Artificial Intelligence Precision Diet Analysis and Health Management System

Abstract

This invention relates to an AI-powered precision diet analysis and health management system, which mainly includes a user device connected to a management platform. This platform contains a database of users or administrators, an AI computing module, an analysis module, and a results and suggestions module. Users can take pictures of food and upload them to the management platform. The AI ​​computing module analyzes the food's nutritional components, and the analysis module combines this with personal physiological data to obtain dietary analysis results and suggestions, which are then displayed on the user device. This allows users to manage their health based on each meal, effectively controlling chronic diseases and improving overall health.

Description

Artificial Intelligence Precision Diet Analysis and Health Management System Technical Field

[0001] This work relates to the field of dietary health management technology. Specifically, it relates to a cross-disciplinary integrated system of artificial intelligence, computer vision, digital health, and health information system, with the primary application being an artificial intelligence precision diet analysis and health management system for chronic diseases. Prior Technology

[0002] Note that the core of a diabetic diet is "balanced diet, regular and measured meals, high fiber, low oil, and low salt." The focus is on controlling the total intake of carbohydrates, choosing more whole grains (such as brown rice and oats), whole foods, and pairing them with high-quality protein (beans, fish, eggs, and meat) and vegetables. The order of eating is also a crucial aspect, usually vegetables, protein, and staple food, or first soup, vegetables, meat, and finally rice, to stabilize blood sugar.

[0003] By following the dietary principles outlined above, both healthy individuals and those with chronic illnesses can enjoy delicious food and maintain stable blood sugar control. However, in daily life, many foods contain hidden sugars, and people are often unaware of how many calories they consume (or what foods to avoid) when faced with tempting foods, leading to failed blood sugar control and health problems. Therefore, the creator has developed the Republic of China Patent No. M635178, "Stable Blood Sugar Food Board Game Teaching Tool." Through card-based teaching tool design, it allows those who wish to control their blood sugar to learn about the category and glycemic index of each food, enabling them to avoid foods that cause blood sugar spikes in their daily diet.

[0004] Furthermore, current diabetes diet management technologies on the market mainly consist of diet record apps, nutrition database query systems, and simple calorie calculation tools. Some systems integrate wearable devices or blood glucose monitoring equipment, providing users with basic diet record keeping, calorie estimation, and blood glucose trend analysis functions. In response, the creator provides Publication No. M650676, "Stable Blood Sugar Diet Cognition and Assessment System," which allows users to learn about the GI value, calories, and food classification of various foods to facilitate diet control.

[0005] However, most systems rely on manual user input or simplified identification mechanisms, which can easily lead to incomplete records or identification errors, affecting the accuracy of dietary analysis. Secondly, current systems primarily focus on calorie calculation or single nutrient analysis, rarely integrating key factors emphasized in diabetes dietary control, such as the order of food intake, glycemic index, identification of hidden sugars and processed foods, making it difficult to provide comprehensive blood sugar control recommendations. Furthermore, most systems lack real-time feedback and personalized adjustment mechanisms, failing to provide dynamic dietary recommendations based on individual user health conditions, medication use, or blood sugar fluctuations. In addition, for the elderly or patients with chronic diseases, the existing interfaces are complex to use, reducing their willingness and adherence to use them.

[0006] The reason is that, with a spirit of striving for excellence and drawing on years of rich design, development and practical production experience in the relevant industry, the creator has further researched and improved upon this, and specially provided an artificial intelligence precision diet analysis and health management system in order to achieve better practical value. Summary of the Invention

[0007] The main purpose of this work is to provide an artificial intelligence-based precision diet analysis and health management system, especially a diet control system that can be used by people with chronic diseases to control chronic diseases and physical health.

[0008] The main purpose and efficacy of this AI-powered precision diet analysis and health management system are achieved through the following specific technical means:

[0009] It mainly includes a user device that connects to a management platform. The management platform contains a database of users or administrators, an AI computing module, an analysis module, and a results and suggestions module. Users can take pictures of food and upload them to the management platform. The AI ​​computing module analyzes the food's nutritional components, and the analysis module combines this with personal physiological data to obtain dietary analysis results and suggestions, which are then displayed on the user device. This allows users to manage their health based on the food they eat each time, effectively controlling chronic diseases and improving their physical health.

[0010] In a preferred embodiment of the AI-powered precision diet analysis and health management system, the user device can be one of a smartphone, tablet, desktop computer, or laptop computer.

[0011] In a preferred embodiment of the AI-powered precision diet analysis and health management system, the user device further includes an alert unit that receives alert signals from the processor. The alert unit is connected to a back-end monitoring station, which is connected to the database of the management platform to obtain the user's personal physiological data and historical data. Simple Explanation of the Diagram

[0012] Image 1: Schematic diagram of the project's architecture. Image 2: Schematic diagram of the user's login screen to the management platform. Image 3: Schematic diagram of the project displaying physiological data and uploading photos. Image 4: Schematic diagram of the project's food analysis components. Image 5: Schematic diagram of the project displaying physiological data and overall health analysis. Image 6: Schematic diagram of the project displaying the analysis report. Image 7: Schematic diagram of the project displaying the dietary analysis results and recommendations generated by the analysis report. Image 8: Schematic diagram of the project displaying the user's historical data. Implementation

[0013] To provide a more complete and clear disclosure of the technical content, creative purpose, and effects achieved in this work, a detailed explanation is provided below. Please also refer to the accompanying drawings and figure numbers:

[0014] First, please refer to Figure 1, which is a schematic diagram of the architecture of the AI-powered precision diet analysis and health management system for this invention. It mainly includes:

[0015] At least one user device (1) is any electronic device capable of computing and executing software applications, such as a smartphone, a tablet computer, a desktop computer, or a laptop computer. The user device (1) includes an application (11), a processor (12), a display unit (13), an image capturing unit (14), and an input interface (15). The application (11) is an APP that starts the diet analysis. The processor (12) controls the central processing unit (CPU) or graphics processing unit (GPU) to execute the operating system and management platform application (11) of the user device (1) and coordinate various data flows. The display unit (13) is used to display the diet analysis results and suggestions. The image capturing unit (14) is used to take pictures of food. The input interface (15) provides users with the opportunity to establish personal physiological data.

[0016] A management platform (2) is provided for at least one user device (1) to connect to. The management platform architecture includes a database (21), an AI computing module (22), an analysis module (23), and a results and suggestions module (24). The database (21) is connected to the input interface (15) and can store the personal physiological data (211) of each user, such as name, gender, age, height, weight, blood sugar, blood lipids, activity level, medical history, etc. It also records the historical data (212) of each user's diet photos, habits, analysis results and suggestions, as well as the administrator information (213). The AI ​​computing module (22) The system receives food photos uploaded by the user through the image capture unit (14) and analyzes the composition, nutritional components and weight of the food in the photos, such as calories, sugar, carbohydrates, sodium content, glycemic index, etc. The analysis module (23) is connected to the AI ​​computing module (22) and the personal physiological data (211) of the database (21) to receive the component data analyzed by the AI ​​computing module (22) and compare it with the user's personal physiological data (211). The result and suggestion module (24) receives the analysis report from the analysis module (23) and generates dietary analysis results and suggestions from the analysis report.

[0017] Please refer to Figures 1-8. In actual use, the AI-powered precision diet analysis and health management system requires each user to download the application (11) [APP] of the management platform (2) [hereinafter referred to as the management platform] through their user device (1). For example, a smartphone is used. The smartphone has a processor, a display unit, an image capture unit, and an input interface. The input interface (15) is the interface that connects to the management platform (2). Personal information is established through the input interface (15), which includes account, password, and personal physiological data (211), such as name, gender, age, height, weight, blood sugar, blood lipids, activity level, medical history, etc. This information is stored in the personal physiological data (211) of the database (21) of the management platform. Users log in to the management platform (2) with their account and password [as shown in the second figure], and the AI ​​computing module (22) can analyze the user's physical function and basic calorie intake [calorie recommendation value, as shown in the third figure, the exclusive recommendation value 2021kcal] or the user's basic information such as gender male, 50 years old, 175cm, 88kg, no diabetes [see the fifth figure] and display it in the display unit (13). Next, when the user eats the first meal or any meal, the user can use the image capture unit (14) to take pictures of the food and upload them to the management platform (2). The AI ​​computing module (22) receives the food photos taken and uploaded by the image capture unit (14) and analyzes the composition, nutritional components and weight of the food in the photos. Here, it shows that the user eats eel soup, which has 320kcal of calories, 2 parts of sugar, 32g of carbohydrates, 900mg of sodium, and a glycemic index of 58, etc., and displays them on the display unit (13) [as shown in Figure 4]. Of course, the user can also upload photos of the food they want to eat in advance [such as uploading photos via mobile phone album or online photos, which means that the analysis is done in advance before eating].

[0018] Please refer to Figure 5. The analysis module (23) receives the component data from the AI ​​calculation module (22) and compares it with the personal physiological data (211) in the database (21) to obtain an analysis report, which is displayed on the display unit (13) [as shown in Figure 6]. The report shows that the total carbohydrates for this meal are 2 servings, total carbohydrates are 32g, total calories are 320kcal, and total sodium content is 900mg [this is the first meal, so the values ​​are the same as before]. When analyzing the second meal, the data from the first meal will be added for comparison. Then, the results and suggestions module (24) receives the analysis report and generates dietary analysis results and suggestions from the analysis report, which are displayed on the display unit (13) [as shown in Figure 7]. The report suggests that eel soup is a thickened food and should be consumed less often. It should be paired with vegetables to maintain a balanced diet and regular exercise.

[0019] In addition, it is particularly worth mentioning that after the user uploads the first food photo, the AI ​​calculation module (22) analyzes the composition, nutritional components and weight of the food in the photo. If the user uploads a second food photo within a certain time, such as 30 minutes after eating, the AI ​​calculation module (22) will analyze the difference between the second food photo and the first food photo. If it is the same food, but the portion is reduced, the AI ​​calculation module (22) will determine that it is the food left over after the user has eaten it. The AI ​​calculation module (22) will convert the data based on the size of the image. The data output by the AI ​​calculation module (22) after analysis is the reduced portion of calories, sugar, carbohydrates, sodium content, glycemic index, etc., so as to obtain the data of the correct serving size.

[0020] Finally, these dietary analysis results and recommendations will be stored in the historical data (212) of the database (21) [as shown in Figure 8] for users to query or analyze and compare. As can be seen from the above, the advantage of this invention is that it uses AI technology to allow users to manage their health based on the food they eat each time, so as to effectively control chronic diseases and physical health.

[0021] Furthermore, an alert unit (16) is constructed in the user device (1). The alert unit (16) is connected to the processor (12) and a back-end monitoring station (3). The back-end monitoring station (3) can be a crisis management office such as a nursing station, hospital, clinic, etc. When the user has an unbalanced diet or has not eaten for a long time, the diet analysis results and suggestions obtained by the user device are already close to the danger level. The processor (12) is connected to the back-end monitoring station (3) through the alert unit (16), and is connected to the database (21) of the management platform (2) through the back-end monitoring station (3). The processor enters the management platform (2) as an administrator and obtains various information of the user [such as personal physiological data and historical data] to initiate medical rescue and reduce or mitigate the crisis.

[0022] The foregoing embodiments or drawings are not intended to limit the structural form of this invention. Any appropriate changes or modifications made by those skilled in the art should be considered as not departing from the patent scope of this invention.

[0023] In conclusion, the embodiments of this invention can indeed achieve the expected effects of use, and the specific structure disclosed therein has not only never been seen in similar products, but has also not been disclosed before the application. It fully complies with the provisions and requirements of the Patent Law. Therefore, I hereby file an application for a utility model patent and respectfully request your review and grant of a patent, which would be of great benefit to you.

[0024] 1: User device 11: Applications 12: Processor 13: Display Unit 14: Image Capture Unit 15: Input Interface 16: Warning Unit 2: Management Platform 21 Database 211: Personal physiological data 212: Historical Data 213: Administrator Information 22: AI computing module 23: Analysis Module 24: Results and Recommendations Module 3: Backend monitoring console

Claims

1. An artificial intelligence precision diet analysis and health management system, which mainly includes: at least one user device, which is an electronic device capable of computing and executing software applications, the user device having an application program for launching diet analysis, an operating system responsible for executing the user device and a processor for the application program, a display unit for displaying diet analysis results and suggestions, an image capturing unit for taking pictures of food, and an input interface for providing users to establish personal physiological data. A management platform is provided for connection to at least one user device. Its internal architecture includes a database, an AI computing module, an analysis module, and a results and suggestions module. The database stores human physiological data, historical data, and administrator information. The human physiological data is created and stored by the user through the input interface of the user device. The AI ​​computing module receives food photos uploaded by the image capture unit and analyzes the various component data of the food in the photos. The analysis module receives the component data analyzed by the AI ​​computing module and compares it with the personal physiological data in the database to generate an analysis report. The results and suggestions module receives the analysis report from the analysis module and generates dietary analysis results and suggestions. The results and suggestions module is connected to the processor of the user device and displayed on the display unit.

2. The AI-powered precision diet analysis and health management system as described in claim 1, wherein the user device may be one of a smartphone, tablet, desktop computer, or laptop computer.

3. The AI-powered precision diet analysis and health management system as described in claim 1 or 2, wherein the user device further includes an alert unit connected to receive alert signals transmitted by the processor, the alert unit connected to a back-end monitoring station, and the back-end monitoring station connected to the database of the management platform to obtain the user's personal physiological data and historical data.

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