Single pane of glass mobile application including erp agnostic realtime data mesh with change data capture

Abstract

To provide a mobile application system and method for integrating, analyzing, and notifying data in real time in a mobile application system.SOLUTION: A system 110 optimizing operations through an adaptive feedback mechanism and protecting security of data through encryption, initiates a data layer and preprocessing phase, and leverages distributed database strategies to store structured data and unstructured data. A real-time data mesh continuously draws data from various platforms, by employing a signal processing method and a machine learning technique for noise removal and data feature extraction. Advanced analytics and a machine learning engine process data, and a determination structure derives a suitable action. A push notification service activates on the basis of an event-driven architecture or a publish-subscribe system, and delivers customized notifications. Technical precision ensures that timely information is sent to end users.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part (CIP) of U.S. Patent Application No. 18 / 341,714, filed June 26, 2023, U.S. Patent Application No. 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 technology The present invention relates to aspects of a real-time data mesh method and system encompassing distribution, supply chain management, and related functionality.

[0003] The global distribution industry faces numerous challenges encompassing distribution management, supply chain management, inventory control, SKU management, compliance, and evolving consumer expectations. Historically, distribution and supply chain management have not been core competencies for many distributors, leading to inefficiencies. Inventory control has long been a major concern, and market fluctuations are driving demand for more flexible distribution and supply chain models. SKU management and localization add layers of complexity due to disparate data from various OEMs and differing jurisdictional requirements. Additionally, compliance with international regulations requires additional vigilance and paperwork. Finally, traditional ways of interacting with customers are rapidly becoming obsolete as the shift toward ecosystem commerce continues.

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

[0005] 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.

[0006] Data inconsistencies present another challenge. When data exists in different formats or units across departments or ERPs, standardizing this data for meaningful analysis can be a tedious process. Businesses often rely on time-consuming manual processes to convert and validate data, further delaying decision-making. Additionally, legacy ERP systems often lack the capabilities 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.

[0007] 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 security capabilities that are fast enough to adapt to the ever-evolving landscape of cybersecurity threats and compliance requirements.

[0008] Finally, consumer expectations for faster service and real-time information are putting additional pressure on legacy systems. Traditional request-based user interfaces in distribution platforms suffer from delayed access to critical distribution and supply chain metrics. These request-based systems can suffer from data staleness, where data is updated only when requested, resulting in outdated information and hindering informed decision-making. Furthermore, requesting data from multiple sources in legacy systems requires significant resources, is time-consuming, and resource-intensive. They are reactive in nature, relying on user-initiated data requests, which limits proactiveness. Integrating data from various sources within these systems can be complex and cumbersome, leading to complex integration challenges. Additionally, request-based systems can face scalability challenges when handling large volumes of data requests. Thus, traditional request-based distribution and supply chain platforms often hinder efficient distribution and logistics management. Summary of the Invention

[0009] The global distribution industry is at a critical juncture, grappling with a series of challenges across multiple domains. These obstacles, both historic and emerging, require the development of innovative and effective solutions to drive the industry toward growth and efficiency. Among these numerous challenges, the most significant exist in the areas of distribution management, supply chain management, inventory and compliance issues, SKU (stock keeping unit) management, the transition to a direct-to-consumer model, and rapidly evolving consumer expectations and behaviors.

[0010] The first key challenge relates to managing the distribution process, which is a core part of a distributor's operations, yet, paradoxically, is typically outside the distributor's core remit. This gap creates inefficiencies in the system and exacerbates the difficulty in managing disruptions, which in turn has a direct bearing on the distributor's ability to deliver products and services efficiently and on time. In addition to these challenges, market trends are increasingly leaning toward direct-to-consumer models. Previous distribution methodologies, involving a significant number of intermediaries, are gradually being replaced. This evolving market force requires a significant reevaluation and readaptation of existing business models and strategies to ensure alignment with this new market reality.

[0011] A typical problem in the distribution sector is inventory management. Given the volatile nature of market demands and trends, companies must ensure they maintain a flexible distribution and supply chain without having to hold inventory positions. This makes the task of fulfilling and delivering goods to customers substantially more complex and difficult. Furthermore, the sheer need to navigate through myriad compliance regulations to transport goods and services across international borders adds yet another layer of additional complexity to the distribution process. This not only makes the distribution process more involved and difficult, but also imposes yet another layer of vigilance and paperwork to maintain compliance.

[0012] To further complicate these challenges, companies must also address issues surrounding product localization, variable distribution rights, and global SKU management. The process of reconciling data from different original equipment manufacturers (OEMs), each with their own systems and processes, adds to the complexity. Furthermore, addressing localization requirements aligned with the laws and regulations of different jurisdictions increases the potential for inefficiency and error.

[0013] Ultimately, to ensure the sustainability of distribution models in evolving market conditions, processes need to be made more efficient and streamlined. This entails shifting the focus of distribution platforms from supply chain management to encompass subscription management, customer visibility, and other key distribution-oriented functionality. The landscape of consumer behavior and expectations is rapidly changing. The shift toward ecosystem commerce requires creating a user-friendly, efficient, and configurable platform for purchasing technology. Traditional ways of interacting with customers are rapidly falling out of favor, making it essential for companies to evolve and meet these new customer expectations.

[0014] Despite these challenges, the distribution model offers several advantages over the direct-to-consumer model. First, it allows manufacturers to focus on their core competencies, leaving the complexities of logistics and distribution to specialized entities. Second, distribution networks often have widespread reach, making products available to customers in far-flung areas that would be unfeasible for manufacturers to reach directly. Third, distributors often offer value-added services, such as after-sales support, installation, and training, that enhance the overall customer experience.

[0015] However, to realize these benefits and keep the distribution model relevant and effective, it is imperative that it evolve and adapt to new and emerging challenges. To ensure the sustainability of the distribution model in evolving market conditions, processes must become more efficient and streamlined. The systems and methodologies described herein are directed toward addressing these challenges. Additionally, the systems described herein can be configured to encompass functionality such as subscription management and other customer-centric areas not effectively managed by previous distribution platforms.

[0016] 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 comprehensive, user-friendly, and efficient platform that streamlines the distribution process.

[0017] 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.

[0018] According to some embodiments, SPoG can consolidate multiple touchpoints into a single platform, emulating a direct consumer channel into a distribution platform. This consolidation provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0019] SPoG provides innovative solutions for improved inventory management through advanced forecasting capabilities. These predictive analytics highlight demand trends and guide companies to manage their inventory more efficiently, reducing the risk of stock-outs or overstocks.

[0020] 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.

[0021] According to some embodiments, to streamline SKU management and product localization, SPoG consolidates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the chance for errors. Furthermore, it allows for efficient management and distribution of localized SKUs, thereby providing performance aligned with the needs and requirements of specific markets.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] SPoG's innovative approach to solving distribution industry challenges makes it an invaluable tool. By increasing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management, and delivering a superior customer experience, it offers a comprehensive solution to complex problems that have long plagued the distribution industry. Through its implementation, distributors can expect to see increased efficiency, reduced errors, and improved customer satisfaction, leading to sustainable growth in an ever-evolving global marketplace.

[0026] Real-Time Data Mesh (RTDM) According to some embodiments, the platform may include an implementation of Real-Time Data Mesh (RTDM). RTDS provides an innovative solution to address these challenges. RTDM is a distributed data architecture that enables real-time data availability across multiple sources and touchpoints. This feature improves supply chain visibility, enables efficient management, and allows distributors to handle disruptions more effectively.

[0027] 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.

[0028] 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.

[0029] RTDM also simplifies SKU management and localization by consolidating data from various OEMs, ensuring data consistency and reducing the chance of errors. Its ability to manage and distribute localized SKUs efficiently aligns with specific market needs.

[0030] 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.

[0031] Benefits of SPoG and RTDM Integration Integrating the SPoG platform with RTDM offers numerous benefits. First, it provides a comprehensive solution to a long-standing problem in the distribution industry. RTDM's capabilities enable SPoG to improve supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and deliver a superior customer experience.

[0032] The real-time tracking and analytics provided by RTDM improves SPoG's ability to effectively manage its supply chain and inventory, providing accurate and up-to-date information that enables distributors to make faster, more informed decisions.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] The SPoG mobile application (mobile app) system and method uniquely addresses challenges within the global distribution industry. Its user-friendly, intuitive interface empowers distributors and customers alike to easily and efficiently navigate complex distribution landscapes. One of its core benefits lies in its ability to improve distribution management by providing real-time visibility into inventory, orders, and supply chain operations. This transparency enables distributors to make informed decisions, optimize processes, and adapt to changing market dynamics in real time.

[0037] The SPoG UI mobile app facilitates connectivity between distributors and customers, aligning with evolving consumer-centric norms. It allows customers to effortlessly access product information, place orders, and track deliveries, all from the convenience of their mobile devices. This direct engagement not only improves customer satisfaction, but also strengthens brand loyalty and trust.

[0038] In some embodiments, the integration of push notifications in the SPoG UI improves the distribution system's ability to keep all users, from vendors to customers, informed and engaged. This enables distributors to proactively communicate order updates, promotions, and related product information, as well as advanced insights, to customers in real time. This level of engagement is invaluable in meeting the rapidly evolving expectations of modern consumers who demand timely and personalized interactions.

[0039] Additionally, the image recognition and SKU mapping capabilities of the SPoG UI offer significant benefits by simplifying the product identification process. Customers can easily scan product images, and advanced AI algorithms accurately map them to the correct stock keeping unit (SKU). This dynamic SKU creation not only streamlines the ordering process, but also reduces manual data entry and potential errors, a major benefit in improving operational efficiency.

[0040] The SPoG UI mobile app's offline data caching ensures uninterrupted functionality even in offline environments. By storing important data locally on the user's device and synchronizing it with a backend server when connectivity is restored, it ensures access to current information at all times. This resilience in the face of connectivity challenges is a significant advantage, especially in areas with sporadic or limited internet access.

[0041] This allows the SPoG UI mobile app to offer comprehensive benefits ranging from improved distribution management and real-time customer engagement to image recognition and offline capabilities. These features empower distributors to thrive in a rapidly evolving distribution landscape while providing a convenient distribution experience to their customers. [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 supply chain and distribution management. [Figure 4] An embodiment of an advanced distribution platform is illustrated, providing a technology distribution platform for optimizing the management and operation of a distribution network. [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 the architecture of a SPoG UI mobile app system and UI according to one embodiment. [Figure 8] FIG. 1 is a flow diagram of a method for real-time data integration, analysis, and notification within a SPoG UI mobile app system, according to some embodiments of the present disclosure. [Figure 9A] FIG. 1 is a flow diagram of a method for image capture and SKU mapping within a SPoG UI mobile app system, according to some embodiments of the present disclosure. [Figure 9B] FIG. 1 is a flow diagram of a method for enhanced product lookup in a SPoG UI mobile app system, according to some embodiments of the present disclosure. [Figure 10] FIG. 1 is a flow diagram of a method for managing an offline data cache in a SPoG UI mobile app, 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. [Figures 13A-13F] 1 illustrates various screens and functionality of the mobile app 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] In some embodiments, a platform supporting supply chain and distribution management is disclosed. In other embodiments, a platform for supporting distribution management rather than supply chain management is provided, where the distribution platform is characterized as a digital-centric environment oriented toward the distribution of technology-oriented products and services, such as cloud services, software, and hardware, specifically targeted toward businesses, IT professionals, and the like. In contrast to platforms such as Amazon, the distribution platform described herein is not involved in the retailing of broad consumer goods, such as clothing, groceries, or household goods. The distribution platform's operational model is generally oriented toward business-to-business transactions rather than a consumer-oriented approach. The platform's infrastructure can be configured to support the distinction between digital and IT distribution, and the platform's logistics are configured specifically to provide recommendations, insights, bundles, and the like to this customer segment rather than a consumer-oriented approach.

[0045] 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.

[0046] 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 parties, 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.

[0047] 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 can also access real-time data and analytics through system 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0048] End customers 130 are 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 and are ensured to have access to the latest technologies and innovations on the market. System 110 allows end customers to track their orders, receive delivery status updates, and access customer support services, thereby enhancing their overall experience.

[0049] 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 leverage system 110 to streamline supply chain operations, manage pricing and promotions, and gain insight into customer preferences and market trends. By integrating with system 110, vendors can expand their reach, access new markets, and improve their overall visibility and competitiveness.

[0050] Resellers 150 are intermediaries in the distribution model, bridging the gap between vendors and customers. Resellers play a key 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 customers. By leveraging system 110, resellers can improve their customer relationships, optimize their product offerings, and increase revenue streams.

[0051] Within the operating environment of the system 110, there are 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 exchanged in real time between stakeholders, 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.

[0052] Scalability and flexibility are key attributes of system 110, allowing it to accommodate the growing demands of IT distribution models, regardless of a growing customer base, an increasing number of vendors, or a wide range of IT products and services. System 110 is designed to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. In addition, system 110 leverages a technology stack that includes .NET, Java, and other suitable technologies, providing a solid foundation for its operation.

[0053] 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 parties. By leveraging real-time data exchange, integration, scalability, and flexibility, system 110 empowers parties to optimize their operations within the IT distribution ecosystem, improve customer experience, and drive business success.

[0054] Figure 2 illustrates a distribution platform operating environment 200 built from 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.

[0055] The operating environment 200 can include the system 110 as a distribution platform that serves as a central hub for managing and facilitating the distribution process. The system 110 can 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 can be integrated to provide a unified, streamlined experience for the parties involved. Additionally, the operating environment 200 can include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points include:

[0056] Customer System Integration: Integration points 210 can enable system 110 to connect with customer systems 220, enabling efficient data exchange and synchronization. Customer systems 220 can include various entities, such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems utilized by customers, such as enterprise resource planning (ERP) or customer relationship management (CRM) systems. Integration with customer systems 220 empowers customers with access to real-time inventory information, pricing details, order tracking, and other relevant data, improving customer visibility and decision-making performance.

[0057] Vendor System Integration: Integration point 210 facilitates connectivity between system 110 and vendor systems 240. Vendor systems 240 may include entities representing inventory management systems, pricing systems, and product catalogs employed by vendors, e.g., vendor system 241, vendor system 242, vendor system 243. Integration with vendor systems 240 ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0058] Reseller System Integration: Integration point 210 provides the capability for reseller systems 260 to connect with system 110. Reseller systems 260 may encompass entities representing the sales systems, customer management systems, and service delivery platforms employed by the reseller, such as reseller system 261, reseller system 262, and reseller system 263. Integration with reseller system 260 empowers the reseller to access up-to-date product information, manage customer accounts, track sales performance, and provide value-added services to customers.

[0059] Other Entity System Integration: Integration point 210 further enables connectivity with other entities involved in the distribution process. These entities may include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures communication and data exchange, facilitating collaboration and an efficient distribution process.

[0060] Integration points 210 within operating environment 200 are 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.

[0061] 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.

[0062] In some embodiments, integration points 210 and data flows within operating environment 200 enable participants 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.

[0063] 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 performance. Furthermore, integration points 210 can 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 stakeholders 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 is processed, harmonized, and made available to relevant stakeholders in real time through system 110. This real-time access to accurate and up-to-date information empowers stakeholders to make informed decisions, optimize supply chain operations, and improve customer experiences.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

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

[0072] The Single Pane of Glass (SPoG) UI 305 serves as a centralized user interface, providing stakeholders 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 gives users access to relevant information and tools, empowering them to make data-driven decisions and efficiently manage supply chain and distribution activities.

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

[0074] SPoG UI 305 integrates with other modules of the system 300 to facilitate real-time data exchange, synchronized operations, and streamlined workflow. 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.

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

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

[0077] To illustrate the capabilities of the RTDM module, consider an example. 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 discrepancies. The harmonized data is then made available in real time, enabling stakeholders across the supply chain to access accurate, up-to-date information.

[0078] 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 is specifically designed to work with a variety of transaction systems, including legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for business operations in diverse environments.

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

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

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

[0082] For example, the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure stock availability during busy periods, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

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

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

[0085] System 300 emphasizes integration and interoperability to connect with existing enterprise systems, such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connections and data flows between these systems, system 300 enables smooth data exchange, process automation, and end-to-end visibility across the supply chain. Integration protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a comprehensive and connected distribution ecosystem.

[0086] The implementation and deployment of system 300 can be tailored to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, easy management, and efficient updates across different environments. The implementation process involves configuring the system to meet specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0087] The system 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by a fragmented distribution ecosystem. It combines the power of the SPoG UI 305, RTDM module 310, and AAML module 315, along with integration with existing systems. 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.

[0088] FIG. 4 illustrates an embodiment of an advanced distribution platform including a system 400 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 400 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 405, a CIM 410, an RTDM module 415, an AI module 420, an interface display module 425, a personalized interaction module 430, a document hub 435, a catalog management module 440, a performance and prospect marker display 445, a predictive analytics module 450, a recommendation system module 455, a notification module 460, a self-onboarding module 465, and a communications module 470.

[0089] System 400, as one embodiment of system 300, enables supply chain and distribution management utilizing 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.

[0090] SPoG UI 405, in some embodiments, serves as a central interface within system 400, providing stakeholders 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 SPoG UI 405 to deliver a user-friendly experience, allowing stakeholders to access relevant information, navigate through different modules, and perform tasks efficiently.

[0091] CIM410, the Customer Interaction Module, employs algorithms and technologies such as Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to handle customer data securely, personalize the customer experience, and provide access control to stakeholders.

[0092] The RTDM module 415, or real-time data mesh module, is a key component of the system 400 and ensures smooth data flow across the distribution ecosystem. It utilizes technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable the RTDM module 415 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 transaction systems, such as legacy ERP and CRM systems. This capability allows stakeholders to access up-to-date and accurate information and make informed decisions.

[0093] AI module 420 within system 400 leverages advanced analytics and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn, to extract valuable insights from data. These algorithms enable the module to automate repetitive tasks, forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, AI module 420 can utilize predictive models to forecast demand, allowing stakeholders to optimize inventory management and minimize out-of-stock or overstock situations.

[0094] The Interface Display module 425 focuses on presenting data and information in a clear and 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 stakeholders to visualize data using various data visualization techniques, such as graphs, charts, and tables, enabling efficient data understanding, comparison, and trend analysis.

[0095] The personalized interaction module 430 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.

[0096] The document hub 435 serves as a central repository for storing and managing documents within the system 400. It utilizes technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, the document hub 435 employs SeeBurger's document management capabilities to categorize and organize documents based on type, such as contracts, invoices, product specifications, or compliance documents, allowing stakeholders to easily access and retrieve relevant documents when needed.

[0097] The catalog management module 440 enables the creation, management, and distribution of up-to-date product catalogs. This ensures that stakeholders have access to the most up-to-date product information, including specifications, pricing, availability, and promotions. It employs technologies such as Kentico and Akamai to facilitate catalog updates, content distribution, and caching. For example, the module can leverage Akamai's content delivery network (CDN) to quickly and efficiently deliver catalog information to stakeholders, regardless of geographic location.

[0098] The performance and outlook marker display 445 collects, analyzes, and visualizes real-time performance metrics and outlooks related to supply chain operations. Utilizing tools such as Splunk and Datadog, it enables effective performance monitoring and provides actionable insights. For example, the module can utilize Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, allowing stakeholders to take proactive measures to optimize operations.

[0099] The predictive analytics module 450 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 stakeholders to optimize inventory levels and minimize costs.

[0100] The recommender system module 455 focuses on providing intelligent recommendations to stakeholders in the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark are employed for data analysis, modeling, and providing targeted recommendations. For example, the module can leverage 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.

[0101] The notification module 460 enables the delivery of real-time notifications to interested parties about 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 interested parties' devices in real time, ensuring timely distribution of relevant information.

[0102] The self-onboarding module 465 facilitates the onboarding process for new participants entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its functionality. It employs technologies such as Okta and Kentico 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 participants, provide them with appropriate permissions, and guide them through the system's functionality.

[0103] The communications module 470 enables communication and collaboration within the system 400. It provides channels for participants to interact, exchange messages, share documents, and collaborate on projects. It employs technologies such as Apigee Edge and Adobe Launch 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 participants, enabling effective collaboration.

[0104] This allows system 400 to incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules, including SPoG UI 405, CIM 410, RTDM module 415, AI module 420, interface display module 425, personalized interaction module 430, document hub 435, catalog management module 440, performance and prospect marker display 445, predictive analytics module 450, recommender system module 455, notification module 460, self-onboarding module 465, and communication module 470, 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.

[0105] Real-time Data Mesh 5 illustrates an RTDM module 500 according to one embodiment. The RTDM module 500 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.

[0106] The RTDM module 500 represents an effective data mesh and change capture component within the overall system architecture, as shown in Figure 5. The module is designed to provide real-time data management and reconciliation capabilities, enabling efficient operations within the supply chain and distribution management domain.

[0107] The RTDM module 500 can include an integration layer 510 (also called "systems 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 510 can handle data exchange and synchronization between the RTDM module 500 and these systems. Data feeds are established to retrieve relevant information from the systems of record, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring the RTDM module operates with the most recent and accurate data.

[0108] The RTDM module 500 can include a data layer 520 configured to process and translate data for search and analysis. At the core of the data layer is a data mesh, a cloud-based infrastructure designed to provide scalable, fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-built data stores (PDSs) are deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS is optimized for efficient data retrieval based on specific use cases and requirements. PDSs are configured to store specific types of data, such as customer data, product data, or financial data. These PDSs act as repositories of harmonized and standardized data, ensuring data consistency and integrity across systems.

[0109] In some embodiments, the RTDM module 500 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 is then processed and harmonized on the fly, transforming it into a standardized format suitable for analysis and integration. This process ensures that data is readily available and up-to-date within the data mesh, facilitating real-time insights and decision-making.

[0110] More specifically, data layer 520 within RTDM module 500 can be configured as a powerful and flexible foundation for managing and processing data within a distribution ecosystem. In some embodiments, data layer 520 can encompass a highly scalable and robust data lake, which can be referred to as data lake 522, along with a set of purpose-built data stores (PDSs), which can be denoted as PDSs 524.1 through 524.N. These components work in unison to ensure efficient data management, reconciliation, and real-time availability.

[0111] At the core of Data Layer 520 sits Data Lake 522, a state-of-the-art storage and processing infrastructure designed to handle the ever-increasing volume, variety, and velocity of data generated within the supply chain. Built on a scalable distributed file system, such as the Apache Hadoop Distributed File System (HDFS) or Amazon S3, the Data Lake provides a unified, scalable platform for storing both structured and unstructured data. By leveraging the elasticity and fault-tolerance of cloud-based storage, Data Lake 522 can accommodate the influx of data from diverse sources.

[0112] Accompanying the data lake 522 may be a population of purpose-built data stores PDS524.1-524.N. Each PDS524 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, PDS524.1 may be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS524.2 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 participants.

[0113] To ensure real-time data synchronization, data layer 520 can be configured to employ one or more advanced change data capture (CDC) mechanisms. These CDC mechanisms 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 520 ensures that the data in data lake 522 and PDS 524 remains up-to-date, providing stakeholders with real-time insight into the distribution ecosystem.

[0114] In some embodiments, data layer 520 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 520 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.

[0115] In terms of data processing and analytics, data layer 520 leverages the capabilities of distributed computing frameworks, such as Apache Spark or Apache Flink, in some non-limiting examples. These frameworks can enable parallel processing and distributed computing across large datasets stored in the data lake and PDS. By leveraging these frameworks, supply chain participants can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, data layer 520 can leverage Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimizing inventory levels, and identifying potential supply chain risks.

[0116] In some embodiments, the data layer 520 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, the data layer 520 can implement data lineage and audit trail mechanisms to enable stakeholders to track the provenance and history of data, ensuring data integrity and compliance with regulatory requirements.

[0117] In some embodiments, data layer 520 can be deployed in a cloud-native environment, leveraging containerization technologies such as Docker and orchestration frameworks like Kubernetes. This approach ensures scalability, resilience, and efficient resource allocation. For example, data layer 520 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.

[0118] The RTDM Module 500's Data Layer 520 incorporates a highly scalable data lake, Data Lake 522, along with purpose-built PDSs, PDSs 524.1 through 524.N. By employing advanced CDC mechanisms, the Data Layer 520 ensures efficient data management, harmonization, and real-time availability. The integration of diverse technology stacks, such as .NET or Java, and distributed computing frameworks like Apache, enables powerful data processing, advanced analytics, and machine learning capabilities. With robust data governance and security measures, the Data Layer 520 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, the Data Layer 520 empowers supply chain stakeholders to make data-driven decisions, optimizing operations and driving business success in dynamic and complex distribution environments.

[0119] The RTDM module 500 may include an AI module 530 configured to implement one or more algorithms and machine learning models to analyze the data stored in the data layer 520 and derive meaningful insights. In some non-limiting examples, the AI module 530 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI module 530 may continuously learn from new data inputs and adapt its models to provide accurate and up-to-date insights. The AI module 530 may generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0120] The data engine layer 540 comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 540 of the RTDM module 500 can include a collection of autonomously operating headless engines 540.1-540.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. Engines 540.1-540.N can leverage harmonized data stored in the data mesh to deliver specific business logic and services. Each engine is designed 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 540 or other exemplary layers of the disclosed system.

[0121] 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.

[0122] Additionally, a data distribution mechanism may be employed to facilitate integration and access to the RTDM module 500. The data distribution mechanism 545 may 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.

[0123] The experience layer 550 focuses on providing an intuitive and user-friendly interface for interacting with supply chain data. The experience layer 550 can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feeds, allowing users to customize views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, tailored to their preferences and roles.

[0124] Thus, in some embodiments, the RTDM module 500 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, up-to-date information and insights, make informed decisions, and optimize supply chain operations. Thus, the RTDM module 500 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 reconciliation, and advanced analytical capabilities. The module's ability to replicate and reconcile data from diverse ERPs while maintaining auditable and 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.

[0125] Single-pane-of-glass UI 6A illustrates an SPoG UI according to one embodiment. SPoG UI 600. In some embodiments, SPoG UI 600 can be an embodiment of SPoG UI 305 and represents a comprehensive, intuitive user interface designed to provide stakeholders with a unified, customizable view of the entire distribution ecosystem. It combines various features and functionality that enable users to gain a comprehensive understanding of their supply chains and efficient management of their operations.

[0126] The SPoG UI 600 can include a unified view (UV) module 605, which provides stakeholders with a centralized, customizable, dashboard-style layout. This module allows users to access real-time data, analytics, and functionality tailored to their specific roles and responsibilities within the distribution ecosystem. The UV module 605 serves as a single entry point for users, providing a comprehensive, holistic view of supply chain operations and empowering them to make data-driven decisions.

[0127] The SPoG UI 600 integrates with a real-time data exchange module 610 to facilitate the continuous exchange of data between the SPoG UI 600 and the RTDM 310, leveraging one or more data sources, which may include one or more ERP, CRM, or other sources. Through this module, stakeholders have access to up-to-date, accurate, and harmonized data. Real-time data synchronization ensures that the information presented in the SPoG UI 600 reflects the latest outlook and developments across the supply chain. This integration enables stakeholders to make informed decisions based on accurate and synchronized data.

[0128] The Collaborative Decision-Making Module 615 within the SPoG UI 600 fosters real-time collaboration and communication between stakeholders. This module enables the exchange of information, initiation of workflows, and sharing of insights and recommendations. By integrating with the RTDM Module 310 / 500, the Collaborative Decision-Making Module 615 ensures stakeholders can collaborate effectively based on accurate and synchronized data. This promotes overall operational efficiency and collaboration within the distribution ecosystem.

[0129] To ensure secure and controlled access to functionality and data, the SPoG UI 600 incorporates a role-based access control (RBAC) module 620. Administrators can define roles, grant permissions, and control user access based on their responsibilities and organizational hierarchy. The RBAC module 620 ensures that only authorized users can access specific functions and information, protecting data privacy, security, and compliance within the distribution ecosystem.

[0130] The customization module 625 empowers users to personalize their dashboards and tailor the interface to their preferences and needs. Users can arrange widgets, charts, and data visualizations to prioritize the information most relevant to their specific roles and tasks. This module allows stakeholders to customize their view of their supply chain operations, providing a user-centric experience that improves productivity and ease of use.

[0131] The SPoG UI 600 integrates a powerful data visualization module 630, which enables stakeholders to analyze and interpret supply chain data through interactive dashboards, charts, graphs, and visual representations. Leveraging advanced visualization techniques, this module presents complex data in a clear and intuitive manner. Users can gain insight into key performance indicators (KPIs), trends, patterns, and anomalies, facilitating data-driven decision-making and strategic planning.

[0132] The SPoG UI 600 can include a mobile and cross-platform accessibility module 635 to ensure accessibility across multiple devices and platforms. Stakeholders can access the interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected and informed while on the go. This module optimizes the user experience for different screen sizes, resolutions, and operating systems, ensuring access to real-time data and functionality across a variety of devices.

[0133] By integrating these reference elements / modules within the SPoG UI 600 and leveraging its integration capabilities with the RTDM module 310 / 500, stakeholders can benefit from a powerful, user-friendly interface for supply chain and distribution management. The Unified View (UV) module 605 provides a customizable, comprehensive view of the supply chain and distribution environment, while the Real-Time Data Exchange module 610 ensures accurate and up-to-date data synchronization. The Collaborative Decision-Making module 615 promotes effective communication and collaboration, and the RBAC module 620 ensures secure access control. The Customization module 625, Data Visualization module 630, and Mobile and Cross-Platform Accessibility module 635 enhance user experience, data analysis, and accessibility, respectively. Together, these modules enable stakeholders to make informed decisions, optimize supply chain operations, and drive business efficiency within the distribution ecosystem.

[0134] SPoG600 is capable of incorporating high-velocity data in data-rich environments. In today's data-rich environments, traditional UI designs often struggle to present large amounts of information in a way that is easy to understand, efficient, and visually appealing. This challenge is amplified when data is dynamic, changing in real time, and needs to be effectively displayed in a single-pane environment that prioritizes clean, whitespace-driven design.

[0135] The SPoG605 UI can be configured to efficiently manage real-time data and maintain a visually clean interface without compromising performance. This innovative approach includes a unique organization of UI structure, responsive data visualization, a way to handle real-time data, an adaptive information architecture, and white space optimization.

[0136] The SPoG 605UI is built around a grid-based layout system, leveraging CSS Grid and Flexbox technologies. This structure provides the flexibility to create fluid layouts, with elements automatically adapting to available space and content. HTML5 and CSS3 serve as the underlying technologies for creating the UI, while JavaScript, specifically React.js, manages the dynamic aspects of the UI.

[0137] It should be understood that the acts shown in the exemplary methods are not exhaustive and that other acts may similarly be performed 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 be varied.

[0138] Mobile Application Architecture Framework Figure 7 illustrates an architectural framework for a mobile app within a cloud distribution platform, highlighting its core components. The mobile app architecture 700 encompasses a user interface (UI) layer 705, a data layer 710, a push notification service 720, an image recognition and SKU mapping engine 730, an offline data cache 740, a security and authentication layer 750, integration with backend systems 760, and device compatibility 770. The architecture is designed to provide users with real-time data access and transaction capabilities, enhancing their experience in a dynamic and evolving marketplace.

[0139] In some embodiments, UI layer 705 can serve as a central point of interaction for a user within system 700. In one non-limiting example, UI 705 can be an embodiment of a SPoG UI, such as SPoG UI 305, 410, 600, or any other UI that allows a user, e.g., one or more vendors, customers, partners, resellers, etc., to easily navigate through different modules, access relevant information, and perform various tasks related to the distribution platform. UI 705 is designed to be intuitive, user-friendly, and responsive, allowing users to efficiently interact with the system.

[0140] The UI layer 705 provides a central point of interaction between users and the app's functionality. It is configured to provide a user-friendly and intuitive experience, providing users, including customers, vendors, partners, resellers, and others, with access to the app's extensive features and capabilities. The UI layer is thoughtfully designed to have a responsive, mobile-friendly layout, meeting the expectations of the diverse range of devices and screen sizes users may employ. The UI layer prioritizes the user's perspective and employs a user-centered design approach. This entails the thoughtful placement of elements such as menus, buttons, and navigation bars, improving the user's ability to efficiently interact with the application. The user interface is crafted to facilitate easy navigation through the app, employing clear, logically structured menus, intuitive icons, and user-friendly labels to guide users through the app's functionality without confusion.

[0141] The UI layer 705 can be user-friendly by design. Thus, the UI optimizes screen real estate, ensuring that information and actions are presented in a clear and organized manner, enhancing readability and ease of use. In some embodiments, the design includes abundant white space to further enhance readability and ease of use. The UI layer 705 is configured to include a responsive design. That is, the user interface dynamically adjusts to different screen sizes and orientations. Whether a user accesses the app on a large desktop monitor or a small smartphone screen, the interface remains user-friendly and visually engaging. The UI layer 705 is optimized for mobile platforms. This optimization encompasses touch-friendly controls, efficient use of screen space, and responsive design elements that adapt to mobile screens. The UI layer 705 ensures a consistent user experience across different platforms, such as web browsers and mobile apps. Users can expect a familiar interface regardless of the device or platform they use to access the application. Accessibility features can be built into the UI layer, enabling individuals with diverse abilities to use the application. These features include keyboard navigation, screen reader compatibility, and compliance with accessibility standards. The UI layer 705 is designed with scalability in mind: it accommodates potential future enhancements and additions to the application, ensuring that the user interface remains adaptable to evolving requirements.

[0142] In some embodiments, the data layer 710 can be configured to enable efficient data flow across the SKU management ecosystem. The data layer 710 can be an embodiment of the RTDM module 310, 415, 500, or any other data layer and can encompass a data lake that serves as a scalable and robust storage infrastructure for storing structured and unstructured data related to SKUs. In some embodiments, the data layer 710 is integrated with the RTDM module to enable real-time data exchange and synchronization. This integration ensures that the data in the data layer 710 is up-to-date and readily available for SKU management operations.

[0143] In some embodiments, data layer 710 can be an embodiment of an RTDM module, such as RTDM module 310 or RTDM module 415, or RTDM module 500. In some embodiments, data layer 710 can be a separate data layer that interacts with the RTDM module. As described above, the RTDM module can be configured to function as an ERP-independent real-time data mesh. In some embodiments, the RTDM module collects data from the system of record layer, including data from various enterprise systems, such as ERP, and incorporates it into a data lake in data layer 710.

[0144] The Data Layer 710 acts as a repository for harmonized and standardized data obtained from the RTDM modules. Within the Data Layer 710, various purpose-built data stores are deployed to store specific types of data, e.g., customer data, product data, financial data, etc. These purpose-built data stores optimize data retrieval based on specific use cases and requirements to ensure efficient distribution and order management.

[0145] By leveraging the data available within the data layer 710 and the real-time capabilities of the RTDM module, the system 700 is configured to enable an efficient and accurate SKU creation process consistent with the most up-to-date information. This interaction between the data layer 710 and the RTDM module facilitates an integrated data flow, enabling dynamic ordering and distribution processes to be effectively executed within the entire distribution ecosystem.

[0146] The Data Layer 710 can incorporate or be operatively connected to a RTDM architecture (e.g., 500) to facilitate real-time data management and processing within the supply chain and distribution management domain. This module is configured to facilitate data retrieval, transformation, and analysis, ensuring users receive up-to-date and accurate information for data-driven decision making.

[0147] In one example, the data layer 710 is coupled with the RTDM module and forms an integral component of the system's architecture. Its primary purpose is to extract, process, and translate data from various sources via the RTDM module 500 to support insight generation and integration with enterprise systems. The data layer 710 integrates with a diverse set of enterprise systems via the RTDM module, including well-known ERPs such as SAP, Impulse, META, and I-SCALA. This integration is achieved through the integration layer 510, often referred to as the "system of record." The integration layer 510 establishes data feeds that facilitate the exchange and synchronization of critical information, such as sales orders, purchase orders, inventory data, and customer details. By maintaining real-time data updates, the data layer 620 ensures that the RTDM module operates with up-to-date and accurate data, a fundamental requirement for efficient supply chain management.

[0148] Within Data Layer 710, a bus / gateway to Data Layer 520 processes and translates data for search and analysis. At the heart of the Data Layer is the Data Mesh, a cloud-based infrastructure known for its scalability and fault-tolerant data storage capabilities. This Data Mesh encompasses multiple Purpose-Driven Data Stores (PDSs) (denoted PDSs 524.1-524.N), each optimized for a specific type of data relevant to the supply chain domain. For example, PDS 524.1 may store customer data, while PDS 524.2 may focus on product data, including SKU details, descriptions, pricing, and inventory levels. These PDSs act as repositories of harmonized and standardized data, ensuring data consistency and integrity across all systems.

[0149] The data layer 710 is configured to implement a data replication mechanism that captures real-time changes from various data sources, including transactional systems like ERP. This captured data is processed in real time and converted into a standardized format suitable for analysis and integration. This real-time data capture and conversion process ensures that the data in the data mesh and PDS remains up-to-date, allowing stakeholders, especially knowledgeable engineers, to access real-time insights to make timely decisions.

[0150] The data layer 710 can also leverage current and future technologies for data processing and analytics. Distributed computing frameworks like Apache Spark or Apache Flink enable parallel processing and distributed computing across large datasets stored in data meshes and PDSs. These frameworks empower supply chain stakeholders, including engineers, to perform complex data analytics, apply machine learning algorithms, and extract valuable insights from the data. For example, these capabilities facilitate demand forecasting, inventory optimization, and risk identification within the supply chain.

[0151] Within this module, data governance and security are paramount. Fine-grained access control mechanisms and robust authentication protocols ensure that only authorized users, including engineers, can access and modify data within the data mesh and PDS. Data encryption techniques protect sensitive supply chain information both at rest and in transit, ensuring protection against unauthorized access. Additionally, the Data Layer 710 implements data lineage and audit trail mechanisms to enable stakeholders, including engineers, to track data history and provenance, ensuring data integrity and regulatory compliance.

[0152] The Data Layer 710 is thereby operatively integrated with the RTDM module 500 and exists as a fundamental element of a supply chain and distribution management system. Its capacity to efficiently manage, transform, and provide real-time access to data empowers stakeholders, especially skilled engineers, with the insight necessary to optimize supply chain operations, make data-driven decisions, and effectively navigate the complexities of the distribution environment. The module's robust architecture, scalability, and integration capabilities position it as a key asset for improving supply chain management in a dynamic and competitive landscape.

[0153] The Push Notification Service 730 is a fundamental component of the mobile app architecture and is tightly integrated with the Data Layer 620 / 710. Operating on an event-driven architecture, event handlers instantiated and / or operably connected via the RTDM module are configured for specific distribution platform events to ensure efficient communication with users. Such events can include price adjustments, inventory updates, order confirmations, etc., and trigger real-time notifications delivered to users' mobile devices.

[0154] The push notification service 730 communicates with RTDM via the data layer 710 for real-time data retrieval based on RTDM from various sources, including enterprise resource planning (ERP) systems and other data repositories. The push notification service 730 can receive events exposed via the data layer 710 that require immediate user attention. Such events encompass matters related to vendors, customers, end customers, resellers, partners, and any other events managed by the distribution platform. These events can include dynamic price fluctuations to inventory updates that support the operation of the distribution platform. Each event triggers the delivery of a real-time notification to the associated user's mobile device. To ensure reliable notification delivery, the service adheres to standardized push notification protocols, such as Firebase Cloud Messaging (FCM) for Android and Apple Push Notification Service (APN) for iOS. Engineers meticulously configure and integrate these protocols to meet platform-specific requirements.

[0155] For Android devices, the push notification service 730 can integrate with Firebase Cloud Messaging (FCM) to optimize handling of notifications through Firebase Cloud Functions. The push notification service 730 can configure a Cloud Function to act as a trigger for sending notifications, aligning FCM with an event-driven architecture. For iOS devices, the push notification service 730 can integrate with the Apple Push Notification Service (APNs), which requires specific configuration steps. Engineers manage encryption keys, set up Apple Developer accounts, and integrate APNs into the event-driven architecture to establish a secure and efficient communication channel with iOS devices.

[0156] The interaction between the push notification service 730 and the data layer 710 enables data-driven intelligence and real-time user engagement. By relying on the data retrieval and processing module's capabilities in accessing diverse data sources and ERPs, the push notification service ensures that users are kept up to date with the distribution platform.

[0157] Additionally, the push notification service 730 may be operatively connected to one or more AI and / or ML modules, such as the AAML module 315. While the AAML module focuses on extracting valuable insights from collected data and performing advanced analytics, predictive modeling, and anomaly detection, the push notification service may complement such operations by ensuring that these insights are immediately communicated to mobile device users.

[0158] In a non-limiting example, the AAML module can be configured to identify seasonal patterns or forecast future demand based on historical sales data, and the push notification service 730 can trigger real-time notifications to inform relevant users. These notifications can include forecasts related to inventory optimization, ensuring stock availability during busy periods and minimizing overstock costs. The integration of the push notification service 710 ensures that actionable forecasts generated by the AAML module are delivered to the appropriate parties in a timely manner.

[0159] Additionally, the push notification service 730 enhances the user experience by providing real-time updates on customer behavior insights generated by the AAML module. This information can be leveraged for targeted / behavioral marketing and personalized customer experiences. For example, if the AAML module identifies cross-selling or up-selling opportunities and recommends related products to individual customers, the push notification service can deliver these recommendations directly to the user's mobile device, enabling immediate action.

[0160] In addition to communicating insights derived via the AAML module, the push notification service 730 can internally integrate with AAML's analytics capabilities. By analyzing data from various sources, such as social media feeds, customer reviews, market trends, etc., the AAML module can provide intent analysis and trend identification. These insights can be used to tailor the content of notifications communicated by the push notification service 730 to ensure alignment with consumer and market intent and preferences.

[0161] Furthermore, the integration and interoperability features related to the data flows established by the integration protocol, APIs, and data connectors enable push notification services to access relevant data generated and parsed by the AAML module, improving the accuracy and relevance of notifications.

[0162] The push notification service 730 thereby bridges the gap between the RTDM module, the AAML module, and end users of the system 700. This ensures that valuable insights and recommendations generated by the AAML module are communicated to users in real time or near real time, empowering users with real-time information and improving their decision-making performance within the supply chain ecosystem. This integration emphasizes the system's commitment to real-time, data-driven decision-making and provides intelligence to optimize distribution operations. Furthermore, by working in conjunction with the RTDM architecture and robust AAML processing, the push notification service 730 effectively scales to handle large volumes of real-time notifications and insights while incorporating error / retry mechanisms to address temporary delivery issues.

[0163] The Image Recognition and SKU Mapping Engine 740 leverages the mobile device's camera to scan product images. Advanced AI algorithms process these images to identify products and map them to their respective stock keeping units (SKUs). This dynamic SKU creation process simplifies the ordering process, reducing manual data entry and potential errors.

[0164] In one embodiment, the image recognition and SKU mapping engine 740 is directly integrated with the mobile device's camera. This integration allows users to efficiently scan product images without the need for external applications or software. Once a user captures an image, the system begins the processing phase. The processing of these product images involves advanced AI algorithms designed for accurate product recognition. These algorithms analyze different features and attributes of the captured image and break them down into identifiable components. This analysis includes examining color, shape, texture, and any markings or labels present on the product.

[0165] In one non-limiting example, consider a user scanning an image of a blue cylindrical object bearing a particular brand label. First, the algorithm identifies the cylindrical shape and the blue color. Then, it focuses on the label to determine the brand and any other relevant details. This level of detail ensures accurate product identification even when the product image may not be of the highest quality.

[0166] Following analysis, the image recognition and SKU mapping engine 740 maps the identified products to their respective stock keeping units (SKUs), which represent unique identifiers for distinct products and variations. For businesses, SKUs facilitate inventory tracking, order fulfillment, and other logistics tasks.

[0167] Engine 740 employs a dynamic SKU creation process. Rather than relying on an existing SKU, this dynamic approach creates a new SKU the first time a product is identified. This capability has proven particularly beneficial for businesses that continually introduce new products or variants. The dynamic SKU creation process maps every product, whether new or not, to a unique identifier.

[0168] This dynamic process reduces the need for manual data entry. In conventional systems, users may have had to manually enter details about new products and assign SKUs. However, with the image recognition and SKU mapping engine 740, this manual process is largely eliminated. The engine's ability to dynamically generate SKUs not only saves time, but also minimizes potential data entry errors.

[0169] Additionally, the image recognition and SKU mapping engine 740's integration with other enterprise systems streamlines the entire product recognition and ordering process. For example, once a product is identified and mapped to its SKU, the system can immediately update the inventory database. This real-time update ensures that inventory levels remain accurate and supports efficient order fulfillment.

[0170] Additionally, Engine 740's AI algorithm continuously learns and adapts. With each product image processed, the algorithm refines its recognition performance. Over time, this iterative learning process improves the engine's accuracy and reduces the likelihood of false positives. For businesses, this means the system becomes more reliable and efficient as more products are scanned and processed.

[0171] In another embodiment, engine 740 also integrates with external data sources. These sources may provide additional product information, such as product specifications, pricing, and supplier details. Once the engine identifies a product, it can incorporate this additional information, increasing the depth of product details available to the user.

[0172] In some embodiments, the image recognition and SKU mapping engine 740 can be configured as an element of the AAML module, a separate module, or a separate element of the system 700. The image recognition and SKU mapping engine 740 is thereby configured to leverage a user's mobile device to handle item identification, order processing, and other tasks. By leveraging the imaging components of a mobile device and coupling them with advanced AI algorithms (and a dynamic SKU creation process), the engine simplifies operations, reduces errors, and streamlines workflow. With its continuous learning capabilities and integration possibilities, the engine is poised to become an essential tool for modern businesses.

[0173] The offline data cache 750 increases utility in situations where internet connectivity is limited or non-existent. The mobile app incorporates an offline data cache 750 that stores important data locally on the user's device. In some non-limiting examples, information related to order status, product details, and other essential data is cached and synchronized with a backend server once connectivity is restored. This ensures uninterrupted functionality and access to important information even in offline environments.

[0174] In one embodiment, offline data cache 750 serves as a foundational component of a mobile app designed for optimal performance in varying connectivity scenarios. Recognizing the unpredictability of network connectivity, especially in remote or congested areas, offline data cache 750 provides users with uninterrupted access to essential data.

[0175] The core functionality of the offline data cache 750 revolves around storing important data locally on the user's device. By caching data locally, the system ensures that the user can access relevant information even if the device loses internet connectivity. This local storage mechanism eliminates the need for real-time server interaction for every data retrieval, thereby improving app responsiveness and reducing latency.

[0176] In one non-limiting example, a user attempting to access order status while traveling through areas with spotty internet coverage can benefit from cached offline data. Rather than retrieving this data in real time from a backend server, which may be hindered due to connectivity issues, the mobile app pulls the needed data from the offline data cache 750. This ensures that the user gets the information they need without delay.

[0177] Additionally, the offline data cache 750 handles various types of data, including product details, order status, user preferences, etc. When a user accesses certain data, the mobile app caches it, allowing for faster subsequent retrieval. Aggressive caching can optimize the user experience by reducing redundant data transfers.

[0178] In some embodiments, the offline data cache 750 can include a synchronization mechanism. Once the device regains internet connectivity, the system initiates a synchronization algorithm with the backend server. This process ensures that locally stored data remains up-to-date and consistent with the primary data source. Changes made by the user offline, such as new orders or modifications to existing orders, can be updated on the backend server during this synchronization.

[0179] The synchronization process also takes into account potential data conflicts. In situations where the same data undergoes offline changes on both the device and the backend server, the system employs conflict resolution protocols. These protocols determine the most recent or winning change and ensure data consistency across platforms.

[0180] The offline data cache 750 also incorporates security measures that recognize the sensitive nature of locally stored data, and the system can be configured to encrypt cached data, protecting the offline data from unauthorized access and mitigating the risks associated with cached data if the device is compromised.

[0181] This allows the offline data cache 750 to provide uninterrupted access to important data in offline scenarios. By storing important data locally, synchronizing it with backend servers when online, and implementing security measures, the offline data cache 750 improves overall app functionality and user satisfaction.

[0182] The Security and Authentication Layer 760 ensures that the security of user data and transactions is paramount. It employs strong encryption mechanisms to protect sensitive information both at rest and in transit. It enforces authentication protocols to verify user identity and grants access only to authorized users. This layer is essential to maintain data integrity and protect user privacy.

[0183] The integration module 770 integrates the mobile app with the back-end systems of the cloud distribution platform. This integration ensures a continuous flow of real-time data between the app and the platform's servers and facilitates secure and efficient transactions, including ordering, payment processing, and inventory management.

[0184] The device compatibility module 780 is an architecture that enables mobile device and operating system diversity, ensuring that mobile apps are accessible and functional across a wide range of devices, including smartphones and tablets, regardless of their operating systems (iOS, Android, etc.). This flexibility caters to the preferences and devices of a broad user base.

[0185] Offline Data Cache 750: The offline data cache focuses primarily on storing data locally for offline access and relies on RTDM for data synchronization. When the mobile app is online and connected to RTDM, it updates its cached data to reflect any changes that occur in real time. This synchronization ensures that the cached data remains current and accurate.

[0186] Figure 7 illustrates one embodiment of a mobile app architecture within the broader context of a distribution platform. Its functionality, encompassing real-time push notifications, image scanning for SKU recognition and other processes, native mobile capabilities, and offline data caching, collectively contributes to an unparalleled distribution experience that not only provides real-time insight but also ensures uninterrupted usability even in scenarios where internet connectivity is limited or unavailable.

[0187] Figure 8 illustrates a process flow 800 for real-time data integration, analysis, and notification in a mobile app system 700 as detailed in Figure 7. Process flow 800 presents the operations for data assimilation, instant processing, and integration within the RTDM and AAML ecosystem.

[0188] In operation 801, the system initializes the data layer, RTDM, and pre-processing phase. The data layer is designed to store structured and unstructured data using a distributed database strategy such as NoSQL. RTDM continuously pulls data from systems such as ERP, CRM, and other platforms. A distributed data architecture is employed to synchronize data in real time. During pre-processing, signal processing methods including Fourier transform and wavelet transform remove noise. Machine learning techniques such as PCA and t-SNE extract and prioritize data features.

[0189] At operation 802, the data is routed to an Advanced Analytics and Machine Learning (AAML) engine, where deep learning algorithms, for example variants of BERT or GPT, process the text data and extract semantic associations and patterns.

[0190] In operation 803, a decision syntax processes the output from the AAML engine. Algorithms ranging from decision trees to Bayesian networks derive suitable actions based on the analyzed data.

[0191] In operation 804, a push notification service may be activated based on an event-driven architecture (EDA), a publish-subscribe (Pub-Sub) system, or another suitable methodology.

[0192] Using EDA techniques, services can respond to distinct data patterns or states, which are considered events. These events typically come from a real-time data mesh (RTDM), which consistently examines data streams for specific patterns. When RTDM identifies a noteworthy pattern, it registers it as an event and directs it to a push notification service. This service can further collaborate with advanced analytics and machine learning (AAML) techniques to dynamically detect and act on changing data patterns.

[0193] Additionally or alternatively, a pub-sub system can be used by push notification services using protocols such as Message Queuing Telemetry Transport (MQTT), a lightweight messaging protocol designed for efficient communication between devices in scenarios where bandwidth and resources may be limited. It can be used in mobile applications where real-time, low-overhead messaging is essential. Here, message producers, known as publishers, create messages without specifying a specific recipient. These messages, arranged by topic, are handled by a broker. When subscribers express interest in a particular topic, the broker filters and distributes messages based on these preferences. The MQTT protocol's inherent quality of service (QoS) levels ensure consistent delivery. A pub-sub architecture using MQTT has two main roles: publishers, which send messages to a central broker, and subscribers, which receive specific types of messages by subscribing to the broker's topics. MQTT is known for its simplicity and efficiency and offers features such as quality of service (QoS) levels for message reliability, retained messages, message waiting (LWT), and security options such as TLS / SSL for encrypted communication. Alternatives to MQTT include (but are not limited to) AMQP (Advanced Message Queuing Protocol), CoAP (Constrained Application Protocol), WebSocket, and HTTP / HTTPS, to list some alternative protocols with specific features and use cases.

[0194] In operation 804, choosing between EDA Pub-Sub or other methodologies depends on the particular process and potential response times. If push notifications make heavy use of RTDM or AAML to invoke previous operations, an EDA method may be preferable. However, if the system requires efficient communication between many separate entities, a Pub-Sub system may be appropriate. The choice may affect subsequent operations and the overall performance of the system.

[0195] In operation 805, the push notification service works in close coordination with an Advanced Analytics and Machine Learning (AAML) module to improve the system's real-time communication performance. This process involves leveraging data patterns and derived insights to deliver highly customized notifications to end users. We will now delve into the technical details without unnecessary embellishment.

[0196] Act 805 can include delivering the relevant information to the user. The push notification service and the AAML module are integrated to achieve sophisticated insights. The AAML module extracts valuable insights from extensive data sets maintained by the system. These insights are derived through advanced analytics techniques, predictive modeling, and anomaly detection. Additionally, the AAML module excels at recognizing and deciphering intricate data patterns, which are paramount for real-time decision making within the system.

[0197] In the context of operation 805, the primary focus is on leveraging the insights generated by the AAML engine. These insights are used as the basis for creating highly tailored notifications, which are then dispatched to relevant users. The notifications can serve as important alerts and provide timely, relevant information to system users.

[0198] For example, the AAML engine initiates a notification process when it identifies a significant business event. This event can encompass a wide range of scenarios, from sudden market fluctuations to inventory-related anomalies detected within the system's data. The AAML engine's ability to quickly recognize such events enables advanced analytical capabilities.

[0199] A key aspect of process flow 800 is the AAML module's outlook integrated with push notification services. This integration ensures that the system responds in real time to emerging patterns or anomalies and translates data-driven insights into actionable notifications. Furthermore, such notifications can be customized to fit the specific context of the detected event or pattern.

[0200] Notification customization encompasses a variety of parameters, including the type of event, its severity, and the target audience. Push notification services have the ability to categorize and prioritize events, ensuring that important notifications are delivered promptly to the appropriate recipients. This prioritization is essential to prevent information overload and ensure that the most urgent matters receive immediate attention.

[0201] Underlying this behavior is a robust and efficient notification delivery mechanism: push notification services adhere to standardized protocols such as Firebase Cloud Messaging (FCM) for Android and Apple Push Notification Service (APN) for iOS. These protocols are carefully configured and integrated to meet the specific requirements of the platform, ensuring reliable and secure notification delivery.

[0202] For Android devices, integration with Firebase Cloud Messaging (FCM) optimizes notification handling. Push notification services can configure Cloud Functions to act as triggers for sending notifications, aligning FCM with the system's event-driven architecture. This approach improves the efficiency of notification delivery on the Android platform.

[0203] Similarly, for iOS devices, the push notification service integrates with the Apple Push Notification Service (APNs). This integration entails a series of specific configuration steps, including managing encryption keys, setting up an Apple Developer account, and integrating APNs into the system's event-driven architecture. These measures establish a secure and efficient communication channel with iOS devices.

[0204] Operation 805 processes data-driven insights for real-time delivery to users. It leverages the AAML module's ability to identify patterns, anomalies, and important business events and transforms this information into actionable notifications. These notifications are meticulously customized and prioritized, empowering stakeholders with the timely information they need to make informed decisions within the dynamic and competitive landscape of the system. The technical precision of this operation, from data analysis to notification delivery, underscores the system's dedication to optimizing communications and facilitating real-time responsiveness.

[0205] In operation 806, the system embeds adaptive feedback mechanisms to optimize its behavior. In some embodiments, operation 806 includes processing and / or training a reinforcement learning model, such as proximal policy optimization, to allow the system to learn from its actions and fine-tune its responses.

[0206] In operation 807, the system secures the interaction log, decisions, and essential metadata through encryption. Methods such as AES (Advanced Encryption Standard) and elliptic curve cryptography ensure data integrity.

[0207] FIG. 9A is a flow diagram of a method 900A for image capture and SKU mapping within a mobile app system that leverages advanced AI algorithms, real-time data integration, and dynamic SKU creation. The method streamlines the process of identifying and mapping products to their respective stock-keeping units (SKUs), ultimately simplifying the ordering process and reducing manual data entry errors. Method 900A outlines a streamlined and efficient process that leverages the capabilities of a mobile app-based SPoG UI to facilitate image recognition and SKU identification. By integrating real-time data, collaborative decision-making, and role-based access control functionality, the mobile SPoG UI enables users to effectively manage and optimize the distribution process. Based on the disclosure herein, the operations of method 900A may be performed in a different order and / or varied as appropriate for specific implementation requirements. The process may involve a series or sequence of technical operations designed to maximize efficiency and accuracy within the real-time data mesh (RTDM) and advanced analytics and machine learning (AAML) modules.

[0208] At operation 901, the process begins with image capture and processing. A user employs a mobile app and captures product images using their mobile device's camera. These images undergo thorough analysis by advanced AI algorithms. The algorithms scrutinize various visual attributes, including color, shape, texture, labels, and markings on the product. For example, if a user captures an image of a blue cylindrical object with a particular brand label, the algorithm first identifies the cylindrical shape, then proceeds to distinguish the blue color, and then determines the brand and other relevant details. This rigorous analysis ensures accurate product identification, even when image quality is suboptimal.

[0209] Following image analysis in operation 902, an image recognition and SKU mapping engine engages in dynamic SKU creation. Unlike previous systems that rely on predefined SKUs, this dynamic approach generates unique SKUs for each product, including new variations. This eliminates the need for manual data entry, significantly reduces the chance of errors, and streamlines the SKU creation process.

[0210] In operation 903, the generated SKUs are integrated with the system's data layer in real time. This integration ensures that SKU information remains up to date and is easily accessible to various actions within the mobile app. Real-time data synchronization is facilitated by a Real-Time Data Mesh (RTDM) module that enables data exchange and synchronization, making SKU information available to stakeholders for informed decision-making and expediting order fulfillment.

[0211] In operation 904, the image recognition and SKU mapping engine undergoes continuous learning and adaptation. With each product image processed, the engine's AI algorithms refine their performance. This iterative learning process increases the engine's accuracy over time and reduces the likelihood of false positives. The system becomes increasingly reliable and efficient as it processes more products.

[0212] Additionally, operation 905 can include integration with external data sources. In one configuration, the engine collaborates with external data sources to augment product information. These sources may provide supplemental details such as product specifications, pricing, supplier information, etc. Once a product is identified, the engine retrieves this additional data, enriching the breadth of product details available to the user.

[0213] Operation 906 configures an image recognition and SKU mapping engine to be incorporated into an Advanced Analytics and Machine Learning (AAML) module or as a separate system component. This adaptation empowers the engine to manage item identification, order fulfillment, and related tasks using a user's mobile device. By leveraging the mobile device's imaging components and advanced AI algorithms, the engine simplifies operations, reduces errors, streamlines workflow, and ultimately evolves into an essential tool for the modern enterprise.

[0214] Finally, in operation 907, the engine's integration with other enterprise systems facilitates real-time inventory updates. Once a product is identified and mapped to its SKU, the system quickly updates the inventory database. This real-time update mechanism ensures accurate inventory levels and contributes to efficient order fulfillment within a proactive distribution ecosystem.

[0215] The image capture and SKU mapping method 900A in a mobile app system provides real-time data integration, dynamic SKU creation, continuous learning, and integration with external data sources and enterprise systems. The method relies on robust technical capabilities to increase the efficiency and accuracy of product recognition and SKU management.

[0216] FIG. 9B is a flow diagram of a method 900B for an enhanced product lookup process within a mobile app system. Method 900B allows users to interact with the system by utilizing advanced search algorithms, RTDM operations, and real-time personalization to enhance product discovery and exploration. By focusing on user-centered design and efficient information retrieval, the process aims to deliver a highly tailored and responsive user experience. This specification guides the reader through each step of the operation and reveals the technical details that enable this enhanced product lookup functionality.

[0217] 9B shows a flow diagram of a method 900B for an enhanced product lookup process using image recognition within a mobile app system. Method 900B enables a user to search for products by capturing an image using a mobile device's camera and leveraging advanced image recognition algorithms, RTDM operations, and real-time personalization.

[0218] Image recognition-based product lookup in mobile app systems provides users with the ability to quickly get product details by snapping a photo of the item. This feature improves user satisfaction and fosters efficient consumer decision-making.

[0219] The process begins with image capture and upload at operation 911. A user captures a photo of an item using the camera on their mobile device, and the image is uploaded to the mobile app system for processing.

[0220] In operation 912, an image recognition engine, e.g., engine 740, processes the uploaded image. Engine 740 uses advanced AI algorithms to analyze image features and attributes, such as color, shape, texture, labels, or marks. Based on this analysis, the engine matches the product to its respective stock keeping unit (SKU) by comparing the image with a comprehensive database of product images stored in RTDM. RTDM, embedded in data layer 710, facilitates real-time data exchange and synchronization, ensuring up-to-date and accurate information.

[0221] Operation 912 involves an image recognition engine acting on the uploaded image. Engine 740 has artificial intelligence capabilities and examines details present in the image. Engine 740's functionality includes using AI algorithms to identify various features and attributes of the image. These include elements such as color patterns, shape outlines, texture properties, and product labels or marks within the image. After decoding the image's characteristics, engine 740 identifies the product. It matches the analyzed product attributes with a corresponding stock-keeping unit, or SKU. The process involves comparing the uploaded image to a database of product images. This product image database is maintained within RTDM, i.e., via data layer 710. The RTDM system facilitates data exchange and synchronization, ensuring up-to-date and accurate information.

[0222] RTDM ensures that data exchange occurs in real time. It also oversees data synchronization, ensuring that the information in the system is up-to-date and accurate. When a user uploads an image of a product with recently updated details to the product database, RTDM ensures that engine 740 has access to current information during the recognition process. This minimizes potential discrepancies.

[0223] During operation 913, a query can be made through RTDM. The identified product details from the image recognition process serve as parameters for querying RTDM. RTDM, integrated with data layer 710, has purpose-built data stores optimized for specific data types, such as product data including SKU details, descriptions, and inventory levels. A search finds items in RTDM that match or are similar to the recognized product in the image. The RTDM module also incorporates data from the system of record layer, including data from various enterprise systems such as ERP. The previously identified product details from the image recognition procedure serve as input criteria for this search in RTDM.

[0224] The RTDM embodied via the data layer 710 contains specialized data stores. These data stores are specifically designed for distinct data types. For example, there is a data store that primarily caters to product-related information. Within this data store are details such as SKU identification, product descriptions, and available stock levels.

[0225] Once a query is initiated, RTDM fetches entries that match or closely resemble the product details identified from the image. It is essential to note that the accuracy of the results depends on the specificity of the recognized product features.

[0226] Moreover, RTDM is not limited to its own internal databases. It also interfaces with the system of record layer, which aggregates data from a wide range of enterprise systems. Notable among these systems are enterprise resource planning, or ERP, systems. By accessing data from these systems, RTDM ensures comprehensive and rich product details, improving the accuracy of the recognition and matching process.

[0227] At operation 914, the retrieved results undergo a filtering and ranking process. Filtering criteria may include price, brand, or category. Ranking organizes products using an algorithm that takes into account user preferences and past searches. Interaction between the Data Layer / RTDM module enables retrieval of ranked and filtered product lists that are in line with the most current information available.

[0228] At operation 915, the mobile app displays the filtered results to the user. The interface displays the identified products along with essential details such as name, image, price, and description.

[0229] At operation 916, the user can access in-depth product information. After selecting a product from the displayed results, the app provides further details, including specifications, reviews, related items, and availability.

[0230] In operation 917, user feedback and personalization occurs. The mobile app collects user feedback regarding the accuracy and relevance of identified products. This data helps refine the image recognition algorithm. Additionally, the system employs machine learning to tailor future product suggestions based on user feedback and search history.

[0231] 10 illustrates a method 1000 for managing an offline data cache for a mobile app, e.g., offline data cache 750 of a mobile app architecture. As described above, offline data cache 750 is designed to optimize performance and ensure uninterrupted access to critical data in scenarios where internet connectivity is limited or nonexistent. This specification provides process flows for performing functions related to the offline data cache.

[0232] In operation 1001, when the mobile app detects that there is limited or no internet connectivity, the offline data cache 750 is triggered to ensure continued access to important data.

[0233] Operation 1002 involves caching essential data types, including order status, product details, and user preferences, on the user's device. This caching process utilizes a local storage mechanism, such as an on-device database, to efficiently store the data. Operation 1002 can include organizing and caching important data categories, such as order status, product details, and user preferences, on the user's device's local storage. An efficient data storage mechanism, such as an SQLite database, is utilized to effectively manage and store the data locally.

[0234] When a user accesses specific data, the system retrieves the required information from the locally cached offline data cache in operation 1003. This process involves querying the on-device database and retrieving data without making a real-time server request, thereby improving app responsiveness and reducing latency. During this phase, the mobile app performs data retrieval by accessing the specific information from the locally cached offline data cache. Structured SQL queries are employed to accurately and quickly retrieve data directly from the on-device database. This approach eliminates the need for a real-time server request, thereby optimizing app responsiveness and reducing latency.

[0235] Operation 1004 encompasses a synchronization mechanism. When the device regains Internet connectivity, the offline data cache initiates synchronization with the backend server to update the locally stored data. This synchronization process involves a data differencing algorithm to identify changes made offline and send these changes to the server. Data synchronization also involves a conflict detection mechanism to handle cases where multiple changes occur simultaneously and ensure data consistency. This operation begins when the device re-establishes Internet connectivity. The offline data cache works with the backend server to synchronize and update the locally cached data. Advanced data differencing algorithms, such as delta encoding or binary differencing, are employed to identify and send only relevant data changes to the server. This streamlined synchronization process minimizes data transfer overhead.

[0236] In the case of a data conflict, operation 1005 employs a conflict resolution protocol. These protocols utilize timestamp-based or version-based strategies to determine the most recent or winning change and ensure data consistency across platforms. Conflict resolution may involve merging conflicting changes or possibly prompting user intervention. In the case of concurrent data modifications, this operation effectively manages data conflicts. An appropriate conflict resolution protocol is employed, which relies on timestamps or versioning mechanisms to determine the most recent or winning data change. Algorithms such as Last-Write-Wins or Three-Way Merge may be applied to ensure data consistency between the device and the backend.

[0237] Operation 1006 highlights security measures implemented within the offline data cache. The cached data may be configured to be encrypted using an industry-standard encryption algorithm, such as AES (Advanced Encryption Standard). This encryption protects the offline data from unauthorized access and potential risks associated with compromised devices. Access to the encrypted data is controlled using secure key management practices. Operation 1006 may include securing the locally cached data. A strong encryption algorithm, for example, AES-256, is employed to encrypt the cached data. A comprehensive key management approach encompassing key generation, secure storage, and access control is implemented to protect the offline data from unauthorized access. The encryption and decryption processes are optimized to take advantage of hardware acceleration for improved performance.

[0238] Thus, method 1000 includes a series of operations for providing uninterrupted access to essential data in offline scenarios. These operations include efficient data caching, data retrieval from local storage, advanced data synchronization, conflict resolution mechanisms, and strong data encryption to enhance app functionality and ensure data security in challenging connectivity situations.

[0239] 11 is a block diagram of example components of a device 1100. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein, as well as combinations and subcombinations thereof. The computer system 1100 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1104. The processor 1104 may be connected to a communication infrastructure, i.e., bus 1106.

[0240] 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 .

[0241] One or more of the processors 1104 may be a graphics processing unit (GPU). In one embodiment, a GPU may be a processor that is a specialized electronic circuit designed to process mathematically intensive applications. A GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common in computer graphics applications, images, video, etc.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery paradigm, including, but not limited to, remote or distributed cloud computing solutions, local or on-premise software ("on-premise" cloud-based solutions), "as a service" models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS)), and / or hybrid models including combinations of the foregoing examples or other service or delivery paradigms.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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 supply chain ecosystem. Vendors can enter basic information such as company details, contact information, product catalog, etc.

[0253] 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.

[0254] 12C shows a vendor onboarding call scheduler that facilitates scheduling a call or meeting between a vendor and a platform partner or representative responsible for guiding the vendor through the onboarding process. The vendor can select a preferred time slot or request a call, ensuring effective communication and assistance throughout the onboarding journey.

[0255] 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.

[0256] 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 completed tasks indicating that the vendor is now officially onboarded into the supply chain ecosystem.

[0257] Figure 12F shows a partner dashboard that provides partners or stakeholders with an aggregated view of relevant information and metrics regarding their partnership with the supply chain ecosystem, providing an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0258] 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 their cart before proceeding to the checkout process.

[0259] Figure 12H shows a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and term. Customers can review and modify subscription details before finalizing their selection.

[0260] 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, quantities, pricing, and any discounts or promotions applied, allowing the customer to review the order before confirming the purchase.

[0261] 12J shows a vendor SKU generation screen for generating unique stock keeping unit (SKU) codes for vendor products. Fields or options may be included that allow vendors to specify product details, attributes, and pricing, and the system will auto-generate the corresponding SKU code.

[0262] Figures 12K and 12L show a dashboard Order Summary for displaying summary information about orders placed within a supply chain ecosystem. They 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 enables stakeholders to efficiently track and manage orders.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] Figure 12P shows Customer Subscription History, which presents a historical record of a customer's subscription activity. It displays a list of past subscriptions, including subscription plan, duration, and status. Customers can review their subscription history, track previous payments, and view details of past subscriptions.

[0267] 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.

[0268] Figures 13A-13F show various screens and functionality of the mobile app-based SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription modifications. A detailed description of each figure is provided below.

[0269] Figure 13A presents a home screen displaying top product picks, thoughtfully curated using the described RTDM and AAML architecture and tailored to a customer's preferences and browsing history. This interface serves as a gateway to an optimized shopping experience, presenting a product selection specifically tailored to the customer's interests. Leveraging advanced recommendation algorithms, the system identifies products likely to resonate with the customer. These top picks encompass a diverse range of categories, from electronics and gadgets to clothing and accessories. Each product can be accompanied by a brief description, high-quality images, and pricing details, allowing customers to effortlessly navigate and make informed choices. The home screen's intuitive layout ensures that customers can browse the displayed products and begin their personalized shopping experience. With a user-centered design and responsive interface, this screen ensures consistent accessibility and user satisfaction across a variety of devices and screen sizes.

[0270] FIG. 13B shows a customer search results (e.g., "Cable") screen within the mobile app interface, designed to provide an efficient and targeted search experience for users seeking, for example, a specific cable product. Users can initiate a search using cable-related keywords, product codes, or specifications, and the system responds with a clear, organized list of relevant search results. Each search result entry provides essential details, including product name, image, specifications, and pricing information. The interface is meticulously designed for fast and effortless navigation, allowing users to quickly identify cable products that meet their requirements. Whether users are searching for HDMI cables, Ethernet cables, or other types of cables, this screen streamlines the search process and ensures users can easily find the right cable. The interface's responsive design ensures an efficient experience across a variety of devices and screen sizes, promoting user satisfaction and facilitating efficient product discovery.

[0271] Figure 13C shows the Customer Favorites screen, designed to provide quick access to customers' favorite or saved items. This screen allows users to conveniently store and revisit products of interest. The Favorites screen provides a comprehensive view of customer-selected items, ranging from fashion ensembles and tech gadgets to household essentials. Each product is accompanied by detailed information, including descriptions, images, and pricing, allowing customers to effortlessly revisit their choices. The intuitive interface allows customers to seamlessly navigate and manage favorite items and includes options to add or remove products as their tastes evolve. The Favorites screen enhances the shopping experience by providing a curated collection of customers' most desired items, streamlining the purchasing process and ensuring a personalized approach to shopping. With a user-friendly design and responsive layout, this screen delivers a consistent and enjoyable user experience across a variety of devices and screen sizes.

[0272] Figure 13D shows the mobile app's customer order tracking screen, which allows customers to track order status and progress within the supply chain. It displays real-time updates on order fulfillment, including processing, packing, and shipping. Customers can monitor order activity and estimate delivery times.

[0273] Figure 13E shows the customer quote list screen within the mobile app interface. This screen serves as a comprehensive overview of all quoted orders for the customer. Users can efficiently navigate through the quote list, and each entry provides a summary of the quoted order, including product description, quantity, pricing breakdown, and can include an estimated delivery timeline. This screen employs a clear and intuitive design that aligns with the user-centered approach that is the core tenet of the mobile app UI. Customers can easily review the quote list, promoting informed purchasing decisions. Responsive elements are incorporated into the interface, allowing customers to access further details or take action on a specific quote with a simple tap or gesture.

[0274] FIG. 13F shows the Customer Quote Details screen, providing users with a deeper overview of the selected quote's particulars. This screen presents a detailed breakdown of the quoted order, including optional information such as a comprehensive product description, exact quantities, itemized pricing, and delivery schedule. The interface maintains a user-friendly and responsive design, allowing customers to fully review and verify the accuracy of the quote information. Within the Customer Quote Details screen, customers have the ability to easily accept, modify, or reject the quoted order, streamlining their interaction with the system. Additionally, the screen is designed for real-time updates, ensuring that quote changes or revisions are reflected quickly, fostering transparency and efficient communication between the customer and the system.

[0275] The UI screens shown are not limiting. In some embodiments, the UI screens of Figures 12A-12Q and 13A-13F collectively represent the diverse functionality and features provided by the SPoG UI, providing stakeholders with a comprehensive, user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the supply chain ecosystem.

[0276] One or more computer systems may be configured to perform particular operations or actions by having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes the system to perform the action. One or more computer programs may be configured to perform particular operations or actions by containing instructions that, when executed by a data processing device, cause the device to perform the action.

[0277] In one general aspect, a computer-implemented method may include incorporating multiple communication channels (i.e., touchpoints) among a population of users into a unified interactive interface within a computer system, the unified interactive interface being referred to herein as an SPoG UI, where the SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality for the population of users, the SPoG UI being positioned to facilitate operations across a supply chain ecosystem. The computer-implemented method may further include utilizing the SPoG UI to manage an end-to-end lifecycle of user interactions. The method may further include collecting data from the user interactions within the SPoG UI. Additionally, the method may include analyzing the collected data to generate one or more prospects for business growth. The method may further include running one or more artificial intelligence and / or machine learning algorithms to improve business operations based on the analyzed data. The method may further include incorporating periodic updates and improvements to the SPoG UI based on the analyzed data. The method may further include a group having distributors, resellers, customers, end customers, vendors, and suppliers, where the population of users may include users selected from two or more diverse groups. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0278] Implementations may include one or more of the following features: integrating may include establishing communication links with multiple existing business platforms; the aggregated interaction points may include one or more websites, customer relationship management systems, vendor platforms, and supply chain management systems; managing the end-to-end lifecycle may include one or more of initial contact, service fulfillment, and follow-up interactions; collecting data may include monitoring and / or logging user activity within the SPoG UI; analyzing the collected data using advanced statistical algorithms; artificial intelligence and machine learning algorithms include predictive analytics to identify market trends; and artificial intelligence and machine learning algorithms include a recommender system to personalize user interactions. Improvements may be based on analytics received through the SPoG UI and / or analyzed user feedback. Implementations of the described techniques may include hardware, a method or process, or a computer-tangible medium.

[0279] In one general aspect, the system may include a communication integration module configured to integrate multiple communication channels (i.e., touchpoints). The system may further include an aggregation module configured to combine the integrated communication channels into a unified interactive interface, referred to herein as an SPoG UI, where the SPoG is a central interface component configured to aggregate user interactions, data, and / or functionality of a population of users, and the SPoG UI is arranged to facilitate operations across a supply chain ecosystem. The system may further include a lifecycle management module configured to manage the end-to-end lifecycle of user interactions within the SPoG. The system may additionally or alternatively include a data collection module configured to automatically collect data from user interactions within the SPoG. The system may further include a data analysis module configured to generate one or more forecasts based on the collected data. The system may further include an artificial intelligence module configured to perform one or more AI and / or ML algorithms based on the analyzed data. The system may further include a group having distributors, resellers, customers, end customers, vendors, and suppliers, where the user population may include users selected from two or more diverse groups. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0280] The system and method for automating SKU management may include a user interface configured to receive catalog files in two or more diverse formats from a user; a catalog transformation module configured to convert the catalog files into a standard format and predict classifications and attribute mappings for at least one catalog item, the catalog transformation module utilizing iterative learning to suggest classifications and attribute mappings; a real-time data mesh (RTDM) module for ensuring real-time data exchange and synchronization across system components, the RTDM module facilitating real-time interaction between users and the system; a master data governance (MDG) module for validating the converted catalog and communicating errors back to the vendor; and a global data repository (GDR) for storing the validated catalog and maintaining data integrity of the stored catalog.

[0281] The system and method may include a search platform for indexing and searching the stored catalog. The user interface is a single pane of glass user interface (SPoG UI). A dynamic SKU creation module for generating SKUs for catalog items in an ERP system, where the catalog items represent one or more non-transactional products, and generating the SKUs allows the one or more products to be added to a customer's cart. The GDR is integrated with the RTDM module to support real-time data synchronization across systems. The MDG module generates a display indicating one or more errors that occurred in the catalog conversion process associated with one or more converted catalogs. A global pricing engine (GPE) for determining real-time pricing for catalog items. The GPE determines product prices based on one or more of vendor price files, market trends, and historical pricing data. Implementations may include one or more of the following features: interactive elements include links to various business platforms; user interactions include actions that may include one or more of clicks, hovers, and input data; collected data is analyzed using advanced statistical algorithms; personalized content is generated based on recommendation algorithms. The method may include updating the SPoG UI based on user feedback and data analysis results. Implementations of the described techniques may include hardware, a method or process, or a computer tangible medium.

[0282] 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.

[0283] 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.

[0284] 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.

[0285] 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 real-time data integration, analysis, and notification in a mobile app system, comprising: Providing a data layer and a pre-processing phase, wherein the data layer is designed to store structured and unstructured data utilizing a distributed database strategy, the RTDM continuously pulls data from systems including ERP and CRM, and the pre-processing includes signal processing methods to remove noise and machine learning techniques to extract and prioritize data features; communicating information with an Advanced Analytics and Machine Learning (AAML) engine; receiving an output from the AAML engine using a decision syntax; Operate a push notification service based on an Event Driven Architecture (EDA), a Publish-Subscribe (Pub-Sub) system, or suitable methodology, where the EDA method responds to distinct data patterns as events, and the Pub-Sub system operates using a protocol such as MQTT for efficient communication; and operating the push notification service in conjunction with the AAML module to deliver customized notifications to end users, leveraging insights generated by the AAML engine.

2. 2. The method of claim 1, wherein the pre-processing phase employs signal processing methods including Fourier transform and wavelet transform to remove noise from the data and ensure data quality for subsequent analysis within the system.

3. 10. The method of claim 1, wherein the AAML engine utilizes deep learning algorithms to process text data and extract semantic associations and patterns for improved analysis.

4. 10. The method of claim 1, wherein the AAML engine employs one or more algorithms, including decision trees and Bayesian networks, to derive suitable actions based on the analyzed data, enabling data-driven decision making within front-end systems.

5. 10. The method of claim 1, wherein the push notification service uses AAML processes to dynamically detect and react to changes in data patterns and deliver real-time notifications to one or more users.

6. 10. The method of claim 1, wherein the push notification service delivers customized notifications to end users based on one or more insights generated by the AAML engine, categorizes and prioritizes events based on their significance and target audience, and ensures efficient communication within the system.

7. The method of claim 1 , further comprising performing an adaptive feedback process to optimize system operation, including processing and training a reinforcement learning model such as proximal policy optimization.

8. 1. A mobile application system, comprising: a user interface (UI) layer configured for intuitive user interaction on user devices to provide a consistent user experience across various devices and platforms; a data layer of a real-time data mesh operatively connected to the one or more headless engines, the data layer comprising a global data lake comprising one or more purpose-built data stores (PDS) that enable real-time analytics based on real-time data in the data mesh, one or more computers configured to capture and process the changed data using a change data capture mechanism, the global data lake configured to transform and harmonize the captured data into a standardized format compatible with analytics and integration processes to ensure data consistency and compatibility across the data mesh; a push notification service operative to deliver real-time notifications based on events in the distribution platform in one or more of an event-driven architecture and a publish-subscribe system; an image recognition and SKU mapping engine that utilizes advanced AI algorithms to scan product images and map them to their respective SKUs to improve the distribution process; and an offline data cache module for storing important data locally on a user device to ensure uninterrupted functionality and synchronizing with a backend server when connectivity is restored.

9. 9. The mobile application system of claim 8, wherein the UI layer promotes efficient user engagement by optimizing screen real estate, employing responsive design elements, and prioritizing the user's perspective, including placement of menus, buttons, and navigation bars, ensuring a consistent, user-friendly experience across various devices and platforms.

10. 10. The mobile application system of claim 8, wherein the data layer includes a global data lake comprising one or more purpose-built data stores (PDS) that enable real-time analytics based on real-time data in the data mesh, wherein one or more computers are configured to capture and process the changed data using a change data capture mechanism, and wherein the global data lake is configured to transform and harmonize the captured data into a standardized format compatible with analytics and integration processes, and ensure data consistency and compatibility across the data mesh, including retrieving data from various enterprise systems such as ERP.

11. 10. The mobile application system of claim 8, wherein the push notification service operates an event-driven architecture and utilizes a standardized push notification protocol for delivering real-time notifications based on events in the distribution platform to communicate information to users regarding events in the distribution platform.

12. 10. The mobile application system of claim 8, wherein the image recognition and SKU mapping engine directly integrates with a mobile device's camera and utilizes advanced AI algorithms to analyze product images, including color, shape, texture, and labels, and accurately map them to their respective stock keeping units (SKUs), simplifying the distribution process and reducing manual data entry.

13. 10. The mobile application system of claim 8, wherein the offline data cache module stores important data including order status, product details, and user preferences locally on the user device to ensure uninterrupted functionality, synchronizes with a backend server when connectivity is restored, optimizes the user experience, and maintains data consistency including data encryption to protect sensitive information both at rest and in transit.

14. 10. The mobile application system of claim 8, further comprising a security and authentication layer that employs strong encryption mechanisms to protect sensitive information both at rest and in transit, strengthens authentication protocols to verify user identity, and includes data lineage and audit trail mechanisms to ensure data integrity and user privacy.

15. 10. The mobile application system of claim 8, further comprising a device compatibility module that ensures the mobile application is accessible and functional across a variety of devices, including smartphones and tablets, regardless of their operating systems, leverages the mobile device imaging components and combines them with advanced AI algorithms for accurate product recognition, and facilitates efficient order processing and inventory management.

16. A computer-readable medium that, when executed by a processor, providing a data layer and a pre-processing phase, wherein the data layer is designed to store structured and unstructured data utilizing a distributed database strategy, the RTDM continuously pulls data from systems including ERP and CRM, and the pre-processing includes signal processing methods to remove noise and machine learning techniques to extract and prioritize data features; communicating information to an Advanced Analytics and Machine Learning (AAML) engine; receiving output from the AAML engine using a decision syntax; activating a push notification service based on an Event Driven Architecture (EDA), a Publish-Subscribe (Pub-Sub) system, or suitable methodology, where the EDA method responds to distinct data patterns as events, and the Pub-Sub system uses a protocol such as MQTT for efficient communication; and operating the push notification service with the AAML module to deliver customized notifications to end users and leverage insights generated by the AAML engine.

17. 20. The computer-readable medium of claim 16, further comprising instructions for performing change data capture using one or more trigger-based machine learning and / or polling-based CDC algorithms.

18. 17. The computer-readable medium of claim 16, wherein the data layer comprises a purpose-built data store (PDS) optimized for efficient retrieval and storage of specific types of data.

19. 17. The computer-readable medium of claim 16, wherein the AAML engine utilizes deep learning algorithms to process text data and extract semantic associations and patterns for improved analysis.

20. 17. The computer-readable medium of claim 16, wherein the push notification service uses AAML processes to dynamically detect and react to changes in data patterns and deliver real-time notifications to one or more users.

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