System and method for providing personalized module recommendations in a digital platform

Abstract

Disclosed herein is a system (100) and a method (300) for providing personalized 5 module recommendations in a digital platform. The system collects user interaction data from multiple sources such as activity logs, navigation paths, and user feedback while using the digital platform. The data is processed to build user profiles that reflect preferences and behavioral patterns. Machine learning algorithms are applied to the user profiles to identify recurring usage patterns and correlations between user 10 actions and module usage. Based on an analysis, the personalized module recommendations are generated for each user. The personalized module recommendations may include existing modules as well as newly launched modules whose functionality aligns with the user's identified patterns and preferences. The personalized module recommendations are then delivered to the users through a 15 notification mechanism, thereby improving interaction with the digital platform.

Description

SYSTEM AND METHOD FOR PROVIDING PERSONALIZEDMODULE RECOMMENDATIONS IN A DIGITAL PLATFORMTECHNICAL FIELD

[0001] The embodiments of the present disclosure generally relate to the field of wireless communication systems. More particularly, the present disclosure relates to a system and a method for providing personalized module recommendations based on behavior and interaction patterns of users while using a digital platform.BACKGROUND OF THE INVENTION

[0002] The subject matter disclosed in the background section should not be assumed or construed to be prior art merely because of its mention in the background section. Similarly, any problem statement mentioned in the background section or its association with the subject matter of the background section should not be assumed or construed to have been previously recognized in the prior art.

[0003] In modem digital environments, users frequently navigate through numerous modules and features offered by digital platforms. These modules enable an execution of various tasks such as content management, collaboration, data analysis, and other platform-specific activities. With ongoing enhancements and regular feature additions, the digital platforms continue to expand in functionality. While this expansion contributes to a richer user experience, the expansion also introduces complexity in identifying and accessing relevant modules efficiently.

[0004] Although the digital platforms provide a wide array of functionalities, the users often face difficulties when attempting to locate appropriate modules for specific needs. Conventional systems typically do not offer personalized assistance or dynamic recommendations, requiring the users to manually explore available features. This manual exploration results in underutilization of the relevant modules, and overall inefficiencies during platform usage.

[0005] Further, existing digital platforms also lacks personalization in presentation and recommendation of the modules. The existing digital platforms often fail to adapt to individual user behavior, preferences, or evolving objectives. As a result, the recommendations, if provided, do not reflect actual requirements of the users. This disconnect reduces an overall effectiveness of the digital platform, potentially causing frustration and disengagement. The users overlook key features or spend unnecessary time locating useful modules without an intelligent mechanism that learns from historical behavior and provides proactive guidance. This hinders productivity and lead to inefficient use of platform resources.

[0006] In light of these challenges, there lies a need for a system and a method for providing personalized module recommendations by monitoring interactions of the users and analyzing their usage patterns while using the digital platforms, while overcoming the challenges associated with the existing digital platforms.SUMMARY

[0007] The following embodiments present a simplified summary to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key / critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0008] According to an aspect of the present disclosure, disclosed herein is a method for providing personalized module recommendations in a digital platform. The method comprises collecting, by a data collection engine from a plurality of sources within the digital platform, data associated with one or more users. The method further comprises performing, by a data processing engine, one or more processing operations on the data to generate processed data. The method further comprises creating, by a user profiling engine, profiles of the one or more users based on the processed data. The method further comprises identifying, by a pattern recognition engine based on the profiles, recurring usage patterns and correlations between actions and module preferences of the one or more users. The methodfurther comprises generating, by a generation engine based on the recurring usage patterns and the correlations, the personalized module recommendations for each user among the one or more users and notifying, by a delivery engine, the personalized module recommendations to the one or more users.

[0009] In one or more implementations, the method further comprises extracting, by the user profiling engine, historical data corresponding to the one or more users from the processed data and analysing, by the user profiling engine, the historical data corresponding to the one or more users to identify usage patterns and the module preferences of the one or more users. The method further comprises periodically updating, by the user profiling engine, the profiles of the one or more users based on the analysis of the historical data.

[0010] In one or more implementations, the data associated with the one or more users is collected from the plurality of sources comprising user activity logs, navigation paths within the digital platform, interactions of the one or more users with a plurality of modules, usage patterns of features of the digital platform.

[0011] In one or more implementations, the one or more processing operations include filtering the data, aggregating the filtered data, and categorizing the aggregated data.

[0012] In one or more implementations, the categorizing the aggregated data comprises categorizing, by the data processing engine, the actions of the one or more users and associating the actions with the module preferences of the one or more users.

[0013] In one or more implementations, the generating the personalized module recommendations comprises applying, by the generation engine, Machine Learning (ML) algorithms to identify the recurring usage patterns and the correlations between the actions and the module preferences of the one or more users and generating the personalized module recommendations based on the identified recurring usage patterns and the correlations.

[0014] According to another aspect of the present disclosure, a system for providing personalized module recommendations in a digital platform is described. The system comprises a data collection engine, a data processing engine, a user profiling engine, a pattern recognition engine, a generation engine, and a delivery engine. The data collection engine is configured to collect, from a plurality of sources within the digital platform, data associated with one or more users. The data processing engine is configured to perform one or more processing operations on the data to generate processed data. The user profiling engine configured to create profiles of the one or more users based on the processed data. The pattern recognition engine is configured to identify, based on the profiles, recurring usage patterns and correlations between actions and module preferences of the one or more users. The generation engine is configured to generate, based on the recurring usage patterns and the correlations, the personalized module recommendations for each user among the one or more users. The delivery engine is configured to notify the personalized module recommendations to the one or more users.BRIEF DESCRIPTION OF DRAWINGS

[0015] Various embodiments disclosed herein will become better understood from the following detailed description when read with the accompanying drawings. The accompanying drawings constitute a part of the present disclosure and illustrate certain non-limiting embodiments of inventive concepts. Further, components and elements shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. For consistency and ease of understanding, similar components and elements are annotated by reference numerals in the exemplary drawings.

[0016] FIG. 1 illustrates a schematic block diagram depicting a communication system for providing personalized module recommendations in a digital platform, in accordance with an exemplary embodiment of the present disclosure.

[0017] FIG. 2 illustrates a block diagram depicting a system architecture of a server, in accordance with an exemplary embodiment of the present disclosure.

[0018] FIG. 3 illustrates a flowchart depicting a method for providing the personalized module recommendations in the digital platform, in accordance with an embodiment of the present disclosure.

[0019] FIG. 4 illustrates a schematic block diagram depicting an architecture of a computing system, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION OF THE INVENTION

[0020] Inventive concepts of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which examples of one or more embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Further, the one or more embodiments disclosed herein are provided to describe the inventive concept thoroughly and completely, and to fully convey the scope of each of the present inventive concepts to those skilled in the art. Furthermore, it should be noted that the embodiments disclosed herein are not mutually exclusive concepts. Accordingly, one or more components from one embodiment may be tacitly assumed to be present or used in any other embodiment.

[0021] The following description presents various embodiments of the present disclosure. The embodiments disclosed herein are presented as teaching examples and are not to be construed as limiting the scope of the present disclosure. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified, omitted, or expanded upon without departing from the scope of the present disclosure.

[0022] The following description contains specific information pertaining to embodiments in the present disclosure. The detailed description uses the phrases “in some embodiments” or “some implementations” which may each refer to one or more or all of the same or different embodiments or implementations. The term“some” as used herein is defined as “one, or more than one, or all.” Accordingly, the terms “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” In view of the same, the terms, for example, “in an embodiment” or “in an implementation” refers to one embodiment or one implementation and the term, for example, “in one or more embodiments” refers to “at least one embodiment, or more than one embodiment, or all embodiments.” Further, the term, for example, “in one or more implementations” refers to “at least one implementation, or more than one implementation, or all implementations.

[0023] The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “contains,” and other similar words are used in either the detailed description, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[0024] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features.

[0025] The description provided herein discloses exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the foregoing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing any of the exemplary embodiments. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it may be understood by one of the ordinary skilled in the art that the embodiments disclosed herein may be practiced without these specific details.

[0026] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein the description, the singular forms "a", "an", and "the" include plural forms unless the context of the invention indicates otherwise.

[0027] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure. Accordingly, unless otherwise defined, all terms, and especially any technical and / or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.

[0028] The present disclosure provides a system and a method for providing personalized module recommendations to users while using a digital platform. By leveraging data collected from user interactions with various modules in the digital platform, the system analyzes individual user behavior, identifies preferences, and detects recurring patterns.

[0029] An aspect of the present disclosure is to provide a system and a method that generates module suggestions aligned with unique preferences of the users and usage patterns.

[0030] Another aspect of the present disclosure is to provide a system and a method that analyzes the user interactions and recommend existing features within the digital platform that reduce user effort and improve workflow efficiency.

[0031] Yet another aspect of the present disclosure is to provide a system and a method that facilitates leveraging an advanced data processing and machine learning techniques to identify recurring patterns in user behavior and correlations between actions and module preferences of the users.

[0032] Several key terms used in the description play pivotal roles in facilitating the system functionality. In order to facilitate an understanding of the description, the key terms are defined below.

[0033] User profiles: The term user profiles may refer to structured digital representations of individual users within the digital platform, generated based on collected and processed data. A user profile may include information such as historical interactions, preferred modules, usage frequency, navigation behavior, and feedback data related to the user.

[0034] Module preferences- The term module preferences may refer to a tendency of the user to interact with certain modules within the digital platform more frequently or favorably than other modules.

[0035] The personalized module recommendations- The term personalized module recommendations may refer to dynamically generated suggestions of one or more modules that are identified as relevant to a particular user, based on the user’s profile, the usage patterns, preferences, and historical behavior.

[0036] The usage patterns- The term usage patterns may refer to observable trends, behaviors, or sequences of actions performed by the user during interaction with the one or more modules or features of the digital platform. These patterns may include, but are not limited to, frequency of module access, duration of module usage, and navigation paths taken within the digital platform.

[0037] Recurring usage patterns- The term recurring usage patterns may refer to the usage patterns that occur repeatedly over the time, thereby indicating consistent behavior by the user.

[0038] The processed data- The term processed data may refer to structured and refined data obtained after performing one or more processing operations on raw user data collected from the digital platform.

[0039] The correlations- The term correlations may refer to statistically or behaviorally linked relationships between user actions and module preferences while using the digital platform.

[0040] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. FIG. 1 to FIG. 4, discussed below, and the one or more embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system or device.

[0041] FIG. 1 illustrates a schematic block diagram depicting a communication system 100 for providing the personalized module recommendations to the users, in accordance with an exemplary embodiment of the present disclosure. Other embodiments of the communication system 100 may be used without departing from the scope of this disclosure.

[0042] As shown in FIG. 1, the communication system 100 (hereinafter may be referred as “the system 100”) may include a user device 110, a network 120, a server 130, and a database 150. The server 130 communicates with each of the user device 110, and the database 150 via the network 120.

[0043] The user device 110 may include a User Interface (UI) 110-1, a communication unit 110-2, a processor 110-3 (may also be referred to as “one or more processors 110-3”) and a memory 110-4. The UI 110-1 may enable interaction between the user and the user device 110. The communication unit 110-2 within the user device 110 may enable communication of the user device 110 with the server 130 for data exchange. The processor 110-3 may execute operating system instructions stored in the memory 110-4 in order to control the overall operation of the user device 110.

[0044] The processor 110-3 is configured to execute programs and instructions stored in the memory 110-4. The processor 110-3 is further configured to move data into or out of the memory 110-4 as required by an executing process. The processor 110-3 may also be configured to execute the one or more applications based on an operating system or in response to signals received from gNBs or an operator. The processor 110-3 may also be coupled to an I / O interface, which provides the user device 110 with an ability to connect to other devices, such as laptop computers and handheld computers. The I / O interface may act as a communication path between the above-described user device components and the processor 110-3. The processor 110-3 may include an intelligent hardware device including a general -purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, or the like, a graphics- only processing unit such as a Graphics Processing Unit (GPU), a microcontroller, a Field-Programmable Gate Array (FPGA), a programmable logic device, a discrete hardware component, or any combination thereof.

[0045] The memory 110-4 may include any type of computer-readable medium usable by a computer or the processor 110-3, such as a Read-Only Memory (ROM), a Random-Access Memory (RAM), a flash memory, a removable storage drive, a Hard Disc Drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read-Only Memory (PROM), an Erasable Programmable Read- Only Memory (EPROM), and / or an Electrically Erasable Programmable Read-Only Memory EEPROM. In an aspect, for example, the memory 110-4 may be a non- transitory computer-readable storage medium that stores one or more computerexecutable codes or instructions.

[0046] The user device 110 may include, but not limited to, smartphones, tablets, laptops, desktop computers, Personal Digital Assistants (PDAs), smartwatches, or any other computing device capable of network connectivity. The user may include, but is not limited to, an end user operating the digital platform, or a system administrator. The user interacts with various modules of the digital platform through the user device 110. The user device 110 communicates with the digitalplatform over the network 120. The digital platform may refer to any software-based system, application, or environment that enables user interaction with various modules or features via the network 120. The digital platform may facilitate user engagement, collects the data on user behavior, processes the data, and delivers personalized content, services, or the personalized module recommendations. For instance, an enterprise productivity suite, within such digital platforms, the users may interact with multiple modules, but not limited to, email, calendars, analytics tools, document editors, dashboards, etc. The system 100 may monitor user activity across these modules to provide the personalized module recommendations.

[0047] The network 120 enables communication between components of the system 100. The network 120 may include suitable logic, circuitry, and interfaces that may be configured to provide several network ports and several communication channels for transmission and reception of data related to operations of various entities of the system 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The network 120 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the various entities of the system 100. The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of several communication channels in the network 120. The communication channels may include, but are not limited to, a wirelesschannel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, an optical fiber network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and / or later developed technologies.

[0048] The server 130 may function as a central entity responsible for storing, processing, and delivering a content to the user device 110 over the network 120. The server 130 responds to requests from the user device 110 and ensures seamless data transmission.

[0049] The server 130 may be a network of computers, a software framework, or a combination thereof, that may provide a generalized approach to create a server implementation. Examples of the server 130 may include, but are not limited to, personal computers, laptops, mini -computers, mainframe computers, any nontransient and tangible machine that can execute a machine-readable code, cloudbased servers, distributed server networks, or a network of computer systems. The server 130 may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a Personal Home Page (PHP) framework, or any web-application framework.

[0050] The server 130 may include a processor 140 (may also be referred as “one or more processors 140”), a communication interface 142, and a memory 144. The processor 140 may include various processing circuitry / units and communicates with the memory 144 and the communication interface 142. The processor 140 is configured to execute the instructions stored in the memory 144 and to perform various processes. The processor 140 may include the intelligent hardware deviceincluding the general -purpose processor, such as, for example, and without limitation, the CPU, the AP, the dedicated processor, or the like, the graphics-only processing unit such as the GPU, the microcontroller, the FPGA, the programmable logic device, the discrete hardware component, or any combination thereof.

[0051] The communication interface 142 may be configured to enable the server 130 to communicate with various entities of the system 100 via the network 120. Examples of the communication interface 142 may include, but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and / or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a Radio Frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a Coder-Decoder (CODEC) chipset, a Subscriber Identity Module (SIM) card, and a local buffer circuit. It will be apparent to a person of ordinary skill in the art that the communication interface 142 may include any device and / or apparatus capable of providing wireless or wired communications between the server 130 and various other entities of the system 100.

[0052] The memory 144 may be configured to store the logic, instructions, circuitry, interfaces, and / or codes of the processor 140 for executing various operations. Examples of the memory 144 may include but are not limited to, the ROM, the RAM, the flash memory, the removable storage drive, the HDD, the solid-state memory, the magnetic storage drive, the PROM, the EPROM, and / or the EEPROM.

[0053] The database 150 may serve as a repository for storing individual user profiles, historical interaction data, the preferences, and behavioral patterns.

[0054] Although FIG. 1 illustrates one example of the system 100, various changes may be made to FIG. 1. For example, the system 100 may include any number of user devices and servers in any suitable arrangement. Further, in another example, the system 100 may include any number of components in addition to the components shown in FIG. 1. Further, various components in FIG. 1 may becombined, further subdivided, or omitted and additional components may be added according to particular needs.

[0055] FIG. 2 illustrates a block diagram depicting a system architecture of the server 130, in accordance with an exemplary embodiment of the present disclosure.

[0056] The server 130 may include the processor 140, the communication interface 142, the memory 144, processing engines 202, and an Input-Output (I / O) interface 204. Each of the components of the server 130 is communicatively coupled to each other via a first communication bus 206.

[0057] The processing engines 202 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the server 130. In non-limiting examples, described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engines 202 may be processor-executable instructions stored on a non-transitory machine- readable storage medium and the hardware for the processor 140 may execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engines 202. In such examples, the server 130 may also comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the server 130 and the processing resource. In other examples, the processing engines 202 may be implemented using an electronic circuitry.

[0058] The I / O interface 204 may include suitable logic, circuitry, interfaces, and / or codes that may be configured to receive input(s) and present (or display) output(s) on the server 130. For example, the I / O interface 204 may have an input interface (not shown) and an output interface (not shown). The input interface may be configured to enable the user to provide input(s) to trigger (or configure) the server 130 for performing data processing operation(s) for identifying the video resolution.Examples of the input interface may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. The output interface may be configured to display (or present) output(s) generated (or provided) by the server 130. In some aspects of the present disclosure, the output interface may provide the output(s) based on an instruction provided by the user of the server 130, by way of the input interface. Examples of the output interface may include, but are not limited to, a digital display, an analog display, a touch screen display, an appearance of a desktop, and / or illuminated characters. Aspects of the present disclosure are intended to include or otherwise cover any type of the input interface and output interface in the I / O interface 204, including known, related art, and / or later developed technologies without deviating from the scope of the present disclosure.

[0059] According to an example embodiment, the processing engines 202 may include a data collection engine 210, a data processing engine 212, a user profiling engine 214, a pattern recognition engine 216, a generation engine 218 and a delivery engine 220. Various modules of the processing engines 202 may be communicatively coupled to each other by way of a second communication bus 208.

[0060] Referring to FIG. 2, the data collection engine 210 is configured to collect data associated with one or more users from a plurality of sources within the digital platform. The data processing engine 212 is configured to perform one or more processing operations on the data to generate processed data. The user profiling engine 214 is configured to create profiles of the one or more users based on the processed data. The pattern recognition engine 216 is configured to identify the recurring usage patterns and the correlations between the actions and the module preferences of the one or more users based on the profiles. The generation engine 218 is configured to generate the personalized module recommendations for each user among the one or more users based on the recurring usage patterns and the correlations. The delivery engine 220 is configured to notify the personalized module recommendations to the one or more users.

[0061] The user profiling engine 214 is further configured to extract historical data corresponding to the one or more users from the processed data and analyze the historical data corresponding to the one or more users to identify the usage patterns and the module preferences of the one or more users. The user profiling engine 214 is further configured to update the profiles of the one or more users based on the analysis of the historical data.

[0062] Further, the generation engine 218 is configured to apply machine learning algorithms to identify the recurring usage patterns and the correlations between the actions and the module preferences of the one or more users and generate the personalized module recommendations based on the identified recurring usage patterns and the correlations.

[0063] Although FIG. 2 illustrates one example of the server 130, various changes may be made to FIG. 2. Further, the server 130 may include any number of components in addition to those shown in FIG. 2, without deviating from the scope of the present disclosure. Further, various components in FIG. 2 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.

[0064] FIG. 3 illustrates a flowchart depicting a method 300 for providing the personalized module recommendations in the digital platform, in accordance with an embodiment of the present disclosure. The method 300 comprises a series of operation steps indicated by blocks 302 through 312. The method 300 starts at step 302.

[0065] At step 302, the data collection engine 210 may collect the data from the plurality of sources within the digital platform. The data may include user activity logs, navigation paths, interactions with various modules and the features, and general usage patterns associated with the one or more users. The data collection process may occur in real-time or over a period to capture historical usage data for enhanced accuracy. In an embodiment, the data may be collected, weekly, monthly or yearly.

[0066] At step 304, the data processing engine 212 may process the collected data by performing one or more processing operations. The operations may include cleaning the raw data, filtering irrelevant or redundant information from the data, aggregating the cleaned data into a unified dataset, and categorizing the aggregated data. The data processing engine 212 may categorize the actions of the users based on the categorized data and associate the actions with the module preferences of the users. The actions here may refer to user-initiated activities performed on the digital platform. The actions may include, but not limited to, logging in to the digital platform, accessing or opening modules, clicking buttons or UI elements, editing, viewing, sharing, downloading, or uploading content, customizing settings on the digital platform. For instance, the user editing a document, then exporting the document to a Portable Document Format (PDF), are both considered as the actions. The module preferences may denote specific modules, tools, or features that the user consistently uses, or engages with more frequently than other modules. The module preferences may be inferred through usage frequency, duration of modules usage, click-through rates or interaction depth, or user-configured settings (e.g., pinned or favorited modules). For instance, If the user consistently uses calendar and task manager modules but ignores an email module, the module preference may reflect a focus on scheduling and productivity planning.

[0067] At step 306, the user profiling engine 214 may create the user profiles based on the processed data. The creation of the user profiles involves organizing the processed data associated with the users to generate a comprehensive behavioral profile. The user profiling engine 214 constructs the user profiles by analyzing parameters such as the frequency of the module access, the duration of the module usage, the navigation paths within the digital platform, historical actions performed and user preferences. For instance, if the user regularly accesses say, a data visualization module within the digital platform and spends the majority of time on dashboard customization, the user profiling engine 214 captures this behavioral pattern and builds the user profile reflecting a high preference for analytical tools. In one or more embodiments, the user profiling engine 214 may extract the historicaldata for each user and analyzes the historical data to identify patterns in usage of the modules, and interaction frequencies. The user profiles are continually updated based on the analysis of the historical data. The historical data may refer to previously collected and stored interaction-related information of the users within the digital platform over a period of time. The historical data represents chronological record of user activities, behaviors, preferences, and module interactions. The historical data may include but is not limited to timestamps of module access events, the frequency of the usage of specific modules or features, sequences and durations of user sessions, user-generated content or inputs in specific modules, system logs capturing the actions. For instance, if the user has accessed the time tracking module 40 times over the past two months and tends to follow the pattern with the report generation module during 60% of the usage of the modules, this user interaction pattern constitutes the historical data. Further, the user profiling engine 214 periodically updates the user profiles by reanalyzing newly collected usage data over the time. This may be done at regular intervals (e.g., daily, weekly) or triggered dynamically when new data exceeds a threshold volume. In an implementation, the user profiling engine 214 updates the user profiles by reflecting any changes in usage behavior such as interest in new module categories or reduced usage of previously popular features.

[0068] At step 308, the pattern recognition engine 216 may identify recurring usage patterns and the correlations between the user actions and the module preferences based on the user profiles. The user profiles and the historical data are analyzed to detect consistent user behaviors i.e. the recurring usage patterns and discover associations between specific user actions and selected modules i.e. the correlations. In one or more embodiments, the identification may utilize statistical techniques or the machine learning algorithms to access the correlations. For instance, if multiple users who frequently edit documents also tend to access collaboration tools, the pattern recognition engine 216 may recognize the correlation between document editing and collaborative module preference.

[0069] The pattern recognition engine 216 may detect common sequences, trends, and the behavioral patterns among the users.

[0070] At step 310, the generation engine 218 may generate the personalized module recommendations specific to each user. By applying the identified usage patterns and the correlations, the generation engine 218 predicts the most relevant modules or features that align with needs or preferences of the user. The usage patterns here are identified by analyzing the user’s interactions with various modules over the time. These interactions may include, but not limited to, metrics such as the frequency of access, the durations of the module usage, the module navigation paths, sequence of actions performed, feature utilization rates, and the time of usage. For instance, if the user consistently accesses a task management module after visiting a project overview module within the digital platform, this indicates a sequential usage pattern. If this behavior is repeated across multiple sessions, then the pattern is considered as the recurring usage pattern. Further, in one or more embodiments, the generation engine 218 may address user challenges while using the digital platform. Few existing features are integrated into the recommendation process to provide a comprehensive solution. By suggesting both the modules and the relevant features, the system delivers complete recommendations that significantly improve the user experience. Further, to ensure that the recommendations remain relevant and aligned with the evolving user needs, the generation engine 218 incorporates a feedback loop. The users are encouraged to provide the feedback on the personalized module recommendations, which is analyzed by the generation engine 218 to modify future suggestions. The machine learning component continuously learns from user input and adapts recommendation strategy over the time. This iterative process ensures that the system remains dynamic and capable of providing up-to-date, effective recommendations.

[0071] At step 312, the delivery engine 220 may deliver the personalized module recommendations to the user. In an embodiment, the personalized module recommendations may be notified via in-app alerts, emails, or personalizeddashboards within the digital platform. This enables the users to easily access and act upon the recommendations, explore new modules, and improve interaction efficiency.

[0072] In one embodiment, the personalized module recommendations generated for the user may include not only existing modules identified through the analysis of the recurring usage patterns and the correlations, but also newly introduced or newly launched modules. The new modules are evaluated to determine if their features or functionalities align with the user’s historical actions and the preferences. If a match is found, such new modules are incorporated into recommendation list to ensure that the user is made aware of potentially relevant modules, even if the users have not yet interacted with such new modules. This allows the system 100 to remain adaptive, thereby improving user engagement and satisfaction. For instance, if the user frequently engages with the document editing and sharing modules, and a new module offering Al-assisted document summarization is launched, the system 100 recognizes the feature alignment and includes the new module in the personalized module recommendations sent to that user.

[0073] FIG. 4 illustrates a schematic block diagram of a computing system 400, in accordance with an embodiment of the present disclosure.

[0074] The computing system 400 includes a network 402, a network interface 404, a processor 406, an Input / Output (I / O) interface 408 and a non-transitory computer readable storage medium 410 (hereinafter may also be referred to as the “storage medium 410” or the “storage media 410”).

[0075] The network interface 404 includes wireless network interfaces such as Bluetooth, Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), or Wideband Code Division Multiple Access (WCDMA) or wired network interfaces such as Ethernet, Universal Serial Bus (USB), or Institute of Electrical and Electronics Engineers-864 (IEEE-864).

[0076] The processor 406 may include various processing circuitry / modules and communicate with the storage medium 410 and the I / O interface 408. The processor 406 is configured to execute instructions stored in the storage medium 410 and to perform various processes. The processor 406 may include an intelligent hardware device including a general -purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), a dedicated processor, or the like, a graphics-only processing unit such as a GPU, a microcontroller, a programmable logic device, a discrete hardware component, or any combination thereof. The processor 406 may be configured to execute computer-readable instructions 410-2 stored in the storage medium 410 to cause the system 100 to perform various functions.

[0077] The storage medium 410 stores a set of instructions i.e., computer program instructions 410-1 (hereinafter may also be referred to as instructions 410-1) required by the processor 406 for controlling its overall operations.

[0078] The storage media 410 may include an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, or the like. For example, the storage media 410 may include, but are not limited to, hard drives, floppy diskettes, optical disks, flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. In one or more embodiments, the storage media 410 includes a Compact Disk-Read Only Memory (CD-ROM), a Compact Disk-Read / Write (CD-R / W), and / or a Digital Video Disc (DVD).

[0079] In one or more implementations, the storage medium 410 stores computer program code configured to cause the computing system 400 to perform at least a portion of the processes and / or methods. Accordingly, in at least one implementation, the computing system 400 performs the method for providing the personalized module recommendations in the digital platform.

[0080] Embodiments of the present disclosure have been described above with reference to flowchart illustrations of methods and systems according toembodiments of the disclosure, and / or procedures, algorithms, steps, operations, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of the flowchart, and combinations of blocks (and / or steps) in the flowchart, as well as any procedure, algorithm, step, operation, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and / or software including one or more computer program instructions embodied in computer-readable program code. As will be appreciated, any such computer program instructions may be executed by one or more computer processors, including without limitation a general-purpose computer or special purpose computer, or other programmable processing apparatus to perform a group of operations comprising the operations or blocks described in connection with the disclosed method.

[0081] Further, these computer program instructions, such as embodied in computer-readable program code, may also be stored in one or more computer- readable memory or memory devices (ex. the storage medium 410) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions 410-2 stored in the computer-readable memory or memory devices produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s).

[0082] It will further be appreciated that the term “computer program instructions” as used herein refer to one or more instructions that can be executed by the one or more processors (for example, the processor 406) to perform one or more functions as described herein. The instructions 410-2 may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.

[0083] Separate instances of these methods / processes may be executed on or distributed across any number of separate computer systems. A variety of alternative implementations will be understood by those having ordinary skill in the art.

[0084] Now, referring to the technical abilities and advantageous effect of the present disclosure, operational advantages that may be provided by one or moreembodiments may include providing the system and the method that enhances user experience by analyzing the user behavior and providing the personalized module recommendations. The users receive suggestions that align with their specific needs and the preferences, leading to a more intuitive and enjoyable interaction with the digital platform.

[0085] Another noteworthy advantage provided by the one or more embodiments may include, but not limited thereto, the personalized module recommendations encourage the users to explore new features and functionalities, increasing their engagement with the digital platform. By highlighting the relevant modules based on the individual usage patterns, the users are more likely to discover and utilize additional capabilities, thereby maximizing the value they derive from the platform. By recommending the features to reduce the user efforts, the disclosure streamlines the workflows and improves productivity.

[0086] The users spend less time searching for the relevant modules and the features, allowing them to accomplish tasks more efficiently and effectively. Whether the users are seeking specific functionalities or exploring new capabilities, the disclosure provides customized solutions that meet their individual needs. Further, the system leverages the machine learning and data analytics to continuously modify and refine the module suggestions based on evolving user behavior and the preferences. The iterative process ensures that the recommendations remain relevant and effective over the time, adapting to changes in the user needs and platform capabilities.

[0087] Those skilled in the art will appreciate that the methodology described herein in the present disclosure may be carried out in other specific ways than those set forth herein in the above disclosed embodiments without departing from essential characteristics and features of the present invention. The above-described embodiments are therefore to be construed in all aspects as illustrative and not restrictive.

[0088] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Any combination of the above features and functionalities may be used in accordance with one or more embodiments.

[0089] In the present disclosure, each of the embodiments has been described with reference to numerous specific details which may vary from embodiment to embodiment. The foregoing description of the specific embodiments disclosed herein may reveal the general nature of the embodiments herein that others may, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications are intended to be comprehended within the meaning of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and is not limited in scope.LIST OF REFERENCE NUMERALS

[0090] The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:100-Communication system / System110 - User device110-1- User Interface (UI)110-2- Communication Unit110-3- Processor110-4- Memory120- Network130- Server140- Processor of the server142- Communication Interface144- Memory of the server150- Database202- Processing engines204- Input / Output interface206- First communication bus208- Second communication bus210- Data collection engine212- Data processing engine214- User profiling engine216- Pattern recognition engine218- Generation engine220- Delivery engine300- Method for providing personalized module recommendations in a digital platform302- 312- Steps to perform method 300400- Computing System402- Network404- Network Interface406- Processor408- I / O interface410- Non transitory computer readable storage medium410-2- Instructions

Claims

We Claim:

1. A method (300) for providing personalized module recommendations in a digital platform, the method comprising: collecting, by a data collection engine (210) from a plurality of sources within the digital platform, data associated with one or more users; performing, by a data processing engine (212), one or more processing operations on the data to generate processed data; creating, by a user profiling engine (214), profiles of the one or more users based on the processed data; identifying, by a pattern recognition engine (216) based on the profiles, recurring usage patterns and correlations between actions and module preferences of the one or more users; generating, by a generation engine (218) based on the recurring usage patterns and the correlations, the personalized module recommendations for each user among the one or more users; and notifying, by a delivery engine (220), the personalized module recommendations to the one or more users.

2. The method (300) as claimed in claim 1, further comprising: extracting, by the user profiling engine (214), historical data corresponding to the one or more users from the processed data; analysing, by the user profiling engine (214), the historical data corresponding to the one or more users to identify usage patterns and the module preferences of the one or more users; and periodically updating, by the user profiling engine (214), the profiles of the one or more users based on the analysis of the historical data.

3. The method (300) as claimed in claim 1, wherein the data associated with the one or more users is collected from the plurality of sources comprising user activity logs, navigation paths within the digital platform, interactions of the one ormore users with a plurality of modules, usage patterns of features of the digital platform.

4. The method (300) as claimed in claim 1, wherein the one or more processing operations include filtering the data, aggregating the filtered data, and categorizing the aggregated data.

5. The method (300) as claimed in claim 4, wherein the categorizing the aggregated data comprises categorizing, by the data processing engine (212), the actions of the one or more users and associating the actions with the module preferences of the one or more users.

6. The method (300) as claimed in claim 1, wherein the generating the personalized module recommendations comprises: applying Machine Learning (ML) algorithms to identify the recurring usage patterns and the correlations between the actions and the module preferences of the one or more users; and generating the personalized module recommendations based on the identified recurring usage patterns and the correlations.

7. A system (100) for providing personalized module recommendations in a digital platform, the system comprising: a data collection engine (210) configured to collect, from a plurality of sources within the digital platform, data associated with one or more users; a data processing engine (212) configured to perform one or more processing operations on the data to generate processed data; a user profiling engine (214) configured to create profiles of the one or more users based on the processed data; a pattern recognition engine (216) configured to identify, based on the profiles, recurring usage patterns and correlations between actions and module preferences of the one or more users;a generation engine (218) configured to generate, based on the recurring usage patterns and the correlations, the personalized module recommendations for each user among the one or more users; and a delivery engine (220) configured to notify the personalized module recommendations to the one or more users.

8. The system (100) as claimed in claim 7, wherein the user profiling engine (214) is further configured to: extract historical data corresponding to the one or more users from the processed data; analyse the historical data corresponding to the one or more users to identify usage patterns and the module preferences of the one or more users; and update the profiles of the one or more users based on the analysis of the historical data.

9. The system (100) as claimed in claim 7, wherein the data associated with the one or more users is collected from the plurality of sources comprising user activity logs, navigation paths within the digital platform, interactions of the one or more users with a plurality of modules, usage patterns of features of the digital platform.

10. The system (100) as claimed in claim 7, wherein the one or more processing operations include filtering the data, aggregating the filtered data, and categorizing the aggregated data.

11. The system (100) as claimed in claim 10, wherein, to categorize the aggregated data, the data processing engine is further configured to categorize the actions of the one or more users and associate the actions with the module preferences of the one or more users.

12. The system (100) as claimed in claim 7, wherein the generation engine (218) is configured to:apply Machine Learning (ML) algorithms to identify the recurring usage patterns and the correlations between the actions and the module preferences of the one or more users; and generate the personalized module recommendations based on the identified recurring usage patterns and the correlations.

13. A computer program product comprising computer-executable instructions that are stored on a non-transitory computer-readable medium and that, when executed by at least one processing unit performs operations comprising: collecting, from a plurality of sources within a digital platform, data associated with one or more users; performing one or more processing operations on the data to generate processed data; creating profiles of the one or more users based on the processed data; identifying, based on the profiles, recurring usage patterns and correlations between actions and module preferences of the one or more users; generating, based on the recurring usage patterns and the correlations, module recommendations for each user among the one or more users; and notifying the module recommendations to the one or more users.

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