Field of the Invention
[0001] Advanced age recognition technology, notably utilising the strength of machine learning algorithms, is the area of invention for the aforementioned subject. Through intelligent analysis of a variety of visual and auditory signals, this cutting-edge system seeks to precisely establish the age of users. The system can successfully recognise facial traits, voice patterns, and other pertinent attributes to accurately estimate an individual's age by using machine learning techniques such as deep neural networks. The idea fills a gap in the market for a more sophisticated and accurate age identification system with potential uses in a variety of fields, including advertising, security, customised user experiences, and age-restricted content management. This ground-breaking innovation pushes the limits of age recognition technology by using machine learning, enabling more accurate and automatic user age identification.
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Background
[0002] The backdrop of the aforementioned invention originates from the rising need for reliable and precise user age recognition systems across a range of sectors and applications. Age estimation techniques have historically depended on crude procedures that frequently lacked accuracy and dependability. Researchers and developers have been able to investigate more complex and data-driven approaches to address this difficulty thanks to developments in machine learning and computer vision techniques.
[0003] A thorough examination of current age recognition technology, including facial recognition algorithms, voice analysis methods, and multimodal data fusion approaches, was done as background research for this patent. The machine learning models were trained and improved using large datasets with labelled age information. To improve the precision and effectiveness of the age identification system, the creators carried out tests and investigated various feature extraction procedures, network designs, and training techniques.
[0004] The purpose of this background research was to design a sophisticated system that could reliably and accurately assess the age of users in practical situations while overcoming the constraints of traditional age estimation methodologies. The goal of the researchers was to develop a machine learning-based solution that would revolutionise age detection technology and pave the way for more customised and bespoke experiences across a range of industries, from age-restricted content management to targeted advertising.
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Objects of the Invention
[0005] The aforementioned patent aims to innovate by creating a sophisticated age detection system that makes use of machine learning algorithms to precisely determine the age of users. The main goal is to deliver a more reliable and accurate answer by overcoming the constraints of the current age estimation techniques. The innovation seeks to increase the age recognition's accuracy and dependability through the use of machine learning techniques, enabling more effective personalization and customised experiences across a range of applications. [0006] The creation of a flexible age identification system that can assess several modalities, including facial features, vocal traits, and maybe other pertinent indicators, is another important goal. This multidimensional strategy offers a more thorough grasp of user age, improving the system's precision. The idea aims to develop a solution that can adapt and improve over time, continuously learning from fresh data and adapting to accommodate varied user groups by utilising the power of machine learning. [0007] A scalable and effective age detection system that can be smoothly included into a variety of platforms and devices is another goal of innovation. The development intends to retain high accuracy while optimising computational resources, making it appropriate for implementation in real-time applications like digital signs, access control systems, or personalised content distribution. The objective is to provide a solution that is easy to use,
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affordable, and able to handle massive amounts of data, enabling broad adoption and useful deployment across diverse industries. [0008] Fitness applications have been the subject of numerous studies and earlier work, with an emphasis on a variety of factors including features, user engagement, effectiveness, and usability. Previous studies looked into how fitness apps affected people's levels of physical activity and overall health outcomes. In order to increase user motivation and adherence to exercise programmes, studies have looked at the usage of gamification components, social support features, and personalised suggestions inside fitness apps. In order to determine the validity and reliability of fitness tracking elements like step counting or calorie estimate, some research has focused on analysing the accuracy of these aspects. In order to enhance the entire user experience and engagement, user experience studies have also been carried out to understand the usability and user satisfaction with various fitness app interfaces. Overall, these previous works advance knowledge of the advantages, difficulties, and possibilities of fitness applications in encouraging physical activity and assisting people in reaching their health and fitness objectives. [0009] To improve the tracking and monitoring capabilities of these applications, wearable gadgets like fitness trackers or smartwatches have been examined in prior work on fitness apps. These research have looked into the accuracy of data collection, the synchronisation of data across wearable devices and apps, and the additional insights that may be gained
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through wearable sensor technology. Additionally, research has been done on the application of machine learning algorithms to analyse the gathered data, offering individualised insights and recommendations based on personal activity patterns. The goal of earlier research in this area was to improve fitness apps' personalization and effectiveness by utilising wearable technology. This work helped to advance and improve fitness apps' ability to support users' fitness journeys and promote healthy lifestyles.
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Drawings
Figure 1: Use-case Diagram of FITSETGO app
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Figure 2: Flowchart of working application
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Brief Description of the Drawing
[0008] The user's login or registration procedure, which ensures secure access to their personal profile, is where the flowchart starts. The user is then given the choice to select goals for things like weight loss, muscle gain, or general fitness enhancement. Once objectives are set, the flowchart splits into different directions based on the user's preferences. For instance, if a user selects a workout regimen, they are shown a list of exercises broken down by muscle types or levels of fitness. Instead, if the user chooses to track their nutrition, they enter their dietary requirements and get tailored meal planning and calorie suggestions. The flowchart also features feedback loops that let users keep tabs on data, track progress, and get energising notifications. The flowchart also includes elements for social interaction, including sharing successes or taking part in challenges. Overall, the flowchart provides a simple and well-organised framework to improve the user experience and engagement while guiding users through the capabilities and user interactions within the fitness app.
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Claims
We Claim:
Claim 1: A fitness app with modules for managing user profiles, goal-setting, exercise routines, and nutrition tracking.
Claim 2: A progress tracking module, a motivational notification module, and a social interaction module are additionally included in the fitness app of claim 1.
Claim 3: A scalable fitness app that meets the requirements of claims 1 or 2 and incorporates machine learning algorithms, interoperability with external devices, and platform compatibility. A scalable age detection system of claim 1 or claim 2, according to claim 3, wherein: