Description:Field of Invention
The Food Facts app provides a seamless login and sign-up experience, making it easy to kick start your health journey. It gathers individual health parameters like age, weight, height, diabetes status, and blood pressure to offer personalized guidance. With an expansive food database, users can effortlessly explore and search for their favorite foods. The app evaluates selected food items against your health profile, offering clear recommendations similar to having a personal nutritionist on hand. It empowers users with personalized guidance to make informed dietary choices, ensuring alignment with their individual health goals.
Objectives of the Invention
The goal of the "Food Facts" app is undoubtedly to assist users in making healthier eating choices. It provides personalized advice by requesting a few personal health details, such as age and burden. It is constructive and easy to use, having been built on the Android base. Customers can find what they prefer among the countless dishes on its table. The program notifies users when they select a snack whether it is tailored to their strengths, such as having a dietician on hand. This aids people in selecting meals that align with their health goals. The invention's objectives are to find a useful app with a large food table and incorporated recommendations. In general, it's about enabling customers to maintain their health by choosing the appropriate meals.
Background of the Invention
Traditional approaches, such as manually searching for individual components or referencing printed dietary recommendations, are tedious and take a considerable amount of time. Many applications utilize a single, fixed database that may lack comprehensiveness or up-to-date information. This results in scattered data and possible inaccuracies. Recently, the significance of maintaining a balanced diet and ensuring sufficient nutrient intake has become increasingly clear to consumers. As mobile devices grow more prevalent, consumers are seeking more accessible and convenient nutritional information. The current methods for collecting nutritional data often come with several shortcomings.
The systems and techniques for the collection and dissemination of real-time nutritional data, especially with regard to mobile devices, are the subject of the current invention. More people are becoming aware of their food choices and looking for resources to assist them in making educated selections as a result of the growing emphasis on health and wellbeing. With customers increasingly focused on individualized nutrition based on their unique health objectives and preferences, there has never been a more pressing need for a comprehensive, user-friendly, and real-time nutritional information system.
Traditionally, people must go to static sources, such product labels, printed documents, or websites, in order to receive comprehensive nutritional information about food items. This frequently leads to a laborious and time-consuming procedure. Furthermore, without a centralized, user-friendly solution, it is challenging to precisely compute nutritional intake and track food consumption in real-time. Furthermore, the integration of various data sources, customization, and real-time updates are all restricted by current systems. Specifically, some mobile applications give broad nutritional databases, but they don't take into account users' changing demands, such providing real-time feedback, customizing for unique dietary requirements, and dynamically gathering data from several food sources.
Modern technology's real-time nature offers a special chance to include cutting-edge techniques for compiling and sending dietary data straight to consumers' mobile devices. Nonetheless, there are still issues with delivering location-based, accurate, and current data while taking user preferences, dietary needs, and medical conditions into account. Additionally, current solutions find it difficult to scale effectively to satisfy the rising need for data-driven, individualized health advice. Growing health issues including obesity, diabetes, heart disease, and other diet-related disorders have made the need for such systems even more urgent. Having access to up-to-date nutritional data can help people make healthier food choices, which will enhance public health outcomes. Consequently, it is essential to offer a solution that integrates real-time data aggregation, individualized health insights, and the most recent developments in mobile technology.
By providing a system and methodology that compiles real-time nutritional data from many sources, customizes the information according to the user's health profile, and distributes it via mobile apps, this invention offers a solution to these problems. In addition to providing a smooth and simple user experience, the solution is intended to increase user engagement, encourage better eating practices, and make it easier for users to manage their nutritional consumption. US9811836B2 Describes the possibility to set up a media targeting system to handle media conflicts and coordinate media delivery to consumers. To guarantee that a consumer only receives non-conflicting offers during specific time periods, the media targeting system may arbitrate in almost real-time between all offers assigned to a specific consumer or available to the customer via one or more channels. In order to make a real-time offer assignment based on the traits, the media targeting system may convert offers into an in-store loyalty environment that is real-time and includes consumer behavior assignment into traits. Customers may be able to share deals with other customers through a bump of their mobile devices if they have an application installed on their device. US10586294B1 discloses the computer-implemented method and system for ordering food remotely or from a vehicle for pickup or delivery is provided by aspects of the disclosure. This system uses a mobile application that provides information that enables the ordering process. An order-in-advance application, which enables a user or the system to automatically place an order based on prior orders, may be a part of the computer-implemented approach and system. Payment for the food may alternatively be made electronically through the computer-implemented method and system. The location, velocity, route, and destination of a user can be determined by using telematics data about the user and/or the vehicle. The computer-implemented approach and system might use this telematics data to give a ranked and prioritized list of eateries for the user. In order to improve the mobile food order method and system and add new features, the computer-implemented method and system might make use of insurance information. US10194770B2 describes the articles, techniques, and systems for individualized cooking appliance control. One or more final attributes for a food product are chosen by the user. When a user selects one or more ending qualities, a processor-based device determines one or more output food preparation parameters. During the cooking process, measurements of temperature, power, or other parameters may be made. The measurements gathered or the analysis of the measurements may be used to update or modify a cooking program that controls the cooking process. The user interface of the cooking appliance or a computing device (such as a smartphone) connected to the user may provide the user with estimates or projections regarding the cooking process. US9817559B2 describes the technique for forecasting the foods a user of a food-logging app would eat is revealed. We obtain loggings of food item consumption. The obtained loggings are used to create a prediction model. One or more additional food items that a target user will consume or is likely to have consumed (for example, at a specific time) are predicted by the predictive model. The prediction is produced by applying the predictive model to one or more data items (for example, real-time data streams from the target user or other users that are pertinent to the target user's food consumption). The forecast of the user's consumption of one or more extra food items may then be conveyed for display in a user interface to the intended user. US20080193600A1 discloses the Edible compositions comprising legume products and tuber products are disclosed. In certain examples, the compositions can comprise bean powder combined with instant mashed potatoes. Such compositions can have excellent taste and improved nutrition. Methods of manufacture and preparation of the legume and tuber products are also disclosed.

Summary of the Invention
There is a need for a system and method that provides real-time, comprehensive, and personalized nutritional information through a mobile platform. The present invention addresses these limitations by providing a system and method for real-time nutritional information aggregation and mobile delivery. This system aggregates data from diverse, verified sources, provides real-time updates, and offers personalized nutritional information based on user profiles and preferences. This system provides a method to verify the nutritional data that is aggregated, to provide a more accurate and safe user experience.
Detailed Description of the Invention
The Food Facts request podium consists of two main details: the attendant side and the customer side. On the admin side, the invention includes several key steps. Firstly, evidence accumulation is essential, that may include group knowledge from miscellaneous sources from WHO or within databases. Once collected, the info is introduced into a dataset that could be stocked in plans in the way that relative datasets. Next the data is inspected in consequence of datasets and the results are reserved.
On the user side, the app offers various functionalities. Users can initialize or enrol the request to access its features. Additionally, consumers can enter their energy analyses, whatever the request then checks against the dataset. A search box helps consumers to opinion their food preferences or queries. Finally, the app returns illustrated results to the end user depended on their health analyses, permissive them to create informed digestive conclusions.
Nutritional data can change due to factors such as recipe modifications, ingredient variations, and new scientific findings. Existing applications may not provide real-time updates, resulting in outdated information. Consumers often need to cross-reference information from multiple sources to obtain a complete nutritional profile. Current applications may lack the ability to automatically aggregate and cross-reference data from diverse sources.
Dietary needs vary significantly based on individual factors such as age, health conditions, and dietary preferences. Existing applications may not adequately personalize nutritional information to meet individual needs. Many applications rely on user generated or unverified data, leading to the possible spread of incorrect nutritional information.
Methods and techniques for offering individualized diet and health management suggestions are provided in the current disclosure. The process might involve creating a food ontology by mapping foods by extracting information from food-related data. The approach might involve gathering and combining many data sets on a user's diet, health, or general well-being. Multiple data sets can be offered in two or more data types from multiple sources. A uniform format that may be customized for each user may be created by transforming the various data sources.
One approach to determining impacts and gathering information about food from a variety of sources may be to apply a prediction model consistently to the user's multiple data sets and the food ontology. At least some of this information is obtained by using one or more automated web crawlers that are configured to continuously search the Internet and update the food ontology in real-time. The information is unstructured, and at least one machine learning algorithm is used to extract information from food-related data at a hardware-based processor of a food analysis system. At least one artificially intelligent computer vision algorithm is among the one or more algorithms (AI) statistical model, deep learning, or optical character recognition (OCR) capabilities.
A mechanism for gathering and combining dietary, health, and/or nutritional data is offered, per another part of the disclosure. A device hub with one or more processors set up to carry out a set of software instructions can be part of the system for gathering and combining food, health, and/or nutritional data. The software instructions can be designed to gather and combine several data sets from multiple APIs, where the many data points are supplied in two or more distinct formats and include multiple user physiological inputs. It is possible to further program the set of software instructions to transform the several data points into a user-specific, standardized format.
An additional component of the discloser is focused on a physical computer-readable media. Instructions that, when carried out by one or more processors, cause one or more processors to carry out a computer-implemented method for gathering and aggregating food, health, and/or nutritional data can be stored on the physical computer readable media. The physical computer readable media can be set up to gather and combine several data sets from multiple APIs, where the many data points are supplied in two or more distinct forms and include multiple human physiological inputs. The physical computer-readable media can be further set up to transform the various data points into a user-specific, standardized format.
The food mapping approach may also entail using the food ontology to develop one or more models that predict the eating habits of one or more users, depending on the context (location and time of day), the user's historical food consumption data, and the relationships between different foods derived from the food ontology. To find patterns in food consumption in large populations, to find patterns in individual food consumption, and to combine the patterns discovered by determining the population type to which a specific user or individual belongs, one or more models can be set up. By altering a person's food intake, one or more models can be created to predict the foods that the user is most likely to be consuming the food selection or selections and recalculating the projections for the likely next meal items. One or more models can be configured to automatically fill in information about a complete meal that the user is consuming based on one or more food items that the user has identified. The auto-completion tool may reduce user inputs or clicks by more than half.
There have been several attempts to compile information on the nutritional value of frequently consumed foods and their impact on human health. However, because data is frequently gathered or crowd sourced from user inputs, these databases typically suffer from inconsistencies, inaccuracy, and overall poor quality. Furthermore, the resulting databases are frequently fragmented in breadth and time since many of the studies have focused on certain populations, regions, or dietary categories during a specified time frame. Their application is limited by this fragmentation. Additionally, these databases rely on many data sources (such as social media, glucose monitors, mobile devices, etc.) that are frequently incompatible with one another. Without a substitute, people still rely on small and insufficient databases and/or services to patch together decisions pertaining to nutrition and food.
Brief description of Drawing
In the figure which is illustrate exemplary embodiments of the invention.
Figure 1, Process of Proposed Invention , Claims:The following claims establish the boundaries of invention:

Claims:
1. A system/method to analyze food facts based on Artificial Intelligence and Machine Learning techniques, said system/method comprising the steps of:
a) The system starts with data collection from various sources (1), from which attributes are extracted to build datasets (2).
b) The proposed invention incorporates preprocessing steps (3) to refine the data and extract relevant features (4). The processed dataset is then inserted into the system (5).
c) The system allows users to interact through a client-side interface (6), where they can log in/signup, search for food-related data, and enter health details (7).
d) The system cross-checks user-entered health details with the dataset (8), leading to the final result generation (9).
2. As mentioned in claim 1, the invented system collects datasets from various sources related to food facts and health information.
3. As mentioned in claim 1, the preprocessing phase filters noisy data, ensuring accuracy and efficiency before inserting it into the dataset for analysis.
4. As mentioned in claim 1, the proposed invention leverages machine learning techniques to process health details entered by users, matching them with the dataset to generate personalized food-related insights.