Abstract: Computerized systems and methods are described for enabling resellers to manage their end-user business within their own business environment on a distribution platform. The system includes a server configured to provide a Single Pane of Glass User Interface (SPoG UI) and a Real-Time Data Mesh (RTDM) module for ingesting and standardizing data from multiple sources. Advanced Analytics and Machine Learning (AAML) models analyze the data to provide predictive analytics, anomaly detection, and personalized recommendations. The SPoG UI presents real-time data and insights through interactive visualizations, enabling resellers to perform actions such as creating quotes, placing orders, and managing customer accounts. The system supports real-time negotiation of pricing, compliance management, and integration with external systems via APIs. The method and system generates a real-time, end-to-end view of both supply and end-user customer interactions.

Abstract: Computerized systems and methods are described for integrating real-time insights across various entities involved in distribution processes. The system includes a Real-Time Data Mesh module for ingesting and harmonizing data from multiple sources, a Data Lake for storing harmonized data, and an Advanced Analytics and Machine Learning (AAML) module for generating insights using predictive analytics, anomaly detection, and recommendation engines. A Single Pane of Glass User Interface (SPoG UI) provides visualizations of these insights through interactive dashboards. The system supports customer, vendor, reseller, and associate systems, enabling efficient data exchange and synchronization. It employs natural language processing (NLP) for sentiment analysis, topic modeling for key theme identification, and clustering algorithms for customer segmentation. Continuous learning mechanisms ensure the system adapts to new data in real-time, enhancing decision-making and operational efficiency.

Abstract: System and methods are provided for automated SKU management. Embodiments include a user interface for receiving diverse catalog files, a Catalog Transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a Search Platform. The Catalog Transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. Embodiments can include a Dynamic SKU Creation module and a Global Pricing Engine for real-time pricing. Embodiments improve data accuracy and SKU management, facilitating integrated order processing and fulfillment.

Abstract: Computerized systems and methods are described for generating and optimizing vendor product roadmaps using predictive insights. Leveraging a Real-Time Data Mesh (RTDM) module, data from diverse sources including market trends, customer feedback, and technological advancements is aggregated and standardized. An Analytics and Machine-Learning (AAML) module analyzes this data to generate predictive insights, facilitating adjustments to existing product roadmaps. Dynamic adjustments are made using a roadmap optimization module, with communication facilitated through a Single Pane of Glass (SPoG) user interface (UI). Scenario analysis, decision-support systems, and continuous monitoring improve strategic decision-making.

Abstract: Computerized systems and methods are described for automated AI-driven customer and vendor segmentation and personalized insights delivery. The method involves collecting real-time data, including purchasing behavior and market trends, analyzing it using AI/ML algorithms for segmentation, and delivering personalized insights through a user-friendly Single Pane of Glass User Interface (SPoG UI). Transaction details are logged for ongoing enhancement. Effectiveness of segmentation is monitored and refined based on user feedback and evolving market dynamics. Multiple data sources and analytics tools are integrated for comprehensive analysis. Users can customize segmentation parameters, and delivery options include push notifications and email alerts. The system facilitates continuous optimization and adaptation, enhancing relevance and precision.

Abstract: Computerized systems and methods are described for managing vendor-agnostic configure-to-order (CTO) and quote-to-order (QTO) processes. A Real-Time Data Mesh (RTDM) is provided for aggregating, standardizing, and normalizing real-time data from various sources. A Single Pane of Glass User Interface (SPoG UI) facilitates dynamic interaction and visibility into vendor performance. An Advanced Analytics and Machine-Learning (AAML) Module analyzes product compatibility, optimizes pricing strategies, and predicts market trends. A Vendor-Agnostic CTO/QTO Integration Module (VACIM) includes a Process Standardization Engine and a Vendor Data Transformation Gateway to ensure uniformity across vendors. Methodologies within the invention automate data processing, integrate transformation gateways for data consistency, and employ rule engines driven by machine learning for decision-making, thereby streamlining vendor processes, enhancing scalability, and optimizing pricing strategies in a scalable, adaptable framework.

Abstract: System and methods are provided for automated SKU management. Embodiments include a user interface for receiving diverse catalog files, a Catalog Transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a Search Platform. The Catalog Transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. Embodiments can include a Dynamic SKU Creation module and a Global Pricing Engine for real-time pricing. Embodiments improve data accuracy and SKU management, facilitating integrated order processing and fulfillment.

Abstract: Computerized systems and methods are provided for managing alerts and notifications within a technology distribution platform. A Single Pane of Glass User Interface (SPoG UI) presents notifications to users enabling interaction and customization. A Real-Time Data Mesh (RTDM) collects, filters, enriches, and standardizes event data from multiple sources into a uniform format. An Event Adapter formats data to be processed by a Notification Engine, configured to determine one or more notification triggers and generate alert content based on established rules and algorithms. A logging and user interaction module tracks user interactions with notifications. User feedback is processed by an Advanced Analytics and Machine Learning (AAML) Module configured to dynamically adapt notification logic. Notification content and delivery mechanisms are refined by a Distribution Module to ensure effective dissemination across various communication channels.

Abstract: Computerized systems and methods are described for converting traditional technology products into an “As a Service” (AaS) model, facilitating the transition from capital expenses (CapEx) to operational expenses (OpEx). Methods include receiving user inputs for technology product selections and accessing a Real-Time Data Mesh (RTDM) to retrieve data. An Advanced Analytics and Machine Learning (AAML) Module analyzes user inputs and market data, optimizing the conversion into subscription-based services. Process results are displayed to the user through a Single Pane of Glass User Interface (SPOG UI). An AaS Conversion Module performs transition of products into customizable subscription packages. This method emphasizes dynamic pricing based on usage, flexibility, and/or scalability of services. Methods are provided for real-time reporting, subscription management, and vendor system integration, enabling a comprehensive AaS conversion process suitable for modern technology products and services.

Abstract: System and methods are provided for dynamically consolidating interaction points in a distribution ecosystem. The method involves integrating multiple touchpoints of communication between distributors, resellers, end-users, vendors, and suppliers into a unified interactive interface. This interface enables the management of end-to-end partner lifecycle, systematic data collection, analysis using advanced statistical algorithms, deployment of artificial intelligence and machine learning algorithms, and continuous updates based on user feedback. The system includes modules for communication integration, consolidation, lifecycle management, data collection, data analysis, and artificial intelligence. The disclosed method and system enhance supply chain operations, generate actionable insights, and provide personalized user experiences, ultimately driving business growth and efficiency.

Abstract: Computerized systems and methods are described for executing personalized bundling processes. Methods include receiving user inputs specifying preferences for product bundles and utilizing a Real-Time Data Mesh (RTDM) to retrieve relevant real-time data. An Advanced Analytics and Machine Learning (AAML) Module analyzes these inputs alongside market data to generate personalized bundle recommendations. Recommendations are then displayed to the user via a Single Pane of Glass User Interface (SPoG UI) and, upon user confirmation, transferred as orders to a vendor system. Validation steps use algorithms within the AAML Module to ensure accuracy and relevance of the bundles. Real-time reports on user engagement and bundle success rates are generated. The system, accessible on multiple devices, integrates machine learning models that continually refine the bundling process based on evolving data patterns and user feedback, enhancing personalization and efficiency.

Abstract: System and methods are provided for dynamically consolidating interaction points in a distribution ecosystem. The method involves integrating multiple touchpoints of communication between distributors, resellers, end-users, vendors, and suppliers into a unified interactive interface. This interface enables the management of end-to-end partner lifecycle, systematic data collection, analysis using advanced statistical algorithms, deployment of artificial intelligence and machine learning algorithms, and continuous updates based on user feedback. The system includes modules for communication integration, consolidation, lifecycle management, data collection, data analysis, and artificial intelligence. The disclosed method and system enhance supply chain operations, generate actionable insights, and provide personalized user experiences, ultimately driving business growth and efficiency.

Abstract: System and methods are provided for dynamically consolidating interaction points in a supply chain and distribution ecosystem. The method involves integrating multiple communication channels (i.e., touchpoints) between distributors, resellers, end-users, vendors, and suppliers into a unified interactive interface. This interface enables the management of end-to-end partner lifecycle, systematic data collection, analysis using advanced statistical algorithms, deployment of artificial intelligence and machine learning algorithms, and continuous updates based on user feedback. The system includes modules for communication integration, consolidation, lifecycle management, data collection, data analysis, and artificial intelligence. The disclosed method and system enhance supply chain and distribution operations, generate actionable insights, and provide personalized user experiences, ultimately driving business growth and efficiency.

Abstract: System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.

Abstract: Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes.

Abstract: System and methods are provided for real-time data integration, analysis, and notification within a Mobile App system. Embodiments include initializing a data layer and preprocessing phase, leveraging distributed database strategies to store structured and unstructured data. The Real-Time Data Mesh (RTDM) continuously draws data from various platforms, employing signal processing methods and machine learning techniques for noise removal and data feature extraction. The Advanced Analytics and Machine Learning (AAML) engine processes data, while decision constructs derive suitable actions. The Push Notification Service activates based on an Event-Driven Architecture (EDA) or a Publish-Subscribe (Pub-Sub) system, delivering customized notifications. Technical precision ensures timely and pertinent information reaches end-users. The system optimizes operations through adaptive feedback mechanisms and secures data through encryption.

Abstract: The disclosed embodiments relate to provisioning of a service, such as a financial service, to a device, such as a mobile device operative to access the service wirelessly or otherwise, in a manner which efficiently provides a consistent user experience which meets a user's expectations as to the functionality and quality of the service, including the user interface therefore and service delivery, which leverages the available capacities of the devices through which the service is provided so as to maximize the functionality and quality of the provided service without diminishing the experience, i.e. without substantially reducing the quality or functionality.

Abstract: The disclosed embodiments relate to provisioning of a service, such as a financial service, to a device, such as a mobile device operative to access the service wirelessly or otherwise, in a manner which efficiently provides a consistent user experience which meets a user's expectations as to the functionality and quality of the service, including the user interface therefore and service delivery, which leverages the available capacities of the devices through which the service is provided so as to maximize the functionality and quality of the provided service without diminishing the experience, i.e. without substantially reducing the quality or functionality.

Abstract: The disclosed embodiments relate to provisioning of a service, such as a financial service, to a device, such as a mobile device operative to access the service wirelessly or otherwise, in a manner which efficiently provides a consistent user experience which meets a user's expectations as to the functionality and quality of the service, including the user interface therefore and service delivery, which leverages the available capacities of the devices through which the service is provided so as to maximize the functionality and quality of the provided service without diminishing the experience, i.e. without substantially reducing the quality or functionality.

Abstract: The disclosed embodiments relate to provisioning of a service, such as a financial service, to a device, such as a mobile device operative to access the service wirelessly or otherwise, in a manner which efficiently provides a consistent user experience which meets a user's expectations as to the functionality and quality of the service, including the user interface therefore and service delivery, which leverages the available capacities of the devices through which the service is provided so as to maximize the functionality and quality of the provided service without diminishing the experience, i.e. without substantially reducing the quality or functionality.