Method and system for enabling accountable interaction in an application

Abstract

The present invention relates to a system (100) for enabling accountable interaction in an application. The system (100) comprises a memory (204) and a processor (202). Further, the system (100) receives a demographic data, an interaction data, and a revenue data associated with a plurality of users. Further, the system (100) identifies one or more patterns associated with the plurality of users based on the interaction data. Further, the system (100) identifies one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users. Further, the system (100) automatically creates one or more interventions during usage of the application by the one or more users at a pre-defined time instant. Further, the system (100) provides the one or more interventions to the one or more users at the pre-defined time instant.

Description

Title of Invention:METHOD AND SYSTEM FOR ENABLING ACCOUNTABLE INTERACTION IN AN APPLICATIONCROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITYThe present application claims priority from the Indian Provisional patent application, having application number 202411031392, filed on 19thApril 2024, incorporated herein by a reference.TECHNICAL FIELDThe present invention, in general, relates to the field of user behavior analytics, and more particularly, relates to a method and system for enabling intervention in the application to ensure ethical usage of the application.BACKGROUNDThis 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 mentioned in the background section.The rapid adoption of digital platforms has significantly transformed user engagement across various domains, including entertainment, finance, social media, and e-commerce. These platforms offer seamless interaction, immersive experiences, and personalized services, making them an integral part of modern life. However, the ease of access and engagement-driven designs have also introduced challenges related to excessive usage, impulsive decision-making, and financial mismanagement. Users often interact with digital platforms in ways that can lead to unintended negative consequences, such as addiction, overspending, and neglect of essential responsibilities. These risks are particularly concerning for vulnerable users who may struggle toself -regulate their interactions effectively.Existing self -regulation tools, such as manual spending limits, screen time restrictions, or opt-in exclusion mechanisms, have proven inadequate in mitigating these risks. These solutions rely on users' awareness and willingness to impose restrictions on themselves, which is often ineffective for individuals who may not recognize the extent of their problematic behavior. Additionally, these tools operate under rigid, one-size-fits-all frameworks that fail to account for individual differences in behavioral patterns, financial capacity, and psychological factors.A critical shortcoming of current systems is the lack of adaptive, real-time interventions that can proactively identify at-risk users based on their unique interaction patterns. For example, users may unknowingly engage in repeated small transactions that accumulate into significant financial strain, or they may exhibit prolonged engagement indicative of unhealthy usage patterns. Without an intelligent mechanism to assess these behaviors dynamically and intervene accordingly, users are left without meaningful support to manage their interactions responsibly.Furthermore, conventional approaches fail to leverage advanced analytics and machine learning to personalize interventions. A truly effective solution should be capable of analyzing demographic data, behavioral interactions, and financial engagement trends to identify users who are at risk of developing unhealthy patterns. Instead of relying solely on user-initiated controls, there is a need for an intelligent system that should proactively determine when intervention is necessary and deliver it at the appropriate moment to encourage responsible behavior.Additionally, existing solutions fail to consider the broader context of an individual’s life, overlooking critical factors such as financial stress, mental health conditions, and external social influences that shape engagement patterns. For example, an individual experiencing high stress or emotional distress may increase their digital platform usage as a coping mechanism, leading to prolonged sessions or impulsive financial decisions. Without an intelligent system capable of analyzing behavioral trends, financial capacity, and psychological indicators in real time, interventions remain reactive rather than proactive. A truly effective approach must recognize these underlying factors and adapt intervention strategies accordingly to prevent harmful outcomes before they escalate.Given these challenges, it is evident that traditional self-regulation methods are insufficient in promoting responsible digital engagement. Therefore, there is a critical need for an adaptive, intelligent system that can dynamically assess user behavior, detect at-risk patterns using machine learning, and deliver personalized interventions at the right moment.Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.SUMMARYThis summary is provided to introduce concepts related to a method and a system for enabling accountable interaction in an application 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 embodiments illustrated herein, a method for enabling accountable interaction in an application is disclosed. The method may comprise various steps performed by a processor. The method may include a step of receiving a demographic data, an interaction data, and a revenue data. In an embodiment, the demographic data, the interaction data, and the revenue data may be associated with a plurality of users. Further, the method may include a step of identifying one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques. Further, the method may include a step of identifying one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users. In an embodiment, the one or more users may be indicative of vulnerable users. Further, the method may include a step of automatically creating one or more interventions during usage of the application by the one or more users at a pre-defined time instant. In an embodiment, the one or more interventions may be identified based on the probability score associated with the one or more users. Further, the method may include a step of providing the one or more interventions to the one or more users at the pre-defined time instant. In an embodiment, the one or more interventions may enable the accountable interaction in theapplication.According to embodiments illustrated herein, a system for enabling accountable interaction in an application is disclosed. 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 receive a demographic data, an interaction data, and a revenue data. In an embodiment, the demographic data, the interaction data, and the revenue data may be associated with a plurality of users. Further, the processor may be configured to identify one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques. Further, the processor may be configured to identify one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users. In an embodiment, the one or more users may be indicative of vulnerable users. Further, the processor may be configured to automatically create one or more interventions during usage of the application by the one or more users at a pre-defined time instant. In an embodiment, the one or more interventions may be identified based on the probability score associated with the one or more users. Further, the processor may be configured to provide the one or more interventions to the one or more users at the pre-defined time instant. In an embodiment, the one or more interventions may enable the accountable interaction in the application.According to embodiments illustrated herein, a non-transitory computer-readable storage medium for enabling accountable interaction in an application is disclosed. 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 involve receiving a demographic data, an interaction data, and a revenue data associated with a plurality of users. Further, the step may involve identifying one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques. Furthermore, the step may involve identifying one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users. In an embodiment, the one or more users may be indicative of vulnerable users. Moreover, the step may involve automatically creating one or more interventions during usage of the application by the one or more users at a pre-defined time instant. In an embodiment, the one or more interventions may beidentified based on the probability score associated with the one or more users. Additionally, the step may involve providing the one or more interventions to the one or more users at the predefined time instant thereby enabling the accountable interaction in the application.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.BRIEF DESCRIPTION OF DRAWINGSThe 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.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:FIG. 1 is a block diagram that illustrates a system (100) for enabling accountable interaction in an application, in accordance with an embodiment of present subject matter.FIG. 2 is a block diagram (200) that illustrates various components of an application server (104) configured for enabling accountable interaction in the application, in accordance with an embodiment of the present subject matter.FIG. 3 is a flowchart that illustrates a method (300) for enabling accountable interaction in the application, in accordance with an embodiment of the present subject matter.FIG. 4 illustrates a block diagram (400) of an exemplary computer system for implementing embodiments consistent with the present subject matter; andDETAILED DESCRIPTIONThe 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.References to “one 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. 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.The objective of the present disclosure is to enable accountable interaction in an application by identifying and monitoring user behavior patterns through machine learning techniques. Another objective of the present disclosure is to detect and classify vulnerable users based on demographic data, interaction data, and revenue data using probability scoring. Another objective of the present disclosure is to provide real-time, context-aware guidance to users at predefined moments. Another objective of the present disclosure is to analyze user spending habits, loss patterns, andemotional states to mitigate impulsive financial decisions. Yet another objective of the present disclosure is to provide users with personalized control options, including time-bound exclusions, financial thresholds, and self-regulation measures.Yet another objective of the present disclosure is to verify revenue data and demographic details through multi-factor authentication and third-party validation to enhance data accuracy. Yet another objective of the present disclosure is to dynamically adapt intervention strategies by employing deep learning models and recurrent neural networks for real-time learning. Yet another objective of the present disclosure is to optimize responsible engagement in various applications, including gaming, stock trading, real-money platforms, and e-commerce. Yet another objective of the present disclosure is to integrate multimodal interaction data, including facial expressions, gaze tracking, and voice inputs, for improved behavioral analysis. Yet another objective of the present disclosure is to enable predictive analytics-based risk assessment for early detection of high-risk spending behavior and addiction patterns.FIG. 1 is a block diagram that illustrates a system (100) for enabling accountable interaction in an application, 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.In one embodiment, the database server (102) may refer to a computing device that may be configured to store one or more interventions, probability score, one or more user inputs, a demographic data, an interaction data comprises one or more actions performed by a plurality of users in the application, application usage data, one or more queries, feedback data, third party application usage data, and user behavior metrics comprising a frequency of application usage, a session duration, and interaction timing. The application usage data comprises a frequency ofwallet top-ups, boot amount value, and withdrawal patterns. The interaction data may be received from a client device / portable device (108) associated with each of the plurality of users. The interaction data comprises facial expressions, gaze information, voice data, a revenue data, user- reported earnings and users gameplay data including but not limited to pattern of gameplay, game selection, play frequency, game outcomes, financial tractions within a platform, chat histories, social media feed, queries, feedback data, users’ facial expressions, users voice, ambient sounds and other intermediate processing data.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, Select, Insert, Update, and Delete. 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 enabling accountable interaction in the application.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).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 procedures such as, but not limited to, programs, routines, or scripts stored in one or more memories for supporting the hosted application or the software service. In an embodiment, the hosted application or the software service may be configured to perform one or more predetermined operations. 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 PHPframework application server, or any other application server framework.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 enabling accountable interaction in the application. In an implementation, the application server (104) corresponds to an infrastructure for implementing the method for enabling accountable interaction in the application.In an embodiment, the application server (104) may be configured to receive the demographic data, the interaction data, and the revenue data. In an embodiment, each of the demographic data, the interaction data, and the revenue data may be associated with the plurality of users. Further, the application server (104) may be configured to identify the one or more patterns associated with the plurality of users based on the interaction data. Further, the one or more patterns may be identified by using one or more machine learning techniques. Further, the application server (104) may be configured to identify the one or more users from the plurality of users based on the one or more patterns and the probability score associated with the one or more users. Further, the one or more users may be indicative of vulnerable users. Moreover, the application server (104) may be configured to create one or more interventions during usage of the application by the one or more users at a pre-defined time instant. Further, the one or more interventions may be identified based on the probability score associated with the one or more users. Additionally, the application server (104) may be configured to provide the one or more interventions to the one or more users at the pre-defined time instant.In an embodiment, the application may correspond to one of a gaming application, a stock trading application, a brokerage application, a real-money transaction-based platform, cryptocurrency trading platform, e-commerce platform, food delivery application, quick commerce application, social media application, media streaming application.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, TransmissionControl 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.In an embodiment, the one or more portable devices (108) may refer to a computing device used by the plurality of users. 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 predetermined operations. In an embodiment, the one or more portable devices (108) may capture and provide the demographic data, the interaction data, and the revenue data associated with a plurality of users. Examples of the one or more portable devices (108) may include, but are not limited to, a personal computer, a laptop, a computer desktop, a personal digital assistant (PDA), a mobile device, a tablet, or any other computing device.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. A critical attribute of the system (100) is that it automatically creates and provide the one or more interventions duringusage of the application by the one or more users at a pre-defined time instant.FIG. 2 illustrates a block (200) diagram illustrating various components of the application server (104) configured for enabling accountable interaction in the application, 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 (I / O) unit (208), a user interface (210), an identification unit (212), an intervention unit (214) and a feedback unit (216). The processor (202) is further preferably communicatively coupled to the memory (204), the transceiver (206), the input / output (I / O) unit (208), the user interface (210), the identification unit (212), the intervention unit (214), and the feedback unit (216) while the transceiver (206) is preferably communicatively coupled to the communication network (106).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 (I / O) unit (208), the user interface (210), the identification unit (212), the intervention unit (214) and the feedback unit (216) for enabling accountable interaction in the application. 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.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 applications and functions of the system (100). 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 adaptability and responsiveness of the application server (104).In another embodiment, the memory (204) may comprise the demographic data, the interaction data, the revenue data, one or more patterns, historical user data and the probability score. Further, the interaction data may include but not limited to one or more actions performed by the plurality of users in the application, facial expressions, gaze information, voice data, application usage data, one or more queries, feedback data, third party application usage data, and user behavior metrics comprising a frequency of application usage, a session duration, and interaction timing. Further, the application usage data comprises a frequency of wallet top-ups, boot amount value, and withdrawal patterns. Further, the revenue data may include but not limited to transactions, payments, deposit, withdrawal, subscription, membership fee, refund, prize, lottery or a combination thereof. Further, the demographic data may include but not limited to age, location, gender, occupation, language spoken or a combination thereof.Further, the one or more patterns includes but not limited to a loss streak pattern indicating consecutive losses beyond a threshold, a user spending patterns, a user profitability fluctuations, a wallet activity, a boot amount value pattern, an expenditure pattern, time spent on the application, frequency of play, an emotional state, a stress level or a combination thereof.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 configuredto 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.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 (I / O) 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 input / output (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 input / output (I / O) unit (208) may allow the system (100) to interact with the user directly or through the portable devices (108). Further, the input / output (I / O) unit (208) may enable the system (100) to communicate with other computing devices, such as web servers and external data servers (not shown). The input / output (I / O) unit (208) canfacilitate 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 user input / output (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 input / output (I / O) unit (208) allows the application server (104) to be logically coupled to other portable devices unit (108), some of which may be built in. Illustrative components include tablets, mobile phones, camera, micro phone, desktop computers, wireless devices, a cell phone, personal digital assistant (PDA), stationary personal computer, IPTV remote control, laptop computer, pocket PC, a television set capable of receiving IP based video services, mobile IP device etc.In an embodiment, input / output (I / O) unit (208) may be configured to receive demographic data, interaction data, and revenue data associated with a plurality of users. Further, input / output (I / O) unit (208) may be configured to receive an input from the one or more users to create the one or more interventions. Further, the input corresponds to a time bound exclusion or a permanent exclusion from the interaction with the application. Further, the interaction data may include but not limited to one or more actions performed by the plurality of users in the application, facial expressions, gaze information, voice data, application usage data, one or more queries, feedback data, third party application usage data, and user behavior metrics comprising a frequency of application usage, a session duration, and interaction timing. Further, the application usage data comprises a frequency of wallet top-ups, boot amount value, and withdrawal patterns. Further, the revenue data may include but not limited to transactions, payments, deposit, withdrawal, subscription, membership fee, refund, prize, lottery or a combination thereof. Further, the demographic data may include but not limited to age, location, gender, occupation, language spoken or a combination thereof. Further, the demographic data may be received in the form of a government-issued identification or other verifiable documents.In an exemplary embodiment, the demographic data, the interaction data, and the revenue data may be received from one or more sensors in smartphones or the tablets, such as an accelerometer, a gyroscope, or a touch screen, to analyze the user's physical interactions with the input device. For example, erratic or aggressive touch patterns, excessive shaking or movement of the device detected by the accelerometer, or changes in the way the user holds the device could indicate agitation, frustration or other negative mental states. Further, a front-facing camera on the inputdevice may be utilized to capture the user's facial expressions and analyze them in real-time. Further, the application may employ facial recognition and emotion detection algorithms to identify signs of distress, anger, disappointment or other concerning emotional states based on the user's facial cues. Furthermore, the input device's microphone may be used to capture the user's voice and any ambient sounds. Further, a voice analysis techniques may be applied to detect changes in the user's tone, pitch, volume or other vocal parameters that may indicate a problematic mental state. For instance, shouting, cursing or aggressive vocalizations may be a sign that the user is becoming overly emotional or losing control.In an embodiment, the processor (202) may be configured to verify the revenue data of the plurality of users using one or more pre-defined workflows. Further, the revenue data may be verified against a third-party data sources. Further, an expenditure capacity of each of the plurality of users may be confirmed by implementing multi-factor checks. Further, the user spending patterns is cross-verified with a transaction history to validate the revenue data. Further, by verifying the revenue, the application may adjust a level of intervention to prevent the one or more users from engaging in activities that could lead to financial distress. Further, the verification of the revenue involves an automated systems capable of processing data efficiently and accurately, as well as manual review in certain cases.In an, the processor (202) may be configured to verify the demographic data of the plurality of users using the one or more pre-defined workflows. Further, the processor (202) may be configured to verify age of the plurality of users to ensure compliance with child protection regulations and to restrict platform access to eligible individuals. Further, the processor (202) may be configured to provide periodic re-verification to ensure ongoing compliance with age-related regulations.In another embodiment, the user interface (210) of the application server (104), is disclosed. In an embodiment, the user interface unit (210) may be configured to display the probability score to the one or more users and then notify the one or more users of their risk level based on interaction data. Further, the user interface (210) may be configured to present the one or more interventions at the pre-defined time instant. Further, the user interface unit (210) may be configured for receiving an input from the one or more users to create the one or more interventions. Further, theinput may corresponds to a time bound exclusion or a permanent exclusion from the interaction with the application. Further, the user interface (210) may facilitate interaction with users who have been profiled based on their behavior and guides the subsequent interventions.In an embodiment, the user interface (210) of the application server (104) may include, but not limited to an app interface, a web interface, a graphical user interface and a touch user interface. Further, the user interface (210) may be a critical component, acting as a bridge between the identification unit (212) and the intervention unit (214).In another embodiment, the identification unit (212) of the application server (104) is disclosed. The identification unit (212) comprises suitable logic, circuitry, interfaces, and / or code that may be configured for identifying the one or more patterns associated with the plurality of users. Further, the one or more patterns may be identified based on the interaction data using the one or more machine learning techniques. Further, the one or more patterns may be identified by identifying correlations between one or more data points and nature of user queries reported in irresponsible behavior scenarios. Further, one or more machine learning models may be trained based on the identified correlations in the interaction data, to identify one or more risk indicators. Further, one or more user behavior pattern and the user behavior metrics may be identified by applying one or more supervised machine learning techniques to historical user data using the correlations. Further, the plurality users may be classified based on the interaction data, and the revenue data including spending habits by employing unsupervised clustering techniques. Further, the identification unit (212) may utilize one or more deep learning models to detect the one or more patterns indicative of vulnerable behavior.Further, the identification unit (212) may be configured for identifying one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users. Further, the one or more users may be indicative of the vulnerable users. Further, the one or more users may be identified based on the interaction data of each of the plurality of users satisfying a predefined criteria. Further, the pre-defined criteria may be based on the revenue data and the demographic data. Further, the identification unit (212) may be configured to compute the probability score for the plurality of users that satisfy the pre-defined criteria using a trained machine learning model. Further, the machine learning model may betrained based on one or more parameters including but not limited to spending capacity of each of the plurality of users, the demographic data, the revenue data, application usage activity, wallet activity, user queries, user provided information, the interaction data or a combination thereof. Further, the probability score may be stored in at least one of a user profile associated with each of the plurality of users or a structured database. Further, the processor (202) may be configured for notifying the probability score to the one or more users.In another embodiment, the intervention unit (214) of the application server (104), is disclosed. The intervention unit (214) comprises suitable logic, circuitry, interfaces, and / or code that may be configured to automatically create the one or more interventions during usage of the application by the one or more users at a pre-defined time instant. Further, the pre-defined time instant may be determined based on features used for calculating the probability score. Further, the features include real-time monitoring of user engagement levels, reaching specific thresholds for wallet additions, loss streaks, or time spent, a combination of historical user behavior patterns and predictive analytics. Further, the pre-defined time instant may be determined for each of the one or more users based on the associated probability score. Further, the one or more interventions may be identified based on the probability score associated with the one or more users.In an embodiment, the one or more interventions may be created at least on a home screen of the application, in between application usage, at a start point of the application, at an end point of the application, prior to executing high-value transactions or application boot, user meeting defined thresholds, during user interactions with a customer support.Further, the one or more intervention may include but not limited to modifying deposit threshold, break interval, modifying withdrawal threshold, modifying time threshold for each interaction type within the application, deleting the application, restricting re-installation of application for a pre-defined time interval, providing dedicated customer support for the one or more users, nudges, creating a wallet restriction where the one or more users are restricted from playing post defined threshold or a combination thereof.In an embodiment, the intervention unit (214) may be configured to provide the one or more interventions to the one or more users at the pre-defined time instant. Further, the one or more interventions may enable the accountable interaction in the application. For example, in a gamingplatform a user with a high likelihood of irresponsible gaming due to frequent loss streaks may receive different interventions compared to a user whose risk is associated with high wallet additions. Further, the interventions could include in-game prompts to encourage taking a break or the activation of a self-limitation mechanisms that allow users to set limits on their gaming activity.In another embodiment, the feedback unit (216) of the application server (104), is disclosed. The feedback unit (216) comprises suitable logic, circuitry, interfaces, and / or code that may be configured for receiving user feedback within the application. Further, the feedback unit (216) may be responsible for gathering insights directly from users, which are essential for refining the application experience. Further, the feedback unit (216) may be configured to collects qualitative data from users, including their concerns, suggestions, and feedback regarding the application experience. Further, this data may be gathered through user interfaces such as in-app pop-ups, feedback forms, or direct communication channels. Further, the feedback unit (216) may be configured to gain insights into user behavior and preferences, which are used enabling accountable interaction in the application.In an embodiment, feedback received by the feedback unit (216) may undergo analysis to identify patterns, specific issues, or sentiments among the user base. Further, this analysis may be performed using natural language processing techniques. Further, the analysed feedback may inform various aspects of the application. Further, it may lead to adjustments in the predictive probability score, refining the criteria for enabling accountable interaction in an application. Further, the feedback unit (216) may also aid in the development of user interface prompts and self-limitation mechanisms. Additionally, the feedback may influence creation of educational resources and an implementation of age verification processes.In an embodiment, the processor (202) may notify the probability score to the one or more users and receiving the input from the one or more users to create the one or more interventions. Further, the input may correspond to a time bound exclusion or a permanent exclusion from the interaction with the application. Further, the processor (202) may enable the one or more users to set boundaries on their application. Further, the input from the one or more users may include opting for either temporary or permanent exclusion from the application.In an embodiment, the processor (202) may provide the self-limitation mechanisms and allow the plurality of users to set limits or take a break. Further, the self-limitation mechanisms may include setting daily or monthly limits on an amount of money the plurality of users can add to their account, establishing time restrictions on their application sessions, opting to take a break from the activities, or choosing to remove the application from their device entirely. Further, these options are made available to users through the user interface (210), which is designed to be accessible and user-friendly.In another embodiment, the processor (202) may be configured to employ the one or more machine learning models to analyse temporal sequences of the interaction data. Further, based on historical interaction data and the analysis of the temporal sequences of the interaction data, the processor (202) may predict a likelihood of future high-risk spending events. Further, the processor (202) may be configured to dynamically adjust the probability score and the one or more interventions based on continuous real-time learning.A person skilled in the art will understand that the scope of the disclosure should not be limited to the field of digital platforms 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.Referring to FIG. 3, a flowchart that illustrates a method (300) for enabling accountable interaction in the application, in accordance with at least one embodiment of the present subject matter. The flowchart is described in conjunction with Figure 1 and Figure 2. The method (300) may be implemented by the processor (202) of the application server (104). The method (300) starts at step (302) and proceeds to step (310).In operation, the method (300) may involve a variety of steps, executed by the processor (202), for enabling accountable interaction in the application. At step (302), the method (300) comprises a step of receiving demographic data, interaction data, and revenue data associated with a plurality of users. At step (304), the method (300) comprises a step of identifying, by the application server (104), one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques. At step (306), the method (300) comprises a step of identifying, by the application server (104), one or more users from the plurality of users basedon the one or more patterns and a probability score associated with the one or more users. In an embodiment, the one or more users are indicative of vulnerable users. At step (308), the method (300) comprises a step of automatically creating, by the application server (104), one or more interventions during usage of the application by the one or more users at a pre-defined time instant. In an embodiment, the one or more interventions being identified based on the probability score associated with the one or more users. At step (310), the method (300) comprises a step of providing, by the application server (104), the one or more interventions to the one or more users at the pre-defined time instant thereby enabling the accountable interaction in the application.Let us delve into a detailed working example of the present disclosure.Example 01: Identifying and intervening for a high-risk user in a gaming application (Online Betting Platform).There are five users P, Q, R, S & T participating in the gaming application that includes real- money transactions. Each user engages in different gaming activities, including placing bets, making in-game purchases, and managing wallet transactions.The system continuously receives interaction data, demographic data, and revenue data from each user in real time. Further, the interaction data includes game session duration, betting frequency, win / loss streaks, in-game spending, transaction timestamps. Further, the demographic data includes each user’s age, location, gaming experience and risk profile. Further, the revenue data includes wallet balance, withdrawal / deposit patterns, spending capacity, external financial data.Further, the system applies machine learning techniques to analyze these data and detect behavioral patterns. Based on the collected data, the system identifies specific behavioral patterns for each user.• User P follows a stable gaming pattern with consistent but small top-ups and withdrawals.• User Q experiences a loss streak pattern, losing consecutive bets beyond a predefined threshold.• User R exhibits high betting frequency and large bet amounts, indicating potential high-risk behavior.• User S shows abnormal withdrawal and deposit patterns with significant fluctuations ingaming expenditure.User T exhibits automated betting activity, suggesting the use of bots for gaming.Based on the identified patterns, the system uses supervised and unsupervised machine learning techniques to computes a probability score for each user, indicating their likelihood of engaging in vulnerable behavior.• User Q is identified as vulnerable due to consecutive losses, with a probability score of 0.82.• User R is identified with excessive betting frequency, triggering a probability score of 0.76.• User S is identified with abnormal financial patterns, receiving a probability score of 0.79.• User T is identified for bot activity, scoring 0.89.Further, the system generates personalized interventions at predefined time intervals based on the computed probability scores. For User 0. who exhibits a loss streak pattern, the system displays an intervention pop-up before placing a high-value bet, warning about recent losses. Further, a temporary betting limit is enforced, and the user is provided with an option to self-exclude for a predefined period.Further, for User R, who demonstrates high betting frequency, the system introduces a session break interval after every five consecutive betting attempts. Further, a notification is sent to highlight the increased betting frequency and suggest self-control measures.Further, for User S, who has abnormal transaction patterns, the system verifies the user’s revenue data and spending behavior through third-party validation. Further, if anomalies persist, a temporary withdrawal threshold is applied, and the user is required to complete an additional identity verification step.Further, for User T, who is detected as using automated betting, the system restricts bot-based interactions and temporarily suspends automated bet placements. Additionally, the system sends a verification request to confirm manual play.These interventions ensure responsible gaming practices while maintaining accountability in financial transactions. The system continuously monitors user behavior and dynamically adjusts interventions as needed to prevent excessive gaming risks. If a user's probability score decreases due to improvedgaming behavior, certain restrictions may be lifted. Conversely, if high-risk behavior persists, stricter interventions such as account suspension, increased verification, or permanent exclusion may be applied.Example 02: Identifying and intervening for high-risk users in a ride-sharing platform.A ride-sharing platform allows users to book rides, make payments, rate drivers, and interact with the platform’s features. Further, five users A, B, C, D & E are actively using the platform.The system continuously receives interaction data, demographic data, and revenue data from each user in real time. Further, the interaction data includes ride frequency, payment method usage, ride cancellation rates, driver-rider chat activity, and customer support requests. Further, the demographic data includes each user’s location, ride history trends, time of travel, and preferred routes. Further, the revenue data includes payment history, refund requests, promotional discount usage, chargeback attempts, and surge pricing behavior.Further, the system applies machine learning techniques to analyze these data and detect behavioral patterns. Based on the collected data, the system identifies specific behavioral patterns for each user.• User A follows a normal ride -booking pattern, occasionally using discounts and making regular payments.• User B exhibits normal behavior, using the service periodically with no policy violations.• User C frequently cancels rides after booking, causing inconvenience to drivers and exploiting the cancellation system.• User D repeatedly disputes payments, often claiming unsuccessful trips to request refunds.• User E engages in suspicious ride -booking behavior, using multiple accounts to exploit referral bonuses and promotions.

[0001] Based on the identified patterns, the system uses supervised and unsupervised machine learning techniques to compute a probability score for each user, indicating their likelihood of engaging in vulnerable behavior.• User C is identified for frequent ride cancellations, with a probability score of 0.81.• User D is identified for payment disputes and refund abuse, receiving a probability score of0.85.• User E is identified for promotion fraud, scoring 0.88.Further, the system generates personalized interventions at predefined time intervals based on the computed probability scores. For User C, flagged for excessive ride cancellations, the system enforces a penalty fee after multiple cancellations within a short period. Further, a cool-down period is applied before the user can request another ride. Further, if the behavior continues, the system temporarily restricts ride-booking privileges.Further, for User D, detected for refund abuse, the system flags excessive dispute claims and requires additional verification before issuing further refunds. Further, if the pattern persists, the system restricts the user from making further refund requests without customer support intervention.Further, for User E, detected for fraudulent promotions, the system prevents multiple accounts from using the same payment method. Further, the referral bonuses are restricted if linked to the same device or location. Further, if fraudulent activity continues, the system blocks further promotional redemptions and may suspend the account.Further, the system continuously monitors user activity and dynamically adjusts interventions based on real-time ride transactions and behavioral patterns. Normal users (A and B) continue using the platform without interruptions, while high-risk users undergo adaptive interventions to maintain a fair and secure ride-sharing ecosystem.The above working examples, clearly demonstrate the practical application of the present disclosure and the technical effect being brought about by the present disclosure.FIG. 4 illustrates a block diagram (400) of an exemplary computer system (401) for implementing embodiments consistent with the present disclosure.Variations of computer system (401) may be used for enabling accountable interaction in the application. 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.Processor (402) may be disposed in communication with one or more input / output (I / O) 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 8O2.n / b / g / n / x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), highspeed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMAX, or the like, for example.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.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.In some embodiments, the processor (402) may be disposed in communication with one or more memory devices (e.g., RAM 413, ROM 414, 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.The memory devices may store a collection of program or database components, including, without limitation, an operating system (416), user interface application (417), web browser (418), mail client / server (419), user / application data (420) (e.g., any data variables or data records discussed inthis 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.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.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.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.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, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.Various embodiments of the disclosure provide a non-transitory computer readable medium and / or storage medium, and / or a non-transitory machine-readable medium and / or storage medium having stored thereon, a machine code and / or a computer program having at least one code section executable by a machine and / or a computer for enabling accountable interaction in an application. The at least one code section in the non-transitory computer readable medium causes the machine and / or computer (application server) including one or more processors to perform the steps, which includes receiving the demographic data, the interaction data, and the revenue data associated with a plurality of users. Further, the processor may be configured for identifying one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques. Further, the processor may be configured for identifying one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users.Further, the one or more users may be indicative of vulnerable users. Further, the processor may be configured for automatically creating one or more interventions during usage of the application by the one or more users at a pre-defined time instant. Further, the one or more interventions may be identified based on the probability score associated with the one or more users. Further, the processor may be configured for providing the one or more interventions to the one or more users at the predefined time instant thereby enabling the accountable interaction in the application.Various embodiments of the disclosure encompass numerous advantages including the system and the method for enabling accountable interaction in the application. The disclosed system and method have several technical advantages, but are not limited to the following:• Enhanced Accountability in User Interactions: By leveraging real-time data analysis and machine learning models, the system ensures accountable interactions by dynamically identifying vulnerable users and automatically providing timely interventions, fostering responsible application usage.• Proactive Risk Management: The ability to predict high-risk behavior, such as consecutive losses or significant fluctuations in user profitability, allows the system to intervene preemptively, reducing the likelihood of users engaging in harmful behaviors.• Secure and Ethical User Data Management: The method utilizes advanced data validation techniques, such as verifying revenue data against third-party sources and implementing multi-factor checks, ensuring that user data is both accurate and protected, contributing to ethical application design.• Predictive and Dynamic Risk Assessment: By employing recurrent neural networks and deep learning models, the system can continuously adjust risk predictions and interventions in real time, ensuring that the application remains responsive to evolving user behaviors.• Data-Driven Decision Making: By incorporating diverse data sources such as interaction history, transaction data, and behavioral metrics, the method provides actionable insights to developers and service providers, enabling informed decision-making for user engagement strategies.• Real-Time User Protection: Continuous monitoring of user behavior ensures that interventions are provided at optimal times, such as before executing high-valuetransactions, minimizing the potential for harmful financial behaviors or application misuse.• Improved Application Integrity and Trust: Through transparent probability scoring and user notifications, the system builds trust by making users aware of their vulnerability scores and involving them in the intervention process, enhancing the integrity of the application’s operation.• Comprehensive Behavioral Insights: With the incorporation of multiple data points, including emotional state, stress levels, and user spending patterns, the system provides a holistic view of user behavior, allowing for deeper analysis and more accurate predictions of future actions.• Versatile Application Scope: The method is adaptable to a wide range of applications, from gaming and stock trading platforms to e-commerce and social media, making it a versatile solution for managing user behavior across various industries.In summary, these technical advantages solve the technical problems of identifying and mitigating vulnerable user behavior in real-time across diverse applications, thereby addressing the technical challenges associated with ensuring responsible and accountable user interactions, such as predicting high-risk behavior, preventing harmful financial decisions, and minimizing application misuse. Additionally, these advantages contribute to improving user engagement, enhancing data-driven decision-making, promoting ethical use of user data, and fostering trust between users and service providers, ultimately ensuring a safer and more transparent application experience across various industries.The claimed invention of a system for enabling accountable interaction in the application involves tangible components, processes, and functionalities that interact to achieve specific technical outcomes. The system integrates various elements such as processors, memory, identification unit, and intervention unit to effectively enable accountable interaction in the application.Furthermore, the invention involves a non-trivial combination of technologies and methodologies that provide a technical solution for a technical problem. The integration of all the different functional units into a comprehensive system for enabling accountable interaction in the application, brings about an improvement and technical advancement in the field of user behavior analytics and ethical usageof applications.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.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.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.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 besubstituted 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 CLAIM:

1. A method (300) for enabling accountable interaction in an application, wherein the method comprising: receiving (302), by a processor (202), a demographic data, an interaction data, and a revenue data associated with a plurality of users; identifying (304), by the processor (202), one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques; identifying (306), by the processor (202), one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users, wherein the one or more users are indicative of vulnerable users; automatically creating (308), by the processor (202), one or more interventions during usage of the application by the one or more users at a pre-defined time instant, wherein the one or more interventions being identified based on the probability score associated with the one or more users; and providing (310), by the processor (202), the one or more interventions to the one or more users at the pre-defined time instant thereby enabling the accountable interaction in the application.

2. The method as claimed in claim 1, comprises notifying the probability score to the one or more users; and receiving an input from the one or more users to create the one or more interventions, wherein the input corresponds to a time bound exclusion or a permanent exclusion from the interaction with the application.

3. The method as claimed in claim 1, comprises verifying the revenue data and the demographic data of the plurality of users using one or more pre-defined workflows, wherein the one or more pre-defined workflows comprises:verifying revenue data against third-party data sources, wherein the revenue data being provided by the plurality of users; implementing multi-factor checks to confirm an expenditure capacity of each of the plurality of users; cross-verifying user spending patterns with transaction history to validate the revenue data.

4. The method as claimed in claim 1, wherein the interaction data comprises one or more actions performed by the plurality of users in the application, application usage data, one or more queries, feedback data, third party application usage data, and user behavior metrics comprising a frequency of application usage, a session duration, and interaction timing, wherein the application usage data comprises a frequency of wallet top-ups, boot amount value, and withdrawal patterns, wherein the interaction data being received from a client device associated with each of the plurality of users, wherein the interaction data comprises facial expressions, gaze information, voice data or a combination thereof.

5. The method as claimed in claim 1, wherein the one or more patterns comprises a loss streak pattern indicating consecutive losses beyond a threshold, user profitability fluctuations, a wallet activity, a boot amount value pattern, an expenditure pattern, time spent on the application, frequency of application usage, emotional state, stress level or a combination thereof.

6. The method as claimed in claim 1 , wherein the one or more interventions being created at least on: a home screen of the application, in between application usage, at a start point of the application, at an end point of the application, prior to executing high-value transactions or application boot, user meeting defined thresholds, during user interactions with a customer support.

7. The method as claimed in claim 1 , wherein the one or more interventions comprises modifying deposit threshold, break interval, modifying withdrawal threshold, modifying time threshold for each interaction type within the application, deleting the application, restricting re-installation of application for a pre-defined time interval, providing dedicated customer support for the one or more users, nudges, creating a wallet restriction where the one or more users are restricted from playing post defined threshold or a combination thereof.

8. The method as claimed in claim 1, wherein the pre-defined time instant being determined based on at least one of: real-time monitoring of user engagement levels, reaching specific thresholds for wallet additions, loss streaks, or time spent, a combination of historical user behavior patterns and predictive analytics; wherein the pre-defined time instant being determined for each of the one or more users based on the associated probability score.

9. The method as claimed in claim 1, wherein the application corresponds to one of a gaming application, a stock trading application, a brokerage application, a real-money transactionbased platform, cryptocurrency trading platform, e-commerce platform, food delivery application, quick commerce application, social media application, media streaming application.

10. The method as claimed in claim 1, wherein identifying the one or more patterns comprises: training one or more machine learning models to identify correlations in the interaction data; applying one or more supervised machine learning techniques to historical user data to identify user behavior metrics based on the correlations; employing unsupervised clustering techniques to classify the plurality of users based on the interaction data, and spending habits; utilizing one or more deep learning models to detect the one or more patterns indicative of vulnerable behavior.

11. The method as claimed in claim 1, wherein identifying one or more users comprises:determining if the interaction data for each of the plurality of users satisfies a predefined criteria, wherein the pre-defined criteria is based on the revenue data and the demographic data; computing the probability score for the plurality of users that satisfy the pre-defined criteria using a trained machine learning model, wherein the probability score is indicative of vulnerable behavior, wherein the machine learning model is trained based on one or more parameters, wherein the one or more parameters comprises spending capacity of each of the plurality of users, the demographic data, the revenue data, application usage activity, wallet activity, user queries, user provided information or a combination thereof; and storing the probability score in at least one of a user profile associated with each of the plurality of users or a structured database.

12. The method as claimed in claim 1, comprises employing the one or more machine learning models to analyse temporal sequences of the interaction data; predicting a likelihood of future high-risk spending events based on historical interaction data and the analysis of the temporal sequences of the interaction data; dynamically adjusting the probability score and the one or more interventions based on continuous real-time learning.

13. A system (100) for enabling accountable interaction in an application, wherein the system comprises: a processor (202); a memory (204) coupled with the processor (202), wherein the memory (204) is configured to store programmed instructions that cause the processor (202) to: receive (302) a demographic data, an interaction data, and a revenue data associated with a plurality of users; identify (304) one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques;identify (306) one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users, wherein the one or more users are indicative of vulnerable users; automatically create (308) one or more interventions during usage of the application by the one or more users at a pre-defined time instant, wherein the one or more interventions being identified based on the probability score associated with the one or more users; and provide (310) the one or more interventions to the one or more users at the predefined time instant thereby enabling the accountable interaction in the application.

14. A non- transitory computer-readable storage medium having stored thereon, a set of computerexecutable instructions causing a computer comprising one or more processors to perform steps comprising: receiving (302) a demographic data, an interaction data, and a revenue data associated with a plurality of users; identifying (304) one or more patterns associated with the plurality of users based on the interaction data using one or more machine learning techniques; identifying (306) one or more users from the plurality of users based on the one or more patterns and a probability score associated with the one or more users, wherein the one or more users are indicative of vulnerable users; automatically creating (308) one or more interventions during usage of the application by the one or more users at a pre-defined time instant, wherein the one or more interventions being identified based on the probability score associated with the one or more users; and providing (310) the one or more interventions to the one or more users at the predefined time instant thereby enabling the accountable interaction in the application.

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