System and method for interactive engagement of users using machine learning

Abstract

An integrated system (106) for user engagement. The system (106) receives data associated with one or more users. The system (106) extracts a set of attributes from the received data to authenticate the users. The system (106) analyze via a machine 5 learning engine (214), the set of attributes to classify the authenticated users into one or more categories. The system (106) generates a predefined schedule associated with the categories to enable participation of the authenticated users from each of the categories in at least an activity for a predetermined period. The system (106) enables communication between a plurality of users of the authenticated users 10 classified into each of the categories during the predetermined period. The system (106) records information associated with the communication between the plurality of users of each of the categories during the predetermined period.

Description

[0001] SYSTEM AND METHOD FOR INTERACTIVE ENGAGEMENT OF USERS USING MACHINE EEARNING

[0002] FIELD OF THE INVENTION

[0003]

[0001] The present disclosure relates to the field of an interactive content delivery and interactive engagement platforms. More specifically, the present disclosure provides a system and method for interactive engagement of users using machine learning.

[0004] BACKGROUND OF THE INVENTION

[0005]

[0002] Mobile technology and social interaction applications have significantly transformed the way people communicate, share, and engage with each other. These advancements have made it easier to connect with friends, family, colleagues, and even strangers, fostering both personal and professional relationships across the globe. Below are some key ways in which mobile technology and social interaction apps impact our lives.

[0006]

[0003] Further, mobile technology and social interaction applications have created new ways for people to connect and communicate. While these applications bring benefits such as global connections, collaboration, and entertainment, they also present challenges related to privacy, security, and mental well-being. As these technologies continue to evolve, they will likely continue to reshape social dynamics and interactions in both positive and complex ways.

[0007]

[0004] Existing solutions providing social interaction often lack verification of the true and actual users, the depth and emotional connection associated with human relationships. This can lead to fake profiles, security concerns misunderstandings, miscommunication, and a sense of isolation. Many social interactions online are shallow, where users may connect with others based on superficial attributes. Further, certain social interaction platforms may inadvertently exclude certain groups due to language barriers, technological access, or platform design. Furthermore, they tend to be limited to one type of content, for example, there are individual platforms not solving the problem of connecting individuals but addressing the limitations in the individual platforms.

[0005] Therefore, there is a need for a method and system that addresses the above-mentioned technical problems by providing a solution which impacts user experience, user identity security and system performance.

[0008] SUMMARY

[0009]

[0006] The present disclosure relates to the field of an interactive content delivery and interactive engagement platforms. More specifically, the present disclosure provides a system and method for interactive engagement of users using machine learning.

[0010]

[0007] The system and method for interactive engagement of users using machine learning (ML) is provided. The present disclosure describes social interaction among users using ML, which involves leveraging algorithms and data-driven techniques to enhance how people connect, communicate, and engage within digital environments. ML can be applied in various ways to improve social interactions, both in user interfaces and in content recommendation systems. The system generates content-based recommendations using ML algorithms, which can analyse user preferences, behaviours, and interactions to suggest relevant content (e.g., articles, videos, social media posts, etc.).

[0011]

[0008] Additionally, the system analyses user behaviour and activity to create or suggest interest groups (e.g., forums or discussion boards) where users can engage with others who have similar hobbies, goals, or concerns. Further, the system may suggest friends or professional connections based on mutual connections, shared interests, or past interactions. The system also generates tailored experiences based on individual preferences, increasing satisfaction and retention. The system may predict user needs and behaviours allowing for timely interventions and content that keeps users engaged. Further, the system provides automated ratings and reviews which allows users to be paired with matches based on a calculated techniques / mechanism. The system also acknowledges user’s availability by automating requests, sending it based on user’s availability.

[0012]

[0009] In view of the above recited background, a few core technical problems that are identified in the existing solutions and that are being solved by the present invention are recited below:

[0010] Enhancing emotional depth in digital communication: Existing solutions (platforms) lack the emotional depth of human relationships, leading to shallow interactions. The problem of how can technology better emulate nuanced, emotionally rich human communication is still unaddressed. However, the system of the present invention uses ML algorithms to tailor interactions and suggest interest groups or forums where users can engage with others who share similar interests or concerns. These curated environments promote deeper connections, fostering meaningful communication and emotional depth.

[0013] [Oil] Overcoming miscommunication in virtual interactions: Misunderstandings and miscommunication are prevalent in online interactions due to the lack of non-verbal cues. The problem of innovative approaches can be developed to reduce miscommunication in digital communication is still unaddressed. However, the system of the present invention by analyzing user behaviour and preferences, the system predicts user needs and recommends relevant content or groups, reducing the chances of misaligned communication. Additionally, the tailored recommendations enhance clarity and relevance, minimizing potential misunderstandings.

[0014]

[0012] Reducing social isolation in digital environments: Despite increased connectivity, users may feel isolated due to the lack of meaningful interactions online. The problem of how can platforms be designed to foster genuine connections and a sense of community is still unaddressed. However, the ML-powered system suggests interest groups and connections based on mutual interests or shared goals, creating a sense of belonging and community. This targeted engagement combats social isolation by fostering relevant, meaningful relationships.

[0015]

[0013] Mitigating privacy and security risks: Mobile and social interaction technologies often compromise user privacy and security. The problem of how can platforms balance the need for connectivity with robust privacy and data protection mechanisms is still unaddressed. However, the system of the present invention by use of advanced ML algorithms incorporates data anonymization and secure behavioural analysis to ensure users’ data is protected while still delivering personalized experiences.

[0014] Addressing accessibility challenges: Certain social interaction platforms may exclude users due to language barriers, lack of technological access, or platform design biases. The problem of what solutions can ensure inclusivity across diverse user groups is still unaddressed. However, the system of the present invention by ML algorithms includes real-time translation features and adaptive user interfaces that cater to diverse linguistic and technological needs, ensuring inclusivity for all users.

[0016]

[0015] Supporting mental well-being: The use of social interaction apps can sometimes negatively affect mental well-being due to cyberbullying, overuse, or unrealistic social comparisons. The problem of how can platforms incorporate features that promote healthy usage and support mental health is still unaddressed. However, the system of the present invention by predicting user needs and behaviors, the system offers timely interventions, such as recommending breaks, promoting positive content, or connecting users to supportive groups. These features encourage healthy usage patterns and enhance mental well-being.

[0017]

[0016] Facilitating deep connections across cultural and language barriers: Language and cultural differences limit global interactions on social platforms. The problem of what technologies (e.g., real-time translation or cultural sensitivity training) can enhance cross-cultural communication? However, the system of the present invention by using ML algorithms suggests culturally sensitive content and provide automatic translation for seamless communication, bridging cultural divides and enhancing global interactions.

[0018]

[0017] Minimizing algorithmic bias in social interaction platforms: Many platforms' algorithms prioritize engagement over meaningful interactions, often leading to echo chambers or marginalization of certain groups. The problem of how can algorithmic designs be restructured to prioritize inclusivity and balanced representation is still unaddressed. However, the system of the present invention by ML approach uses diverse datasets and prioritizes user satisfaction by recommending connections and content based on shared interests and inclusive factors, reducing bias and enhancing balanced engagement.

[0019]

[0018] Improving collaborative tools for professional use: While mobile technology facilitates collaboration, it often lacks the sophistication needed fordeep professional collaboration. The problem of how can platforms be enhanced to support diverse and complex professional workflows is still unaddressed. However, the system of the present invention has ability to suggest professional connections and create interest groups based on shared goals enhances collaboration. Tailored recommendations for professional content also support in-depth workflows and network growth.

[0020]

[0019] As compared to the conventional solutions, the ML-driven system provides a comprehensive framework to address the core technical problems associated with mobile technology and social interaction platforms. Its use of personalized recommendations, behaviour analysis, and real-time adaptability significantly enhances the quality, inclusivity, and depth of digital interactions.

[0021]

[0020] Further, the existing social networking platforms like social networking applications (e.g., Facebook, Linkedln, Instagram) and professional tools (e.g., Slack, Teams) offer user authentication, communication, and data recording. However, these systems do not explicitly classify users into categories using machine learning to dynamically generate activity schedules. The present invention provides a combination of machine learning-driven user categorization with tailored scheduling and engagement mechanisms stands out compared to existing solutions, which primarily rely on manual grouping or simpler algorithms. While machine learning is widely used in user profiling and recommendation systems, its application to classify users for activity scheduling and engagement within predefined periods represents an inventive integration. It can be further noted that the present invention does not merely combine existing functionalities (authentication, ML classification, scheduling, communication, recording) but does so in a way that addresses specific engagement challenges, such as promoting active participation and structured interactions within user groups.

[0022] DEFINITIONS

[0023]

[0021] Integrated System: An integrated system facilitates seamless communication and collaboration through a combination of tools, technologies, and interfaces, enabling users to connect, share, and interact across multiple channels like text, voice, video, and social media. These systems enhance bothpersonal and professional engagements, ranging from simple messaging to immersive virtual experiences.

[0024]

[0022] Activities: Activities include sharing content such as photos, videos, and updates; engaging with others through likes, comments, and private conversations; live streaming with real-time interactions; and participating in forums or competitive gaming. Additionally, they encompass real-time collaboration on projects, document editing, and task organization within platforms to streamline teamwork.

[0025]

[0023] Specialists: Specialists are professionals focused on enhancing communication and interaction among individuals and groups. Working in fields like psychology, education, or business, they aim to improve interpersonal skills, resolve conflicts, and foster social cohesion through mediation, empathy-building, and problem- solving strategies.

[0026] BRIEF DESCRIPTION OF DRAWINGS

[0027]

[0024] FIG. 1 illustrates an example system architecture of a proposed system, in accordance with an example embodiment; FIG. 2 illustrates an example block diagram of a proposed system, in accordance with an example embodiment; FIG.

[0028] 3 illustrates a flow diagram of an example method implemented by the proposed system, in accordance with an example embodiment; FIG. 4 illustrates an example block diagram representing user types and functionalities of the proposed system, in accordance with an example embodiment; FIG. 5 illustrates an example block diagram representing various modules implemented by the proposed system, in accordance with an example embodiment; FIG. 6A and 6B illustrate example interfaces implemented by the proposed system, in accordance with an example embodiment;

[0029] DETAILED DESCRIPTION

[0030]

[0025] The system and method for interactive engagement of users using machine learning (ML) is provided. The present disclosure describes social interaction among users using ML, which involves leveraging algorithms and data-driven techniques to enhance how people connect, communicate, and engage within digital environments. ML can be applied in various ways to improve social interactions, both in user interfaces and in content recommendation systems. Thesystem generates content-based recommendations using ML algorithms, which can analyse user preferences, behaviours, and interactions to suggest relevant content (e.g., articles, videos, social media posts, etc.).

[0031]

[0026] Additionally, the system analyses user behaviour and activity to create or suggest interest groups (e.g., forums or discussion boards) where users can engage with others who have similar hobbies, goals, or concerns. Further, the system may suggest friends or professional connections based on mutual connections, shared interests, or past interactions. The system also generates ttailored experiences based on individual preferences, increasing satisfaction and retention. The system may predict user needs and behaviours allowing for timely interventions and content that keeps users engaged.

[0032]

[0027] Embodiments of the present disclosure may provide a system and a method for interactive engagement of users using machine learning (ML). The system and the method are described with reference to FIGs. 1 to 6B.

[0033]

[0028] FIG. 1 illustrates example system architecture 100 of the proposed system 106, in accordance with an example embodiment. The system 106 may be connected to one or more user equipments (102-1, 102-2... 102-N) through a network 104. A person of ordinary skill in the art will understand that the one or more user equipments (102-1, 102-2... 102-N) may be collectively referred as the user equipments 102 and individually referred as the user equipment 102. One or more users may access the system 106 through the UEs 102.

[0034]

[0029] The communication network 104 may be wired, wireless, or any combination of wired and wireless communication networks, such as cellular, WiFi, internet, local area networks, or the like. In one embodiment, the communication network 104 may comprise one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet- switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fibre-optic network, and the like, or any combination thereof. The network 104 may include, by way of example butnot limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth.

[0035]

[0030] In one example embodiment, the system 106 receives data associated with one or more users through the UEs 102. The one or more users may access the system 106 for engaging in various activities with at least a user among the one or more users. Further, the system 106 extracts a set of attributes (facial and visual) from the received data to authenticate the users through corresponding UEs 102.

[0036]

[0031] In one example embodiment, the system 106 analyses via a machine learning (ML) engine, the set of attributes to classify the authenticated one or more users into one or more categories.

[0037]

[0032] In one example embodiment, the system 106 generates a predefined schedule associated with the one or more categories to enable participation of the authenticated one or more users (users herein after) from each of the one or more categories in at least an activity for a predetermined period. To generate the predefined schedule, the system 106 is configured to determine one or more preferences (preferences herein after) associated with the authenticated users classified into each of the one or more categories (categories herein after) based on the set of attributes. To generate the predefined schedule, the system 106 is configured to match the authenticated users classified into each of the categories based on the determined preferences. To generate the predefined schedule, the system 106 is configured to request one or more approvals (approvals herein after) from each of the authenticated users classified into each of the categories for participation in the said at least activity based on the determined preferences. The system 106 is configured to enable the participation of the plurality of users at a predetermined location during said at least activity for the predetermined period. The system 106 may allow users to access various facilities and communities without the need for physical visits. The system 106 may enable automated generation of identity cards / passes for accessing the facilities, each accompaniedby a date and time stamp, enhancing security measures and preventing fraudulent duplication.

[0038]

[0033] In one example embodiment, the system 106 may allow the users to enter their email and password for accessing the activity. The users may enter their registered email address and password associated with their community connectivity platform account. The system 106 may also offer an OTP (One-Time Password) option for password recovery. When users select this method, they will receive a unique OTP on their registered mobile number or email address. After verifying the OTP, users may proceed to reset their password and regain access to their account.

[0039]

[0034] For example, in one embodiment, by integrating geo -location among the predetermined location, the system 106 enables users to discover and access nearby facilities conveniently. Customers / Users registered with the system 106 may effortlessly, match make, schedule appointments, online sessions, webinars, classes, or events and make secure payments directly. Additionally, the system 106 facilitates real-time communication between facility managers, staff, coaches and community members, fostering better engagement and enhancing the overall user experience. Users may receive updates, announcements, and personalized notifications based on their preferences and interests, creating a dynamic and interactive ecosystem. The system 106 may allow the users to select the desired date, time, location, and specify preferences regarding gender, skill level, and fitness goals. Users may choose a date, time, and location for their desired in-person session. Users may also specify preferences such as the gender of their partner, skill level, images filters, and fitness goals. By applying these filters, users may narrow down their search and find potential partners who align with their specific criteria. This streamlined experience allows users to explore potential matches without filtering through specific preferences, making it easier to connect with compatible partners based on other shared interests and values. Further, the system 106 enables a personalized interaction feature that enables users, once matched, to send training, health, or social requests to each other. The recipient has the choice to accept or decline the request, and if accepted, they may seamlessly integrate it into their current mobile or tablet calendar. Thisstreamlined process enhances user convenience, fostering effective communication and coordination.

[0040]

[0035] Further, in another example embodiment, the system 106 is configured to record feedback associated with said at least activity performed by the plurality of users for the predetermined period. The system 106 is configured to subsequently recommend activities based on the determined preferences and the recorded information to enable the participation of the authenticated users from each of the one or more categories. To generate the activities, the system 106 is configured to receive at least an input from one or more specialists (specialists herein after) on the determined preferences and recorded information. Further, the system 106 is configured to analyze the recorded feedback and prioritize scheduling of the activities based on the determined preferences and the recorded information.

[0041]

[0036] In one example embodiment, the system 106 is configured to allow at least a user from each of the categories to select partners (partners herein after) from each of the categories for participating in said at least activity for the predetermined period. Hence the system 106 enables individuals / users to connect with each other and encourages active user participation through feedback, fostering a supportive community and offering a dynamic social media feature. Users may share exercises, recipes, playlists, guides, posts, contributing to a personalized and engaging experience. To ensure a safe community, the system 106 comprises member verification, adding an extra layer of authenticity and trust.

[0042]

[0037] For example, in another embodiment, the system 106 allows users to schedule traditional coffee dates or allows active pursuits such as sporting exercises or yoga sessions with verified members in a safe place. The verification process may be implemented through the use of blockchain technology, ensuring secure identity checks without storing sensitive information on conventional servers. Additionally, the integration of artificial intelligence (Al) enhances the matching process, contributing to a personalized and trustworthy dating environment. The system 106 with the Al technology automates the dating process by generating personalized matchmaking requests. This feature analyzesuser profiles, preferences, and physical features to suggest potential matches, streamlining the dating experience and promoting meaningful connections. The platform enhances user convenience by seamlessly integrating dating activities into personal calendars. Users may schedule dates, plan meetups, or engage in virtual interactions with matches, and these events are automatically added to their calendars. This ensures organized and efficient coordination of dating activities. Users may message specialists, seeking advice on relationship topics, enhancing their dating strategies, or addressing any concerns they may have. Robust measures may be implemented by the system 106 to safeguard user data, ensuring that sensitive information is protected during messaging interactions with dating specialists. This emphasis on security contributes to a safe and trusted dating environment. The system 106 not only facilitates interactions with specialists but also serves as a mechanism to reward user engagement. Users may earn reward points through various activities, fostering a sense of community and incentivizing positive contributions.

[0043]

[0038] In one example embodiment, the system 106 enable communication between a plurality of users of the authenticated users classified into each of the categories during the predetermined period. The system 106 may also incorporate a group chat support function that provides users with the option to participate anonymously or using a chosen name. This group chat feature fosters a supportive and inclusive environment where community members / users may connect and discuss various topics whilst specialists may facilitate the conversations. The system 106 records information associated with the communication between the plurality of users of each of the categories during the predetermined period.

[0044]

[0039] In one example embodiment, the system 106 may enable community staff, individuals, and entrepreneurs to upload schedules, classes, timetables, and more. The system 106 allows community coaches, trainers, and staff to respond to member's issues and wellness concerns through a messaging and chat functionality. The system 106 may enable users to openly share their thoughts, feelings, and challenges within a safe and supportive environment. By providing various communication channels, such as group chat support and interactions with specialists from around the world, the system 106 creates opportunities for usersto express themselves freely and seek assistance when needed. The system 106 may also provide access to wellbeing-related content like blogs, videos, and posts, users may engage with wellness specialists and gain valuable insights into various aspects of well-being. This exposure to educational resources empowers individuals to become more self-aware and recognize potential signs of distress or difficulties early on. By providing various communication channels, such as but not limited to messaging, forums, and announcements, the system 106 facilitates seamless interaction among users. The system 106 may create a dynamic and inclusive environment where users may easily connect, share information, and stay informed about relevant updates. This not only strengthens the sense of community but also provides a centralized platform for meaningful and efficient communication.

[0045]

[0040] In one example embodiment, the system 106 may uses ML related to education, training, or skill development, where ML may enable personalized learning experiences. The system adjusts based on the learner's performance, providing additional resources or changing the difficulty of tasks based on the user’s capabilities and needs. ML may be used to analyze and categorize users into different segments based on their behavior, enabling user interactions to specific groups. Further, ML algorithms may identify unusual patterns in user behavior, which may be used to flag security concerns (e.g., unusual login attempts or potential fraud) or provide proactive customer support. ML may process facial expressions, voice tones, and text sentiment to understand a user's emotional state. This enables the system 106 to adjust their responses accordingly, such as providing comforting responses when detecting frustration or providing more engaging content when detecting excitement.

[0046]

[0041] For example, in one embodiment, ML algorithms may analyze user’s performance and adjust the difficulty level of tasks or challenges in real-time. For example, the system 106 may adapt quizzes based on the user’s previous performance to ensure they are neither too easy nor too hard. Further, by analyzing user behavior, preferences, and patterns, ML may suggest personalized rewards or incentives that resonate with individual users, enhancing motivation. Natural language Processing (NLP), a branch of ML, may power gamifiedchatbots that interact with users in real-time. These chatbots may offer personalized advice, guidance, or even in-game support, making the experience feel more engaging and interactive.

[0047]

[0042] For example, in one embodiment, for providing dating matches, the ML by analyzing past successful matches may predict which profiles are more likely to lead to a meaningful conversation or relationship. ML may predict when users are most likely to be active and engage with matches, enabling applications to send notifications or suggest new connections at the optimal time. ML may also help in identifying fraudulent or fake profiles. The ML algorithms may analyze user behavior, profile consistency, and image metadata to flag suspicious profiles, reducing the chances of catfishing or scams. Further, NLP may be used to assess the tone of messages, ensuring that users maintain polite and respectful conversations, which may improve the overall dating experience.

[0048]

[0043] For example, in one embodiment, the system 106 may provide access to content related to wellbeing, comprising blogs, videos, and posts, may offer a variety of resources to help individuals improve their physical, mental, and emotional health. These platforms typically cover topics such as mindfulness, stress management, fitness, nutrition, self-care, mental health, and personal growth. The system 106 enables users to access, interact with, and share wellbeing-related content, engage with other users through likes, shares, and comments, and communicate with specialists through messaging.

[0049]

[0044] For example, in one embodiment, the system 106 may enable users to access gym facilities and events by offering a seamless and efficient digital experience. By eliminating the need for physical visits to purchase memberships or event tickets, the platform allows users to easily browse, select, and pay for gym services and events from the comfort of their own homes or on the go. This streamlined purchasing process ensures convenience, accessibility, and flexibility, allowing users to access gym facilities and events when it fits their schedule, without the traditional barriers of in-person transactions. With secure payment systems and real-time availability updates, users may manage their fitness journey effortlessly. Further, the users may select a preferred date and time for their workout or session. The users may choose a location whether it’s a gym, home,outdoor space, or virtual session. Based on the user's preferences (gender, skill level, and fitness goals), the system 106 may match available trainers who meet these criteria. The system 106 may enable users to provide feedback on their training sessions and an overall experience with their partners. After each session, users may provide feedback associated with their training partner, wellbeing coach or dating coach on session quality. Further, users may rate their partner, leave comments, and share suggestions.

[0050]

[0045] For example, in one embodiment, the system 106 may provide access to an events calendar showcasing community-wide activities, including sports events, workshops, seminars, and social gatherings, but not limited to the like, may be a valuable tool for fostering engagement and communication within a community. The system 106 may include a web-based or mobile solution (using the UEs 102), depending on the target audience's preferences.

[0051]

[0046] For example, in one embodiment, the system 106 may improve the user experience by providing seamless access to community facilities, offering convenience and time-saving features, and ensuring that users may make the most out of their local amenities. Users who want to book multiple facilities (like a gym session followed by lunch at the cafeteria) may do so in one seamless transaction. A unified account may allow users to manage bookings across different locations (e.g., sports arenas in different districts or libraries in various neighbourhoods).

[0052]

[0047] For example, in one embodiment, the system 106 may comprise a login interface which comprises fields for users to enter their email and password, along with buttons and features to facilitate account access. Further, the login interface may comprise a “Forgot Password” link or button and also offer an OTP (One-Time Password) option for password recovery. When users select this option, they will receive a unique OTP on their registered mobile number or email address. After verifying the OTP, users may proceed to reset their password and regain access to their account. If users forget their password, they may click on the “Forgot Password” link or button. This action will redirect them to a password recovery interface, where they may provide their registered email address. Further, the system 106 may comprise a password recovery interface that allows users to recover or reset their password when they forget their password or need toupdate the password for security reasons. This interface typically involves a series of steps to ensure identity verification and provides a secure way to reset or retrieve the password.

[0053]

[0048] For example, in one embodiment, the system 106 may comprise a social media feed interface where users may share and view posts related to exercises, recipes, playlists, music, and other fitness or health-related content. The system 106 may enable clean, user-friendly, and interactive layout that encourages engagement. The system 106 may implement user accounts, post storage, and media hosting. Cloud storage solutions may be used for storing user-generated content (images, videos). The system 106 may further store user profiles, post metadata, interactions (likes, comments, shares). The system 106 may use technologies to provide real-time updates (new posts, comments, likes). The system 106 may ensure proper data encryption and content moderation for a safe and positive user experience.

[0054]

[0049] For example, in one embodiment, the system 106 may comprise a platform where users may connect with qualified wellbeing specialists from all over the world via an in-built messaging interface. This in-built messaging interface may be used for various wellbeing needs, such as mental health support, stress management, life coaching, or even physical wellness guidance. This may allow users to chat directly with wellbeing specialists in real-time. Further, the system 106 may implement both text and voice messaging to offer flexibility in communication with an option for video calls for more personalized sessions. Specialists from various time zones and cultural backgrounds may cater to diverse needs. Users may be able to view detailed profiles of wellbeing specialists, including their qualifications, areas of expertise, certifications, and user reviews. The system 106 may ensure encrypted messaging for user privacy, allow users to control their data and manage their profiles, and enable specialists to adhere to professional ethics and confidentiality standards.

[0055]

[0050] Further as shown in FIG. 2, the system 106 includes a processor 202 and memory 204 working together for data processing and storage. The processor can include various hardware types (e.g., CPU, GPU, ASIC) and supports advanced tasks like multithreading and big data analysis. The memory stores data,instructions, and buffered inputs, which are non-transitory and include volatile and non-volatile types (e.g., RAM, flash memory).

[0056]

[0051] An interface 206 connects the system to external devices, servers, and peripherals, enabling data communication and user input via graphical or hardware interfaces. The processing engine 208 combines hardware and software to execute system functionalities, including a data ingestion engine 212 and an ML engine 214 for advanced data processing. A database 210 stores data generated or used by the system components.

[0057]

[0052] In an embodiment, the processor 202 receives data through the data ingestion engine 212. The data may be associated with the users. Further, the processor 202 records the information in the database 210. The processor 202 extracts a set of attributes from the received data to authenticate the users. The processor 202 analyzes via the ML engine 214, the set of attributes to classify the authenticated users into categories.

[0058]

[0053] In an embodiment, the processor 202 generates a predefined schedule associated with the categories to enable participation of the authenticated users from each of the categories in at least an activity for a predetermined period. To generate the predefined schedule, the processor 202 is configured to determine preferences associated with the authenticated users classified into each of the categories based on the set of attributes. To generate the predefined schedule, the processor 202 is configured to match the authenticated users classified into each of the categories based on the determined preferences. To generate the predefined schedule, the processor 202 is configured to request approvals from each of the authenticated users classified into each of the categories for participation in the said at least activity based on the determined preferences.

[0059]

[0054] In an embodiment, the processor 202 records feedback associated with said at least activity performed by the plurality of users for the predetermined period. The processor 202 subsequently recommends activities based on the determined preferences and the recorded information to enable the participation of the authenticated users from each of the categories. The processor 202 is configured to analyze the recorded feedback and prioritize scheduling of the activities based on the determined preferences and the recorded information.Further, to generate the one or more activities, the processor 202 is configured to receive at least an input from specialists on the determined preferences and recorded information.

[0060]

[0055] In an embodiment, the processor 202 is configured to allow at least a user from each of the categories to select partners from each of the categories for participating in said at least activity for the predetermined period. Further, the processor 202 is configured to enable the participation of the plurality of users at a predetermined location during said at least activity for the predetermined period. The processor 202 enables communication between the plurality of users classified into each of the categories during the predetermined period. Further, the processor 202 records information associated with the communication between the plurality of users of each of the categories during the predetermined period.

[0061]

[0056] FIG. 3 illustrates a flow diagram 300 of the method implemented by system 106, where each step can be executed via hardware, firmware, or software with computer program instructions. These instructions, stored in memory 204 and executed by the processor 202, enable the system to perform the flowchart’s functions. The instructions can be loaded onto a computer or programmable device to create a machine or process for executing the specified tasks. The blocks in the flow diagram represent functions that can be implemented through hardware, software, or their combination.

[0062]

[0057] The method 300 is implemented by the system architecture 200. At step 302, method 300 comprises the steps of receiving data, by the system 106, associated with users. The data may be collected and entered into the system 106 using mobile devices associated with the users.

[0063]

[0058] At step 304, method 300 further comprises the steps of extracting a set of attributes (facial and visual), by the system 106, from the received data to authenticate the users.

[0064]

[0059] At step 306, method 300 comprises the steps of analyzing, by the system 106 via a ML engine, the set of attributes to classify the authenticated users into categories. Further, at step 308, method 300 comprises the steps of generating, by the system 106, a predefined schedule associated with thecategories to enable participation of the authenticated users from each of the categories in at least an activity for a predetermined period.

[0065]

[0060] In one example embodiment, at step 306 and 308, the method 300 comprises the steps of determining, by the system 106, preferences associated with the authenticated users classified into each of the categories based on the set of attributes. Further, the method 300 comprises the steps of matching, by the system 106, the authenticated users classified into each of the categories based on the determined preferences. Furthermore, the method 300 comprises the steps of requesting, by the system 106, approvals from each of the authenticated users classified into each of the categories for participation in the said at least activity based on the determined preferences.

[0066]

[0061] In one example embodiment, at step 306 and 308, the method 300 comprises the steps of recording feedback, by the system 106, associated with said at least activity performed by the plurality of users for the predetermined period. Further, the method 300 comprises the steps of subsequently recommending, by the system 106, activities based on the determined preferences and the recorded information to enable the participation of the authenticated users from each of the categories. Furthermore, the method 300 comprises the steps of analyzing, by the system 106, the recorded feedback and prioritizing scheduling of the activities based on the determined preferences and the recorded information. The method 300 comprises the steps of receiving, by the system 106, at least an input from specialists on the determined preferences and recorded information for generating the activities.

[0067]

[0062] In one example embodiment, at step 306 and 308, the method 300 comprises the steps of allowing, by the system 106, at least a user from each of the categories to select partners from each of the categories for participating in said at least activity for the predetermined period. Further, the method 300 comprises the steps of enabling, by the system 106, the participation of the plurality of users at a predetermined location during said at least activity for the predetermined period.

[0063] Further, at step 310, method comprises the steps of enabling, by the system 106, communication between a plurality of users of the authenticated users classified into each of the categories during the predetermined period.

[0068]

[0064] Further, at step 312, method comprises the steps of recording, by the system 106, information associated with the communication between the plurality of users of each of the categories during the predetermined period.

[0069]

[0065] FIG. 4 illustrates an example block diagram representing user types and functionalities of the proposed system, in accordance with an example embodiment. The system 106 may comprise members 404 that access the system 106 for interacting with various users through the activities. For example, the members 404 may connect with other members through the gym connect platform 402. Further, the gym connect platform 402 may comprise trainers for training the members 404. An administrator 408 may be appointed by the gym connect platform 402 for managing the various activities associated with the gym connect platform 402. In addition, a supplier 410 may supply the requirements required by the members 404 of the gym connect platform 402.

[0070]

[0066] FIG. 5 illustrates an example block diagram representing various modules implemented by the proposed system, in accordance with an example embodiment. In an example embodiment, the users may use user devices 502 (previously UEs 102) for accessing the system 106 through the mobile application 504. The system 106 may comprise a backend server 506 and a content management module 508 for managing and providing the activities to the users. The user management module 510 may be connected to the backend server 506 for categorizing the users for various activities via a training module 512, a dating module 514, a forum module 516, and a sessions module 518 respectively. For example, in an embodiment, the users choosing dating as the activity may be categorized through the dating module 514 and one or more sessions associated with dating may be provided by the dating module 514 for the predetermined period.

[0071]

[0067] FIGs. 6A-6B illustrates an example interfaces (600A, 600B) implemented by the proposed system 106, in accordance with an example embodiment. In an example embodiment, the system 106 may comprise aninterface 602 that allows the user / member to login 604 into the interface 602. The interface 602 may be accessed by the users by using a sign in 606 option and entering an email 608 and a password 610. Further, the interface 602 may comprise a “Forgot Password” field 612 that allows the users to remember 614 their password by providing various options. In another example embodiment, the users may access a gym connect platform 616 through logging into the interface 602. The gym connect platform 616 may further comprise a training module 618, a dating module 620, a forum module 622, a sessions module 624 respectively, and a user management module 626 respectively for allowing the users / members to access the various activities provided the system 106.

[0072]

[0068] As will be appreciated by those skilled in the art, the techniques described in the various embodiments discussed above are not routine, or conventional, or well understood in the art. The techniques discussed above provide for innovative solutions to address the challenges associated with generating holistic responses based on structural and semantic queries. The disclosed techniques offer several advantages over the existing methods as listed in below paragraphs.

[0073]

[0069] The present disclosure introduces a system and a method for connecting individuals with similar interests on a versatile and customizable mobile platform designed around several key features that enhance community building and engagement. The system enables users to connect, share, and collaborate based on mutual interests, hobbies, and goals. The system provides customizable and dynamic networking tools to foster community interaction in a personalized way. Users may join specific interest-based communities, interact with others, participate in events, and even collaborate on projects.

[0074]

[0070] Additionally, the system can provide tailored experience to users, where users can customize their profiles based on specific interests, hobbies, or goals. The system suggests connections and groups that align with these preferences, ensuring more meaningful interactions.

[0075]

[0071] Several of the key advantages of this solution lies in leveraging machine learning, by which the system can improve its ability to match users with like-minded individuals by analyzing behaviors, preferences, and interactions.The system fosters community engagement by bringing together individuals who share similar passions, whether in professional, recreational, or social contexts. This promotes networking, collaboration, and idea-sharing. The system helps users form specialized groups or clubs, making it easier to organize events, discussions, or projects around a shared interest. The system connects individuals from around the world, regardless of geographical location. This can lead to a more diverse and inclusive community. The system can also work across various devices and integrate with other platforms (e.g., social media), enabling users to access the community at any time and from any location. The system enables users to connect with people who have similar goals and interests, saving time and effort. The system recommends events, meetups, and content tailored to user interests, further enhancing the connection opportunities. Further, the users can adapt the platform to their evolving needs, whether they are focused on professional networking, hobby-related groups, or social interaction. Whether for personal interests, educational purposes, professional networking, or advocacy, the platform can serve a wide range of communities.

[0076]

[0072] Thus, the present disclosure provides a highly efficient, user-centric solution for networking, community-building, and engagement. It offers tailored experiences, fosters collaboration, and ensures a safe and accessible environment for all users.

[0077]

[0073] To summarize, the invention relates to an integrated system (106) for user engagement that leverages advanced technologies such as machine learning (ML), artificial intelligence (Al), blockchain, and graph neural networks (GNNs) to create a dynamic and adaptive platform for enhancing social interactions and collaborative activities. The system ensures personalized and secure engagement through features like user authentication, preference-based activity scheduling, and gamification, fostering meaningful and inclusive interactions. Key features and functionalities that the present invention has are as follows:

[0078]

[0074] Data Processing and User Classification: The system receives data from multiple users and extracts attributes to authenticate them. A machine learning engine classifies users into specific categories based on these attributes (e.g., shared interests, professional goals).

[0075] Activity Scheduling and Participation: Predefined schedules are generated for each user category, enabling structured participation in activities over predetermined periods. User preferences and environmental factors (e.g., location, device context) dynamically adjust these schedules. The system facilitates group activities, ensures timely approvals for participation, and collects feedback post-activity to refine future recommendations.

[0079]

[0076] Blockchain-Based Security: Blockchain technology secures user authentication, tracks communication logs, and manages data sharing between users. This ensures transparency, privacy, and data integrity across all interactions. Ensures tamper-proof, transparent, and privacy-preserving user interactions, addressing a critical limitation in many current platforms.

[0080]

[0077] Multi-Modal Interaction and Immersive Engagement: Users can communicate and participate in activities via multiple modalities, including voice, text, video, augmented reality (AR), and virtual reality (VR). These diverse channels ensure inclusivity and cater to varied user preferences. Creates immersive and inclusive environments for user engagement, catering to diverse user preferences and abilities. For example, AR / VR could facilitate virtual gatherings or team-building exercises. Further, ensures long-term user engagement by adapting to changing interests, preferences, and behaviours.

[0081]

[0078] Gamification for Motivation: Gamification elements such as earning points, badges, or rewards encourage user engagement and foster healthy competition among participants. Encourages active participation and fosters a sense of achievement among users. It further, enhances communication quality and group cohesion by proactively addressing issues before they escalate. Furthermore, it expands inclusivity and ensures equal engagement opportunities for all users

[0082]

[0079] Graph Neural Networks (GNNs) for Advanced Recommendations: GNNs identify complex relationships and hidden affinities among users to generate precise recommendations and create highly relevant user groupings. It offers highly refined group formation and content suggestions, surpassing traditional recommendation systems.

[0080] Expert Input and User-Selected Partners: The system incorporates inputs from specialists to enhance activity design. Users can select activity partners based on their preferences and compatibility within categorized groups.

[0083]

[0081] Feedback Integration and Continuous Improvement: The system records user feedback and continuously refines schedules, activities, and groupings to improve engagement over time.

[0084]

[0082] Examples of Use Cases:

[0085]

[0083] Virtual Networking Events: A professional networking platform uses the system to authenticate users, analyze their skills and interests, and organize tailored virtual events.

[0086]

[0084] Collaborative Learning Platforms: Students participating in online courses are classified into study groups using GNNs based on shared academic goals. Activities, such as problem- solving sessions, are scheduled dynamically, and gamification rewards participation with points redeemable for course credits.

[0087]

[0085] Health and Wellness Communities: The system categorizes users based on their fitness goals and health data. Activities like virtual yoga sessions or fitness challenges are organized, with Al assistants providing real-time guidance and tracking progress.

[0088]

[0086] Global Social Platforms: Users from diverse backgrounds join discussion groups tailored to their interests (e.g., book clubs, gaming communities). Blockchain ensures secure communication and data sharing, while gamification encourages active participation.

[0089]

[0087] Hybrid Work Environments: Teams in a corporate setting use the system to schedule brainstorming sessions and collaborative activities. AR / VR interfaces enhance the experience of remote workers, while sentiment analysis ensures positive engagement during meetings.

[0090]

[0088] The implementation of the method for user engagement, particularly in a gym connection environment, incorporates various advanced technologies, algorithms, and techniques to realize each of the steps outlined in the invention. Below is a breakdown of these steps with examples of techniques and their application in the gym context:

[0089] Receiving and Authenticating Data: The system receives user data, such as gym membership details, fitness goals, past workout history, and device data (e.g., wearable fitness trackers). Techniques like secure API integration and data encryption protocols ensure secure data transmission. For authentication, the system uses blockchain technology to verify users securely, leveraging distributed ledgers to validate membership and prevent unauthorized access.

[0091]

[0090] Extracting Attributes and Classifying Users: Attributes such as fitness goals, exercise preferences, and skill levels are extracted using natural language processing (NLP) to analyze user-inputted text data or structured data processing techniques. A machine learning (ML) engine applies clustering algorithms (e.g., k-means or hierarchical clustering) to classify users into categories such as beginners, intermediate lifters, or yoga enthusiasts.

[0092]

[0091] Generating a Predefined Schedule: The ML engine determines suitable workout schedules for user categories based on preferences, class availability, and gym capacity. A collaborative filtering algorithm recommends time slots and group activities (e.g., spin classes or strength training sessions). Dynamic scheduling employs reinforcement learning to adjust class timings in real-time based on user attendance patterns and feedback.

[0093]

[0092] Enabling Communication: Users within the same category (e.g., a yoga group) are connected through communication channels such as group chats or video calls. Diverse modalities, including augmented reality (AR) or virtual reality (VR), enable immersive interactions like virtual personal training or collaborative group workouts. Secure communication protocols powered by blockchain manage chat logs and ensure privacy.

[0094]

[0093] Gamification for Engagement: Gamification techniques such as reward systems provide users with points for completing workouts or attending classes. Game design algorithms integrate leaderboards, challenges, and virtual badges, motivating users to maintain consistency. For instance, a user completing a weekly HIIT challenge might earn a badge and free access to a specialty class.

[0095]

[0094] Using Graph Neural Networks (GNNs): To form highly relevant workout groups or recommend ideal gym partners, the system uses graph neural networks (GNNs). GNNs analyze connections among users based on sharedinterests, mutual gym sessions, or past co-participation to uncover hidden affinities.

[0096]

[0095] Recording Feedback and Adjusting Schedules: The system records user feedback using structured forms and sentiment analysis of comments. A feedback prioritization algorithm ranks user suggestions to refine future schedules. For instance, if multiple users in a strength training group express dissatisfaction with a specific instructor, the system adjusts the schedule to assign a different trainer.

[0097]

[0096] Specialist Input and Partner Selection: Specialists such as trainers provide input on group activities using supervised learning models that incorporate expert annotations. Users can manually select partners from recommended options generated by collaborative filtering, ensuring compatibility based on goals or training intensity.

[0098]

[0097] Enabling Participation at a Predetermined Location: Users receive location-based reminders and AR / VR guidance for navigating to specific gym areas or attending virtual sessions. Location-based recommendation systems and AR overlays guide users in real-time.

Claims

1. An integrated system (106) for user engagement, the system (106) comprising:a processor (202);a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions which, when executed by the processor (202), cause the processor (202) to:receive data associated with one or more users;extract a set of attributes from the received data to authenticate the one or more users;analyze via a machine learning engine (214), the set of attributes to classify the authenticated one or more users into one or more categories;generate a predefined schedule associated with the one or more categories to enable participation of the authenticated one or more users from each of the one or more categories in at least an activity for a predetermined period;enable communication between a plurality of users of the one or more authenticated users classified into each of the one or more categories during the predetermined period; andrecord information associated with the communication between the plurality of users of each of the one or more categories during the predetermined period.

2. The integrated system (106) according to claim 1, wherein to generate the predefined schedule, the processor (202) is configured to:determine one or more preferences associated with the authenticated one or more users classified into each of the one or more categories based on the set of attributes;match the authenticated one or more users classified into each of the one or more categories based on the determined one or more preferences; andrequest one or more approvals from each of the authenticated one or more users classified into each of the one or more categories for participation in the said at least activity based on the determined one or more preferences; record feedback associated with said at least activity performed by the plurality of users for the predetermined period; andsubsequently recommend one or more activities based on the determined one or more preferences and the recorded information to enable the participation of the authenticated one or more users from each of the one or more categories.

3. The integrated system (106) according to claim 1, wherein the processor (202) is configured to:utilize blockchain to securely authenticate the plurality of users, track communication logs between the plurality of users, and manage data sharing between of the plurality of users;enable the plurality of users to engage in one or more activities and communication through diverse modalities selected from any or a combination of voice, text, video, augmented reality (AR), or virtual reality (VR);provide, by means of a gamification technique, earning points, badges, or rewards to the plurality of users for participation and engagement in the one or more activities;utilize graph neural networks (GNNs) to identify complex relationships and hidden user affinities across the one or more authenticated users to generate one or more recommendations and groupings there among.

4. The integrated system (106) according to claim 2, wherein the processor (202) is configured to:analyze the recorded feedback and prioritize scheduling of the one or more activities based on the determined one or more preferences and the recorded information;receive at least an input from one or more specialists on the determined one or more preferences and recorded information to generate the one or more activities;allow at least a user from each of the one or more categories to select one or more partners from each of the one or more categories for participating in said at least activity for the predetermined period; andenable the participation of the plurality of users at a predetermined location during said at least activity for the predetermined period.

5. A method (300) for user engagement, the method (300) comprising:receiving data (302), by a processor (202), associated with one or more users (102);extracting (304) a set of attributes, by the processor (202), from the received data to authenticate the one or more users;analyzing (306), by the processor (202), via a machine learning engine (214), the set of attributes, to classify the authenticated one or more users into one or more categories;generating (308), by the processor (202), a predefined schedule, associated with the one or more categories to enable participation of the authenticated one or more users from each of the one or more categories in at least an activity for a predetermined period;enabling (310), by the processor (202), communication between a plurality of users of the authenticated one or more users classified into each of the one or more categories during the predetermined period; andrecording (312), by the processor (202), information associated with the communication between the plurality of users of each of the one or more categories during the predetermined period.

6. The method (300) according to claim 5, for generating the predefined schedule, the method (300) comprises:determining, by the processor (202), one or more preferences associated with the authenticated one or more users classified into each of the one or more categories based on the set of attributes;matching, by the processor (202), the authenticated one or more users classified into each of the one or more categories based on the determined one or more preferences; andrequesting, by the processor (202), one or more approvals from each of the authenticated one or more users classified into each of the one or more categories for participation in the said at least activity based on the determined one or more preferences.

7. The method (300) according to claim 5, comprising:utilizing blockchain to securely authenticate the plurality of users, track communication logs between the plurality of users, and manage data sharing between of the plurality of users;enabling the plurality of users to engage in one or more activities and communication through diverse modalities selected from any or a combination of voice, text, video, augmented reality (AR), or virtual reality (VR);providing, by means of a gamification technique, earning points, badges, or rewards to the plurality of users for participation and engagement in the one or more activities;utilizing graph neural networks (GNNs) to identify complex relationships and hidden user affinities across the one or more authenticated users to generate one or more recommendations and groupings there among.

8. The method (300) according to claim 7, comprising:recording feedback, by the processor (202), associated with said at least activity performed by the plurality of users for the predetermined period; and subsequently recommending, by the processor (202), one or more activities based on the determined one or more preferences and the recorded information to enable the participation of the authenticated one or more users from each of the one or more categories;allowing at least a user from each of the one or more categories to select one or more partners from each of the one or more categories for participating in said at least activity for the predetermined period; andenabling the participation of the plurality of users at a predetermined location during said at least activity for the predetermined period.

9. The method (300) according to claim 8, the method further comprising: analyzing, by the processor (202), the recorded feedback and prioritizing scheduling of the one or more activities based on the determined one or more preferences and the recorded information;allowing, by the processor (202), at least a user from each of the one or more categories to select one or more partners from each of the one or more categories for participating in said at least activity for the predetermined period;enabling, by the processor (202), the participation of the plurality of users at a predetermined location during said at least activity for the predetermined period.

10. The method (300) according to claim 5, further comprising: for generating the one or more activities, the method comprises receiving, by the processor (202), at least an input from one or more specialists on the determined one or more preferences and recorded information.

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