System and method for alert and notification in advanced distribution platform

Abstract

To provide systems and methods for managing alerts and notifications within a technology distribution platform so as to increase operational inefficiency and error possibility.SOLUTION: In a system 300, a single pane of glass (SPoG) UI 305 functions as a centralized user interface, and provides an integrated view of the whole supply chain for a user. A real-time data mesh (RTDM) module 310 is another component of the system 300, and takes responsibility of guaranteeing a data flow in a distribution ecosystem. An advanced analysis and machine learning (AAML) module 315 utilizes powerful analysis tool and algorithm to extract a valuable prospect from collected data.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application is a continuation-in-part (CIP) of U.S. Patent Application No. 18 / 341,714, filed June 26, 2023, and U.S. Patent Application No. 18 / 349,836, filed July 10, 2023. This application also claims the benefit of priority to 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, and U.S. Provisional Application No. 63 / 515,076, filed July 21, 2023. Each of these applications is incorporated herein by reference in its entirety.

[0002] (background) Traditional ordering processes in distribution and supply chain platforms are plagued by inefficiencies, delays, and inaccuracies. In traditional environments, multiple systems and vendors typically perform each activity independently, from creating bills of materials to registering transactions, applying pricing, generating quotes, and issuing orders. This approach leads to operational inefficiencies and a high likelihood of error.

[0003] Enterprise resource planning (ERP) systems have served as the workhorse in managing business processes, including distribution and supply chains. These systems act 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 environment. One of the main challenges is data fragmentation. Data silos across different departments or separate ERP systems make it difficult to achieve real-time visibility. Users lack a comprehensive view of key distribution and supply chain metrics, which negatively impacts the decision-making process.

[0004] Furthermore, ERP systems often do not offer effective data integration capabilities. Traditional ERP systems are not designed for efficient integration with external systems or between different modules within the same ERP suite. This design results in cumbersome, error-prone manual processes for transferring data between systems, negatively impacting the flow of information throughout the supply chain. When information exists in different formats across systems, data inconsistencies arise, preventing accurate data analysis and leading to uninformed decision-making.

[0005] Data inconsistencies present another challenge. When data exists in different formats or units across departments or ERPs, standardizing this data for meaningful analysis can be a tedious process. Businesses often rely on time-consuming manual processes to convert and validate data, further delaying decision-making. Additionally, legacy ERP systems often lack the ability to effectively handle large amounts of data. These systems struggle to provide timely insights for operational improvement, which is particularly problematic for businesses dealing with complex and expanding distribution and supply chain networks.

[0006] Data security is another concern, especially considering the sensitivity of supply chain data, which includes customer details, pricing, and contracts. Ensuring compliance with global regulations on data security and governance adds another layer of complexity. Traditional ERP systems often lack robust enough security features to adapt to the ever-evolving landscape of cybersecurity threats and compliance requirements. Summary of the Invention

[0007] The automated alert and notification process is designed to address inefficiencies in the technology distribution industry by integrating disparate systems and activities into a unified interface. This integration allows information, from operational alerts to service updates, to be managed and distributed across the technology distribution platform. This transformation to a unified communication model facilitates the entire notification management process and improves the efficiency of activities such as user interaction, dynamic content customization, and subscription notification management. The platform effectively integrates and accelerates the notification process while ensuring data security and compliance.

[0008] The global distribution industry requires innovative solutions to challenges such as inefficient communication channels, real-time updates, and a shift to a more user-centric model. Traditional notification methods are becoming increasingly inadequate, especially with shifting user engagement and regulatory requirements. By integrating functionality for real-time alerts, user interaction management, and system performance visibility, the platform supports the shift from traditional communication methods to a flexible, user-centric notification model.

[0009] According to some embodiments, the alert optimization module can be configured to incorporate algorithms for optimizing notification content and delivery based on real-time data and user preferences. The system includes a module that integrates with a real-time data mesh (RTDM) and a single pane of glass user interface (SPoG UI) to optimize information dissemination. Advanced algorithms are used to adapt notifications based on real-time system performance data and user interaction patterns, improving the relevance and timeliness of communications.

[0010] In non-limiting examples, a user engagement recommendation engine employs algorithms to provide users with personalized notification options, and a content customization engine uses models such as multivariate linear regression or random forests to predict and tailor notification content based on actual user engagement, system events, and individual user preferences.

[0011] In one embodiment, a notification management and real-time interaction module operatively connected to the RTDM and SPoG UI manages the lifecycle of user notifications. This module optimizes notification delivery based on real-time data using algorithms to dynamically adjust content and user interaction options. The system includes a content engine for customizing messages and adapting to variables such as user feedback and system conditions.

[0012] In some embodiments, the system allows users to personalize notification settings with a single click via the SPoG UI and includes modules to check user preferences and aggregate interaction options based on the current system context, facilitating the notification management process.

[0013] 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 to ensure consistent and up-to-date information across the notification system.

[0014] The embodiments disclosed herein integrate multiple systems and automate and validate processes to automate the management of information across technology distribution platforms. By implementing intelligent rules and validations, the system efficiently performs complex tasks, reducing the time and errors associated with manual notification management. The adaptability of the system ensures that it remains current and evolves to meet operational and user demands.

[0015] The system uses a data-driven approach to automate the customization and management of notification packages based on user interaction patterns. This involves assembling various alerts and updates into a coherent communication flow that aligns with individual user behavior and preferences. The system generates user profiles based on comprehensive data analysis that encompasses aspects such as digital engagement and response patterns. This data enables the customization of notification packages to meet specific user requirements in areas such as system updates, service alerts, and operational changes.

[0016] 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 relevant and engaging notification packages. Automatic notification bundling integrates information and updates from different system components, ensuring each communication package meets the user's information and interaction needs. Automated alert and notification generation, which can include combining system alerts with compatible service updates and operational changes, is designed to improve user awareness and operational efficiency.

[0017] Single Pane of Glass Single Pane of Glass (SPoG) can provide a comprehensive solution aimed at addressing these multifaceted challenges. It can be configured to provide a holistic, user-friendly, and efficient platform that expedites the distribution process.

[0018] 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 analytics, SPoG can provide valuable insight into inventory levels and product status, ensuring supply chain and distribution management processes are handled efficiently.

[0019] According to some embodiments, SPoG can consolidate multiple touchpoints into a single platform, emulating a direct consumer channel into a distribution platform. This consolidation provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0020] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics highlight demand trends and guide companies to manage their inventory more efficiently, reducing the risk of stock-outs or overstocks.

[0021] According to some embodiments, the SPoG can include a global compliance database that is updated in real time, allowing distributors to stay abreast of the latest international regulations. This feature significantly reduces the burden of manual tracking and ensures smooth and compliant cross-border transactions.

[0022] According to some embodiments, to facilitate SKU management and product localization, SPoG consolidates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the potential for errors. Furthermore, it provides the ability to efficiently manage and distribute localized SKUs, thereby meeting the needs and requirements of specific markets.

[0023] 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 meeting the expectations of a new generation of technology buyers.

[0024] Additionally, SPoG's advanced analytical capabilities provide valuable insights that can drive strategy and decision-making. Trends can be tracked and analyzed in real time, enabling companies to stay ahead of the curve and adapt to changing market conditions.

[0025] SPoG's flexibility and scalability make it a future-proof solution, able to adapt to changing business needs and allowing companies to scale operations up or down as needed without making major changes to their infrastructure.

[0026] SPoG's innovative approach to solving distribution industry challenges is an invaluable tool. By increasing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management, and delivering a superior customer experience, it offers a comprehensive solution to the complex problems that have long plagued the distribution industry. Through implementation, distributors can expect to see increased efficiency, reduced errors, and improved customer satisfaction, leading to sustainable growth in an ever-evolving global marketplace.

[0027] Real-Time Data Mesh (RTDM) According to some embodiments, the platform may include an implementation of Real-Time Data Mesh (RTDM). RTDS provides an innovative solution to address these challenges. RTDM is a distributed data architecture that enables real-time data availability across multiple sources and touchpoints. This feature improves supply chain visibility, enables efficient management, and allows distributors to handle disruptions more effectively.

[0028] RTDM's predictive analytics capabilities provide a solution for efficient inventory control. By providing insight into demand trends, it helps companies manage their inventory and reduces the risk of overstocking or running out of stock.

[0029] RTDM's global compliance database is updated in real time, ensuring distributors are up to date with international regulations, significantly reducing the burden of manual tracking and enabling cross-border transactions.

[0030] RTDM also simplifies SKU management and localization by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. Its ability to manage and distribute localized SKUs efficiently aligns with specific market needs.

[0031] RTDM enhances the customer experience with an intuitive interface, making technology easier to access and purchase, and meeting the expectations of a new generation of technology buyers.

[0032] Benefits of SPoG and RTDM Integration Integrating the SPoG platform with RTDM offers numerous benefits. First, it provides a holistic solution to a long-standing problem in the distribution industry. RTDM's capabilities enable SPoG to improve supply chain visibility, facilitate inventory management, ensure compliance, simplify SKU management, and deliver a superior customer experience.

[0033] The real-time tracking and analytics provided by RTDM improves SPoG's ability to effectively manage its supply chain and inventory, providing accurate, current information that enables distributors to make informed decisions quickly.

[0034] Integrating SPoG with RTDM also ensures data consistency and reduces errors in SKU management, and provides a centralized platform for managing data from various OEMs, simplifying product localization and helping to meet market needs.

[0035] RTDM's global compliance database is integrated with SPoG to facilitate compliant cross-border transactions and reduce the burden of manual tracking, saving significant time and resources.

[0036] In some embodiments, a distribution platform incorporates SPoG and RTDM to provide an improved, comprehensive distribution system that can leverage the advantages of the distribution model, address its existing challenges, and position it for sustained growth in a constantly evolving global marketplace. [Brief explanation of the drawings]

[0037] [Figure 1] 1 illustrates one embodiment of an operating environment for a distribution platform, referred to herein as a system. [Figure 2]1 illustrates an embodiment of an operating environment for a distribution platform, according to some embodiments. [Figure 3] 1 illustrates an embodiment of a distribution management system. [Figure 4] 1 illustrates a system for automated alert and notification management, according to one embodiment. [Figure 5] 1 illustrates an RTDM module according to one embodiment. [Figure 6] 1 illustrates a SPoG UI according to one embodiment. [Figure 7] 1 illustrates a system for automated alert and notification management, according to one embodiment. [Figure 8] FIG. 1 is a flow diagram of a method for automated alert and notification management according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a flow diagram of automated alert and notification management in a technology distribution platform, according to some embodiments of the present disclosure. [Figure 10] FIG. 1 illustrates a flow diagram of user interaction with notifications in a technology distribution platform, according to some embodiments of the present disclosure. [Figure 11] FIG. 2 is a block diagram of exemplary components of a device according to some embodiments of the present disclosure. [Figures 12A-12Q] 1 illustrates various screens and functionality of the SPoG UI, according to some embodiments. Detailed Description of the Invention

[0038] The present embodiments may be implemented in hardware, firmware, software, or a combination thereof. The present 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 a 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 read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing particular actions. However, it should be understood that such description is merely for convenience and that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0039] It should be understood that the acts shown in the example methods are not exhaustive and that other acts may similarly occur before, after, or between any of the acts shown. In some embodiments of the present disclosure, acts may be performed in a different order and / or may differ.

[0040] 1 illustrates a distribution platform operating environment 100, referred to in this embodiment as system 110. System 110 operates within the context of an information technology (IT) distribution model and serves the needs of various users, such as customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment encompasses a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0041] 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 diverse range 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 suitable IT solution based on their requirements. Through system 110, customers also have access to real-time data and analytics, enabling them to make informed decisions and optimize their IT infrastructure.

[0042] End customers 130 can be the ultimate beneficiaries of the IT solutions provided by system 110. End customers can include businesses or individuals who use 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 on the market. System 110 allows end customers to track orders, receive delivery status updates, and access customer support services, thereby enhancing their overall experience.

[0043] Vendors 140 include manufacturers, distributors, and suppliers that offer a diverse range of IT products and services. System 110 serves as a centralized platform for vendors to showcase their offerings, manage inventory, and facilitate transactions with customers and resellers. Vendors leverage system 110 to streamline supply chain operations, manage pricing and promotions, and gain insight into customer preferences and market trends. By integrating with system 110, vendors can expand their reach, access new markets, and improve their overall visibility and competitiveness.

[0044] Resellers 150 can be intermediaries in the distribution model that bridge the gap between vendors and customers. Resellers play a vital role in the IT distribution ecosystem by connecting customers to the right IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipeline, and provide value-added services to their customers. By leveraging system 110, resellers can improve customer relationships, optimize their product offerings, and increase revenue streams.

[0045] Within the operating environment of the system 110, various dynamics and characteristics can exist that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. The system 110 ensures that relevant data can be exchanged between users in real time, enabling accurate decision-making and timely action. Integration with existing enterprise systems, such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems, enables communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0046] The system 110 can provide scalability and flexibility to accommodate the growing demands of IT distribution models, whether they involve a growing customer base, an increasing number of vendors, or a broad range of IT products and services. The system 110 can be configured to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. Additionally, the system 110 utilizes a technology stack that includes .NET, Java, and other suitable technologies.

[0047] In summary, the operating environment of system 110 within the IT distribution model encompasses customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. System 110 serves as a centralized platform that facilitates efficient collaboration, communication, and transaction processes among these users. By leveraging real-time data exchange, integration, scalability, and flexibility, system 110 enables users to optimize operations, improve customer experience, and drive business success within the IT distribution ecosystem.

[0048] 2 illustrates a distribution platform operating environment 200, which may be one embodiment of operating environment 100 of FIG. 1. The environment may include integration points 210, which enable data flow and connectivity between various systems, such as customer systems 220, vendor systems 240, reseller systems 260, and other entities for the implementation of a comprehensive alert and notification system. Operating environment 200 is designed to support real-time, event-driven notifications using advanced data processing and AI / ML techniques.

[0049] The alert and notification system within environment 200 involves real-time monitoring and alerting of important events such as inventory changes, price updates, and delivery tracking. Data from diverse sources, including customer interaction and service metrics, is aggregated from systems such as CRM and analytics tools into a real-time data mesh (RTDM), which processes and normalizes this data and serves as a dynamic repository for immediate access and delivery of notifications.

[0050] The system uses advanced AI algorithms for real-time analysis and predictive notifications. It employs machine learning models, including neural networks and decision trees, to process and interpret large amounts of data, enabling timely and relevant notifications. The system leverages ML algorithms for intelligent alert generation and employs techniques such as ensemble learning and reinforcement learning to continuously refine the notification process.

[0051] In this operational environment, system 110 serves as a central hub for coordinating the alert and notification process, bridging customer systems 220, vendor systems 240, reseller systems 260, and other related entities. It unifies communication and data exchange to ensure a cohesive and efficient notification experience across the distribution network. The environment utilizes a hybrid architecture that combines RESTful APIs and WebSockets to ensure real-time data exchange and synchronization over SSL / TLS protocols for data security.

[0052] Integration with customer systems 220, such as CRM and ERP platforms, is crucial to the alert system, enabling real-time distribution of notifications relevant to customer-specific needs and preferences, improving decision-making and operational efficiency.

[0053] Data exchange between customer systems 220, vendor systems 240, and reseller systems 260 can incorporate ETL processes to ensure data consistency and reliability in alert generation. Predefined business rules and logic direct data flow and processing, and advanced mapping and transformation tools harmonize disparate data formats for unified notification delivery.

[0054] Integration with trading partner systems 230 through integration points 210 supports the streamlining of the alert and notification process by providing market and product data for accurate and timely alert generation.

[0055] Vendor system integration ensures that vendors connected to system 110 can receive real-time notifications on key indicators, such as inventory levels and pricing changes, which are essential for staying up-to-date in a dynamic market environment.

[0056] Reseller System Integration: Reseller systems 260 connect to system 110 through integration points 210 to gain access to real-time notifications on product availability, pricing updates, and customer order status, enabling them to more effectively manage customer relationships and sales processes.

[0057] Other Entity System Integration: Integration points 210 further connect with other entities in the distribution process, facilitating efficient collaboration and distribution through the real-time exchange of alerts and notifications. This integration is central to maintaining a responsive and informed distribution ecosystem.

[0058] The system 110 employs AI and ML capabilities to automate and optimize alert and notification processes in response to dynamic market conditions and individual user preferences, ensuring timely and relevant notifications across the distribution network.

[0059] Integration points 210 also connect with systems of record 280, enabling additional data management and integration. These systems encompass ERP and CRM platforms, providing a rich source of data for alert and notification systems, enabling real-time updates and ensuring accurate information dissemination.

[0060] Integration points 210 within operating environment 200 are established through standardized protocols and APIs to ensure compatibility and secure data transfer. System 110 employs protocols such as RESTful API, SOAP, and GraphQL for efficient communication and data exchange.

[0061] To ensure secure access and data protection, the system 110 incorporates authentication and authorization mechanisms utilizing technologies such as OAuth or JSON Web Tokens (JWT), which maintain data integrity and confidentiality across the notification network.

[0062] Data flow within 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 among customer systems 220, vendor systems 240, reseller systems 260, and other entities to improve operational efficiency and decision-making.

[0063] The system 110 may utilize advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, and C# to support communication within the integration points 210 and the operating environment 200.

[0064] The architecture of system 110 also facilitates the processing, harmonization, and real-time accessibility of data across the distribution network, providing users with immediate access to relevant, real-time information for timely decision-making.

[0065] Each of the customer systems, such as customer system 220, is configured to receive and interact with alerts and notifications, facilitating immediate action and reaction to real-time information. This includes devices such as desktops, laptops, mobile phones, and smartwatches, each capable of presenting alerts through a variety of user interfaces.

[0066] The components of the customer system are configured using applications such as a web browser running on a central processing unit such as an Intel Pentium processor or similar. The distribution platform (system 110) and its components are similarly configured to ensure integrated interaction and notification management.

[0067] The machine-readable storage medium contains instructions for programming a computer to execute the processes of the alert and notification system. The computer code for operating and configuring the platform can be stored on various memory media and transmitted over conventional network connections using standard communication protocols.

[0068] The system implementation can be in a variety of programming languages ​​and run on different platforms, depending on the particular requirements of the distribution platform and the environment in which it operates.

[0069] In this way, 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 that significantly improves responsiveness and operational efficiency across the distribution network.

[0070] In conclusion, Figure 2 depicts a dynamic, interconnected operating environment 200 in which real-time alerts and notifications are central to the distribution process, facilitating informed decision-making and efficient operations across various entities in the supply chain. The integration of advanced AI / ML techniques, real-time data processing, and comprehensive alert mechanisms positions the system 110 as a core component in the modernization and optimization of distribution and supply chain management.

[0071] Figure 3 illustrates a system 300 for supply chain and distribution management. System 300 (Figure 3) is a supply chain and distribution management solution designed to address the challenges faced by a fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, improve collaboration, and drive business efficiency.

[0072] The Single Pane of Glass (SPoG) UI 305 serves as a centralized user interface, providing users with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to users' specific roles and responsibilities. By offering a customizable and intuitive dashboard-style layout, the SPoG UI allows users to access relevant information and tools, enabling data-driven decision-making and efficient supply chain and distribution activity management.

[0073] For example, logistics managers can use the SPoG UI to monitor shipment status, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize the data through interactive charts, such as a map showing the current location of each shipment, or a bar graph showing inventory levels by product category. Having a unified view of the supply chain allows logistics managers to identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0074] SPoG UI 305 integrates with other modules in the system 300 to facilitate real-time data exchange, synchronized operations, and streamlined workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the distribution ecosystem. Designed with a user-centric approach, SPoG UI 305 features an intuitive and responsive layout. Utilizing front-end technology, it provides dynamic and interactive data visualizations. Customizable dashboards allow users to tailor views based on their 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 allow users to efficiently navigate and access relevant supply chain data and insights.

[0075] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, triggers notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0076] The Real-Time Data Mesh (RTDM) module 310 is another component of the system 300 that is responsible for ensuring the flow of data within the distribution ecosystem, collecting and harmonizing data from multiple sources and ensuring its availability in real time.

[0077] In 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 harmonizing formats, standardizing units of measure, and reconciling any discrepancies. The harmonized data can then be made available in real time, allowing users to access accurate, current information across the supply chain.

[0078] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. It employs a change data capture (CDC) mechanism that constantly monitors transaction systems to detect updates or modifications. The CDC component can be specifically designed to work with a variety of transaction systems, including legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for business operations in diverse environments.

[0079] Having access to real-time data allows users to make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden spike in demand for a particular product, it can trigger an alert to the production team so that they can adjust production schedules to prevent stockouts.

[0080] The RTDM module 310 facilitates data management within supply chain operations. It enables real-time reconciliation of data from multiple sources, freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This increased flexibility supports improved efficiency, customer service, and innovation.

[0081] Another component of the system 300 is the Advanced Analytics and Machine Learning (AAML) module 315. Leveraging powerful analytics tools and algorithms such as Apache Spark, TensorFlow, or scikit-learn, the AAML module extracts valuable insights from the collected data, enabling advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0082] 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 stock availability during busy periods, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0083] In addition to forecasting demand, the AAML module can provide insight into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or up-selling opportunities and recommend relevant products to individual customers.

[0084] Additionally, the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of customer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0085] 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 comprehensive and connected distribution ecosystem.

[0086] The implementation and deployment of system 300 can be tailored 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, easy management, and efficient updates across different environments. The implementation process involves configuring the system to meet specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0087] 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 power of the SPoG UI 305, RTDM module 310, and AAML module 315, along with 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 herein are non-limiting and can be customized to meet specific industry requirements, driving efficiency and success in supply chain and distribution management.

[0088] 4 illustrates one embodiment of a system 400, focusing primarily 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 mesh (RTDM 410), and advanced AI / ML technologies (AI module 460). System 400 is configured for integration with existing reseller systems to ensure efficient data exchange, synchronization, and real-time alert functionality.

[0089] The SPoG UI 405 serves as the primary user interface and 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. The interface displays real-time data from the data mesh 410 and allows users to configure notification preferences, access alert history, and interact with real-time alerts. The SPoG UI 405 was developed using responsive web technologies and is accessible across multiple devices, ensuring users stay informed regardless of device.

[0090] The data mesh 410 forms the core of the alert and notification system. It aggregates and harmonizes data from diverse sources, including ERPs, vendor platforms, and third-party databases, ensuring that operational modules within the system 400 have access to consistent and current information. This harmonization is critical for generating accurate and timely alerts and notifications, especially in a dynamic distribution environment.

[0091] The AI ​​module 460 of the system 400 is directed to improving the alert and notification system. It uses machine learning algorithms and predictive modeling to identify patterns and trends that trigger alerts. This module dynamically processes data from the data mesh 410 and generates real-time notifications about important events such as inventory changes, pricing updates, and shipping status, improving user decision-making and response times.

[0092] In some embodiments, the AI ​​module 460 incorporates deep learning neural networks for pattern recognition, which is essential for predictive alerts in the distribution process. The module also uses decision trees and clustering algorithms to classify and segment alert types, ensuring users receive relevant, customized notifications.

[0093] The real-time processing capabilities of the AI ​​module 460 based on data from the data mesh 410 allows the system to adapt notifications to current market conditions and user behavior, including employing advanced analytics for proactive alert generation, ensuring users are quickly informed of important changes and updates in the distribution network.

[0094] The AI ​​module's 460 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 to further improve the alert customization process.

[0095] Data mesh 410 provides real-time tracking of data for the alert and notification system, driving predictive and responsive alerts. Data mesh 410 can be implemented in system 400 to enable tracking of usage patterns and market feedback, facilitating a responsive alert system in a dynamic distribution environment.

[0096] The predictive analytics tools within the AI ​​module 460 use time series forecasting and optimization algorithms to foresee future trends and demand in the distribution network and inform users through predictive alerts. This proactive approach to notification allows users to effectively prepare for and respond to changing market conditions.

[0097] The alert management module 430 of the system 400 oversees the lifecycle of alerts and notifications, managing their initiation, modification, and distribution, ensuring that alerts are delivered in compliance with user preferences and contractual agreements, and maintaining a high level of relevance and accuracy.

[0098] The alert analysis module 440 of 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, providing strategic insights for business decision-making.

[0099] The Alert Customization and Recommendation Engine 450 is integrated with the SPoG UI 405 and AI module 460 to enable personalization of the alert and notification system, recommending alert configurations and preferences based on user input and historical data analysis, and enhancing the user experience by providing tailored alert content.

[0100] The asset tracking module 470 of the system 400 supports the alert and notification system by tracking and managing the allocation of assets related to alerts, including monitoring inventory levels and ensuring that alerts regarding asset availability are accurate and timely.

[0101] System 400 thereby transforms the traditional distribution model by leveraging a highly responsive alert and notification system that is equipped with real-time data processing, AI-driven analysis, and user customization capabilities to ensure users are proactively informed and able to make timely decisions based on real-time alerts and notifications in a technological distribution environment.

[0102] FIG. 5 illustrates an embodiment of an advanced distribution platform including a system 500 for managing a complex distribution network, which may be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of a distribution network. System 500 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include a SPoG UI 505, a CIM 510, an 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 prospect marker display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-onboarding module 565, and a communications module 570.

[0103] System 500, as an embodiment of system 300, can enable supply chain and distribution management using a wide range of technologies and algorithms that facilitate efficient data processing, personalized interactions, real-time analytics, secure communications, and effective management of documents, catalogs, and performance standards.

[0104] The SPoG UI 505, in some embodiments, serves as a central interface within the system 500, providing users with a unified view of the entire distribution network. Front-end technologies such as ReactJS, TypeScript, and Node.js are utilized 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.

[0105] CIM510, the Customer Interaction Module, employs algorithms and technologies from Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to handle customer data securely, personalize the customer experience, and provide access control to users.

[0106] The RTDM module 515, or real-time data mesh module, is a key component of the system 500 and ensures smooth data flow across the distribution ecosystem. It utilizes 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 volumes 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 legacy ERP and CRM systems. This capability allows users to access current and accurate information and make informed decisions.

[0107] The AI ​​module 520 in the system 500 can extract valuable insights from data using advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn. These algorithms enable the module to automate repetitive tasks, forecast 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.

[0108] The Interface Display module 525 focuses on presenting data and information in a clear, user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks like 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, enabling efficient data understanding, comparison, and trend analysis.

[0109] The personalized interaction module 530 utilizes customer data, historical behavior, and machine learning algorithms to generate personalized recommendations for products or services. It employs technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and providing targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, improve customer satisfaction, and drive sales.

[0110] The document hub 535 serves as a central repository for storing and managing documents within the system 500. It utilizes technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, the document hub 535 can employ SeeBurger's document management capabilities to categorize and organize documents based on type, such as contracts, invoices, product specifications, or compliance documents, allowing users to easily access and find relevant documents when needed.

[0111] The catalog management module 540 enables the creation, management, and distribution of current product catalogs. This ensures that users have access to the most up-to-date product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be employed to facilitate catalog updates, content distribution, 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 geographic location.

[0112] The Performance and Outlook Marker Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. Utilizing tools such as Splunk and Datadog, it enables effective performance monitoring and provides actionable insights. For example, the module can utilize Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, allowing users to take proactive measures to optimize operations.

[0113] The predictive analytics module 550 employs machine learning algorithms and forecasting models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can use TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing users to optimize inventory levels and minimize costs.

[0114] The recommender system module 555 focuses on providing intelligent recommendations to users within 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 employed for data analysis, modeling, and providing targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels, improving customer engagement and driving sales.

[0115] The notification module 560 enables the delivery of real-time notifications to users regarding important events, updates, or alerts within the supply chain. It utilizes message queues, event-driven architectures, and technologies such as Apigee X and TIBCO for notification delivery. For example, the module can utilize TIBCO's messaging infrastructure to send notifications to users' devices in real time, ensuring timely distribution of relevant information.

[0116] 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 become familiar with the system and its functionality. Technologies such as Okta and Kentico can be employed 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 permissions, and guide them through the system's functionality.

[0117] The communications module 570 enables communication and collaboration within the system 500. It provides users with channels for interaction, messaging, document sharing, and project collaboration. Technologies such as Apigee Edge and Adobe Launch can be employed to facilitate secure and efficient communication, document sharing, and version control. For example, the module can leverage the API management capabilities of Apigee Edge to ensure secure and reliable communication between users, enabling effective collaboration.

[0118] This allows the system 500 to incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules, including the 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 prospect marker display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-onboarding module 565, and communication module 570, work together to provide end-to-end visibility, data-driven decision-making, personalized interactions, real-time analytics, and streamlined communication within the distribution network. The incorporation of specific technologies and algorithms enables efficient data management, secure communication, personalized experiences, and effective performance monitoring, contributing to improved operational efficiency and success in supply chain and distribution management.

[0119] Real-time Data Mesh 6 illustrates an RTDM module 600 according to one embodiment. The RTDM module 600 may be an embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0120] The RTDM module 600 represents an effective data mesh and change capture component within the overall system architecture, as shown in Figure 5. The module can be configured to provide real-time data management and standardization capabilities, enabling efficient operations within the fields of supply chain and distribution management.

[0121] The RTDM module 600 can include an integration layer 610 (also called a "system of record") that integrates with various enterprise systems. These enterprise systems can include, for example, ERPs such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 can handle data exchange and synchronization between the RTDM module 600 and these systems. Data feeds can be established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring the RTDM module operates with the most recent and accurate data.

[0122] The RTDM module 600 can include a data layer 620 configured to process and translate data for acquisition and analysis. The data layer 620 includes a data mesh, a cloud-based infrastructure configured to provide scalable, fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-built 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. A PDS can be configured to store specific types of data, such as customer data, product data, or financial data. These PDSs act as repositories of normalized and / or standardized data, ensuring data consistency and integrity across systems.

[0123] In some embodiments, the RTDM module 600 implements a data replication mechanism to capture real-time changes from multiple data sources, including transactional systems like ERP (e.g., SAP, Impulse, META, I-SCALA). The captured data can be processed and normalized on the fly and transformed into a standardized format suitable for analysis and integration. This process ensures that data is readily available and current within the data mesh, facilitating real-time insights and decision-making.

[0124] More specifically, the data layer 620 within the RTDM module 600 can be configured as a powerful, flexible foundation for managing and processing data within a distribution ecosystem. In some embodiments, the data layer 620 can encompass a highly scalable and robust data lake, which can be referred to as a data lake 622, along with a set of purpose-built data stores (PDSs), which can be denoted as PDSs 624.1 through 624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0125] 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 the Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified, scalable platform for storing both structured and unstructured data. By leveraging the elasticity and fault-tolerance of cloud-based storage, data lake 622 can accommodate the influx of data from diverse sources.

[0126] Accompanying the data lake 622 may be a population of purpose-built data stores PDS624.1-624.N. Each PDS624 may serve as a dedicated repository optimized for storing and retrieving a particular type of data related to the supply chain domain. In some non-limiting examples, PDS624.1 may be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS624.2 may focus on product data, encompassing details regarding SKU codes, descriptions, pricing, and inventory levels. These purpose-built data stores enable efficient data retrieval, analysis, and processing to meet the diverse needs of supply chain users.

[0127] To ensure real-time data synchronization, 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 legacy ERPs like SAP, Impulse, META, and I-SCALA, as well as other enterprise-wide systems. CDC constantly monitors these systems for updates, modifications, or new transactions and captures them in real time. By capturing these changes, data layer 620 ensures that the data in data lake 622 and PDS 624 remains current, providing users with a real-time view into the distribution ecosystem.

[0128] In some embodiments, data layer 620 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring compatibility with a wide range of existing systems and providing flexibility for customization and extensibility. For example, data layer 620 can utilize a Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with systems of record with a diverse population 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.

[0129] In terms of data processing and analytics, the data layer 620 can use the power of distributed computing frameworks, such as Apache Spark or Apache Flink, in some non-limiting examples. These frameworks can enable parallel processing and distributed computing across large datasets stored in the data lake and PDS. 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, the data layer 620 can use Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimizing inventory levels, and identifying potential supply chain risks.

[0130] In some embodiments, data layer 620 can incorporate data governance and security measures. Elaborate access control mechanisms and authentication protocols ensure that only authorized users can access and modify data in the data lake and PDS. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. Additionally, data layer 620 can implement data lineage and audit trail mechanisms to enable users to track data provenance and history, ensuring data integrity and compliance with regulatory requirements.

[0131] In some embodiments, data layer 620 can be deployed in a cloud-native environment using containerization technologies such as Docker and orchestration frameworks like Kubernetes. This approach ensures scalability, resilience, and efficient resource allocation. For example, data layer 620 can be deployed on cloud infrastructure offered by AWS, Azure, or Google Cloud to take advantage of their managed services and scalable storage options. This enables scaling of resources based on demand, minimizes operational overhead, and provides an elastic infrastructure for managing supply chain data.

[0132] The data layer 620 of the RTDM module 600 can incorporate a highly scalable data lake, Data Lake 622, along with purpose-built PDSs, PDSs 624.1-624.N. By employing a CDC mechanism, the data layer 620 ensures efficient data management, standardization, and real-time availability. In a non-limiting example, the data layer 620 can be implemented using appropriate technologies, such as .NET or Java, and / or distributed computing frameworks like Apache Spark, enabling powerful data processing, advanced analytics, and machine learning capabilities. Through 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, optimizing operations and driving business success in dynamic, complex distribution environments.

[0133] 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, current insights. The AI ​​module 630 may generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0134] The data engine layer 640 comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 640 of the RTDM module 600 can include a collection of autonomously operating headless engines 640.1-640.N. These engines represent distinct functionality within the system and can include, for example, one or more recommendation engines, forecasting engines, and subscription management engines. The engines 640.1-640.N can deliver specific business logic and services using standardized data stored in the data mesh. Each engine can be configured to be pluggable, allowing for flexibility and future expansion of the module's capabilities. Exemplary engines are shown in FIG. 5 and are not intended to be limiting. Additional headless engines can be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0135] These systems can be configured to receive data from multiple sources, such as transactional 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 are applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration.

[0136] Additionally, a data distribution mechanism can be employed to facilitate integration and access to the RTDM module 600. The data distribution mechanism 645 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints, including user interfaces, micro-frontends, and external systems.

[0137] The experience layer 650 focuses on providing an intuitive, user-friendly interface for interacting with supply chain data. It can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search 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 feeds, allowing users to customize views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, tailored to their preferences and roles.

[0138] Thus, in some embodiments, the RTDM module 600 for supply chain and distribution management may include integration with systems of record and may include one or more data layers with a data mesh and purpose-built data stores, 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, efficient data processing and analysis, and integration with existing enterprise systems. Technical feeds and searches within the module ensure users can find relevant, current information and insights, 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 diverse ERPs while maintaining auditable, repeatable transactions offers a distinct advantage: enabling a unified view for vendors, resellers, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0139] Automated Notification Engine In one embodiment, FIG. 7 illustrates a system 700 as an alert and notification system within a technology distribution platform, comprising a Single Pane of Glass User Interface (SPoG UI) 705, a Real-Time Data Mesh (RTDM) 710, an Advanced Analytics and Machine Learning (AAML) module 715, and an integrated notification engine 720.

[0140] The SPoG UI 705 serves as the central command for alert interactions within the system 700. Built with a user-focused design, it provides a customizable dashboard for managing alerts and notifications. The interface integrates real-time data visualization tools, allowing users to monitor alert status through graphical displays. It includes an intuitive layout that facilitates the configuration workflow of notification parameters and provides a comprehensive view of both active alerts and alert history. The UI is optimized for responsiveness across various devices and platforms, ensuring accessibility and user engagement.

[0141] 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 and features rules-based automation of user responses to notifications. It includes a History Tracking submodule that logs user actions and preferences to refine future alerts. The Notification Customization Module 707 allows users to configure notification parameters and supports custom alert thresholds and delivery modes to ensure a personalized user experience. The system's data processing capabilities are extended to integrate with Impulse data, for example, via RTDM 710, enabling resellers to receive notifications relevant to their business. This integration ensures resellers keep up with relevant information, such as inventory changes or pricing updates, in real time, fostering a responsive and informed reseller network.

[0142] The RTDM 710 is configured as a comprehensive data management system for alert and notification processes within the system 700. It includes an integration layer, a data processing layer, and subsystems that facilitate real-time data analysis and management. The system 700 establishes a link with the cloud marketplace via the RTDM 710 through an event handling adapter (e.g., for Knowledge Article Generator (KAG) integration). This adapter can act as a conduit for event-driven data, capturing marketplace activity and facilitating its translation into structured events suitable for the notification engine 720. This connection enables the system to consume cloud-based events and translate marketplace dynamics into actionable notifications.

[0143] The RTDM 710's integration layer interconnects with various enterprise systems and handles data exchange and synchronization to ensure 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. The system 700 incorporates data paths originating from the cloud management platform (CMP), directing both active data streams, such as user-generated emails, and passive data, such as user engagement metrics, to the notification engine. These data paths are enriched through intermediate data enrichment nodes, ensuring notification content is context-rich and tailored to the user's profile and preferences.

[0144] The RTDM 710 includes a data layer configured to translate and process data for search and analysis. It encompasses a scalable data mesh 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 and ensure consistency and integrity across systems.

[0145] The data layer can also include a data replication mechanism to capture real-time changes from transactional systems and convert them into a standardized format for immediate analysis and action. This functionality ensures that notification systems operate with the most up-to-date data, enabling real-time insights and decision-making.

[0146] The RTDM 710 data layer 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 volume and breadth of data generated. This data lake serves as a centralized platform for structured and unstructured data, supporting complex analytical tasks and machine learning algorithms to optimize the relevance and timing of notifications. The notification engine 720 leverages the data lake, which serves 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 improving the personalization and relevance of alerts.

[0147] Additionally, the module incorporates a change data capture mechanism to maintain real-time data synchronization with various systems, using a framework that is compatible with a wide range of enterprise systems to ensure integration and facilitate smooth data exchange.

[0148] RTDM710 also prioritizes data governance and security, implementing strict access control and encryption to protect sensitive information. Containerization technology within a cloud-native environment ensures the module is scalable and fault-tolerant, allowing it to adapt to the dynamic needs of the technology distribution platform. RTDM710 is a dynamic component within the system 700 and is central to the efficient operation of the notification engine. It ensures real-time data availability and facilitates the generation of precise, actionable alerts and notifications. The module's advanced data management capabilities are essential to maintaining the integrity and responsiveness of the alert and notification system within the technology distribution framework.

[0149] The AAML module 715 integrates a suite of advanced machine learning algorithms and data processing tools to serve as the analytical core of the system 700. It analyzes incoming data streams and detects anomalies, trends, and patterns that trigger notifications. The module is configured with neural network and decision tree algorithms that adapt to and learn from data patterns over time, improving predictive accuracy. The AAML module 715 also incorporates natural language processing (NLP) capabilities to analyze unstructured data for sentiment and content that can affect the relevance of notifications. The module's design includes a feedback loop mechanism that uses the output of the notification system to continuously recalibrate the analytical model, thereby improving the context and timing of alerts.

[0150] The notification engine 720 acts as a central processing unit within the system 700 for evaluating events and creating notifications. It is built with 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 evaluate event severity, categorization, and user preferences. The notification engine 720 is also designed with template processing capabilities, allowing for the creation of notification content that is consistent with and tailored to the alert context. It can include an orchestration layer that manages the notification generation workflow from event detection to dispatch, ensuring each notification is accurately processed and delivered in real time. Events are emitted by source systems and processed through the event adapter 730, which structures the raw event data into a format compatible with the notification engine 720. After processing, notifications are distributed through various channels managed by the distribution module 735, ensuring timely delivery to intended recipients. The notification engine 720 also includes diversified channels specifically designed to dispatch 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, thereby optimizing the reach and impact of all issued alerts.

[0151] The operational flow of the system 700 begins with the publication of an event by a source system. The event adapter 730 is structured to parse and format raw event data, utilizing JSON schema and XML for interoperability across system boundaries. The adapter is capable of handling high-volume event streams and converting them into a standardized format for subsequent processing. After conversion, the distribution module 735 manages the multi-channel distribution of notifications. This module is designed for scalability and supports various communication protocols, such as MQTT and AMQP for event broadcasting. It ensures notifications are delivered according to user preferences and channel availability, leveraging push technology for instantaneous delivery. Interaction with consumer systems is coordinated through a subscription model, allowing entities such as the XAC Web Message Center to subscribe to specific event patterns. This model enables the system to distribute targeted alerts and allows users to access a historical log of notifications, ensuring continuity and reference of ongoing and past events.

[0152] Within the SPoG UI 705, the consumer interaction module 740 allows end users to view and manage notifications, providing a user-centric platform that interfaces with consumer systems and facilitates interaction with generated alerts. The user experience is further refined by the interactive capabilities of the platform's web interface, which displays notifications and maintains a history of alerts presented on the screen. Users can interact with this history, providing a feedback loop that informs future notification refinement and user mapping strategies.

[0153] The data analysis and storage component 745 is part of the RTDM 710 and allows for the storage of vast amounts of event and notification data, supports analysis, and provides historical references to improve notification accuracy. The data analysis and storage component 745 is configured to maintain data integrity and accessibility.

[0154] The notification customization module 750 in the SPoG UI 705 provides the ability for users to personalize notifications, facilitating an interactive and tailored alert experience.

[0155] The system 700 architecture ensures an efficient and dynamic process for managing alerts and notifications, consolidating data from multiple sources into a unified interface via the SPoG UI 705, automating alert processing via the AAML module 715, and maintaining a real-time, standardized data repository via the RTDM 710. The system improves the overall responsiveness and operational efficiency of the technology distribution platform and ensures that users stay informed about the information most relevant to overall responsiveness and operational efficiency.

[0156] In one embodiment, FIG. 8 illustrates a notification architecture 800 for a technology distribution platform, built to process and manage numerous event data from diverse interfaces and effectively distribute information to end users. The notification architecture 800 can incorporate an embodiment of the notification engine 720 within the system 700. As shown in FIG. 8, the notification architecture 800 illustrates a cloud-integrated alert and notification architecture. In some embodiments, the notification architecture 800 integrates the RTDM 710 from the system 700, which handles high-speed data streams and provides a data management layer that enables real-time analytics for notification processing.

[0157] The notification architecture 800 is configured to evaluate event data and format it 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 in the notification architecture 800 may also include an advanced data processing subsystem that facilitates the operationalization of analytics, enabling the transformation of raw data into actionable insights for customization of notifications.

[0158] The gateway interface 840, referred to as the API gateway, acts as a controlled access point for incoming data, ensuring secure data transfer by enforcing required protocols and directing data flow to the correct subsystem within the notification engine.

[0159] Portal interface 810 is the entry point for user interaction, portal interface 810 is dedicated to general purpose user interaction, portal interface 811 is designed for logistics and returns management, and portal interface 812 is allocated for client service management. These interfaces facilitate user engagement with the system and provide a customized dashboard experience for monitoring and managing notifications.

[0160] The API wrapper 820 acts as a domain-specific wrapper API, providing a modular, domain-tailored path for event subscription and translation, converting business events into standardized notification requests for processing by the notification engine.

[0161] Event source applications 830, also referred to as source application interfaces 830, include various enterprise systems, such as ERPs and WMSs, that emit operational events that are captured and processed by the notification engine 800 for notification triggers. In some embodiments, RTDM 710 is employed to establish an integrated link, filtering, and normalization between event source applications 830 and the notification architecture 800, facilitating data flow and ensuring notifications are generated based on current real-time operational events.

[0162] The messaging interface 850 manages event messages through a publish / subscribe mechanism, depicted as Pub / Sub topics. This interface is key to the asynchronous nature of message queuing, allowing the system to handle varying loads and maintain a consistent flow of notifications. In some embodiments, the messaging interface 850 is operatively connected and / or integrated with RTDM 710 to prioritize and route messages and optimize the delivery of notifications based on real-time data analysis and user engagement metrics.

[0163] The notification engine 720 comprises sub-modules, including the notification engine 720 itself and the notification API 870, which are responsible for evaluating events and generating notifications. These sub-modules apply criteria to incoming data to determine notification triggers and manage the workflow from event detection to notification delivery. The notification engine 720 can incorporate or be operatively connected to an AAML module 715 configured to apply machine learning algorithms for pattern detection and predictive analysis, thereby refining the criteria used for notification triggers within the notification API 870.

[0164] In some embodiments, the notification API 870 interconnects with the notification engine 720 and applies predetermined criteria to incoming data to trigger appropriate notifications. The channel provider 871 manages the sending of notifications through email services, ensuring reliable delivery to users / subscribers. In some embodiments, the SPoG UI 705 provides an integrated platform that allows users to interact, manage, and personalize their notification experience, thereby optimizing the interface for different user segments. The SPoG UI 705 can include a user-centric design, allowing for real-time monitoring and management of alerts and notifications through a customizable dashboard. In some embodiments, the AAML module 715 is configured to improve the functionality of the notification API 870 by incorporating user behavior models that adapt over time, providing a feedback loop mechanism for continuous improvement of the notification system.

[0165] 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 secure connections to cloud services and incorporates security measures such as automatic patch management and network intrusion detection, thereby improving the system's data security framework. In some embodiments, the load balancing interface 880 is integrated into or configured to utilize the real-time processing capabilities of the RTDM 710 and is configured to dynamically adjust network traffic distribution, thereby maintaining system performance under fluctuating loads.

[0166] The cloud service interfaces can include a cloud SQL interface 860 and a cloud storage interface 861, each performing a specific role in data management. The cloud SQL interface 860 handles structured data and provides managed database services, while the cloud storage interface 861 is configured for storage of unstructured data and supports analytics and historical data retrieval. In certain embodiments, the cloud SQL interface 860 and the cloud storage interface 861 can be integrated with RTDM 710 to support structured and unstructured data flows, thereby configuring RTDM 710 to provide real-time data warehousing and complementing the data management and retrieval capabilities of cloud services.

[0167] A channel provider 871 is operatively connected to the notification API 870 to send notifications through email services. This provider interacts with the distribution platform and ensures reliable notification delivery to users / subscribers, such as end users, who receive notifications and manage preferences through an interface provided by the notification engine.

[0168] The load balancing interface 880 distributes incoming requests to prevent bottlenecks and ensure system reliability. This interface employs algorithms to dynamically allocate system resources to maintain performance.

[0169] Serverless VPC Connector 890 establishes secure connections to cloud services, facilitates direct communication within the cloud provider's network, and improves data security through measures such as encryption and intrusion detection.

[0170] The notification engine 720 incorporated into the notification architecture 800 thus provides resilient and scalable notifications to meet the evolving complexities and demands of the technology distribution platform. The notification architecture 800 allows users and systems to stay connected with current, real-time information, improving the responsiveness and operational efficiency of the overall platform.

[0171] 9 shows a flow diagram of a method 900 for managing data flow from event generation to notification delivery within the system 700. The flowchart outlines the sequential processing steps through which data transitions from raw events to refined notifications and highlights the operational synergies between the RTDM 710, event adapter 730, and distribution module 735. The method demonstrates the transformation of diverse data inputs into structured, actionable alerts that meet user specifications and are delivered across multiple channels.

[0172] At 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 activity that publish operational data core to the alert generation process. Operation 901 may include ingesting the 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 and maintain the integrity of the notification system. Operation 901 may also include data enrichment performed by RTDM 710, where contextual information may be added to and / or attached to the data, enhancing it with additional details necessary to create comprehensive notifications. This enriched data may include user preferences, past interaction data, and associated metadata. Operation 904 may further include normalizing the data ingested through RTDM 710. Data from disparate sources may be converted into a unified format, which is essential for subsequent processing steps and ensures consistency across the notification system.

[0173] In operation 902, the normalized data is replicated to a data lake within the RTDM 710 to ensure that a persistent historical record of events and notifications is maintained for analysis and audit purposes.

[0174] At operation 903, the method proceeds with sending the standardized data to the event adapter 730, where the data is structured into a format compatible with a notification engine, often utilizing a JSON schema or XML format for interoperability and integration.

[0175] At operation 904, the event adapter 730 sends the formatted data to the notification engine, where at operation 908 predefined rules and dynamic algorithms are applied to determine if a notification trigger is needed.

[0176] In operation 905, once the triggering event is determined, the notification engine leverages its template processing capabilities to create notification content and generate alerts that are consistent and contextual to the event and user profile.

[0177] In operation 906, the notification engine passes the generated notification to the distribution module 735, which is responsible for distributing the alert to various channels.

[0178] In operation 907, the distribution module 735 employs a multi-channel approach to ensure that notifications are delivered through the most appropriate and effective medium, such as email, SMS, or application-based push notification, depending on user preferences and channel availability.

[0179] In operation 908, the notification is received by the end user, who can interact through the SPoG UI to respond to the alert, thereby closing the loop on the notification sending process and facilitating a continuous feedback mechanism for the system 700.

[0180] Method 900 thereby provides an efficient process flow for notification delivery from the point of data capture to final user interaction, enhancing the user-centric design of system 700.

[0181] 10 shows a flow diagram of a method 1000 for user interaction of notifications within system 700. The flowchart details the user engagement sequence and shows how a user receives, reviews, and interacts with notifications through a single pane of glass user interface (SPoG UI). The method shows the responsiveness of system 700 to user input ranging from basic notification acknowledgment to complex user-driven configuration adjustments.

[0182] At operation 1001, the method 1000 begins by delivering notifications to the user through the SPoG UI 705. These notifications are the result of the process detailed in Figure 9, where they are presented to the user in a consistent and user-friendly manner on their device interface.

[0183] In operation 1002, upon receiving the notification, the user engages with the alert through the SPoG UI 705. This engagement can be an acknowledgment of receipt, a request for additional details, or an immediate response action, depending on the nature and urgency of the notification.

[0184] In operation 1003, user interactions are logged by the consumer interaction module 706 in the SPoG UI 705. This module captures and records the user's actions and preferences, contributing to a comprehensive history of the user's interactions with notifications.

[0185] In operation 1004, the notification customization module 707 in the SPoG UI 705 allows the user to adjust notification parameters. Users can set preferences for notification frequency, channel, and format to tailor the alert experience to their individual needs.

[0186] At operation 1005, the method 1000 includes processing user feedback received through the SPoG UI 705. This feedback can be about the relevance, timeliness, or quality of content of the notification and is used to refine future notifications.

[0187] In operation 1006, the user preferences and feedback are synchronized with the user profile in the backend system to ensure all future notifications are consistent with the updated user settings and preferences. Operation 1006 may include utilizing RTDM 710 in combination with AAML module 715 to analyze the aggregated user interaction data to identify trends and patterns in user behavior and notification interactions.

[0188] In operation 1007, based on the analysis performed by the RTDM 710 and AAML module 715, the system 700 can dynamically adjust the notification logic within the notification engine 720. This adjustment can include changing notification rules, updating templates, or modifying delivery channels.

[0189] At operation 1008, the method 1000 proceeds with iterative refinement of the notification content and delivery mechanism. Continuous loops of user interaction data contribute to the evolution of the notification system, leading to an increasingly personalized and relevant user experience.

[0190] In operation 1009, the refined notification is redeployed through the distribution module 735, closing the feedback loop by sending the improved notification back to the user via the user's preferred channel.

[0191] System 700 thereby provides a user-centric notification process in method 1000, where each user interaction is captured, analyzed, and used to inform the system's operation. Method 700 emphasizes the adaptability of the notification system to user preferences and behaviors, providing a personalized and engaging user experience within a technology distribution platform.

[0192] 11 is a block diagram of example components of a device 1100. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. The computer system 1100 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1104. The processor 1104 may be connected to a communication infrastructure, i.e., bus 1106.

[0193] The computer system 1100 may also include user input / output devices 1103 , such as a monitor, keyboard, pointing device, etc., which may communicate with a communications infrastructure 1106 through a user input / output interface 1102 .

[0194] One or more of the 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 structure that can be efficient for parallel processing of large blocks of data, such as mathematically intensive data common in computer graphics applications, images, videos, etc.

[0195] The computer system 1100 may also include a main or primary memory 1108, such as random access memory (RAM). The main memory 1108 may include one or more levels of cache. The main memory 1108 may have control logic (i.e., computer software) and / or data stored therein.

[0196] The computer system 1100 may also include one or more secondary storage devices or memories 1110. The secondary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0197] The removable storage drive 1114 may interact with a removable storage unit 1118. The removable storage unit 1118 may include a computer-usable or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1118 may be a program cartridge and cartridge interface (such as those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage unit and associated interface. The removable storage drive 1114 may read from and / or write to the removable storage unit 1118.

[0198] Secondary memory 1110 may include other means, devices, components, intermediaries, or other approaches for allowing computer programs and / or other instructions and / or data to be accessed by computer system 1100. Such means, devices, components, intermediaries, or other approaches may include, for example, removable storage unit 1122 and interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as found in a video game device), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage unit and associated interface.

[0199] Computer system 1100 may further include a communications or network interface 1124. Communications interface 1124 may enable computer system 1100 to communicate and interact with a combination of external devices, external networks, external entities, etc. (individually and collectively referred to by reference numeral 1128). For example, communications interface 1124 may enable computer system 1100 to communicate with external or remote devices 1128 via communications path 1126, which may be wired and / or wireless (or a combination thereof) and may include a combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to and from computer system 1100 via communications path 1126.

[0200] Additionally, computer system 1100 may be a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or any combination thereof, to name a few non-limiting examples.

[0201] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery paradigm, including, but not limited to, remote or distributed cloud computing solutions, local or on-premise software ("on-premise" 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)), and / or hybrid models including combinations of the foregoing examples or other service or delivery paradigms.

[0202] Applicable data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), Yet Another Markup Language (YAML), Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or other functionally similar representations, alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used, either exclusively or in combination with known or open standards.

[0203] In some embodiments, a tangible, non-transitory apparatus 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 embodying combinations of the foregoing. Such control logic, when executed by one or more data processing devices (such as computer system 1100), may cause such data processing devices to operate as described herein.

[0204] Figures 12A-12Q show various screens and functionality of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription changes. A detailed description of each figure is provided below.

[0205] Figure 12A shows the Vendor Onboarding Start Screen, which represents the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in joining the distribution ecosystem. Vendors can enter basic information such as company details, contact information, and product catalog.

[0206] Figure 12B shows a vendor onboarding guide that displays a step-by-step guide or checklist for vendors to follow during the onboarding process, outlining the necessary tasks and requirements to ensure the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0207] 12C shows a vendor onboarding call scheduler that facilitates scheduling a call or meeting between a vendor and a platform partner or representative responsible for guiding the vendor through the onboarding process. The vendor can select a preferred time slot or request a call, ensuring effective communication and assistance throughout the onboarding journey.

[0208] FIG. 12D shows a vendor onboarding task list that presents a comprehensive task list or dashboard outlining the specific steps and actions required for successful vendor onboarding. An overview of pending tasks, completed tasks, and upcoming deadlines is provided to help vendors track progress and ensure timely completion of each onboarding task.

[0209] 12E shows a vendor onboarding completion screen confirming successful completion of the vendor onboarding process, which may display a congratulatory message or a summary of the tasks completed indicating that the vendor is now officially onboarded into the distribution ecosystem.

[0210] Figure 12F shows the Partner Dashboard, which provides partners or users with an aggregated view of relevant information and metrics regarding their partnerships with the distribution ecosystem, providing an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0211] FIG. 12G shows a customer product cart, representing a customer's product cart, to which the customer can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of the cart before proceeding to the checkout process.

[0212] Figure 12H shows the customer subscription cart, which allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify subscription details before finalizing their selection.

[0213] FIG. 12I shows a customer order summary that provides a summary of the customer's order, including details such as the products or subscriptions purchased, quantity, pricing, and any discounts or promotions applied, allowing the customer to review their order before confirming their purchase.

[0214] 12J shows a vendor SKU generation screen for generating unique stock-keeping unit (SKU) codes for vendor products, which may include fields or options that allow vendors to specify product details, attributes, and pricing, and the system will auto-generate the corresponding SKU code.

[0215] Figures 12K and 12L show a dashboard order summary for displaying summary information about orders placed within a distribution ecosystem. These present key order details, such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity and allows users to efficiently track and manage orders.

[0216] Figure 12M shows the customer subscription cart, which allows customers to add, modify, or delete subscription plans. A list of selected subscriptions, pricing, and renewal dates can be displayed. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0217] Figure 12N shows the Customer Order Tracking screen, which allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment, including processing, packing, and shipping. Customers can monitor the movement of their orders and estimate delivery times.

[0218] FIG. 12O shows customer shipment tracking, which provides customers with real-time tracking information about their shipments. Details such as the carrier, tracking number, current location, and estimated delivery date may be included. Customers can stay informed about the whereabouts of their shipments.

[0219] Figure 12P shows Customer Subscription History, which presents a historical record of a customer's subscription activity. It displays a list of past subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track previous payments, and view their subscription history.

[0220] Figure 12Q shows the Customer Subscription Modification dialog, which 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 according to their evolving needs or preferences.

[0221] The UI screens shown are not limiting. In some embodiments, the UI screens of Figures 12A-12Q collectively represent the diverse functionality and features provided by the SPoG UI, providing users with a comprehensive, user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the distribution ecosystem.

[0222] It is understood that the Detailed Description section, and not the Abstract section, is intended to be used to interpret the claims. The Abstract section may describe one or more, but not all, example embodiments of the invention as contemplated by the inventors, and thus is not intended to limit the invention and the appended claims in any way.

[0223] The present invention has been described above with the aid of functional building blocks illustrating the implementation of certain functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for convenience of description. Alternative boundaries may be defined so long as the certain functions and relationships thereof are properly performed.

[0224] The foregoing description of specific embodiments fully discloses the general nature of the present invention, and by applying the knowledge of those skilled in the art, such specific embodiments can be readily modified and / or adapted for various uses without undue experimentation and without departing from the general concept of the present invention. Such adaptations and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It should be understood that the phraseology or terminology used herein is for the purpose of description and not of limitation, and should therefore be interpreted in light of the teaching and guidance provided by those skilled in the art.

[0225] 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. 1. A system for managing alerts and notifications within a technology distribution platform, comprising: means for collecting event data from multiple source systems and presenting notifications to a user via a Single Pane of Glass User Interface (SPoG UI); a real-time data mesh (RTDM) configured to perform initial filtering, data enrichment, and standardization of the event data into a unified format; a persistent storage means for replicating the standardized data for analysis and audit purposes; an event adapter configured to format data for processing by a notification engine; a notification engine designed to determine notification triggers and generate alert content based on rules and algorithms; a logging and user interaction module that captures and records user interactions with notifications and allows customization of notification settings; an Advanced Analytics and Machine Learning (AAML) module for processing user feedback and dynamically adapting notification logic; a distribution module for iteratively refining notification content and delivery mechanisms and redeploying improved notifications across multiple channels.

2. The system of claim 1 , wherein the SPoG UI includes a real-time visualization tool for monitoring alert status and managing notification preferences.

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

4. The system of claim 1 , wherein the notification engine is configured to coordinate a workflow of notification generation from event detection to delivery.

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

6. The system of claim 1 , wherein the user interaction module allows a user to customize alert thresholds, notification frequencies, and channel preferences.

7. The system of claim 1 , further comprising an analytics and reporting means for generating real-time reports on notification engagement metrics and user response rates.

8. 1. A system for managing alerts and notifications within a technology distribution platform, comprising:

1. A notification engine configured to process event data from a variety of user portals and source application interfaces, the notification engine comprising: an API gateway that facilitates secure data ingress and routing to a messaging interface; a cloud services interface that integrates cloud-based services for data management and analysis; a load balancing interface designed to manage and distribute incoming requests across said platform; a Serverless VPC Connector for establishing a secure connection to a cloud service; a distribution module for distributing the notification via various communication channels; a single pane of glass user interface (SPoG UI) configured to enable a user to interact with, manage, and personalize their notification experience.

9. The system of claim 8 , wherein the portal interface is optimized to provide a dashboard experience tailored to different user segments.

10. The system of claim 8 , wherein the API wrapper is modular and configurable to support different business domain requirements within the platform.

11. The system of claim 8 , wherein the cloud services interface includes a scalable data lake for management of structured and unstructured data.

12. The system of claim 8 , wherein the channel providers include dedicated channels for email, SMS, and application-based push notifications.

13. The system of claim 8 , wherein the load balancing interface employs a dynamic distribution algorithm to manage incoming API calls and system load.

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

15. The system of claim 8 , wherein the notification engine includes a feedback loop mechanism for continuous improvement based on user interaction data.

16. 1. A computerized method for managing alerts and notifications within a technology distribution platform, the method comprising: Collecting event data from multiple source systems and presenting notifications to users via a single pane of glass user interface (SPoG UI); processing the event data through a real-time data mesh (RTDM) to convert it into a unified format for notification processing by applying one or more of initial filtering, data enrichment, and data normalization; replicating said standardized data to persistent storage for future analysis and auditing purposes; processing the data with an event adapter to generate formatted data; inputting the formatted data into a notification engine, wherein the formatted data is formatted according to processing requirements of the notification engine; determining notification logic comprising a plurality of notification triggers based on one or more rules and / or dynamic algorithms; generating notification content by the notification engine 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 customization input and / or the interaction data with an Advanced Analytics and Machine Learning (AAML) module to dynamically adapt notification logic; iteratively refining notification content and delivery mechanisms based on user interaction data and redeploying the improved notifications across multiple channels via a distribution module.

16. 16. The method of claim 15, further comprising maintaining real-time synchronization with the source system to ensure current data accuracy by the RTDM.

17. The method of claim 15 further comprising coordinating, by the notification engine, a workflow of notification generation from event detection to delivery.

18. 16. The method of claim 15, further comprising broadcasting notifications utilizing one or more communication protocols, the one or more protocols selected from MQTT and AMQP and using JSON schema and XML formatting for data interoperability across system boundaries.

19. The method of claim 15 , wherein the notification customization module receives one or more of an alert threshold, a notification frequency, and a channel preference input by the user.

20. The method of claim 15 , wherein the method includes generating real-time reports on notification engagement metrics and user response rates.

Images