Single glass pane mobile application including ERP agnostic real-time data grid and change data capture

Abstract

Systems and methods for real-time data integration, analysis, and notification within a mobile application system are provided. Embodiments include initializing a data layer and a pre-processing phase to store structured and unstructured data with a distributed database policy. A real-time data grid (RTDM) continuously extracts data from various platforms using signal processing methods and machine learning techniques for noise removal and data feature extraction. An advanced analysis and machine learning (AAML) engine processes the data, while a decision construct derives appropriate actions. A push notification service is activated based on an event-driven architecture (EDA) or a publish-subscribe (publish-subscribe) system to deliver customized notifications. The technical precision ensures that relevant information reaches the end user in time. The system optimizes operation through a self-adaptive feedback mechanism and protects data through encryption.

Description

[0001] Cross - Reference to Related Applications

[0002] This application is a Continuation - in - Part (CIP) of the following U.S. patent applications: U.S. Patent Application No. 18 / 341,714, filed on June 26, 2023; U.S. Patent Application No. 18 / 349,836, filed on July 10, 2023; U.S. Provisional Application No. 63 / 513,073, filed on July 11, 2023; U.S. Provisional Application No. 63 / 513,078, filed on July 11, 2023; U.S. Provisional Application No. 63 / 515,075, filed on July 21, 2023; and U.S. Provisional Application No. 63 / 515,076, filed on July 21, 2023. Each of these applications is hereby incorporated by reference in its entirety. Technical Field

[0003] The present invention relates to aspects of a real - time data grid method and system that includes distribution, supply chain management, and related functions. Background Art

[0004] The traditional global distribution industry faces many challenges, including distribution management, supply chain management, inventory control, SKU management, compliance, and evolving consumer expectations. Traditionally, distribution and supply chain management (not the core competency of many distributors) has been inefficient. Inventory control has long been an important issue, and market volatility requires flexible distribution and supply chain models. SKU management and localization add layers of complexity due to different data from various OEMs and requirements from different jurisdictions. Complying with international regulations also requires additional vigilance and paperwork. Finally, traditional customer interaction methods are rapidly becoming obsolete with the shift towards ecosystem commerce.

[0005] Enterprise Resource Planning (ERP) systems have been used as the backbone for managing business processes, including distribution and supply chain. 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. Although ERP is comprehensive, they pose several challenges in today's complex distribution and supply chain environment. One of the major challenges is data fragmentation. Data silos across different departments or even separate ERP systems make it difficult to achieve real - time visibility. Users lack a comprehensive understanding of key distribution and supply chain metrics, which adversely affects the decision - making process.

[0006] In addition, ERP systems typically do not provide effective data integration capabilities. Traditional ERP systems are not designed to integrate effectively with external systems or even between different modules within the same ERP suite. This design results in a cumbersome and error-prone manual process for transferring data between systems and affects the information flow across the entire supply chain. Data inconsistencies occur when information exists in different formats across systems, which hinders accurate data analysis and leads to ill-informed decision-making.

[0007] Another challenge lies in data inconsistencies. When data exists in different formats or units across departments or ERPs, standardizing this data for meaningful analysis becomes a laborious process. Enterprises often resort to time-consuming manual processes for data conversion and validation, which further delays decision-making. Additionally, traditional ERP systems typically lack the ability to effectively handle large volumes of data. These systems struggle to provide timely insights for operational improvement, especially for enterprises dealing with complex and extensive distribution and supply chain networks.

[0008] Data security is another concern, especially considering the sensitive nature of supply chain data, which includes customer details, pricing, and contracts. Ensuring compliance with global regulations regarding data security and governance adds an additional layer of complexity. Traditional ERP systems often lack robust security features that are flexible enough to adapt to evolving scenarios of cybersecurity threats and compliance requirements.

[0009] Finally, consumers' expectations for faster service and real-time information have added further pressure on traditional systems. The conventional request-based user interfaces in distribution platforms are challenged by delayed access to key distribution and supply chain metrics. These request-based systems may suffer from data staleness as data is only updated when requested, resulting in outdated information that hinders informed decision-making. Moreover, requesting data from multiple sources in conventional systems requires significant resources and can be time-consuming, making them resource-intensive. They are inherently passive, relying on user-initiated requests for data, which limits proactivity. Integrating data from various sources in these systems can be complex and cumbersome, leading to complex integration challenges. Additionally, request-based systems may face scalability challenges when dealing with a large number of data requests. Therefore, traditional request-based distribution and supply chain platforms typically impede efficient distribution and distribution management. Summary of the Invention

[0010] The global distribution industry is at a critical juncture, struggling to address a series of challenges spanning multiple domains. These historical and emerging obstacles require the development of innovative and effective solutions to steer the industry towards growth and efficiency. Among these numerous obstacles, the most significant ones lie in the areas of distribution management, supply chain management, inventory and compliance issues, SKU (Stock Keeping Unit) management, the shift towards direct-to-consumer models, and the rapidly evolving consumer expectations and behaviors.

[0011] The first key challenge pertains to the management of the distribution process, which is a central part of any distributor's operations. Paradoxically, however, it is often not within the core competencies of the distributor. This gap creates inefficiencies in the system and complicates the difficulty of managing disruptions, which directly impacts the distributor's ability to deliver products and services efficiently and on time. In addition to these challenges, market trends are favoring more direct-to-consumer models. Traditional distribution methods involving many intermediaries are being gradually replaced. This changing market dynamic requires a significant reevaluation and repositioning of existing business models and strategies to ensure alignment with this new market reality.

[0012] A quantifiable issue in the distribution realm is inventory management. Given the volatile nature of market demand and trends, companies must ensure that they maintain flexible distribution and supply chains without having to hold positions in inventory. This makes the task of promising and delivering goods to customers inherently more complex and challenging. Additionally, the sheer necessity of numerous compliance regulations for transporting goods and services across international borders adds an additional layer of complexity to the distribution process. This not only makes the distribution process more complex and challenging but also imposes an additional layer of vigilance and paperwork to remain compliant.

[0013] Complicating these challenges further is the need to address issues surrounding product localization, different distribution authorities, and managing global SKUs. The process of coordinating data from different original equipment manufacturers (OEMs), each with its unique systems and processes, adds complexity. Moreover, addressing the localization requirements to comply with the laws and regulations of different jurisdictions increases inefficiencies and potential errors.

[0014] Finally, the process should be made more efficient and streamlined to ensure the sustainability of the distribution model in an evolving market environment. This involves shifting the focus of the distribution platform from supply chain management to encompassing subscription management, customer visibility, and other key distribution-oriented functions. The landscape of consumer behavior and expectations is changing rapidly. The shift towards ecosystem commerce requires the creation of user-friendly, efficient, and configurable platforms for purchasing technology. Traditional methods of customer interaction are quickly falling out of favor, compelling companies to evolve and cater to these new customer expectations.

[0015] Despite these challenges, the distribution model has several advantages over the direct-to-consumer model. First, it allows manufacturers to focus on their core competencies, leaving the complexity of logistics and distribution to specialized entities. Second, distribution networks typically have a wide reach, allowing products to be accessible to customers in remote areas that may be infeasible for manufacturers to directly cover. Third, distributors often provide value-added services that enhance the overall customer experience, such as after-sales support, installation, and training.

[0016] However, in order to realize these benefits and to keep the distribution model relevant and effective, it must evolve and adapt to emerging challenges. The process should be made more efficient and streamlined to ensure the sustainability of the distribution model in the evolving market landscape. The systems and methods described in this article address these challenges. Additionally, the systems described in this article can be configured to cover features such as subscription management and other customer-centric areas that traditional distribution platforms have not effectively managed.

[0017] Single-pane glass

[0018] Single-pane glass (SPoG) can provide a comprehensive solution designed to address these multifaceted challenges. It can be configured to provide an overall, user-friendly, and efficient platform for streamlining the distribution process.

[0019] According to some embodiments, SPoG can be configured to address supply chain and distribution management by enhancing visibility and control over the supply chain process. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and the status of goods, ensuring that the processes of supply chain and distribution management are handled efficiently.

[0020] According to some embodiments, SPoG can integrate multiple touchpoints into a single platform to emulate the direct consumer channel into the distribution platform. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing the complexity of the supply chain and enhancing the overall customer experience.

[0021] SPoG provides an innovative solution for improved inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends, guiding companies to manage their inventory more effectively and mitigate the risks of out-of-stock or overstock situations.

[0022] According to some embodiments, SPoG can include a global compliance database. Through real-time updates, this database enables distributors to stay in sync with the latest international laws and regulations. This feature significantly reduces the burden of manual tracking, ensuring smooth and compliant cross-border transactions.

[0023] According to some embodiments, to streamline SKU management and product localization, SPoG integrates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the likelihood of errors. Additionally, it provides the ability to efficiently manage and distribute localized SKUs, thus aligning with specific market needs and requirements.

[0024] According to some embodiments, SPoG is a highly configurable and user-friendly platform. Its intuitive interface allows users to easily access and purchase technologies, thus aligning with the expectations of the new generation of technology buyers.

[0025] Furthermore, the advanced analytics capabilities of SPoG provide extremely useful insights that can drive strategies and decisions. It can track and analyze trends in real time, allowing companies to stay ahead of the curve and adapt to changing market conditions.

[0026] The flexibility and scalability of SPoG make it a future-proof solution. It can adapt to changing business needs, allowing companies to expand or contract their operations as needed without significant infrastructure changes.

[0027] SPoG's innovative approach to solving challenges in the distribution industry makes it an extremely useful tool. By enhancing supply chain visibility, streamlining inventory management, ensuring compliance, simplifying SKU management, and providing an excellent customer experience, it offers a comprehensive solution to the complex problems that have long plagued the distribution sector. Through its implementation, distributors can expect increased efficiency, reduced errors, and improved customer satisfaction, thus achieving sustainable growth in the evolving global market.

[0028] Real-Time Data Mesh (RTDM)

[0029] According to some embodiments, the platform can include the implementation of a Real-Time Data Mesh (RTDM). RTDS provides an innovative solution to address these challenges. RTDM (Distributed Data Architecture) enables real-time data availability across multiple sources and touchpoints. This feature enhances the visibility of the supply chain, allows for efficient management, and enables distributors to handle disruptions more effectively.

[0030] The predictive analytics capabilities of RTDM provide solutions for efficient inventory control. By providing insights into demand trends, it helps companies manage inventory, thus reducing the risk of overstocking or stockouts.

[0031] The globally compliant database of RTDM with real-time updates ensures that distributors stay abreast of current international regulations. This significantly reduces the manual tracking burden, enabling cross-border transactions.

[0032] RTDM also simplifies SKU management and localization by integrating data from various OEMs, ensuring data consistency and reducing the likelihood of errors. Its ability to manage and distribute localized SKUs efficiently meets specific market requirements.

[0033] RTDM enhances the customer experience with its intuitive interface, allowing easy access to and purchase of technologies, meeting the expectations of the new generation of technology buyers.

[0034] Advantages of the SPoG and RTDM Integration

[0035] Integrating the SPoG platform with RTDM offers numerous advantages. First, it provides an overall solution to long-standing problems in the distribution industry. Leveraging the capabilities of RTDM, SPoG can enhance supply chain visibility, streamline inventory management, ensure compliance, simplify SKU management, and provide an excellent customer experience.

[0036] The real-time tracking and analytics provided by RTDM improve SPoG's ability to effectively manage the supply chain and inventory. It provides accurate and up-to-date information, enabling distributors to make quick and informed decisions.

[0037] Integrating SPoG with RTDM also ensures data consistency and reduces errors in SKU management. By providing a centralized platform for managing data from various OEMs, it simplifies product localization and helps align with market requirements.

[0038] The global compliance database of RTDM integrated with SPoG facilitates compliant cross-border transactions. It also reduces the burden of manual tracking, saving significant time and resources.

[0039] In some embodiments, the distribution platform includes SPoG and RTDM to provide an improved and comprehensive distribution system. The platform can fully utilize the advantages of the distribution model, address its existing challenges, and position itself for continued growth in the evolving global market.

[0040] The SPoG mobile application (mobile App) system and method uniquely address challenges within the global distribution industry. Its user-friendly and intuitive interface enables distributors and customers to easily and efficiently navigate the complex distribution environment. One of the core advantages is its ability to enhance distribution management by providing real-time visibility into inventory, orders, and supply chain operations. This transparency allows distributors to make informed decisions, optimize their processes, and adapt to changing market dynamics in real time.

[0041] The SPoG UI mobile application facilitates the connection between distributors and customers, in line with the evolving consumer-centric paradigm. It allows customers to effortlessly access product information, place orders, and track their deliveries, all from the convenience of their mobile devices. This direct engagement not only enhances customer satisfaction but also strengthens brand loyalty and trust.

[0042] In some embodiments, the integration of push notifications in the SPoG UI enhances the distribution system's ability to keep all users, from sellers to customers, informed and engaged. It enables distributors to proactively send order updates, promotions, and relevant product information, as well as sophisticated insights, to customers in real-time. This level of engagement is highly useful in meeting the rapidly evolving expectations of modern consumers who require timely and personalized interactions.

[0043] Furthermore, the image recognition and SKU mapping capabilities of the SPoG UI offer significant advantages by simplifying the product identification process. Customers can easily scan product images, and advanced AI algorithms accurately map them to the correct stock keeping units (SKUs). This dynamic SKU creation not only streamlines the ordering process but also reduces manual data entry and potential errors, which are important advantages in improving operational efficiency.

[0044] The offline data caching in the SPoG UI mobile application ensures uninterrupted functionality, even in offline environments. By locally storing critical data on the user's device and synchronizing it with the backend server when the connection is restored, it guarantees 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.

[0045] Thus, the SPoG UI mobile application offers comprehensive advantages ranging from enhanced distribution management and real-time customer engagement to image recognition and offline capabilities. These features together enable distributors to thrive in the rapidly evolving distribution landscape while providing customers with a convenient distribution experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 An embodiment of the operating environment of a distribution platform, referred to as the system in this embodiment, is shown.

[0047] Figure 2 An embodiment of the operating environment of a distribution platform built on the components introduced in Figure 1 is shown.

[0048] Figure 3 An embodiment of a system for distribution management in a supply chain is shown.

[0049] Figure 4Depicts an embodiment of an advanced distribution platform that provides a technology distribution platform for optimizing the management and operation of a distribution network.

[0050] Figure 5 Shows an RTDM module according to an embodiment.

[0051] Figure 6 Shows an SPoGUI according to an embodiment.

[0052] Figure 7 Shows the architecture of an SPoG UI mobile application system and UI according to an embodiment.

[0053] Figure 8 Is a flowchart of a method for real-time data integration, analysis, and notification within an SPoG UI mobile application system according to some embodiments of the present disclosure.

[0054] Figure 9A Is a flowchart of a method for image capture and SKU mapping within an SPoG UI mobile application system according to some embodiments of the present disclosure.

[0055] Figure 9B Is a flowchart of a method for enhanced product search in an SPoG UI mobile application system according to some embodiments of the present disclosure.

[0056] Figure 10 Is a flowchart of a method for managing offline data caching in an SPoG UI mobile application according to some embodiments of the present disclosure.

[0057] Figure 11 Is a block diagram of an example component of a device according to some embodiments of the present disclosure.

[0058] Figures 12A to 12Q Depicts various screens and functions of an SPoGUI according to some embodiments.

[0059] Figures 13A to 13F Depicts various screens and functions of a mobile application SPoGUI according to some embodiments. Detailed Description

[0060] Embodiments may be implemented in hardware, firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. The 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, the machine-readable medium may include read-only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices, etc. Additionally, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be understood that such descriptions are merely for convenience, and such actions are actually generated by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0061] In some embodiments, a platform for 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 hub environment involving the distribution of technology-oriented products and services (such as cloud services, software, and hardware, particularly for enterprises, IT professionals, etc.). Compared to platforms such as AMAZON, the distribution platform described herein does not participate in the retail of a large number of consumer goods (such as clothing, groceries, or household items). The operating model of the distribution platform is typically targeted at business-to-business interactions rather than a consumer-oriented approach. The infrastructure of the platform may be configured to support the distinction between digital and IT distribution, where the logistics of the platform are specifically configured to provide recommendations, insights, bundling, etc. to that audience rather than a consumer-oriented approach.

[0062] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the shown operations. In some embodiments of the present disclosure, the operations may be performed in a different order and / or varied.

[0063] Figure 1 An operating environment 100 of a distribution platform referred to as system 110 in this embodiment is shown. System 110 operates in the context of an information technology (IT) distribution model, catering to various stakeholders such as customers 120, end customers 130, sellers 140, distributors 150, and other entities involved in the distribution process. The operating environment includes a wide range of features and dynamics that contribute to the success and efficiency of the distribution platform.

[0064] Customers 120 within the operating environment of System 110 represent enterprises or individuals seeking IT solutions to meet their specific needs. These customers may require a variety of IT products, such as hardware components, software applications, network devices, or cloud-based services. System 110 provides customers with a user-friendly interface that allows them to browse, search, and select the most suitable IT solutions based on their needs. Customers can also access real-time data and analytics through System 110, empowering them to make informed decisions and optimize their IT infrastructure.

[0065] End customers 130 are the ultimate beneficiaries of the IT solutions provided by System 110. They can include enterprises or individuals who utilize IT products and services to enhance their operations, productivity, or daily activities. End customers rely on System 110 to access a wide range of IT solutions, ensuring that they have access to the latest technologies and innovations in the market. System 110 enables end customers to track their orders, receive updates on delivery status, and access customer support services, enhancing their overall experience.

[0066] Vendors 140 play an important role in the operating environment of System 110. These vendors include manufacturers, distributors, and suppliers who offer a variety of IT products and services. System 110 serves as a centralized platform for vendors to showcase their products, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage System 110 to streamline their supply chain operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with System 110, vendors can expand their reach, access new markets, and enhance their overall visibility and competitiveness.

[0067] Resellers 150 are intermediaries within the distribution model that bridge the gap between vendors and customers. They play a crucial role in the IT distribution ecosystem by connecting customers with the right IT solutions from various vendors. Resellers can include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access an integrated catalog of IT solutions, manage their sales channels, and provide value-added services to customers. By leveraging System 110, resellers can strengthen their customer relationships, optimize their product offerings, and increase their revenue streams.

[0068] Within the operating environment of 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. System 110 ensures the real-time exchange of relevant data among stakeholders, enabling accurate decision-making and timely actions. Integration with existing enterprise systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems allows for integrated communication and interoperability, eliminating data silos and enabling end-to-end visibility.

[0069] Scalability and flexibility are key features of system 110. It can adapt to the growing demands of the IT distribution model, whether it involves an expanding customer base, an increasing number of resellers, or a wider range of IT products and services. System 110 is designed to handle large-scale data processing, storage, and analysis, ensuring that it can support the evolving needs of the distribution platform. Additionally, system 110 makes full use of a technology stack including.NET, Java, and other suitable technologies to provide a robust foundation for its operations.

[0070] In summary, the operating environment of system 110 within the IT distribution model includes customers 120, end-customers 130, resellers 140, distributors 150, and other entities involved in the distribution process. System 110 serves as a centralized platform that facilitates efficient collaboration, communication, and transaction processing among these stakeholders. By leveraging real-time data exchange, integration, scalability, and flexibility, system 110 empowers stakeholders to optimize their operations, improve the customer experience, and drive business success within the IT distribution ecosystem.

[0071] Figure 2 An embodiment of the operating environment 200 of a distribution platform built on the components introduced in Figure 1 is shown. Within this operating environment, integration points 210 facilitate the data flow and connectivity among various customer systems 220, reseller systems 240, distributor systems 260, and other entities involved in the distribution process. This figure illustrates the interconnectivity and mechanisms for achieving efficient collaboration and data-driven decision-making.

[0072] The operating environment 200 may include system 110, which acts as a distribution platform serving as a central hub for managing and facilitating the distribution process. System 110 can be configured to perform functions and operations that act as a bridge between customer systems 220, reseller systems 240, distributor systems 260, and other entities within the ecosystem. It can integrate communication, data exchange, and transaction processing to provide a unified and streamlined experience for stakeholders. Additionally, the operating environment 200 may include one or more integration points 210 to ensure smooth data flow and connectivity. These integration points include:

[0073] Customer System Integration: Integration point 210 enables system 110 to connect with customer system 220, thus achieving efficient data exchange and synchronization. Customer system 220 can include various entities, such as customer system 221, customer system 222, and customer system 223. These systems represent the internal systems used by customers, such as enterprise resource planning (ERP) or customer relationship management (CRM) systems. Integration with customer system 220 authorizes customers to access real-time inventory information, pricing details, order tracking, and other relevant data, thereby enhancing their visibility and decision-making capabilities.

[0074] Vendor System Integration: Integration point 210 facilitates the connection between system 110 and vendor system 240. Vendor system 240 can include entities such as vendor system 241, vendor system 242, and vendor system 243, thus representing the inventory management system, pricing system, and product catalog adopted by the vendor. Integration with vendor system 240 ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details.

[0075] Distributor System Integration: Integration point 210 provides the ability for distributor system 260 to connect with system 110. Distributor system 260 can include entities such as distributor system 261, distributor system 262, and distributor system 263, thus representing the sales system, customer management system, and service delivery platform adopted by the distributor. Integration with distributor system 260 allows distributors to access the latest product information, manage customer accounts, track sales performance, and provide value-added services to their customers.

[0076] Other Entity System Integration: Integration point 210 also enables connections with other entities involved in the distribution process. These entities can include entities such as entity system 271, entity system 272, and entity system 273. Integration with these systems ensures communication and data exchange, thus facilitating collaboration and an efficient distribution process.

[0077] The integration point 210 within the operating environment 200 is facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and the connected systems. System 110 adopts industrial standard protocols such as RESTful API, SOAP, or GraphQL to establish communication channels and achieve integrated data exchange.

[0078] In some embodiments, system 110 can incorporate authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) can be adopted to authenticate users, authorize data access, and maintain the integrity and confidentiality of the exchanged information.

[0079] In some embodiments, the integration points 210 and data flows within the operating environment 200 enable stakeholders 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 the customer system 220, the reseller system 240, the distributor system 260, and other entities. This data exchange promotes real-time visibility, enables data-driven decision-making, and improves the operational efficiency of the entire distribution platform.

[0080] In some embodiments, the system 110 makes full use of advanced technologies such as Typescript, NodeJS, ReactJS, NETcore, C#, and other suitable technologies that support the integration points 210 and enable communication within the operating environment 200. These technologies provide a robust foundation for the system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Additionally, the integration points 210 can also employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize the distribution process, and personalize the customer experience. The integration points 210 and data flows within the 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, coordinated, and made available to relevant stakeholders in real time by the 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 the customer experience.

[0081] Figure 2 Several elements in the depicted operating environment may include conventional, well-known elements that are only briefly explained here. For example, each customer system (such as the customer system 220) may include a desktop personal computer, a workstation, a laptop computer, a PDA, a cellular phone, or any other computing device that supports a wireless access protocol (WAP)-enabled device, or any other computing device capable of directly or indirectly docking with the Internet or other network connections. Each customer system typically can run an HTTP client, such as Microsoft's Edge browser, Google's Chrome browser, Opera's browser, or a WAP-enabled browser for mobile devices, thereby allowing the customer system to access, process, and view information, pages, and applications available from the distribution platform via the network.

[0082] In addition, each customer system can typically be equipped with user interface devices for interacting with the graphical user interface (GUI) provided by the browser, such as a keyboard, mouse, trackball, touchpad, touch screen, pen, or similar devices. These user interface devices enable the users of the customer system to navigate the GUI, interact with pages, forms, and applications, and access the data and applications hosted by the distribution platform.

[0083] The customer system and its components can be operator-configurable using applications (including web browsers) running on a central processing unit such as an Intel Pentium processor or similar processors. Similarly, the distribution platform (System 110) and its components can be operator-configured using applications running on a central processing unit (such as a processor system) that can include an Intel Pentium processor or similar processors and / or multiple processor units.

[0084] Computer program product embodiments include machine-readable storage media that contain instructions for programming a computer to perform the processes described herein. The computer code for operating and configuring the distribution platform and customer systems, seller systems, dealer systems, and other entity systems to communicate with each other, 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 card, or optical card, nanosystems, or any suitable medium for storing instructions and data.

[0085] In addition, the computer code for implementing the embodiments can be transmitted and downloaded from a software source via communication media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. over the Internet or any other conventional network connection. The code can also be transmitted over an extranet, VPN, local area network, 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, etc.

[0086] It should be understood that the embodiments can be implemented in various programming languages that execute on a client system, server, or server system, and the choice of language can depend on the specific requirements and environment of the distribution platform.

[0087] Thus, the operating environment 200 can couple the distribution platform with one or more integration points 210 and data flows to enable efficient collaboration and a streamlined distribution process.

[0088] Figure 3 A system 300 for supply chain and distribution management is shown. System 300 (Figure 3 ) is a supply chain and distribution management solution designed to address the challenges faced by the fragmented distribution ecosystems in the global distribution industry. System 300 can include a number of interconnected components and modules that work in concert to optimize supply chain and distribution operations, enhance collaboration, and drive business efficiency.

[0089] The SPoG UI 305 serves as a centralized user interface, providing stakeholders with a unified view of the entire supply chain. It consolidates information from various sources and presents real-time data, analytics, and functionality customized to the specific roles and responsibilities of the users. By providing a customizable and intuitive dashboard-style layout, the SPoG UI 305 enables users to access relevant information and tools, empowering them to make data-driven decisions and effectively manage their supply chain and distribution activities.

[0090] For example, a logistics manager can use the SPoG UI 305 to monitor the status of goods, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize the data through interactive charts and graphs, such as a map showing the current location of each shipped item or a bar chart depicting inventory levels by product category. With a unified view of the supply chain, the logistics manager can identify bottlenecks, optimize routes, and ensure the timely delivery of goods.

[0091] The SPoG UI 305 integrates with other modules of System 300, facilitating real-time data exchange, synchronized operations, and streamlined workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, the SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the distribution ecosystem.

[0092] For example, when a purchase order is generated in the SPoG UI, the system automatically updates the inventory levels, triggers a notification to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0093] The Real-Time Data Mesh (RTDM) module 310 is another key component of System 300, responsible for ensuring data flow within the distribution ecosystem. It aggregates data from multiple sources, coordinates it, and ensures its availability in real time.

[0094] To illustrate the functionality of the RTDM module, let's 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 coordinates this data by aligning formats, standardizing measurement units, and reconciling any discrepancies. The unified data is then made available in real time, allowing stakeholders to access accurate and up-to-date information across the supply chain.

[0095] The RTDM module 310 can be configured to capture in real time changes in data across multiple transaction systems. It employs an advanced change data capture (CDC) mechanism that continuously monitors the transaction systems to detect any updates or modifications. The CDC component is specifically designed to work with a variety of transaction systems, which may include legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for businesses operating in diverse environments.

[0096] By having access to real-time data, stakeholders can make decisions in a timely manner 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, enabling them to adjust the manufacturing schedule and prevent stockouts.

[0097] The RTDM module 310 facilitates data management operations within the supply chain. It enables real-time coordination of data from multiple sources, liberating vendors, distributors, customers, and end-customers from the constraints imposed by legacy ERP systems. This enhanced flexibility supports improved efficiency, customer service, and innovation.

[0098] 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. It supports advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0099] For example, the AAML module 315 can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure inventory availability during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0100] In addition to demand forecasting, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or upselling opportunities and recommend relevant products to individual customers.

[0101] In addition, the AAML module can analyze data from various sources, such as social media feedback, customer reviews, and market trends, to gain a deeper understanding of customer sentiment and preferences. This information can be used for informed product development decisions, identifying emerging market trends, and adjusting business strategies to meet evolving customer expectations.

[0102] 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, thus creating an integrated and connected distribution ecosystem.

[0103] The implementation and deployment of System 300 can be customized to meet specific business requirements. 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 align with specific supply chain requirements, integrating with existing systems, and customizing modules and components based on business needs and preferences.

[0104] System 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by fragmented distribution ecosystems. It combines the functions of the SPoG UI 305, the RTDM module 310, and the AAML module 315, as well as integration with existing systems. By leveraging different technology stacks, scalable architectures, and robust integration capabilities, System 300 provides end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided in this specification are non-limiting and can be customized to meet specific industry requirements, drive efficiency, and achieve success in supply chain and distribution management.

[0105] Figure 4Depicts an embodiment of an advanced distribution platform including system 400 for managing complex distribution networks, which can be an embodiment of system 300, and provides a technology distribution platform for optimizing the management and operation of distribution networks. System 400 includes several interconnected modules, each with a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include SPoG UI 405, Customer Interaction Module CIM410, RTDM Module 415, AI Module 420, Interface Display Module 425, Personalized Interaction Module 430, Document Hub 435, Catalog Management Module 440, Performance and Insights Tag Display 445, Predictive Analytics Module 450, Recommendation System Module 455, Notification Module 460, Self-Login Module 465, and Communication Module 470.

[0106] System 400, as an embodiment of system 300, utilizes a series of technologies and algorithms to achieve supply chain and distribution management. These technologies and algorithms facilitate efficient data processing of documents, catalogs, and performance metrics, personalized interactions, real-time analytics, secure communication, and effective management.

[0107] In some embodiments, SPoG UI 405 serves as the central interface within system 400, providing stakeholders with a unified view of the entire distribution network. It utilizes front-end technologies such as ReactJS, TypeScript, and Node.js to create an interactive and responsive user interface. These technologies enable SPoG UI 405 to deliver a user-friendly experience, allowing stakeholders to access relevant information, navigate through different modules, and perform tasks efficiently.

[0108] CIM 410 or 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 securely process customer data, personalize the customer experience, and provide access control for stakeholders.

[0109] RTDM Module 415 or the Real-Time Data Mesh Module is a key component of system 400 that ensures smooth data flow across the distribution ecosystem. It utilizes technologies such as or Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable 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 transactional systems such as legacy ERP systems and CRM systems. This functionality allows stakeholders to access the latest and accurate information for informed decision-making.

[0110] The AI module 420 within the system 400 utilizes advanced analytics and machine learning algorithms (including Apache Spark, TensorFlow, and scikit-learn) to extract valuable insights from data. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI module 420 can utilize predictive models to forecast demand, allowing stakeholders to optimize inventory management and minimize out-of-stock or overstock situations.

[0111] 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 (such as 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.

[0112] The personalized interaction module 430 utilizes customer data, historical trends, and machine learning algorithms to generate personalized recommendations for products or services. It can employ technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and delivering targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, enhancing customer satisfaction and boosting sales.

[0113] The document hub 435 serves as a centralized repository for storing and managing documents within the system 400. It can utilize technologies such as SeeBurger and elastic cloud for efficient document management, storage, and retrieval. For example, the document hub 435 can adopt the document management capabilities of SeeBurger to classify and organize documents based on their types (such as contracts, invoices, product specifications, or compliance documents), allowing stakeholders to easily access and retrieve relevant documents when needed.

[0114] The catalog management module 440 enables the creation, management, and distribution of up-to-date product catalogs. It ensures that stakeholders can access the latest product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be utilized to facilitate catalog updates, content delivery, and caching. For example, the module can utilize Akamai's content delivery network (CDN) to quickly and effectively deliver catalog information to stakeholders regardless of their geographical location.

[0115] The Performance and Insights Tagging Display 445 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. It utilizes tools such as Splunk and Datadog to enable effective performance monitoring and provide actionable insights. For example, this module can leverage Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling stakeholders to take proactive measures to optimize operations.

[0116] The Predictive Analytics Module 450 employs machine learning algorithms and predictive 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 prediction. For example, this module can leverage TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing stakeholders to optimize inventory levels and minimize costs.

[0117] The Recommendation System Module 455 focuses on providing intelligent recommendations to stakeholders within the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. It can employ technologies such as Adobe Target and Apache Spark for data analysis, modeling, and delivering targeted recommendations. For example, this module can utilize Adobe Target's recommendation engine to analyze customer preferences and behavior and deliver personalized product recommendations across various channels, enhancing customer engagement and driving sales.

[0118] The Notification Module 460 enables the distribution of real-time notifications to stakeholders regarding important events, updates, or alerts within the supply chain. It can utilize technologies such as Apigee X and TIBCO for message queuing, event-driven architectures, and notification delivery. For example, this module can leverage TIBCO's messaging infrastructure to send real-time notifications to stakeholders' devices, ensuring timely and relevant information dissemination.

[0119] The Self-Login Module 465 facilitates the login process for new stakeholders entering the distribution network. It provides guided steps, tutorials, or documentation to help users familiarize themselves with the system and its functions. It can utilize technologies such as Okta and Kentico to ensure secure user authentication, access control, and self-learning resources. For example, this module can leverage Okta's identity and access management capabilities to securely onboard new stakeholders, provide them with appropriate access rights, and guide them through the system's functionality.

[0120] The communication module 470 enables communication and collaboration within the system 400. It provides channels for stakeholders to interactively exchange messages, share documents, and collaborate on projects. It can utilize technologies such as Apigee Edge and Adobe Launch to facilitate secure and efficient communication, document sharing, and version control. For example, this module can utilize the API management capabilities of Apigee Edge to ensure secure and reliable communication among stakeholders, enabling them to collaborate effectively.

[0121] Thus, the system 400 can include various modules that utilize various technologies and algorithms to optimize supply chain and distribution management. These modules (including the SPoG UI 405, CIM 410, RTDM module 415, AI module 420, interface display module 425, personalized interaction module 430, document hub 435, directory management module 440, performance and insight marker display 445, predictive analytics module 450, recommendation system module 455, notification module 460, self-login module 465, and communication module 470) work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analysis, and streamlined communication within the distribution network. The combination of specific technologies and algorithms enables efficient data management, secure communication, personalized experiences, and effective performance monitoring, contributing to increased operational efficiency and the success of supply chain and distribution management.

[0122] Real-time data grid

[0123] Figure 5 Shows an RTDM module 500 according to an embodiment. The RTDM module 500 can be an embodiment of the RTDM module 310, which can include interconnected components, processes, and subsystems configured to implement real-time data management and analysis.

[0124] As Figure 5 shown, the RTDM module 500 represents an effective data grid and change capture component within the overall system architecture. This module is designed to provide real-time data management and coordination capabilities, enabling efficient operations within the supply chain and distribution management domain.

[0125] The RTDM module 500 can include an integration layer 510 (also referred to as the "system of record") that integrates with various enterprise systems. These enterprise systems can include ERPs such as SAP, Impulse, META, and I-SCALA, among others, 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 and ensure that the RTDM module operates with the latest and accurate data.

[0126] The RTDM module 500 may include a data layer 520 configured to process and transform data for retrieval and analysis. At the core of the data layer is a data grid, which is a cloud-based infrastructure designed to provide scalable and fault-tolerant data storage capabilities. Within the data grid, multiple Purchase 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. The PDSs are configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs serve as repositories for coordinating and standardizing data, ensuring data consistency and integrity across systems.

[0127] 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., Impulse, META, I-SCALA). Then, the captured data is processed and coordinated in-flight, transforming it into a standardized format suitable for analysis and integration. This process ensures that the data is readily available and up-to-date within the data grid, thus facilitating real-time insights and decision-making.

[0128] More specifically, the data layer 520 within the RTDM module 500 can be configured as a powerful and flexible foundation for managing and processing data within a distribution ecosystem. In some embodiments, the data layer 520 may include a highly scalable and robust data lake, which may be referred to as the data lake 522, and a set of purposeful data stores (PDSs), which may be denoted as PDS 524.1 to 524.N. These components work together to ensure efficient data management, coordination, and real-time availability.

[0129] At the core of the data layer 520 is the data lake, namely the data lake 522, which is a state-of-the-art storage and processing infrastructure designed to handle the ever-increasing volume, variety, and velocity of data generated within a supply chain. Relying on scalable distributed file systems such as the Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified and scalable platform for storing both structured and unstructured data. Leveraging the elasticity and fault tolerance of cloud-based storage, the data lake 522 can accommodate data inflows from different sources.

[0130] Associated with the data lake 522, a set of purposeful data stores, PDSs 524.1 to 524.N can be adopted. Each PDS 524 can be used as a dedicated repository optimized for storing and retrieving specific types of data related to the supply chain domain. In some non-limiting examples, PDS 524.1 can be dedicated to customer data, storing information such as customer profiles, preferences, and transaction histories. PDS 524.2 can focus on product data, including details about SKU codes, descriptions, pricing, and inventory levels. These purposeful data stores allow for efficient data retrieval, analysis, and processing, thus meeting the diverse needs of supply chain stakeholders.

[0131] To ensure real-time data synchronization, the data layer 520 can be configured to employ one or more sophisticated change data capture (CDC) mechanisms. These CDC mechanisms are integrated with transaction systems, such as traditional ERPs like SAP, Impulse, META, and I-SCALA, as well as other enterprise-wide systems. CDC continuously monitors any updates, modifications, or new transactions in these systems and captures them in real time. By capturing these changes, the data layer 520 ensures that the data within the data lake 522 and PDS 524 remains up-to-date, thus providing stakeholders with real-time insights into the distribution ecosystem.

[0132] In some embodiments, the data layer 520 can be implemented to use one or more frameworks such as.NET or Java to facilitate integration with existing enterprise systems, thus ensuring compatibility with various existing systems and providing flexibility for customization and scalability. For example, the data layer 520 can utilize the Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with the record systems of a group with multiple ERP systems and other enterprise-wide solutions. This can promote smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0133] In terms of data processing and analysis, the 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-scale datasets stored in the data lake and PDS. By leveraging these frameworks, supply chain stakeholders can perform complex analysis tasks, apply machine learning algorithms, and derive valuable insights from the data. For example, the data layer 520 can utilize the machine learning library of Apache Spark to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0134] In some embodiments, the data layer 520 can incorporate robust data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authorized users can access and modify the data within the data lake and PDS. During rest and transit, data encryption techniques protect sensitive supply chain information from unauthorized access. Additionally, the data layer 520 can implement data lineage and audit trail mechanisms, allowing stakeholders to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0135] In some embodiments, the data layer 520 can be deployed in a cloud-native environment using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability, elasticity, and efficient resource allocation. For example, the data layer 520 can be deployed on cloud infrastructure provided by AWS, Azure, or Google Cloud using its managed services and scalable storage options. This allows for efficient scaling of resources based on demand, minimizing operational overhead and providing a resilient infrastructure for managing supply chain data.

[0136] The data layer 520 of the RTDM module 500 can incorporate a highly scalable data lake (Data Lake 522) as well as purpose-built PDSs (PDSs 5241 to 524.N), and employ sophisticated CDC mechanisms. The data layer 520 ensures efficient data management, coordination, and real-time availability. Integration of various technology stacks such as.NET or Java and distributed computing frameworks such as Apache Spark enables powerful data processing, advanced analytics, and machine learning capabilities. Leveraging 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 allows supply chain stakeholders to make data-driven decisions, optimize operations, and drive business success in dynamic and complex distribution environments.

[0137] The RTDM module 500 can include an AI module 530, which is 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 can apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI module 530 can continuously learn from new data inputs and adapt its models to provide accurate and up-to-date insights. The AI module 530 can generate predictions, recommendations, and alerts, and publish such insights to a dedicated data feedback.

[0138] The data engine layer 540 includes 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 headless engines 540.1 to 540.N that operate autonomously. These engines represent different functions within the system and can include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 540.1 to 540.N can utilize the coordinated data stored in the data grid to deliver specific business logic and services. Each engine 5 is designed to be pluggable, allowing for flexibility and future expansion of module capabilities. Figure 5 Exemplary engines are shown, which is not meant to be restrictive. Any additional headless engines can be included in the data engine layer 540 or other exemplary layers of the disclosed system.

[0139] These systems can be configured to receive data from multiple sources, such as transaction systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms are applied to clean, aggregate, and enrich the data, preparing it for further analysis and integration.

[0140] In addition, to facilitate integration and access to the RTDM module 500, a data distribution mechanism 545 can be employed. The data distribution mechanism can be configured to include one or more APIs to facilitate the distribution of data from the data grid and engines to various endpoints, including user interfaces, micro frontends, and external systems.

[0141] The experience layer 550 focuses on delivering 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 features. Through this layer, users can retrieve and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The user experience layer supports personalized data feedback, allowing users to customize their views and receive relevant updates based on their roles and responsibilities. Users can subscribe to specific data updates customized to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0142] Thus, in some embodiments, the RTDM module 500 for supply chain and distribution management may include integration with record systems and include one or more of a data layer with a data grid and purposeful data storage, an AI component, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, data processing and analysis, and efficient integration with existing enterprise systems. Technical feedback and retrieval within the module ensure that users can retrieve relevant and up-to-date information and insights to 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 allows for rich integration of different data sources, efficient data coordination, and advanced analytics capabilities. The module's ability to replicate and reconcile data from different ERPs while maintaining auditable and repeatable transactions provides distinct advantages in achieving a unified view of vendors, distributors, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0143] Single-pane glass UI

[0144] Figure 6 Shows a SPoG UI according to an embodiment. The SPoG UI 600. In some embodiments, the SPoG UI 600 may be an embodiment of the SPoG UI 305, which represents a comprehensive and intuitive user interface designed to provide a unified and customizable view of the entire distribution ecosystem. It combines a series of features and functions that enable users to gain a comprehensive understanding of the supply chain and effectively manage their operations.

[0145] The SPoG UI 600 may include a UV (Unified View) module 605, which provides a centralized and customizable dashboard-style layout for stakeholders. This module allows users to access real-time data, analytics, and functions customized for their specific roles and responsibilities within the distribution ecosystem. The UV module 605 serves as a single entry point for users, providing an overall and comprehensive view of supply chain operations and empowering them to make data-driven decisions.

[0146] The SPoG UI 600 is integrated with the real-time data exchange module 610 to facilitate continuous exchange of data between the SPoG UI 600 and the RTDM 310 to leverage one or more data sources, which may include one or more ERPs, CRMs, or other sources. Through this module, stakeholders can access the latest, accurate, and reconciled data. Real-time data synchronization ensures that the information presented in the SPoG UI 600 reflects the latest insights and developments across the supply chain. This integration enables stakeholders to make informed decisions based on accurate and synchronized data.

[0147] The collaborative decision-making module 615 within the SPoG UI 600 facilitates real-time collaboration and communication among 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 that stakeholders can effectively collaborate based on accurate and synchronized data. This promotes overall operational efficiency and collaboration within the distribution ecosystem.

[0148] To ensure secure and controlled access to functions and data, the SPoG UI 600 incorporates this role-based access control (RBAC) module 620. Administrators can define roles, assign permissions, and control user access based on their responsibilities and organizational hierarchy. This RBAC module 620 ensures that only authorized users can access specific features and information, thus protecting data privacy, security, and compliance within the distribution ecosystem.

[0149] The customization module 625 allows users to personalize their dashboards and customize the interface according 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 the supply chain operations, thus providing a user-centric experience that enhances productivity and usability.

[0150] 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 insights into key performance indicators (KPIs), trends, patterns, and anomalies, thus facilitating data-driven decision-making and strategic planning.

[0151] 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, thus allowing them to stay connected and informed while on the move. This module optimizes the user experience for different screen sizes, resolutions, and operating systems, thus ensuring access to real-time data and functions across various devices.

[0152] By integrating these reference components / modules within the SPoG UI600 and leveraging its integration capabilities with the RTDM modules 310 / 500, stakeholders can benefit from a powerful and user-friendly interface for supply chain and distribution management. The Unified View (UV) module 605 provides a customizable and holistic 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 module 615 facilitates effective communication and collaboration, and the RBAC module 620 ensures secure access control. The Customization module 625, the Data Visualization module 630, and the Mobile and Cross-Platform Accessibility module 635 enhance the 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.

[0153] The SPoG 600 can incorporate high-speed data in a data-rich environment. In contemporary data-rich environments, conventional UI designs often struggle to present large amounts of information in an understandable, efficient, and visually appealing manner. The challenge intensifies when the data is dynamic, changing in real-time, and needs to be effectively displayed in a single-pane environment that emphasizes a clean, whitespace-oriented design.

[0154] The SPoG 605 UI can be configured to effectively manage real-time data while maintaining a visually clean interface without sacrificing performance. This innovative approach includes a unique configuration of the UI structure, responsive data visualization, real-time data processing methods, adaptive information architecture, and whitespace optimization.

[0155] The SPoG 605 UI can be constructed around a grid-based layout system using CSS grid and Flexbox technologies. This structure provides the flexibility to create fluid layouts with components that automatically adjust to the available space and content. HTML5 and CSS3 are used as the base technologies for creating the UI, while JavaScript, specifically React.js, manages the dynamic aspects of the UI.

[0156] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the shown operations. In some embodiments of the present disclosure, the operations may be performed in a different order and / or varied.

[0157] Figure 7Shows the architectural framework of a mobile application within a cloud distribution platform, highlighting its core components. The mobile application architecture 700 includes a User Interface (UI) layer 705, a push notification service 730, an image recognition engine 740, an offline data cache 750, a security and authentication layer 760, an integration with backend systems 770, a device compatibility module 780, and a real-time data grid 710. This architecture is designed to provide users with real-time data access and transaction capabilities, enhancing their experience in a dynamic and evolving market.

[0158] In some embodiments, the UI layer 705 can serve as the central point of interaction for users within the system 700. In a non-limiting example, the UI 705 can be an embodiment of the SPoG UI, such as the SPoG UI 305, 410, 600, or any other UI, which allows users (such as one or more vendors, customers, colleagues, dealers, etc.) to easily navigate through different modules, access relevant information, and perform various tasks related to the distribution platform. The UI 705 is designed to be intuitive, user-friendly, and responsive, enabling users to interact with the system efficiently.

[0159] The UI layer 705 provides a central point of interaction between the user and the functionality of the application. It is configured to provide a user-friendly and intuitive experience, giving users (including customers, vendors, partners, dealers, etc.) access to a wide range of features and capabilities of the application. The UI layer is carefully designed to have a responsive and mobile-friendly layout to accommodate the different range of devices and screen sizes that users may employ. Adopting a user-centered design approach, the UI layer prioritizes the user's perspective. This requires careful arrangement of elements such as menus, buttons, and navigation bars to enhance the user's ability to interact with the application efficiently. The user interface is designed to facilitate effortless navigation through the application. It uses clear and logically structured menus, intuitive icons, and user-friendly labels to guide users through the application's functionality without confusion.

[0160] The UI layer 705 can be purposefully user-friendly. Thus, the UI optimizes the actual screen usage area, ensuring that information and actions are presented in a clear and organized manner, enhancing readability and usability. In some embodiments, the design includes abundant white space to further enhance readability and usability. The UI layer 705 is configured to include responsive design. That is, the user interface dynamically adjusts to different screen sizes and orientations. Whether the user accesses the application on a large desktop monitor or on a small smartphone screen, the interface remains user-friendly and visually appealing. The UI layer 705 is optimized for the mobile platform. This optimization includes touch-friendly controls, efficient use of screen space, and responsive design elements that adapt to the mobile screen. The UI layer 705 ensures a consistent user experience across different platforms such as web browsers and mobile applications. Users can expect a familiar interface regardless of the device or platform they use to access the application. Accessibility features can be incorporated into the UI layer so that the application can be used by individuals with different abilities. 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. [[ID= ]] [[ID=

[0161] ]]

[0161] In some embodiments, the data layer 710 can be configured to implement an efficient data flow across the SKU management ecosystem. The data layer 710, which can be an embodiment of the RTDM modules 310, 415, 500 or any other data layer, can include 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, enabling real-time data exchange and synchronization. This integration ensures that the data within the data layer 710 is up-to-date and readily available for SKU management operations. [[ID= ]] [[ID=

[0162] ]]

[0162] In some embodiments, the data layer 710 can be an RTDM module, such as an embodiment of the RTDM module 310 or the RTDM module 415 or the RTDM module 500. In some embodiments, the 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 act as an ERP-agnostic real-time data grid. In some embodiments, the RTDM module collects data from systems in the record layer (including data from various enterprise systems such as ERP) and merges the data into the data lake within the data layer 710. [[ID= ]] [[ID=

[0163] ]]

[0163] The data layer 710 serves as a repository for the coordinated and standardized data obtained from the RTDM module. Within the data layer 710, various purpose-built data stores are deployed to store specific types of data, such as customer data, product data, financial data, and so on. These purpose-built data stores optimize data retrieval based on specific use cases and requirements, thereby ensuring efficient distribution and order management.

[0164] By leveraging the data available within the data layer 710 and the real-time capabilities of the RTDM module, the system 710 is configured to achieve an efficient and accurate SKU creation process that is consistent with the most current information. This interaction between the data layer 710 and the RTDM module facilitates an integrated data flow and enables dynamic order and distribution processes to be effectively executed across the entire distribution ecosystem.

[0165] The RTDM architecture (e.g., 500) or the data layer 710 operably connected to the RTDM architecture (e.g., 500) can facilitate the management and processing of real-time data within the supply chain and distribution management domains. This module is configured to facilitate data retrieval, transformation, and analysis, ensuring that users have received current and accurate information for data-driven decision-making.

[0166] In an example, the data layer 710 is coupled with the RTDM module to form an integrated component of the system architecture. Its primary objective is to extract, process, and translate data from various sources through the RTDM module 500 to support the generation of insights and integration with enterprise systems. The data layer 710 is integrated with a set of different enterprise systems via the RTDM module, including prominent ERPs such as SAP, Impulse, META, and I-SCALA, among others. This integration is achieved through the integration layer 510, which is commonly 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 the latest and accurate data, which is an essential requirement for efficient supply chain management.

[0167] Within the data layer 710, the bus / gateway to the data layer 520 processes and transforms data for retrieval and analysis. The core of the data layer is the data grid, a cloud-based infrastructure known for its scalability and fault-tolerant data storage capabilities. This data grid includes multiple purpose-built data stores (PDSs) (represented as PDS524.1 to 524.N), each of which is optimized for a specific type of data related to the supply chain domain. For example, PDS524.1 can hold customer data, while PDS524.2 can focus on product data, including SKU details, descriptions, pricing, and inventory levels. These PDSs serve as repositories for coordinated and standardized data, thereby ensuring data consistency and integrity across the entire system.

[0168] The data layer 710 is configured to implement a data replication mechanism that captures real-time changing data from various data sources, including transaction systems like ERP. The captured data is processed in real time and transformed into a standardized format suitable for analysis and integration. This real-time data capture and transformation process ensures that the data within the data grid and PDS remains up-to-date, enabling stakeholders, especially well-versed engineers, to access real-time insights and make timely decisions.

[0169] In addition, the data layer 710 can make full use of current and future technologies for data processing and analysis. Distributed computing frameworks such as Apache Spark or Apache Flink enable parallel processing and distributed computing across large datasets stored in the data grid and PDS. These frameworks allow supply chain stakeholders, including engineers, to perform complex data analysis, 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.

[0170] Data governance and security are of utmost importance in this module. Fine-grained access control mechanisms and robust authentication protocols ensure that only authorized users, including engineers, can access and modify the data within the data grid and PDS. Data encryption techniques protect sensitive supply chain information both at rest and in transit, ensuring its protection from unauthorized access. Additionally, the data layer 710 implements data lineage and audit trail mechanisms, enabling stakeholders, including engineers, to track the history and origin of the data, thus ensuring data integrity and regulatory compliance.

[0171] Thus, the data layer 710, which is operably integrated with the RTDM module 500, exists as a fundamental element of the supply chain and distribution management system. Its ability to effectively manage, transform, and provide real-time access to data enables stakeholders, especially proficient engineers, to have the necessary insights to optimize supply chain operations, make data-driven decisions, and effectively navigate the complexities of the distribution environment. The robust architecture, scalability, and integration capabilities of this module position it as a key asset for enhancing supply chain management in a dynamic and competitive environment.

[0172] The push notification service 730 is a fundamental component of the mobile application architecture and is tightly integrated with the data layer 620 / 710. It operates on an event-driven architecture where event processors embodied and / or operably connected via the RTDM module are configured to process specific distribution platform events, ensuring efficient communication with users. Such events can include price adjustments, inventory updates, order confirmations, etc., which trigger real-time notifications delivered to the user's mobile device.

[0173] The push notification service 730 communicates with RTDM via the data layer 710 for real-time data retrieval from RTDM based on data from various sources, including enterprise resource planning (ERP) systems and other data repositories. The push notification service 730 can receive events that require immediate user attention published via the data layer 710. Such events include issues related to vendors, customers and end-customers, distributors, partners, and any other events managed by the distribution platform. These events can include dynamic price fluctuations for inventory updates that support the operations of the distribution platform. Each event initiates the delivery of a real-time notification to the mobile devices of the associated users. To ensure reliable notification delivery, the service follows 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.

[0174] For Android devices, the push notification service 730 can be integrated with Firebase Cloud Messaging (FCM) to optimize the handling of notifications via Firebase Cloud Functions. The push notification service 730 can configure cloud functions to act as triggers for sending notifications that align FCM with an event-driven architecture. For iOS devices, the push notification service 730 can be integrated with Apple Push Notification Service (APN), which requires specific configuration steps. Engineers manage encryption keys, set up Apple developer accounts, and integrate APN into the event-driven architecture to establish a secure and efficient communication channel with iOS devices.

[0175] 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 ability to access different data sources and ERPs, the push notification service ensures that users are always kept up-to-date on the distribution platform.

[0176] In addition, the push notification service 730 can be operably 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 the collected data and performing advanced analytics, predictive modeling, and anomaly detection, the push notification service can complement such operations by ensuring that these insights are promptly communicated to mobile device users.

[0177] In a non - limiting example, the AAML module can be configured to identify seasonal patterns or predict 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 predictions related to inventory optimization, ensuring inventory availability during peak seasons and minimizing excess inventory costs. The integration of the push notification service 710 ensures that the actionable insights generated by the AAML module are delivered to relevant personnel in a timely manner.

[0178] Furthermore, 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 fully utilized for targeted / behavioral marketing and personalized customer experiences. For example, when the AAML module identifies cross - selling or upselling opportunities and recommends relevant products to individual customers, the push notification service can directly deliver these recommendations to the user's mobile device, enabling immediate action.

[0179] In addition to communicating the insights derived via the AAML module, the push notification service 730 can also be integrated internally with the analytical capabilities from AAML. By analyzing data from various sources such as social media feeds, customer reviews, market trends, etc., the AAML module can provide sentiment analysis and trend identification. These insights can be used to customize the content of the notifications delivered by the push notification service 730, ensuring alignment with consumer and market sentiment and preferences.

[0180] Moreover, the integration and interoperability features related to the data flow established by integration protocols, APIs, and data connectors enable the push notification service to access relevant data generated and analyzed by the AAML module, thereby improving the accuracy and relevance of the notifications.

[0181] Thus, the push notification service 730 bridges the RTDM module, the AAML module, and the end - users of the system 700. It ensures that the valuable insights and recommendations generated by the AAML module are delivered to users in real - time or near real - time, enabling them to stay informed with real - time information and enhancing their decision - making capabilities within the supply chain ecosystem. This integration emphasizes the system's commitment to real - time data - driven decision - making, providing the intelligence to optimize distribution operations. Additionally, the push notification service 730 can operate effectively at scale by combining the RTDM architecture and robust AAML processing to handle a large volume of real - time notifications and insights, while incorporating error / retry mechanisms to address transient delivery issues.

[0182] The image recognition and SKU mapping engine 740 makes full use of the camera of the mobile device to scan product images. Advanced AI algorithms process these images to identify the products and map the products to their respective stock - keeping units (SKUs). This dynamic SKU creation process simplifies the sorting process, reducing manual data entry and potential errors.

[0183] In an embodiment, the image recognition and SKU mapping engine 740 is directly integrated with the camera of the mobile device. This integration allows the user to efficiently scan product images without any external applications or software. Once the user captures an image, the system initiates 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, breaking it down into recognizable components. This analysis includes examining the colors, shapes, textures, and any markings or labels present on the product.

[0184] In a non - limiting example, consider a user scanning an image of a blue cylindrical object with a specific brand label. The algorithm first 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 recognition even when the image of the product may not be of the highest quality.

[0185] After the analysis, the image recognition and SKU mapping engine 740 maps the identified product to its corresponding Stock Keeping Unit (SKU). An SKU represents a unique identifier for each different product and variant. For enterprises, SKUs facilitate inventory tracking, order processing, and other logistics tasks.

[0186] The engine 740 employs a dynamic SKU creation process. When a product is first identified, this dynamic approach creates a new SKU rather than relying on pre - existing SKUs. This feature proves particularly beneficial for enterprises that are constantly introducing new products or variations. The dynamic SKU creation process ensures that every product, regardless of its novelty, is mapped to a unique identifier.

[0187] This dynamic process reduces the need for manual data entry. In a conventional system, a user may need to manually enter details about a new product and assign it to an SKU. However, with the image recognition and SKU mapping engine 740, this manual process has largely become obsolete. The engine's ability to dynamically generate SKUs not only saves time but also minimizes potential data entry errors.

[0188] Furthermore, the integration of the image recognition and SKU mapping engine 740 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 aids in efficient order processing.

[0189] In addition, the AI algorithm of the engine 740 continuously learns and adapts. As each product image is processed, the algorithm improves its recognition ability. Over time, this iterative learning process increases the accuracy of the engine and reduces the likelihood of misidentification. For enterprises, this means that as more products are scanned and processed, the system becomes more reliable and efficient.

[0190] In another embodiment, the engine 740 is also integrated with external data sources. These sources can provide additional product information such as product specifications, pricing, and supplier details. When the engine identifies a product, it can retrieve this additional information, thereby enhancing the depth of product details available to the user.

[0191] In some embodiments, the image recognition and SKU mapping engine 740 can be configured as an element of the AAML module, another module, or a separate element of the system 700. Thus, the image recognition and SKU mapping engine 740 is configured to process item identification, order processing, and other tasks by making full use of the mobile device. By making full use of the imaging components of the mobile device and coupling them to advanced AI algorithms (and the dynamic SKU creation process), the engine simplifies operations, reduces errors, and streamlines the workflow. With its continuous learning ability and integration possibilities, the engine is poised to become an indispensable tool for modern enterprises.

[0192] The offline data cache 750 enhances usage in scenarios with limited or no internet connectivity. The mobile application includes the offline data cache 750, which locally stores critical data on the user's device. In some non-limiting examples, when the connection is restored, information related to order status, product details, and other necessary data is cached and synchronized with the backend server. This ensures uninterrupted functionality and access to important information even in an offline environment.

[0193] In one embodiment, the offline data cache 750 serves as a fundamental component of the mobile application, which is designed for optimal performance in various connectivity scenarios. Recognizing the unpredictability of network connections, especially in remote or crowded areas, the offline data cache 750 provides the user with uninterrupted access to essential data.

[0194] The core function of the offline data cache 750 revolves around locally storing critical data on the user's device. By caching data locally, the system ensures that the user can access relevant information even when the device loses its internet connection. This local storage mechanism eliminates the need for real-time server interactions for each data retrieval, thereby enhancing application responsiveness and reducing latency.

[0195] In a non - limiting example, a user attempting to access order status while traveling in an area with unstable internet coverage can benefit from cached offline data. The mobile application extracts the required data from the offline data cache 750 instead of retrieving it in real - time from the backend server, which may be hindered due to connectivity issues. This ensures that the user obtains the necessary information without any delay.

[0196] Additionally, the offline data cache 750 processes various types of data, including product details, order status, user preferences, etc. When the user accesses specific data, the mobile application caches it, allowing for faster subsequent retrievals. Proactive caching can optimize the user experience by reducing redundant data transfers.

[0197] In some embodiments, the offline data cache 750 may include a synchronization mechanism. Once the device regains an internet connection, the system initiates a synchronization algorithm with the backend server. This process ensures that the locally stored data remains up - to - date and consistent with the main data source. During this synchronization, any changes made by the user offline, such as modifications to new or existing orders, can be updated on the backend server.

[0198] The synchronization process also takes into account potential data conflicts. In cases where the same data has undergone changes both offline on the device and on the backend server, the system employs conflict - resolution protocols. These protocols determine the most recent or preferred changes, ensuring data integrity across platforms.

[0199] Furthermore, the offline data cache 750 contains security measures to identify the sensitivity of the data stored locally. The system can be configured to encrypt the cached data. Encryption protects the offline data from unauthorized access, mitigating the risks associated with cached data in the event of a device attack.

[0200] Thus, the offline data cache 750 provides uninterrupted access to important data in offline scenarios. By locally storing critical data, synchronizing with the backend server when online, and implementing security measures, the offline data cache 750 enhances the overall application functionality and user satisfaction.

[0201] The security and authentication layer 760 ensures the security of user data and transactions is of utmost importance. The security and authentication layer 760 employs robust encryption mechanisms to protect sensitive information both at rest and in transit. It implements authentication protocols to verify user identities, thus granting access only to authorized users. This layer is necessary for maintaining data integrity and protecting user privacy.

[0202] The integration module 770 integrates the mobile application with the backend system of the cloud distribution platform. This integration ensures the continuous flow of real-time data between the application and the platform's servers. It also facilitates secure and efficient transactions, including order placement, payment processing, and inventory management.

[0203] The device compatibility module 780 enables the diversity of mobile devices and operating systems. This architecture ensures that the mobile application is 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.

[0204] Offline Data Cache 750: Although mainly focused on local storage of data for offline access, the offline data cache relies on RTDM for data synchronization. When the mobile application is online and connected to RTDM, it updates the cached data to reflect any changes that occur in real time. This synchronization ensures that the cached data remains up-to-date and accurate.

[0205] Figure 7 An embodiment of the mobile application architecture within the broader context of the distribution platform is depicted. Its features, including real-time push notifications, image scanning for SKU identification and other processes, local mobile capabilities, and offline data caching, collectively contribute to a seamless distribution experience. This experience not only provides real-time insights but also ensures uninterrupted availability even in scenarios where internet connectivity is limited or unavailable.

[0206] Figure 8 Depicts as Figure 7 the process flow 800 of real-time data integration, analysis, and notification in the mobile application system 700 as detailed in. The process flow 800 depicts operations for performing data assimilation, instantaneous processing, and integration within the RTDM and AAML ecosystems.

[0207] In operation 801, the system initializes the data layer, RTDM, and the preprocessing stage. The data layer is designed to utilize a distributed database strategy such as NoSQL to store structured and unstructured data. RTDM continuously extracts data from systems such as ERP, CRM, and other platforms. It employs a distributed data architecture to synchronize data in real time. During preprocessing, signal processing methods including Fourier and wavelet transforms remove noise. Machine learning techniques such as PCA and t-SNE extract data features and prioritize them.

[0208] In operation 802, the data is routed to the Advanced Analytics and Machine Learning (AAML) engine. Here, deep learning algorithms such as BERT or GPT variants process the text data to extract semantic associations and patterns.

[0209] In operation 803, the decision-making construct processes the output from the AAML engine. A series of algorithms from decision trees to Bayesian networks derive appropriate actions based on the analyzed data.

[0210] In operation 804, the push notification service can be activated based on an event-driven architecture (EDA), a publish-subscribe system, or another suitable method.

[0211] Using the EDA approach, the service can respond to different data patterns or states that are considered events. These events typically come from a real-time data grid (RTDM) that consistently examines specific patterns in the data stream. When the RTDM identifies a significant pattern, it registers it as an event, thus prompting the push notification service. The service can also work with advanced analytics and machine learning (AAML) techniques, enabling it to dynamically detect and address changing data patterns.

[0212] Additionally or alternatively, using a publish-subscribe system, the push notification service uses a protocol such as MQTT (Message Queuing Telemetry Transport), which is 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 necessary. Here, a message generator called a publisher creates messages without naming certain receivers. These messages, arranged into topics, are processed by a broker. Subscribers express interest in certain topics, causing the broker to filter and distribute messages based on these preferences. The inherent quality of service (QoS) levels of the MQTT protocol ensure different delivery guarantees. In a publish-subscribe architecture using MQTT, there are two main roles: the publisher, which sends messages to a central broker; and the subscriber, which receives specific types of messages by subscribing to topics on the broker. MQTT is well-known for its simplicity and efficiency, offering features such as quality of service (QoS) levels for message reliability, retained messages, last will and testament (LWT), and security options such as TLS / SSL for encrypted communication. Alternatives to MQTT can include (but are not limited to) AMQP (Advanced Message Queuing Protocol), CoAP (Constrained Application Protocol), WebSocket, and HTTP / HTTPS, to list some alternative protocols and use cases with specific characteristics.

[0213] In operation 804, the choice between EDA Pub-Sub or other methods depends on the specific process and the potential response time. If the push notification uses RTDM or AAML to a large extent and invokes earlier operations, the EDA approach may be advantageous. However, if the system requires efficient communication between many separate entities, the Pub-Sub system may be appropriate. This choice can affect subsequent operations and the overall performance of the system.

[0214] In operation 805, the push notification service operates in close conjunction with the Advanced Analytics and Machine Learning (AAML) module to enhance the system's real-time communication capabilities. This process involves leveraging data patterns and derived insights to deliver highly customized notifications to end-users. Here, we explore the technical details without unnecessary embellishments:

[0215] Operation 805 may include delivering relevant information to users. The push notification service and the AAML module are integrated to achieve refined insights. The AAML module extracts valuable insights from the extensive dataset maintained by the system. These insights are derived through advanced analytics techniques, predictive modeling, and anomaly detection. Additionally, the AAML module is excellent at identifying and decrypting complex data patterns, which is crucial for real-time decision-making within the system.

[0216] Within the context of operation 805, the main focus is on leveraging the insights generated by the AAML engine. These insights are used as the basis for crafting highly customized notifications that are dispatched to relevant users. The notifications can serve as critical alerts, thereby providing timely and relevant information to the system users.

[0217] For example, when the AAML engine identifies a business event of significance, it initiates the notification process. The event can cover a wide range of scenarios, from sudden market fluctuations to inventory-related anomalies detected within the system's data. The AMML engine's ability to quickly identify these events enables sophisticated analytical capabilities.

[0218] A key aspect of process flow 800 is the insights of the AAML module integrated with the push notification service. This integration ensures that the system can respond in real-time to emerging patterns or anomalies, thereby converting data-driven insights into actionable notifications. Additionally, such notifications can be customized to fit the specific context of the detected event or pattern.

[0219] The customization of notifications covers various parameters, including the type of event, its importance, and the target audience. The push notification service is equipped with the ability to classify and prioritize events, thereby ensuring that critical notifications are promptly delivered to the appropriate recipients. This prioritization is necessary to prevent information overload and ensure that the most urgent matters receive immediate attention.

[0220] The foundation of this operation is a robust and efficient notification delivery mechanism. The push notification service follows standardized protocols, such as Firebase Cloud Messaging (FCM) for Android and Apple Push Notification Service (APN) for iOS. These protocols are meticulously configured and integrated to meet the specific requirements of the platform, thereby ensuring the reliable and secure delivery of notifications.

[0221] For Android devices, integration with Firebase Cloud Messaging (FCM) optimizes the handling of notifications. The push notification service can configure cloud functions to act as triggers for sending notifications, thus aligning FCM with the system's event-driven architecture. This approach improves the efficiency of notification delivery on the Android platform.

[0222] Similarly, for iOS devices, the push notification service integrates with the Apple Push Notification service (APN). This integration requires a series of specific configuration steps, including the management of password keys, the setup of an Apple developer account, and the integration of APN into the system's event-driven architecture. These measures establish a secure and efficient communication channel with iOS devices.

[0223] Operation 805 executes a process for delivering real-time, data-driven insights to its users. It leverages the ability of the AAML module to identify patterns, anomalies, and critical business events, converting this information into actionable notifications. These notifications (meticulously customized and prioritized) empower stakeholders with the timely information they need to make informed decisions within the system's dynamic and competitive landscape. From data analysis to notification delivery, the technical precision of this operation underscores the system's commitment to optimizing communication and facilitating real-time response.

[0224] In operation 806, the system embeds an adaptive feedback mechanism to optimize its operation. In some embodiments, operation 806 includes processing and / or training a reinforcement learning model, such as Proximal Policy Optimization, where the system learns to fine-tune its responses based on its actions.

[0225] In operation 807, the system protects interaction logs, decisions, and necessary metadata through encryption. Methods such as AES (Advanced Encryption Standard) and elliptic curve cryptography ensure data protection.

[0226] Figure 9AIt is a flowchart of Method 900A for image capture and SKU mapping within a mobile application system that utilizes advanced AI algorithms, real-time data integration, and dynamic SKU creation. This method simplifies the process of identifying products and mapping them to their corresponding Stock Keeping Units (SKUs), thereby ultimately streamlining the ordering process and reducing manual data entry errors. Method 900A outlines a simplified and efficient process that leverages the capabilities of a mobile application-based SPoG UI to facilitate image discrimination and SKU identification. By integrating real-time data, collaborative decision-making, and role-based access control capabilities, the mobile SPoG UI enables users to effectively manage and optimize the distribution process. Based on the disclosure herein, the operations in Method 900A can be performed in a different order and / or altered to accommodate specific implementation requirements. The process can involve a series of technical operations or sequences of technical operations designed to maximize efficiency and precision within a Real-Time Data Mesh (RTDM) as well as an Advanced Analytics and Machine Learning (AAML) module.

[0227] In operation 901, the process begins with image capture and processing. The user employs a mobile application to capture product images using the camera of their mobile device. These images are meticulously analyzed by advanced AI algorithms. The algorithms closely examine various visual attributes, including the color, shape, texture, labels, and markings of the product. For example, if the user captures an image of a blue cylindrical object with a specific brand label, the algorithm initially identifies the cylindrical shape, proceeds to distinguish the blue color, and subsequently determines the brand and other relevant details. Even when the image quality is sub-optimal, this rigorous analysis ensures accurate product identification.

[0228] After the image analysis in operation 902, the image recognition and SKU mapping engine engages in dynamic SKU creation. Different from traditional 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, thereby significantly reducing the likelihood of errors and simplifying the SKU creation process.

[0229] In operation 903, the generated SKUs are integrated with the data layer of the system in real-time. This integration ensures that the SKU information remains current and readily accessible for various operations within the mobile application. Real-time data synchronization is facilitated by the Real-Time Data Mesh (RTDM) module, which enables data exchange and synchronization, thereby making the SKU information available for stakeholders to make informed decisions and expedite order processing.

[0230] In operation 904, the image recognition and SKU mapping engine performs continuous learning and adaptation. By leveraging each processed product image, the engine's AI algorithms refine their capabilities. This iterative learning process improves the engine's accuracy over time, reducing the likelihood of misidentifications. As the system processes more products, it gradually becomes reliable and efficient.

[0231] Additionally, operation 905 can include integration with external data sources. In certain configurations, the engine collaborates with external data sources to augment product information. These external data sources can contribute supplementary details such as product specifications, pricing, and vendor information. When a product is recognized, the engine retrieves this additional data, enriching the breadth of product details available to the user.

[0232] Operation 906 configures the 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 use the user's mobile device to manage item recognition, order processing, and related tasks. By leveraging the mobile device's imaging components and advanced AI algorithms, the engine simplifies operations, reduces errors, and streamlines workflows, ultimately evolving into an indispensable tool for modern enterprises.

[0233] Finally, in operation 907, the integration of the engine with other enterprise systems facilitates real-time inventory updates. Once a product is recognized and mapped to its SKU, the system promptly updates the inventory database. This real-time update mechanism ensures accurate inventory levels and contributes to efficient order processing within the overall distribution ecosystem.

[0234] The image capture and SKU mapping method 900A within the mobile application system performs real-time data integration, dynamic SKU creation, continuous learning, and integration with external data sources and enterprise systems. This method improves the efficiency and accuracy of product identification and SKU management, building on robust technical capabilities.

[0235] Figure 9B is a flowchart of method 900B for an enhanced product finding process within a mobile application system. Method 900B enables users to interact with the system by leveraging advanced search algorithms, RTDM operations, and real-time personalization to enhance product discovery and exploration. By focusing on user-centric design and efficient information retrieval, this process aims to provide a highly customized and responsive user experience. This description will guide you through each step of the operation to clarify the technical intricacies that enable this enhanced product finding functionality.

[0236] Figure 9BA flowchart of method 900B for an enhanced product search process using image recognition within a mobile application system is shown. Method 900B allows a user to search for products by capturing an image using the camera of their mobile device, leveraging sophisticated image recognition algorithms, RTDM operations, and real-time personalization.

[0237] Image recognition-based product search in a mobile application system provides the user with the ability to quickly obtain product details by taking a photo of an item. This feature enhances user satisfaction and facilitates efficient consumer decision-making.

[0238] In operation 911, the process begins with image capture and upload. The user uses the camera of their mobile device to capture a photo of the product. The image is then uploaded to the mobile application system for processing.

[0239] In operation 912, an image recognition engine (e.g., engine 740) processes the uploaded image. Engine 740 uses advanced AI algorithms to analyze the features and attributes of the image, such as color, shape, texture, and any labels or markings. Based on this analysis, the engine matches the product to its corresponding stock keeping unit (SKU) by comparing the image to a comprehensive database of product images housed in the RTDM. The RTDM implemented in data layer 710 facilitates real-time data exchange and synchronization, ensuring current and accurate information.

[0240] Operation 912 involves the image recognition engine acting on the uploaded image. Engine 740 with artificial intelligence capabilities examines the details present within the image. The functions of engine 740 include using AI algorithms to identify various features and attributes of the image. These include elements such as color patterns, shape contours, texture characteristics, and any labels or markings regarding the product within the image. After deciphering the characteristics of the image, engine 740 identifies the product. It matches the analyzed attributes of the product to its corresponding stock keeping unit or SKU. This process involves comparing the uploaded image to a database of product images. This product image database is maintained in the RTDM via data layer 710. The RTDM system facilitates data exchange and synchronization, ensuring current and accurate information.

[0241] The RTDM ensures that data exchange occurs in real time. It also oversees the synchronization of data, ensuring that the information in the system is current and accurate. When a user uploads an image of a product with details that have been recently updated in the product database, the RTDM ensures that engine 740 accesses current information during the identification process. This minimizes potential mismatches.

[0242] In operation 913, a query can be executed via RTDM. The recognized product details from the image recognition process serve as parameters for querying the RTDM. The RTDM integrated with the data layer 710 has a purpose-built data repository optimized for specific data types such as product data, which includes SKU details, descriptions, and inventory levels. The search retrieves items in the RTDM that match or are similar to the recognized product of the image. The RTDM module also includes data from the system of record layer, including data from various enterprise systems such as ERP. The product details previously recognized from the image recognition program are used as the input criteria for this search within the RTDM.

[0243] The RTDM implemented via the data layer 710 contains specialized data repositories. These data repositories are specifically designed for different types of data. For example, there is a data repository that mainly caters to product-related information. In this data repository, there are details such as SKU identifiers, product descriptions, and available inventory levels.

[0244] Once the query starts, the RTDM retrieves entries that match or are very similar to the product details recognized from the image. It is crucial to note that the accuracy of the results depends on the specificity of the recognized product features.

[0245] In addition, the RTDM is not limited to its own internal database. It also interfaces with the system of record layer. This layer aggregates data from various enterprise systems. Notably among these systems is the enterprise resource planning or ERP system. By accessing data from these systems, the RTDM ensures comprehensive and rich product details, thereby enhancing the accuracy of the recognition and matching process.

[0246] In operation 914, a filtering and sorting process is performed on the retrieved results. The filtering criteria can include price, brand, or category. Sorting uses an algorithm that takes into account user preferences and previous searches to organize the products. The interaction between the data layer / RTDM module enables the retrieval of a sorted and filtered product list consistent with the latest available information.

[0247] In operation 915, the mobile application displays the filtered results to the user. The interface presents the recognized product with essential details such as name, image, price, and description.

[0248] In operation 916, the user can access in-depth product information. After selecting a product from the displayed results, the application provides further details, including specifications, reviews, related items, and availability.

[0249] In operation 917, user feedback and personalization are performed. The mobile application collects user feedback on the accuracy and relevance of the identified products. This data helps to refine the image recognition algorithm. Additionally, the system uses machine learning to customize future product recommendations based on user feedback and search history.

[0250] Figure 10 Method 1000 for managing an offline data cache (such as offline data cache 750 of a mobile application architecture) in a mobile application is shown. As noted above, offline data cache 750 is designed to optimize performance and ensure uninterrupted access to critical data in situations with limited or no internet connectivity. This description provides a process flow for performing functions related to the offline data cache.

[0251] In operation 1001, when the mobile application detects limited or no internet connectivity, offline data cache 750 is triggered to ensure continued access to critical data.

[0252] Operation 1002 involves caching necessary data types on the user device, including order status, product details, and user preferences. This caching process utilizes a local storage mechanism (such as a database on the device) to efficiently store the data. Operation 1002 may include systematically caching critical data categories (such as order status, product details, and user preferences) onto the local storage device of the user device. It uses an efficient data storage mechanism such as an SQLite database to effectively manage and store data locally.

[0253] When the user accesses specific data, in operation 1003, the system retrieves the required information from the locally cached offline data cache. This process involves querying the database on the device and retrieving the data without making real-time server requests, which enhances application responsiveness and reduces latency. During this stage, the mobile application performs data retrieval by accessing specific information from the locally cached offline data cache. Structured SQL queries are used to perform precise and fast data retrieval directly from the database on the device. This method eliminates the need for real-time server requests, thus optimizing application responsiveness and reducing latency.

[0254] Operation 1004 covers the synchronization mechanism. When the device regains an Internet connection, the offline data cache initiates synchronization with the backend server to update the data stored locally. This synchronization process involves a data differential algorithm to identify the changes made offline and transmit these changes to the server. Data synchronization also involves a conflict detection mechanism to handle the situation where multiple changes occur simultaneously, thus ensuring data consistency. This operation is initiated when the device re-establishes an Internet connection. The offline data cache coordinates with the backend server to synchronize and update the data cached locally. It employs advanced data differential algorithms, such as Delta encoding or binary differencing, to identify the relevant data changes and send only the relevant data changes to the server. This simplified synchronization process minimizes the data transfer overhead.

[0255] In the case of data conflicts, Operation 1005 employs conflict resolution protocols. These protocols utilize timestamp - based or version - based strategies to determine the most recent or prioritized changes, thus ensuring data integrity across platforms. Conflict resolution may involve merging conflicting changes or, in some cases, prompting user intervention. In the case of concurrent data changes, this operation effectively manages data conflicts. It utilizes precise conflict resolution protocols that rely on timestamp or version control mechanisms to determine the most recent or prioritized data changes. Algorithms such as last - write - wins or three - way merge can be applied to ensure data consistency between the device and the backend.

[0256] Operation 1006 emphasizes the security measures implemented within the offline data cache. It can be configured to use industry - standard encryption algorithms such as AES (Advanced Encryption Standard) to encrypt the cached data. This encryption protects the offline data from unauthorized access and potential risks associated with an attacked device. Secure key management practices are used to control access to the encrypted data. Operation 1006 can include protecting the data cached locally. A robust encryption algorithm such as AES - 256 is used to encrypt the cached data. A comprehensive key management approach including 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 utilize hardware acceleration for enhanced performance.

[0257] Therefore, Method 1000 includes a series of operations for providing uninterrupted access to essential data in an offline scenario. These operations include efficient data caching, data retrieval from local storage, complex data synchronization, conflict resolution mechanisms, and robust data encryption to enhance application functionality and ensure data security in challenging connectivity situations.

[0258] Figure 11A block diagram of an example component of device 1100. One or more computer systems 1100 can be used to implement, for example, any of the embodiments discussed herein, as well as any combinations and sub - combinations thereof. The computer system 1100 can include one or more processors (also referred to as central processing units or CPUs), such as processor 1104. The processor 1104 can be connected to a communication infrastructure or bus 1106.

[0259] The computer system 1100 can also include user input / output device(s) 1103, such as monitors, keyboards, pointing devices, etc., which can communicate with the communication infrastructure 1106 through user input / output interface(s) 1102.

[0260] One or more processors 1104 can be a graphics processing unit (GPU). In one embodiment, the GPU can be a processor that is a dedicated electronic circuit designed to process math - intensive applications. The GPU can have a parallel architecture that is efficient at performing parallel processing of large blocks of data (common math - intensive data such as for computer graphics applications, images, videos, etc.).

[0261] The computer system 1100 can also include main memory or primary storage 1108, such as random access memory (RAM). The primary storage 1108 can include one or more levels of cache. Control logic (i.e., computer software) and / or data can be stored in the primary storage 1108.

[0262] The computer system 1100 can also include one or more secondary storage devices or memories 1110. The secondary storage 1110 can include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0263] The removable storage drive 1114 can interact with a removable storage unit 1118. The removable storage unit 1118 can include a computer - usable or readable storage device on which computer software (control logic) and / or data is stored. The removable storage unit 1118 can be a program cartridge and cartridge interface (such as those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. The removable storage drive 1114 can read from and / or write to the removable storage unit 1118.

[0264] The auxiliary storage 1110 may include other devices, apparatuses, components, means, or other paths for allowing the computer system 1100 to access computer programs and / or other instructions and / or data. Such devices, apparatuses, components, means, or other paths may include, for example, a removable storage unit 1122 and an interface 1120. Examples of the removable storage unit 1122 and the interface 1120 may include a program cartridge and a cartridge interface (such as those present in video game devices), a removable memory chip (such as an EPROM or PROM) and an associated socket, a memory stick and a USB port, a memory card and an associated memory card slot, and / or any other removable storage unit and associated interface.

[0265] The computer system 1100 may also include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate and interact with any combination of external devices, external networks, external entities, etc. (collectively and individually referred to by the reference numeral 1128). For example, the communication interface 1124 may allow the computer system 1100 to communicate with an external or remote device 1128 via a communication path 1126, which may be wired and / or wireless (or a combination thereof), and may include any combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to and from the computer system 1100 via the communication path 1126.

[0266] The computer system 1100 may also be any one of a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet computer, a smart phone, a smart watch or other wearable device, an appliance, a part of the Internet of Things, and / or an embedded system (to name just a few non-limiting examples), or any combination thereof.

[0267] The computer system 1100 may be a client or a server that accesses or hosts any application and / or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; local or on-premises software (“local” cloud-based solutions); “as a service” models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.); and / or a hybrid model that includes any combination of the foregoing examples or other services or delivery paradigms.

[0268] Any available data structures, file formats, and schemas in computer system 1100 can 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), message packets, XML User Interface Language (XUL), or any other functionally similar representations, either alone or in combination. Alternatively, proprietary data structures, formats, or schemas can be used exclusively or in combination with known or open standards.

[0269] In some embodiments, a tangible non-transitory device or article of manufacture including a tangible non-transitory computer-usable or readable medium having control logic (software) stored thereon may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer system 1100, main memory 1108, secondary memory 1110, and removable storage units 1118 and 1122, as well as tangible articles of manufacture implementing any combination of the foregoing. When executed by one or more data processing devices, such as computer system 1100, such control logic can cause such data processing devices to operate as described herein.

[0270] Figures 12A to 12Q Depicts various screens and functions of the SPoGUI related to vendor login, partner dashboard, customer shopping cart, order summary, SKU generation, order tracking, shipped goods tracking, subscription history, and subscription modification. A detailed description of each figure is provided below:

[0271] Figure 12A Depicts a vendor login startup screen representing the initial steps of the vendor login process. It provides a form or interface where vendors can express their interest in joining the supply chain ecosystem. Vendors can enter their basic information, such as company details, contact information, and product catalogs.

[0272] Figure 12B Depicts a vendor login guide that shows a step-by-step guide or checklist for vendors to follow during the login process. It outlines the necessary tasks and requirements, ensuring that vendors have a clear understanding of the login process and can proceed smoothly.

[0273] Figure 12C Depicts a vendor login call scheduler that facilitates scheduling calls or meetings between vendors and platform affiliates or representatives responsible for guiding them through the login process. Vendors can select a suitable time slot or request a call, ensuring effective communication and assistance throughout the login journey.

[0274] Figure 12DDepicts a vendor login task list, which presents a comprehensive task list or dashboard that outlines the specific steps and actions required for a successful vendor login. It provides an overview of pending tasks, completed tasks, and upcoming deadlines, thus helping vendors track their progress and ensure the timely completion of each login task.

[0275] Figure 12E Depicts a vendor login completion screen, which confirms the successful completion of the vendor login process. It can display a congratulatory message or summary of the completed tasks, indicating that the vendor is now officially logged into the supply chain ecosystem.

[0276] Figure 12F Depicts a partner dashboard that provides a centralized view of relevant information and metrics related to their partnership with the supply chain ecosystem for partners or stakeholders. It offers a summary of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0277] Figure 12G Depicts a customer product shopping cart that represents the customer's product shopping cart, where they can add items they wish to purchase. It displays a list of the selected products, quantities, prices, and other relevant details. Customers can view and modify the contents of their shopping cart before proceeding to the checkout process.

[0278] Figure 12H Depicts a customer subscription shopping cart that allows customers to manage their subscription-based purchases. It shows the selected subscription plans, pricing, and duration. Customers can view and modify their subscription details before finalizing their selections.

[0279] Figure 12I Depicts a customer order summary that provides an overview of the customer's order, including details such as the purchased products or subscriptions, quantities, pricing, and any applied discounts or promotions. It allows customers to view their order before confirming the purchase.

[0280] Figure 12J Depicts a vendor SKU generation screen for generating unique stock keeping unit (SKU) codes for vendor products. It can include fields or options where the vendor can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU code.

[0281] Figure 12K and Figure 12L Depicts a dashboard order summary that displays summary information about orders placed within the 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 activities, enabling stakeholders to efficiently track and manage orders.

[0282] Figure 12M Depicts a customer subscription shopping cart that allows customers to add, modify, or unsubscribe from a plan. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0283] Figure 12N Depicts a customer order tracking screen that enables customers to track the status and progress of their orders within the supply chain. It shows real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and anticipate delivery times.

[0284] Figure 12O Depicts customer shipment tracking that provides customers with real-time tracking information about their shipments. It can include details such as the vehicle, tracking number, current location, and estimated delivery date. Customers can stay informed about the whereabouts of their shipments.

[0285] Figure 12P Depicts a customer subscription history that presents the history of a customer's subscription activities. It shows a list of previous subscriptions, including the subscription plan, duration, and status. Customers can view their subscription history, track past payments, and refer to previous subscription details.

[0286] Figure 12Q Depicts a customer subscription modification dialog that allows customers to modify their existing subscriptions. It provides options for upgrading or downgrading the subscription plan, changing billing details, or adjusting other subscription-related preferences. Customers can manage their subscriptions according to their evolving needs or preferences.

[0287] Figures 13A to 13F Depicts various screens and functions of a mobile application-based SPoGUI related to vendor login, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipment tracking, subscription history, and subscription modification. A detailed description of each figure is provided below:

[0288] Figure 13APresents the main screen, which uses the described RTDM and AAML architectures to thoughtfully curate and display the top product picks customized to the customer's preferences and browsing history. This interface serves as the gateway to an optimized shopping experience, presenting a selection of products specifically chosen to align with the customer's interests. By leveraging advanced recommendation algorithms, the system identifies products that are highly likely to resonate with the customer. These top picks cover a wide range of categories, from electronics and accessories to clothing and fashion items. Each product can be accompanied by a brief description, high-quality image, and pricing details, enabling the customer to effortlessly explore and make informed choices. The intuitive layout of the main screen ensures that the customer can browse the displayed products and initiate a personalized shopping journey. By utilizing user-centered design and a responsive interface, this screen accommodates various devices and screen sizes, ensuring consistent accessibility and user satisfaction.

[0289] Figure 13B Depicts the customer search results (e.g., "cables") screen within the interface of the mobile application, which is designed, for example, to provide an efficient and precise search experience for users seeking specific cable products. The user can initiate a search using cable-related keywords, product codes, or specifications, and the system responds with a clear and organized list of relevant search results. Each search result entry provides the necessary details, including product name, image, specifications, and pricing information. The interface is meticulously designed for quick and effortless navigation, allowing the user to rapidly identify the cable product that meets their requirements. Whether the user is searching for HDMI cables, Ethernet cables, or any other type of cable, this screen simplifies the search process, ensuring that the user can easily find the right cable. The responsive design of the interface ensures an efficient experience across various devices and screen sizes, promoting user satisfaction and facilitating efficient product discovery.

[0290] Figure 13C Depicts the customer favorites screen, which is designed to provide the customer with quick access to their preferred and saved items. This screen enables the user to conveniently store and revisit products of interest. The favorites screen presents a comprehensive view of the customer's selected items, ranging from fashion ensembles and tech accessories to household essentials and more. Each product is accompanied by detailed information, including description, image, and pricing, enabling the customer to effortlessly recall their selections. The intuitive interface allows for seamless navigation and management of favorite items, including the option to add or remove products as preferences evolve. The favorites screen enhances the shopping experience by providing a curated collection of the customer's most desired items, streamlining the purchasing process, and ensuring a personalized shopping approach. By leveraging user-friendly design and a responsive layout, this screen accommodates various devices and screen sizes, providing a consistent and enjoyable user experience.

[0291] Figure 13D Depicts a customer order tracking screen in a mobile application that enables customers to track the status and progress of their orders within the supply chain. It shows real-time updates on order fulfillment, including processing, packing, and shipping. Customers can monitor the movement of their orders and anticipate delivery times.

[0292] Figure 13E Depicts a customer quote list screen within the interface of a mobile application. This screen serves as a comprehensive overview of all the orders quoted to the customer. Users can efficiently navigate through their quote list, where each entry provides a summary of the quoted order, including product description, quantity, pricing breakdown, and may include an estimated delivery timeline. The screen features a clean and intuitive design, consistent with the user-centered approach of the mobile application's UI philosophy. Customers can easily view their quote list, facilitating informed purchasing decisions. Responsive elements are integrated into the interface, enabling customers to access more details or take actions on specific quotes with a simple tap or gesture.

[0293] Figure 13F Depicts a customer quote details screen that provides users with a deeper overview of the details of the selected quote. This screen presents the detailed breakdown of the quoted order, including a comprehensive product description, exact quantity, line-item pricing, and optional information such as a delivery schedule. The interface maintains its user-friendly and responsive design, ensuring that customers can thoroughly view and verify the accuracy of the quoted information. Within the customer quote details screen, customers have the ability to accept, modify, or decline the quoted order, streamlining their interaction with the system. Additionally, the screen is designed for real-time updates, guaranteeing that any changes or revisions to the quote are promptly reflected, facilitating transparent and efficient communication between the customer and the system.

[0294] The depicted UI screens are not restrictive. In some embodiments, Figures 12A to 12Q the UI screens of 13 and 13A through 13F together represent the various functions and features provided by the SPoG UI, providing a comprehensive and user-friendly interface for stakeholders for vendor login, partnership management, customer interaction, order management, subscription management, and tracking within the supply chain ecosystem.

[0295] A system of one or more computers can be configured to perform particular operations or actions by causing software, firmware, hardware, or a combination thereof to be installed on the system, which in operation causes the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by including instructions that, when executed by a data processing apparatus, cause the apparatus to perform the actions.

[0296] In one general aspect, a computer-implemented method can include integrating multiple communication channels (i.e., touchpoints) among user groups into a unified interactive interface in a computer system, the unified interactive interface being referred to herein as the SPoG UI, where the SPoG is a central interface component configured to merge user interactions, data, and / or functions of user groups, and where the SPoG UI is arranged to facilitate operations across a supply chain ecosystem. The computer-implemented method can also include managing the end-to-end lifecycle of user interactions using the SPoG UI. The method can also include collecting data from the user interactions within the SPoG UI. Additionally, the method can include analyzing the collected data to generate one or more insights for business growth. Further, the method can include performing one or more artificial intelligence and / or machine learning algorithms to enhance business operations based on the analyzed data. The method can also include incorporating routine updates and improvements into the SPoG UI based on the analyzed data. Additionally, the method can include the case where the user groups can include users selected from two or more different groups, the groups having distributors, resellers, customers, end customers, sellers, and suppliers. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0297] Embodiments can include one or more of the following features. A method where the integration includes establishing communication links with multiple pre-existing business platforms. A method where the merged touchpoints can include one or more websites, customer relationship management systems, seller platforms, and supply chain management systems. A method where the management of the end-to-end lifecycle can include one or more of initial contact, service fulfillment, and subsequent interactions. A method where collecting data can include monitoring and / or officially recording user activities within the SPoG UI. A method where advanced statistical algorithms are used to perform the analysis of the collected data. A method where the artificial intelligence and machine learning algorithms include predictive analytics to identify market trends. A method where the artificial intelligence and machine learning algorithms include a recommendation system for personalizing user interactions. The improvements can be based on the analysis received through the SPoG UI and / or the analyzed user feedback. Embodiments of the described technology can include hardware, methods, or processes, or computer tangible media.

[0298] In one general aspect, a system can include a communication integration module configured to integrate multiple communication channels (i.e., contacts). The system can also include a consolidation module configured to combine the integrated communication channels into a unified interactive interface, which is referred to herein as the SPoG UI, where SPoG is a central interface component configured to consolidate user interactions, data, and / or functions of user groups, and where the SPoG UI is arranged to facilitate operations across a supply chain ecosystem. Additionally, the system can include a lifecycle management module configured to manage the end-to-end lifecycle of user interactions within SPoG. The system can additionally or alternatively include a data collection module configured to automatically collect data from user interactions within SPoG. Further, the system can include a data analysis module configured to generate one or more insights based on the collected data. The system can also include an artificial intelligence module configured to execute one or more AI and / or ML algorithms based on the analyzed data. Additionally, the system can include the following: the user groups can include users selected from two or more different groups, the groups having distributors, resellers, customers, end customers, sellers, and suppliers. 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.

[0299] A system and method for automating SKU management can include: a user interface configured to receive from a user catalog files in two or more different formats; a catalog conversion module configured to convert the catalog files into a standard format to predict the classification and attribute mapping of at least one catalog item, where the catalog conversion module utilizes iterative learning to propose the classification and attribute mapping; a real-time data grid (RTDM) module for ensuring real-time data exchange and synchronization across system components, where the RTDM module facilitates real-time interaction between the user and the system; a master data governance (MDG) module for validating the converted catalog and feeding back any errors to the seller; and a global data repository (GDR) for storing the validated catalog and maintaining the data integrity of the stored catalog.

[0300] The system and method may include a search platform for indexing and retrieving stored directories. The user interface is a single-pane glass user interface (SPoG UI). The dynamic SKU creation module is used to generate SKUs in the ERP system of the catalog items, where the catalog items represent one or more non-trading products, and where generating SKUs allows one or more products to be added to the customer shopping cart. The GDR is integrated with the RTDM module to support real-time data synchronization across systems. The MDG module generates indications to represent one or more errors that occur during the catalog transformation process associated with one or more transformed catalogs. The Global Pricing Engine (GPE) is used to determine the real-time pricing of catalog items. The GPE determines the price of a product based on one or more of the following: the seller price file, market trends, and historical pricing data. Implementations may include one or more of the following features. A method in which the interaction elements include links to various business platforms. An action in which the user interaction includes may include one or more clicks, hovers, and methods of entering data. A method in which the collected data is analyzed using advanced statistical algorithms. A method in which personalized content is generated based on a recommendation algorithm. The method may include updating the SPoG UI based on user feedback and data analysis results. Implementations of the described technology may include hardware, methods or processes, or computer tangible media.

[0301] It should be understood that the detailed description section, rather than the summary and abstract sections, is intended to be used to interpret the claims. The summary and abstract sections may set forth one or more but not all exemplary embodiments of the invention as contemplated by the inventor, and are therefore not intended to limit the invention and the appended claims in any way.

[0302] The present invention has been described above by means of functional building blocks of embodiments that specify functions and their relationships. For ease of description, the boundaries of these functional building blocks have been arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and their relationships are appropriately performed.

[0303] The foregoing description of specific embodiments will so fully disclose the general nature of the invention that others can, without departing from the general concept of the invention, readily modify and / or adapt it for various applications, such as the specific embodiments, by applying the knowledge of those skilled in the art, without undue experimentation. Therefore, such adaptations and modifications are intended to be within the meaning and range of the equivalent embodiments of the disclosed embodiments based on the teachings and guidance presented herein. It should be understood that the language or terminology herein is for the purpose of description and not of limitation, such that the terminology or wording of this specification is to be interpreted by those skilled in the art in light of the teachings and guidance.

[0304] The breadth and scope of the present invention should not be limited by any of the above exemplary embodiments, but should be defined only in accordance with the appended claims and their equivalents.

Claims

1. A computerized method for real-time data integration, analysis, and notification in a mobile application system, comprising: Providing a data layer and a preprocessing stage, wherein the data layer is designed to store structured and unstructured data using a distributed database strategy, RTDM continuously extracts data from systems including ERP and CRM, and the preprocessing includes signal processing methods for removing noise and machine learning techniques for extracting data features and prioritizing the data features; Transmitting information to an advanced analytics and machine learning engine, namely the AAML engine; Receiving the output from the AAML engine using a decision construct; Activating a push notification service based on an event-driven architecture, i.e., EDA, a publish-subscribe system, i.e., Pub-Sub system, or a suitable method, wherein the EDA method responds to different data patterns as events, and the Pub-Sub system uses 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, thereby leveraging the insights generated by the AAML engine.

2. The method according to claim 1, wherein The preprocessing stage employs signal processing methods, including Fourier transform and wavelet transform, to remove noise from the data, thereby ensuring the data quality for subsequent analysis within the system.

3. The method according to claim 1, wherein The AAML engine utilizes deep learning algorithms to process text data, thereby extracting semantic associations and patterns for enhanced analysis.

4. The method according to claim 1, wherein, The AAML engine employs one or more algorithms including decision trees and Bayesian networks to derive appropriate actions based on the analyzed data, thereby enabling data-driven decision-making within the system.

5. The method according to claim 1, wherein The push notification service uses the AMML process to dynamically detect and address changing data patterns to deliver real-time notifications to one or more users.

6. The method according to claim 1, wherein The push notification service delivers customized notifications to end-users based on one or more insights generated by the AAML engine, and classifies and prioritizes the events based on the importance of the events and the target audience, thereby ensuring efficient communication within the system.

7. The method according to claim 1, wherein The method further includes performing an adaptive feedback process to optimize system operation, including processing and training an enhanced learning model, such as proximal policy optimization, to enhance system responsiveness.

8. A mobile application system, comprising: A user interface layer, i.e., UI layer, which is configured for intuitive user interaction on a user device to provide a consistent user experience across various devices and platforms; The data layer of the real-time data grid, operatively connected to one or more headless engines, the data layer including a global data lake, the global data lake including one or more purposeful data repositories (PDSs) to enable real-time analysis based on real-time data within the data grid, wherein one or more computers are configured to capture and process changed data using a change data capture mechanism, and wherein the global data lake is configured to transform and reconcile the captured data into a standardized format compatible with analysis and integration processes, thereby ensuring data consistency and compatibility on the data grid; A push notification service, operating on one or more of an event-driven architecture and a publish-subscribe system, to deliver real-time notifications based on distribution platform events; An image recognition and SKU mapping engine, utilizing advanced AI algorithms to scan product images and map the product images to their respective SKUs to improve the distribution process; And An offline data cache module for locally storing critical data on a user device to ensure uninterrupted functionality and synchronizing with a backend server when the connection is restored.

9. The mobile application system according to claim 8, wherein, The UI layer optimizes the actual screen usage area, employs responsive design elements, and promotes efficient user engagement by prioritizing the perspective of the user, including the arrangement of menus, buttons, and navigation bars, thereby ensuring a consistent and user-friendly experience across various devices and platforms.

10. The mobile application system according to claim 8, wherein, The data layer includes a global data lake, the global data lake including one or more purposeful data repositories (PDSs), enabling real-time analysis based on real-time data within the data grid, the one or more computers being configured to capture and process changed data using a change data capture mechanism, and the global data lake being configured to transform and reconcile the captured data into a standardized format compatible with analysis and integration processes, thereby ensuring data consistency and compatibility on the data grid, including data retrieval from various enterprise systems such as ERP.

11. The mobile application system according to claim 8, wherein, The push notification service operates on an event-driven architecture and utilizes a standardized push notification protocol to deliver real-time notifications based on distribution platform events, thereby conveying information about distribution platform events to users.

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

13. The mobile application system according to claim 8, wherein, The offline data cache module locally stores critical data including order status, product details, and user preferences on a user device to ensure uninterrupted functionality and synchronizes with a backend server when the connection is restored, thereby optimizing the user experience and maintaining data consistency, including data encryption to protect sensitive information at rest and in transit.

14. The mobile application system according to claim 8, wherein, The mobile application system further includes a security and authentication layer that employs a robust encryption mechanism to protect sensitive information at rest and in transit, implements an authentication protocol to verify user identities, and ensures data integrity and user privacy, including data lineage and audit trail mechanisms.

15. The mobile application system according to claim 8, wherein, The mobile application system further includes a device compatibility module that ensures the mobile application is accessible and operative on a wide range of devices including smart phones and tablets, regardless of their operating systems, thereby leveraging the imaging components of the mobile devices and coupling them to advanced AI algorithms for accurate product identification, thus facilitating efficient order processing and inventory management.

16. A computer-readable medium comprising instructions that, when executed by a processor, perform the following steps: Provide a data layer and a preprocessing stage, where the data layer is designed to store structured and unstructured data using a distributed database strategy, RTDM continuously extracts data from systems including ERP and CRM, and the preprocessing includes signal processing methods for noise removal and machine learning techniques for extracting and prioritizing data features; Transmit the information to an advanced analytics and machine learning engine, namely the AAML engine; Receive the output from the AAML engine using a decision construct; Activate a push notification service based on an event-driven architecture, i.e., EDA, a publish-subscribe system, i.e., Pub-Sub system, or an appropriate method, where the EDA method responds to different data patterns as events, and the Pub-Sub system uses a protocol such as MQTT for efficient communication; And Operate the push notification service in conjunction with the AAML module to deliver customized notifications to end users, thereby leveraging the insights generated by the AAML engine.

17. The computer-readable medium according to claim 16, wherein, The computer-readable medium further includes instructions for implementing change data capture using one or more trigger-based, machine learning-based, and / or polling-based CDC algorithms.

18. The computer-readable medium according to claim 16, wherein, The data layer includes a purposeful data repository (PDS) optimized for efficient retrieval and storage of specific types of data.

19. The computer-readable medium according to claim 16, wherein, The AAML engine utilizes deep learning algorithms to process text data, thereby extracting semantic associations and patterns for enhanced analysis.

20. The computer-readable medium according to claim 16, wherein, The push notification service uses an AMML process to dynamically detect and address changing data patterns to deliver real-time notifications to one or more users.

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