequency, and intensity of exercises planned based on the user's health condition.

[0471] This invention is a system that efficiently supports individualized lifestyle improvements for diabetic patients. The system mainly consists of a user terminal, a server that receives and processes data, and a network that connects each device. A specific embodiment of the system is shown below.

[0472] Users input basic information and health data using a dedicated application on their own devices. Basic information includes age, height, and weight, while health data includes recent exercise history, diet, and blood sugar levels. This information is transmitted from the device to the server via the network.

[0473] The server uses Python and SQL to store and manage received user data in a database. It also uses a generative AI model to analyze user data and generate personalized meal recipes. The generative AI model is implemented using machine learning frameworks such as TensorFlow or PyTorch. The AI ​​model suggests optimal meal options based on the user's past data and similar cases.

[0474] Furthermore, users take photos of their daily meals with their device's camera and send the images to a server. The server analyzes these images using image recognition technology and calculates the contents and calories of the meal using OpenCV and the Google Cloud Vision API. Based on this information, users can meticulously manage their own diet.

[0475] Furthermore, the server creates a personalized exercise plan based on the user's information. It determines the type, frequency, and intensity of exercise, taking into account exercise history, age, and daily diet, and sends this information to the user's device. This allows users to utilize it for daily health management.

[0476] As a concrete example, if a 40-year-old male user takes a picture of his breakfast and sends the data to the server, the server analyzes the image, recognizes that it is a meal consisting of toast, eggs, and coffee, and estimates the calorie content to be 350 calories. Based on this, it suggests a salad and chicken for lunch, vegetable soup for dinner, and recommends a 30-minute walk as exercise for the day.

[0477] Example of a prompt:

[0478] "We have a 40-year-old user whose recent diet consists of toast, eggs, and coffee. Please use AI to generate recommended recipes for lunch and dinner for this user. Also, please suggest a suitable exercise plan."

[0479] In this way, this system uses AI technology to support users in improving their lifestyle habits and provides them with optimal health management.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] Users open a dedicated application on their own devices and log in. At this time, they enter basic information such as age, height, weight, past exercise history, diet, and recent blood glucose levels into a form. The entered information is temporarily stored on the device. Specifically, the process involves the user entering data into the form and pressing the "Submit" button, after which the information is compiled on the device.

[0483] Step 2:

[0484] The terminal transmits collected user information to the server via the network. The data is securely encrypted using the HTTPS protocol. Specifically, a background process encrypts the data and then sends it to the server. The input is user health information, and the output is the arrival of the data at the server.

[0485] Step 3:

[0486] The server saves the received user information to the database. This database operation is performed using Python and SQL. Specifically, the server executes SQL INSERT statements to store the user information in the appropriate table. The input contains user data, and the output is the information stored in the database.

[0487] Step 4:

[0488] The user takes a picture of their meal and sends the image data to the server via their device. Specifically, they use the "Photo Shooting" function within the app and upload the captured image to the server using the "Send Image" button. The input is the photographed meal image, and the output is the image sent to the server.

[0489] Step 5:

[0490] The server analyzes the received image data using image recognition technology. It performs image analysis using OpenCV and the Google Cloud Vision API to identify the contents of the meal and calculate the calories. Specifically, an AI model runs a process to calculate calories based on the analysis results. The input is the received image, and the output is the calorie information from the analysis results.

[0491] Step 6:

[0492] The server uses a generative AI model to analyze accumulated user data and similar cases to generate personalized meal recipes. This model is built using TensorFlow and PyTorch. The AI ​​model creates individually optimized recipes from the data and sends them from the server to the terminal. The input is user data, and the output is a recommended meal recipe.

[0493] Step 7:

[0494] The server generates an exercise plan, determining the optimal type, frequency, and intensity of exercise based on the user's basic information and exercise history. The generated exercise plan is sent to the terminal, where the user can use it for daily health management. Specifically, an algorithm calculates the plan individually, packages it as data, and sends it. The input is the user's health information and dietary details, and the output is the suggested exercise plan.

[0495] Step 8:

[0496] The device displays meal recipes and exercise plans received from the server to the user, who then takes specific actions to improve their lifestyle based on this information. This information is conveyed to the user through the app's interface, and options are also provided to help the user manage their health. The input is data sent from the server, and the output is the presentation of information to the user.

[0497] (Application Example 1)

[0498] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] In modern times, managing diabetes, a lifestyle-related disease, is considered crucial, but many patients struggle with effective dietary management and exercise planning. Furthermore, choosing appropriate foods and maintaining consistent health management are burdensome for patients. There is a need for a system that develops personalized plans and proposes them in an easily implementable format.

[0500] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0501] In this invention, the server includes means for inputting biometric data and dietary history, means for generating a nutrition plan using a predictive algorithm, means for identifying nutrients from meal images and calculating calories, and means for presenting food ingredients in cooperation with an online shopping site. This provides individually optimized meal and exercise plans, enabling users to manage their health by easily purchasing the suggested food ingredients.

[0502] "Biometric data" refers to information about an individual's body, including their age, height, weight, and health status.

[0503] "Dietary history" refers to the contents of meals consumed by the user in the past and the records thereof.

[0504] A "predictive algorithm" is a computational method used to generate optimal nutrition plans and exercise plans based on input data.

[0505] A "nutrition plan" is a suggestion of recommended meals based on the user's health condition and lifestyle.

[0506] "Meal images" refer to photographic data of meals taken by users.

[0507] "Identifying nutrients" means using image recognition technology to identify the ingredients contained in a food image and recognize their nutritional components.

[0508] "Calculating calories" means determining the total energy content of a meal based on the quantity and type of food items identified.

[0509] A "physical activity plan" is a proposal for individualized exercise content and frequency, formulated based on the user's data.

[0510] "Integrating with e-commerce sites" means transmitting information so that users can obtain food ingredients suggested based on their nutrition plan through external e-commerce platforms.

[0511] "Food ingredients" refer to the raw materials and ingredients necessary to implement a recommended nutrition plan.

[0512] This invention relates to the implementation of a system aimed at comprehensive health management for patients with lifestyle-related diseases, such as diabetes. The main components are user terminals, a server, and a network connecting them.

[0513] First, users log in to the system using their own devices and enter their biometric data and dietary history. The entered information is sent to a server via the network and stored in a database. The server processes this data and generates a personalized nutrition plan using predictive algorithms. The software used in this process includes AI models such as TensorFlow and PyTorch.

[0514] Furthermore, users take photos of their daily meals with their smartphones and send these images to the server. The server uses image recognition technology to identify the nutrients in the meal and calculate the calories. For this purpose, the server can utilize libraries such as Scikit-learn and OpenCV.

[0515] Once the analysis of each meal is complete, the server develops and proposes an optimal physical activity plan based on this analysis. The proposed nutrition plan also includes a function that links to online shopping sites to suggest relevant food ingredients. This function allows users to conveniently purchase the suggested food ingredients.

[0516] As a concrete example, consider a scenario where a user sends a picture of their breakfast to a server. The server recognizes the meal consists of toast, eggs, and coffee, and then creates a suitable lunch and dinner plan. Links are then provided to purchase the suggested food items through an online shopping site. A possible prompt for the generative AI model might be: "A user's meal image has been sent to the server. Identify the specific foods that make up this meal and calculate the calories."

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] Users access the system via a login screen using their device and enter biometric data and past meal history. The entered data is compiled on the device and sent to the server via the network. Examples of entered data include the user's age, weight, and meal content for the past week. The server stores the received data in a database.

[0520] Step 2:

[0521] Users take photos of their daily meals using their smartphones and upload the images to a server. These images are processed on the device and converted into a format that clearly identifies the contents of the meal. The server receives these images and uses image recognition technology to identify the nutrients contained in the meal. Specifically, OpenCV is used as the image processing library, and the output includes the names of the ingredients and an estimated calorie amount.

[0522] Step 3:

[0523] The server executes a predictive algorithm based on the biometric data saved in Step 1 and the dietary information identified in Step 2. Using TensorFlow, it analyzes the various input data and generates an individualized nutrition plan. The output provides a recommended daily meal menu and its nutritional balance.

[0524] Step 4:

[0525] The server creates an optimal physical activity plan based on the generated nutrition plan. Using Scikit-learn, it determines exercise intensity and frequency, taking into account the user's age and exercise history. The output of this step is a specific exercise plan proposed to the user.

[0526] Step 5:

[0527] The server displays food ingredients corresponding to the recommended nutrition plan in conjunction with online shopping sites. This allows users to easily purchase the necessary ingredients. The server accesses the online shopping site's database via an API, and the output provides available product information and purchase links.

[0528] Step 6:

[0529] The user reviews the meal and exercise plans presented through the device and, if necessary, purchases food ingredients from the suggested online shopping site. The operation is performed via the device's GUI, and the actual purchase process is completed by redirection to an external online shopping site. Output includes a purchase completion notification and a display of the healthcare action plan.

[0530] This series of processes allows users to manage their health more efficiently and easily. Examples of prompts for the generating AI model include: "New user data and meal images have been received. Please generate a personalized nutrition plan based on them."

[0531] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0532] This invention is a system for effectively managing the lifestyle habits of diabetic patients, and by combining it with an emotion engine, it enables responses tailored to the user's emotional state. The system consists of a user's terminal, a server, and a network connection.

[0533] Users first log in to the system using their device and enter information such as age, height, weight, exercise history, past meals, and blood glucose levels. Users also take pictures of their daily meals and send this information from their device to the server. Furthermore, users can express their emotional state through voice input and facial recognition.

[0534] The server receives the user's basic information and entered meal images and stores them in a database. Based on the received data, an AI model is used to create meal management and exercise plans. This AI model uses a predictive model to generate optimal meal recipes tailored to each individual user. The generated meal recipes are provided to the user via the terminal. At this stage, an emotion engine analyzes the user's emotional state and adjusts the meal recipes and exercise plans accordingly.

[0535] The emotion engine identifies the user's emotional state from their voice and facial expressions, thereby determining their stress level and motivation. For example, if the server detects that the user is under high stress, it will suggest meals containing stress-relieving foods and recommend relaxing exercise plans (such as yoga or stretching).

[0536] For example, if a user says "I'm tired" in a voice that indicates high stress, the emotion engine analyzes the voice and determines that the user is experiencing high stress. The server takes this analysis into account and suggests a light meal and an exercise plan to help reduce stress. Furthermore, if the user sends a picture of their meal with a smile, they are considered highly motivated, and the system can suggest a more challenging exercise routine.

[0537] Thus, the present invention realizes a system that not only aims to manage diabetes but also supports the user's psychological state.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The user logs into the system via a terminal and enters information such as age, height, weight, past exercise history, and recent meals and blood sugar levels. The terminal collects this information and prepares it for transmission.

[0541] Step 2:

[0542] The user takes a picture of their meal using their device and sends the image along with emotional data such as audio and facial expressions to the server. The device then packages this data and sends it to the server.

[0543] Step 3:

[0544] The server stores personal information, food images, and emotional data received from the terminal in a database. An image recognition algorithm is used to analyze the food content and estimate calorie intake.

[0545] Step 4:

[0546] The server uses an emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the analysis results, it understands the user's stress level and motivation.

[0547] Step 5:

[0548] The server uses an AI model to analyze each user's personal information and past eating data to generate optimal meal recipes. It creates meal recipes and exercise plans that take into account the results of sentiment analysis.

[0549] Step 6:

[0550] The server sends generated meal recipes, estimated calories, and an exercise plan based on emotions to the user's device. The user can then review this information via their device and use it to improve their lifestyle.

[0551] Step 7:

[0552] Based on the information received, users adjust their daily lifestyle habits. For example, they manage their health by incorporating stress-reducing foods and exercise.

[0553] (Example 2)

[0554] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] In managing the lifestyle habits of diabetic patients, there is a need to achieve integrated and personalized health management that takes into account not only individual physical information and past consumption history, but also the user's psychological state. While conventional technologies could provide plans based on consumption records and personal attribute information, they lacked specific suggestions that reflected the user's psychological state, which led to decreased user motivation and a lack of continuity in health management.

[0556] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0557] In this invention, the server includes means for inputting user attribute information and historical consumption information, means for generating a meal plan using a predictive algorithm based on the attribute information and consumption information, and means for identifying the user's psychological state through emotion analysis and adjusting the meal plan and activity plan accordingly. This enables comprehensive health management based on both the user's physical attributes and psychological state.

[0558] "User attribute information" is a general term for basic physical information used to identify and characterize an individual, such as the user's age, height, and weight.

[0559] "Historical consumption information" refers to a record of meals consumed by a user in the past, including details such as the type, timing, and quantity of food eaten.

[0560] A "predictive algorithm" refers to mathematical methods and models used to calculate and generate future trends and optimal plans based on input data.

[0561] "Captured consumption images" refer to photographic data of meals taken by users, and are the subject of image analysis.

[0562] "Nutritional value" is a numerical indicator that shows the amount of energy and specific nutrients that food provides to the human body.

[0563] An "activity plan" is a plan of exercise and training designed to promote the user's physical activity and maintain their health.

[0564] "Emotional analysis" refers to the technology and process of identifying a user's emotional state from audio and images and analyzing their psychological characteristics.

[0565] "Psychological state" refers to an internal state related to the user's psychology, such as emotions, stress, and motivation.

[0566] In an embodiment of this invention, the system mainly consists of a user terminal, a server, and a network. The user accesses the system using the terminal and inputs their basic physical information (age, height, weight, etc.) and past meal records. The terminal uses its built-in camera and microphone to acquire images of meals and voice data of the user, and transmits this to the server.

[0567] The server receives this data and stores it in a high-performance database. Based on the information stored in the database, a predictive algorithm is used to generate personalized meal and activity plans for each individual user. This predictive algorithm uses a generative AI model, enabling personalized suggestions that meet the user's expectations. In addition, an emotion analysis module analyzes voice and images to understand the user's psychological state and adjust the suggested plans accordingly. This system achieves more effective health management by taking into account the user's stress and motivation.

[0568] Specifically, the system has a function that allows users to leave voice comments reflecting their emotional state, such as "I'm very tired today," and then suggests relaxing meals and activity plans such as yoga or stretching. In this way, it provides comprehensive feedback based on individual physical information and psychological state.

[0569] An example of a prompt would be, "Generate a meal and exercise plan to suggest when the emotion engine determines that the user is in a high-stress state." The server would then return personalized health suggestions accordingly.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The user enters their personal attribute information (age, height, weight, etc.) and past consumption information into the terminal. The terminal packages this information as structured data and sends it to the server. At this stage, data integrity and format checks are performed.

[0573] Step 2:

[0574] The server receives user attribute and consumption information sent from the terminal and stores it in the database. Here, it performs processing to ensure data consistency by integrating it with historical data. Furthermore, it integrates historical and new data and processes it into a format suitable for analysis.

[0575] Step 3:

[0576] Users take pictures of their meals and upload them to their devices. They also use voice input to express their emotional state. The devices compress the image data and send it to the server along with the voice data. The image data is preprocessed and converted into a format suitable for analysis.

[0577] Step 4:

[0578] The server analyzes the received image data to identify the content of consumption and nutritional value. It utilizes a generative AI model to perform highly accurate image analysis and stores the identification results in a database. This allows for detailed identification of meal contents, which can then be used to plan future meals.

[0579] Step 5:

[0580] The server uses voice data to perform sentiment analysis. The sentiment analysis module analyzes the tone and content of the voice to identify the user's psychological state. Based on this, it estimates the emotional state and adds it to the user's profile.

[0581] Step 6:

[0582] The server uses a generative AI model to generate meal and activity plans based on the user's attribute information, consumption history, and current emotional state. A predictive algorithm analyzes the generated data and suggests the optimal plan.

[0583] Step 7:

[0584] The generated plan is sent from the server to the terminal and provided to the user. The terminal presents the plan through an intuitive and easy-to-use user interface. This plan includes meal and activity suggestions to support personalized health management for the user.

[0585] (Application Example 2)

[0586] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] Lifestyle management for diabetic patients tends to be limited to general dietary management and exercise planning, but appropriate responses that take into account the psychological state and emotional fluctuations of individual patients are required. However, current systems have difficulty properly analyzing users' emotions and providing personalized guidance and support based on that analysis. Therefore, there is a need to develop a system that can provide more nuanced support in diabetes management.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0589] In this invention, the server includes means for inputting the user's personal information and past meal records, means for generating dietary guidelines using a predictive model, means for identifying intake amounts and estimating energy amounts from captured meal images, and means for adjusting dietary guidelines and exercise plans according to the user's emotional state. This enables appropriate lifestyle management according to the user's emotional state.

[0590] "Personal information" refers to information about an individual user's profile, such as their age, height, weight, exercise history, and blood glucose level information.

[0591] A "meal record" is data that records the details of meals a user has eaten in the past, and may include the type and quantity of food consumed.

[0592] A "predictive model" is a computational method that includes algorithms and AI used to generate optimal dietary guidelines and exercise plans based on input data.

[0593] "Meal images" are image data taken to visually record the contents of meals consumed by users.

[0594] "Intake" refers to the amount of each food item included in a meal and serves as the basic data for calculating calories and nutrients.

[0595] "Energy amount" refers to the total amount of calories obtained from ingested food, and is a numerical value that serves as an energy source for human activity and metabolism.

[0596] An "exercise plan" is a program that specifically outlines exercises suitable for the user's health condition and lifestyle, including the type, duration, and frequency of exercise.

[0597] "Emotional state" refers to the psychological state of the user, and includes the type and intensity of emotions detected from voice, facial expressions, etc.

[0598] "Emotion recognition means" refers to a method or device for identifying a user's emotional state using voice analysis and facial expression analysis technology.

[0599] "Adjustment measures" refer to functions and methods for modifying previously presented dietary guidelines and exercise plans according to the individual circumstances and emotional state of the user.

[0600] The system for realizing this invention consists of a user terminal, a server, and a network connecting them. The user terminal is a device such as smart glasses or a smartphone, through which it can input images of meals and voice, and express its emotional state to the system. The server receives this data and performs the necessary processing for analysis.

[0601] The server analyzes the received image data using computer vision technology (e.g., OpenCV) to analyze the meal content and estimate the amount of food consumed and the amount of energy. For audio data, it uses speech recognition technology (e.g., Google Speech-to-Text API) to estimate the emotional state and uses emotion recognition tools to identify the emotional state from the audio and facial expressions.

[0602] Subsequently, the server uses a predictive model (e.g., TensorFlow) to generate dietary guidelines based on the user's personal information and past meal records. These guidelines are then adjusted to take into account the user's emotional state and are also used to provide exercise plans. The AI ​​model enables the provision of lifestyle management optimized for individual emotions.

[0603] For example, if a user wears smart glasses and sends an image of themselves smiling after breakfast to the system, the system can determine from that smile that the user is highly motivated and suggest a more active exercise plan than usual. Such specific responses can be obtained through a prompt example from the generative AI model: "If the user was smiling after breakfast this morning, what kind of exercise suggestion should be made based on that emotional state?"

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The user operates the device to enter personal information and past meal records. This includes age, height, weight, exercise history, and meal details. The entered data is collected on the device and sent to the server.

[0607] Step 2:

[0608] The user sends images of their meals and voice messages to the system using a device. The image data records the meals the user has consumed, and the voice messages indicate their emotional state. This data is transmitted to the server using a secure protocol.

[0609] Step 3:

[0610] The server analyzes the received meal images. Using computer vision technology, it recognizes the contents of the meal from the image and calculates the intake and energy content. The input is a meal image, and the output is data on calories and nutrients.

[0611] Step 4:

[0612] The server analyzes voice messages to identify emotional states. Using a speech recognition API, it estimates emotions from voice tone and patterns using an emotion recognition model. The input is voice data, and the output is data indicating the user's emotional state.

[0613] Step 5:

[0614] The server uses a predictive model to generate dietary guidelines based on the user's personal information and meal records. The generating AI model creates guidelines tailored to the user's health condition and preferences, and data processing yields individually optimized recipes.

[0615] Step 6:

[0616] The server adjusts dietary guidelines based on emotional state and generates exercise plans that reflect individual needs. For example, if a high-stress state is detected, it recommends relaxing exercises. This enables more personalized health management.

[0617] Step 7:

[0618] The device displays adjusted dietary guidelines and exercise plans sent from the server to the user visually or audibly. Based on this, the user can improve their daily lifestyle and manage their health.

[0619] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0620] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0621] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0625] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0626] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0627] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0628] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0629] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0630] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0631] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0632] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0633] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0634] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0635] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] This invention is implemented as a system to efficiently support lifestyle improvements for diabetic patients. The system mainly consists of user terminals, a server, and a network connecting them.

[0637] First, users log in to the system using their own devices and enter basic information and recent health status. This includes age, height, weight, past exercise history, diet, and recent blood glucose levels. Users can also take pictures of their daily meals and send the meal records to the server.

[0638] Next, user information received through the device is sent to the server. The server stores the received information in a database and uses an AI model to evaluate diet management and exercise plans. The AI ​​model analyzes past data entered by the user and similar cases to generate personalized meal recipes. The server sends these meal recipes to the device, and the user can check the recommended meals through the app.

[0639] The server also analyzes the received meal images and calculates calories from the captured food. By using image recognition technology to return information about the contents and quantity of the meal, as well as the corresponding calorie information, users can easily manage their own diet.

[0640] Furthermore, regarding the exercise plan, the server creates an optimal exercise plan based on the user's age, exercise history, and daily diet. Specific types of exercise, frequency, and duration are suggested and provided to the user via the device.

[0641] As a concrete example, suppose a 40-year-old male user takes a picture of his breakfast and sends it to the server. The server analyzes the image, recognizes that the meal consists of toast, eggs, and coffee, and estimates the calorie content to be approximately 350 calories. Furthermore, based on this information, the server suggests a salad and chicken for lunch and generates a recipe for dinner that includes vegetable soup. At the same time, it recommends a 30-minute walk as exercise for the day, thus comprehensively supporting the user's health management.

[0642] In this way, this system realizes a concrete embodiment that utilizes AI technology to support users' health management.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] Users log in to the app using their device and enter personal information such as age, height, weight, exercise history, past meals, and recent blood glucose levels. The device collects this data and prepares it for transmission to the server.

[0646] Step 2:

[0647] The terminal collects information entered by the user into data packets and sends them to the server. The server receives this data and stores it in its database.

[0648] Step 3:

[0649] The user takes a photo of their meal and sends the image from their device to the server. The server receives the image and uses image recognition technology to analyze the ingredients and quantities.

[0650] Step 4:

[0651] The server estimates the calorie content of the meal based on the image analysis results. The estimated calorie information is then sent back to the user's device, allowing the user to check their calorie intake.

[0652] Step 5:

[0653] The server uses an AI model to analyze the user's personal information and past meal data, and generates meal recipes that are effective for managing blood sugar levels. This suggestion is sent to the user's device, and the user reviews the recommended meal plan.

[0654] Step 6:

[0655] The server uses an AI model to calculate the optimal exercise plan based on the user's age, exercise history, and daily meal information. The created exercise plan is then sent to the device.

[0656] Step 7:

[0657] Users can manage their diabetes by reviewing suggested meal recipes and exercise plans through their devices and incorporating them into their daily lives.

[0658] (Example 1)

[0659] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] There is a need to efficiently and individually provide appropriate health management and lifestyle improvement support for patients with diabetes, a lifestyle-related disease. Currently, inputting information and managing daily meals and exercise is cumbersome, and there is a lack of specific guidance tailored to individual lifestyles. As a result, users find it difficult to manage their health properly on their own and struggle to sustain an effective improvement plan.

[0661] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0662] In this invention, the server includes means for inputting the user's basic information and health status, means for transmitting the data of the basic information and health status to the server, means for generating personalized meal recipes using a generation AI model with a server that provides a service for accumulating and analyzing diverse data, means for analyzing meal images, detecting ingested nutrients using image recognition technology and calculating calories, and means for providing an exercise plan based on the user's basic information, exercise history and daily meal content. As a result, users can receive optimal meal and exercise plans tailored to their individual health status, enabling them to efficiently and continuously improve their lifestyle habits.

[0663] "Users" refers to all individuals who use the system and who input and receive information for the purpose of managing their health status and improving their lifestyle.

[0664] "Basic information" refers to personal data necessary for managing the user's health, such as age, height, and weight.

[0665] "Health status" refers to information about the user's physical condition, such as exercise records, dietary information, and blood sugar levels, which they input into the system.

[0666] A "server" refers to a computer device connected via a network for receiving and analyzing data.

[0667] A "generative AI model" refers to an artificial intelligence system that uses machine learning to generate appropriate output (e.g., meal recipes or exercise plans) based on input data.

[0668] "Meal images" refer to photographic data that users use to visually record the meals they have eaten and send to the system for analysis.

[0669] "Image recognition technology" refers to technologies that include computer vision techniques for analyzing input images and understanding their content.

[0670] "Calories" are a numerical representation of the amount of energy contained in food, and are an important indicator in dietary management.

[0671] An "exercise plan" refers to a proposal document that includes the content, frequency, and intensity of exercises planned based on the user's health condition.

[0672] This invention is a system that efficiently supports individualized lifestyle improvements for diabetic patients. The system mainly consists of a user terminal, a server that receives and processes data, and a network that connects each device. A specific embodiment of the system is shown below.

[0673] Users input basic information and health data using a dedicated application on their own devices. Basic information includes age, height, and weight, while health data includes recent exercise history, diet, and blood sugar levels. This information is transmitted from the device to the server via the network.

[0674] The server uses Python and SQL to store and manage received user data in a database. It also uses a generative AI model to analyze user data and generate personalized meal recipes. The generative AI model is implemented using machine learning frameworks such as TensorFlow or PyTorch. The AI ​​model suggests optimal meal options based on the user's past data and similar cases.

[0675] Furthermore, users take photos of their daily meals with their device's camera and send the images to a server. The server analyzes these images using image recognition technology and calculates the contents and calories of the meal using OpenCV and the Google Cloud Vision API. Based on this information, users can meticulously manage their own diet.

[0676] Furthermore, the server creates a personalized exercise plan based on the user's information. It determines the type, frequency, and intensity of exercise, taking into account exercise history, age, and daily diet, and sends this information to the user's device. This allows users to utilize it for daily health management.

[0677] As a concrete example, if a 40-year-old male user takes a picture of his breakfast and sends the data to the server, the server analyzes the image, recognizes that it is a meal consisting of toast, eggs, and coffee, and estimates the calorie content to be 350 calories. Based on this, it suggests a salad and chicken for lunch, vegetable soup for dinner, and recommends a 30-minute walk as exercise for the day.

[0678] Example of a prompt:

[0679] "We have a 40-year-old user whose recent diet consists of toast, eggs, and coffee. Please use AI to generate recommended recipes for lunch and dinner for this user. Also, please suggest a suitable exercise plan."

[0680] In this way, this system uses AI technology to support users in improving their lifestyle habits and provides them with optimal health management.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] Users open a dedicated application on their own devices and log in. At this time, they enter basic information such as age, height, weight, past exercise history, diet, and recent blood glucose levels into a form. The entered information is temporarily stored on the device. Specifically, the process involves the user entering data into the form and pressing the "Submit" button, after which the information is compiled on the device.

[0684] Step 2:

[0685] The terminal transmits collected user information to the server via the network. The data is securely encrypted using the HTTPS protocol. Specifically, a background process encrypts the data and then sends it to the server. The input is user health information, and the output is the arrival of the data at the server.

[0686] Step 3:

[0687] The server saves the received user information to the database. This database operation is performed using Python and SQL. Specifically, the server executes SQL INSERT statements to store the user information in the appropriate table. The input contains user data, and the output is the information stored in the database.

[0688] Step 4:

[0689] The user takes a picture of their meal and sends the image data to the server via their device. Specifically, they use the "Photo Shooting" function within the app and upload the captured image to the server using the "Send Image" button. The input is the photographed meal image, and the output is the image sent to the server.

[0690] Step 5:

[0691] The server analyzes the received image data using image recognition technology. It performs image analysis using OpenCV and the Google Cloud Vision API to identify the contents of the meal and calculate the calories. Specifically, an AI model runs a process to calculate calories based on the analysis results. The input is the received image, and the output is the calorie information from the analysis results.

[0692] Step 6:

[0693] The server uses a generative AI model to analyze accumulated user data and similar cases to generate personalized meal recipes. This model is built using TensorFlow and PyTorch. The AI ​​model creates individually optimized recipes from the data and sends them from the server to the terminal. The input is user data, and the output is a recommended meal recipe.

[0694] Step 7:

[0695] The server generates an exercise plan, determining the optimal type, frequency, and intensity of exercise based on the user's basic information and exercise history. The generated exercise plan is sent to the terminal, where the user can use it for daily health management. Specifically, an algorithm calculates the plan individually, packages it as data, and sends it. The input is the user's health information and dietary details, and the output is the suggested exercise plan.

[0696] Step 8:

[0697] The device displays meal recipes and exercise plans received from the server to the user, who then takes specific actions to improve their lifestyle based on this information. This information is conveyed to the user through the app's interface, and options are also provided to help the user manage their health. The input is data sent from the server, and the output is the presentation of information to the user.

[0698] (Application Example 1)

[0699] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] In modern times, managing diabetes, a lifestyle-related disease, is considered crucial, but many patients struggle with effective dietary management and exercise planning. Furthermore, choosing appropriate foods and maintaining consistent health management are burdensome for patients. There is a need for a system that develops personalized plans and proposes them in an easily implementable format.

[0701] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0702] In this invention, the server includes means for inputting biometric data and dietary history, means for generating a nutrition plan using a predictive algorithm, means for identifying nutrients from meal images and calculating calories, and means for presenting food ingredients in cooperation with an online shopping site. This provides individually optimized meal and exercise plans, enabling users to manage their health by easily purchasing the suggested food ingredients.

[0703] "Biometric data" refers to information about an individual's body, including their age, height, weight, and health status.

[0704] "Dietary history" refers to the contents of meals consumed by the user in the past and the records thereof.

[0705] A "predictive algorithm" is a computational method used to generate optimal nutrition plans and exercise plans based on input data.

[0706] A "nutrition plan" is a suggestion of recommended meals based on the user's health condition and lifestyle.

[0707] "Meal images" refer to photographic data of meals taken by users.

[0708] "Identifying nutrients" means using image recognition technology to identify the ingredients contained in a food image and recognize their nutritional components.

[0709] "Calculating calories" means determining the total energy content of a meal based on the quantity and type of food items identified.

[0710] A "physical activity plan" is a proposal for individualized exercise content and frequency, formulated based on the user's data.

[0711] "Integrating with e-commerce sites" means transmitting information so that users can obtain food ingredients suggested based on their nutrition plan through external e-commerce platforms.

[0712] "Food ingredients" refer to the raw materials and ingredients necessary to implement a recommended nutrition plan.

[0713] This invention relates to the implementation of a system aimed at comprehensive health management for patients with lifestyle-related diseases, such as diabetes. The main components are user terminals, a server, and a network connecting them.

[0714] First, users log in to the system using their own devices and enter their biometric data and dietary history. The entered information is sent to a server via the network and stored in a database. The server processes this data and generates a personalized nutrition plan using predictive algorithms. The software used in this process includes AI models such as TensorFlow and PyTorch.

[0715] Furthermore, users take photos of their daily meals with their smartphones and send these images to the server. The server uses image recognition technology to identify the nutrients in the meal and calculate the calories. For this purpose, the server can utilize libraries such as Scikit-learn and OpenCV.

[0716] Once the analysis of each meal is complete, the server develops and proposes an optimal physical activity plan based on this analysis. The proposed nutrition plan also includes a function that links to online shopping sites to suggest relevant food ingredients. This function allows users to conveniently purchase the suggested food ingredients.

[0717] As a concrete example, consider a scenario where a user sends a picture of their breakfast to a server. The server recognizes the meal consists of toast, eggs, and coffee, and then creates a suitable lunch and dinner plan. Links are then provided to purchase the suggested food items through an online shopping site. A possible prompt for the generative AI model might be: "A user's meal image has been sent to the server. Identify the specific foods that make up this meal and calculate the calories."

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] Users access the system via a login screen using their device and enter biometric data and past meal history. The entered data is compiled on the device and sent to the server via the network. Examples of entered data include the user's age, weight, and meal content for the past week. The server stores the received data in a database.

[0721] Step 2:

[0722] Users take photos of their daily meals using their smartphones and upload the images to a server. These images are processed on the device and converted into a format that clearly identifies the contents of the meal. The server receives these images and uses image recognition technology to identify the nutrients contained in the meal. Specifically, OpenCV is used as the image processing library, and the output includes the names of the ingredients and an estimated calorie amount.

[0723] Step 3:

[0724] The server executes a predictive algorithm based on the biometric data saved in Step 1 and the dietary information identified in Step 2. Using TensorFlow, it analyzes the various input data and generates an individualized nutrition plan. The output provides a recommended daily meal menu and its nutritional balance.

[0725] Step 4:

[0726] The server creates an optimal physical activity plan based on the generated nutrition plan. Using Scikit-learn, it determines exercise intensity and frequency, taking into account the user's age and exercise history. The output of this step is a specific exercise plan proposed to the user.

[0727] Step 5:

[0728] The server displays food ingredients corresponding to the recommended nutrition plan in conjunction with online shopping sites. This allows users to easily purchase the necessary ingredients. The server accesses the online shopping site's database via an API, and the output provides available product information and purchase links.

[0729] Step 6:

[0730] The user reviews the meal and exercise plans presented through the device and, if necessary, purchases food ingredients from the suggested online shopping site. The operation is performed via the device's GUI, and the actual purchase process is completed by redirection to an external online shopping site. Output includes a purchase completion notification and a display of the healthcare action plan.

[0731] This series of processes allows users to manage their health more efficiently and easily. Examples of prompts for the generating AI model include: "New user data and meal images have been received. Please generate a personalized nutrition plan based on them."

[0732] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0733] This invention is a system for effectively managing the lifestyle habits of diabetic patients, and by combining it with an emotion engine, it enables responses tailored to the user's emotional state. The system consists of a user's terminal, a server, and a network connection.

[0734] Users first log in to the system using their device and enter information such as age, height, weight, exercise history, past meals, and blood glucose levels. Users also take pictures of their daily meals and send this information from their device to the server. Furthermore, users can express their emotional state through voice input and facial recognition.

[0735] The server receives the user's basic information and entered meal images and stores them in a database. Based on the received data, an AI model is used to create meal management and exercise plans. This AI model uses a predictive model to generate optimal meal recipes tailored to each individual user. The generated meal recipes are provided to the user via the terminal. At this stage, an emotion engine analyzes the user's emotional state and adjusts the meal recipes and exercise plans accordingly.

[0736] The emotion engine identifies the user's emotional state from their voice and facial expressions, thereby determining their stress level and motivation. For example, if the server detects that the user is under high stress, it will suggest meals containing stress-relieving foods and recommend relaxing exercise plans (such as yoga or stretching).

[0737] For example, if a user says "I'm tired" in a voice that indicates high stress, the emotion engine analyzes the voice and determines that the user is experiencing high stress. The server takes this analysis into account and suggests a light meal and an exercise plan to help reduce stress. Furthermore, if the user sends a picture of their meal with a smile, they are considered highly motivated, and the system can suggest a more challenging exercise routine.

[0738] Thus, the present invention realizes a system that not only aims to manage diabetes but also supports the user's psychological state.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The user logs into the system via a terminal and enters information such as age, height, weight, past exercise history, and recent meals and blood sugar levels. The terminal collects this information and prepares it for transmission.

[0742] Step 2:

[0743] The user takes a picture of their meal using their device and sends the image along with emotional data such as audio and facial expressions to the server. The device then packages this data and sends it to the server.

[0744] Step 3:

[0745] The server stores personal information, food images, and emotional data received from the terminal in a database. An image recognition algorithm is used to analyze the food content and estimate calorie intake.

[0746] Step 4:

[0747] The server uses an emotion engine to analyze the user's emotional state from their voice and facial expressions. Based on the analysis results, it understands the user's stress level and motivation.

[0748] Step 5:

[0749] The server uses an AI model to analyze each user's personal information and past eating data to generate optimal meal recipes. It creates meal recipes and exercise plans that take into account the results of sentiment analysis.

[0750] Step 6:

[0751] The server sends generated meal recipes, estimated calories, and an exercise plan based on emotions to the user's device. The user can then review this information via their device and use it to improve their lifestyle.

[0752] Step 7:

[0753] Based on the information received, users adjust their daily lifestyle habits. For example, they manage their health by incorporating stress-reducing foods and exercise.

[0754] (Example 2)

[0755] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In managing the lifestyle habits of diabetic patients, there is a need to achieve integrated and personalized health management that takes into account not only individual physical information and past consumption history, but also the user's psychological state. While conventional technologies could provide plans based on consumption records and personal attribute information, they lacked specific suggestions that reflected the user's psychological state, which led to decreased user motivation and a lack of continuity in health management.

[0757] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0758] In this invention, the server includes means for inputting user attribute information and historical consumption information, means for generating a meal plan using a predictive algorithm based on the attribute information and consumption information, and means for identifying the user's psychological state through emotion analysis and adjusting the meal plan and activity plan accordingly. This enables comprehensive health management based on both the user's physical attributes and psychological state.

[0759] "User attribute information" is a general term for basic physical information used to identify and characterize an individual, such as the user's age, height, and weight.

[0760] "Historical consumption information" refers to a record of meals consumed by a user in the past, including details such as the type, timing, and quantity of food eaten.

[0761] A "predictive algorithm" refers to mathematical methods and models used to calculate and generate future trends and optimal plans based on input data.

[0762] "Captured consumption images" refer to photographic data of meals taken by users, and are the subject of image analysis.

[0763] "Nutritional value" is a numerical indicator that shows the amount of energy and specific nutrients that food provides to the human body.

[0764] An "activity plan" is a plan of exercise and training designed to promote the user's physical activity and maintain their health.

[0765] "Emotional analysis" refers to the technology and process of identifying a user's emotional state from audio and images and analyzing their psychological characteristics.

[0766] "Psychological state" refers to an internal state related to the user's psychology, such as emotions, stress, and motivation.

[0767] In an embodiment of this invention, the system mainly consists of a user terminal, a server, and a network. The user accesses the system using the terminal and inputs their basic physical information (age, height, weight, etc.) and past meal records. The terminal uses its built-in camera and microphone to acquire images of meals and voice data of the user, and transmits this to the server.

[0768] The server receives this data and stores it in a high-performance database. Based on the information stored in the database, a predictive algorithm is used to generate personalized meal and activity plans for each individual user. This predictive algorithm uses a generative AI model, enabling personalized suggestions that meet the user's expectations. In addition, an emotion analysis module analyzes voice and images to understand the user's psychological state and adjust the suggested plans accordingly. This system achieves more effective health management by taking into account the user's stress and motivation.

[0769] Specifically, the system has a function that allows users to leave voice comments reflecting their emotional state, such as "I'm very tired today," and then suggests relaxing meals and activity plans such as yoga or stretching. In this way, it provides comprehensive feedback based on individual physical information and psychological state.

[0770] An example of a prompt would be, "Generate a meal and exercise plan to suggest when the emotion engine determines that the user is in a high-stress state." The server would then return personalized health suggestions accordingly.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The user enters their personal attribute information (age, height, weight, etc.) and past consumption information into the terminal. The terminal packages this information as structured data and sends it to the server. At this stage, data integrity and format checks are performed.

[0774] Step 2:

[0775] The server receives user attribute and consumption information sent from the terminal and stores it in the database. Here, it performs processing to ensure data consistency by integrating it with historical data. Furthermore, it integrates historical and new data and processes it into a format suitable for analysis.

[0776] Step 3:

[0777] Users take pictures of their meals and upload them to their devices. They also use voice input to express their emotional state. The devices compress the image data and send it to the server along with the voice data. The image data is preprocessed and converted into a format suitable for analysis.

[0778] Step 4:

[0779] The server analyzes the received image data to identify the content of consumption and nutritional value. It utilizes a generative AI model to perform highly accurate image analysis and stores the identification results in a database. This allows for detailed identification of meal contents, which can then be used to plan future meals.

[0780] Step 5:

[0781] The server uses voice data to perform sentiment analysis. The sentiment analysis module analyzes the tone and content of the voice to identify the user's psychological state. Based on this, it estimates the emotional state and adds it to the user's profile.

[0782] Step 6:

[0783] The server uses a generative AI model to generate meal and activity plans based on the user's attribute information, consumption history, and current emotional state. A predictive algorithm analyzes the generated data and suggests the optimal plan.

[0784] Step 7:

[0785] The generated plan is sent from the server to the terminal and provided to the user. The terminal presents the plan through an intuitive and easy-to-use user interface. This plan includes meal and activity suggestions to support personalized health management for the user.

[0786] (Application Example 2)

[0787] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0788] Lifestyle management for diabetic patients tends to be limited to general dietary management and exercise planning, but appropriate responses that take into account the psychological state and emotional fluctuations of individual patients are required. However, current systems have difficulty properly analyzing users' emotions and providing personalized guidance and support based on that analysis. Therefore, there is a need to develop a system that can provide more nuanced support in diabetes management.

[0789] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0790] In this invention, the server includes means for inputting the user's personal information and past meal records, means for generating dietary guidelines using a predictive model, means for identifying intake amounts and estimating energy amounts from captured meal images, and means for adjusting dietary guidelines and exercise plans according to the user's emotional state. This enables appropriate lifestyle management according to the user's emotional state.

[0791] "Personal information" refers to information about an individual user's profile, such as their age, height, weight, exercise history, and blood glucose level information.

[0792] A "meal record" is data that records the details of meals a user has eaten in the past, and may include the type and quantity of food consumed.

[0793] A "predictive model" is a computational method that includes algorithms and AI used to generate optimal dietary guidelines and exercise plans based on input data.

[0794] "Meal images" are image data taken to visually record the contents of meals consumed by users.

[0795] "Intake" refers to the amount of each food item included in a meal and serves as the basic data for calculating calories and nutrients.

[0796] "Energy amount" refers to the total amount of calories obtained from ingested food, and is a numerical value that serves as an energy source for human activity and metabolism.

[0797] An "exercise plan" is a program that specifically outlines exercises suitable for the user's health condition and lifestyle, including the type, duration, and frequency of exercise.

[0798] "Emotional state" refers to the psychological state of the user, and includes the type and intensity of emotions detected from voice, facial expressions, etc.

[0799] "Emotion recognition means" refers to a method or device for identifying a user's emotional state using voice analysis and facial expression analysis technology.

[0800] "Adjustment measures" refer to functions and methods for modifying previously presented dietary guidelines and exercise plans according to the individual circumstances and emotional state of the user.

[0801] The system for realizing this invention consists of a user terminal, a server, and a network connecting them. The user terminal is a device such as smart glasses or a smartphone, through which it can input images of meals and voice, and express its emotional state to the system. The server receives this data and performs the necessary processing for analysis.

[0802] The server analyzes the received image data using computer vision technology (e.g., OpenCV) to analyze the meal content and estimate the amount of food consumed and the amount of energy. For audio data, it uses speech recognition technology (e.g., Google Speech-to-Text API) to estimate the emotional state and uses emotion recognition tools to identify the emotional state from the audio and facial expressions.

[0803] Subsequently, the server uses a predictive model (e.g., TensorFlow) to generate dietary guidelines based on the user's personal information and past meal records. These guidelines are then adjusted to take into account the user's emotional state and are also used to provide exercise plans. The AI ​​model enables the provision of lifestyle management optimized for individual emotions.

[0804] For example, if a user wears smart glasses and sends an image of themselves smiling after breakfast to the system, the system can determine from that smile that the user is highly motivated and suggest a more active exercise plan than usual. Such specific responses can be obtained through a prompt example from the generative AI model: "If the user was smiling after breakfast this morning, what kind of exercise suggestion should be made based on that emotional state?"

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The user operates the device to enter personal information and past meal records. This includes age, height, weight, exercise history, and meal details. The entered data is collected on the device and sent to the server.

[0808] Step 2:

[0809] The user sends images of their meals and voice messages to the system using a device. The image data records the meals the user has consumed, and the voice messages indicate their emotional state. This data is transmitted to the server using a secure protocol.

[0810] Step 3:

[0811] The server analyzes the received meal images. Using computer vision technology, it recognizes the contents of the meal from the image and calculates the intake and energy content. The input is a meal image, and the output is data on calories and nutrients.

[0812] Step 4:

[0813] The server analyzes voice messages to identify emotional states. Using a speech recognition API, it estimates emotions from voice tone and patterns using an emotion recognition model. The input is voice data, and the output is data indicating the user's emotional state.

[0814] Step 5:

[0815] The server uses a predictive model to generate dietary guidelines based on the user's personal information and meal records. The generating AI model creates guidelines tailored to the user's health condition and preferences, and data processing yields individually optimized recipes.

[0816] Step 6:

[0817] The server adjusts dietary guidelines based on emotional state and generates exercise plans that reflect individual needs. For example, if a high-stress state is detected, it recommends relaxing exercises. This enables more personalized health management.

[0818] Step 7:

[0819] The device displays adjusted dietary guidelines and exercise plans sent from the server to the user visually or audibly. Based on this, the user can improve their daily lifestyle and manage their health.

[0820] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0821] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0822] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0823] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0824] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0825] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0826] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0827] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0828] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0829] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0830] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0831] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0832] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0833] 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.

[0834] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0835] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0836] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0837] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0838] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0839] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0840] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0841] The following is further disclosed regarding the embodiments described above.

[0842] (Claim 1)

[0843] A means of inputting the user's personal information and past meal records,

[0844] A means for generating meal recipes using a predictive model based on the aforementioned personal information and meal records,

[0845] A method for identifying intake amounts and estimating calories from captured images of meals,

[0846] A means of providing an exercise plan based on user information,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, comprising means for analyzing meal images from users and suggesting the next recommended meal based on the identified meal content.

[0850] (Claim 3)

[0851] The system according to claim 1, comprising means for adjusting the exercise intensity based on the user's age and exercise history when generating an exercise plan.

[0852] "Example 1"

[0853] (Claim 1)

[0854] A device for inputting the user's basic information and health status,

[0855] A device that transmits the aforementioned basic information and health status data to a server,

[0856] A device that uses a server that provides a service for accumulating and analyzing diverse data to generate AI models and create personalized meal recipes,

[0857] A device that analyzes food images, uses image recognition technology to detect the nutrients consumed, and calculates calories,

[0858] A device that provides an exercise plan based on the user's basic information, exercise history, and daily meal content,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, comprising a device that analyzes a meal image and presents the next suggested meal using identified meal information.

[0862] (Claim 3)

[0863] The system according to claim 1, comprising a device that adjusts the exercise intensity and frequency based on the user's age, exercise history, and dietary content when generating an exercise plan.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A means of inputting the user's biometric data and past meal history,

[0867] A means for generating a nutrition plan using a predictive algorithm based on the aforementioned biometric data and dietary history,

[0868] A method for identifying nutrients and calculating calories from photographed food images,

[0869] A means of providing a physical activity plan based on user data,

[0870] A means of providing food ingredients based on a suggested meal plan in conjunction with an online shopping site,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, comprising means for analyzing meal images from users and suggesting the next recommended meal based on the identified meal content.

[0874] (Claim 3)

[0875] The system according to claim 1, comprising means for adjusting exercise intensity based on the user's age and exercise history when generating a physical activity plan, and further comprising means for generating purchase links for food ingredients based on the generated nutrition plan.

[0876] "Example 2 of combining an emotion engine"

[0877] (Claim 1)

[0878] A means of inputting user attribute information and historical consumption information,

[0879] A means for generating a meal plan using a predictive algorithm based on the aforementioned attribute information and consumption information,

[0880] A means for identifying consumption amounts from captured consumption images and estimating nutritional value,

[0881] A means of providing an activity plan based on user information,

[0882] A means for identifying the user's psychological state through emotion analysis and adjusting the meal plan and activity plan accordingly,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, comprising means for analyzing consumption images from users and suggesting the next recommended consumption based on the identified consumption content.

[0886] (Claim 3)

[0887] The system according to claim 1, comprising means for adjusting the activity intensity based on the user's age and activity history when generating an activity plan.

[0888] "Application example 2 when combining with an emotional engine"

[0889] (Claim 1)

[0890] A means of inputting the user's personal information and past meal records,

[0891] A means for generating dietary guidelines using a predictive model based on the aforementioned personal information and dietary records,

[0892] A means for identifying intake amounts and estimating energy content from captured images of meals,

[0893] A means of providing an exercise plan based on user information,

[0894] An emotion recognition method for analyzing the emotional state of the user,

[0895] A means of adjusting dietary guidelines and exercise plans according to emotional state,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] A means for analyzing meal images from users and suggesting the next meal based on the identified meal content,

[0899] The system according to claim 1, comprising means for adjusting the contents of a meal according to the emotional state.

[0900] (Claim 3)

[0901] In generating an exercise plan, a means for adjusting exercise intensity based on the user's age and exercise history,

[0902] The system according to claim 1, comprising means for adjusting movement that takes into account emotional state. [Explanation of symbols]

[0903] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting the user's personal information and past meal records, A means for generating meal recipes using a predictive model based on the aforementioned personal information and meal records, A method for identifying intake amounts and estimating calories from captured images of meals, A means of providing an exercise plan based on user information, A system that includes this.

2. The system according to claim 1, comprising means for analyzing meal images from users and suggesting the next recommended meal based on the identified meal content.

3. The system according to claim 1, comprising means for adjusting the exercise intensity based on the user's age and exercise history when generating an exercise plan.

Images