Method and system for personalized resolving of issues associated with users in a multi-user environment

Abstract

The present disclosure relates to a method and system for personalized resolving of one or more issues associated with one or more users in a multi-user environment. The method includes analyzing user data in real-time to create a dynamic and personalized user profile based on user behavior, user intent, and user sentiment. One or more insights are generated based on the user profile and provided to support agents during a support interaction session to assist in resolving user issues. Further, a personalized support interaction is generated during a support interaction session based on the user profile and insights to provide resolutions tailored to the user's context.

Description

[0001] TITLE OF INVENTION

[0002] METHOD AND SYSTEM FOR PERSONALIZED RESOLVING OF ISSUES ASSOCIATED WITH USERS IN A MULTI-USER ENVIRONMENT

[0003] CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

[0004] The present application claims priority from the Indian patent application having application number 202411034701 filed on 01 May 2024, incorporated herein by a reference.

[0005] TECHNICAL FIELD

[0006] The presently disclosed embodiments are related, in general, to the field of personalized user support and issue resolution. More particularly, the presently disclosed embodiments are related to a method and system for dynamically resolving user issues in a multi-user environment by leveraging real-time user data, personalized insights, and intelligent support interactions.

[0007] BACKGROUND

[0008] This section is intended to introduce the reader to various aspects of art (the relevant technical field or area of knowledge to which the invention pertains), which may be related to various aspects of the present disclosure that are described or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements in this background section are to be read in this light, and not as admissions of prior art. Similarly, a problem is mentioned in the background section.

[0009] In modern multi-user environments, managing and utilizing data effectively plays a pivotal role in ensuring operational efficiency and user satisfaction. Organizations increasingly rely on data- driven insights to deliver tailored support and enhance the user experience. However, traditional data management systems face significant challenges when handling large-scale, dynamic user interactions across diverse platforms.

[0010] In today’s fast-paced digital environment, customer support plays a pivotal role in enhancing user experience and ensuring long-term user satisfaction. Traditional customer support systems, however, often rely on static user data and predefined issue categories, which fail to accurately capture the dynamic and evolving nature of user behaviour and preferences. These systems also lack the ability to anticipate user needs, offering generic solutions that can lead to inefficient interactions, repetitive troubleshooting, and lower overall satisfaction.

[0011] Conventional methods often rely on static segmentation techniques, which categorize users into broad groups based on past interactions or limited attributes. These techniques typically operate on lagged data, making them unsuitable for real-time support operations. Furthermore, they struggle to process and analyze data from multiple sources, including user devices, external systems, and feedback repositories. As a result, existing systems frequently fail to provide personalized and contextually relevant recommendations.

[0012] Excel-based segmentation and other manual approaches are still commonly used for analyzing user data. While these methods offer some insights, they are time-consuming, lack scalability, and are incapable of adapting to dynamic changes in user behaviour. Similarly, automated segmentation systems that rely on predefined parameters often have limited analytical depth and are restricted to small datasets or feature sets.

[0013] Given the increasing complexity of multi-user environments and the growing demand for personalized support, there is a pressing need for a system that can manage user data dynamically. Such a system should be capable of real-time profiling, integrating data from various sources, and generating actionable recommendations that cater to individual user needs. Addressing these gaps can transform support operations, enabling organizations to deliver a seamless and adaptive user experience.

[0014] Further, these traditional systems often struggle with effectively prioritizing user requests, resulting in delays and prolonged response times. Such inefficiencies can cause frustration among users, impacting their overall experience and potentially leading to a loss of trust. As industries continue to innovate and expand, the need for more flexible, personalized, and efficient customer support solutions has become increasingly crucial to meet evolving user expectations and build lasting relationships.

[0015] Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through the comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and concerning the drawings.

[0016] SUMMARY

[0017] This summary is provided to introduce concepts related to a method and system for personalized resolving of one or more issues associated with one or more users in a multi-user environment and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter. According to one embodiment illustrated herein, the method for personalized resolving of one or more issues associated with one or more users in a multi-user environment. The method may comprise various steps performed by a processor of an application server. The method may include a step of analyzing in real-time user data associated with the one or more users. Further, the method may comprise a step of analyzing a user profile associated with each of the one or more users based on the user data. Further, the user profile may be dynamic and personalized to each of the one or more users based on user behaviour, user intent, user sentiment identified by analyzing the real-time user data. Further, the method may comprise a step of generating one or more insights associated with each of the one or more users based on the corresponding user profile. Further, the method may comprise a step of providing the one or more insights to one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced the one or more users. The one or more issues being provided by the one or more users to the one or more support agents via an interactive user interface. Furthermore, the method may comprise a step of generating a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile. Further, the personalized support interactions may provide one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile.

[0018] According to one embodiment illustrated herein, a system for personalized resolving of one or more issues associated with one or more users in a multi-user environment. The system may comprise a processor, a memory coupled with the processor. The memory may be configured to store programmed instructions that cause the processor to perform various steps. The processor may be configured to analyze real-time user data associated with the one or more users. Further, processor may be configured to analyzea user profile associated with each of the one or more users based on the user data. Further, the user profile may be dynamic and personalized to each of the one or more users based on user behaviour, user intent, user sentiment identified by analyzing the real-time user data. Further, the system may generate one or more insights associated with each of the one or more users based on the corresponding user profile. Furthermore, the system may provide the one or more insights to one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced the one or more users. Further, the one or more issues being provided by the one or more users to the one or more support agents via an interactive user interface. Furthermore, the system may generate a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile. Further, the personalized support interactions may provide one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile.

[0019] According to embodiments illustrated herein, a non-transitory computer-readable storage medium for personalized resolving of one or more issues associated with one or more users in a multi-user environment. The non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising a processor to perform steps. The step may comprise analyzing in real-time user data associated with the one or more users. Further, the step may comprise a step of analyzing a user profile associated with each of the one or more users based on the user data. Further, the user profile may be dynamic and personalized to each of the one or more users based on user behaviour, user intent, user sentiment identified by analyzing the real-time user data. Further, the step may comprise a step of generating one or more insights associated with each of the one or more users based on the corresponding user profile. Further, the step may comprise a step of providing the one or more insights to one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced the one or more users. Further, the one or more issues being provided by the one or more users to the one or more support agents via an interactive user interface. Furthermore, the step may comprise a step of generating a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile. Further, the step may comprise personalized support interactions that may provide one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile.

[0020] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

[0021] BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings illustrate the various embodiments of systems, methods, and other aspects of the disclosure. Any person with ordinary skills in art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Further, the elements may not be drawn to scale.

[0023] Various embodiments will hereinafter be described in accordance with the appended drawings, which are provided to illustrate and not to limit the scope in any manner, wherein similar designations denote similar elements, and in which:

[0024] FIG. 1 is a block diagram that illustrates a system (100) for personalized resolving of one or more issues associated with one or more users in a multi-user environment, in accordance with an embodiment of present subject matter.

[0025] FIG. 2 is a block diagram that illustrates various components of an application server (104) configured for personalized resolving of one or more issues associated with one or more users in a multi-user environment, in accordance with an embodiment of the present subject matter.

[0026] FIG. 3 is a flowchart that illustrates a method (300) for personalized resolving of one or more issues associated with one or more users in a multi-user environment, in accordance with an embodiment of the present subject matter.

[0027] FIG. 4 is a block diagram (400) of an exemplary computer system for implementing embodiments consistent with the present subject matter.

[0028] DETAILED DESCRIPTION

[0029] The present disclosure may be best understood with reference to the detailed figures and description set forth herein. Various embodiments are discussed below with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions given herein with respect to the figures are simply for explanatory purposes as the methods and systems may extend beyond the described embodiments. For example, the teachings presented, and the needs of a particular application may yield multiple alternative and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond the particular implementation choices in the following embodiments described and shown.

[0030] References to “one embodiment,” “various embodiments,” “some embodiments,” or “an embodiment” “at least one embodiment,” “an embodiment,” “one example,” “an example,” “for example,” and so on indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, property, element, or limitation but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Thus, appearances of the phrases “in various embodiments,” “in some embodiments,” “in one embodiment,” or “in an embodiment” in places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. Further, repeated use of the phrase “in an embodiment” does not necessarily refer to the same embodiment. The terms “comprise”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system or method. In other words, one or more elements in a system or apparatus preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[0031] The objective of the present invention is to provide a method and system for personalized resolving of one or more issues associated with one or more users in a multi-user environment. Another objective of the present invention is to anticipate user needs by understanding individual preferences and potential issues based on their profile, thereby allowing for more efficient and proactive customer support. Another objective of the present invention is to offer personalized solutions by tailoring support interactions and suggesting relevant solutions that are specific to each user's context, enhancing the overall user experience. Another objective of the present invention is to prioritize requests efficiently by identifying high-value users or complex issues, ensuring that these are given quicker attention for faster resolution.

[0032] Yet another objective of the present invention is to reduce response times by streamlining the process of issue identification and categorization, leading to faster and more accurate support responses. Yet another objective of the present invention is to improve user satisfaction by delivering personalized and efficient solutions, thus fostering long-term loyalty and trust between the user and the support system. Yet another objective of the present invention is to enhance the adaptability of the support system by continuously learning from user interactions and feedback, enabling the system to evolve and better meet user needs over time. Yet another objective of the present invention is to optimize resource allocation by ensuring that high-priority issues are addressed first, leading to better management of support resources and reduced overall workload.

[0033] FIG. 1 is a block diagram that illustrates a system (100) for personalized resolving of one or more issues associated with one or more users in a multi-user environment, in accordance with an embodiment of present subject matter. The system (100) typically includes a database server (102), an application server (104), a communication network (106), and one or more portable devices (108). The database server (102), the application server (104), and the one or more portable devices (108) are typically communicatively coupled with each other via the communication network (106). In an embodiment, the application server (104) may communicate with the database server (102), and the one or more portable devices (108) using one or more protocols such as, but not limited to, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol / Internet Protocol (TCP / IP), Wireless Application Protocol (WAP), RF mesh, Bluetooth Low Energy (BLE), and the like, to communicate with one another.

[0034] In an embodiment, the database server (102) may include a special purpose operating system specifically configured to perform one or more database operations on the stored content. Examples of database operations may include, but are not limited to, storing, retrieving, comparing, and updating data. In an embodiment, the database server (102) may include hardware that may be configured to perform one or more predetermined operations. In an embodiment, the database server (102) may be realized through various technologies such as, but not limited to, Microsoft® SQL Server, Oracle®, IBM DB2®, Microsoft Access®, PostgreSQL®, MySQL®, SQLite®, distributed database technology and the like. In an embodiment, the database server (102) may be configured to utilize the application server (104) for implementing the method for personalized resolving of one or more issues associated with one or more users in a multi-user environment.

[0035] In an embodiment, the database server (102) may be configured to securely store and manage realtime user data, which is critical for enabling responsive and dynamic system functionality. In another embodiment, the real-time user data may include but is not limited to, user interactions, behavioural patterns, preferences, and activity logs.

[0036] In another embodiment, the real-time user data may be stored in but not limited, to a data lake, a data warehouse, and / or a relational database. Each of these may be selected based on the scale, structure, and accessibility requirements of the data. A data lake may be used for storing large volumes of raw, unstructured data, allowing for flexible analysis and future data processing. A data warehouse, on the other hand, may be utilized for structured data storage, enabling fast queries and analytics. In another embodiment, the traditional databases may be employed for transactional data that requires high consistency and integrity. The flexibility to use multiple data storage solutions may allow for optimized data handling, ensuring that the system can scale efficiently and provide real-time insights to support dynamic user interactions and decisions.

[0037] A person with ordinary skills in art will understand that the scope of the disclosure is not limited to the database server (102) as a separate entity. In an embodiment, the functionalities of the database server (102) can be integrated into the application server (104) or into the one or more portable device (108).

[0038] In an embodiment, the application server (104) may refer to a computing device or a software framework hosting an application or a software service. In an embodiment, the application server (104) may be implemented to execute operations such as, but not limited to, data retrieval, data storage, and data manipulation, utilizing one or more stored procedures or queries to support user interactions. In an embodiment, the hosted application or the software service may be configured to ensure data integrity and facilitate efficient access to the stored content. The application server (104) may be realized through various types of application servers such as, but are not limited to, a Java application server, a .NET framework application server, a Base4 application server, a PHP framework application server, or any other application server framework.

[0039] In an embodiment, the application server (104) may be configured to utilize the database server (102) and the one or more portable device (108), in conjunction, for implementing the method for personalized resolving of one or more issues associated with one or more users in a multi-user environment. In an implementation, the application server (104) may correspond to an infrastructure for implementing the method for personalized resolving of one or more issues associated with one or more users in a multi-user environment.

[0040] In an exemplary embodiment, the multi-user environment may correspond to a gaming platform, virtual training simulations, educational platforms, social networking spaces, fitness applications, and creative design tools. In another embodiment, the multi-user environment may include e- commerce platforms, healthcare applications, music and audio production tools, virtual reality experiences, productivity applications, financial management tools, cooking and recipe apps and travel planning applications.

[0041] In another embodiment, the multi-user environment may correspond to various interactive platforms, including but not limited to a gaming platform, a social media sharing platform, a ridesharing platform, an e-commerce platform, a collaboration and communication platform, and / or a content streaming platform. These multi-user environments may facilitate real-time interactions and engagements among multiple users, each with distinct roles, preferences, and behaviours.

[0042] In an embodiment, the communication network (106) may correspond to a communication medium through which the application server (104), the database server (102), and the one or more portable device (108) may communicate with each other. Such a communication may be performed in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols include but are not limited to, Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), Wireless Application Protocol (WAP), File Transfer Protocol (FTP), ZigBee, EDGE, infrared IR), IEEE 802.11, 802.16, 2G, 3G, 4G, 5G, 6G, 7G cellular communication protocols, and / or Bluetooth (BT) communication protocols. The communication network (106) may either be a dedicated network or a shared network. Further, the communication network (106) may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like. The communication network (106) may include, but is not limited to, the Internet, intranet, a cloud network, a Wireless Fidelity (Wi-Fi) network, a Wireless Local Area Network (WLAN), a Local Area Network (LAN), a cable network, the wireless network, a telephone network (e.g., Analog, Digital, POTS, PSTN, ISDN, xDSL), a telephone line (POTS), a Metropolitan Area Network (MAN), an electronic positioning network, an X.25 network, an optical network (e.g., PON), a satellite network (e.g., VSAT), a packet- switched network, a circuit- switched network, a public network, a private network, and / or other wired or wireless communications network configured to carry data.

[0043] In an embodiment, the one or more portable devices (108) may refer to a computing device used by a user. The one or more portable devices (108) may comprise of one or more processors and one or more memory. The one or more memories may include computer readable code that may be executable by one or more processors to perform various functions necessary for personalized resolving of one or more issues associated with one or more users in a multi-user environment. Examples of the user one or more portable devices (108) may include, but are not limited to, a personal computer, a laptop, a personal digital assistant (PDA), a mobile device, a tablet or any other computing device.

[0044] The system (100) can be implemented using hardware, software, or a combination of both, which includes using where suitable, one or more computer programs, mobile applications, or “apps” by deploying either on-premises over the corresponding computing terminals or virtually over cloud infrastructure. The system (100) may include various micro-services or groups of independent computer programs which can act independently in collaboration with other micro -services. The system (100) may also interact with a third-party or external computer system. Internally, the system (100) may be the central processor of all requests for transactions by the various actors or users of the system. In a specific embodiment, the system (100) is implemented for personalized resolving of one or more issues associated with one or more users in a multi-user environment.

[0045] FIG. 2 illustrates a block (200) diagram illustrating various components of the application server (104) configured for personalized resolving of one or more issues associated with one or more users in a multi-user environment, in accordance with an embodiment of the present subject matter. Further, FIG. 2 is explained in conjunction with elements from FIG. 1. Here, the application server (104) preferably includes a processor (202), a memory (204), a transceiver (206), an Input / Output unit (208), a User Interface unit (210), a Profiling unit (212), an Insight Generation unit (214) and a Support unit (216). The processor (202) is further preferably communicatively coupled to the memory (204), the transceiver (206), the Input / Output unit (208), the user interface unit (210), the profiling unit (212), the insight generation unit (214) and the support unit (216), while the transceiver (206) is preferably communicatively coupled to the communication network (106).

[0046] The processor (202) comprises suitable logic, circuitry, interfaces, and / or code that may be configured to execute a set of instructions stored in the memory (204), and may be implemented based on several processor technologies known in the art. The processor (202) works in coordination with the transceiver (206), the Input / Output unit (208), the user interface unit (210), the profiling unit (212), the insight generation unit (214) and the support unit (216), for personalized resolving of one or more issues associated with one or more users in a multi-user environment. Examples of the processor (202) include, but not limited to, standard microprocessor, microcontroller, central processing unit (CPU), an X86-based processor, a Reduced Instruction Set Computing (RISC) processor, an Application- Specific Integrated Circuit (ASIC) processor, and a Complex Instruction Set Computing (CISC) processor, distributed or cloud processing unit, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions and / or other processing logic that accommodates the requirements of the present invention.

[0047] The memory (204) comprises suitable logic, circuitry, interfaces, and / or code that may be configured to store the set of instructions, which are executed by the processor (202). Preferably, the memory (204) is configured to store one or more programs, routines, or scripts that are executed in coordination with the processor (202). Additionally, the memory (204) may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic randomaccess memory (DRAM), and / or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, a Hard Disk Drive (HDD), flash memories, Secure Digital (SD) card, Solid State Disks (SSD), optical disks, magnetic tapes, memory cards, virtual memory and distributed cloud storage. The memory (204) may be removable, non-removable, or a combination thereof. Further, the memory (204) may include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The memory (204) may include programs or coded instructions that supplement the applications and functions of the system (100). The memory (204) may store programs or coded instructions that enhance the applications and functionalities of the system (100), including but not limited to, data validation algorithms, state management procedures, and user interface rendering logic.

[0048] In one embodiment, the memory (204), amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the programs or the coded instructions. These programs may enable the processor (202) to execute the steps outlined in the method for personalized resolving of one or more issues associated with one or more users in a multi-user environment, ensuring that user inputs are properly analysed, user profiles are dynamically created and analyzed based on real-time data, and personalized insights are generated. In one embodiment, the memory (204), amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the programs or the coded instructions. In yet another embodiment, the memory (204) may be managed under a federated structure that enables the adaptability and responsiveness of the application server (104).

[0049] The transceiver (206) comprises suitable logic, circuitry, interfaces, and / or code that may be configured to receive, process or transmit information, data or signals, which are stored by the memory (204) and executed by the processor (202). The transceiver (206) is preferably configured to receive, process or transmit, one or more programs, routines, or scripts that are executed in coordination with the processor (202). The transceiver (206) is preferably communicatively coupled to the communication network (106) of the system (100) for communicating all the information, data, signal, programs, routines or scripts through the network.

[0050] The transceiver (206) may implement one or more known technologies to support wired or wireless communication with the communication network (106). In an embodiment, the transceiver (206) may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a Universal Serial Bus (USB) device, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and / or a local buffer. Also, the transceiver (206) may communicate via wireless communication with networks, such as the Internet, an Intranet and / or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and / or a metropolitan area network (MAN). Accordingly, the wireless communication may use any of a plurality of communication standards, protocols and technologies, such as: Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and / or IEEE 802.1 In), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for email, instant messaging, and / or Short Message Service (SMS). The input / output (I / O) unit (208) comprises suitable logic, circuitry, interfaces, and / or code that may be configured to receive or present information. The input / output unit (208) comprises various input and output devices that are configured to communicate with the processor (202). Examples of the input devices include but are not limited to, a keyboard, a mouse, a joystick, a touch screen, a microphone, a camera, and / or a docking station. Examples of the output devices include, but are not limited to, a display screen and / or a speaker. The I / O unit (208) may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I / O unit (208) may allow the system (100) to interact with the user directly or through the portable devices (108). Further, the VO unit (208) may enable the system (100) to communicate with other computing devices, such as web servers and external data servers (not shown). The I / O unit (208) can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I / O unit (208) may include one or more ports for connecting a number of devices to one another or to another server. In one embodiment, the I / O unit (208) allows the application server (104) to be logically coupled to other portable devices (108), some of which may be built in. Illustrative components include tablets, mobile phones, desktop computers, wireless devices, etc.

[0051] In an embodiment, the input / output unit (208) may comprise an input device namely a keyboard, touchpad, trackpad may be configured to allow the one or more users to interact with the system by providing input data. The input / output unit (208) may enable the one or more users to enter commands, select options, navigate through user interfaces, and provide relevant information regarding the issues they are facing. The input / output unit (208) may be configured to receive and transmit user inputs, facilitating seamless communication between the user and the system, while also providing feedback to the user based on the analysis of real-time user data and insights derived from their personalized user profiles. This interaction may help optimize the support interaction process and improve the overall resolution of user issues.

[0052] In another embodiment, the user interface unit (210) may be configured to present a dynamic, interactive interface through which users can engage with the system during a support interaction. The user interface unit (210) may display real-time insights, relevant notifications, and personalized recommendations based on the user profile and analysis of user data. It may allow users to input information regarding the issues they are experiencing, track the progress of their support session, and receive personalized responses from support agents. Additionally, the user interface unit (210) may adopt the interface layout and content based on user behaviour, intent, and sentiment, ensuring a seamless and intuitive user experience throughout the support interaction.

[0053] In another embodiment, the user interface unit (210) may be configured to provide an intuitive and responsive interface for users, enabling seamless interaction with the system. The user interface unit (210) may dynamically adjust its content and layout based on real-time analysis of user inputs, behaviour, and preferences. It may display personalized information such as insights, issue tracking, and recommended resolutions based on the user profile. Additionally, the user interface unit (210) may allow users to engage with support agents through an interactive platform, supporting various communication modes such as text, voice, or video, ensuring a tailored and effective support experience.

[0054] In an exemplary embodiment, the user interface unit (210) may facilitate a seamless and personalized user experience by dynamically adapting to the user's preferences and context. The user interface unit (210) may display real-time updates, such as insights, suggested solutions, and notifications, based on the analysis of user behaviour and data. The user interface unit (210) may allow users to easily interact with the system through customizable elements, such as menus, buttons, and input fields, enabling them to communicate their issues or requests effectively. Additionally, the user interface unit (210) may integrate with support agents, presenting relevant user information and real-time data to assist in resolving user issues efficiently.

[0055] In another embodiment, the profiling unit (212) of the application server (104) is disclosed. The profiling unit (212) may create, analyze and update user profiles. The user profiles may include dynamic data reflecting user behaviour, user intent, and user sentiment, as well as data indicative of the user's preferences, activities, and interactions with the system. In another embodiment, these user profiles are continuously updated to incorporate the latest data and provide an accurate and personalized representation of each user's gaming experience or application usage experience. In another embodiment, by analyzing the real-time data associated with a user, the profiling unit (212) may ensure that the user profile remains current, enabling more accurate insights and tailored support interaction sessions. The user profiles may then be used by the processor (202) to personalize the support interactions and resolve issues based on the user's context and behaviour, ensuring the most effective and relevant support solutions.

[0056] In another embodiment, the profiling unit (212) may analyze the real-time user data associated with the one or more users. In another embodiment, analyzing user data may comprise extracting one or more patterns and one or more trends over a time interval by leveraging statistical analysis, machine learning models, and time-series data analysis. In another embodiment, statistical analysis may be used to identify recurring trends by evaluating variations in user interactions over time. Time-series data analysis may further assist in recognizing periodic patterns, fluctuations, and seasonal behaviours in user engagement.

[0057] In another embodiment, analyzing user data may comprise extracting one or more patterns and one or more trends over a time interval by using deep learning models to detect anomalies in user behaviour. Clustering algorithms may group users with similar behavioural attributes, while anomaly detection models may identify deviations from normal interaction trends, indicating fraudulent behaviour, unusual activity spikes, or emerging user concerns. In another embodiment, the deep learning models may help identify outliers or unusual patterns that may indicate emerging issues or areas for further investigation.

[0058] In another embodiment, deep learning models may process large volumes of historical and realtime user data to refine behaviour classifications and detect complex patterns. These models may analyze user queries, transaction histories, and engagement levels to predict potential future issues or fraudulent activities. In another embodiment, the extracted insights may be used to improve decision-making in personalized support interactions. By integrating statistical and machine learning-based analysis, the system ensures dynamic adaptation to evolving user behaviours, enabling proactive issue resolution and efficient support allocation.

[0059] In another embodiment, the profiling unit (212) may analyze both historical and real-time data for behaviour clustering, where users may be grouped into segments based on similar actions, preferences, or issue occurrences. In another embodiment, behaviour clustering may help in understanding different user profiles and anticipating their needs more effectively.

[0060] In another embodiment, the profiling unit (212) may be configured for extracting one or more behavioral patterns and one or more behavioral trends over a time interval based on comparison between the real-time user data and historical interaction data using at least one of statistical analysis and machine learning techniques. In an embodiment, the one or more behavioral patterns and the one or more behavioral trends are indicative of the user preferences.

[0061] In another embodiment, the profiling unit (212) may use reinforcement learning models to continuously refine prediction accuracy, allowing the system to improve its understanding of user behaviour and make more accurate predictions regarding future interactions and issues. Through these methods, the profiling unit (212) may provide insights that assist in delivering more personalized and proactive support to the users.

[0062] In another embodiment, the profiling unit (212) may analyze user profiles based on the one or more machine learning techniques. In another embodiment, the user profile may include, but not limited to, user engagement metrics, sentiment analysis data derived from past interactions, predictive scoring for issue resolution probability and / or one or more gamification patterns to optimize user experience.

[0063] In another embodiment, unsupervised learning techniques such as clustering algorithms (e.g., k- means clustering, hierarchical clustering, or DBSCAN) may be used to segment users into different behavioural groups based on interaction frequency, support history, and engagement patterns. These clusters / segments enable personalized support strategies and targeted engagement approaches.

[0064] In another embodiment, supervised learning techniques such as classification models (e.g., decision trees, random forests, and boosting algorithms like XGBoost) may be employed to classify incoming user queries based on historical data. These models may predict the nature of queries, determine whether an issue is complex or straightforward, and assign appropriate priority levels for resolution. In another embodiment, sentiment analysis may be performed using natural language processing (NLP) models, such as recurrent neural networks (RNNs) or transformerbased models, to derive sentiment scores from past user interactions. These sentiment insights allow for more empathetic and context-aware support interactions.

[0065] In another embodiment, predictive modeling techniques may be applied to determine the probability of successful issue resolution based on past resolution times, user behavior, and engagement metrics. Regression models or reinforcement learning approaches may be used to continuously refine these predictions by incorporating real-time user feedback.

[0066] In another embodiment, gamification patterns may be integrated into the user profile using reinforcement learning methods that adapt dynamically based on user interactions. These patterns may be designed to enhance user engagement, reward participation, and encourage proactive problem-resolution behaviours. In another embodiment, by integrating clustering, classification, sentiment analysis, and predictive modelling, the profiling unit (212) ensures that user profiles are dynamically updated and refined, leading to a more adaptive and personalized support experience.

[0067] In another embodiment, the insight generation unit (214) of the application server (104) is disclosed. The insight generation unit (214) is configured for generating one or more insights associated with each of the one or more users based on the corresponding user profile. In one embodiment, the one or more insights may consist of user preferences and one or more potential issues associated with each of the one or more users. The insight generation unit (214) is configured for applying the one or more machine learning techniques on the one or more behavioral patterns and the one or more behavioral trends to predict the one or more potential issues that occur at a future time instant. The insight generation unit (214) is configured for identifying one or more fraudulent users from the one or more users based on the one or more behavioral patterns and the one or more behavioral trends using reinforcement learning models. In an embodiment, the personalized support interaction for the one or more fraudulent users comprises alerting the user about fraudulent activity within the application

[0068] In one embodiment, the insight generation unit (214) may be configured for providing the one or more insights to one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced the one or more users. In one embodiment, the one or more issues may be provided by the one or more users to the one or more support agents via an interactive user interface.

[0069] In one embodiment, the one or more insights being generated by the insight generation unit (214) may involve analyzing both real-time and historical user interaction data. In another embodiment, the insight generation unit (214) may extract user behavioural trends by identifying common issues, preferences, and engagement patterns over time. This analysis may involve classifying users based on their interaction frequency, resolution history, and severity of past issues. In another embodiment, the insight generation unit (214) may extract user behavioural trends based on this data, identifying common issues, preferences, and engagement patterns. In another embodiment, the machine learning models may be applied to predict potential future issues that the user may encounter, allowing for proactive support and issue resolution.

[0070] In another embodiment, the insight generation unit (214) may apply machine learning models to predict potential future issues that the user may encounter. These predictions may be based on analyzing historical issue patterns, the complexity of past queries, and behavioural anomalies indicative of emerging concerns. Additionally, fraud detection mechanisms may be employed to identify users who have previously raised false queries, ensuring that support resources are allocated efficiently.

[0071] In another embodiment, the user may be segmented and categorised into engagement-based segments, tailoring the support experience to the individual needs and behaviours of the users. Such segmentation may be determined based on qualitative and quantitative metrics, including transaction volume, duration of engagement, and historical response effectiveness. By categorizing users accordingly, the system ensures that priority support is extended to those requiring immediate resolution while optimizing response strategies for other users. In another embodiment, the one or more insights may then be delivered to the support agents, enabling them to offer personalized and efficient assistance, thereby enhancing the support experience and increasing the likelihood of resolving user issues effectively.

[0072] In another embodiment, the support unit (216) of the application server (104) is disclosed. The support unit (216) may be configured for generating a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile. In another embodiment, the support unit (216) may provide one or more promotional content to the one or more users based on the user profile. In another embodiment, the promotional content may be dynamically adjusted based on real-time user activity and engagement trends.

[0073] In another embodiment, the support unit (216) may be configured for providing personalized support interactions with one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile. The user context may include various factors such as past behaviour, preferences, sentiment, recent interactions, and engagement patterns, ensuring that the provided support is highly relevant. In another embodiment, personalized interactions are based on a comprehensive understanding of the user context, which is determined by analyzing one or more insights derived from the user’s historical and real-time data, as well as the detailed user profile. In another embodiment, the user context may include various factors, such as past behaviour, preferences, sentiment, recent interactions, and engagement patterns, all of which are taken into account to ensure that the support provided is highly relevant and appropriate to the user's needs at any given moment. Furthermore, sentiment analysis may be employed during interactions, allowing the support unit (216) to adjust the tone, style, and depth of responses to ensure an empathetic and effective support experience.

[0074] In another embodiment, the support unit (216) may integrate these insights with real-time information, enabling it to adapt and adjust the support interaction dynamically. For example, if a user has experienced a recurring issue in the past, the system may prioritize addressing that issue quickly and offer tailored solutions based on the user’s previous experiences. Furthermore, if the user’s mood or emotional state is detected through sentiment analysis during the interaction, the support unit (216) may adjust the tone, style, and depth of the support, ensuring the interaction remains empathetic and effective.

[0075] In another embodiment, the user profde, which may include detailed data about the user’s preferences, usage history, engagement level, and even feedback from past support interactions, can be continuously updated in real time, allowing the system to evolve and optimize the personalized support it offers. This dynamic, context-aware support ensures that users are not only provided with immediate solutions but also that their past preferences and current circumstances are always taken into account to offer the most relevant and effective resolutions.

[0076] In another embodiment, the support unit (216) may improve the overall user experience by providing a seamless and personalized journey through each support interaction, making it more likely that the user’s issues are resolved in a manner that meets their expectations and needs.

[0077] In another embodiment, the support unit (216) may identify a priority user from the one or more users for providing the personalized support interaction based on the user profile. The user profile may include a variety of factors, such as transaction history, past interactions, urgency of current issues, and engagement levels. This user profile enables the support unit (216) to determine which users require more immediate or specialized attention during a support interaction. In another embodiment, a priority user may be someone who has experienced frequent issues, has a high- value account, or is engaged in time-sensitive activities that necessitate quick resolution.

[0078] In another embodiment, the support unit (216) may identify one or more complex issues from the one or more issues being faced by the one or more users based on a pre-defined criteria. The predefined criteria for identifying complex issues may include, but are not limited to, resolution time, severity of the query, user’s qualitative and quantitative metrics, historical issue complexity, transaction volume, and duration of user engagement. These factors enable the classification of issues based on their complexity and ensure that high-priority concerns are addressed efficiently through personalized support interactions.

[0079] In another embodiment, based on this identification, the priority user may be assigned a higher level of priority for providing the one or more resolutions, ensuring that their concerns are addressed promptly and effectively, in comparison to the remaining users. The personalized support for these users may be tailored not only to resolve their current issues but also to enhance their overall experience by providing specialized care. In another embodiment, the priority user may be assigned a higher level of priority for providing the one or more resolutions as compared to the remaining one or more users.

[0080] In another embodiment, the support unit (216) may identify one or more complex issues from the one or more issues being faced by the one or more users based on a pre-defined criteria. These issues may require more detailed analysis, advanced troubleshooting, or multi-step resolutions. The identification of complex issues is based on a pre-defined set of criteria that help distinguish between simple and more intricate issues.

[0081] In yet another embodiment, the pre-defined criteria for identifying complex issues may include, but are not limited to complex issues that may arise more frequently in users who engage with the platform extensively. A high volume of transactions may indicate the presence of systemic or recurring issues that are not easily resolved with standard support methods. The higher the transaction volume, the more likely it is that a user may face nuanced or interdependent issues that require more in-depth analysis.

[0082] In yet another embodiment, users with a history of encountering complex or recurring issues may experience similar issues in the future. If a user has faced issues in the past that were difficult to resolve, the support unit (216) may categorize their new issue as complex, especially if it shares similarities with previous problems. These users may require advanced support to address the persistent nature of their problems. In an embodiment, the support unit (216) may be configured for categorizing the one or more users into one or more segments to further customize the personalized support interaction. In an embodiment, the one or more segments comprises the one or more fraudulent users, high priority users, potential chum user, engaging user. Further the support unit (216) may be configured for dynamically adjusting the personalized support interaction to allocate additional resources, escalate unresolved issues, or further customize response strategies based on the user profde, priority user, and the one or more complex issues.

[0083] In yet another embodiment, the support unit (216) may indicate the complexity of the issues based on the length of time a user has been actively engaged with the platform. Long-term users may face more complex issues due to their deeper integration with the system and greater number of interactions. The more interactions a user has, the more likely it is that their problems will involve historical context or accumulated data, which requires more time and resources to resolve.

[0084] In yet another embodiment, the support unit (216) may classify issues as complex based on their potential to significantly disrupt the user's experience. For example, if an issue prevents the user from completing essential tasks such as transaction processing, accessing their account, or utilizing core platform features, the support unit (216) may prioritize these issues due to their adverse impact on the user experience. These issues often require more immediate attention from the support unit (216) to mitigate any long-term effects on user satisfaction and platform usage.

[0085] In yet another embodiment, the support unit (216) may identify issues that involve the interaction of multiple factors, such as technical errors, user behavior, and platform- specific limitations, which complicate the resolution process. These multifactorial issues often demand more in-depth investigation and collaboration between different teams or specialized experts within the support unit (216). The support unit (216) may coordinate efforts across departments to analyze and resolve the issue, ensuring all contributing factors are addressed comprehensively.

[0086] In yet another embodiment, the support unit (216) may also flag issues as complex when they involve users with a history of escalated concerns or high dissatisfaction. By analyzing the escalation history and user sentiment, the support unit (216) can identify patterns that indicate recurring or unresolved issues. If the sentiment analysis of user interactions reveals heightened frustration, urgency, or negative feedback, the support unit (216) may assign a higher priority to addressing these concerns, recognizing that complex problems often require more attention to restore the user's trust and satisfaction.

[0087] In yet another embodiment, the one or more complex issues may be assigned a higher level of priority as compared to the remaining one or more issues. In another embodiment, the pre-defined criteria may comprise but are not limited to one of user transaction volume, historical issue complexity, and / or a duration of user engagement.

[0088] In an exemplary embodiment, the support unit (216) may provide support interactions to one or more support agents, where the support agents may be either human support agents or virtual support bots. In another embodiment, the virtual support bot may be configured to dynamically adapt the personalized support interaction based on real-time sentiment and contextual analysis of the user profile. This allows the virtual support bot to understand the user's emotional state, preferences, and recent behaviours, thereby enabling a more tailored and empathetic response.

[0089] In an exemplary embodiment, the virtual support bot may be designed to automatically adjust the tone of the personalized support interaction by considering factors such as the user’s mood, recent engagement levels, and any historical interactions. For example, if the user has exhibited frustration or disengagement in previous interactions, the bot may adjust its tone to be more supportive, understanding, and encouraging to improve the overall user experience.

[0090] In another exemplary embodiment, the virtual support bot may be built on top of Large Language Models (LLMs) and may process input data comprising user interaction patterns, inferred behavioural trends, decision-making logic, and predefined rules to generate accurate and context- aware resolutions. This allows the virtual support bot to develop deeper contextual awareness and enhance its ability to provide relevant solutions dynamically.

[0091] In another exemplary embodiment, the virtual support bot may leverage Al-driven, autonomous learning capabilities, enabling it to self-leam from past interactions to continuously enhance user engagement. By analyzing historical interactions and feedback, the bot may refine its communication strategies and response patterns, ultimately improving its ability to address user needs in a more effective manner.

[0092] In another exemplary embodiment, the Al model may incorporate reinforcement learning mechanisms to iteratively refine its response strategies. In another exemplary embodiment, the Al model may learn from past successes and failures in user engagement, adjusting its approach to better align with individual user preferences. This dynamic learning process allows the virtual support bot to evolve and optimize its interactions over time, providing a more personalized, intuitive, and efficient support experience. Additionally, natural language processing (NLP) models integrated into the virtual support bot may continuously analyze user sentiment, engagement trends, and contextual cues to ensure that responses are emotionally intelligent and contextually relevant. Through these capabilities, the virtual support bot may act as an evolving support agent, capable of offering a progressively improved user engagement experience, driven by continuous self-learning and adaptation.

[0093] A person skilled in the art will understand that the scope of the disclosure should not be limited to the online gaming domain and using the aforementioned techniques. Further, the examples provided in supra are for illustrative purposes and should not be construed to limit the scope of the disclosure.

[0094] Referring to FIG. 3, a flowchart that illustrates a method (300) for personalized resolving of one or more issues associated with one or more users in a multi-user environment, in accordance with at least one embodiment of the present subject matter. The method (300) may be implemented by an electronic device (108) including the one or more processors (202) and the memory (204) communicatively coupled to the processor (202) and the memory (204) is configured to store processor-executable programmed instructions, caused the processor (202) to perform the following steps.

[0095] At step (301), the processor (202) is configured to analyze real-time user data associated with the one or more users. At step (302), the processor (202) is configured to analyze a user profile associated with each of the one or more users based on the user data. Further, the user profile is dynamic and personalized to each of the one or more users based on user behaviors, user intent, user sentiment identified by analyzing the real-time user data.

[0096] At step (303), the processor (202) is configured to generate the one or more insights associated with each of the one or more users based on the corresponding user profiles. At step (304), the processor (202) is configured to provide the one or more insights to the one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced by the one or more users. Further, the one or more issues being provided by the one or more users to the one or more support agents via an interactive user interface. At step (305), the processor (202) is configured to generate a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile. Furthermore, the personalized support interactions provide one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile.

[0097] Let us delve into detailed working examples of the present disclosure. The following examples illustrate how the system and method for personalized resolving of one or more issues associated with one or more users in a multi-user environment can be effectively implemented to ensure efficient and tailored support solutions.

[0098] Example 1: Personalized Technical Support for Software Issues

[0099] A user encounters a recurring issue with a software application and contacts customer support through a chat-based interactive interface.

[0100] User Data Analysis: The system collects real-time user data, including past interactions, error logs, and usage patterns.

[0101] Dynamic User Profile Creation and Analysis: The system updates the user’s profde, identifying patterns such as frequent crashes or failed login attempts.

[0102] Insight Generation: The system detects that the user has previously attempted troubleshooting steps without success.

[0103] Support Agent Assistance: The system provides the agent with insights, such as the user’s frustration level, prior interactions, and a probable solution.

[0104] Personalized Resolution: The support agent, guided by system-generated insights, suggests a tailored solution, such as a software patch, and offers proactive assistance.

[0105] Example 2: E-commerce Customer Query Resolution

[0106] A user contacts support regarding an undelivered order and an incorrect charge.

[0107] User Data Analysis: The system retrieves order details, payment history, and past interactions.

[0108] User Profile Analysis: The system identifies that the user frequently orders from a specific category and has a high engagement rate.

[0109] Insight Generation: The system flags that the order was delayed due to a logistic issue and that a partial refund is eligible.

[0110] Support Agent Assistance: The agent is provided with contextual data, including pre-approved refund options. Personalized Resolution: The agent reassures the user, processes the refund, and offers a personalized discount for future purchases.

[0111] In another working example, let us consider a skill-based gaming application, allows multiple users to participate in competitive tournaments, real-money gaming, and leaderboard challenges. Players frequently encounter issues such as transaction failures, ranking discrepancies, gameplay lags, or unfair matchups.

[0112] To enhance the user experience, the gaming platform implements the present disclosure using realtime data analysis, machine learning-based profiling, and dynamic support interventions to provide personalized resolving of issues

[0113] The system continuously analyzes real-time user data collected from multiple sources, including gameplay performance (win rates, accuracy, skill progression), transaction data (deposits, withdrawals, in-game purchases), past customer support interactions, and engagement trends (daily logins, session durations). For example, a high-engagement user, U 1002, who plays for long hours and frequently disputes losses, is flagged by the system due to a high probability of dissatisfaction. Meanwhile, U1001, a casual player experiencing failed deposits, is monitored for potential transaction issues.

[0114] Using this data, the system dynamically creates and analyzes personalized user profiles, incorporating engagement metrics, sentiment analysis, predictive scoring for issue likelihood, and behavioural trends such as gaming style and dispute frequency. For instance, U1002 has a history of complaints related to lag and fairness, so the system assigns them a high-risk score, indicating the need for priority support. Meanwhile, U1001, with a minor transactional issue, is handled by an Al chatbot.

[0115] Once profiles are established, the system generates real-time insights and predictive issue detection to prevent escalations. If U1002 frequently disputes losses, the system proactively flags their game logs for fairness verification before they even raise a complaint. Similarly, if U1001 has repeated transaction failures, an automatic alert is sent to the finance team, suggesting alternative payment methods before frustration builds.

[0116] To ensure efficient prioritization, users and their issues are categorized based on pre-defined criteria such as transaction volume, historical complaint complexity, and engagement duration. High-value users (VIP players spending over INR 5000 / month or participating in more than ten tournaments weekly) receive instant prioritized support. In this case, U1002, being a highly engaged but dissatisfied player, is assigned to a human agent, who is provided with an Al- generated case summary for faster resolution. Conversely, U1001 receives a standard Al chatbot response, such as: "We noticed a failed deposit attempt. Try using PayPal or UPI for an instant transaction."

[0117] During the support session, the system further enhances user experience through personalized interactions. If frustration is detected in U 1002’ s tone, the Al hot automatically adjusts its response style to be empathetic: "We understand your concern. Your last match had a 10ms lag spike. We’ve credited 10 bonus points as compensation." Additionally, if U1002 prefers human support, their query is immediately escalated to a live agent, avoiding prolonged frustration.

[0118] To maintain engagement and retention, the system sends personalized notifications and promotions. For U1001, a 24-hour bonus offer of INR 50 on their next deposit is triggered to encourage further spending. For U1002, the system suggests: "We noticed you love Ivl battles! Join the Grand Tournament for a chance to win INR 10,000!" thereby leveraging gamification strategies to retain the player.

[0119] The entire personalized support framework operates through machine learning techniques that extract patterns and trends from real-time data, allowing predictive issue resolution and adaptive support. The system dynamically adjusts interactions based on sentiment analysis, engagement levels, and recent activity, ensuring that every user receives contextual, real-time support. As a result, complaint rates drop by 40%, user retention improves by 25%, and VIP players receive faster, more effective assistance, ultimately enhancing the overall gaming experience.

[0120] These examples demonstrate the system’s adaptability in dynamically analyzing real-time user data, generating personalized user profiles, and providing tailored support interactions based on user behaviour, intent, and sentiment. The system effectively assists support agents by offering actionable insights, enabling proactive issue resolution, and continuously refining user profiles to enhance future support interactions.

[0121] A person skilled in the art will understand that the scope of the disclosure is not limited to scenarios based on the aforementioned factors and using the aforementioned techniques and that the examples provided do not limit the scope of the disclosure.

[0122] FIG. 4 illustrates a block diagram of an exemplary computer system (401) for implementing embodiments consistent with the present disclosure.

[0123] Variations of a computer system (401) may be used for performing the one or more tasks in the environment. The computer system (401) may comprise a central processing unit (“CPU” or “processor”) (402). The processor (402) may comprise at least one data processor for executing program components for executing user or system generated requests. A user may include a person, a person using a device such as those included in this disclosure, or such a device itself. Additionally, the processor (402) may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, or the like. In various implementations the processor (402) may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, for example. Accordingly, the processor (402) may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), or Field Programmable Gate Arrays (FPGAs), for example.

[0124] Processor (402) may be disposed in communication with one or more input / output (VO) devices via I / O interface (403). Accordingly, the I / O interface (403) may employ communication protocols / methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS / 2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n / b / g / n / x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMAX, or the like, for example.

[0125] Using the I / O interface (403), the computer system (401) may communicate with one or more I / O devices. For example, the input device (404) may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device / source, or visors, for example. Likewise, an output device (405) may be a user’s smartphone, tablet, cell phone, laptop, printer, computer desktop, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light- emitting diode (LED), plasma, or the like), or audio speaker, for example. In some embodiments, a transceiver (406) may be disposed in connection with the processor (402). The transceiver (406) may facilitate various types of wireless transmission or reception. For example, the transceiver (406) may include an antenna operatively connected to a transceiver chip (example devices include the Texas Instruments® WiLink WL1283, Broadcom® BCM4750IUB8, Infineon Technologies® X-Gold 618-PMB9800, or the like), providing IEEE 802.1 la / b / g / n, Bluetooth, FM, global positioning system (GPS), and / or 2G / 3G / 5G / 6G HSDPA / HSUPA communications, for example.

[0126] In some embodiments, the processor (402) may be disposed in communication with a communication network (408) via a network interface (407). The network interface (407) is adapted to communicate with the communication network (408). The network interface (407) may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), transmission control protocol / internet protocol (TCP / IP), token ring, or IEEE 802.1 la / b / g / n / x, for example. The communication network (408) may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), or the Internet, for example. Using the network interface (407) and the communication network (408), the computer system (401) may communicate with devices such as shown as a laptop (409) or a mobile / cellular phone (410). Other exemplary devices may include, without limitation, personal computer(s), server(s), fax machines, printers, scanners, various mobile devices such as cellular telephones, smartphones (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), tablet computers, desktop computers, eBook readers (Amazon Kindle, Nook, etc.), laptop computers, notebooks, gaming consoles (Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. In some embodiments, the computer system (401) may itself embody one or more of these devices.

[0127] In some embodiments, the processor (402) may be disposed in communication with one or more memory devices (e.g., RAM 513, ROM 514, etc.) via a storage interface (412). The storage interface (412) may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE- 1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, or solid-state drives, for example.

[0128] The memory devices may store a collection of program or database components, including, without limitation, an operating system (416), user interface application (517), web browser (418), mail client / server (419), user / application data (420) (e.g., any data variables or data records discussed in this disclosure) for example. The operating system (416) may facilitate resource management and operation of the computer system (401). Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS / 2, Microsoft Windows (XP, Vista / 7 / 8, etc.), Apple iOS, Google Android, Blackberry OS, or the like.

[0129] The user interface (417) is for facilitating the display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces (417) may provide computer interaction interface elements on a display system operatively connected to the computer system (401), such as cursors, icons, check boxes, menus, scrollers, windows, or widgets, for example. Graphical user interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS / 2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, or web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), for example.

[0130] In some embodiments, the computer system (401) may implement a web browser (418) stored program component. The web browser (418) may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, or Microsoft Edge, for example. Secure web browsing may be provided using HTTPS (secure hypertext transport protocol), secure sockets layer (SSL), Transport Layer Security (TLS), or the like. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, or application programming interfaces (APIs), for example. In some embodiments, the computer system (401) may implement a mail client / server (419) stored program component. The mail server (419) may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ActiveX, ANSI C++ / C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, or WebObjects, for example. The mail server (419) may utilize communication protocols such as internet message access protocol (IMAP), messaging application programming interface (MAPI), Microsoft Exchange, post office protocol (POP), simple mail transfer protocol (SMTP), or the like. In some embodiments, the computer system (401) may implement a mail client (420) stored program component. The mail client (420) may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, or Mozilla Thunderbird.

[0131] In some embodiments, the computer system (401) may store user / application data (421), such as the data, variables, records, or the like as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase, for example. Alternatively, such databases may be implemented using standardized data structures, such as an array, hash, linked list, struct, structured text file (e.g., XML), table, or as object- oriented databases (e.g., using Objectstore, Poet, Zope, etc.). Such databases may be consolidated or distributed, sometimes among the various computer systems discussed above in this disclosure. It is to be understood that the structure and operation of any computer or database component may be combined, consolidated, or distributed in any working combination.

[0132] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non- transitory. Examples include Random Access Memory (RAM), Read- Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

[0133] Various embodiments of the disclosure encompass numerous advantages including methods and systems for personalized resolving of one or more issues associated with one or more users in a multi-user environment. The disclosed method and system have several technical advantages, but not limited to the following:

[0134] Real-Time Data Analysis: The system continuously analyzes user data in real-time to detect issues, user intent, and sentiment, enabling proactive and dynamic issue resolution.

[0135] Dynamic and Adaptive User Profiling: The system generates, and updates user profiles dynamically based on real-time interactions, behavioural patterns, and contextual data, ensuring personalized support solutions.

[0136] AI-Driven Insight Generation: The system leverages artificial intelligence to derive insights from historical interactions, user behaviour, and real-time system conditions, optimizing support efficiency.

[0137] Context- A ware Support Interaction: By utilizing a user’s historical and real-time data, the system enables context-aware support interactions that improve resolution accuracy and response time.

[0138] Automated and Intelligent Recommendations: The system provides automated, Al-driven recommendations to support agents, reducing manual effort and enhancing support decisionmaking.

[0139] Scalability in Multi-User Environments: The disclosed system efficiently handles large-scale, multi-user environments by dynamically managing multiple user profiles and prioritizing support interactions based on urgency and context.

[0140] Continuous Learning and System Adaptation: The system refines its response mechanisms through machine learning, continuously improving resolution strategies based on past interactions and real-time data.

[0141] In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.

[0142] The present disclosure may be realized in hardware, or a combination of hardware and software. The present disclosure may be realized in a centralized fashion, in at least one computer system, or in a distributed fashion, where different elements may be spread across several interconnected computer systems. A computer system or other apparatus adapted for carrying out the methods described herein may be suited. A combination of hardware and software may be a general -purpose computer system with a computer program that, when loaded and executed, may control the computer system such that it carries out the methods described herein. The present disclosure may be realized in hardware that comprises a portion of an integrated circuit that also performs other functions.

[0143] A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above disclosed system elements, modules, and other features and functions, or alternatives thereof, may be combined to create other different systems or applications.

[0144] Those skilled in the art will appreciate that any of the aforementioned steps and / or system modules may be suitably replaced, reordered, or removed, and additional steps and / or system modules may be inserted, depending on the needs of a particular application. In addition, the systems of the aforementioned embodiments may be implemented using a wide variety of suitable processes and system modules, and are not limited to any particular computer hardware, software, middleware, firmware, microcode, and the like. The claims can encompass embodiments for hardware and software, or a combination thereof. While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure is not limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.

Claims

WE CLAIM1. A method for personalized resolving of one or more issues associated with one or more users in a multi-user environment, the method comprising: analyzing, by a processor, in real-time user data associated with the one or more users; analyzing, by the processor, a user profile associated with each of the one or more users based on the user data, wherein the user profile is dynamic and personalized to each of the one or more users based on user behavior, user intent, user sentiment identified by analyzing the real-time user data; generating, by the processor, one or more insights associated with each of the one or more users based on the corresponding user profile; providing, by the processor, the one or more insights to one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced the one or more users, wherein the one or more issues being provided by the one or more users to the one or more support agents via an interactive user interface; and generating, by the processor, a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile, wherein the personalized support interactions provide one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile.

2. The method as claimed in claim 1, wherein the real-time user data comprises application usage preferences, spending patterns, past interactions, activity levels associated with the one or more users, and wherein the real-time user data being received from a plurality of data sources.

3. The method as claimed in claim 1, wherein the multi-user environment corresponds to a gaming platform, a social media sharing platform, and a ride sharing platform.

4. The method as claimed in claim 1, wherein the one or more insights comprises user preferences and one or more potential issues associated with each of the one or more users.

5. The method as claimed in claim 1, comprises providing predictive support actions to the one or more users based on the one or more potential issues.

6. The method as claimed in claim 1, wherein the real-time user data being stored in a data lake, data warehouse, a database.

7. The method as claimed in claim 1, comprises: identifying a priority user from the one or more users for providing the personalized support interaction based on the user profile, wherein the priority user being assigned a higher level of priority for providing the one or more resolutions as compared to the remaining one or more users; identifying one or more complex issues from the one or more issues being faced by the one or more users based on a pre-defined criteria, wherein the one or more complex issues being assigned a higher level of priority as compared to the remaining one or more issues, wherein the pre-defined criteria comprises at least one of user transaction volume, historical issue complexity, a duration of user engagement, resolution time associated with the one or more issues, and a severity of the one or more issues or a combination thereof; categorizing the one or more users into one or more segments to further customize the personalized support interaction, wherein the one or more segments comprises the one or more fraudulent users, high priority users, potential churn user, engaging user; and dynamically adjusting the personalized support interaction to allocate additional resources, escalate unresolved issues, or further customize response strategies based on the user profile, priority user, and the one or more complex issues.

8. The method as claimed in claim 1, comprises generating one or more personalized notifications to each of the one or more users based on the user profile and the one or more insights; and providing the one or more personalized notifications to the one or more users.

9. The method as claimed in claim 1, wherein analyzing user data comprises extracting one or more behavioral patterns and one or more behavioral trends over a time interval based on comparison between the real-time user data and historical interaction data using at least one of statistical analysis and machine learning techniques, wherein the one or morebehavioral paterns and the one or more behavioral trends are indicative of the user preferences.

10. The method as claimed in claim 9, wherein the one or more insights being generated by: applying the one or more machine learning techniques on the one or more behavioral patterns and the one or more behavioral trends to predict the one or more potential issues that occur at a future time instant; and identifying one or more fraudulent users from the one or more users based on the one or more behavioral patterns and the one or more behavioral trends using reinforcement learning models, wherein the personalized support interaction for the one or more fraudulent users comprises alerting the user about fraudulent activity within the application.

11. The method as claimed in claim 1, wherein the user profile being created and analyzed based on the one or more machine learning techniques, wherein the user profile comprises User engagement metrics, sentiment analysis data derived from past interactions, predictive scoring for issue resolution probability, one or more gamification patterns to optimize user experience.

12. The method as claimed in claim 1, comprises providing one or more promotional content to the one or more users based on the user profile, and wherein the promotional content is dynamically adjusted based on real-time user activity and engagement trends.

13. The method as claimed in claim 1, wherein the one or more support agents corresponds to one of a human support agent or a virtual support bot, wherein the virtual support bot is configured to adapt the personalized support interaction dynamically based on real-time sentiment and contextual analysis of the user profile, wherein the virtual support bot is configured to automatically adjust a tone of the personalized support interaction based on recent user data, user mood and recent user engagement levels.

14. A system for personalized resolving of one or more issues associated with one or more users in a multi-user environment, the system comprising: a processor; and a computer-readable medium communicatively coupled to the processor, wherein the computer-readable medium stores processor-executable instructions, which when executed by the processor, cause the processor to: analyze user data associated with the one or more users in real-time; analyze a user profile associated with each of the one or more users based on the user data, wherein the user profile is dynamic and personalized to each of the one or more users based on user behavior, user intent, and user sentiment identified by analyzing the real-time user data; generate one or more insights associated with each of the one or more users based on the corresponding user profile; provide the one or more insights to one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced by the one or more users, the one or more issues being provided by the one or more users to the one or more support agents via an interactive user interface; and generate a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile, the personalized support interaction providing one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile.

15. A non-transitory computer-readable storage medium having stored thereon a set of computer-executable instructions that, when executed by a processor, cause the processor to perform steps comprising: analyzing user data associated with one or more users in real-time; analyzing a user profile associated with each of the one or more users based on the user data, the user profile being dynamic and personalized based on user behavior, user intent, and user sentiment identified by analyzing the real-time user data; generating one or more insights associated with each of the one or more users based on the corresponding user profile;providing the one or more insights to one or more support agents to assist the one or more users during a support interaction session to resolve one or more issues being faced by the one or more users, the one or more issues being provided by the one or more users to the one or more support agents via an interactive user interface; and generating a personalized support interaction within the support interaction session with the one or more users based on the one or more insights and the user profile, the personalized support interaction providing one or more resolutions for the one or more issues based on a user context determined based on the one or more insights and the user profile.

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