Description:We Claim
1. A system for aggregating and optimizing multi-platform eCommerce and quick commerce shopping, comprising:

a computing device comprising a processor for executing instructions from a products aggregator module located within the computing device, wherein the products aggregator module is configured to enable a user to search and select one or more products by entering product names and preferences into a search interface, thereby transmitting the user-selected one or more product details and preferences to a cloud server over a network;

the cloud server configured to receive one or more product details and preferences from the products aggregator module, wherein the cloud server comprises a products data aggregation and optimization module configured to collect and aggregate one or more products data from multiple eCommerce and quick commerce platforms using APIs and web scraping technologies based on the received one or more product details and preferences, the products data aggregation and optimization module configured to compare product prices, availability, and promotional offers from multiple eCommerce and quick commerce platforms, and determine optimal product selections based on user-defined preferences for price, delivery speed, and platform choice;

the products data aggregation and optimization module generates a customized shopping list for the user, which allocates one or more selected products to specific platforms according to price, delivery speed, and availability, thereby the products data aggregation and optimization module transmits the generated customized shopping list to the computing device over the network; and

the computing device comprises the products aggregator module configured to receive the generated customized shopping list, the products aggregator module enables the user to access the generated customized shopping list and enable the user to review the listed products, select desired products, confirm, and finalize the shopping order, whereupon the products aggregator module transmits the confirmed order details to the cloud server for execution, and redirect the user to a payment process upon confirmation of the order, wherein the products aggregator module provides an interface for the user to manage preferences, track order status, and receive alerts for future deals and offers across selected platforms.

2. The system as claimed in claim 1, wherein the processor executes instructions from the products aggregator module, the products aggregator module comprises a products selection module is configured to enable the user to search and filter one or more products from multiple eCommerce and quick commerce platforms based on criteria such as price, brand, availability, and user ratings.

3. The system as claimed in claim 1, wherein the processor executes instructions from the products aggregator module, the products aggregator module comprises a shopping list creation module is configured to allow the user to create, modify, and save one or more shopping lists for future purchases, including support for multiple lists categorized by product type, preferences, or purchase timeframes.

4. The system as claimed in claim 1, wherein the processor executes instructions from the products aggregator module, the products aggregator module comprises a user preferences selection module is configured to allow the user to set platform-specific preferences, including brand preferences, price ranges, and delivery speed preferences.

5. The system as claimed in claim 4, wherein the user preferences selection module is configured to allow the user to select specific platform-specific preferences (e.g., always order fruits from Provider A and grains from Provider B) and set default delivery timelines for specific products.

6. The system as claimed in claim 1, wherein the processor executes instructions from the products aggregator module, the products aggregator module comprises an order management and scheduling module is configured to enable the user to schedule orders for specific delivery times and prioritize items based on perishability, thereby allowing flexible delivery arrangements for each order.

7. The system as claimed in claim 1, wherein the processor executes instructions from the products aggregator module, the products aggregator module comprises an offers alerting module is configured to provide real-time notifications to the user about active discounts, flash deals, and promotions that match user preferences, ensuring timely access to cost-saving opportunities.

8. The system as claimed in claim 1, wherein the processor executes instructions from the products aggregator module, the products aggregator module comprises a subscription management module is configured to allow the user to subscribe to regular deliveries of frequently purchased products, such as daily or weekly essentials, including features to adjust delivery frequency and pause or resume subscriptions.

9. The system as claimed in claim 1, wherein the cloud server executes instructions from the products data aggregation and optimization module, the products data aggregation and optimization module comprises a data collection module is configured to gather product information from multiple eCommerce and quick commerce platforms, including details such as product prices, availability, descriptions, ratings, and promotional offers.

10. The system as claimed in claim 1, wherein the cloud server executes instructions from the products data aggregation and optimization module, the products data aggregation and optimization module comprises a price comparison module is configured to compare prices of identical or similar products across multiple eCommerce and quick commerce platforms, thereby identifying the most cost-effective options for each product based on real-time data, including base prices, discounts, and promotional offers.

11. The system as claimed in claim 1, wherein the cloud server executes instructions from the products data aggregation and optimization module, the products data aggregation and optimization module comprises an AI recommendation module is configured to analyze user purchasing history and provide personalized recommendations, including suggested order frequencies based on historical shopping patterns.

12. The system as claimed in claim 1, wherein the cloud server executes instructions from the products data aggregation and optimization module, the products data aggregation and optimization module comprises a dynamic order scheduling module is configured to schedule and prioritize deliveries of selected products based on user preferences, product urgency, and availability, adjusts delivery times automatically in accordance with platform-specific delivery slots and constraints to meet the user’s requirements.

13. The system as claimed in claim 1, wherein the cloud server executes instructions from the products data aggregation and optimization module, the products data aggregation and optimization module comprises an order splitting module is configured to allocate products within the user’s shopping list to different platforms to optimize cost, delivery speed, and product availability.

14. The system as claimed in claim 1, wherein the cloud server executes instructions from the products data aggregation and optimization module, the products data aggregation and optimization module comprises a notifications generating module is configured to provide real-time alerts regarding price changes, availability updates, promotions, and delivery status for the user’s selected products.

15. A method for aggregating and optimizing multi-platform eCommerce and quick commerce shopping, comprising:

enabling a user to search and select one or more products by entering product names and preferences into a search interface by a products aggregator module enabled in a computing device;

transmitting the user-selected one or more product details and preferences to a cloud server over a network by the products aggregator module;

receiving the user-selected one or more product details and preferences by the cloud server from the computing device;

collecting and aggregating product data from multiple eCommerce and quick commerce platforms based on the received product details and preferences by a products data aggregation and optimization module enabled in the cloud server;

comparing product prices, availability, and promotional offers across the multiple eCommerce and quick commerce platforms by the products data aggregation and optimization module;

determining optimal product selections based on user-defined preferences for price, delivery speed, and platform choice by the products data aggregation and optimization module;

generating a customized shopping list with the user selected products to specific platforms according to price, delivery speed, and availability by the products data aggregation and optimization module;

transmitting the generated customized shopping list from the cloud server to the computing device by the products data aggregation and optimization module;

receiving the customized shopping list by the products aggregator module and enabling the user to access the generated customized shopping list, review the listed products, select desired products, confirm, and finalize the shopping order;

transmitting the confirmed order details from the computing device to the cloud server for execution, and redirecting the user to a payment process upon confirmation of the order; and
enabling the user to manage preferences, track order status, and receive alerts for future deals and offers across selected platforms by the products aggregator module
, Claims:TECHNICAL FIELD
[001] The present disclosure generally relates to the field of eCommerce and quick commerce technologies. More particularly, the present disclosure relates to a system and method for aggregating and optimizing multi-platform ecommerce and quick commerce shopping. Additionally, the present disclosure employs artificial intelligence (AI) to analyze user purchasing patterns, provide personalized recommendations, and optimize order placement across various eCommerce and quick commerce platforms.

BACKGROUND
[002] In the digital era, online shopping has become a convenient alternative to traditional shopping, with eCommerce and quick commerce platforms providing users with access to a wide array of products. However, this convenience comes with its own challenges, as users often face a fragmented shopping experience when purchasing everyday essentials. Specifically, users frequently shop across multiple platforms to take advantage of unique product offerings, exclusive brands, and platform-specific discounts. This cross-platform shopping behavior stems from the fact that each eCommerce and quick commerce platform, such as Zepto, Blinkit, Big Basket, Amazon, and Flipkart, often has exclusive partnerships, distinct product selections, and varied pricing structures. Each platform frequently offers different products, prices, delivery options, and exclusive discounts, making it challenging for users to identify the optimal purchasing strategy across platforms.

[003] This fragmented ecosystem poses several technical challenges. First, users must manually search through various applications or websites, a time-consuming process that requires substantial effort and attention to detail. This manual approach can result in inconsistencies in pricing and delivery options, where users may miss out on the most cost-effective or fastest options available due to limited visibility across platforms. Additionally, customers often have specific preferences regarding where certain items are sourced, based on quality, reliability, or brand loyalty, but existing systems lack a centralized mechanism to apply such preferences automatically. One major issue is that prices for similar items can differ significantly across platforms, with each provider offering its own set of discounts, promotions, and price points. This results in a notable variation in grocery costs; users may save up to 12-15% on their monthly purchases by optimizing orders across different platforms. However, this process of manually comparing prices and availability across multiple applications is time-consuming and labor-intensive, requiring users to switch between platforms to identify the best deals for each item.

[004] Another technical challenge is managing delivery expectations. With the rise of quick commerce platforms offering rapid delivery for select items, users may want some items delivered within minutes, while other less urgent items can be scheduled for later delivery. Current systems do not provide a way to customize delivery speeds across platforms, requiring users to place separate orders or compromise on delivery timing. This fragmented experience and lack of an integrated solution create a pressing need for a system that can unify the shopping process, enabling users to effortlessly compare products, optimize their shopping lists, and set customized delivery options across platforms. Such a system would not only improve user convenience but also ensure cost savings by automatically aggregating products and recommending optimal ordering based on user-defined parameters, purchasing history, and real-time data.

[005] In the light of the aforementioned discussion, there exists a need for a system with novel methodologies that would overcome the above- mentioned challenges.

SUMMARY
[006] The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[007] Exemplary embodiments of the present disclosure are directed towards a system and method for aggregating and optimizing multi-platform ecommerce and quick commerce shopping.

[008] An objective of the present disclosure is directed towards a system that provides a unified shopping experience by aggregating products across multiple eCommerce and quick commerce platforms, allowing users to access a variety of products through a single platform, thereby eliminating the need to search across multiple applications.

[009] Another objective of the present disclosure is directed towards a system that enables significant cost savings by automatically comparing prices, discounts, and promotions across platforms, potentially reducing users' grocery bills by up to 12-15%.

[0010] Another objective of the present disclosure is directed towards a system that employs artificial intelligence to analyze user purchasing patterns, offering personalized recommendations based on purchase frequency and user preferences, thereby enhancing convenience and relevance.

[0011] Another objective of the present disclosure is directed towards a system that allows users to customize delivery preferences based on urgency, providing options for rapid delivery of perishable products and more flexible scheduling for non-urgent products, thus accommodating both immediate and scheduled delivery needs.

[0012] Another objective of the present disclosure is directed towards a system that optimizes orders based on real-time availability, delivery speed, and cost, ensuring that users receive the best possible outcomes across all selected platforms.

[0013] Another objective of the present disclosure is directed towards a system that allows users to set preferred providers for specific categories, such as ordering fruits from one provider and grains from another, to create a personalized and consistent shopping experience.

[0014] Another objective of the present disclosure is directed towards a system that enables intelligent order splitting across multiple providers, ensuring the best deals and quickest deliveries without additional effort from the user.

[0015] Another objective of the present disclosure is directed towards a system that provides subscription-based ordering options for frequently used products, such as daily milk delivery, offering users convenience and consistency with minimal manual input.

[0016] Another objective of the present disclosure is directed towards a system that collaborates with manufacturers to offer direct promotions and exclusive products to end customers, providing additional savings and unique purchasing options.

[0017] Another objective of the present disclosure is directed towards a system that supports the optimization of delivery logistics for providers, potentially improving service speed and efficiency for both users and providers.

[0018] Another objective of the present disclosure is directed towards a system that automates the process of adding products to the respective carts on each platform after the user has finalized their selections, reducing manual effort and streamlining the checkout process.

[0019] Another objective of the present disclosure is directed towards a system that dynamically schedules orders and adjusts product sourcing in real-time based on factors like availability, cost, and delivery speed, ensuring that users receive the most optimal combination for their purchases.

[0020] Another objective of the present disclosure is directed towards a system that offers insights and optimization tools to eCommerce and quick commerce providers, enhancing their delivery efficiency through data gathered from user demand patterns and activity on the platform.

[0021] Another objective of the present disclosure is directed towards a system that allows users to view and compare prices and availability for specific products across multiple platforms on a single screen, simplifying the process of finding the best deals or preferred brands.

[0022] Another objective of the present disclosure is directed towards a system that enables users to create shopping lists which the system automatically optimizes by selecting the best sourcing options for each product based on price, availability, delivery speed, and user preferences.

[0023] Another objective of the present disclosure is directed towards a system that offers fully automated order placement across platforms, either by automating the checkout process on respective platforms or through APIs, enabling seamless ordering directly.

[0024] Another objective of the present disclosure is directed towards a system that provides users with real-time notifications of active discounts or flash deals on products of interest, ensuring that users do not miss time-sensitive offers.

[0025] Another objective of the present disclosure is directed towards a system that helps users locate unique brands or hard-to-find products available only on specific platforms, enhancing user access to diverse and exclusive product offerings.

[0026] Another objective of the present disclosure is directed towards a system that uses AI to analyze user purchase frequency and predict order timing, providing personalized recommendations for recurring items, such as monthly staples or frequently purchased perishables.

[0027] Another objective of the present disclosure is directed towards a system that efficiently handles multiple user sessions simultaneously through a scalable pool of virtual networks.

[0028] Another objective of the present disclosure is directed towards a system that prevents service disruptions caused by IP blacklisting through periodic flushing of the virtual network pool.

[0029] Another objective of the present disclosure is directed towards a system that seamlessly integrates with service providers, ensuring accurate and up-to-date data retrieval for users.

[0030] Another objective of the present disclosure is directed towards a system that maintains secure and consistent user sessions through session affinity mechanisms.

[0031] Another objective of the present disclosure is directed towards a system that enhances the overall shopping experience by combining reliability, scalability, and security, ensuring uninterrupted access and optimization of shopping across multiple platforms.

[0032] According to an exemplary aspect of the present disclosure, a computing device comprising a processor for executing instructions from a products aggregator module located within the computing device, wherein the products aggregator module is configured to enable a user to search and select one or more products by entering product names and preferences into a search interface, thereby transmitting the user-selected one or more product details and preferences to a cloud server over a network.

[0033] According to another exemplary aspect of the present disclosure, the cloud server configured to receive one or more product details and preferences from the products aggregator module, whereby the cloud server comprises a products data aggregation and optimization module configured to collect and aggregate one or more products data from multiple eCommerce and quick commerce platforms using APIs and web scraping technologies based on the received one or more product details and preferences, the products data aggregation and optimization module configured to compare product prices, availability, and promotional offers from multiple eCommerce and quick commerce platforms, and determine optimal product selections based on user-defined preferences for price, delivery speed, and platform choice.

[0034] According to another exemplary aspect of the present disclosure, the products data aggregation and optimization module generates a customized shopping list for the user, which allocates one or more selected products to specific platforms according to price, delivery speed, and availability, thereby the products data aggregation and optimization module transmits the generated customized shopping list to the computing device over the network.

[0035] According to another exemplary aspect of the present disclosure, the computing device comprises the products aggregator module configured to receive the generated customized shopping list, the products aggregator module enables the user to access the generated customized shopping list and enable the user to review the listed products, select desired products, confirm, and finalize the shopping order, whereupon the products aggregator module transmits the confirmed order details to the cloud server for execution, and redirect the user to a payment process upon confirmation of the order, wherein the products aggregator module provides an interface for the user to manage preferences, track order status, and receive alerts for future deals and offers across selected platforms.

BRIEF DESCRIPTION OF THE DRAWINGS
[0036] In the following, numerous specific details are set forth to provide a thorough description of various embodiments. Certain embodiments may be practiced without these specific details or with some variations in detail. In some instances, certain features are described in less detail so as not to obscure other aspects. The level of detail associated with each of the elements or features should not be construed to qualify the novelty or importance of one feature over the others.

[0037] FIG. 1 is a block diagram depicting a schematic representation of a system for aggregating and optimizing multi-platform ecommerce and quick commerce shopping, in accordance with one or more exemplary embodiments.

[0038] FIG. 2 is a block diagram depicting an embodiment of the products aggregator module 112 as shown in Fig. 1, in accordance with one or more exemplary embodiments.

[0039] FIG. 3 is a block diagram depicting an embodiment of the products data aggregation and optimization module 116 as shown in Fig. 1, in accordance with one or more exemplary embodiments.

[0040] FIG. 4 is a block diagram illustrating an embodiment of the system architecture for aggregating and optimizing multi-platform eCommerce and quick commerce shopping, in accordance with one or more exemplary embodiments.

[0041] FIG. 5 is a block diagram depicting an integration of a dynamic IP management system for optimizing multi-platform eCommerce and quick commerce shopping, in accordance with one or more exemplary embodiments.

[0042] FIG. 6 is a flow diagram depicting a method for aggregating and optimizing multi-platform eCommerce and quick commerce shopping, in accordance with one or more exemplary embodiments.

[0043] FIG. 7 is a flow diagram depicting a method for managing a dynamic pool of virtual IPs to prevent blacklisting, in accordance with one or more exemplary embodiments.

[0044] FIG. 8 is a block diagram illustrating the details of a digital processing system in which various aspects of the present disclosure are operative by execution of appropriate software instructions.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0045] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0046] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0047] Referring to FIG. 1 is a block diagram 100 depicting a schematic representation of a system for aggregating and optimizing multi-platform ecommerce and quick commerce shopping, in accordance with one or more exemplary embodiments. The system 100 includes a computing device 102, a network104, a cloud server 106, a processor108, a memory110, a products aggregator module 112, an API 114, a products data aggregation and optimization module 116, a database server 118, and a database120.

[0048] The computing device 102 may include but is not limited to, a personal digital assistant, smartphones, personal computers, a mobile station, computing tablets, a handheld device, an internet enabled calling device, an internet enabled calling software, a telephone, a mobile phone, a digital processing system, and so forth. The computing device 102 may include the processor 108 in communication with the memory 110. The processor 108 may be a central processing unit. The memory 110 is a combination of flash memory and random-access memory. The processor 108 may execute instructions and process data within the system. The memory 110 may be configured to store program instructions, data, and temporary information needed for system operations. The computing device 102 may be communicatively connected with the cloud server 106 via the network 104. The network 104 may include, but not limited to, an Internet of things (IoT network devices), an Ethernet, a wireless local area network (WLAN), or a wide area network (WAN), a Bluetooth low energy network, a ZigBee network, a WIFI communication network e.g., the wireless high speed internet, or a combination of networks, a cellular service such as a 4G (e.g., LTE, mobile WiMAX) or 5G cellular data service, a RFID module, a NFC module, wired cables, such as the world-wide-web based Internet, or other types of networks may include Transport Control Protocol/Internet Protocol (TCP/IP) or device addresses (e.g. network-based MAC addresses, or those provided in a proprietary networking protocol, such as Modbus TCP, or by using appropriate data feeds to obtain data from various web services, including retrieving XML data from an HTTP address, then traversing the XML for a particular node) and so forth without limiting the scope of the present disclosure.

[0049] Although the computing device 102 is shown in FIG. 1, an embodiment of the system 100 may support any number of computing devices. The computing device 102 supported by the system 100 is realized as a computer-implemented or computer-based device having the hardware or firmware, software, and/or processing logic needed to carry out the computer-implemented methodologies described in more detail herein. The products aggregator module 112 may be any suitable applications downloaded from GOOGLE PLAY® (for Google Android devices), Apple Inc.'s APP STORE® (for Apple devices), or any other suitable database. The products aggregator module 112 may be a desktop application which runs on Windows or Linux or any other operating system and may be downloaded from a webpage or a CD/USB stick etc. In some embodiments, the products aggregator module 112 may be software, firmware, or hardware that is integrated into the computing device 102. The computing device 102 may present a web page to the user by way of a browser, wherein the webpage comprises a hyper-link may direct the user to uniform resource locator (URL).

[0050] The computing device 102 may be configured to provide an interface for the user to interact with the system. This computing device 102 may include but is not limited to, a smartphone, tablet, or personal computer. The computing device 102 may enable the user to browse products, view price comparisons, set preferences, and finalize orders across multiple eCommerce and quick commerce platforms. The computing device 102 may communicate with other system components through the network 104. The network 104 may be configured to provide communication between the computing device 102, the cloud server 106, and external eCommerce platforms and quick commerce platforms. The network 104 may include various types of networks, such as the Internet, local area networks (LAN), wide area networks (WAN), and cellular networks. The network 104 enables the transfer of data between the user interface on the computing device 102 and backend services on the server 106. The cloud server 106 may be configured to manage and coordinate the overall operation of the system 100. The cloud server 106 acts as a central processing unit, receiving requests from the computing device 102, processing data from multiple platforms, and delivering optimized shopping recommendations back to the user. The cloud server 106 is further responsible for coordinating interactions with the products data aggregation and optimization module 116 and the database 120.

[0051] The processor 108 may be configured to execute instructions to perform functions within the computing device 102. For example, the processor 108 may process user inputs, handle communication protocols, and perform calculations necessary for optimizing product selections. The processor 108 may work in conjunction with memory 110 to store and retrieve instructions and temporary data during processing. The memory 110 may be configured to store data and instructions for the processor 108. The memory 110 may include various types, such as random-access memory (RAM) for temporary data storage and non-volatile memory for storing application data and user preferences. The products aggregator module 112 may be the primary user interface within the computing device 102, where the users may view product listings from various platforms, set preferences, and create shopping lists. The products aggregator module 112 may provide the functionality to aggregate and display product options, enable comparison across platforms, and offer personalized recommendations based on historical data. The API 114 may be configured to facilitate communication between the products aggregator module 112 and the products data aggregation and optimization module 116. The API 114 may allow the system to interact with external eCommerce platforms and quick commerce platforms by retrieving product data, price information, availability, and other relevant details needed to optimize the shopping experience. The products data aggregation and optimization module 116 may be configured to perform backend functions such as aggregating data from multiple platforms, comparing prices, analyzing user preferences, and optimizing order placement. This products data aggregation and optimization module 116 may utilize artificial intelligence (AI) algorithms to recommend products, predict order frequencies, and select the most efficient delivery options based on user preferences and real-time data. The database server 118 may be configured to manage access to the database 120. The database server 118 coordinates data read and write requests, ensuring that the products data aggregation and optimization module 116 to store and retrieve data as needed for processing. The database 120 may be configured to store essential data for system operation. This data may include product details, user preferences, historical purchasing data, platform-specific configurations, and records of completed transactions. The database 120 ensures that all relevant information is available for the system to make informed recommendations and optimizations.
[0052] In accordance with one or more exemplary embodiments of the present disclosure, the computing device 102 may include the processor 108 may be configured to execute instructions from the products aggregator module 112 located within the computing device 102. The products aggregator module 112 may be configured to enable the user to search and select one or more products by entering product names and preferences into a search interface. The products aggregator module 112 may be configured to transmit the user-selected one or more product details and preferences to the cloud server 106 over the network 104. The cloud server 106 may be configured to receive one or more product details and preferences from the products aggregator module 112. The cloud server 106 may include the products data aggregation and optimization module 116 may be configured to collect and aggregate one or more products data from multiple eCommerce and quick commerce platforms using APIs 114 and web scraping technologies based on the received one or more product details and preferences. The products data aggregation and optimization module 116 may be configured to compare product prices, availability, and promotional offers from multiple eCommerce and quick commerce platforms, and determine optimal product selections based on user-defined preferences for price, delivery speed, and platform choice. The products data aggregation and optimization module 116 may be configured to generate a customized shopping list for the user, which allocates one or more selected products to specific platforms according to price, delivery speed, and availability. The products data aggregation and optimization module 116 may transmit the generated customized shopping list to the computing device 102 over the network 104. The computing device 102 may include the products aggregator module 112 may be configured to receive the generated customized shopping list, and the products aggregator module 112 may be configured to enable the user to access the generated customized shopping list and enable the user to review the listed products, select desired products, confirm, and finalize the shopping order, whereupon the products aggregator module transmits the confirmed order details to the cloud server 106 for execution, and redirect the user to a payment process upon confirmation of the order. The products aggregator module 112 may be configured to provide an interface for the user to manage preferences, track order status, and receive alerts for future deals and offers across selected platforms.

[0053] Referring to FIG. 2 is a block diagram 200 depicting an embodiment of the products aggregator module 112 as shown in Fig. 1, in accordance with one or more exemplary embodiments. The products aggregator module 112 includes a bus 201, a products selection module 202, a shopping list creation module 204, a user preferences selection module 206, an order management and scheduling module 208, an offers alerting module 210, a subscription management module 212, and a user interface module 214. The bus 201 may include a path that permits communication among the modules of the products aggregator module 112 installed on the computing device 102. The term “module” is used broadly herein and refers generally to a program resident in the memory 110 of the computing device 102.

[0054] The products selection module 202 may be configured to allow the user users to search and select products from various eCommerce and quick commerce platforms. The products selection module 202 may aggregate product data from various platforms, displaying options to users for comparison. The products selection module 202 may also provide filtering and sorting options based on factors such as price, brand, availability, and user ratings to help users make informed purchasing decisions. The shopping list creation module 204 may be configured to enable users to create, modify, and save shopping lists. The shopping list creation module 204 may be configured to allow the user to add selected products to a shopping list, facilitating easy reference and organization for future purchases. The shopping list creation module 204 may support multiple lists, allowing users to categorize products based on types, preferences, or intended purchase times. The shopping list creation module 204 may also provide an intelligent shopping list feature that suggests frequently purchased or related products, helping to users efficiently build comprehensive lists based on previous shopping patterns and user-defined categories.

[0055] The user preferences selection module 206 may be configured to allow the user to set preferences related to their shopping experience. These preferences may include specific brands, delivery speed preferences, platform choices for certain products, and price ranges. The user preferences selection module 206 may store these preferences to customize the shopping experience, tailoring recommendations and selections to match user-defined criteria. The user preferences selection module 206 may allow the user to specify platform-specific preferences (e.g., always order fruits from Provider A and grains from Provider B) and set default delivery timelines for specific products. These preferences may be saved and used to personalize future shopping experiences. The order management and scheduling module 208 may be configured to manage the process of order placement and scheduling based on user preferences and delivery requirements. The order management and scheduling module 208 may be configured to allow the user to review, confirm, and schedule their shopping orders. The order management and scheduling module 208 may provide options for immediate or scheduled deliveries and may allow users to prioritize specific products for faster delivery. The order management and scheduling module 208 may coordinate with backend systems to optimize delivery times and order splitting, ensuring products arrive according to user expectations. The order management and scheduling module 208 may support different delivery timeframes based on user urgency, enabling users to select quick delivery for perishable products (e.g., milk or bread) and standard delivery for non-perishable products (e.g., dry fruits or grains). The scheduling options allow users to create a flexible, customized delivery plan for each order.

[0056] The offers alerting module 210 may be configured to provide users with real-time alerts about discounts, flash deals, and promotions available on products of interest. The offers alerting module 210 continuously monitors active offers across platforms and notifies users of time-sensitive deals, helping them save money. The offers alerting module 210 can customize alerts based on user preferences, such as alerting only for specific categories, brands, or platforms. The offers alerting module 210 may be configured to generate real-time alerts and reminders, ensuring that users do not miss out on discounts or offers that match their preferences or products on their shopping list. The subscription management module 212 may be configured to enable users to subscribe to regular deliveries of frequently used products, such as daily milk or weekly bread deliveries. The subscription management module 212 automates recurring orders, providing convenience and ensuring a steady supply of essential products without requiring repeated manual orders. The subscription management module 212 may allow the user to adjust delivery frequency, pause or resume subscriptions, and manage payment options for subscribed products. The user interface module 214 may be configured to provide a seamless and user-friendly interface for interaction. The user interface module 214 may display aggregated product listings, shopping lists, preferences, and order statuses. The user interface module 214 may be configured to enable the user to navigate through various features, view detailed product information, and access alerts and recommendations. The user interface module 214 facilitates smooth communication to ensure an interactive, responsive user experience. The user interface module 214 may consolidate all sub-module functionalities into a cohesive interface, displaying product listings, shopping lists, preferences, order summaries, and notifications in an intuitive layout. The user interface module 214 may support a variety of interface elements, such as buttons, sliders, and dropdown menus, ensuring that users can navigate easily and perform tasks with minimal effort.

[0057] Referring to FIG. 3 is a block diagram 300 depicting an embodiment of the products data aggregation and optimization module 116 as shown in Fig. 1, in accordance with one or more exemplary embodiments. The products data aggregation and optimization module 116 includes a bus 301, a data collection module 302, a price comparison module 304, an AI recommendation module 306, a dynamic order scheduling module 308, an order splitting module 310, a provider integration module 312, and a notifications generating module 314.

[0058] The data collection module 302 may be configured to gather and aggregate product data from multiple eCommerce and quick commerce platforms. This data may include product prices, availability, descriptions, ratings, and any promotional details. The data collection module 302 may utilize APIs or web scraping techniques to retrieve updated information in real-time, ensuring that users have access to the most current product data across platforms. The price comparison module 304 may be configured to compare prices of identical or similar products across various platforms, helping users identify the most cost-effective option for each item. The price comparison module 304 may analyze factors such as base price, discounts, and promotional offers to calculate the final price. The price comparison module 304 may display price differences to users, offering insights that enable them to make informed purchasing decisions. The AI recommendation module 306 may be configured to provide personalized product recommendations based on the user’s purchase history, browsing behavior, and preferences. The AI recommendation module 306 may employ machine learning algorithms to analyze historical shopping patterns and suggest items that users frequently purchase or may be interested in. The AI recommendation module 306 may also predict optimal purchase intervals for certain products (e.g., monthly for staples, weekly for perishables), enhancing user convenience through tailored suggestions. The dynamic order scheduling module 308 may be configured to manage and optimize order scheduling based on user preferences and real-time data from eCommerce platforms. The dynamic order scheduling module 308 schedule deliveries for specific times or days and prioritizes urgent items for faster delivery. The dynamic order scheduling module 308 may also adjust delivery times automatically based on factors such as availability, delivery slots, and platform-specific constraints, ensuring that items arrive at the most convenient time for the user.

[0059] The order splitting module 310 may be configured to divide a user’s order across multiple providers to ensure the best possible combination of price, delivery speed, and availability. For example, the order splitting module 310 may allocate perishable items to a quick commerce provider for immediate delivery and non-perishable items to a standard eCommerce provider. The order splitting module 310 optimizes the order fulfillment process by strategically selecting providers to meet the user’s needs for each item in the shopping list. The provider integration module 312 may be configured to facilitate seamless communication and integration with various eCommerce and quick commerce platforms. The provider integration module 312 may handle API requests, authentication, and data formatting to ensure compatibility with each provider's system. The provider integration module 312 enables the system to retrieve up-to-date product information, manage orders, and synchronize preferences with third-party platforms, enhancing interoperability within the system. The notifications generating module 314 may be configured to provide real-time notifications and alerts to users about various aspects of their shopping experience. This may include updates on price changes, availability, promotions, or delivery status. The notifications generating module 314 may also alert users to active discounts and flash deals, particularly those matching their preferences or items in their shopping list. The notifications generating module 314 ensures that users remain informed of any relevant updates, enhancing the overall shopping experience.

[0060] Referring to FIG. 4 is a block diagram 400 illustrating an embodiment of the system architecture for aggregating and optimizing multi-platform eCommerce and quick commerce shopping, in accordance with one or more exemplary embodiments. The system includes a client application, a network security group with an API gateway and load balancer, and a private application subnet containing APIs, microservices, and request/response queues. The microservices layer incorporates services for various eCommerce platforms, such as Blinkit and Zepto, along with provider-specific queues to streamline the integration with other quick commerce providers. The client application 402 includes interfaces accessible through various devices, such as a web app, mobile app, and desktop app. The client application 402 allows users to interact with the system, including browsing products, managing preferences, and finalizing shopping orders. A web application firewall 426 and an API gateway with a load balancer 428 are implemented to enhance security and manage incoming requests. These components filter and route requests from the client application to the internal components within the private network security group. The private network security group 406 protects the internal components from external threats, while the private application subnet 408 hosts the core APIs and microservices responsible for handling various aspects of the shopping experience. The API layer 410 contains various APIs, such as those for login/logout, preferences, product search, product details, cart management, checkout initiation, order verification, delivery charges, feedback, and refund processing. These APIs serve as intermediaries between the client application 402 and the backend microservices, ensuring a smooth flow of data and requests. The system includes separate request and response queues for each eCommerce provider. For instance, Blinkit request and response queues 412 and Zepto request and response queues 414 handle communication with these specific platforms, ensuring that data and actions are processed efficiently. The microservices layer comprises individual microservices for each supported eCommerce platform: Blinkit Microservices 418: This includes services for login, preferences management, product search, product details retrieval, cart management, checkout initiation, feedback, order verification, coupon management, delivery charges, and refund processing. Blinkit’s integration supports UI automation, web scraping, or Blinkit APIs. Zepto Microservices 420: Similar to Blinkit, Zepto has its own set of microservices for handling user login, preferences, product searches, cart management, and other functionalities. Zepto integration also supports UI automation, web scraping, or Zepto APIs. The system includes provider queues 416 and microservices 422 for additional quick commerce providers beyond Blinkit and Zepto, allowing seamless integration and expansion with other platforms. These microservices provide similar functionalities as described for Blinkit and Zepto, facilitating a consistent shopping experience across multiple platforms. The database 424 stores data related to user preferences, product information, order details, and other relevant data necessary for system functionality and optimization. This centralized storage allows efficient data retrieval and management across different components.

[0061] Referring to FIG. 5 is a block diagram 500 depicting an integration of a dynamic IP management system for optimizing multi-platform eCommerce and quick commerce shopping, in accordance with one or more exemplary embodiments. The system provides a pool of virtual networks hosted on the cloud and web microservices to maintain seamless integration with service providers such as BlinkIt, Instamart, and others, ensuring a robust and scalable user experience. The computing device 102 may be configured to initiate and manage user sessions for shopping activities. The computing device 102 may include a processor 108 and memory 110, and the memory 110 may store program instructions for the products aggregator module 112. The products aggregator module 112 may be configured to provide the user with an interface to search for products, manage preferences, and initiate shopping sessions. The computing device 102 may be configured to communicate with the cloud server 106 and various service providers 502 through the network 104. The network 104 may include various types of communication technologies, such as Wi-Fi, Ethernet, or cellular networks, to facilitate data transfer between the computing device 102 and external components. The API 114 may be configured to enable seamless communication between the products aggregator module 112 and the cloud server 106. It may retrieve relevant data, including product details, pricing, availability, and promotional offers, from service providers 502 and transfer it to the aggregator module for display on the user interface.

[0062] The cloud server 106 may be configured to manage a pool of virtual networks comprising multiple machines, such as the first virtual network 504, the second virtual network 506, up to Nth virtual network 508. These virtual networks may be dynamically assigned to user sessions to facilitate secure and efficient communication with service providers 502. When a user starts a session in the application, the cloud server 106 may be configured to assign a machine from this pool to the user's session. The cloud server 106 may be configured to maintain session affinity by ensuring that all service provider calls during the session originate from the same assigned virtual network. This approach ensures that the service provider perceives the activity as originating from a consistent IP address, thereby enhancing reliability and preventing disruptions. In addition to maintaining session affinity, the cloud server 106 may be configured to execute a parallel job to flush the pool of virtual networks at regular intervals (e.g., hourly). This dynamic flushing mechanism ensures that the system prevents IP blacklisting by service providers. In cases where a session overlaps with a flushing event, subsequent calls from the same session may be routed through a different IP (e.g., from IP1 to IP2), maintaining operational continuity while mitigating the risk of detection or blocking. The web microservices 510 running on the cloud server 106 may be configured to integrate directly with the service providers 502. These microservices may handle tasks such as retrieving product data, managing orders, and processing user requests in real-time. They ensure that the interactions between the computing device 102 and the service providers 502 are efficient, secure, and scalable.

[0063] In accordance with one or more exemplary embodiments of the present disclosure, the system is designed to provide the capabilities of more powerful user devices, such as high-performance smartphones or computing devices, to eliminate dependency on the server for interactions with service providers. This architecture significantly reduces system complexity and enhances scalability by shifting the core functionality directly onto the user’s device. Device-Based Interactions: When running on a more powerful device, the products aggregator module 112 may be configured to perform all interactions with service providers directly from the device itself. This eliminates the need for the cloud server 106 to mediate requests. The products aggregator module 112 may be configured to handle API calls, web scraping, or UI automation directly to interact with service providers 502 such as BlinkIt, Instamart, and others. The products aggregator module 112 may be configured to interact with service providers through their publicly available or private APIs. Using APIs, the products aggregator module 112 may fetch real-time product data, including prices, availability, and promotions, directly to the user interface. The APIs may also facilitate seamless order placement, tracking, and other related functionalities. Web Scraping: In scenarios where APIs are unavailable or insufficient, the products aggregator module 112 may be configured to execute web scraping techniques to extract product information from service provider websites. The products aggregator module 112 may include a scraping technology capable of navigating web pages, parsing HTML content, and retrieving relevant data while adhering to ethical and legal guidelines. UI Automation: To simulate user interactions for platforms without APIs or web scraping capabilities, the products aggregator module 112 may be configured to utilize UI automation techniques. This functionality enables the products aggregator module 112 to perform tasks such as logging into user accounts, adding products to carts, and completing the checkout process. UI automation replicates user actions on the service provider’s platform while ensuring precision and security. The products aggregator module 112 may be configured to optimize the use of the device’s resources, such as its processor and memory, to efficiently handle these tasks. By offloading server responsibilities to the user device, the products aggregator module 112 may reduce latency and enhance responsiveness, particularly when processing time-sensitive interactions with service providers. When the products aggregator module 112 interacts directly with service providers, user credentials and sensitive data may be securely managed within the products aggregator module 112 itself. The products aggregator module 112 may include advanced encryption and tokenization techniques to protect data during transmission and storage. Additionally, this approach eliminates the need for user data to pass through an intermediary server, further enhancing privacy. The products aggregator module 112 may be configured to dynamically adapt its operational mode based on the device’s capabilities. For less powerful devices, the products aggregator module 112 may rely on the server for backend processing. On more capable devices, the products aggregator module 112 transitions to direct interactions with service providers, ensuring flexibility and an optimal user experience across a range of hardware.

[0064] Referring to FIG. 6 is a flow diagram depicting a method for aggregating and optimizing multi-platform eCommerce and quick commerce shopping, in accordance with one or more exemplary embodiments. The method 600 may be carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3, FIG. 4, and FIG. 5. However, the method 600 may also be carried out in any desired environment. Further, the aforementioned definitions may equally apply to the description below.

[0065] The exemplary method 600 commences at step 602, enabling a user to search and select one or more products by entering product names and preferences into a search interface by a products aggregator module enabled in a computing device. Thereafter at step 604, transmitting the user-selected one or more product details and preferences to a server over a network by the products aggregator module. Thereafter at step 606, receiving the user-selected one or more product details and preferences by the server from the computing device. Thereafter at step 608, collecting and aggregating product data from multiple eCommerce and quick commerce platforms based on the received product details and preferences by a products data aggregation and optimization module enabled in the server. Thereafter at step 610, comparing product prices, availability, and promotional offers across the multiple eCommerce and quick commerce platforms by the products data aggregation and optimization module. Thereafter at step 612, determining optimal product selections based on user-defined preferences for price, delivery speed, and platform choice by the products data aggregation and optimization module. Thereafter at step 614, generating a customized shopping list with the user selected products to specific platforms according to price, delivery speed, and availability by the products data aggregation and optimization module. Thereafter at step 616, transmitting the generated customized shopping list from the server to the computing device by the products data aggregation and optimization module. Thereafter at step 618, receiving the customized shopping list by the products aggregator module and enabling the user to access the generated customized shopping list, review the listed products, select desired products, confirm, and finalize the shopping order. Thereafter at step 620, transmitting the confirmed order details from the computing device to the server for execution, and redirecting the user to a payment process upon confirmation of the order. Thereafter at step 622, enabling the user to manage preferences, track order status, and receive alerts for future deals and offers across selected platforms by the products aggregator module

[0066] Referring to FIG. 7 is a flow diagram depicting a method for managing a dynamic pool of virtual IPs to prevent blacklisting, in accordance with one or more exemplary embodiments. The method 700 may be carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3, FIG. 4, FIG. 5, and FIG. 6. However, the method t00 may also be carried out in any desired environment. Further, the aforementioned definitions may equally apply to the description below.

[0067] The exemplary method 700 commences at step 702, enabling a user to initiating a user session by a products aggregator module enabled in a computing device to search for products. Thereafter at step 704, dynamically assigning a virtual network machine from a pool of virtual networks hosted on a cloud server. Thereafter at step 706, maintaining session affinity by ensuring that all subsequent service provider calls during the user session originate from the same assigned virtual network, thereby maintaining a consistent IP address for the session and enhancing service reliability. Thereafter at step 708, flushing the pool of virtual networks at regular intervals to prevent IP blacklisting, wherein the flushing process results in routing subsequent calls from the same user session through a different IP address, ensuring continued service continuity. Thereafter at step 710, allowing a session to exhibit two or more IP addresses during the same user session, where part of the activity is routed through a first IP address (IP1) and later part of the activity is routed through a second IP address (IP2). Thereafter at step 712, retrieving product data, pricing, availability, and promotional offers from the one or more service providers via web microservices running on the cloud server. Thereafter at step 714, transferring the retrieved data to the products aggregator module, wherein the data is displayed to the user on the computing device interface for browsing, selection, and order placement across multiple eCommerce and quick commerce platforms.

[0068] In accordance with one or more exemplary embodiments of the present disclosure, the processor 108 executes instructions from the products aggregator module 112, the products aggregator module 112 may include the products selection module 202 may be configured to enable the user to search and filter one or more products from multiple eCommerce and quick commerce platforms based on criteria such as price, brand, availability, and user ratings.

[0069] In accordance with one or more exemplary embodiments of the present disclosure, the processor 108 executes instructions from the products aggregator module 112, the products aggregator module 112 may include the shopping list creation module 204 may be configured to allow the user to create, modify, and save one or more shopping lists for future purchases, including support for multiple lists categorized by product type, preferences, or purchase timeframes.

[0070] In accordance with one or more exemplary embodiments of the present disclosure, the processor 108 executes instructions from the products aggregator module 112, the products aggregator module 112 may include the user preferences selection module 206 may be configured to allow the user to set platform-specific preferences, including brand preferences, price ranges, and delivery speed preferences.

[0071] In accordance with one or more exemplary embodiments of the present disclosure, the user preferences selection module 206 may be configured to allow the user to select specific platform-specific preferences (e.g., always order fruits from Provider A and grains from Provider B) and set default delivery timelines for specific products.

[0072] In accordance with one or more exemplary embodiments of the present disclosure, the processor 108 executes instructions from the products aggregator module 112, the products aggregator module 112 may include the order management and scheduling module 208 may be configured to enable the user to schedule orders for specific delivery times and prioritize items based on perishability, thereby allowing flexible delivery arrangements for each order.

[0073] In accordance with one or more exemplary embodiments of the present disclosure, the processor 108 executes instructions from the products aggregator module 112, the products aggregator module 112 may include the offers alerting module 210 may be configured to provide real-time notifications to the user about active discounts, flash deals, and promotions that match user preferences, ensuring timely access to cost-saving opportunities.

[0074] In accordance with one or more exemplary embodiments of the present disclosure, the processor 108 executes instructions from the products aggregator module 112, the products aggregator module 112 may include the subscription management module 212 may be configured to allow the user to subscribe to regular deliveries of frequently purchased products, such as daily or weekly essentials, including features to adjust delivery frequency and pause or resume subscriptions.

[0075] In accordance with one or more exemplary embodiments of the present disclosure, the cloud server 106 executes instructions from the products data aggregation and optimization module 116, the products data aggregation and optimization module 116 may include the data collection module 302 may be configured to gather product information from multiple eCommerce and quick commerce platforms, including details such as product prices, availability, descriptions, ratings, and promotional offers.

[0076] In accordance with one or more exemplary embodiments of the present disclosure, the cloud server 106 executes instructions from the products data aggregation and optimization module, the products data aggregation and optimization module may include the price comparison module 304 may be configured to compare prices of identical or similar products across multiple eCommerce and quick commerce platforms, thereby identifying the most cost-effective options for each product based on real-time data, including base prices, discounts, and promotional offers.

[0077] In accordance with one or more exemplary embodiments of the present disclosure, the cloud server 106 executes instructions from the products data aggregation and optimization module 116, the products data aggregation and optimization module 116 may include the AI recommendation module 306 may be configured to analyze user purchasing history and provide personalized recommendations, including suggested order frequencies based on historical shopping patterns.

[0078] In accordance with one or more exemplary embodiments of the present disclosure, the cloud server 106 executes instructions from the products data aggregation and optimization module 116, the products data aggregation and optimization module 116 may include the dynamic order scheduling module 308 may be configured to schedule and prioritize deliveries of selected products based on user preferences, product urgency, and availability, adjusts delivery times automatically in accordance with platform-specific delivery slots and constraints to meet the user’s requirements.

[0079] In accordance with one or more exemplary embodiments of the present disclosure, the cloud server 106 executes instructions from the products data aggregation and optimization module 116, the products data aggregation and optimization module 116 may include the order splitting module 310 may be configured to allocate products within the user’s shopping list to different platforms to optimize cost, delivery speed, and product availability.

[0080] In accordance with one or more exemplary embodiments of the present disclosure, the cloud server 106 executes instructions from the products data aggregation and optimization module 116, the products data aggregation and optimization module 116 may include the notifications generating module 314 may be configured to provide real-time alerts regarding price changes, availability updates, promotions, and delivery status for the user’s selected products.

[0081] Referring to FIG. 8 is a block diagram 800 illustrating the details of a digital processing system 800 in which various aspects of the present disclosure are operative by execution of appropriate software instructions. The Digital processing system 800 may correspond to the computing devices (or any other system in which the various features disclosed above can be implemented).

[0082] Digital processing system 800 may contain one or more processors such as a central processing unit (CPU) 810, random access memory (RAM) 820, secondary memory 830, graphics controller 860, display unit 870, network interface 880, and input interface 890. All the components except display unit 870 may communicate with each other over communication path 850, which may contain several buses as is well known in the relevant arts. The components of Figure 8 are described below in further detail.

[0083] CPU 810 may execute instructions stored in RAM 820 to provide several features of the present disclosure. CPU 810 may contain multiple processing units, with each processing unit potentially being designed for a specific task. Alternatively, CPU 810 may contain only a single general-purpose processing unit.

[0084] RAM 820 may receive instructions from secondary memory 830 using communication path 850. RAM 820 is shown currently containing software instructions, such as those used in threads and stacks, constituting shared environment 825 and/or user programs 826. Shared environment 825 includes operating systems, device drivers, virtual machines, etc., which provide a (common) run time environment for execution of user programs 826.

[0085] Graphics controller 860 generates display signals (e.g., in RGB format) to display unit 870 based on data/instructions received from CPU 810. Display unit 870 contains a display screen to display the images defined by the display signals. Input interface 890 may correspond to a keyboard and a pointing device (e.g., touch-pad, mouse) and may be used to provide inputs. Network interface 880 provides connectivity to a network (e.g., using Internet Protocol), and may be used to communicate with other systems connected to the network.

[0086] Secondary memory 830 may contain hard drive 835, flash memory 836, and removable storage drive 837. Secondary memory 830 may store the data software instructions (e.g., for performing the actions noted above with respect to the Figures), which enable digital processing system 800 to provide several features in accordance with the present disclosure.

[0087] Some or all of the data and instructions may be provided on removable storage unit 840, and the data and instructions may be read and provided by removable storage drive 837 to CPU 810. Floppy drive, magnetic tape drive, CD-ROM drive, DVD Drive, Flash memory, removable memory chip (PCMCIA Card, EEPROM) are examples of such removable storage drive 837.

[0088] Removable storage unit 840 may be implemented using medium and storage format compatible with removable storage drive 837 such that removable storage drive 837 can read the data and instructions. Thus, removable storage unit 840 includes a computer readable (storage) medium having stored therein computer software and/or data. However, the computer (or machine, in general) readable medium can be in other forms (e.g., non-removable, random access, etc.)

[0089] In this document, the term "computer program product" is used to generally refer to removable storage unit 840 or hard disk installed in hard drive 835. These computer program products are means for providing software to digital processing system 800. CPU 810 may retrieve the software instructions, and execute the instructions to provide various features of the present disclosure described above.

[0090] The term “storage media/medium” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage memory 830. Volatile media includes dynamic memory, such as RAM 820. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

[0091] Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fibre optics, including the wires that comprise bus (communication path) 850. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[0092] Reference throughout this specification to “one embodiment”, “an embodiment”, or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0093] Although the present disclosure has been described in terms of certain preferred embodiments and illustrations thereof, other embodiments and modifications to preferred embodiments may be possible that are within the principles of the invention. The above descriptions and figures are therefore to be regarded as illustrative and not restrictive.

[0094] Thus the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.