Systems and methods for alerts and notifications in advanced distribution platforms

Abstract

Computerized systems and methods for managing alarms and notifications within a technology distribution platform are provided. A single glass pane user interface (SPoG UI) presents notifications to the user to enable interaction and customization. A real-time data grid (RTDM) collects, filters, enriches and standardizes event data from multiple sources into a unified format. The event adapter formats the data for processing by a notification engine configured to determine one or more notification triggers based on established rules and algorithms and generate alarm content. A recording and user interaction module tracks user interactions with notifications. User feedback is processed by an advanced analysis and machine learning (AAML) module configured to dynamically adjust notification logic. Notification content and delivery mechanisms are refined through a distribution module to ensure effective spread over various communication channels.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is a continuation-in-part (CIP) of the following U.S. patent applications: U.S. Patent Application No. 18 / 341,714, filed June 26, 2023; U.S. Patent Application No. 18 / 349,836, filed July 10, 2023. This application also claims the benefit of U.S. Provisional Application No. 63 / 513,073, filed July 11, 2023; U.S. Provisional Application No. 63 / 513,078, filed July 11, 2023; U.S. Provisional Application No. 63 / 515,075, filed July 21, 2023; U.S. Provisional Application No. 63 / 515,076, filed July 21, 2023; and U.S. Patent Application No. 18 / 599,388, filed March 8, 2024. Each of these applications is incorporated herein by reference in its entirety. Background Art

[0003] Traditional ordering processes within distribution and supply chain platforms are plagued by inefficiencies, delays, and inaccuracies. Traditionally, multiple systems and vendors perform each activity independently, from creating a bill of materials to registering transactions, applying pricing, generating quotes, and submitting orders. This approach leads to operational inefficiencies and increases the potential for errors.

[0004] Enterprise resource planning (ERP) systems have long served as the backbone for managing business processes, including distribution and supply chains. These systems serve as a central repository where different departments, such as finance, human resources, and inventory management, can access and share real-time data. While comprehensive, ERP systems present several challenges in today's complex distribution and supply chain environments. One of the primary challenges is data fragmentation. Data silos across different departments or even separate ERP systems make it difficult to achieve real-time visibility. Users lack a comprehensive understanding of key distribution and supply chain metrics, which negatively impacts decision-making.

[0005] Furthermore, ERP systems often lack effective data integration capabilities. Traditional ERP systems are not designed to effectively integrate with external systems, or even between different modules within the same ERP suite. This design leads to cumbersome, error-prone manual processes for transferring data between systems, impacting the flow of information throughout the supply chain. When information exists in different formats across systems, data inconsistencies arise, hindering accurate data analysis and leading to uninformed decisions.

[0006] Data inconsistency presents another challenge. When data exists in different formats or units across departments or ERP systems, standardizing that data for meaningful analysis becomes a laborious process. Businesses often resort to time-consuming manual processes for data conversion and validation, further delaying decision-making. Furthermore, traditional ERP systems often lack the ability to efficiently process large volumes of data. These systems struggle to provide timely insights for operational improvements, a particular problem for businesses operating complex and sprawling distribution and supply chain networks.

[0007] Data security is another concern, especially given the sensitive nature of supply chain data, which can include customer details, pricing, and contracts. Ensuring compliance with global regulations on data security and governance adds an extra layer of complexity. Traditional ERP systems often lack robust security features to adapt to the evolving landscape of cybersecurity threats and compliance requirements. Summary of the Invention

[0008] The automated alert and notification process aims to address inefficiencies in the technology distribution industry by integrating various systems and activities into a unified interface. This integration enables information management and dissemination (from operational alerts to service updates) across the technology distribution platform. The shift to this unified communications model facilitates the entire notification management process, increasing the efficiency of activities such as user interaction, dynamic content customization, and subscription notification management. The platform ensures data security and compliance while effectively integrating and accelerating the notification process.

[0009] In the global distribution industry, challenges such as inefficient communication channels, real-time updates, and the transition to a more user-centric model require innovative solutions. Traditional notification methods are increasingly inadequate, especially as user engagement and regulatory requirements shift. By integrating features for real-time alerts, user interaction management, and visibility into system capabilities, the platform supports the transition from traditional communication methods to a flexible, user-centric notification model.

[0010] According to some embodiments, the alert optimization module can be configured to incorporate algorithms to optimize notification content and delivery based on real-time data and user preferences. The system includes a module that integrates with the Real-Time Data Grid (RTDM) and Single Pane of Glass User Interface (SPoG UI) to optimize the dissemination of information. This is achieved by using advanced algorithms to adjust notifications based on real-time system performance data and user interaction patterns, thereby enhancing the relevance and timeliness of communications.

[0011] In one non-limiting example, a user engagement recommendation engine employs algorithms to provide personalized notification options to users. A content customization engine uses models such as multivariate linear regression or random forests to predict and adjust notification content based on actual user engagement, system events, and individual user settings.

[0012] In one embodiment, notification management and real-time interaction modules operatively connected to RTDM and SPoG UI manage the lifecycle of user notifications. The modules optimize notification delivery based on real-time data, using algorithms to dynamically adjust content and user interaction options. The system includes a content engine for message customization, adapting to variables such as user feedback and system status.

[0013] In some embodiments, the system enables users to personalize their notification settings with a single click via the SPoG UI. It includes a module for checking user preferences and aggregating interactive options based on the current system state, thereby facilitating the notification management process.

[0014] Additionally, or alternatively, the system employs validation algorithms such as support vector machines to ensure accuracy of notification delivery. It synchronizes real-time data from various sources, ensuring consistent and up-to-date information across the notification system.

[0015] The embodiments disclosed herein integrate multiple systems, automate processes, and perform validation to automate the management of information across technology distribution platforms. By implementing intelligent rules and validation, the system efficiently performs complex tasks, reducing the time and errors associated with manual notification management. The system's adaptability ensures it remains current and continuously evolves to meet operational and user needs.

[0016] The system uses a data-driven approach to automate the customization and management of notification packages based on user interaction patterns. This includes assembling various alerts and updates into a coherent communication flow that aligns with individual user behaviors and preferences. The system generates user profiles based on comprehensive data analysis, including aspects such as digital engagement and response patterns. This data informs the customization of notification packages that meet specific user requirements in areas such as system updates, service alerts, and operational changes.

[0017] The system incorporates advanced algorithms to analyze user data, including historical interaction patterns, to identify preferences and anticipate communication needs. This facilitates the creation of highly relevant and engaging notification packages. Automatic notification bundling integrates information and updates from disparate system components, ensuring that each communication package meets the user's information and interaction needs. Automatic alert and notification generation can combine system alerts with compatible service updates and operational changes, aiming to improve user awareness and operational efficiency.

[0018] Single glass pane

[0019] Single Pane of Glass (SPoG) can provide a comprehensive solution configured to address these multifaceted challenges. It can be configured to provide a holistic, user-friendly and efficient platform that facilitates the distribution process.

[0020] According to some embodiments, SPoG can be configured to address supply chain and distribution management by enhancing visibility and control over supply chain processes. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and goods status, thereby ensuring that supply chain and distribution management processes are handled efficiently.

[0021] According to some embodiments, SPoG can integrate multiple touchpoints into a single platform to emulate a direct consumer channel into a distribution platform. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing the complexity of the supply chain and enhancing the overall customer experience.

[0022] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends, leading companies to manage their inventory more effectively and mitigate the risk of out-of-stock or overstocking.

[0023] According to some embodiments, the SPoG may include a global compliance database. This database, updated in real time, enables distributors to stay up-to-date with the latest international laws and regulations. This feature significantly reduces the burden of manual tracking, ensuring smooth and compliant cross-border transactions.

[0024] According to some embodiments, to facilitate SKU management and product localization, SPoG integrates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the possibility of errors. Furthermore, it provides the ability to efficiently manage and distribute localized SKUs, aligned with specific market needs and requirements.

[0025] According to some embodiments, SPoG is a highly configurable and user-friendly platform whose intuitive interface allows users to easily access and purchase technology, thereby aligning with the expectations of a new generation of technology buyers.

[0026] In addition, SPoG's advanced analytical capabilities provide extremely useful insights that can drive strategy and decision-making. It can track and analyze trends in real time, allowing companies to stay ahead and adapt to changing market conditions.

[0027] The flexibility and scalability of SPoG make it a future-proof solution that can adapt to changing business needs, allowing companies to expand or contract their operations as needed without requiring significant infrastructure changes.

[0028] SPoG's innovative approach to addressing distribution industry challenges makes it an extremely valuable tool. By enhancing supply chain visibility, facilitating inventory management, ensuring regulatory compliance, streamlining SKU management, and providing an exceptional customer experience, it offers a comprehensive solution to the complex issues that have long plagued the distribution sector. Through its implementation, distributors can expect increased efficiency, reduced errors, and improved customer satisfaction, enabling continued growth in an evolving global marketplace.

[0029] Real-Time Data Grid (RTDM)

[0030] According to some embodiments, the platform may include an implementation of a real-time data grid (RTDM). RTDS offers an innovative solution to these challenges. RTDM (Distributed Data Architecture) enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allows for efficient management, and enables distributors to more effectively handle disruptions.

[0031] RTDM's predictive analytics capabilities provide a solution for efficient inventory control. By providing insights into demand trends, it helps companies manage inventory, thereby reducing the risk of overstocking or stockouts.

[0032] RTDM’s global compliance database, updated in real time, ensures distributors are aware of current international regulations. This significantly reduces the burden of manual tracking, enabling cross-border transactions.

[0033] RTDM also simplifies SKU management and localization by integrating data from various OEMs, ensuring data consistency and reducing the possibility of errors. Its ability to manage and distribute localized SKUs efficiently meets specific market requirements.

[0034] RTDM enhances the customer experience with its intuitive interface, allowing easy access and purchase of technology, meeting the expectations of the next generation of technology buyers.

[0035] Advantages of SPoG and RTDM integration

[0036] Integrating the SPoG platform with RTDM offers numerous advantages. First, it provides a holistic solution to a long-standing problem in the distribution industry. Leveraging RTDM's capabilities, SPoG can enhance supply chain visibility, facilitate inventory management, ensure regulatory compliance, streamline SKU management, and deliver a superior customer experience.

[0037] The real-time tracking and analysis provided by RTDM enhances SPoG’s ability to effectively manage its supply chain and inventory. It provides accurate and current information, enabling distributors to make informed decisions quickly.

[0038] Integrating SPoG with RTDM also ensures data consistency and reduces errors in SKU management. By providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps align with market demands.

[0039] RTDM's global compliance database integrated with SPoG facilitates compliant cross-border transactions. It also reduces the burden of manual tracking, saving significant time and resources.

[0040] In some embodiments, the distribution platform incorporates SPoG and RTDM to provide an improved and comprehensive distribution system that can leverage the advantages of the distribution model, address its existing challenges, and position it for continued growth in an ever-evolving global market. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 One embodiment of an operating environment for a distribution platform, referred to in this embodiment as a system, is shown.

[0042] Figure 2 One embodiment of an operating environment for a distribution platform according to some embodiments is shown.

[0043] Figure 3 One embodiment of a system for distribution management is shown.

[0044] Figure 4 A system for automated alert and notification management, according to one embodiment.

[0045] Figure 5 An SPoG UI according to one embodiment is shown.

[0046] Figure 6 An RTDM module according to one embodiment is shown.

[0047] Figure 7 A system for automated alert and notification management is shown according to one embodiment.

[0048] Figure 8 is a flow chart of a method for automatic alert and notification management according to some embodiments of the present disclosure.

[0049] Figure 9 is a flow chart of automatic alert and notification management in a technology distribution platform according to some embodiments of the present disclosure.

[0050] Figure 10is a flow diagram of user interaction with notifications in a technology distribution platform according to some embodiments of the present disclosure.

[0051] Figure 11 is a block diagram of example components of a device according to some embodiments of the present disclosure.

[0052] Figures 12A to 12Q Depicted are various screens and functions of the SPoG UI according to some embodiments. DETAILED DESCRIPTION

[0053] Embodiments may be implemented in hardware, firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include a read-only memory (ROM); a random access memory (RAM); a magnetic disk storage medium; an optical storage medium; a flash memory device, etc. In addition, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be understood that such descriptions are merely for convenience, and that such actions are actually generated by a computing device, a processor, a controller, or other device that executes the firmware, software, routines, instructions, etc.

[0054] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the operations shown. In some embodiments of the present disclosure, operations may be performed in a different order and / or varied.

[0055] Figure 1 The operating environment 100 of a distribution platform, referred to in this embodiment as system 110, is shown. System 110 operates in the context of an information technology (IT) distribution model, catering to a variety of users such as customers 120, end customers 130, vendors 140, distributors 150, and other entities involved in the distribution process. The operating environment includes a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0056] Customers 120 within the operating environment of system 110 represent businesses or individuals seeking IT solutions to meet their specific needs. These customers may require a variety of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most appropriate IT solution based on their needs. Customers can also access real-time data and analytics through system 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0057] End customers 130 may be the ultimate beneficiaries of the IT solutions provided by system 110. They may include businesses or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers rely on system 110 to access a wide range of IT solutions, ensuring they have access to the latest technologies and innovations in the market. System 110 enables end customers to track their orders, receive updates on delivery status, and access customer support services, thereby enhancing their overall experience.

[0058] Sellers 140 may include manufacturers, distributors, and suppliers offering a variety of IT products and services. System 110 serves as a centralized platform for sellers to showcase their products, manage inventory, and facilitate transactions with customers and distributors. Sellers can leverage system 110 to streamline their supply chain operations, manage pricing and promotions, and gain insight into customer preferences and market trends. By integrating with system 110, sellers can expand their reach, access new markets, and enhance their overall visibility and competitiveness.

[0059] Distributors 150 can be intermediaries within the distribution model, bridging the gap between vendors and customers. They play a vital role in the IT distribution ecosystem by connecting customers with the right IT solutions from a variety of vendors. Distributors can include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables distributors to access a comprehensive catalog of IT solutions, manage their sales channels, and provide value-added services to customers. By fully leveraging system 110, distributors can strengthen their customer relationships, optimize their product offerings, and increase their revenue streams.

[0060] Within the operating environment of system 110, various dynamics and features may exist that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. System 110 ensures that relevant data can be exchanged between users in real time, enabling accurate decisions and timely actions. Integration with existing enterprise systems (such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems) allows for communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0061] System 110 is scalable and flexible. It can adapt to the growing demands of IT distribution models, whether it involves an expanding customer base, a growing number of vendors, or a wider range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of distribution platforms. In addition, system 110 fully utilizes a technology stack that includes .NET, Java, and other suitable technologies.

[0062] In summary, the operating environment of system 110 within the IT distribution model includes clients 120, end-customers 130, vendors 140, distributors 150, and other entities involved in the distribution process. System 110 serves as a centralized platform that facilitates efficient collaboration, communication, and transaction processing among these users. By leveraging real-time data exchange, integration, scalability, and flexibility, system 110 empowers users to optimize their operations, enhance the customer experience, and drive business success within the IT distribution ecosystem.

[0063] Figure 2 The operating environment 200 of the distribution platform is shown, which may be Figure 1 The present invention provides an embodiment of an operating environment 100 for managing customer data. The environment may include integration points 210 that enable data flow and connections between various systems (such as customer systems 220, vendor systems 240, dealer systems 260, and other entities) to implement a comprehensive alert and notification system. The operating environment 200 is designed to support real-time, event-driven notifications using advanced data processing and AI / ML techniques.

[0064] The alert and notification system within environment 200 involves real-time monitoring and alerting of key events, such as inventory changes, pricing updates, and delivery tracking. Data from various sources, including customer interactions and service metrics, is aggregated from systems such as CRM and analytics tools into a real-time data grid (RTDM). This data is then processed and standardized by RTDM, which serves as a dynamic repository, for immediate access and dissemination of notifications.

[0065] Advanced AI algorithms within the system perform real-time analysis and predictive notifications. Machine learning models, including neural networks and decision trees, process and interpret large amounts of data, enabling timely and relevant notifications. The system leverages ML algorithms for intelligent alert generation, employing techniques such as ensemble learning and reinforcement learning to continuously refine the notification process.

[0066] In this operating environment, system 110 serves as a central hub for coordinating alert and notification processes, bridging customer systems 220, vendor systems 240, dealer systems 260, and other related entities. It integrates communication and data exchange, ensuring a cohesive and efficient notification experience across the distribution network. This environment utilizes a hybrid architecture combining RESTful APIs and WebSockets, ensuring real-time data exchange and synchronization with SSL / TLS protocols for data security.

[0067] Integration with customer systems 220, such as CRM and ERP platforms, is crucial for alert systems. This allows notifications related to a customer's specific needs and preferences to be disseminated in real time, thereby improving decision-making and operational efficiency.

[0068] Data exchange between customer system 220, vendor system 240, and dealer system 260 can be combined with ETL processes to ensure data consistency and reliability for alert generation. Predefined business rules and logic dictate the flow and processing of data, while advanced mapping and transformation tools reconcile disparate data formats for unified notification delivery.

[0069] Integration with associated systems 230 through integration points 210 supports the efficiency of the alerting and notification process by providing market and product data, thereby generating accurate and timely alerts.

[0070] Vendor system integration ensures that vendors connected to system 110 can receive real-time notifications about key metrics such as inventory levels and pricing changes, which is crucial for maintaining up-to-date information in a dynamic market environment.

[0071] Dealer System Integration: Through integration point 210, dealer systems 260 are connected to system 110, enabling access to real-time notifications about product availability, pricing updates, and customer order status, enabling them to more effectively manage customer relationships and the sales process.

[0072] Integration with other entities: Integration points 210 also connect other entities in the distribution process, thereby promoting efficient collaboration and distribution through real-time exchange of alerts and notifications. This integration is critical to maintaining a responsive and informed distribution ecosystem.

[0073] The system 110 employs AI and ML capabilities to automate and optimize the alert and notification process based on dynamic market conditions and individual user preferences. This ensures timely and relevant notifications across the distribution network.

[0074] Integration points 210 also connect to systems of record 280, allowing for additional data management and integration. These systems, including ERP and CRM platforms, provide a rich data source for alert and notification systems, enabling real-time updates and ensuring accurate information dissemination.

[0075] Integration points 210 within the operating environment 200 are established through standardized protocols and APIs, ensuring compatibility and secure data transfer. The system 110 uses protocols such as RESTful API, SOAP, or GraphQL for efficient communication and data exchange.

[0076] To ensure secure access and data protection, the system 110 incorporates authentication and authorization mechanisms, leveraging technologies such as OAuth or JSON Web Tokens (JWT). This maintains data integrity and confidentiality across the notification network.

[0077] The data flow within the operating environment 200 enables users to operate within a connected ecosystem of real-time alerts and notifications. Data generated at various stages of the distribution process is shared between customer systems 220, vendor systems 240, dealer systems 260, and other entities, thereby increasing operational efficiency and enhancing decision-making.

[0078] The system 110 can utilize advanced technologies such as Typescript, NodeJS, ReactJS, NET Core, C#, etc. to support communication within the integration point 210 and the operating environment 200.

[0079] The architecture of system 110 also facilitates the processing, coordination, and real-time access to data across the distribution network, thereby empowering users with immediate access to relevant, real-time information to make timely decisions.

[0080] Each of the client systems, such as client system 220, is configured to receive and interact with alerts and notifications, thereby facilitating immediate action and response to real-time information. This includes devices such as desktops, laptops, mobile phones, and smart watches, each of which is capable of presenting alerts through various user interfaces.

[0081] The components of the client system are configured using an application such as a web browser running on a central unit such as an Intel Pentium processor or similar. The distribution platform (system 110) and its components are similarly configured, ensuring integrated interaction and notification management.

[0082] The machine-readable storage medium contains instructions for programming a computer to execute the processes of the alarm and notification system. The computer code for operating and configuring the platform can be stored on a variety of storage media and transmitted over conventional network connections using standard communication protocols.

[0083] Depending on the specific requirements of the distribution platform and the environment in which it operates, the implementation of the system can be performed in various programming languages ​​and executed on different platforms.

[0084] Thus, the operating environment 200 couples the distribution platform with integration points 210 and data flows, enabling efficient collaboration and streamlined distribution processes through a responsive alert and notification system. This system significantly improves the responsiveness and operational efficiency of the entire distribution network.

[0085] In short, Figure 2The system depicts a dynamic and interconnected operating environment 200 where real-time alerts and notifications are at the core of the distribution process, facilitating informed decision-making and efficient operations across every entity in the supply chain. The integration of advanced AI / ML technology, real-time data processing, and comprehensive alert mechanisms positions System 110 as a key component in modernized and optimized distribution and supply chain management.

[0086] Figure 3 A system 300 for supply chain and distribution management is shown. System 300 ( Figure 3 ) is a supply chain and distribution management solution that is configured to address the challenges faced by the fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in concert to optimize supply chain and distribution operations, enhance collaboration, and drive business efficiency.

[0087] SPoG UI 305 serves as a centralized user interface, providing users with a unified view of the entire supply chain. It consolidates information from various sources and presents real-time data, analytics, and functionality tailored to the user's specific role and responsibilities. By providing a customizable and intuitive summary tabular layout, SPoG UI enables users to access relevant information and tools, empowering them to make data-driven decisions and effectively manage their supply chain and distribution activities.

[0088] For example, logistics managers can use SPoG UI 305 to monitor the status of shipments, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize data through interactive charts and graphs, such as a map showing the current location of each shipped shipment or a bar chart showing inventory levels by product category. By having a unified view of the supply chain, logistics managers can identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0089] The SPoG UI 305 is integrated with other modules of the system 300, facilitating real-time data exchange, synchronized operations, and streamlined workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, the SPoG UI 305 ensures a smooth flow of information and enables collaborative decision-making across the distribution ecosystem. The SPoG UI 305 is designed with a user-centric approach, featuring an intuitive and responsive layout. It utilizes front-end technology to present dynamic and interactive data visualizations. Customizable summary tables allow users to customize their views based on specific roles and requirements. The UI supports drag-and-drop functionality for ease of use, and its adaptive design ensures compatibility across various devices and platforms. Advanced filtering and search capabilities enable users to efficiently navigate to and access relevant supply chain data and insights.

[0090] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers a notification to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and enhances overall supply chain visibility.

[0091] The Real-Time Data Grid (RTDM) module 310 is another component of the system 300 and is responsible for ensuring the flow of data within the distribution ecosystem. It aggregates data from multiple sources, coordinates it, and ensures its availability in real time.

[0092] Within a distribution network, the RTDM module collects data from various systems, including inventory management systems, point-of-sale terminals, and customer relationship management systems. It harmonizes this data by aligning formats, standardizing units of measurement, and reconciling any discrepancies. This unified data is then made available in real time, allowing users across the supply chain to access accurate and current information.

[0093] The RTDM module 310 can be configured to capture changes in data across multiple transaction systems in real time. It employs a change data capture (CDC) mechanism that continuously monitors transaction systems to detect any updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, including traditional ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, thereby ensuring compatibility and flexibility for businesses operating in various environments.

[0094] By having access to real-time data, users can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden spike in demand for a specific product, it can trigger an alert to the production team, allowing them to adjust manufacturing schedules and prevent stockouts.

[0095] The RTDM module 310 facilitates data management operations within the supply chain. It enables real-time coordination of data from multiple sources, freeing vendors, distributors, customers, and end-customers from the constraints imposed by traditional ERP systems. This enhanced flexibility supports improved efficiency, customer service, and innovation.

[0096] Another component of system 300 is the Advanced Analytics and Machine Learning (AAML) module 315. Leveraging powerful analytical tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from collected data. It supports advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0097] For example, the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure inventory availability during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0098] In addition to demand forecasting, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-sell or up-sell opportunities and recommend relevant products to individual customers.

[0099] Additionally, the AAML module can analyze data from various sources, such as social media feedback, customer reviews, and market trends, to gain a deeper understanding of consumer sentiment and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adjust business strategies to meet evolving consumer expectations.

[0100] System 300 emphasizes integration and interoperability to connect with existing enterprise systems such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connections and data flows between these systems, System 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a holistic and connected distribution ecosystem.

[0101] The implementation and deployment of system 300 can be customized to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability across diverse environments, ease of management, and efficient updates. Implementation involves configuring the system to align with specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0102] The system 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by a fragmented distribution ecosystem. It combines the functionality of the SPoG UI 305, the RTDM module 310, and the AAML module 315, as well as integration with existing systems. The system 300 can be configured to provide end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided in this specification are non-limiting and can be customized to meet specific industry requirements, drive efficiency, and success in supply chain and distribution management.

[0103] Figure 4An embodiment of a system 400 is depicted, primarily focusing on an alert and notification system within a technology distribution platform. The system integrates a single pane of glass user interface (SPoG UI 405), a real-time data grid (RTDM 410), and advanced AI / ML technology (AI module 460). System 400 is configured to integrate with existing dealer systems, ensuring efficient data exchange, synchronization, and real-time alerting capabilities.

[0104] SPoG UI 405, the primary user interface, is central to the alert and notification system. It provides users with an interactive platform for receiving and managing alerts related to various distribution activities. This interface displays real-time data from a data grid 410 and allows users to configure notification preferences, access alert history, and interact with real-time alerts. Developed using responsive web technologies, SPoG UI 405 is accessible on multiple device types, ensuring users stay informed regardless of their device.

[0105] Data grid 410 forms the core of the alert and notification system. It aggregates and coordinates data from various sources, including ERP, vendor platforms, and third-party databases, ensuring that operational modules within System 400 access consistent and current information. This coordination is crucial for generating accurate and timely alerts and notifications, especially in dynamic distribution environments.

[0106] AI module 460 in system 400 is customized to improve alert and notification systems. It uses machine learning algorithms and predictive modeling to identify patterns and trends that trigger alerts. This module dynamically processes data from data grid 410 to generate real-time notifications about key events such as inventory changes, pricing updates, and shipping status, thereby improving user decision-making and response time.

[0107] In some embodiments, AI module 460 incorporates deep learning neural networks for pattern recognition, which is crucial for predictive alerts during distribution. The module also uses decision trees and clustering algorithms to classify and segment alert types, ensuring that users receive relevant and customized notifications.

[0108] Based on data from the data grid 410, the real-time processing capabilities of the AI ​​module 460 enable the system to adjust notifications based on current market conditions and user behavior. This includes using advanced analytics to proactively generate alerts, ensuring that users are informed of critical changes or updates in the distribution network.

[0109] The AI ​​module 460's reinforcement learning algorithms continuously refine the notification process, ensuring that alerts remain relevant and actionable over time. The module also uses natural language processing (NLP) techniques to interpret user feedback, further improving the alert customization process.

[0110] Data grid 410 provides real-time data tracking for alert and notification systems, driving predictive and responsive alerts. Data grid 410 can be implemented in system 400 to enable tracking of usage patterns and market feedback, thereby facilitating a responsive alert system in a dynamic distribution environment.

[0111] The predictive analytics tools within AI module 460 use time series forecasting and optimization algorithms to anticipate future trends and demands in the distribution network, notifying users through predictive alerts. This proactive notification approach enables users to effectively prepare for and respond to changing market conditions.

[0112] The alert management module 430 in the system 400 oversees the lifecycle of alerts and notifications, managing their initiation, modification, and dissemination. It ensures that alerts are delivered in accordance with user preferences and contractual agreements, maintaining a high level of relevance and accuracy.

[0113] The alert analysis module 440 in the system 400 is configured to support the alert and notification system by providing financial and usage insights based on alert data. This module helps understand the impact of alerts on user behavior and subscription changes, thereby providing strategic insights for business decisions.

[0114] The alert customization and recommendation engine 450 integrated with the SPoG UI 405 and the AI ​​module 460 enables personalization of the alert and notification system. It recommends alert configurations and preferences based on user input and historical data analysis, thereby enhancing the user experience by delivering customized alert content.

[0115] The asset tracking module 470 in the system 400 supports the alarm and notification system by tracking and managing the allocation of assets related to alarms. This includes monitoring inventory levels and ensuring that alarms related to asset availability are accurate and timely.

[0116] Thus, system 400 transforms the traditional distribution model by utilizing a responsive alert and notification system. Powered by real-time data processing, AI-driven analytics, and user customization capabilities, the system ensures that users are proactively informed and can make timely decisions based on real-time alerts and notifications in a technical distribution environment.

[0117] Figure 5An embodiment of an advanced distribution platform including a system 500 for managing complex distribution networks is depicted, which can be an embodiment of system 300 and provides a technology distribution platform for optimizing the management and operation of distribution networks. System 500 includes several interconnected modules, each of which has a specific function and contributes to the overall efficiency of supply chain operations. In some embodiments, these modules can include an SPoG UI 505, a customer interaction module CIM 510, a RTDM module 515, an AI module 520, an interface display module 525, a personalized interaction module 530, a document hub 535, a catalog management module 540, a performance and insight badge display 545, a predictive analysis module 550, a recommendation system module 555, a notification module 560, a self-login module 565, and a communication module 570.

[0118] System 500, as an embodiment of system 300, can implement supply chain and distribution management using a series of technologies and algorithms that facilitate efficient data processing, personalized interaction, real-time analysis, secure communication, and effective management of documents, catalogs, and performance metrics.

[0119] In some embodiments, the SPoG UI 505 serves as a central interface within the system 500, providing users with a unified view of the entire distribution network. It utilizes front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. These technologies enable the SPoG UI 505 to deliver a user-friendly experience, allowing users to access relevant information, navigate through different modules, and perform tasks efficiently.

[0120] The CIM 510, or Customer Interaction Module, uses algorithms and technologies such as Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to securely process customer data, personalize the customer experience, and provide access control to users.

[0121] The RTDM module 515, or real-time data grid module, is a key component of system 500 that ensures smooth data flow across the distribution ecosystem. It leverages technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable the RTDM module 515 to handle real-time data streams, process large amounts of data, and ensure low-latency data processing. Additionally, the module employs a change data capture (CDC) mechanism to capture real-time data updates from various transactional systems, such as traditional ERP and CRM systems. This functionality allows users to access current and accurate information for informed decision-making.

[0122] The AI ​​module 520 within the system 500 can use advanced analytics and machine learning algorithms (including Apache Spark, TensorFlow, and scikit-learn) to extract valuable insights from data. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI ​​module 520 can utilize predictive models to forecast demand, allowing users to optimize inventory management and minimize out-of-stock or overstock situations.

[0123] The interface display module 525 focuses on presenting data and information in a clear and user-friendly manner. It uses technologies such as HTML, CSS, and JavaScript frameworks (such as ReactJS) to create interactive and responsive user interfaces. These technologies allow users to visualize data using various data visualization techniques (such as graphs, charts, and tables), thereby enabling efficient data understanding, comparison, and trend analysis.

[0124] The personalized interaction module 530 leverages customer data, historical trends, and machine learning algorithms to generate personalized recommendations for products or services. It can employ technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and delivering targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, thereby increasing customer satisfaction and boosting sales.

[0125] Document Hub 535 serves as a centralized repository for storing and managing documents within System 500. It can leverage technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, Document Hub 535 can utilize SeeBurger's document management capabilities to categorize and organize documents based on their type (e.g., contracts, invoices, product specifications, or compliance documents), allowing users to easily access and retrieve relevant documents when needed.

[0126] The catalog management module 540 enables the creation, management, and distribution of up-to-date product catalogs. It ensures that users have access to current product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be utilized to facilitate catalog updates, content delivery, and caching. For example, the module can use Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to users regardless of their geographic location.

[0127] Performance and Insights Monitor 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. It leverages tools such as Splunk and Datadog to enable effective performance monitoring and provide actionable insights. For example, the module can leverage Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling users to take proactive measures to optimize operations.

[0128] The predictive analytics module 550 employs machine learning algorithms and forecasting models to predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It leverages technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can leverage TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing users to optimize inventory levels and minimize costs.

[0129] The recommendation system module 555 focuses on providing intelligent recommendations to users in the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be used for data analysis, modeling, and delivery of targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behavior and deliver personalized product recommendations across various channels, thereby enhancing customer engagement and promoting sales.

[0130] The notification module 560 enables the distribution of real-time notifications to users regarding important events, updates, or alerts within the supply chain. It leverages technologies such as Apigee X and TIBCO for message queuing, event-driven architecture, and notification delivery. For example, the module can leverage TIBCO's messaging infrastructure to send real-time notifications to users' devices, ensuring timely and relevant information dissemination.

[0131] The self-onboarding module 565 facilitates the onboarding process for new users entering the distribution network. It provides guided steps, tutorials, or documentation to help users familiarize themselves with the system and its functionality. Technologies such as Okta and Kentico can be leveraged to ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identity and access management capabilities to securely onboard new users, provide them with appropriate access rights, and guide them through the system's functionality.

[0132] The communication module 570 enables communication and collaboration within the system 500. It provides a channel for users to exchange messages, share documents, and collaborate on projects. Technologies such as Apigee Edge and Adobe Launch can be used to promote secure and efficient communication, document sharing, and version control. For example, the module can utilize the API management capabilities of Apigee Edge to ensure secure and reliable communication between users, enabling them to collaborate effectively.

[0133] Therefore, the system 500 can include various modules that utilize various technologies and algorithms to optimize supply chain and distribution management. These modules (including SPoG UI 505, CIM 510, RTDM module 515, AI module 520, interface display module 525, personalized interaction module 530, document hub 535, catalog management module 540, performance and insight tag display 545, predictive analysis module 550, recommendation system module 555, notification module 560, self-login module 565 and communication module 570) work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analysis and simplified communication within the distribution network. The combination of specific technologies and algorithms can achieve efficient data management, secure communication, personalized experience and effective performance monitoring, thereby contributing to improved operational efficiency and the success of supply chain and distribution management.

[0134] Real-time Data Grid

[0135] Figure 6 An RTDM module 600 is shown according to an embodiment. The RTDM module 600 may be an embodiment of the RTDM module 310, which may include interconnected components, processes, and subsystems configured to implement real-time data management and analysis.

[0136] like Figure 6 As shown, the RTDM module 600 represents an effective data grid and change capture component within the overall system architecture. This module can be configured to provide real-time data management and standardization capabilities, thereby enabling efficient operations within the supply chain and distribution management domain.

[0137] The RTDM module 600 may include an integration layer 610 (also referred to as a "system of record") for integration with various enterprise systems. These enterprise systems may include ERP systems such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 handles data exchange and synchronization between the RTDM module 600 and these systems. Data feeds are established to retrieve relevant information from the system of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates and ensure that the RTDM module operates with the latest and most accurate data.

[0138] The RTDM module 600 may include a data layer 620 configured to process and transform data for retrieval and analysis. The data layer 620 includes a data grid, which is a cloud-based infrastructure configured to provide scalable and fault-tolerant data storage capabilities. Within the data grid, multiple purchase data stores (PDSs) can be deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS can be optimized for efficient data retrieval based on specific use cases and requirements. The PDS can be configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs serve as repositories for normalized and / or standardized data, thereby ensuring data consistency and integrity across systems.

[0139] In some embodiments, the RTDM module 600 implements a data replication mechanism to capture real-time changes from multiple data sources, including transactional systems such as ERP (e.g., Impulse, META, I-SCALA). The captured data can then be processed and standardized on the fly, converting it into a standardized format suitable for analysis and integration. This process ensures that data is readily available and current within the data grid, thereby facilitating real-time insights and decision-making.

[0140] More specifically, the data layer 620 within the RTDM module 600 can be configured as a powerful and flexible foundation for managing and processing data within the distribution ecosystem. In some embodiments, the data layer 620 can include a highly scalable and robust data lake, which can be referred to as data lake 622, and a set of purpose-built data stores (PDSs), which can be represented as PDSs 624.1 through 624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0141] Data layer 620 includes data lake 622, a state-of-the-art storage and processing infrastructure configured to handle the ever-increasing volume, variety, and velocity of data generated within the supply chain. Built on a scalable distributed file system (such as Apache Hadoop Distributed File System (HDFS) or Amazon S3), the data lake provides a unified and scalable platform for storing both structured and unstructured data. Leveraging the elasticity and fault tolerance of cloud-based storage, data lake 622 can adapt to the influx of data from diverse sources.

[0142] Associated with data lake 622, a set of purpose-built data stores can be employed: PDSs 624.1 through 624.N. Each PDS 624 can serve as a specialized repository optimized for storing and retrieving a specific type of data related to the supply chain domain. In some non-limiting examples, PDS 624.1 can be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS 624.2 can focus on product data, including details about SKU codes, descriptions, pricing, and inventory levels. These purpose-built data stores allow for efficient data retrieval, analysis, and processing, thereby meeting the diverse needs of supply chain users.

[0143] To ensure real-time data synchronization, the data layer 620 can be configured to employ one or more change data capture (CDC) mechanisms. These CDC mechanisms can integrate with transactional systems, such as traditional ERP systems like SAP, Impulse, META, and I-SCALA, as well as other enterprise-wide systems. CDC continuously monitors these systems for any updates, modifications, or new transactions and captures them in real time. By capturing these changes, the data layer 620 ensures that the data within the data lake 622 and PDS 624 remains current, providing users with real-time insights into the distribution ecosystem.

[0144] In some embodiments, the data layer 620 can be implemented to facilitate integration with existing enterprise systems using one or more frameworks such as .NET or Java, thereby ensuring compatibility with various existing systems and providing flexibility for customization and extensibility. For example, the data layer 620 can utilize the Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with systems of record across a variety of ERP systems and other enterprise-wide solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0145] For data processing and analysis, data layer 620 can leverage the capabilities of distributed computing frameworks, such as Apache Spark or Apache Flink, as non-limiting examples. These frameworks enable parallel processing and distributed computing across large-scale datasets stored in data lakes and PDSs. By leveraging these frameworks, supply chain users can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, data layer 620 can use Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0146] In some embodiments, the data layer 620 can incorporate data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authorized users can access and modify data within the data lake and PDS. Data encryption technologies protect sensitive supply chain information from unauthorized access while at rest and in transit. Additionally, the data layer 620 can implement data lineage and audit trail mechanisms, allowing users to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0147] In some embodiments, the data layer 620 can be deployed in a cloud-native environment using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability, elasticity, and efficient resource allocation. For example, the data layer 620 can be deployed on cloud infrastructure provided by AWS, Azure, or Google Cloud, leveraging its managed services and scalable storage options. This allows for efficient resource scaling based on demand, minimizes operational overhead, and provides a resilient infrastructure for managing supply chain data.

[0148] The data layer 620 of the RTDM module 600 can combine a highly scalable data lake (data lake 622) and purpose-built PDSs (PDSs 624.1 to 624.N), and employ a CDC mechanism to ensure efficient data management, standardization, and real-time availability. In a non-limiting example, the data layer 620 can be implemented using any appropriate technology (such as .NET or Java) and / or a distributed computing framework (such as Apache Spark), enabling powerful data processing, advanced analytics, and machine learning capabilities. Utilizing data governance and security measures, the data layer 620 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, the data layer 620 enables supply chain users to make data-driven decisions, optimize operations, and drive business success in dynamic and complex distribution environments.

[0149] The RTDM module 600 may include an AI module 630 configured to implement one or more algorithms and machine learning models to analyze the data stored in the data layer 620 and derive meaningful insights. In some non-limiting examples, the AI ​​module 630 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 630 may continuously learn from new data inputs and adapt its models to provide accurate and current insights. The AI ​​module 630 may generate predictions, recommendations, and alerts, and publish such insights to dedicated data feeds.

[0150] The data engine layer 640 includes a set of interconnected systems responsible for data ingestion, processing, conversion, and integration. The data engine layer 640 of the RTDM module 600 may include a collection of autonomous headless engines 640.1 to 640.N. These engines represent different functions within the system and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 640.1 to 640.N can use standardized data stored in the data grid to deliver specific business logic and services. Each engine 6 can be configured to be pluggable, allowing flexibility and future expansion of module capabilities. Figure 6 An exemplary engine is shown in FIG, which is not meant to be limiting. Any additional headless engines may be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0151] These systems can be configured to ingest data from multiple sources, such as transaction systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms can be applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration.

[0152] Furthermore, to facilitate integration and access to the RTDM module 600, a data distribution mechanism 645 can be employed. The data distribution mechanism can be configured to include one or more APIs to facilitate distribution of data from the data grid and engine to various endpoints, including user interfaces, micro frontends, and external systems.

[0153] The Experience Layer 650 focuses on delivering an intuitive and user-friendly interface for interacting with supply chain data. This layer can include data visualization tools, interactive summary tables, and user-centric functionality. Through this layer, users can retrieve and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The User Experience Layer supports personalized data feedback, allowing users to customize their views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates tailored to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0154] Thus, in some embodiments, the RTDM module 600 for supply chain and distribution management can include integration with systems of record and include one or more of a data layer with a data grid and purposeful data storage, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, data processing and analysis, and efficient integration with existing enterprise systems. Technical feedback and retrieval within the module ensure that users can retrieve relevant, current information and insights to make informed decisions and optimize supply chain operations. Thus, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable, real-time data management solution. Its innovative architecture enables rich integration of disparate data sources, efficient data standardization, and advanced analytical capabilities. The module's ability to replicate and standardize data from different ERP systems while maintaining auditable and repeatable transactions offers significant advantages in achieving a unified view of vendors, distributors, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0155] Automatic notification engine

[0156] In one embodiment, Figure 7 System 700 is shown as an alert and notification system within a technology distribution platform, including a single pane of glass user interface (SPoG UI) 705, a real-time data grid (RTDM) 710, an advanced analytics and machine learning (AAML) module 715, and an integrated notification engine 720.

[0157] The SPoG UI 705 serves as the central command for alert interactions within the system 700. It is architected with a user-focused design, providing a customizable summary table for managing alerts and notifications. The interface integrates real-time data visualization tools, allowing users to monitor alert status through graphical representations. It can include an intuitive layout that facilitates the workflow of setting notification parameters and provides a comprehensive view of both active and historical alerts. The UI is optimized for responsiveness across various devices and platforms, ensuring accessibility and user engagement.

[0158] Within the SPoG UI 705, the consumer interaction module 706 facilitates direct user engagement with the notification system. This module is optimized for interaction efficiency, with rule-based automation for user responses to notifications. It includes a history tracking submodule that records user actions and preferences to refine future alerts. The notification customization module 707 enables users to configure notification parameters, thereby supporting customized alert thresholds and delivery modes, ensuring a personalized user experience. For example, the system's data processing capabilities are extended via RTDM 710 to integrate with Impulse data, enabling dealers to receive notifications relevant to their operations. This integration ensures that dealers are kept up to date with relevant information in real time, such as inventory changes or pricing updates, thereby cultivating a responsive and informed dealer network.

[0159] RTDM 710 is configured as a comprehensive data management system for executing alert and notification processes within system 700. It consists of an integration layer, a data processing layer, and subsystems that facilitate real-time data analysis and management. System 700 establishes a link to the cloud marketplace via RTDM 710 through an event processing adapter (e.g., for Knowledge Article Generator (KAG) integration). This adapter can serve as a channel for event-driven data, capturing market activity and facilitating its conversion into structured events suitable for notification engine 720. This connection enables the system to control and utilize cloud-based events, thereby converting market dynamics into actionable notifications.

[0160] The integration layer of RTDM 710 interconnects various enterprise systems, handling data exchange and synchronization to ensure that notifications are generated from the most current and accurate data. This includes data feeds from enterprise systems that provide real-time updates on operational metrics, user activity, and system events. System 700 incorporates data paths originating from a cloud management platform (CMP), where both active data streams (such as user-generated emails) and passive data (such as user engagement metrics) are directed to the notification engine. These data paths are enriched through intermediate data enrichment nodes to ensure that notification content is both context-rich and customized for user profiles and preferences.

[0161] RTDM 710 includes a data layer that is configured to translate and process data for retrieval and analysis. It includes a scalable data grid and multiple purpose-built data stores (PDSs), each optimized for a specific data type such as user alerts, system events, or operational metrics. These PDSs store normalized data, ensuring consistency and integrity across systems.

[0162] The data layer can also include data replication mechanisms to capture real-time changes from transactional systems and transform them into a standardized format for immediate analysis and action. This capability ensures that notification systems operate with the latest data, enabling real-time insights and decisions.

[0163] The data layer of RTDM 710 is a powerful foundation for managing the vast amounts of data in the distribution ecosystem. It includes a scalable data lake equipped to handle the large amounts of diverse data generated. This data lake serves as a centralized platform for structured and unstructured data, enabling complex analytical tasks and machine learning algorithms to optimize notification relevance and timing. The notification engine 720 leverages the data lake (as a repository and analytical foundation) to map and merge user data with notification logic. This integration is configured to enrich notifications with user-specific information and historical data, thereby enhancing the personalization and relevance of alerts.

[0164] Furthermore, the module incorporates a change data capture mechanism to maintain real-time data synchronization with various systems. It uses a framework compatible with various enterprise systems to ensure integration and facilitate smooth data exchange.

[0165] RTDM 710 also prioritizes data governance and security, implementing fine-grained access controls and encryption to protect sensitive information. Containerization within a cloud-native environment ensures the module's scalability and resilience, adapting to the dynamic needs of the technology distribution platform. RTDM 710 is a dynamic component within System 700 and core to the efficient operation of the notification engine. It ensures real-time data availability, facilitating the generation of precise and actionable alerts and notifications. The module's advanced data management capabilities are crucial to maintaining the integrity and responsiveness of the alert and notification system within the technology distribution framework.

[0166] The AAML module 715 integrates a set of advanced machine learning algorithms and data processing tools to serve as the analytical core of the system 700. It analyzes incoming data streams to detect anomalies, trends, and patterns that trigger notifications. The module is configured with neural network and decision tree algorithms that can adapt to and learn from data patterns over time to enhance predictive accuracy. The AAML module 715 also incorporates natural language processing (NLP) capabilities to analyze unstructured data for opinions and content that may affect the relevance of notifications. The design of the module includes a feedback loop mechanism in which the output of the notification system is used to continuously recalibrate the analytical model to improve the context and timing of the alerts.

[0167] The notification engine 720 serves as the central processing unit within system 700, evaluating events and generating notifications. It is built upon a rules engine that applies a set of criteria to incoming data to determine notification triggers. These criteria are based on both static rules and dynamic algorithms that assess event severity, classification, and user preferences. The notification engine 720 is also designed with template processing capabilities, enabling the creation of customized notification content that is consistent with the context of the alert. It includes an orchestration layer that manages the workflow from event detection to scheduled notification generation, ensuring that each notification is accurately processed and disseminated in real time. Events are emitted by source systems and processed by event adapters 730, which structure the raw event data into a format compatible with the notification engine 720. After processing, notifications are disseminated through various channels managed by a distribution module 735, ensuring timely delivery to intended recipients. The notification engine 720 is also equipped with a variety of channels specifically designed for sending alerts via email and push notifications. These channels are calibrated for high throughput and can adjust notification delivery mechanisms based on user preferences and channel efficiency to optimize the reach and impact of each issued alert.

[0168] The operational flow of system 700 begins with an event being emitted by a source system. Event adapters 730 are constructed to parse and format raw event data using JSON schemas and XML for interoperability across system boundaries. The adapters are capable of handling high-volume event streams and converting them into standardized formats for subsequent processing. After conversion, a distribution module 735 manages the multi-channel propagation of notifications. This module is designed for scalability and supports various communication protocols, such as MQTT and AMQP, for event broadcasting. It ensures that notifications are distributed based on user preferences and channel availability, thereby utilizing push technology for instant delivery. Interactions with consumer systems are coordinated through a subscription model, where entities such as the XAC web message center can subscribe to specific event patterns. This model enables the system to disseminate targeted alerts and allows users to access a historical log of notifications, thereby ensuring continuity and reference to ongoing and past events.

[0169] Within the SPoG UI 705, the consumer interaction module 740 allows end users to view and manage notifications, providing a user-centric platform. This aligns with the consumer system, facilitating interaction with generated alerts. The user experience is further refined through the interactive capabilities of the platform's web interface, which displays notifications and maintains a history of the alerts presented on screen. Users can interact with this history, providing a feedback loop that informs future notification refinement and user mapping strategies.

[0170] The data analysis and storage component 745 (part of the RTDM 710) enables storage of large amounts of event and notification data to support analysis and provide historical reference to improve notification accuracy. The data analysis and storage component 745 is configured to maintain the integrity and accessibility of the data.

[0171] The notification customization module 750 within the SPoG UI 705 provides users with the ability to personalize their notifications, thereby facilitating an interactive and customized alerting experience.

[0172] The architecture of system 700 ensures an efficient and dynamic process for managing alerts and notifications, integrating data from multiple sources into a unified interface via SPoG UI 705, automating alert processing via AAML module 715, and maintaining a real-time, standardized data repository via RTDM 710. The system enhances the overall responsiveness and operational efficiency of the technology distribution platform, ensuring that users obtain the most relevant information at the moment to improve overall responsiveness and operational efficiency.

[0173] In one embodiment, Figure 8 The notification architecture 800 for a technology distribution platform is shown, which is configured to process and manage large amounts of event data from various interfaces and effectively disseminate information to end users. The notification architecture 800 can be incorporated into an embodiment of the notification engine 720 within the system 700. Figure 8 As shown, notification architecture 800 depicts a cloud-integrated alert and notification architecture. In some embodiments, notification architecture 800 integrates RTDM 710 from system 700, which provides a data management layer capable of processing high-speed data streams and enabling real-time analysis for notification processing.

[0174] The notification architecture 800 is configured to evaluate event data and format the event data into notifications, and includes a gateway interface 840, an API wrapper 820, an event source application 830, a messaging interface 850, a cloud SQL interface 860, a cloud storage interface 861, a notification API 870, a serverless VPC connector 890, and a channel provider 871. The RTDM 710 within the notification architecture 800 may also include an advanced data processing subsystem that facilitates the operationalization of analytics, thereby allowing raw data to be transformed into actionable insights for notification customization.

[0175] The gateway interface 840 is represented as an API gateway, which serves as a controlled access point for incoming data. It ensures secure data transfer by executing the necessary protocols and directing the data flow to the correct subsystem within the notification engine.

[0176] Portal interface 810 is the entry point for user interaction, wherein portal interface 810 is dedicated to general user interaction, portal interface 811 is designed for logistics and return management, and portal interface 812 is allocated for customer service management. These interfaces promote user participation in the system, providing a customized summary form experience for monitoring and managing notifications.

[0177] The API wrapper 820 acts as a domain-specific wrapper API, providing a modular and domain-customizable tracking channel for event subscription and translation, thereby converting business events into standardized notification requests for processing by the notification engine.

[0178] Event source applications 830 (referred to as source API 830) include various enterprise systems (such as ERP and WMS) that emit operational events to be captured and processed by notification engine 800 for notification triggering. In some embodiments, RTDM 710 is employed to establish integrated connectivity, filtering, and normalization between event source applications 830 and notification architecture 800, thereby facilitating data flow and ensuring that notifications are generated based on current, real-time operational events.

[0179] The messaging interface 850 (represented as a Pub / Sub topic) manages event messages via a publish / subscribe mechanism. This interface is crucial for the asynchronous nature of message queuing, allowing the system to handle varying loads and maintain a consistent notification flow. In some embodiments, the messaging interface 850 is operably connected and / or integrated with the RTDM 710 to prioritize and route messages, thereby optimizing the delivery of notifications based on real-time data analysis and user engagement metrics.

[0180] The notification engine 720 includes submodules, including the notification engine 720 itself and the notification API 870, which are responsible for event evaluation and notification formulation. These submodules apply criteria to incoming data to determine notification triggers and manage the workflow from event detection to notification scheduling. The notification engine 720 can be combined with or operatively connected to the AAML module 715, which is configured to apply machine learning algorithms for pattern detection and predictive analysis, thereby refining the criteria used for notification triggering within the notification API 870.

[0181] In some embodiments, a notification API 870 interconnected with the notification engine 720 applies predetermined criteria to incoming data to trigger appropriate notifications. A channel provider 871 manages the scheduling of notifications through an email service, thereby ensuring reliable delivery to users / subscribers. In some embodiments, the SPoG UI 705 provides an integrated platform that enables users to interact with, manage, and personalize their notification experience, thereby optimizing the interface for various user groups. The SPoG UI 705 can include a user-centric design, enabling real-time monitoring and management of alerts and notifications through customizable summary tables. In some embodiments, the AAML module 715 is configured to enhance the functionality of the notification API 870 by incorporating user behavior models that adapt over time, thereby providing a feedback loop mechanism for continuous enhancement of the notification system.

[0182] The load balancing interface 880 is designed with a dynamic distribution algorithm to fairly manage incoming requests, ensuring system reliability and avoiding bottlenecks. The serverless VPC connector 890 establishes a secure connection with cloud services, incorporating security measures such as automated patch management and network intrusion detection, thereby enhancing the system's data security framework. In some embodiments, the load balancing interface 880 can be integrated into the real-time processing capabilities of RTDM 710 or otherwise configured to utilize RTDM 710 to dynamically adjust the distribution of network traffic to maintain system performance even under varying load conditions.

[0183] The cloud service interface may include a cloud SQL interface 860 and a cloud storage interface 861, each of which performs a specific role in data management. Cloud SQL interface 860 processes structured data, thereby providing managed database services, while cloud storage interface 861 is configured to store unstructured data, thereby supporting analysis and historical data retrieval. In certain embodiments, cloud SQL interface 860 and cloud storage interface 861 may be integrated with RTDM 710 to support structured and unstructured data streams, allowing RTDM 710 to be configured to provide a real-time data warehouse to supplement the data management and retrieval capabilities of the cloud service.

[0184] A channel provider 871 operatively connected to the notification API 870 dispatches notifications via an email service. The provider ensures reliable delivery of notifications to users / subscribers (e.g., end users who interact with the distribution platform and receive notifications and manage their preferences through an interface provided by the notification engine).

[0185] The load balancing interface 880 distributes incoming requests to prevent bottlenecks and ensure system reliability. The interface uses an algorithm that dynamically allocates system resources to maintain performance.

[0186] The serverless VPC connector 890 establishes a secure connection to cloud services, facilitating direct communication within the cloud provider's network, thereby enhancing data security through measures such as encryption and intrusion detection.

[0187] Thus, the notification engine 720 incorporated into the notification architecture 800 provides flexible and scalable notifications to meet the evolving complexity and needs of the technology distribution platform. The notification architecture 800 enables users and systems to always engage with current real-time information, thereby improving the overall responsiveness and operational efficiency of the platform.

[0188] Figure 9 A flowchart is shown of a method 900 for managing the flow of data from event generation to notification dispatch within system 700. The flowchart outlines the sequential processing steps for converting data from raw events to refined notifications, highlighting the operational synergy between RTDM 710, event adapter 730, and distribution module 735. The method illustrates the conversion of various data inputs into structured, actionable alerts that are customized to the user's specifications and distributed across multiple channels.

[0189] In operation 901, the method begins by collecting raw data from multiple event source applications. These sources may include, but are not limited to, ERP systems, WMS platforms, and direct user activities, which emit operational data that is critical to the alarm generation process. Operation 901 may include ingesting data through RTDM 710, where filtering and validation checks may be performed. Operation 901 may be performed to ensure accurate data is processed and filtered based on relevance, thereby maintaining the integrity of the notification system. Operation 901 may also include performing data enrichment through RTDM 710, where contextual information may be attached to the data and / or associated with the data, thereby enhancing it with the additional details required to create a comprehensive notification. This enriched data may include user preferences, historical interaction data, and related metadata. Operation 904 may also include standardizing the ingested data through RTDM 710. Data from disparate sources may be converted into a unified format, which is crucial for subsequent processing steps and ensures consistency across the notification system.

[0190] In operation 902 , the normalized data is copied to a data lake within the RTDM 710 , ensuring that a persistent history of events and notifications is maintained for analytical and auditing purposes.

[0191] In operation 903, the method proceeds to sending the normalized data to the event adapter 730. Here, the data is structured into a compatible format for the notification engine, typically using JSON schema or XML format, for interoperability and integration.

[0192] In operation 904 , the event adapter 730 delivers the formatted data to the notification engine, which applies predefined rules and dynamic algorithms to determine the necessity of a notification trigger in operation 908 .

[0193] In operation 905 , after determining the triggering event, the notification engine crafts the notification content, utilizing template processing capabilities to generate consistent alerts and contextually customize based on the event and user profile.

[0194] In operation 906, the notification engine then passes the generated notification to the distribution module 735, which is tasked with disseminating the alert on various channels.

[0195] In operation 907 , the distribution module 735 employs a multi-channel approach, ensuring that notifications are delivered through the most appropriate and efficient medium (such as email, SMS, or app-based push notifications) based on user preferences and channel availability.

[0196] In operation 908 , the end user receives the notification and can interact with and respond to the alert through the SPoG UI, thereby closing the loop on the notification scheduling process and facilitating the continuous feedback mechanism of the system 700 .

[0197] Thus, the method 900 provides an efficient process for notification scheduling from the point of data capture to end-user interaction, thereby reinforcing the user-centric design of the system 700 .

[0198] Figure 10 A flow chart of a method 1000 for user interaction with notifications within the system 700 is shown. The flow chart details the user engagement sequence, illustrating how a user receives, acknowledges, and interacts with notifications through a single pane of glass user interface (SPoG UI). The method illustrates the responsiveness of the system 700 to user input, from basic notification acknowledgement to complex user-driven configuration adjustments.

[0199] In operation 1001, the method 1000 begins by delivering notifications to the user via the SPoG UI 705. These notifications are Figure 9 The results of the process described in detail in are now presented to the user in a coherent and user-friendly manner on their respective device interface.

[0200] In operation 1002, after receiving the notification, the user interacts with the alert through the SPoG UI 705. Depending on the nature and urgency of the notification, this interaction may be an acknowledgement of receipt, a request for additional details, or an immediate response action.

[0201] In operation 1003, user interactions are recorded by the consumer interaction module 706 within the SPoG UI 705. This module captures and records the user's actions and preferences, thereby facilitating a comprehensive history of user-notification interactions.

[0202] In operation 1004, the notification customization module 707 within the SPoG UI 705 enables the user to adjust notification parameters. The user can set preferences for notification frequency, channel, and format, thereby customizing the alert experience according to their personal needs.

[0203] At operation 1005, method 1000 includes processing user feedback received via the SPoG UI 705. This feedback may be related to the relevance, timeliness, or content quality of the notification and used to refine future notifications.

[0204] In operation 1006, the user's preferences and feedback are synchronized with the user's profile in the backend system to ensure that all future notifications are consistent with the updated user settings and preferences. Operation 1006 may include analyzing the aggregated user interaction data using RTDM 710 in conjunction with AAML module 715 to identify trends and patterns in user behavior and notification interactions.

[0205] In operation 1007, based on the analysis performed by RTDM 710 and AAML module 715, system 700 may dynamically adjust notification logic within notification engine 720. The adjustment may involve changing notification rules, updating templates, or modifying delivery channels.

[0206] The method 1000 proceeds to iteratively refine the notification content and delivery mechanism at operation 1008. The continuous loop of user interaction data informs the evolution of the notification system, resulting in an increasingly personalized and relevant user experience.

[0207] In operation 1009 , the refined notification is then redeployed by the distribution module 735 , thereby closing the feedback loop by delivering the enhanced notification back to the user via the user's preferred channel.

[0208] Thus, system 700 provides a user-centric notification process in method 1000, wherein each user interaction is captured, analyzed, and used to inform the behavior of the system. Method 700 emphasizes the adaptability of the notification system to user preferences and behaviors, thereby providing a personalized and engaging user experience within the technology distribution platform.

[0209] Figure 11A block diagram of example components of a device 1100 is depicted. One or more computer systems 1100 can be used, for example, to implement any of the embodiments discussed herein, as well as any of their combinations and subcombinations. The computer system 1100 can include one or more processors (also referred to as central processing units or CPUs), such as a processor 1104. The processor 1104 can be connected to a communication infrastructure or bus 1106.

[0210] The computer system 1100 may also include user input / output device(s) 1103 , such as a monitor, keyboard, pointing device, etc., which may communicate with the communication infrastructure 1106 through the user input / output interface(s) 1102 .

[0211] One or more processors 1104 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor that can be a specialized electronic circuit configured to process mathematically intensive applications. A GPU may have a parallel architecture that can efficiently process large blocks of data (common mathematically intensive data such as computer graphics applications, images, and videos).

[0212] The computer system 1100 may also include a main memory or main storage 1108, such as random access memory (RAM). The main storage 1108 may include one or more levels of cache. The main storage 1108 may store control logic (ie, computer software) and / or data.

[0213] Computer system 1100 may also include one or more secondary storage devices or memories 1110. Secondary storage 1110 may include, for example, a hard drive 1112 and / or a removable storage device or drive 1114.

[0214] Removable storage drive 1114 can interact with removable storage unit 1118. Removable storage unit 1118 can include a computer-usable or readable storage device having computer software (control logic) and / or data stored thereon. Removable storage unit 1118 can be a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated sockets, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. Removable storage drive 1114 can read from and / or write to removable storage unit 1118.

[0215] Secondary storage 1110 may include other devices, equipment, components, tools, or other pathways for allowing computer system 1100 to access computer programs and / or other instructions and / or data. Such devices, equipment, components, tools, or other pathways may include, for example, a removable storage unit 1122 and an interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and an associated socket, a memory stick and a USB port, a memory card and an associated memory card slot, and / or any other removable storage unit and associated interface.

[0216] The computer system 1100 may also include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate and interact with any combination of external devices, external networks, external entities, and the like (individually and collectively referred to as 1128). For example, the communication interface 1124 may allow the computer system 1100 to communicate with an external or remote device 1128 via a communication path 1126, which may be wired and / or wireless (or a combination thereof) and may include any combination of a LAN, a WAN, the Internet, and the like. Control logic and / or data may be transmitted to and from the computer system 1100 via the communication path 1126.

[0217] The computer system 1100 may also be any one of a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet computer, a smart phone, a smart watch or other wearable device, an appliance, part of the Internet of Things, and / or an embedded system, to name a few non-limiting examples, or any combination thereof.

[0218] The computer system 1100 can be a client or server accessing or hosting any application and / or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; local or on-premises software ("local" cloud-based solutions); "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.); and / or a hybrid model including any combination of the foregoing examples or other services or delivery paradigms.

[0219] Any available data structures, file formats, and schemas in the computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), another markup language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), message bundles, XML User Interface Language (XUL), or any other functionally similar representation (alone or in combination). Alternatively, proprietary data structures, formats, or schemas may be used exclusively or in combination with known or open standards.

[0220] In some embodiments, a tangible, non-transitory device or article of manufacture comprising a tangible, non-transitory computer-usable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1100, main memory 1108, secondary memory 1110, and removable storage units 1118 and 1122, as well as tangible articles of manufacture implementing any combination of the foregoing. When executed by one or more data processing devices (such as computer system 1100), such control logic may cause such data processing devices to operate as described herein.

[0221] Figures 12A to 12Q Depicts the various screens and features of the SPoG UI related to seller login, partner summary form, customer shopping cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription modification. A detailed description of each diagram is provided below:

[0222] Figure 12A Depicted is a merchant login splash screen, which represents the initial step of the merchant login process. It provides a form or interface in which merchants can express their interest in joining the distribution ecosystem, and merchants can enter their basic information such as company details, contact information, and product catalog.

[0223] Figure 12B Depicts a vendor onboarding guide that shows a step-by-step guide or checklist for vendors to follow during the onboarding process. It outlines the necessary tasks and requirements, ensuring that the vendor clearly understands the onboarding process and can proceed smoothly.

[0224] Figure 12C Depicts the seller onboarding call scheduler, which helps schedule calls or meetings between sellers and platform employees or representatives who will guide them through the onboarding process. Sellers can select a suitable time slot or request a call, ensuring effective communication and assistance throughout the onboarding journey.

[0225] Figure 12DDepicted is a vendor login task list that presents a comprehensive task list or summary table outlining the specific steps and actions required for a successful vendor login. It provides an overview of pending tasks, completed tasks, and upcoming deadlines, helping vendors track their progress and ensure timely completion of each login task.

[0226] Figure 12E Depicts the seller login completion screen confirming the successful completion of the seller login process. It can display a congratulatory message or a summary of the completed tasks, indicating that the seller is now officially logged into the distribution ecosystem.

[0227] Figure 12F Depicts a partner summary table that provides partners or users with a centralized view of relevant information and metrics related to their partnership with the distribution ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0228] Figure 12G Depicts a customer's product shopping cart representing a customer's product shopping cart, where they can add items they want to purchase. It displays a list of selected products, quantities, prices, and other relevant details. Customers can review and modify their shopping cart contents before proceeding to the checkout process.

[0229] Figure 12H Depicts a customer subscription shopping cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify their subscription details before finalizing their selection.

[0230] Figure 12I Depicts a customer order summary that provides a summary of the customer's order, including details such as the product or subscription purchased, quantity, pricing, and any applied discounts or promotions. It allows customers to review their order before confirming their purchase.

[0231] Figure 12J A vendor SKU generation screen for generating unique stock keeping unit (SKU) codes for vendor products is depicted. It may include fields or options where the vendor can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU code.

[0232] Figure 12K and Figure 12LSummary tables, Order Summaries, are depicted to display summary information about orders placed within the distribution ecosystem. They present key order details such as order number, customer name, product or subscription information, quantity, and order status. Summary tables provide an overview of order activity, enabling users to efficiently track and manage orders.

[0233] Figure 12M Depicts a customer subscription cart that allows customers to add, modify, or remove subscription plans. It displays a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes based on their preferences and requirements.

[0234] Figure 12N Depicts the customer order tracking screen that enables customers to track the status and progress of their orders through the supply chain. It displays real-time updates on order fulfillment, including processing, packing, and shipping. Customers can monitor the progress of their orders and anticipate delivery times.

[0235] Figure 12O Depicts customer shipment tracking that provides customers with real-time tracking information about their shipments. It can include details such as the carrier, tracking number, current location, and estimated delivery date. Customers can stay informed about the whereabouts of their shipments.

[0236] Figure 12P Depicts a customer subscription history that presents a history of a customer's subscription activity. It displays a list of previous subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track past payments, and reference previous subscription details.

[0237] Figure 12Q Depicts the customer subscription modification dialog that allows customers to modify their existing subscriptions. It provides options to upgrade or downgrade subscription plans, change billing details, or adjust other subscription-related preferences. Customers can manage their subscriptions based on their evolving needs or preferences.

[0238] The UI screens depicted are not limiting. In some embodiments, Figures 12A to 12Q The UI screens collectively present the various functions and features available through the SPoG UI, providing users with a comprehensive and user-friendly interface for seller onboarding, partnership management, customer interaction, order management, subscription management and tracking within the distribution ecosystem.

[0239] It should be understood that the Detailed Description section, and not the Summary and Abstract sections, is intended to be used to interpret the claims. The Summary and Abstract sections may set forth one or more, but not all, exemplary embodiments of the present invention as contemplated by the inventor(s), and thus, are not intended to limit the present invention and the appended claims in any way.

[0240] The present invention has been described above by means of functional building blocks illustrating embodiments of specified functions and relationships thereof. For ease of description, the boundaries of these functional building blocks have been arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and relationships thereof are appropriately performed.

[0241] The foregoing description of specific embodiments will fully reveal the general nature of the invention so that others can easily make modifications and / or adjustments for various applications (such as specific embodiments) by applying the knowledge of those skilled in the art without excessive experimentation without departing from the overall concept of the invention. Therefore, based on the teachings and guidance presented herein, such adjustments and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments. It should be understood that the wording or terminology herein is for descriptive and not limiting purposes, so that the terms or wording of this specification are interpreted by the skilled person in accordance with the teachings and guidance.

[0242] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. A system for managing alerts and notifications within a technology distribution platform, wherein: The system comprises: means for collecting event data from multiple source systems and presenting notifications to the user through a single pane of glass user interface, i.e., SPoG UI; A real-time data grid (RTDM) configured to initially filter, enrich and normalize the event data into a unified format; Persistent storage for replicating normalized data for analytical and auditing purposes; an event adapter, which is configured to format the data for processing by the notification engine; a notification engine, which is designed to determine notification triggers and generate alert content based on rules and algorithms; The recording and user interaction module is used to capture and record user interactions with notifications and enable notification settings customization; Advanced Analytics and Machine Learning (AAML) module, which processes user feedback and dynamically adjusts notification logic; and A distribution module for iteratively refining notification content and delivery mechanisms, and redeploying enhanced notifications across multiple channels.

2. The system according to claim 1, wherein: The SPoG UI includes real-time visualization tools for monitoring alert status and managing notification preferences.

3. The system according to claim 1, wherein: The RTDM is configured to maintain real-time synchronization with the source system to ensure the accuracy and timeliness of the data.

4. The system according to claim 1, wherein: The notification engine is configured to orchestrate a workflow generated from event detection to scheduled notifications.

5. The system according to claim 1, wherein The distribution module supports multiple communication protocols including one or more of MQTT and AMQP for broadcast notifications, and wherein the system supports interoperability utilizing JSON schema and XML formats for data compatibility.

6. The system according to claim 1, wherein: The user interaction module allows users to customize alert thresholds, notification frequency, and channel preferences.

7. The system according to claim 1, wherein: The system also includes analysis and reporting means for generating real-time reports on notification engagement metrics and user response rates.

8. A system for managing alerts and notifications within a technology distribution platform, wherein: The system comprises: A notification engine configured to process event data from different user portals and source application interfaces, wherein the notification engine comprises: An API gateway that facilitates secure data entry and routing to the messaging interface; a cloud service interface that integrates cloud-based services for data management and analysis; A load balancing interface designed to manage and distribute incoming requests across the platform; Serverless VPC Connectors, which are used to establish secure connections to cloud services; and a distribution module for disseminating notifications via various communication channels; and A single pane of glass user interface (SPoG UI) that is configured to enable users to interact with, manage, and personalize their notification experience.

9. The system according to claim 8, wherein: The portal interface is optimized to provide a customized summary table experience for various user groups.

10. The system according to claim 8, wherein: The API wrapper is modular and can be configured to support different business domain requirements within the platform.

11. The system according to claim 8, wherein The cloud service interface includes a scalable data lake for structured and unstructured data management.

12. The system according to claim 8, wherein: Channel providers include dedicated channels for email, SMS, and app-based push notifications.

13. The system according to claim 8, wherein: The load balancing interface uses a dynamic distribution algorithm to manage incoming API calls and system load.

14. The system according to claim 8, wherein: The serverless VPC connector is configured with security measures including automated patch management and network intrusion detection.

15. The system according to claim 8, wherein The notification engine includes a feedback loop mechanism for continuous refinement based on user interaction data.

16. A computerized method for managing alerts and notifications within a technology distribution platform, wherein: The method comprises: Collect event data from multiple source systems and present notifications to users via a single pane of glass user interface (SPoG UI); Processing the event data into a unified format for notification processing by applying one or more of initial filtering, data enrichment, and data normalization via a real-time data mesh (RTDM); Copy normalized data to persistent storage for future analysis and auditing purposes; The event adapter processes the data to generate formatted data; inputting the formatted data into a notification engine, wherein the formatted data is formatted according to a processing requirement of the notification engine; determining notification logic including a plurality of notification triggers based on one or more rules and / or dynamic algorithms; The notification engine generates notification content based on the notification trigger; Capturing one or more user interactions with the notification as interaction data for historical tracking; Receiving user customization input via a notification customization module; Processing the user customized input and / or the interaction data through an advanced analytics and machine learning (AAML) module to dynamically adjust notification logic; Iteratively refine notification content and delivery mechanisms based on user interaction data, and redeploy enhanced notifications across multiple channels through a distribution module.

17. The method according to claim 16, wherein The method further includes maintaining real-time synchronization with the source system via the RTDM to achieve current data accuracy.

18. The method according to claim 16, wherein The method also includes orchestrating, by the notification engine, a workflow from event detection to scheduled notification generation.

19. The method according to claim 16, wherein The method further includes broadcasting the notification using one or more communication protocols, one or more of the protocols being selected from MQTT and AMQP, and wherein JSON schema and XML format are used for data interoperability across system boundaries.

20. The method according to claim 16, wherein One or more alert thresholds, notification frequencies, and channel preferences input by the user are received by the notification customization module.

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