Systems and methods for personalizing bundles based on personas

Abstract

To provide a computerized system and method for performing an individualized bundling process.SOLUTION: The method utilizes a Real-Time Data Mesh (RTDM) to retrieve relevant real-time data and analyze these inputs along with market data in an Advanced Analysis and Machine Learning (AAML) module to generate individualized bundle recommendations, which are displayed to the user via a Single Pane of Glass User Interface (SPoGUI) and, upon user approval, forwarded to the vendor system as an order. The validation step uses algorithms within the AAML module to ensure the accuracy and relevance of the bundle. The system integrates a machine learning model that is accessible on multiple devices and continuously refines the bundling process based on evolving data patterns and user feedback to improve personalization and efficiency.SELECTED DRAWING: Figure 8

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application is a continuation-in-part (CIP) of U.S. Patent Application Nos. 18 / 341,714, filed June 26, 2023, and 18 / 349,836, filed July 10, 2023, 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 increases the likelihood of inefficiencies and errors.

[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 ERP is comprehensive, it comes with 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 pose another challenge. When data exists in different formats or units across departments or ERPs, standardizing this data for meaningful analysis is a difficult 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, personalized bundling process is designed to address the above-mentioned deficiencies in the distribution industry by providing a unified platform experience. This platform consolidates various activities and systems into a single interface, allowing users to streamline the entire bundling process. It reduces the time required for activities such as product and service selection, bundle customization, pricing application, and ordering. There is a critical need for technology solutions that effectively integrate, streamline, and accelerate these complex processes while ensuring data security and compliance.

[0008] Distribution platforms are transitioning from a model of selling individual stock-keeping units (SKUs) to one that embraces a more integrated approach focused on delivering comprehensive solutions. The transformation described herein is driven by the rapidly evolving nature of technology, which encompasses a wide range of hardware, software, and subscription-based services. Systems and methods leverage an automated, personalized bundling process to automatically bridge this complexity and satisfy diverse user needs. The systems and methods utilize advanced AI models to correlate and analyze user preferences and behaviors, enabling them to dynamically assemble tailored bundles containing combinations of products and services. This approach simplifies the user experience by providing a single, coherent solution and improves platform performance to keep pace with technological advancements and market trends. By integrating real-time data analytics, these systems ensure bundled products are relevant and compelling, addressing the challenges of maintaining compatibility and synergy across different technology variations. The systems and methods are configured to adapt to the transition to solutions across individual SKUs in response to changing conditions, maximizing utility for users while embracing the ongoing innovation inherent in technology products and services.

[0009] In the global distribution industry, challenges such as inefficient distribution management, SKU management, and the transition to a direct-to-consumer model require innovative solutions. Traditional distribution methods are becoming increasingly inadequate, especially due to shifts in consumer expectations and regulations. This invention addresses these challenges by integrating a comprehensive set of functionality focused on distribution management, supply chain management, and customer visibility into one platform.

[0010] A key challenge in technology distribution is how to discern compatibility and synergy between a vast array of products and SKUs across hardware, software, as-a-service (AaS), and other technology offerings. The systems and methods in the embodiments described herein leverage the integration of interfaces, data layers, and analytics engines detailed below to provide scalable solutions. The complexity of precisely bundling diverse technology solutions to meet individual user needs while achieving scalability to keep up with the rapid pace of technological advancements presents the need for an advanced, adaptable platform. Previous methods struggle to filter relevant signals about the right technology solution from the noise. The embodiments described herein provide effective personalization.

[0011] According to some embodiments, the personalized bundling module can be integrated with a real-time data mesh (RTDM) and a single pane of glass user interface (SPoG UI). This module uses algorithms to optimize product and service selection based on real-time market data and customer preferences. Advanced algorithms are employed to intelligently combine products and services into tailored bundles.

[0012] In a non-limiting example, the bundle recommendation engine within the personalized bundling module uses decision tree algorithms and entropy minimization techniques to provide users with compatible, customized choices. Alternative embodiments employ machine learning models such as neural networks for more tailored and nuanced bundle recommendations. The pricing engine uses multivariate linear regression models to predict bundle costs, and in another embodiment, more advanced machine learning models such as random forests.

[0013] In one embodiment, the personalized bundling module interacts with the RTDM and the SPoG UI. Upon receiving a user request, the bundle recommendation engine can fetch real-time market and customer data from the RTDM. The module can utilize a decision tree algorithm with entropy minimization to optimize bundle selection. Alternatively, machine learning models such as neural networks can be employed to refine user selection based on nuanced data patterns. The bundle pricing engine can perform cost prediction and customization. In some embodiments, the pricing engine employs a multivariate linear regression model for cost prediction that considers variables such as base price, customer-specific discounts, and market conditions. An alternative option can implement a random forest algorithm for more complex pricing structures.

[0014] In some embodiments, the personalized bundling module can initiate bundling requests via the SPoG UI. An authorization checker can verify user permissions against role-based access control policies. A bundle configuration aggregator can query RTDM for current product and service options. In a non-limiting example, the bundle configuration aggregator can assemble bundles using a weighted score algorithm. In some embodiments, a bundle template filler can populate the bundle template based on user preferences and market data. An error checking integrator reviews the bundle configuration using predefined rules and algorithms.

[0015] Additionally or alternatively, the module's error-checking integrator can use a set of validation algorithms. These can include support vector machines trained on historical data for bundle accuracy. Real-time data can be fetched from various systems via RTDM to ensure synchronization. SQL queries pull account-specific data, such as customer preferences or previously identified bundle patterns, directly from the integrated database via RTDM.

[0016] The embodiments disclosed herein integrate multiple systems, automate processes, and validate bundle configurations based on intelligent rules, enabling complex tasks to be performed efficiently without specialized knowledge, saving time and minimizing errors. Furthermore, the present invention is adaptable and configurable to meet evolving market and customer demands, thereby maintaining the relevance and sustainability of distribution models. Thus, the present invention provides an efficient, integrated, and adaptable solution for automating the personalized bundling process in the distribution industry.

[0017] Automated personalized bundling based on personas provides an advanced data-driven methodology for creating combinations of diverse product categories, such as hardware, software, cloud services, and other related offerings, customized to the unique preferences and requirements of different user archetypes or personas. This method assembles various products and services into integrated bundles tailored to the specific characteristics, behaviors, and needs of disparate customer segments. In embodiments disclosed herein, automated personalized bundling can keep up with rapid technological changes that challenge static algorithms and human analysis. By leveraging a data-driven methodology in real time, the systems and methods described herein dynamically generate personas and personalized bundles based on real-time technology and inventory updates, market research, user data, and evolving technology trends. A process is provided for enabling the assembly of product and service bundles that are not coherent and up-to-date with the latest technological advancements, ensuring relevance and appeal to various customer segments.

[0018] This process automatically generates personas based on comprehensive market research and real user data, encompassing a wide range of attributes including demographics, purchasing patterns, digital engagement, and preferences across various product categories. Identifying personas enables a granular understanding of customer needs in areas such as technology, software applications, cloud computing solutions, and hardware requirements.

[0019] Leveraging advanced machine learning algorithms and predictive analytics, the system analyzes a rich array of data, including purchase history, user browsing activity, and interaction data across different product categories. This analysis identifies the unique preferences or latent needs of each persona, facilitating the creation of relevant and compelling bundles.

[0020] Some embodiments are directed to a platform that employs advanced machine learning algorithms and dynamic data analysis to address the inherent variability and frequent updates in technology product features (e.g., which laptops have adequate battery life, which peripherals have desired features, etc.). By continuously analyzing the latest product information and user interaction data, the system and methodology can adapt bundle recommendations in real time. This ensures the delivery of the most relevant, compatible, and up-to-date technology solutions customized for each persona.

[0021] Automated personalized bundling integrates products and services from different categories so that each bundle is coherent and comprehensive in meeting a particular user's technology and service needs. This may involve combining current hardware with compatible software solutions, aligning cloud service offerings with user data requirements, integrating product suites that collectively improve a user's productivity and efficiency, etc. The system is configured to deliver a highly personalized user experience, driving increased engagement, customer satisfaction, and loyalty by offering relevant bundles curated for an individual's specific professional or personal technology needs.

[0022] (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 for delivering the distribution process.

[0023] 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.

[0024] According to some embodiments, SPoG can consolidate multiple touchpoints into a single platform and emulate 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.

[0025] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics highlight demand trends and guide companies to manage inventory more efficiently, reducing the risk of stock-outs or overstocks.

[0026] 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.

[0027] According to some embodiments, to facilitate personalized bundling and manage user personas, SPoG consolidates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the chance of errors. Furthermore, it provides the ability to efficiently perform personalized bundling, thereby meeting the needs and requirements of specific markets.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] SPoG's innovative approach to solving distribution industry challenges is an invaluable tool. By increasing supply chain visibility, facilitating inventory management, ensuring compliance, facilitating personalized bundling, and delivering a superior customer experience, it offers a comprehensive solution to a complex problem that has 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.

[0032] (Real-time Data Mesh (RTDM)) According to some embodiments, the platform may include an implementation of Real-Time Data Mesh (RTDM). RTDM 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.

[0033] 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.

[0034] 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.

[0035] RTDM also simplifies SKU management and localization by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. Its capability for personalized bundling efficiently aligns with specific market needs.

[0036] RTDM enhances the customer experience with its intuitive interface, making technology easier to access and purchase, and meeting the expectations of a new generation of technology buyers.

[0037] (SPoG and RTDM Integration Advantages) 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.

[0038] 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.

[0039] Integrating SPoG with RTDM also ensures data consistency and reduces errors in SKU management, and by providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps meet market needs.

[0040] 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.

[0041] 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]

[0042] [Figure 1] 1 illustrates one embodiment of an operating environment for a distribution platform, referred to herein as a system. [Figure 2] 2 illustrates one embodiment of an operating environment for a distribution platform built with the elements introduced in FIG. 1. [Figure 3] 1 illustrates one embodiment of a system for distribution management. [Figure 4] 1 illustrates a system for an automated personalized bundling process, 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 an automated personalized bundling process, according to one embodiment. [Figure 8]FIG. 1 is a flow diagram of a method for an automated personalized bundling process according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a flow diagram for an automated data processing and analysis process in a personalized bundling system, according to some embodiments of the present disclosure. [Figure 10] FIG. 1 is a flow diagram for an automated user interaction and feedback process in a personalized bundling system, 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

[0043] The present embodiments may be implemented in hardware, firmware, software, or any 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 any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include 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.

[0044] 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.

[0045] 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.

[0046] 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. Customers also have access to real-time data and analytics through system 110, enabling them to make informed decisions and optimize their IT infrastructure.

[0047] End customers 130 can be the ultimate beneficiaries of the IT solutions provided by system 110. End customers may include businesses or individuals who utilize IT products and services to improve their operations, productivity, or daily activities. End customers rely on system 110 to access a wide range of IT solutions, ensuring they have access to the latest technologies and innovations 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.

[0048] Vendors 140 play a key role within the operating environment of system 110. These vendors 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 can leverage system 110 to facilitate 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.

[0049] 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.

[0050] Within the operating environment of the system 110, there may be various dynamics and characteristics 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 is exchangeable 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.

[0051] System 110 provides 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 broader range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. Additionally, system 110 leverages a technology stack that includes .NET, Java, and other suitable technologies, providing a solid foundation for its operation.

[0052] 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.

[0053] Figure 2 illustrates a distribution platform operating environment 200 built with the elements introduced in Figure 1. Within this operating environment, integration points 210 facilitate data flow and connectivity between various customer systems 220, vendor systems 240, reseller systems 260, and other entities involved in the distribution process. The diagram illustrates the interconnectivity and mechanisms that enable efficient collaboration and data-driven decision-making. The operating environment is configured to utilize advanced artificial intelligence (AI) and machine learning (ML) techniques to automate the personalized bundling process and to integrate, process, and analyze data.

[0054] In some embodiments, the automated process for personalized bundling is a systematic approach designed to create and deliver customized product or service bundles to users with minimal human intervention. The process integrates several key technology components. The process can involve the collection of diverse user data, including demographic information, browsing habits, purchase history, and user interactions. This data is aggregated from various sources, such as CRM systems, web analytics tools, and other platforms. Utilizing a real-time data mesh (RTDM), the system processes and normalizes the collected data, ensuring it is ready for analysis. The RTDM serves as a centralized repository facilitating real-time updates and searches of user data. Advanced analytics and machine learning algorithms within the AAML module analyze the aggregated data to identify distinct user personas. This involves segmenting the user base into groups based on common characteristics and predicted behavioral patterns. The personalized bundling module utilizes insights gained from the AAML module to generate customized bundle recommendations for each identified persona. This module applies predictive models and heuristic algorithms to select products or services that fit each persona's specific preferences and needs. The recommended bundles are presented to the user through an interactive, user-friendly single-pane-of-glass user interface (SPoG UI). This interface allows the user to browse, customize, and confirm their bundle selection. The system can incorporate a feedback loop, where user responses to the recommended bundles are collected and analyzed. This feedback is used to continuously refine and optimize the bundling algorithm, ensuring that recommendations remain relevant and effective.

[0055] In some embodiments, AI algorithms can be applied to the personalized bundling process for real-time inventory management, customization options, and user selection optimization. Machine learning models, such as neural networks and decision trees, can be employed to provide more sophisticated and personalized options to users. Similarly, the personalized bundling process can use ML-based algorithms for real-time generation of personalized bundles. Advanced analytics in the form of ensemble learning or reinforcement learning can be implemented to continuously optimize the personalized bundling process. AI and ML technologies within operating environment 200 utilize either or both supervised and unsupervised learning algorithms. These include convolutional neural networks for pattern recognition and logistic regression models for decision-making processes. AI aspects and components can dynamically adapt to changing data inputs, such as user preferences and market conditions, and employ reinforcement learning to optimize decision-making paths. ML aspects can utilize decision trees and clustering algorithms for predictive analysis to continuously refine their output by incorporating new data, thereby improving the accuracy and relevance of the personalized bundling process.

[0056] The operating environment 200 may include the system 110 as a distribution platform that serves as a central hub for managing and facilitating the distribution process. The system 110 may be configured to function and operate as a bridge between the customer system 220, the vendor system 240, the reseller system 260, and other entities in the ecosystem. Communication, data exchange, and transaction processes may be integrated to provide users with a unified, streamlined experience. Additionally, the operating environment 200 may include one or more integration points 210 to ensure smooth data flow and connectivity. The integration points 210 may employ a hybrid architecture combining RESTful APIs, WebSockets, and the like to facilitate real-time data exchange and synchronization. This architecture may be secured through SSL / TLS encryption protocols to ensure confidentiality and integrity of data in transit. These integration points include:

[0057] Customer System Integration: Integration point 210 allows system 110 to connect with customer system 220, facilitating efficient data exchange and synchronization. Customer system 220 may include entities such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems used by customers, such as ERP or CRM systems. Integration with customer system 220 allows customers to access real-time information about personalized bundles, pricing details, order tracking, and other related data, improving their decision-making capabilities. This integration provides an automated, real-time solution for creating and managing personalized bundles, improving the customer's operational efficiency.

[0058] Data exchange between customer systems 220, vendor systems 240, and reseller systems 260 is enabled by robust ETL (extract, transform, load) as described below with reference to a real-time data mesh architecture, ensuring data consistency and reliability. This interaction can be governed by predefined business rules and logic that dictate data flow and processing methods. Advanced mapping and transformation tools are employed to harmonize disparate data formats and enable data integration and utilization across these systems. Orchestrated data exchange supports synchronized operations, enabling efficient, informed decision-making across the distribution network.

[0059] Trading Partner System Integration: Integration points 210 allow system 110 to connect to trading partner systems 230, facilitating efficient data exchange and synchronization. These systems contribute to the overall efficiency of personalized bundling by providing relevant market and product data.

[0060] Vendor System Integration: Integration point 210 facilitates connectivity between system 110 and vendor systems 240. Vendor systems 240 may include entities such as vendor system 241, vendor system 242, and vendor system 243, which represent inventory management, pricing systems, and product catalogs. Integration with vendor systems 240 ensures that vendors can efficiently update their product offerings and receive real-time notifications, facilitating the personalized bundling process.

[0061] Reseller System Integration: Integration point 210 allows reseller system 260 to connect with system 110. Reseller system 260 encompasses entities such as reseller system 261, reseller system 262, and reseller system 263 that handle sales, customer management, and service delivery. The integration allows resellers to access up-to-date product information and effectively manage customer relationships.

[0062] Other Entity System Integration: Integration points 210 further connect with other entities involved in the distribution process to facilitate collaboration and efficient distribution. This integration ensures real-time data exchange for personalized bundling processes and decision-making in the distribution ecosystem.

[0063] The system 110 configuration includes advanced AI and ML capabilities to tailor product offerings and bundles according to individual customer personas and ensure relevance and optimization in the distribution process.

[0064] Integration point 210 also enables connectivity with systems of record 280 for additional data management and integration. Systems of record 280 can represent enterprise resource planning (ERP) systems or customer relationship management (CRM) systems, including both legacy ERP systems (e.g., SAP, Impulse, META, I-SCALA, etc.) as well as future systems. Systems of record can include one or more storage repositories of critical legacy business data. This facilitates integrated data exchange and synchronization between distribution platforms, systems 110, and ERPs, enabling real-time updates and ensuring the availability of accurate, up-to-date information. Integration point 210 establishes connectivity between systems of record 280 and distribution platforms, enabling stakeholders to leverage the rich data stored in ERPs for efficient collaboration, data-driven decision-making, and streamlined distribution processes. These systems represent internal systems utilized by customers, vendors, and others.

[0065] Integration points 210 within operating environment 200 can be facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. System 110 employs industry-standard protocols, such as RESTful APIs, SOAP, or GraphQL, to establish communication channels and enable data exchange.

[0066] In some embodiments, the system 110 may incorporate authentication and authorization mechanisms to ensure secure access and data integrity. Technologies such as OAuth or JSON Web Token (JWT) may be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0067] In some embodiments, integration points 210 and data flow within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various stages of the distribution process, including customer orders, inventory updates, shipping details, and sales analytics, flows between customer systems 220, vendor systems 240, reseller systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves operational efficiency across the distribution platform.

[0068] In some embodiments, system 110 utilizes advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies to support integration points 210 and enable communication within operating environment 200. These technologies provide a solid foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, integration points 210 may also employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. Integration points 210 and the data flow within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between different entities, systems, and components. The integrated data can be processed, harmonized, and made available to relevant users in real time through system 110. This real-time access to accurate and current information enables users to make informed decisions, optimize supply chain operations, and improve customer experiences.

[0069] 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, a workstation, a laptop, a PDA, a mobile phone, or any Wireless Access Protocol (WAP)-enabled device, or any other computing device capable of interfacing directly or indirectly with the Internet or other network connection. Each of the customer systems is typically capable of running an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, enabling the customer systems to access, process, and display information, pages, and applications available from the distribution platform over a network.

[0070] Additionally, each customer system may typically be equipped with a user interface device, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device for interacting with the graphical user interface (GUI) provided by the browser. These user interface devices enable users of the customer systems to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0071] The customer system and its components may be operator-configurable using applications, including a web browser, running on a central processing unit, such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator-configurable using applications running on a central processing unit, such as an Intel Pentium processor or similar processor, and / or a processor system that may include multiple processing units.

[0072] An embodiment of a computer program product includes a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and customer systems, vendor systems, reseller systems, and systems of other entities to intercommunicate and process web pages, applications, and other data can be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as ROM, RAM, floppy disk, optical disk, DVD, CD, microdrive, magneto-optical disk, magnetic or optical card, nanosystem, or any suitable medium for storing instructions and data.

[0073] Furthermore, computer code for implementing the present embodiments can be transmitted and downloaded from a software source via the Internet or any other conventional network connection using communications media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transmitted over an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, or others.

[0074] It will be appreciated that the present embodiment can be implemented in a variety of programming languages ​​executed on the client system, server, or server system, and the choice of language may depend on the particular requirements and environment of the distribution platform.

[0075] This allows the operating environment 200 to couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0076] Figure 3 illustrates a system 300 for supply chain and distribution management. System 300 (Figure 3) is a supply chain and distribution management solution configured 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.

[0077] 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 providing a customizable and intuitive dashboard-style layout, the SPoG UI enables users to access relevant information and tools to make data-driven decisions and efficiently manage supply chain and distribution activities.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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 inconsistencies. The harmonized data can then be made available in real time, allowing users access to accurate, current information across the supply chain.

[0083] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. It employs an advanced change data capture (CDC) mechanism that constantly monitors transaction systems to detect updates or modifications. The CDC component can be specifically configured 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, the system 300 provides 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.

[0093] FIG. 4 illustrates one embodiment of a system 400 for personalized bundling based on specific user personas. System 400 is configured for integration with existing reseller systems, ensuring efficient data exchange and system synchronization. An embodiment of system 400 enables the creation of customized product and service bundles, individualized to specific user requirements and preferences. The system leverages real-time data aggregation and analytics to adaptively configure bundles incorporating various technology products, including hardware, software, and subscription-based services. This adaptability ensures the bundling process remains relevant in the face of a rapidly evolving technology landscape and user needs. By integrating AI-driven forecasting and user interaction data, the system facilitates dynamic bundling strategies that not only address current market trends but also anticipate future shifts. This approach improves correlation capabilities with AI models, enabling users to make informed decisions and access comprehensive solutions aligned with their unique technology and operational goals.

[0094] The SPoG UI 405 serves as the primary user interface. Users interact with this interface to perform a variety of tasks, providing straightforward interaction and customization. It displays information and options relevant to the reseller's unique business model and customer demographics. It displays real-time data from the data mesh 410 and provides controls for initiating actions within the system 400. For example, a user can generate personalized bundles directly from the SPoG UI 405. The SPoG UI was developed using web-based technology, allowing it to be accessed from various types of devices, including desktop computers, laptops, tablets, and smartphones. The SPoG UI 405 provides a comprehensive view of the entire distribution ecosystem, aggregating data and functionality from various modules into a centralized, easy-to-navigate platform. The SPoG UI 405 simplifies the management of complex distribution tasks and provides a streamlined experience for resellers.

[0095] Data Mesh 410 is an advanced data management layer. It aggregates and harmonizes data from various sources, including ERPs, vendor platforms, third-party databases, etc. This component ensures that all operational modules within System 400 have access to consistent and up-to-date information. System 400 can synchronize with existing reseller systems, ensuring efficient data exchange and system functionality.

[0096] The system 400 includes a unified product catalog 420 that maintains an extensive and constantly updated list of IT products and services. Integration with an AI module 460 ensures that the catalog reflects the latest market demands and user preferences. The catalog offers a wide range of choices across hardware, software, and cloud solutions to meet the diverse needs of resellers.

[0097] The enhanced search interface 430 optimizes the product and service discovery process. By leveraging natural language processing and user-specific data, the interface facilitates efficient and relevant search results. The enhanced search interface 430 can be a component of the SPoG UI 405 or can be a separate component designed for efficient and relevant product and service discovery. Leveraging natural language processing and user data, it streamlines the search process and helps resellers quickly identify the items they need.

[0098] The data aggregation and analytics module 440 can be configured to collect and process data from a variety of sources, including vendor and reseller systems, as well as external market intelligence to generate insights. The insights generated by this module inform the operation of the AI ​​module 460 and the unified product catalog 420, aiding in understanding market dynamics and customer needs. The system 400 incorporates a data aggregation and analytics module 440 that is responsible for collecting and processing data from a variety of sources. This module supports the personalization engine 410 and the unified product catalog 420 with insights to aid in understanding market dynamics and customer needs.

[0099] The data aggregation and analysis module 440 of the system 400 plays a vital role in maintaining the system's responsiveness to rapid technological changes and user needs. This module is designed to continuously collect and analyze data from multiple sources, including real-time market trends, customer feedback, and global technological advancements. It integrates data from enterprise resource planning (ERP) systems, vendor platforms, third-party databases, and direct customer input to create a comprehensive data pool. This module employs advanced data processing techniques, such as predictive and sentiment analysis, to extract actionable insights from this vast data set. For example, if there is a surge in demand for a particular type of software solution in the market, the module quickly identifies this trend and reflects it in the system's recommendations and catalog updates. Similarly, customer feedback, whether it relates to product preferences or service experiences, is analyzed and used to refine product offerings and user interfaces. This integration ensures that the system 400 remains agile and adaptive, effectively responding to the evolving technology environment and nuanced customer requirements. As a result, resellers using System 400 can confidently navigate the fast-paced IT market equipped with the latest insights and a platform that evolves in tandem with industry trends and customer expectations.

[0100] The AI ​​module 460 automates various distribution processes, including personalized bundling. According to some embodiments, the AI ​​module 460 can be provided as a personalized bundling module integrated with the SPoG UI 405 and the data mesh 410. The AI ​​module 460 automates and enhances the bundling process. By analyzing purchasing patterns, customer preferences, and market data, the module intelligently suggests bundles that are likely to meet the specific needs of different user personas within the reseller organization. For example, based on the purchasing history and preferences of the reseller's customers, the AI ​​module 460 may suggest bundles that combine a specific model laptop with a suitable software suite and cloud services. The AI ​​module 460 uses algorithms to optimize product and service selection based on real-time market data and customer preferences. It employs advanced algorithms to intelligently combine products and services into tailored bundles.

[0101] In a non-limiting example, AI module 460 may include a personalized recommendation engine 465 employing decision tree algorithms and entropy minimization techniques to provide compatible, customized selections to the user. In additional or alternative embodiments, personalized recommendation engine 465 may employ machine learning models, such as neural networks, for further tailored and nuanced bundle recommendations. Personalized recommendation engine 465 may include a pricing engine 466, for example, utilizing multivariate linear regression models, random forests, or other methodologies to predict bundle costs.

[0102] In one embodiment, the personalized recommendation engine 465 interacts with the data mesh 410 and the SPoG UI 405. Upon receiving a user request, the personalized recommendation engine 465 can fetch real-time market and customer data from the RTDM. The module can utilize a decision tree algorithm with entropy minimization to optimize bundle selection. Alternatively, machine learning models such as neural networks can be employed to refine user selections based on nuanced data patterns. The personalized recommendation engine 465 can perform cost prediction and customization. In some embodiments, the pricing engine 466 employs a multivariate linear regression model for cost prediction that takes into account base prices, customer-specific discounts, and market conditions. An alternative option can implement a random forest algorithm for more complex pricing structures.

[0103] In some embodiments, the personalized recommendation engine 465 can initiate bundling requests via the SPoG UI 405. An authorization checker can verify user permissions against role-based access control policies. The bundle configuration aggregator 467 can query the data mesh 410 for current product and service options. In a non-limiting example, the bundle configuration aggregator 467 can assemble bundles using a weighted score algorithm. In some embodiments, the bundle template filler 468 can populate bundle templates based on user preferences and market data. The error checking integrator 469 can be configured to review bundle configurations using predefined rules and algorithms.

[0104] In some embodiments, the personalized recommendation engine 465 can utilize machine learning and pattern recognition algorithms via the AI ​​module 460 to analyze user profiles and historical interaction data to match one or more bundles with potential consumers of the matched bundles. This can be accomplished by employing a compatibility scoring system that evaluates each bundle against patterns associated with the user's preferences, past purchases, etc., and / or patterns associated with anticipated potential future needs. For example, a technology startup interested in scalability and innovation may be recommended a bundle that includes cloud computing services, innovative cybersecurity solutions, and collaborative work tools. The personalized recommendation engine 465 can analyze the associated data to recognize potential conversions associated with the user.

[0105] In this regard, AI module 460 acts as a personalization engine to analyze reseller activity and market trends to generate user-specific offerings, dynamically adapting to changes in user behavior and market conditions. Working in conjunction with personalization recommendation engine 465, AI module 460 can also incorporate advanced filtering algorithms to distinguish between essential and non-essential features in technology solutions, personalizing bundles tailored to the user's specific operational requirements and strategic goals. For example, for a small business focused on digital marketing, AI module 460 can utilize filtering algorithms to prioritize solutions with strong CRM and analytics capabilities, effectively filtering out unrelated enterprise-level infrastructure services.

[0106] The AI ​​module 460 can be configured to customize the reseller experience, as described above, facilitating the creation of personalized bundles of hardware, software, and services. The AI ​​module 460 provides an advanced engine for creating personalized bundles of products and services, utilizing real-time data analysis and user behavior patterns. This module utilizes sophisticated algorithms that analyze a vast array of data points, including purchase history, browsing history, and specific user interactions within the platform. By interpreting this data, the AI ​​module 460 can discern the nuanced trends and preferences unique to each user persona within the reseller organization.

[0107] For example, if a user frequently explores cloud storage solutions and cybersecurity software, AI module 460 correlates that interest with current market offerings and the user's unique requirements. It then intelligently combines compatible and complementary products, such as pairing a popular cloud storage service with highly rated cybersecurity software, thereby creating a customized bundle. This process is dynamic, allowing the module to continuously learn and adapt to changing user preferences and market conditions. As a result, AI module 460 not only proactively suggests personalized bundles tailored to current needs, but can also predict future requirements, ensuring resellers can satisfy customer demand ahead of time.

[0108] The AI ​​module 460 is configured to support a personalized user experience for different personas within the reseller organization. Each user's interaction with the system is tailored based on their role, preferences, and behavior. For example, a user focused on procurement and finance will see different options and recommendations compared to a user involved in sales or IT management. The AI ​​module 460 generates personalized bundles based on the user persona. This functionality enables the system 400 to implement IT distribution via a cloud- and software-focused approach. The AI ​​module 460 utilizes AI and ML algorithms to understand the specific needs and preferences of different user personas within the reseller organization.

[0109] For example, if a user persona within a reseller organization primarily deals with cloud-based solutions, AI module 460 can identify and suggest bundles that combine related cloud services with complementary hardware and software products. This not only improves the user experience by providing appropriate options, but also streamlines the procurement process. Furthermore, system 400's ability to provide an agnostic marketplace is essential in addressing the industry challenge of hyperscalers operating siloed marketplaces. Unlike traditional marketplaces, system 400 provides a platform where resellers, whether hyperscalers or manufacturers, can access a wide range of products and services.

[0110] To facilitate access to a broad array of technology solutions, system 400 integrates with multiple vendor platforms through standardized APIs, ensuring the latest products and services are available in a unified product catalog 420. This enables system 400 to support an agnostic marketplace, creating and dynamically updating bundles with the latest, relevant solutions to address rapidly evolving technology needs.

[0111] The system 400 further includes a reporting and analytics module 450 that provides detailed insight into customer behavior, market trends, and the effectiveness of personalized bundles. This module enables resellers to make informed decisions based on data-driven insights and further optimize their offerings and operations.

[0112] System 400 facilitates scenarios where resellers can meet diverse customer needs through a single platform. For example, reseller personnel can quickly identify and procure the bundles that best suit their segment based on recommendations and data provided by system 400. This process not only improves efficiency but also ensures that offerings are aligned with market demands and customer preferences. System 400 combines AI / ML technologies to provide a solution for IT delivery that identifies and adheres to reseller requirements and market trends. By providing a platform that unifies the creation of personalized bundles based on user personas, system 400 addresses the needs of agnostic markets and offers a comprehensive suite of tools for efficient IT distribution.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The RTDM module 515, or real-time data mesh module, is a component of the system 500 that 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, accurate information and make informed decisions.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 employs 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.

[0122] The catalog management module 540 enables the creation, management, and distribution of current product catalogs. This ensures that users have access to current 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.

[0123] 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 leverage Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, allowing users to take proactive measures to optimize operations.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] (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.

[0131] 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 supply chain and distribution management domain.

[0132] 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 current and accurate data.

[0133] The RTDM module 600 can include a data layer 620 configured to process and translate data for search 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, financial data, etc. These PDSs act as repositories of normalized and / or standardized data, ensuring data consistency and integrity across systems.

[0134] 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 then 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.

[0135] 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.

[0136] The data layer 620 includes a 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. Using the elasticity and fault-tolerance of cloud-based storage, the data lake 622 can accommodate an influx of data from diverse sources.

[0137] 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 a 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.

[0138] 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.

[0139] 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.

[0140] 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 using 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.

[0141] In some embodiments, data layer 620 can incorporate robust 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.

[0142] 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.

[0143] 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, to enable powerful data processing, advanced analytics, and machine learning capabilities. With robust 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.

[0144] 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.

[0145] 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 provide 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. Any additional headless engines can be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0146] 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.

[0147] 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.

[0148] 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, depending on their preferences and role.

[0149] 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.

[0150] (Personalized bundling based on personas) In one embodiment, FIG. 7 illustrates a system 700 for personalized bundling based on specific user personas. The system 700 includes a real-time data mesh 710, a single-pane-of-glass user interface (SPoG UI) 705, an advanced analytics and machine learning (AAML) module 715, and a personalized bundling module 720. The system 700 provides a framework for real-time data processing and analysis and emphasizes adaptability to changing technology and user demands. The system 700 implements AI and machine learning processes that dynamically curate personalized bundles tailored to users' current needs and are scalable to accommodate future technological advancements. The system 700 can be implemented as an integrated solution of hardware, software, and subscription services, providing users with an intuitive platform for accessing personalized technology solutions. The integration conforms to a market adaptation increasingly focused on delivering value through complete solutions rather than individual products, increasing user engagement and satisfaction through a solution-centric distribution model.

[0151] The RTDM 710 aggregates and standardizes real-time data from various sources, ensuring the personalized bundling module 720 operates with up-to-date and relevant information. This includes data on product availability, user behavior patterns, and market trends. The RTDM 710 provides a unified, centralized data hub that aggregates and standardizes data from multiple sources, including ERP, CRM systems, and market intelligence. It employs a combination of data warehouses and data lakes to manage structured and unstructured data. The RTDM 710 uses ETL processes and data normalization techniques to ensure data uniformity and accessibility. This standardized data enables the personalized bundling module 720 and provides the necessary input for accurate and efficient bundle generation. The RTDM 710 maintains data integrity and relevance, enabling and facilitating the automated process of personalized bundling.

[0152] In one embodiment, the personalized bundling module 720 includes various functional subcomponents designed for the personalized bundling process, which interact closely with other system components: the Single Pane of Glass User Interface (SPoG UI) 705, the Real-Time Data Mesh (RTDM) 710, and the Advanced Analytics and Machine Learning (AAML) module 715.

[0153] The personalized bundling module 720 is dedicated to creating customized product and service bundles. It contains subroutines and algorithms for tasks such as analyzing user behavior, generating personalized product combinations, and applying dynamic pricing. When a user initiates a bundling action from the SPoG UI 705, the request is routed to the personalized bundling module 720 in the AAML 715. The module processes the request, interacts with the real-time data mesh 710 for needed data, and performs the bundling configuration. The results are then displayed in the SPoG UI 705. Additionally, the personalized bundling module 720 includes a logging mechanism to track all configuration changes and user interactions for auditing and optimization purposes.

[0154] The personalized bundling module 720 employs a combination of machine learning models and heuristic algorithms for bundle recommendation and pricing. It uses decision tree models to determine optimal product combinations and linear regression for dynamic pricing strategies. The module incorporates real-time error-checking algorithms to ensure the accuracy and feasibility of each bundle. It dynamically adjusts recommendations based on user feedback and market trends, utilizing a feedback mechanism to continuously improve bundling accuracy. This module is essential for providing users with customized, relevant, and error-free bundles, improving customer satisfaction and operational efficiency.

[0155] The personalized bundling module 720 can employ the AAML module 715 to filter and distinguish relevant from irrelevant data in real time. This allows the personalized bundling module 720 to employ advanced filtering algorithms and noise reduction techniques to process the vast amount of data received from the RTDM 710. In one non-limiting example, it can distinguish between highly important signals, such as emerging market trends and user engagement metrics, and noise, such as outdated or irrelevant product information. By prioritizing data that accurately reflects current market trends and user preferences, the personalized bundling module 720 can recognize and recommend potential bundles that are customized to the user's immediate needs and also match offers and trends that improve conversions. This optimization engine is integrated with the advanced analytics and machine learning (AAML) module 715, leveraging both historical and real-time data to continuously improve its filtering algorithms and increase the accuracy of bundle recommendations.

[0156] The personalized bundling module 720 can integrate crucial subcomponents such as a bundle recommendation engine 720.1, which guides the user through the product selection process based on the user's profile and preferences. The bundle recommendation engine 720.1 is activated when the user is searching for products in the SPoG UI 705 and can fetch real-time data from the RTDM 710, ensuring all recommendations are based on current availability and user preferences. This engine can use decision tree algorithms to provide compatible and complementary selections and optimize the bundling process.

[0157] Another subcomponent, the Bundle Composition Generator 720.2, assembles a detailed list of products and services for each personalized bundle. Using a RESTful API or similar data connector, it matches user preferences and market trends with real-time inventory data from the RTDM 710. This component ensures that each bundle is optimally configured to meet user needs and market demand.

[0158] The Personalized Pricing Adjuster 720.3 dynamically calculates the total cost of personalized bundles, fetching real-time pricing data from the RTDM 710 and applying user-specific discounts or promotional offers. This component ensures that pricing for each bundle is competitive and tailored to the individual user profile.

[0159] The personalized bundling module 720 also includes an error checking integrator 720.4. This component validates all user selections and bundle configurations using algorithms stored in AAML 715. This ensures that each personalized bundle is free of errors and inconsistencies before finalization.

[0160] The personalized bundling module 720 supports JSON and XML data formats for creating and customizing bundles. This flexibility allows it to accommodate a wide range of user preferences and system requirements.

[0161] The AAML module 715 acts like a central processing unit for the personalized bundling process. It contains intelligent rules and algorithms designed for specific tasks in the bundling process, such as analyzing user behavior, optimizing product mixes, and applying dynamic pricing. The AAML module 715 can utilize analytical tools for big data processing and deep learning capabilities. The AAML module 715 can perform sentiment analysis, trend forecasting, and behavioral analysis to understand and anticipate user needs. The AAML module 715 integrates and trains machine learning algorithms based on historical datasets to identify patterns and create predictive recommendations. It adapts its algorithms based on a continuous feedback loop, refining its accuracy over time. This module performs the functions corresponding to the automated bundling process and ensures that recommendations are tailored to each individual user's profile and preferences.

[0162] As a result, the system 700 is configured to integrate data from multiple sources into a unified interface via the SPoG UI 705, automate various tasks in the personalized bundling process via the AAML 715, and maintain a real-time, standardized data repository via the RTDM 710. This architecture enables an efficient and accurate user-specific bundling process, improving the overall customer experience in the IT distribution industry.

[0163] 8 illustrates a flow diagram of a method 800 for a personalized bundling process according to an embodiment of the present disclosure. The flowchart depicts operations from start to finish and highlights the personalized bundling module 720 of the system 700.

[0164] In operation 801, a user begins the bundling process by interacting with a Single Pane of Glass User Interface (SPoG UI) 705. Operation 801 includes collecting user inputs such as product preferences and specific requirements. The SPoG UI 705 then communicates with an Advanced Analytics and Machine Learning (AAML) module 715 to process these inputs.

[0165] In operation 802, the AAML module 715 performs a preliminary analysis to identify the user's specific needs for bundling. Algorithms within the AAML 715 analyze the user's input and determine optimal bundle recommendations, taking into account factors such as past purchasing behavior and current market conditions.

[0166] In operation 803, the request proceeds to the Real Time Data Mesh (RTDM) 710. The RTDM 710 utilizes RESTful APIs for these operations to retrieve data related to the user's preferences, including real-time inventory status and related product information.

[0167] At operation 804, the personalized bundling module 720 processes the request. This module utilizes components such as a bundle recommendation engine 720.1 and a bundle composition generator 720.2 to create customized bundles. The bundle recommendation engine 720.1 employs algorithms to select products and services that match the user's preferences, while the bundle composition generator 720.2 assembles them into coherent bundles.

[0168] Operation 804 may include a personalized bundling module 720 that refines the proposed bundles using filtering and / or pattern recognition processes. This operation may utilize advanced filtering algorithms and noise reduction techniques to evaluate preliminary bundle configurations against current market trends and user-specific data to ensure that only the most relevant and compatible products and services are included. The personalized bundling module 720 processes aggregate data from RTDM 710 to identify and prioritize key signals, such as user preference trends, compatibility of selected products, and real-time market data, while effectively filtering out irrelevant information. This optimization not only ensures that each bundle is tailored to the user's specific needs and preferences, but also reflects the latest available technological solutions, thereby increasing the value and appeal of personalized bundles.

[0169] In some embodiments, the personalized bundling module 720 can match the optimized bundles against a set of predefined rules and criteria to ensure compatibility and compliance with industry standards. In non-limiting examples, this may include checking the latest versions of software, hardware compatibility, and service contract terms to ensure the bundles meet all requirements necessary for a seamless user experience. Such adjustments can ensure that the final bundle recommendations are personalized and suitable for immediate implementation by the relevant users.

[0170] In operation 805, a validation step by the Error Check Integrator 720.4 ensures the accuracy and feasibility of the proposed bundle. This integrator uses algorithms stored in the AAML 715 to verify the integrity of the bundle.

[0171] In operation 806, the personalized bundle, including detailed components and pricing, is presented again to the user on the SPoG UI 705 for review and approval.

[0172] In operation 807, machine learning models in personalized bundling module 720 perform post-processing analysis. These models use techniques such as predictive analytics to refine the bundling process and improve future recommendations based on evolving user data and market conditions.

[0173] In operation 808, a logging mechanism within the personalized bundling module 720 records transaction details, including user preferences and final bundle composition, for future analysis and ongoing improvement of the system.

[0174] In operation 809, the user reviews and approves or modifies the bundle presented on the SPoG UI 705. If the user approves, the personalized bundling process is complete.

[0175] This operational flow integrates the SPoG UI 705, RTDM 710, AAML 715, and personalized bundling module 720, each performing specific functions to automate and optimize the personalized bundling process. Alternative embodiments can include variations in machine learning algorithms, data retrieval methods, and user interaction interfaces, providing adaptability and scalability of the system 700.

[0176] 9 illustrates a flow diagram of a method 900 for data processing and analysis utilizing a system 700 including, for example, a real-time data mesh 710 and an advanced analytics and machine learning (AAML) module 715. The method 900 illustrates a series of operations involved in aggregating, transforming, and utilizing data for real-time analytics and insight generation.

[0177] In operation 901, the process begins with data collection. Various data sources, such as enterprise resource planning (ERP) systems, vendor platforms, third-party databases, and direct input from users, are fed into the real-time data mesh 710. This step involves aggregating a variety of data types, including inventory levels, user behavior data, market trends, and product information.

[0178] In operation 902, the aggregated data undergoes standardization and transformation within the real-time data mesh 710. This includes aligning data formats, normalizing measurements, and resolving data discrepancies. The RTDM 710 ensures that the data is processed into a unified format suitable for subsequent analysis.

[0179] In operation 903, the standardized data is transferred from the RTDM 710 to the AAML module 715. This transfer is facilitated through a secure and efficient data communication protocol to ensure data integrity and authenticity.

[0180] In operation 904, the AAML module 715 begins the analysis process. Advanced algorithms and machine learning models within this module analyze the incoming data to extract meaningful insights, including identifying patterns, predicting market trends, and understanding user behavior.

[0181] Given the diverse nature of the data (user behavior, market trends, inventory data), some embodiments may include performing segmentation based on both user characteristics (demographics, purchase history) and product category in operation 904. This segmentation may be performed to enable or improve targeted and relevant analysis. In some embodiments, operation 904 may include predictive modeling, and the AAML module 715 may implement one or more statistical methods (e.g., regression analysis for sales forecasting, etc.) and / or machine learning techniques (e.g., neural networks for complex pattern recognition and demand forecasting, etc.). As described herein, these models may be continually refined with incoming data to improve their accuracy.

[0182] In some embodiments, operation 904 may include sentiment analysis, such as processing customer feedback and market sentiment data using an NLP model trained on a large text corpus, allowing for understanding customer satisfaction and market attitudes for personalizing offerings in personalized bundling module 720.

[0183] In operation 905, the AAML module 715 uses the derived insights to inform various functions within the system 700. Specifically, these insights contribute to the operational efficiency of the personalized bundling module 720. In some embodiments, predictive and trend analysis can improve the accuracy and relevance of product bundle recommendations.

[0184] In some embodiments, operation 905 may include the generation and / or retrieval by the personalized bundling module 720 of detailed customer profiles based on behavioral analytics to tailor bundles of products and services. Customization may utilize deep insights such as the customer's lifestyle indicators, past purchasing patterns, and time-sensitive factors such as recent browsing history. In a non-limiting example, if analytics indicate a growing customer interest in sustainable products, the bundling module may adjust its algorithms to prioritize environmentally friendly options in product bundles.

[0185] In some embodiments, operation 905 may include translating the data-driven insights and analysis into actionable implementation by the AAML module 715 to create tailored product and service bundles applicable to different user personas. The insights generated by the AAML module 715 are based on the patterns, trends, etc. identified in operation 904. Operation 905 may include leveraging this information to dynamically adjust product and bundle offerings in real time. For example, if the AAML module 715 detects a surge in a particular product or category, the personalized bundling module 720 can instantly incorporate such trends into bundling options. Real-time integration ensures that bundle recommendations remain relevant and meaningful to users, improving user engagement.

[0186] In some embodiments, operation 905 may include the personalized bundling module 720 implementing a dynamic pricing model based on market demand and / or insights into customer price sensitivity. For example, operation 905 may include adjusting prices depending on various factors, such as inventory levels, competitor pricing, and customer willingness to pay. The personalized bundling module 720 may use machine learning models, for example, via the AAML module 715, to predict optimal prices for each bundle to maximize both revenue and customer satisfaction.

[0187] Second, the trend analysis performed by the AAML module 715 provides the personalized bundling module 720 with information about current market dynamics. This analysis includes evaluating user engagement metrics, purchase history, and feedback patterns to identify product and service combinations that resonate with different customer segments. For example, if a growing trend toward environmentally friendly products is detected among a certain user demographic, the personalized bundling module 720 can adjust its algorithms to prioritize these products in bundle recommendations for that segment.

[0188] Additionally, insights from the AAML module 715 allow the personalization bundling module 720 to fine-tune its recommendation engine. This involves adjusting the weighting of different user attributes and behaviors in its algorithms. For example, if insights indicate a high correlation between recent browsing history and purchasing decisions, the recommendation engine can be calibrated to place more weight on browsing data.

[0189] In addition to influencing product bundle recommendations, insights from the AAML module 715 further guide dynamic pricing strategies within the personalized bundling module 720. By understanding the price sensitivity of different user groups and market demand curves, the module can dynamically adjust the prices of personalized bundles to improve sales potential and customer satisfaction.

[0190] Additionally, these insights contribute to improving the user interface and experience of the SPoG UI 705. By understanding user preferences and behavior, the UI can be optimized to present the most relevant information and options to the user, improving user engagement and simplifying the decision-making process.

[0191] In summary, operation 905 within system 700 is the essential process of translating the analytical results of AAML module 715 into actionable strategies and operational enhancements, particularly for personalized bundling module 720. This operation allows the system to remain agile and responsive, adapting to the changing needs and behaviors of users.

[0192] XXX

[0193] In operation 906, the AAML module 715 continuously updates its algorithms and models based on the most recent data. This iterative learning process allows the analytical component of the system to remain dynamic and adapt to changing market conditions and user needs.

[0194] In operation 907, the AAML module 715 communicates the analysis results back to the real-time data mesh 710. This step updates the RTDM 710 with new perspectives, ensuring that the data mesh always contains the most current and processed information.

[0195] In operation 908, the real-time data mesh 710 is now updated with the latest perspective and is ready to support subsequent data requests from the various components of the system 700. This ensures a continuous cycle of data processing and utilization, maintaining the effectiveness and responsiveness of the system.

[0196] In operation 909, the SPoG UI 705 accesses the updated data from the RTDM 710. This allows the user interface to reflect the most current data and perspectives, improving the user experience and decision-making capabilities.

[0197] As shown in Figure 9, method 900 integrates real-time data mesh 710 with AAML module 715 for efficient data processing, analysis, and utilization. This methodology ensures that the system is informed by real-time data and perspectives, significantly contributing to the personalized bundling process and overall system performance. Alternative embodiments of this method can include variations in data sources, analytical models, and communication protocols, providing adaptability and scalability to data processing and analytical operations within system 700.

[0198] 10 illustrates a flow diagram of a method 1000 for user interaction and feedback in system 700. Method 1000 performs functions to incorporate user interaction to enable continuous improvement and training of the system.

[0199] In operation 1001, the process begins with user input and selections made in the SPoG UI 705. The user interacts with the interface by selecting products, indicating preferences, and providing feedback. Operation 1001 can include capturing real-time user behavior and preferences.

[0200] In operation 1002, user data collected from the SPoG UI 705 is sent to the real-time data mesh 710, where the data is aggregated with other relevant information, such as current market trends and inventory status, and prepared for analysis.

[0201] In operation 1003, the enriched data is sent from the RTDM 710 to the AAML module 715. The AAML module 715 then processes this data using advanced analytics and machine learning algorithms to derive insights into user behavior, preferences, and feedback.

[0202] In operation 1004, the AAML module 715 communicates the processed perspectives to the personalized bundling module 720. These perspectives include user satisfaction scores, bundle success rates, and other relevant metrics that are crucial for refining the bundling process.

[0203] In operation 1005, the personalized bundling module 720 utilizes these insights to adjust and improve the bundling algorithm. This step involves updating bundle recommendations, personalization strategies, and user interaction protocols to better align with user expectations and market demands.

[0204] In operation 1006, the updated bundling strategies and recommendations are implemented in the SPoG UI 705. Users interact with these updated interfaces and offerings, providing a continuous loop of interaction and feedback.

[0205] In operation 1007, the user's reactions to the updated strategy are again captured by the SPoG UI 705. This includes both explicit feedback, such as user ratings or comments, and implicit feedback, such as click patterns and time spent on different pages.

[0206] In operation 1008, this new set of user data is sent back to the RTDM 710 and subsequently to the AAML module 715, continuing the cycle of feedback and improvement. This continuous process ensures that the system adapts and evolves in response to user interactions.

[0207] In operation 1009, performance metrics and user satisfaction scores are periodically reviewed to measure the effectiveness of iterative improvements, which may involve analyzing trends over time, comparing user engagement metrics before and after changes, and evaluating overall system performance.

[0208] As illustrated in FIG. 10 , method 1000 establishes a comprehensive feedback loop within system 700, integrating user interaction data from SPoG UI 705 with the analytical capabilities of AAML module 715 and the operational coordination of personalized bundling module 720. This method emphasizes the importance of user feedback in the continuous improvement of the system, ensuring that the system remains responsive and adaptive to user needs and market changes. By capturing and utilizing real-time user interactions, system 700 dynamically evolves to provide increasingly sophisticated and personalized bundle recommendations. This continuous feedback loop not only improves user satisfaction but also contributes to the overall efficiency and effectiveness of the bundling process.

[0209] Method 1000 thereby establishes a process for continuous learning and adaptation within system 700. Effectively integrating user feedback into the system's operational framework ensures that the SPoG UI 705, real-time data mesh 710, AAML module 715, and personalized bundling module 720 are constantly aligned with user preferences and market dynamics. This adaptive approach is key to maintaining the relevance and effectiveness of the personalized bundling process and ultimately improving user experience and system performance. Alternative embodiments of method 1000 can include variations in data collection methods, feedback processing algorithms, and user interaction interfaces, providing adaptability and scalability to the operation of user interactions and feedback loops within system 700.

[0210] 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 or bus 1106.

[0211] 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 .

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery model, 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 models.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 12C illustrates 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 process.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] Figure 12P shows Customer Subscription History, which presents a historical record of a customer's subscription activity. It displays a list of previous subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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 computerized method for performing a personalized bundling process, comprising: receiving user input specifying preferences for product bundling; retrieving real-time data related to the user's preferences from a real-time data mesh (RTDM); analyzing user input and market data via an Advanced Analytics and Machine Learning (AAML) module; generating one or more personalized bundle recommendations through a personalized bundling module based on the analyzed user input and market data; Displaying the personalized bundle recommendations to the user via a Single Pane of Glass user interface (SPoG UI); receiving input from the user to complete the individualized bundle; forwarding an individualized bundle order to a vendor system based on the completed individualized bundle; and executing the personalized bundle order by integrating data from the SPoG UI, RTDM, and vendor systems; A method wherein the method is performed by a computer system that comprises a unified platform that integrates data from multiple sources for real-time personalization.

2. 10. The method of claim 1, further comprising validating the personalized bundle using rules and algorithms by the AAML module to ensure the validity of the personalized bundle recommendation.

3. The method of claim 1 , wherein the AAML module utilizes dynamic machine learning algorithms to adapt the personalized bundle recommendations based on changing user preferences and market conditions.

4. The method of claim 1 , wherein the RTDM is continuously updated with real-time inventory, user behavior data, and market trends.

5. 10. The method of claim 1, further comprising generating one or more real-time reports related to the personalized bundling process, the reports comprising user engagement metrics and / or personalized bundle success rates.

6. The method of claim 1 , wherein the vendor system for fulfilling the personalized bundle is selected based on criteria including product availability and / or delivery capabilities.

7. The method of claim 1 , further comprising sending a notification to the user upon successful completion of the personalized bundle order.

8. 1. A computerized method for optimizing individualized bundling decisions, comprising: communicating with the personalized bundling module via the SPoG UI to initiate a bundling request; Retrieving user preference and history data; analyzing the user's preferences and historical data; Querying the RTDM to fetch the real-time data comprising product and / or user behavior; applying, by a personalized bundling module, a predictive analytics process based on the real-time data and the analyzed historical data to determine an optimal combination of products for bundling; populating a bundle configuration template based on the analyzed user preferences and historical data; validating the individualized bundle configuration with an Advanced Analytics and Machine Learning (AAML) module; and presenting the final personalized bundle to the user via the SPoG UI; Logging the personalized bundling process for future analysis and system refinement; and and initiating a feedback loop within the personalized bundling module for continuous improvement of the bundle recommendations.

9. 10. The method of claim 8, further comprising utilizing a machine learning algorithm to perform the feedback loop to analyze user feedback and system performance for continuous optimization of the personalized bundling process.

10. The method of claim 8 , wherein the RTDM captures real-time data comprising information pertaining to current market conditions and inventory.

11. 10. The method of claim 8, further comprising generating real-time reports related to the personalized bundling process, the reports comprising metrics related to user satisfaction and bundle customization.

12. The method of claim 8 , wherein product selection for the personalized bundle is made based on predefined criteria including user preferences, market trends, and product compatibility.

13. The method of claim 8 , further comprising sending a notification to the user based on successful generation of the personalized bundle.

14. 10. The method of claim 8, wherein the feedback loop for personalized bundling decisions is based on user engagement and / or analytics and occurs within a defined timeframe.

15. 1. A system for automating a personalized bundling process, comprising: a real-time data mesh configured to aggregate and provide data including user preferences, market trends, and product information; a single pane of glass user interface that enables user interaction and displays personalized bundle options; an advanced analytics and machine learning module responsible for processing the data and generating intelligent bundle recommendations; a personalized bundling module that interacts with the SPoG UI and RTDM to perform a personalized bundling process including user preference analysis, product selection, and bundle generation.

16. 16. The system of claim 15, wherein the personalized bundling module further comprises a logging mechanism for tracking user interactions and bundling selections for auditing and / or analysis.

17. 16. The system of claim 15, wherein the personalized bundling module is integrated with the AAML module for validation and optimization of one or more personalized bundle recommendations, and the personalized bundling module applies algorithms stored in the AAML module to refine the bundle recommendations.

18. 16. The system of claim 15, wherein the SPoG UI is designed to be accessible and responsive across a variety of devices, providing a unified user experience for bundle customization.

19. 16. The system of claim 15, wherein the RTDM is configured to standardize and harmonize data from diverse sources for consumption and analysis via the AAML module.

20. 16. The system of claim 15, wherein the AAML module incorporates a machine learning model applied by the personalized bundling module, the machine learning module being continuously trained to optimize the bundling process based on one or more of user feedback and evolving market data.

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