System and method for generating artificial intelligence driven integrated insights

Abstract

Computerized systems and methods for automated AI-driven customer and vendor subdivision and personalized insight delivery are described. The method involves collecting real-time data including purchase behavior and market trends, analyzing it using an AI / ML algorithm for subdivision, and delivering personalized insights through a user-friendly single glass pane user interface (SPoG UI). Transaction details are recorded for continuous enhancement. Based on user feedback and evolved market dynamics, the subdivision effectiveness is monitored and refined. A plurality of data sources and analysis tools are integrated for comprehensive analysis. The user may customize the tessellation parameters, and the delivery options include push notifications and email alerts. The system facilitates continuous optimization and adaptation, thereby enhancing correlation and accuracy.

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 June 26, 2023; U.S. Patent Application No. 18 / 349,836, filed July 10, 2023; U.S. Patent Application No. 18 / 424,193, filed January 26, 2024; U.S. Patent Application No. 18 / 583,256, filed February 21, 2024; U.S. Patent Application No. 18 / 583,337, filed February 21, 2024; U.S. Patent Application No. 18 / 599,388, filed March 8, 2024; U.S. Patent Application No. 18 / 614,517, filed March 22, 2024; U.S. Patent Application No. 18 / 732,227, filed June 3, 2024; U.S. Patent Application No. 18 / 768,998, filed July 10, 2024; U.S. Patent Application No. 18 / 768,971, filed July 10, 2024; U.S. Patent Application No. 18 / 789,602, filed July 30, 2024; and U.S. Patent Application No. 18 / 793,346, filed August 2, 2024. Each of these applications is incorporated by reference herein in its entirety. BACKGROUND

[0003] Traditional ordering processes in distribution and supply chain platforms suffer from inefficiencies, delays, and inaccuracies. In traditional scenarios, multiple systems and sellers often perform each activity independently, from creating a bill of materials to registering a transaction, applying pricing, generating a quote, and submitting an order. This approach results in operational inefficiencies and an increased likelihood of errors.

[0004] Enterprise Resource Planning (ERP) systems have been used as a backbone to manage 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. While ERPs are comprehensive, they present several challenges in today’s complex distribution and supply chain environment. One of the main challenges is data fragmentation. Data silos across different departments or 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 impacts the decision-making process.

[0005] Further, ERP systems generally 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 cumbersome and error-prone manual processes for transferring data between systems and impacts the flow of information throughout the supply chain. When information exists across systems in different formats, data inconsistencies occur, hindering accurate data analysis and leading to uninformed decisions.

[0006] Data inconsistency presents another challenge. When data exists across departments or ERP in different formats or units, standardizing that data for meaningful analysis becomes a laborious process. Businesses often resort to time-consuming manual processes for data conversion and validation, further delaying decision-making. Additionally, traditional ERP systems generally lack the ability to effectively handle large volumes of data. These systems struggle to provide timely insights for operational improvements, particularly for businesses handling complex and large distribution and supply chain networks.

[0007] Data security is another issue, particularly given 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 generally lack robust security features that are flexible enough to adapt to the evolving landscape of cybersecurity threats and compliance requirements. SUMMARY

[0008] The automated, AI-driven segmentation and insight generation process aims to address the shortcomings in technical distribution by integrating various systems and activities into a unified interface, enabling the delivery of AI-driven insights to users. This transformation leverages AI algorithms to optimize product and service selection based on real-time market data and user preferences, enhancing the flexibility and scalability of service options while ensuring data security and compliance. The platform integrates functionalities for segmentation analysis, real-time market monitoring, and personalized insight delivery.

[0009] In the global distribution industry, challenges such as inefficient distribution management and the transition to direct-to-consumer models demand innovative solutions. Particularly with the shift in consumer expectations and regulations, traditional distribution methods are increasingly inadequate. By integrating functionalities for segmentation analysis, real-time market monitoring, and personalized insight delivery, the platform supports the transition to flexible, AI-driven insights and adaptive recommendation engines that evolve with changing market conditions and user preferences.

[0010] According to some embodiments, the AI-driven segmentation module can be configured to contain algorithms that optimize product and service selection based on real-time market data and user preferences. The system includes a segmentation module that integrates with a real-time data mesh (RTDM) and a single pane of glass user interface (SPoG UI) that delivers AI-driven insights to users in real-time. Using advanced algorithms that adjust offerings based on real-time market data and user behavior patterns, the relevance and value of service options are enhanced.

[0011] In one non-limiting example, the AI-driven segmentation and insight process employs algorithms to provide dynamic, personalized insights and recommendations to users. The system and process can be configured to implement machine learning models, such as multivariate linear regression or random forest, for predicting and adjusting insights based on real-time market dynamics and user-specific factors.

[0012] In one embodiment, a segmentation management and / or real-time insight delivery module operatively connected with the RTDM and SPoG UI manages the delivery of AI-driven insights to users. The module can optimize service segmentation, insights, and delivery based on real-time data, using algorithms to dynamically adjust insight and segmentation configurations. The system can include an insight generation engine for predictive analytics, adapting to variables like user behavior patterns and market trends.

[0013] In some embodiments, the system enables users to receive personalized insights and recommendations via the SPoG UI with minimal input requirements. Embodiments can include one or more modules for validating user preferences and aggregating insight options based on real-time data, facilitating the insight delivery process.

[0014] Additionally, or alternatively, the system can employ validation algorithms, such as support vector machines, to ensure the accuracy of insight configurations. It synchronizes real-time data from various systems, ensuring consistent and up-to-date information across the insight delivery model. Embodiments disclosed herein integrate multiple systems, automating the process, and validating to automatically deliver AI-driven insights to users. By implementing intelligent rules and validations, the system effectively performs complex tasks, reducing time and errors. The adaptability of the system ensures it stays current and evolves to meet market and user demands.

[0015] In some embodiments, the system uses a data-driven approach to automatically create and deliver personalized insights based on user consumption patterns. This includes delivering AI-driven insights that align with individual user preferences and behavior patterns, enhancing user engagement and satisfaction. The system generates user profiles based on comprehensive data analysis, including aspects like digital engagement and technology preferences, to inform the delivery of relevant insights.

[0016] In some embodiments, AI-driven segmentation analysis is automatically performed based on comprehensive market research and real user data, thereby identifying user segments with different behavior patterns and preferences. The identification of user segments allows for a nuanced understanding of customer needs, enabling targeted insights into the needs of specific users for information in areas such as technology, software applications, cloud computing solutions, and hardware requirements.

[0017] Single pane of glass

[0018] A single pane of glass (SPoG) can provide a comprehensive solution configured to address these multifaceted challenges. It can be configured to provide an overall, user-friendly, and efficient platform that facilitates the distribution process.

[0019] According to some embodiments, the 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, the SPoG can provide valuable insights into inventory levels and shipment status, ensuring that the process of supply chain and distribution management is handled efficiently.

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

[0021] The SPoG provides an innovative solution for improved inventory management through advanced predictive capabilities. These predictive analyses can highlight demand trends, guiding companies to manage their inventory more effectively and mitigate the risk of stockouts or overstocking.

[0022] According to some embodiments, the 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 facilitate AI-driven segmentation and insight generation, the SPoG integrates data from various OEMs into a single platform. This not only ensures data consistency but also significantly reduces the likelihood of errors. Furthermore, it provides the ability to efficiently manage segmentation and insight generation, aligning with specific market needs and requirements.

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

[0025] In addition, the advanced analytics capabilities of SPoG provide extremely useful insights that can drive strategy and decision-making. 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 timeless solution. It can adapt to changing business needs, allowing companies to expand or contract their operations as needed without significant infrastructure changes.

[0027] The innovative approach of SPoG to solving challenges in the distribution industry makes it an extremely useful tool. By enhancing supply chain visibility, facilitating inventory management, ensuring compliance, executing AI-driven segmentation and insight generation, and providing an excellent customer experience, it offers a comprehensive solution to complex problems that have long plagued the distribution sector. Through its implementation, distributors can expect increased efficiency, reduced errors, and improved customer satisfaction, enabling sustained growth in the ever-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 these challenges. RTDM (Distributed Data Architecture) enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allowing efficient management and enabling distributors to handle disruptions more effectively.

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

[0031] The globally compliant database of RTDM, updated in real-time, ensures that distributors are up-to-date with current international regulations. It significantly reduces the burden of manual tracking, enabling cross-border transactions.

[0032] RTDM also simplifies AI-driven segmentation and insight generation by integrating data from various OEMs, ensuring data consistency and reducing the likelihood of errors. Its ability to manage product and market data efficiently meets the specific needs of certain markets.

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

[0034] Advantages of SPoG integration with RTDM

[0035] Integrating the SPoG platform with RTDM offers several advantages. First, it provides a holistic solution to long-standing problems in the distribution industry. Leveraging the capabilities of RTDM, SPoG can enhance supply chain visibility, generate AI-driven segmentation and insights.

[0036] Real-time tracking and analytics provided by RTDM enhance SPoG's ability to effectively manage its supply chain and inventory. It offers accurate and up-to-date information, enabling distributors to make informed decisions quickly.

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

[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 incorporates SPoG and RTDM to provide an improved and comprehensive distribution system. The platform can fully leverage the advantages of distribution models, address their existing challenges, and is positioned for continued growth in an ever-evolving global market. Attached Figure Description

[0040] Figure 1 An embodiment of the operating environment of the distribution platform referred to in this embodiment is shown.

[0041] Figure 2 It shows in Figure 1 An example of the operating environment of a distribution platform built on the components introduced in the document.

[0042] Figure 3 An example of a system for distribution management is shown.

[0043] Figure 4 A system for automating an AI-driven user segmentation and insight generation process is described according to one embodiment.

[0044] Figure 5 An SPoG UI according to one embodiment is shown.

[0045] Figure 6 An RTDM module according to one embodiment is shown.

[0046] Figure 7 A system for automated, AI-driven user segmentation and insight generation is illustrated according to one embodiment.

[0047] Figure 8 This is a flowchart of a method for automating an AI-driven user segmentation process according to some embodiments of the present disclosure.

[0048] Figure 9 This is a flowchart of AI-driven insight generation for automation, based on some embodiments of this disclosure.

[0049] Figure 10 This is a flowchart illustrating the automated delivery of AI-driven insights and recommendations in an automated AI-driven user segmentation and insight generation system, according to some embodiments of the present disclosure.

[0050] Figure 11 This is a block diagram of example components of a device according to some embodiments of the present disclosure.

[0051] Figures 12A to 12Q Various screens and functions of the SPoG UI according to some embodiments are depicted. Detailed Implementation

[0052] Implementations may be carried out in hardware, firmware, software, or any combination thereof. Implementations may also be carried out as instructions stored on a machine-readable medium that can be read and executed by one or more processors. Machine-readable media may include any mechanism for storing or transmitting information in a machine-readable form (e.g., a computing device). For example, machine-readable media may include read-only memory (ROM); random access memory (RAM); disk storage media; optical storage media; flash memory devices, etc. Furthermore, firmware, software, routines, and 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 the computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0053] 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 this disclosure, the operations may be performed in different orders and / or varied.

[0054] Figure 1 An operating environment 100 for a distribution platform, referred to as system 110 in this embodiment, is illustrated. System 110 operates within the context of an information technology (IT) distribution model, catering to a variety of users such as customers 120, end customers 130, retailers 140, distributors 150, and other entities involved in the distribution process. This operating environment includes a wide range of features and dynamics that contribute to the success and efficiency of the distribution platform.

[0055] Customers 120 within the operating environment of System 110 represent businesses or individuals seeking IT solutions to meet their specific needs. These customers may require a wide variety of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, allowing them to browse, search, and select the most suitable IT 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.

[0056] End customers 130 can be the ultimate beneficiaries of the IT solutions provided by system 110. These can include businesses or individuals utilizing IT products and services to improve their operations, productivity, or daily activities. End customers rely on system 110 to access a wide range of IT solutions, ensuring they have access to the latest technologies and innovations in the market. System 110 enhances the overall experience by enabling end customers to track their orders, receive updates on delivery status, and access customer support services.

[0057] Vendors 140 play a crucial role in the operational environment of System 110. These vendors can include manufacturers, distributors, and suppliers offering a wide range of IT products and services. System 110 acts 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 enhance 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.

[0058] Resellers 150 can be intermediaries within a distribution model, bridging 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 a variety of vendors. Resellers can include retailers, value-added resellers (VARs), systems integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales channels, and provide value-added services to customers. By fully leveraging System 110, resellers can enhance their customer relationships, optimize their product offerings, and increase their revenue streams.

[0059] Within the operating environment of System 110, various dynamics and characteristics can contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. System 110 ensures that relevant data can be exchanged between users in real time, enabling accurate decision-making and timely action. Integration with existing enterprise systems, such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and warehouse management systems, allows for communication and interoperability, thereby eliminating data silos and achieving end-to-end visibility.

[0060] System 110 offers scalability and flexibility. It can adapt to the growing needs of IT distribution models, whether involving an expanding customer base, a growing number of vendors, or a wider range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analysis, ensuring it can support the evolving needs of the distribution platform. Furthermore, System 110 fully leverages a technology stack including .NET, Java, and other suitable technologies, providing a robust foundation for its operation.

[0061] In summary, the operating environment of System 110 within the IT distribution model includes customers 120, end customers 130, retailers 140, distributors 150, and other entities involved in the distribution process. System 110 serves as a centralized platform to facilitate efficient collaboration, communication, and transaction processes among these users. By fully leveraging real-time data exchange, integration, scalability, and flexibility, System 110 empowers users to optimize their operations, enhance customer experience, and drive business success within the IT distribution ecosystem.

[0062] Figure 2 The operating environment of the distribution platform is described in section 200, which expands upon... Figure 1 The components introduced in this environment feature integration point 210, which enables data flow and connectivity between various systems, such as customer system 220, retailer system 240, dealer system 260, and other entities in the AI-driven segmentation and insight process. Figure 2 This demonstrates the interconnectivity of the network and the mechanisms that facilitate collaborative and data-driven decision-making through AI-driven segmentation and insights. The operating environment 200 is configured to automate the AI-driven segmentation and insights process using AI and ML technologies, thereby processing and analyzing data for service transformation.

[0063] Some embodiments of AI-driven segmentation and insight processes involve a systematic approach to analyzing user behavior and preferences to tailor services accordingly. This process comprises several technical components: collecting various data, including product specifications, user service interactions, and usage patterns; aggregating this data from sources such as CRM systems and web analytics tools and feeding it into a Real-Time Data Grid (RTDM); processing and standardizing the data in the RTDM as a centralized repository for real-time data updates and retrieval; analyzing this aggregated data to determine the optimal strategy for generating service segmentation and insights; segmenting services based on data-driven insights and predicted user preferences; and customizing service offerings for each user or market segment based on insights communicated by the AAML module. It applies predictive models and heuristics to determine service offerings that meet specific user requirements. Users can interact with these services through the SPoG UI to customize and confirm their segmentation, insights, and delivery choices. The system includes a feedback loop in which responses to insights are collected and analyzed to continuously refine service offerings.

[0064] AI algorithms in the AI-driven segmentation and insight process address inventory management, service customization, and optimization of user choices. Machine learning models, such as neural networks and decision trees, refine service offerings. The AI-driven segmentation and insight process uses ML-based algorithms for real-time service provisioning. Advanced analytics, such as ensemble learning or reinforcement learning, continuously optimize the AI-driven segmentation and insight process. The AI ​​and ML technologies in the operating environment 200 employ supervised and unsupervised learning algorithms, including convolutional neural networks for pattern recognition and logistic regression for decision-making. These components dynamically adapt to changing data inputs (such as user preferences and market conditions), thereby optimizing decision paths through reinforcement learning. ML components leverage predictive analytics to continuously refine the output by incorporating new data, enhancing the accuracy and relevance of segmentation.

[0065] Operating environment 200 includes system 110, which serves as a central hub for managing AI-driven segmentation and insight processes. System 110 acts as a bridge between customer systems 220, retailer systems 240, reseller systems 260, and other entities. It integrates communication, data exchange, and transaction processes to provide a cohesive experience. Furthermore, environment 200 features integration point 210, enabling real-time data exchange and synchronization using a hybrid architecture combining RESTful APIs and WebSockets. This architecture is secured using SSL / TLS protocols to protect data during transmission.

[0066] Customer System Integration: Integration point 210 enables system 110 to connect with customer system 220, facilitating efficient data exchange and synchronization. Customer system 220 may include entities such as customer system 221, customer system 222, and customer system 223. These systems represent internal systems used by the customer, such as ERP or CRM systems. Integration with customer system 220 allows customers to access real-time information on AI-driven segmentation and insights, including personalized bundling, pricing details, order tracking, and other relevant data, thereby enhancing their decision-making capabilities. This integration provides an automated, real-time solution for creating and managing AI-driven segmentation and insights processes, thereby improving customer operational efficiency.

[0067] Data exchange between customer system 220, retailer system 240, and distributor system 260 is achieved through a robust ETL (Extract, Transform, Load) mechanism described below with reference to the real-time data grid architecture, ensuring data consistency and reliability. This interaction can be managed by predefined business rules and logic that define data flows and processing methods. Advanced mapping and transformation tools are employed to harmonize disparate data formats, allowing for data integration and utilization across these systems. Coordinated data exchange supports synchronous operations, enabling efficient and informed decision-making across the distribution network.

[0068] Related System Integration: Integration point 210 enables system 110 to connect with related system 230, thereby facilitating efficient data exchange and synchronization. These systems contribute to the overall efficiency of AI-driven segmentation and insight processing by providing relevant market and product data.

[0069] Retailer System Integration: Integration point 210 facilitates the connection between system 110 and retailer system 240. Retailer system 240 may include entities such as retailer system 241, retailer system 242, and retailer system 243, representing inventory management, pricing systems, and product catalogs. Integration with retailer system 240 ensures that retailers can effectively update their offerings and receive real-time notifications, facilitating AI-driven segmentation and insight processes.

[0070] Distributor System Integration: Integration point 210 allows distributor system 260 to connect to system 110. Distributor system 260 includes entities such as distributor system 261, distributor system 262, and distributor system 263, thereby handling sales, customer management, and service delivery. Integration enables authorized distributors to access the latest product information and effectively manage customer relationships.

[0071] Integration with other entities: Integration point 210 also connects other entities involved in the distribution process, thereby facilitating collaboration and efficient distribution. This integration ensures real-time data exchange for AI-driven segmentation and insight processing and decision-making within the distribution ecosystem.

[0072] System 110 is configured with sophisticated AI and ML capabilities to automatically perform AI-driven segmentation and insight processing based on individual preferences, thereby ensuring relevance and optimization in the distribution process.

[0073] Integration point 210 also enables connectivity with record system 280 for additional data management and integration. Record system 280 can represent an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system, including both future-proof and traditional ERP systems such as SAP, Impulse, META, I-SCALA, etc. Record system can include one or more repositories of critical and traditional business data. It facilitates the integration of data exchange and synchronization between the distribution platform, system 110, and ERP, enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes a connection between record system 280 and the distribution platform, allowing stakeholders to leverage the rich data stored in the ERP for efficient collaboration, data-driven decision-making, and streamlined distribution processes. These systems represent internal systems used by customers, distributors, etc.

[0074] Standardized protocols, APIs, and data connectors facilitate integration points 210 within the operating environment 200. These mechanisms ensure compatibility, interoperability, and secure data transmission between the distribution platform and the connected systems. System 110 employs industry-standard protocols such as RESTful APIs, SOAP, or GraphQL to establish communication channels and enable integrated data exchange.

[0075] In some embodiments, system 110 may incorporate authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) may be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0076] In some embodiments, the integration point 210 and data flow within the operating environment 200 enable users to operate within a connected ecosystem. Data generated at various stages of the distribution process (including customer orders, inventory updates, shipping details, and sales analytics) flows between customer systems 220, retailer systems 240, distributor systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves the operational efficiency of the entire distribution platform.

[0077] In some embodiments, system 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other suitable technologies that support integration point 210 and enable communication within operating environment 200. These technologies provide a robust foundation for system 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, integration point 210 can employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. Integration point 210 and data flows within operating environment 200 enable users to operate within a connected ecosystem. Data generated at various touchpoints (including customer orders, inventory updates, pricing changes, or delivery status) flows between different entities, systems, and components. System 110 can process, coordinate, and make integrated data available to relevant users in real time. This real-time access to accurate and current information empowers users to make informed decisions, optimize supply chain operations, and improve customer experience.

[0078] Figure 2 Several elements in the operating environment described herein may include conventional, well-known elements, which are only briefly explained herein. For example, each client system (such as client system 220) may include a desktop personal computer, workstation, laptop computer, PDA, mobile phone, or any device that supports Wireless Access Protocol (WAP) enabled, or any other computing device capable of directly or indirectly interfacing with the Internet or other networks. Each client system may typically run an HTTP client, such as Microsoft Edge, Google Chrome, Opera, or a WAP-enabled browser for mobile devices, allowing the client system to access, process, and view information, pages, and applications available from the distribution platform via the network.

[0079] In addition, each client system may typically be equipped with a user interface device for interacting with a browser-based graphical user interface (GUI), such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar device. These user interface devices enable users of the client system to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0080] The client system and its components may be operator-configurable using applications (including web browsers) running on a central processing unit such as an Intel Pentium processor or similar processor. Similarly, the distribution platform (system 110) and its components may be operator-configurable using applications running on a central processing unit (such as a processor system) that may include an Intel Pentium processor or similar processor and / or multiple processor units.

[0081] Computer program product embodiments include machine-readable storage media containing instructions for programming a computer to perform the processes described herein. Computer code for operating and configuring distribution platforms and customer systems, retailer systems, distributor 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, nanosystem, or any suitable medium for storing instructions and data.

[0082] Furthermore, the computer code used to implement the embodiments can be transferred and downloaded from the software source via the Internet or any other conventional network connection using communication media and protocols such as TCP / IP, HTTP, HTTPS, Ethernet, etc. The code can also be transferred via extranets, VPNs, local area networks, or other networks, and executed on client systems, servers, or server systems using programming languages ​​such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, etc.

[0083] It should be understood that the embodiments can be implemented in various programming languages ​​that execute on the client system, server, or server system, and the choice of language may depend on the specific requirements and environment of the distribution platform.

[0084] Thus, the operating environment 200 can couple the distribution platform with one or more integration points 210 and data flows to achieve efficient collaboration and a streamlined distribution process.

[0085] Figure 3 System 300 for supply chain and distribution management is shown. System 300 ( Figure 3 System 300 is a supply chain and distribution management solution configured to address the challenges of the fragmented distribution ecosystem in the global distribution industry. System 300 may include several interconnected components and modules that work in a coordinated manner to optimize supply chain and distribution operations, enhance collaboration, and drive business efficiency.

[0086] SPoG UI 305 serves as a centralized user interface, providing users with a unified view of the entire supply chain. It integrates information from various sources and presents real-time data, analytics, and functionalities tailored to the specific roles and responsibilities of users. By offering a customizable and intuitive summary table layout, SPoG UI empowers users to access relevant information and tools, enabling them to make data-driven decisions and effectively manage their supply chain and distribution activities.

[0087] For example, logistics managers can use SPoG UI 305 to monitor the status of goods, track delivery routes, and view real-time inventory levels across multiple warehouses. They can visualize data through interactive charts and graphs, such as maps showing the current location of each shipped item or bar charts illustrating inventory levels by product category. With a unified view of the supply chain, logistics managers can identify bottlenecks, optimize routes, and ensure timely delivery of goods.

[0088] SPoG UI 305 integrates with other modules of System 300, facilitating real-time data exchange, synchronized operations, and workflows. Through API integration, data synchronization mechanisms, and an event-driven architecture, SPoG UI 305 ensures smooth information flow and enables collaborative decision-making across the distribution ecosystem. Designed with a user-centric approach, SPoG UI 305 features an intuitive and responsive layout. It utilizes front-end technologies to present dynamic and interactive data visualizations. Customizable summary tables allow users to tailor their views based on specific roles and requirements. The UI supports drag-and-drop functionality, is easy to use, and its adaptive design ensures compatibility across various devices and platforms. Advanced filtering and search capabilities enable users to efficiently navigate to and access relevant supply chain data and insights.

[0089] For example, when a purchase order is generated in the SPoG UI, the system automatically updates 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 enhances overall supply chain visibility.

[0090] The Real-Time Data Grid (RTDM) module 310 is another 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.

[0091] Within the distribution network, the RTDM module collects data from various systems, including inventory management systems, point-of-sale terminals, and customer relationship management systems. It harmonizes this data by aligning formats, standardizing units of measurement, and reconciling any discrepancies. This allows unified data to be available in real time, enabling users to access accurate and current information across the supply chain.

[0092] The RTDM module 310 can be configured to capture changes in data across multiple transaction systems in real time. It employs a sophisticated Change Data Capture (CDC) mechanism that continuously monitors transaction systems to detect any updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, including traditional ERP systems, customer relationship management (CRM) systems, and other enterprise-wide systems, ensuring compatibility and flexibility for business operations across diverse environments.

[0093] With access to real-time data, users can make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden surge in demand for a particular product, it can trigger an alert to the production team, enabling them to adjust manufacturing schedules and prevent stockouts.

[0094] The RTDM Module 310 facilitates data management operations within the supply chain. It enables real-time coordination of data from multiple sources, freeing retailers, distributors, customers, and end customers from the constraints imposed by traditional ERP systems. This enhanced flexibility supports improved efficiency, customer service, and innovation.

[0095] Another component of 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 collected data. It supports advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0096] For example, the AAML module can analyze historical sales data to identify seasonal patterns and forecast future demand. It can generate forecasts that help optimize inventory levels, ensure inventory availability during peak seasons, and minimize the costs of excess inventory. By fully leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0097] Beyond demand forecasting, this AAML module provides 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 upsell opportunities and recommend relevant products to individual customers.

[0098] Furthermore, this AAML module can analyze data from various sources, such as social media feedback, customer reviews, and market trends, to gain a deeper understanding of consumer sentiment and preferences. This information can be used for informed product development decisions, identifying emerging market trends, and adjusting business strategies to meet evolving consumer expectations.

[0099] 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, thereby creating a holistic and connected distribution ecosystem.

[0100] System 300's implementation and deployment can be customized to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies (such as Docker) and orchestration frameworks (such as Kubernetes). This approach ensures scalability, manageability, and efficient updates across diverse 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.

[0101] System 300 for Supply Chain and Distribution Management is a comprehensive and innovative solution that addresses the challenges of a fragmented distribution ecosystem. It combines the functionality of SPoG UI 305, the RTDM module 310, and the AAML module 315 with integration with existing systems. By leveraging a diverse technology stack, a scalable architecture, 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 ensure success in supply chain and distribution management.

[0102] Figure 4 An embodiment of an AI-driven, comprehensive insight system 400 for automated customer and reseller segmentation is described, which interacts with and combines SPoG UI, RTDM, and AI / ML technologies to achieve a comprehensive insight generation system. System 400 is configured to integrate with existing reseller systems to ensure efficient data exchange and system synchronization.

[0103] SPoG UI 405 serves as the primary user interface. Users interact with this interface to perform various tasks, providing direct interaction and customization. It displays information and options related to different business models and customer statistics of the distributor. It displays real-time data from data grid 410 and provides control for initiating actions in system 400. For example, users can interact directly from SPoG UI 405 with dynamic displays of service options, segmented and insightful customized interactive elements, and real-time feedback tools regarding user choices. It integrates with other system components to reflect accurate service information and user customization options. SPoG UI is developed using web-based technologies, allowing access from various types of devices such as desktops, laptops, tablets, and smartphones. SPoG UI 405 provides a comprehensive view of the entire distribution ecosystem, consolidating data and functionality from various modules into a centralized, easy-to-navigate platform. SPoG UI 405 simplifies the management of complex distribution tasks, providing distributors with a streamlined experience. In some embodiments, SPoG 405 includes dynamic pricing tools that display variable costs based on individual user consumption patterns.

[0104] Data Grid 410 is a complex data management layer. It aggregates and coordinates data from various sources, including ERP, dealer platforms, and third-party databases. This component ensures that all operational modules in System 400 access consistent and up-to-date information. System 400 can synchronize with existing dealer systems, thereby ensuring efficient data exchange and system functionality.

[0105] Data Grid 410 aggregates, harmonizes, and ensures the real-time availability of data from various systems such as inventory management, point of sale, and CRM. It employs Change Data Capture (CDC) to track real-time changes within transactional systems. This module standardizes data formats and units to ensure data consistency and accuracy in decision-making processes related to service offerings.

[0106] AI Module 460 uses machine learning algorithms and predictive modeling to automate the generation of insights for customer and retailer segments. AI Module 460 analyzes market trends, user preferences, and consumer data to dynamically generate and adjust these insights.

[0107] AI module 460 includes a decision support system for personalized insights based on complex data analysis. In some embodiments, AI module 460 employs deep learning neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for pattern recognition and time series analysis. For example, CNNs can be used to identify trends and patterns in market data, while RNNs, particularly LSTM (Long Short-Term Memory) networks, can analyze sequential data, such as time-based user interaction patterns. In some embodiments, AI module 460 can use decision trees for classification and regression tasks. These trees analyze user data and market conditions to segment users into different categories based on their service preferences. Integration methods of random forests and gradient boosting algorithms with decision trees provide improved accuracy and stability in prediction. In some embodiments, clustering, particularly K-means and hierarchical clustering, is employed to segment the market and user groups into distinct groups. Market / user segmentation helps AI module 460 understand different user preferences and generate AI-driven insights for different market segments.

[0108] In some embodiments, the AI ​​module 460 may use reinforcement learning (RL) to adjust service offerings based on user feedback. RL algorithms, particularly Q-learning and policy gradient methods, can tune the model to maximize user satisfaction, thereby learning from each interaction to improve recommendation accuracy. The module integrates reinforcement learning algorithms to continuously adjust service offerings based on user feedback, thereby improving the accuracy and relevance of customized segmentation and insights over time. Furthermore, NLP techniques can be used to analyze user feedback and queries. By leveraging tokenization, sentiment analysis, and named entity recognition, the AI ​​module 460 interprets user feedback, thereby improving the insight generation process.

[0109] Real-time processing via the data grid 410 enables the AI ​​module 460 to dynamically adjust service offerings based on current usage patterns and immediate market feedback. The data grid 410 also enables precise tracking of real-time usage data to achieve usage-based pricing strategies. The data grid 410 may include collaborative filtering and content-based recommendation systems to analyze user behavior and preferences, comparing them to similar user profiles or content characteristics to provide appropriate insights.

[0110] In some embodiments, the AI ​​module 460 can integrate predictive analytics tools to employ time-series forecasting methods (e.g., autoregressive composite moving average, exponential smoothing, etc.) to predict future service demand. Optimization algorithms, such as linear programming and genetic algorithms, can facilitate optimal segmentation and insight configurations, taking into account various factors such as cost, user preferences, and resource availability to recommend the most effective service bundles. The AI ​​module 460 can employ Monte Carlo simulations and scenario analysis for risk assessment and strategic planning, thereby simulating different market scenarios and evaluating various generated insights under different conditions.

[0111] System 400 may include a service management module 420 for monitoring the delivery of insights and ensuring alignment with user expectations and contractual agreements. It integrates tools for compliance monitoring, request processing, and service change procedures to support efficient segmentation and personalized experiences.

[0112] The Insight Delivery Module 408 serves as the interface within the SPoG UI 405, allowing users to access and interact with insights from the platform. It ensures the real-time, continuous delivery of insights within the same platform where transactions are conducted. This module allows insights to be tailored to a user's role within their company, ensuring relevant and actionable intelligence. For example, management users may receive different insights compared to purchasing users. This module integrates with its system components to reflect accurate insights and user-customized options, providing decision-makers with a streamlined experience.

[0113] The Insight Engine 430 is configured to aggregate and harmonize data from both internal and external sources via the data grid 410. This enables comprehensive and up-to-date real-time availability of information, facilitating integration with existing systems. By standardizing data formats and units, this module ensures data consistency and accuracy in decision-making processes related to insight delivery.

[0114] The Insight Engine 430 is configured to personalize insights for specific segments of customers and vendors by considering their roles and permissions. This engine enhances the decision-making process by providing personalized insights relevant to users' specific needs and responsibilities. The Insight Engine 430 can be configured to process data in real time using AI and machine learning algorithms via the AI ​​Module 460. It ingests data from various sources, including market trends, competitive comparisons, and predictive analytics, to generate actionable insights for users. This engine ensures that insights are continuously updated and reflect the latest market conditions and internal data changes, moving away from static insights typically delivered periodically (e.g., monthly). The Insight Engine 430 can be configured to deliver insights regardless of the technology stack used by the enterprise. This enables integration with different technology solutions, providing users with flexibility and accessibility. This module allows enterprises to leverage insights regardless of whether they utilize cloud, hardware, or other technologies.

[0115] The Deployment and Integration Management Module 440 is configured to manage the deployment and integration of the AI-driven insights platform across different countries and systems. It focuses on enhancing and expanding the platform's capabilities to ensure scalability and impact. This management module ensures the platform is deployed in a way that maximizes its value while minimizing disruption to existing operations.

[0116] Therefore, System 400 leverages real-time data and AI to segment customers and resellers. By integrating these modules into its infrastructure, the platform provides a competitive advantage through enabling informed, data-driven decision-making within a unified transaction ecosystem. System 400 performs AI-driven segmentation and insight generation. System 400 utilizes real-time data processing, AI-driven analytics, and user customization capabilities to deliver an insight-centric experience.

[0117] Figure 5 An embodiment of an advanced distribution platform, including system 500 for managing a complex distribution network, is described. This platform can be an embodiment of system 300, and provides a technical distribution platform for optimizing the management and operation of the distribution network. System 500 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 an SPoG UI 505, a customer interaction module CIM 510, an RTDM module 515, an AI module 520, an interface display module 525, a personalized interaction module 530, a document hub 535, a catalog management module 540, a performance and insight marker display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-login module 565, and a communication module 570.

[0118] System 500, as an embodiment of System 300, can utilize a range of technologies and algorithms to implement supply chain and distribution management. These technologies and algorithms facilitate efficient data processing of documents, catalogs, and performance metrics, personalized interaction, real-time analytics, secure communication, and effective management.

[0119] In some embodiments, SPoG UI 505 serves as the central interface within system 500, providing users with a unified view of the entire distribution network. It utilizes front-end technologies such as ReactJS, TypeScript, and Node.js to create interactive and responsive user interfaces. These technologies enable SPoG UI 505 to deliver a user-friendly experience, allowing users to access relevant information, navigate through different modules, and perform tasks efficiently.

[0120] The CIM 510, or 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 customer experiences, and provide access control for users.

[0121] The RTDM module 515, or Real-Time Data Grid module, is a key component of System 500, ensuring a smooth data flow across the distribution ecosystem. It utilizes technologies such as Apache Kafka, Apache Flink, or Apache Pulsar for data ingestion, processing, and stream management. These technologies enable the RTDM module 515 to handle real-time data streams, process large volumes of data, and ensure low-latency data processing. Additionally, the module employs a Change Data Capture (CDC) mechanism to capture real-time data updates from various transaction systems, such as traditional ERP and CRM systems. This feature allows users to access current and accurate information for informed decision-making.

[0122] The AI ​​module 520 within System 500 can use 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, AI module 520 can leverage predictive models to forecast demand, allowing users to optimize inventory management and minimize stockouts or overstocking.

[0123] The interface display module 525 focuses on presenting data and information in a clear and user-friendly manner. It utilizes technologies such as HTML, CSS, and JavaScript frameworks (like ReactJS) to create interactive and responsive user interfaces. These technologies allow users to visualize data using various data visualization techniques such as graphs, charts, and tables, enabling efficient data understanding, comparison, and trend analysis.

[0124] The personalized interaction module 530 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, thereby enhancing customer satisfaction and boosting sales.

[0125] Document Hub 535 serves as a centralized repository for storing and managing documents within the system 500. It can leverage technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, Document Hub 535 can utilize SeeBurger's document management capabilities to categorize and organize documents based on their type (such as contracts, invoices, product specifications, or compliance documents), allowing users to easily access and retrieve relevant documents when needed.

[0126] The catalog management module 540 enables the creation, management, and distribution of up-to-date product catalogs. It ensures users have access to current product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be leveraged to facilitate catalog updates, content delivery, and caching. For example, the module can use Akamai's Content Delivery Network (CDN) to deliver catalog information to users quickly and efficiently, regardless of their geographic location.

[0127] The Performance and Insights Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. It leverages tools such as Splunk and Datadog to enable effective performance monitoring and provide actionable insights. For example, the module can utilize Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling users to take proactive steps to optimize operations.

[0128] The predictive analytics module 550 employs machine learning algorithms and predictive models to forecast demand patterns, optimize inventory levels, and improve overall supply chain efficiency. It leverages technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module can utilize TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing users to optimize inventory levels and minimize costs.

[0129] The recommendation system module 555 focuses on providing intelligent recommendations to users within the distribution network. It generates personalized recommendations for products or services based on customer data, historical trends, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be used for data analysis, modeling, and delivering targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behaviors and deliver personalized product recommendations across various channels, thereby enhancing customer engagement and driving sales.

[0130] The notification module 560 enables the distribution of real-time notifications to users regarding important events, updates, or alerts within the supply chain. It utilizes technologies such as Apigee X and TIBCO for message queuing, event-driven architecture, and notification delivery. For example, the module can leverage TIBCO's messaging infrastructure to send real-time notifications to users' devices, ensuring timely and relevant information dissemination.

[0131] The self-login module 565 facilitates the login process for new users entering the distribution network. It provides guided steps, tutorials, or documentation to help users familiarize themselves with the system and its functions. Technologies such as Okta and Kentico can be utilized to ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identity and access management capabilities to securely log in new users, grant them appropriate access permissions, and guide them through the system's functionality.

[0132] The communication module 570 enables communication and collaboration within the system 500. It provides users with channels for interactive message exchange, document sharing, and project collaboration. Technologies such as Apigee Edge and Adobe Launch can be utilized to facilitate secure and efficient communication, document sharing, and version control. For example, the module can leverage Apigee Edge's API management capabilities to ensure secure and reliable communication between users, enabling them to collaborate effectively.

[0133] Therefore, System 500 can include various modules that utilize diverse technologies and algorithms to optimize supply chain and distribution management. These modules (including SPoG UI 505, CIM 510, RTDM module 515, AI module 520, interface display module 525, personalized interaction module 530, document hub 535, catalog management module 540, performance and insight marker display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-login module 565, and communication module 570) work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analytics, 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, thereby contributing to improved operational efficiency and successful supply chain and distribution management.

[0134] Real-time data grid

[0135] Figure 6 An RTDM module 600 according to an embodiment is shown. The RTDM module 600 may be an embodiment of the RTDM module 310, which may include interconnect components, processes, and subsystems configured to enable real-time data management and analysis.

[0136] like Figure 6 As shown, RTDM module 600 represents the effective data grid and change capture component within the overall system architecture. This module can be configured to provide real-time data management and standardization capabilities, thereby enabling efficient operations in the supply chain and distribution management domains.

[0137] The RTDM module 600 may include an integration layer 610 (also referred to as a "record system") for integration with various enterprise systems. These enterprise systems may include ERP systems such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 handles data exchange and synchronization between the RTDM module 600 and these systems. Data feedback can be established to retrieve relevant information from the record system, such as sales orders, purchase orders, inventory data, and customer information. This feedback enables real-time data updates and ensures that the RTDM module operates with the latest and most accurate data.

[0138] The RTDM module 600 may include a data layer 620 configured to process and transform data for retrieval and analysis. The data layer 620 includes a data grid, a cloud-based infrastructure configured to provide scalable and fault-tolerant data storage capabilities. Within the data grid, multiple Purchase Data Stores (PDSs) can be deployed to store specific types of data, such as customer data, product data, or inventory data. Each PDS can be optimized for efficient data retrieval based on specific use cases and requirements. PDSs can be configured to store specific types of data, such as customer data, product data, financial data, etc. These PDSs serve as repositories for normalized and / or standardized data, thereby ensuring data consistency and integrity across systems.

[0139] In some embodiments, the RTDM module 600 implements a data replication mechanism to capture real-time changes from multiple data sources, including transaction systems such as ERPs (e.g., Impulse, META, I-SCALA). The captured data can then be processed and standardized in-process, 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, thereby facilitating real-time insights and decision-making.

[0140] More specifically, the data layer 620 within the RTDM module 600 can be configured as a robust and flexible foundation for managing and processing data within the distribution ecosystem. In some embodiments, the data layer 620 may include a highly scalable and robust data lake, which may be referred to as data lake 622, and a set of purposeful data stores (PDS), which may be represented as PDS 624.1 to 624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0141] Data Layer 620 includes Data Lake 622, a state-of-the-art storage and processing infrastructure configured to handle the ever-increasing volume, variety, and speed of data generated within the supply chain. Built upon a scalable distributed file system such as Apache Hadoop Distributed File System (HDFS) or Amazon S3, Data Lake provides a unified and scalable platform for storing both structured and unstructured data. Leveraging the resilience and fault tolerance of cloud-based storage, Data Lake 622 can adapt to data inflows from diverse sources.

[0142] Associated with Data Lake 622, a set of purposeful data stores can be employed: PDS 624.1 to 624.N. Each PDS 624 can serve as a dedicated repository optimized for storing and retrieving specific types of data relevant to the supply chain domain. In some non-limiting examples, PDS 624.1 can be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS 624.2 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 to meet the diverse needs of supply chain users.

[0143] To ensure real-time data synchronization, data layer 620 can be configured to employ one or more change data capture (CDC) mechanisms. These CDC mechanisms can be integrated with transactional systems such as traditional ERPs like SAP, Impulse, META, and I-SCALA, as well as other enterprise-wide systems. CDC continuously monitors these systems for any updates, modifications, or new transactions and captures them in real time. By capturing these changes, data layer 620 ensures that the data within data lake 622 and PDS 624 remains current, thereby providing users with real-time insights into the distribution ecosystem.

[0144] In some embodiments, data layer 620 may be implemented to facilitate integration with existing enterprise systems using one or more frameworks such as .NET or Java, thereby ensuring compatibility with a variety of existing systems and providing flexibility for customization and scalability. For example, data layer 620 may leverage the Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with a group of record systems with multiple ERP systems and other enterprise-wide solutions. This can facilitate smooth data exchange across the supply chain, process automation, and end-to-end visibility.

[0145] In terms of data processing and analysis, Data Layer 620 can utilize the capabilities of distributed computing frameworks, such as Apache Spark or Apache Flink in some unrestricted examples. These frameworks enable parallel processing and distributed computing across large-scale datasets stored in data lakes and PDSs. By leveraging these frameworks, supply chain users can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from data. For example, Data Layer 620 can use Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0146] In some embodiments, data layer 620 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 data within the data lake and PDS. Data encryption technologies protect sensitive supply chain information from unauthorized access both at rest and in transit. Additionally, data layer 620 can implement data traceability and audit trail mechanisms, allowing users to track the origin and history of data, ensuring data integrity and regulatory compliance.

[0147] In some embodiments, data layer 620 can be deployed in a cloud-native environment using containerization technologies (such as Docker) and coordination frameworks (such as Kubernetes). This approach ensures scalability, resilience, and efficient resource allocation. For example, data layer 620 can be deployed on cloud infrastructure provided by AWS, Azure, or Google Cloud, leveraging its managed services and scalable storage options. This allows for efficient scaling of resources based on demand, minimizes operational overhead, and provides a resilient infrastructure for managing supply chain data.

[0148] The data layer 620 of the RTDM module 600 can combine a highly scalable data lake (data lake 622) with a purposeful PDS (PDS 624.1 to 624.N) and employs a CDC mechanism. The data layer 620 ensures efficient data management, standardization, and real-time availability. In a non-limiting example, the data layer 620 can be implemented using any suitable technology (such as .NET or Java) and / or distributed computing framework (such as Apache Spark), enabling powerful data processing, advanced analytics, and machine learning capabilities. With robust data governance and security measures, the data layer 620 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, the data layer 620 enables supply chain users to make data-driven decisions, optimize operations, and drive business success in dynamic and complex distribution environments.

[0149] RTDM module 600 may include AI module 630, which is configured to implement one or more algorithms and machine learning models to analyze data stored in data layer 620 and derive meaningful insights. In some non-limiting examples, AI module 630 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. AI module 630 can continuously learn from new data inputs and adapt its model to provide accurate and current insights. AI module 630 can generate predictions, recommendations, and alerts, and publish such insights to dedicated data feedback.

[0150] The data engine layer 640 includes a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 640 of the RTDM module 600 may include a collection of autonomous, headless engines 640.1 to 640.N. These engines represent different functions within the system and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 640.1 to 640.N can use standardized data stored in a data grid to deliver specific business logic and services. Each engine can be configured to be pluggable, allowing for flexibility and future expansion of module capabilities. Figure 5 An exemplary engine is shown, but this is not intended to be limiting. Any additional headless engine may be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0151] These systems can be configured to receive data from multiple sources, such as trading systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and transforming it into a standardized format. Data processing algorithms can be applied to cleanse, summarize, and enrich the data, preparing it for further analysis and integration.

[0152] Furthermore, to facilitate integration and access to the RTDM module 600, a data distribution mechanism 645 can be employed. This data distribution mechanism can be configured to include one or more APIs to facilitate the distribution of data from the data grid and engine to various endpoints, including user interfaces, micro-frontends, and external systems.

[0153] Experience Layer 650 focuses on delivering an intuitive and user-friendly interface for interacting with supply chain data. Experience Layer 650 may include data visualization tools, interactive summary tables, and user-centric functionality. 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 tailored to their preferences and roles, such as inventory changes, pricing updates, or new SKU notifications.

[0154] Therefore, in some embodiments, the RTDM module 600 for supply chain and distribution management may include integration with a record system and includes one or more of the following: a data layer with data grids 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 current information and insights to make informed decisions and optimize supply chain operations. Thus, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable real-time data management solution. Its innovative architecture allows for rich integration of diverse data sources, efficient data standardization, and advanced analytics capabilities. The module's ability to replicate and standardize data from different ERPs while maintaining auditable and repeatable transactions offers significant advantages in achieving a unified view of retailers, distributors, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0155] Comprehensive insights driven by automation and AI

[0156] In one embodiment, Figure 7 A system 700 is described for generating comprehensive AI-driven insights for customer and reseller segmentation. System 700 includes a real-time data grid 710, a single-pane glass user interface (SPoG UI) 705, an advanced analytics and machine learning (AAML) module 715, and a customer and reseller segmentation engine (CVSE) 720.

[0157] In some embodiments, SPoG UI 705 (which may be an embodiment of SPoG UI described above) can be enhanced by accessing an AI-driven, comprehensive insights platform, enabling users to interact with the platform to access personalized, real-time, and customizable insights into their roles within their organizations and enhancing the user experience.

[0158] The RTDM 710 aggregates and standardizes real-time data from various sources for generating insights within an AI-driven integrated insights platform. This includes internal data from the platform host and user-relevant external data sources. The RTDM 710 establishes a centralized, unified data hub for the AI-driven integrated insights platform, aggregating and standardizing data from multiple sources such as ERP, CRM systems, and market intelligence. It leverages a combination of data warehouses and data lakes to effectively handle both structured and unstructured data. The RTDM 710 employs ETL processes and data normalization techniques to ensure data uniformity and accessibility. This standardized data is crucial for the operation of the CVSE 720 and the AI-driven integrated insights platform 720, providing the necessary input for accurate and effective AI-driven segmentation and insight generation. The RTDM 710 maintains data integrity and relevance, which is essential for automating the user segmentation and insight generation process. In some embodiments, the RTDM 710 is configured to interface with an asset management system, thereby supporting service providers' ownership of physical assets while allowing users to access and utilize these assets under an integrated service agreement.

[0159] The AAML module 715 serves as the central processing unit of the AI-driven comprehensive insights platform 720. It contains specialized rules and algorithms designed for tasks such as market data analysis, customer segmentation, and predictive analytics. The AAML module 715 employs analytics tools with big data processing and deep learning capabilities to analyze data and generate actionable insights in real time. It performs sentiment analysis, trend prediction, and behavioral analysis to understand and anticipate market and user needs. The AAML module 715 integrates and trains machine learning algorithms based on historical datasets to identify relevant insights. It refines its algorithms based on continuous feedback loops, improving their accuracy over time to enhance the precision and relevance of the insights provided. This module performs functions crucial for automating the AI-driven insights process, ensuring that services are aligned with individual user preferences and market conditions.

[0160] In one embodiment, the Customer and Vendor Segmentation Engine (CVSE) 720 within the AI-driven Integrated Insights Platform serves as a key component for effectively segmenting customers and vendors based on various parameters extracted from the RTDM 710. The CVSE 720 utilizes sophisticated algorithms to analyze data streams to identify patterns in purchasing behavior, demographic information, transaction history, and other relevant factors. By employing techniques such as clustering analysis, decision trees, or neural networks, the CVSE 720 identifies meaningful segments in the data, allowing for precise targeting and personalized insights. For example, it can differentiate between high-value customers and casual buyers based on their geographic location and product preferences, or segment vendors.

[0161] The Personalization and Recommendation Engine (PRE) 730 leverages enriched data from RTDM 710 and insights generated by AAML module 715 to deliver highly customized recommendations to users. PRE 730 employs a combination of collaborative filtering, content-based filtering, and matrix factorization techniques to analyze user preferences, historical interactions, and market trends. For example, it can recommend products or services based on past purchases, similar user profiles, or emerging trends in the market. Through continuous learning and adaptation, PRE 730 ensures that recommendations remain relevant and valuable to users over time.

[0162] The Real-Time Insights Delivery Module (RIDM) 740 plays a crucial role in ensuring timely and effective delivery of insights to users within the SPoG UI 705. Employing real-time data streaming technology and an event-driven architecture, the RIDM 740 delivers insights as soon as they become available. It supports various delivery options, including push notifications, in-app messages, and email alerts, allowing users to receive insights in their preferred format and channel. For example, the RIDM 740 can notify purchasing managers of sudden price drops in key product categories, enabling them to capitalize on the opportunity immediately.

[0163] The Feedback and Adaptation Mechanism (FAM) 750 enables the AI-driven comprehensive insights platform to continuously evolve and improve based on user feedback and changing market conditions. FAM 750 collects user feedback through the interactive interface within the SPoG UI 705, sentiment analysis of user interactions, and direct input mechanisms. It monitors the effectiveness of delivered insights, thereby measuring key performance indicators such as engagement rates, conversion rates, and user satisfaction ratings. Based on this feedback, FAM 750 dynamically adjusts the algorithms and models within the AAML module 715, fine-tuning them to better meet user needs and preferences.

[0164] Regarding integration and deployment, the components of the AI-driven comprehensive insights platform are integrated within System 700, thereby promoting interoperability and scalability across different regions and user groups. Ongoing algorithm development enhances the platform's models and capabilities through iterative feedback ingestion, updates, and additions. This continuous improvement ensures the platform delivers personalized intelligence to users.

[0165] Figure 8 The diagram illustrates a flowchart of a method 800 for AI-driven customer and reseller segmentation, configured to initiate a segmentation process implementing AI algorithms within system 700. This method outlines operations designed to effectively segment customers and resellers based on real-time data, thereby enhancing the accuracy and relevance of the generated insights.

[0166] In some embodiments, method 800 can be configured to effectively segment customers and retailers using one or more AI / ML algorithms. Specifically, in a non-limiting example, method 800 can analyze real-time data, such as purchasing behavior, demographic information, transaction history, market trends, etc., and the platform can employ one or more techniques, such as cluster analysis, decision trees, neural networks, etc., to identify meaningful segments in the data. These segmentation techniques can achieve precise targeting and provide customized, personalized insights for specific customer and retailer segments. The method enhances the relevance and effectiveness of insights by ensuring that users receive information relevant to their segments, ultimately leading to improved decision-making and business outcomes.

[0167] In operation 801, real-time data, including factors such as purchasing behavior, demographic information, and transaction history, is collected from various sources. Operation 801 may include actions performed by the RTDM 710, such as collecting real-time data required for segmentation analysis. In some embodiments, the RTDM 710 utilizes a RESTful API to retrieve current transaction data, demographic information, data related to market trends, etc., thereby facilitating the segmentation process based on updated information.

[0168] In Operation 802, AI / ML algorithms (including clustering analysis, decision trees, or neural networks) are used to process and analyze the collected data to identify meaningful segments within the data. Operation 802 may include one or more segmentation processes utilizing information input by one or more users interacting with the platform via SPoG UI 705. Users can provide inputs that can be used for segmentation analysis, including purchasing behavior, demographic information, transaction history, etc.

[0169] Operation 802 may include AAML module 715 performing preliminary analysis to identify relevant parameters for segmentation. AAML 715 uses algorithms to analyze user input, taking into account factors such as historical purchasing patterns and prevailing market trends, to develop the optimal segmentation strategy.

[0170] Operation 802 may include CVSE 720 using techniques such as cluster analysis, decision trees, or neural networks to process the collected data. CVSE 720 can identify meaningful segments in the data, thereby facilitating precise targeting and personalized insights for users.

[0171] Operation 802 may include performing one or more processes to verify the accuracy and consistency of the segmentation results using error-checking algorithms within the platform. This verification process ensures the integrity of the segmentation analysis, minimizes errors, and improves the quality of the generated insights. In one non-limiting example, operation 802 may include CVSE module 720 employing one or more machine learning models to analyze the effectiveness of the implemented segmentation process. In another non-limiting example, predictive analytics may be applied to refine the segmentation strategy based on updated data and user feedback. Operation 802 may include various analytical techniques and / or combinations of analytical techniques to verify the generated segments.

[0172] In Operation 803, the platform generates customized, personalized insights for specific customer and sales segments based on segmentation analysis. As described in more detail by Method 900 (which may be an embodiment of Operation 803), one or more PRE 730 processes may be executed to generate segmented insights and recommendations for users.

[0173] In operation 804, as described below in method 1000, personalized insights are delivered to the user to enhance the relevance and effectiveness of the decision-making process.

[0174] Optionally, additional operations can be performed, including operation 805, in which transaction details related to the segmentation process are recorded within the platform, thereby facilitating continuous enhancement and optimization. Operation 805 may include capturing user interactions, segmentation results, feedback, etc., thereby promoting iterative improvement of the segmentation method.

[0175] Optionally, in Operation 806, CVSE 720 may employ one or more machine learning models to analyze the effectiveness of the post-implementation / delivery segmentation process. Predictive analytics can be applied to refine the segmentation strategy based on updated data and user feedback. Operation 806 may include refining the segmentation strategy based on updated data and evolving market dynamics to ensure continuous improvement and adaptation.

[0176] This workflow integrates SPoG UI 705, RTDM 710, AAML 715, and AI-driven CVSE 720. Thus, method 800 automates and refines the segmentation process, thereby enhancing the relevance and effectiveness of the generated insights. Alternative embodiments may integrate changes in machine learning algorithms, data acquisition techniques, and user interface aspects to further enhance adaptability and scalability.

[0177] Figure 9The diagram illustrates a flowchart of a method 900 for a personalization and recommendation engine within system 700. This method describes the operation of delivering highly personalized and segmented insights and recommendations to users via an AI-driven engine. In some embodiments, method 900 executes one or more processes of an AI / ML-driven personalization and recommendation engine to deliver highly personalized insights to users. The engine analyzes real-time data, including insights generated by AI modules, to provide personalized recommendations using techniques such as collaborative filtering, content-based filtering, and matrix factorization. By leveraging user preferences, historical interactions, and market trends, the platform generates relevant recommendations consistent with user interests and needs. Continuous learning and adaptation further enhance user engagement and drive business growth by ensuring that recommendations remain up-to-date and valuable over time.

[0178] In operation 901, real-time data from various sources is collected and processed, including insights generated by the AI ​​module. Operation 901 can be an embodiment of operation 801 described above. In some embodiments, RTDM 710 collects real-time data required for recommendation analysis. RTDM 710 utilizes a RESTful API to retrieve data that may include current transaction data, demographic information, and market trends, thereby ensuring that recommendations are based on up-to-date information.

[0179] In Operation 902, the personalization and recommendation engine uses techniques such as collaborative filtering, content-based filtering, and matrix factorization to analyze the collected data to generate relevant recommendations based on one or more segments generated through Method 800.

[0180] Operation 902 may include one or more users interacting with the platform via SPoG UI 705. In some non-limiting examples, users may provide inputs such as preferences, historical interactions, and product / service interests that can be considered for recommendation / insight analytics.

[0181] In some embodiments, operation 902 may include AAML module 715 performing preliminary analysis to identify relevant parameters for personalized recommendations. AAML 715 uses algorithms to analyze user input, historical interactions, and market trends to formulate the optimal recommendation strategy.

[0182] Operation 902 may include PRE 730 using techniques such as collaborative filtering, content-based filtering, and matrix factorization to process the collected data. PRE 730 can analyze user preferences, historical interactions, and market trends to generate highly personalized recommendations for users.

[0183] Operation 902 can include using error-checking algorithms within the platform to verify the accuracy and relevance of generated recommendations. This verification process ensures the integrity of AI-driven insights, minimizing errors and improving the quality of the user experience.

[0184] In Operation 903, the platform delivers personalized recommendations to users based on their preferences, historical interactions, and market trends. Operation 903 can include delivering insights that can be presented to users via SPoG UI 705, allowing them to view and interact with personalized recommendations and insights. The intuitive interface enables users to explore recommendations that align with their specific preferences and interests, thereby enhancing their overall satisfaction.

[0185] Action 903 may include providing users with the opportunity to provide feedback on the insights and recommendations presented by SPoG UI 705. The feedback mechanism within the platform allows users to express satisfaction or dissatisfaction with the recommendations they receive, thereby contributing to continuous improvement.

[0186] In Operation 904, the feedback mechanism monitors the effectiveness of recommendations and dynamically adjusts algorithms and models to better meet user needs and preferences, thereby promoting engagement and satisfaction. Operation 904 may include FAM 750 implementing user-provided feedback to dynamically adjust the recommendation algorithms and models within AAML module 715, CVSE 720, and / or PRE 730. This continuous adaptation ensures that recommendations remain relevant and valuable to users over time, thereby enhancing user engagement and satisfaction.

[0187] This workflow integrates SPoG UI 705, RTDM 710, AAML 715, PRE 730, and FAM 750 to automate and refine the insight / recommendation generation process. Alternative embodiments can integrate changes in machine learning algorithms, data acquisition techniques, and user interface design to further enhance adaptability and scalability.

[0188] Figure 10 A flowchart of method 1000 for delivering AI-generated insights and recommendations to one or more users based on segmentation is described. This method focuses on delivering AI-generated insights to users in real time through an intuitive interface and channels. Real-time data streaming technology and an event-driven architecture ensure timely delivery of insights as they become available. Various delivery options are supported, such as push notifications, in-app comments, and email alerts, allowing users to receive insights in their preferred formats and channels. Feedback mechanisms play a crucial role in continuously improving the platform based on user input and changing market conditions, ensuring the platform remains responsive, adaptive, and capable of delivering valuable intelligence to users.

[0189] In Operation 1001, insights generated by the AI ​​modules can be delivered to users in real-time, queued form via an intuitive interface and channel. Operation 1001 may include integrating AI-generated insights into the SPoG UI 705 (the central hub for user interaction). In some embodiments, System 700 may implement protocols such as WebSocket to establish a persistent, full-duplex communication channel between the SPoG UI and RIDM 740, thereby facilitating the immediate delivery of insights without relying on traditional HTTP polling mechanisms. System 700 fully provides an AI and event-driven architecture, where AI-driven insights provide triggers within the system that prompt the initiation of the delivery process. Events generated by the AI ​​modules (including CVSE 720 and PRE 730), signal detection of new insights or significant data changes, and signals captured by the SPoG UI via RIDM 740 trigger the real-time propagation of updates to the user.

[0190] In some embodiments, to efficiently handle the large ingestion and generation of real-time data, system 700 can utilize asynchronous message queuing technologies such as Apache Kafka or RabbitMQ, thereby ensuring scalability and fault tolerance. SPoG UI 705 dynamically renders incoming insights on the client side, enabling updates using JavaScript frameworks such as React or Angular without requiring page reloads. Content prioritization and filtering algorithms can be applied in the SPoG UI to personalize insights delivery based on user preferences and segmentation / relevance, thus mitigating information overload. Operation 1001 achieves cross-platform compatibility, ensuring users can access the SPoG UI and receive updates across various devices and operating systems, maintaining a consistent and responsive user experience.

[0191] In addition to utilizing asynchronous message queuing technology, System 700 can also employ dynamic scaling to adapt to fluctuating workloads. The auto-scaling mechanism monitors system metrics such as CPU utilization and incoming message rate, automatically configuring or deconfiguring resources to maintain optimal performance. Parallel processing frameworks such as Apache Spark can be used to distribute computational tasks across multiple nodes, maximizing throughput and reducing processing time. Furthermore, data partitioning strategies ensure efficient resource utilization by distributing data across multiple nodes for parallel processing, thereby minimizing bottlenecks and enhancing scalability.

[0192] In some embodiments, to protect real-time data transmission, the system uses industry-standard cryptographic protocols such as Transport Layer Security (TLS) for end-to-end encryption. Access control lists (ACLs) and role-based access control (RBAC) mechanisms are implemented to restrict access to sensitive data and functions based on user roles and permissions. Furthermore, data integrity checks and message authentication mechanisms (such as digital signatures) are used to verify the authenticity and integrity of transmitted data, thereby preventing tampering or unauthorized modification. Regular security audits and penetration tests are conducted to identify and mitigate potential vulnerabilities, ensuring compliance with industry regulations and standards.

[0193] In some embodiments, operation 1001 may include system 700 integrating one or more error handling mechanisms to manage failures and ensure operational continuity. Fault-tolerant architectures, such as microservices deployed in a containerized environment using platforms like Kubernetes, enable seamless failover and recovery in the event of node failures. Circuit breaker patterns can be employed to detect and isolate faulty components, preventing cascading failures and maintaining system stability. Automatic monitoring and alerting systems proactively detect anomalies and performance degradation, triggering remedial actions such as automatic rollback or resource scaling. Furthermore, comprehensive disaster recovery plans and data backup strategies can be implemented to mitigate the impact of catastrophic events, ensuring business continuity and data resilience.

[0194] In Operation 1002, real-time data streaming technology and an event-driven architecture facilitate the timely delivery of insights as they become available. In some embodiments, System 700 may utilize real-time data streaming technologies, such as APACHEKAFKA or APACE PULSAR, to facilitate the timely delivery of insights as they become available. System 700 can be configured to process large volumes of data and efficiently distribute insights to users. SPoG UI 705 provides an efficient interface to the event-driven architecture and is used to trigger the delivery of insights based on specific events or conditions. For example, when significant changes occur in market conditions or user behavior, System 700 can generate relevant insights in real time and deliver them, for example, via push notifications.

[0195] In operation 1003, various delivery options, such as push notifications, in-app messages, and email alerts, enable users to receive insights in their preferred format and channel. In some embodiments, delivery options can be personalized based on user preferences. This includes push notifications, in-app messages, email alerts, or SMS notifications. Users can customize their notification preferences to receive insights in their preferred format and channel. For example, when PRE 730 identifies new business insights or product recommendations for a user, an event is triggered prompting RIDM 740 to deliver the recommendations to the user's mobile device via push notification.

[0196] In operation 1004, the feedback mechanism collects user input and sentiment analysis to continuously improve the platform based on changing market conditions and user needs, thereby promoting engagement and satisfaction. In some embodiments, SPoG UI 705 may enable users to provide feedback on the relevance and usefulness of the insights received. These interfaces may include feedback forms or rating systems. In some embodiments, SPoG UI 705 may employ one or more techniques via AAML 715 to process sentiment analysis algorithms used to analyze user interactions. Thus, operation 1001 may include a process for measuring the sentiment of one or more users towards the delivered insights. Positive or negative sentiment metrics can be used to assess the effectiveness of the insights.

[0197] In some embodiments, users can provide feedback or suggestions directly through a dedicated input mechanism integrated into the platform. This can include text input fields or voice commands for providing feedback. For example, after receiving an AI-driven business insight or recommendation for a product, a user can interact with the recommendation by clicking on it to view more details. The system tracks this interaction and interprets it as positive feedback, indicating that the recommendation is relevant to the user's interests.

[0198] Thus, Method 1000 enables the automatic, real-time delivery of insights generated by the AI ​​module to users through an intuitive interface and channels. Real-time data streaming technology and an event-driven architecture ensure timely delivery of insights as they become available. Various delivery options are supported, such as push notifications, in-app messages, and email alerts, allowing users to receive insights in their preferred formats and channels. Feedback mechanisms play a crucial role in continuously improving the platform based on user input and changing market conditions, ensuring the platform remains responsive, adaptable, and capable of providing valuable intelligence to users.

[0199] Figure 11 A block diagram depicting example components of device 1100 is provided. One or more computer systems 1100 may be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof. Computer system 1100 may include one or more processors (also referred to as central processing units or CPUs), such as processor 1104. Processor 1104 may be connected to communication infrastructure or bus 1106.

[0200] The computer system 1100 may also include multiple user input / output devices 1103, such as monitors, keyboards, pointing devices, etc., which can communicate with the communication infrastructure 1106 through multiple user input / output interfaces 1102.

[0201] One or more processors 1104 may be graphics processing units (GPUs). In one embodiment, the GPU may be a processor that can function as a dedicated electronic circuit configured to process mathematically intensive applications. The GPU may have a parallel architecture capable of efficiently performing parallel processing of large blocks of data, such as common mathematically intensive data like computer graphics applications, images, and videos.

[0202] Computer system 1100 may also include main memory or main memory 1108, such as random access memory (RAM). Main memory 1108 may include one or more levels of cache. Main memory 1108 may store control logic (i.e., computer software) and / or data.

[0203] The computer system 1100 may also include one or more auxiliary storage devices or memories 1110. The auxiliary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0204] Removable storage drive 1114 can interact with removable storage unit 1118. Removable storage unit 1118 may include a computer-usable or readable storage device on which computer software (control logic) and / or data are stored. Removable storage unit 1118 may be a program box and box interface (such as those present in video game devices), a removable memory chip (such as EPROM or PROM) and associated slots, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. Removable storage drive 1114 can read from and / or write to removable storage unit 1118.

[0205] Auxiliary storage 1110 may include other means, devices, components, tools, or other paths for allowing computer system 1100 to access computer programs and / or other instructions and / or data. Such devices, devices, components, tools, or other paths may include, for example, removable storage unit 1122 and interface 1120. Examples of removable storage unit 1122 and interface 1120 may include a program box and box interface (such as those present in video game devices), a removable memory chip (such as EPROM or PROM) and associated slots, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0206] Computer system 1100 may also include a communication or network interface 1124. The communication interface 1124 enables computer system 1100 to communicate and interact with any combination of external devices, external networks, external entities, etc. (each individually and collectively referred to by reference numeral 1128). For example, communication interface 1124 may allow computer system 1100 to communicate with external or remote devices 1128 via communication path 1126, which may be wired and / or wireless (or a combination thereof), and may include any combination of LAN, WAN, Internet, etc. Control logic and / or data may be transmitted to and from computer system 1100 via communication path 1126.

[0207] 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 smartphone, a smartwatch or other wearable device, an appliance, part of the Internet of Things and / or an embedded system (to name just a few non-limiting examples), or any combination thereof.

[0208] Computer system 1100 can be a client or server that accesses or manages any application and / or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; on-premises or pre-installed software (“on-premises” 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 hybrid models that include any combination of the foregoing examples or other services or delivery paradigms.

[0209] Any available data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), YAML, Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), packet processing, XML User Interface Language (XUL), or any other functionally similar representation (alone or in combination). Alternatively, proprietary data structures, formats, or schemas may be used exclusively or in combination with known or open standards.

[0210] In some embodiments, a tangible non-transitory device or article of art, including a tangible non-transitory computer-usable or readable medium on which control logic (software) is stored, 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 art implementing any combination thereof. 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.

[0211] Figures 12A to 12Q This section describes the various screens and functions of the SPoG UI related to seller login, partner summary forms, customer shopping carts, order summaries, SKU generation, order tracking, shipment tracking, subscription history, and subscription modifications. Detailed descriptions of each diagram are provided below:

[0212] Figure 12A It depicts a seller login splash screen, representing the initial steps of the seller login process. It provides a form or interface where sellers can express their interest in joining the distribution ecosystem, and can enter their basic information, such as company details, contact information, and product catalog.

[0213] Figure 12B A seller registration guide is provided, outlining the step-by-step instructions or checklist that sellers must follow during the registration process. It outlines the necessary tasks and requirements to ensure sellers clearly understand the registration process and can complete it smoothly.

[0214] Figure 12C This document describes a seller login call scheduling procedure that helps arrange calls or meetings between sellers and platform staff or representatives responsible for guiding them through the login process. Sellers can select appropriate time slots or request calls, thereby ensuring effective communication and assistance throughout the login process.

[0215] Figure 12D It outlines the seller login task list, presenting a comprehensive task list or summary table that outlines the specific steps and actions required for a successful seller login. It provides an overview of pending tasks, completed tasks, and upcoming deadlines to help sellers track their progress and ensure timely completion of each login task.

[0216] Figure 12E This depicts a seller login completion screen that confirms the successful completion of the seller login process. It can display a congratulatory message or a summary of completed tasks, indicating that the seller is now officially logged into the distribution ecosystem.

[0217] Figure 12F A partner summary table is presented, providing partners or users with a centralized view of relevant information and metrics related to their partnerships with the distribution ecosystem. It offers an overview of performance metrics, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0218] Figure 12G This describes a customer's product shopping cart, where they can add items they want to purchase. It displays a list of selected products, quantities, prices, and other relevant details. Customers can view and modify their cart contents before proceeding to checkout.

[0219] Figure 12H It depicts a customer subscription cart that allows customers to manage their subscription-based purchases. It displays the selected subscription plan, pricing, and duration. Customers can review and modify their subscription details before finalizing their choices.

[0220] Figure 12I A customer order summary provides a detailed overview of the customer's order, including details such as the purchased product or subscription, quantity, pricing, and any applicable discounts or promotions. It allows customers to review their orders before confirming a purchase.

[0221] Figure 12J This describes a vendor SKU generation screen used to generate unique stock unit (SKU) codes for vendor products. It can include fields or options where vendors can specify product details, attributes, and pricing, and the system will automatically generate the corresponding SKU code.

[0222] Figure 12K and Figure 12L Summary tables depict order summaries, displaying concise information about orders placed within the distribution ecosystem. These tables present key order details such as order number, customer name, product or subscription information, quantity, and order status. The summary tables provide an overview of order activity, enabling users to efficiently track and manage orders.

[0223] Figure 12M This describes a customer subscription cart that allows customers to add, modify, or remove subscription plans. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes based on their preferences and requirements.

[0224] Figure 12NThis describes a customer order tracking screen that allows customers to track the status and progress of their orders throughout the supply chain. It displays real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and anticipate delivery times.

[0225] Figure 12O This describes a customer shipment tracking system that provides customers with real-time tracking information about their shipments. This can include details such as the carrier, tracking number, current location, and estimated delivery date. Customers can stay informed about the whereabouts of their goods.

[0226] Figure 12P This section describes a customer's subscription history, presenting a record of their subscription activity. It displays 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.

[0227] Figure 12Q This describes a customer subscription modification dialog box that allows customers to modify their existing subscriptions. It provides options for upgrading or downgrading subscription plans, changing billing details, or adjusting other subscription-related preferences. Customers can manage their subscriptions based on their evolving needs or preferences.

[0228] The UI screens depicted are not limiting. In some embodiments, Figures 12A to 12Q The UI screens together present the various functions and features offered by SPoG UI, providing users with a comprehensive and user-friendly interface for seller login, partnership management, customer interaction, order management, subscription management and tracking within the distribution ecosystem.

[0229] It should be understood that the Detailed Description section, rather than the Summary and Abstract section, is intended to interpret the claims. The Summary and Abstract section may set forth one or more, but not all, exemplary embodiments of the invention as contemplated by the inventors(s), and is therefore not intended to limit the invention and the appended claims in any way.

[0230] The invention has been described above using functional building blocks that illustrate implementations of specified functions and their relationships. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and their relationships are properly performed.

[0231] The foregoing description of specific embodiments will so fully reveal the general nature of the invention that, without departing from the overall conception of the invention, others can easily make modifications and / or adjustments for various applications (such as the specific embodiments) by applying the knowledge of those skilled in the art without excessive experimentation. Therefore, based on the teachings and guidance presented herein, such modifications and adjustments are intended to be within the meaning and scope of equivalents of the disclosed embodiments. It should be understood that the wording or terminology herein is for descriptive and not limiting purposes, and that the terminology or terminology of this specification should be interpreted by those skilled in the art based on the teachings and guidance.

[0232] The breadth and scope of this invention should not be limited by any of the exemplary embodiments described above, but should be defined only by the appended claims and their equivalents.

Claims

1. A computerized method for automated artificial intelligence (AI) driven customer and vendor segmentation through a system for generating comprehensive insights, the method comprising: collecting real-time data including purchase behavior, demographic information, transaction history, and market trends from various sources through a real-time data mesh (RTDM); analyzing the collected data using AI / ML algorithms via advanced analytics and machine learning (AAML) modules by a customer and vendor segmentation engine (CVSE) to identify meaningful segments in the data, the algorithms including one or more algorithms for performing cluster analysis, decision trees, or neural networks; generating personalized insights tailored for specific customer and vendor segments based on the segmentation analysis; delivering the personalized insights to users through a single pane of glass user interface (SPoG UI) enabling a decision-making process based on the relevance of the personalized insights.

2. The computerized method of claim 1, wherein, The computerized method further comprises recording transaction details related to the segmentation process within the platform for ongoing enhancement and optimization efforts facilitated by data recording mechanisms within the RTDM and CVSE modules within the system for generating comprehensive insights.

3. The computerized method of claim 1, wherein, The computerized method further comprises analyzing the effectiveness of the segmentation process using machine learning models and predictive analytics post-implementation to refine segmentation strategies based on updated data and user feedback, leveraging a feedback and adaptation mechanism (FAM) within the system for generating comprehensive insights.

4. The computerized method of claim 1, wherein, The computerized method further comprises iteratively refining the segmentation analysis based on evolving market dynamics and user interactions, ensuring ongoing improvement and adaptation within the system for generating comprehensive insights.

5. The computerized method of claim 1, wherein, The computerized method further comprises dynamically adjusting segmentation algorithms and models based on real-time data streams and user feedback, enhancing the accuracy and relevance of insights generated within the system for generating comprehensive insights.

6. The computerized method of claim 1, wherein, The computerized method further comprises integrating multiple data sources and analytical tools within the system for generating comprehensive insights to facilitate comprehensive segmentation analysis, ensuring the generation of complete and accurate insights.

7. The computerized method of claim 1, wherein, The computerized method further comprises providing users with customizable segmentation parameters and criteria within the SPoG UI, allowing for tailored segmentation analysis and insight delivery within the system for generating comprehensive insights based on individual user preferences.

8. A system for automated generation and delivery of AI driven comprehensive insights, comprising: a real-time data mesh (RTDM) module configured to aggregate and standardize real-time data from various sources, establishing a centralized data hub for generating insights within an AI driven comprehensive insights platform; a single pane of glass user interface (SPoG UI) enhancing access to the AI driven comprehensive insights platform, facilitating user interaction and access to real-time, customizable insights personalized for user roles within an organization; an advanced analytics and machine learning, AAML, module operatively connected to the RTDM and configured as a central processing unit for executing specialized rules and algorithms based on information retrieved from the RTDM to perform one or more of market data analysis, customer segmentation, and / or predictive analysis; and a customer and vendor segmentation engine, CVSE, configured to execute one or more algorithms via the AAML module to segment customers and vendors based on parameters extracted from the RTDM module, wherein the CVSE utilizes techniques such as cluster analysis, decision trees, or neural networks to identify meaningful segments in the data, allowing for precise targeting and personalized insights.

9. The system of claim 8, wherein, The system further includes a personalization and recommendation engine, PRE module, configured to utilize data from the RTDM module and insights generated by the AAML module to deliver highly customized recommendations to users.

10. The system of claim 9, wherein, The PRE module employs a combination of collaborative filtering, content-based filtering, and matrix factorization techniques to analyze user preferences, historical interactions, and market trends, generating highly personalized recommendations for users.

11. The system of claim 8, wherein, The system further includes a real-time insights delivery module, RIDM module, configured to employ real-time data streaming techniques and event-driven architecture for efficient insights delivery to users within the SPoG UI.

12. The system of claim 11, wherein, The RIDM module supports various delivery channels, including push notifications, in-app messages, and email alerts, and is configured to enable insights delivery based on user preferences for format and channel.

13. The system of claim 8, wherein, The system further includes a feedback and adaptation mechanism, FAM module, enabling continuous evolution and improvement of the AI-driven comprehensive insights platform based on user feedback and changing market conditions, wherein the FAM module collects feedback from users through interactive interfaces within the SPoG UI, sentiment analysis of user interactions, and direct input mechanisms, dynamically adjusting algorithms and models within the AAML module, CVSE, and / or PRE module.

14. The system of claim 1, wherein, The RTDM module is configured to perform one or more extract, transform, and load, ETL, processes and data normalization techniques to generate unified, accessible data.

15. The system of claim 1, wherein, The AAML module adjusts algorithms based on a continuous feedback loop, refining the accuracy of the AAML module processes over time to enhance the relevance of generated insights.

16. A computerized method for generation and delivery of personalized insights within a system utilizing AI-driven engines, comprising: collecting real-time data from various sources, wherein the real-time data includes one or more of insights generated by AI modules, user interactions and behavior, market trends and conditions, transaction data, demographic information, historical data, and / or product / service preferences; The collected data is analyzed by a personalization and recommendation engine (PRE) to generate one or more relevant insights based on one or more computerized algorithms and / or machine learning models; Personalized recommendations are provided to users via a single pane of glass user interface (SPoG UI), wherein the SPoG user interface is configured to: (i) utilize real-time data streaming technology and event-driven architecture to facilitate timely delivery of insights as they become available; (ii) employ multiple delivery options, including two or more of push notifications, in-app messages, and / or email alerts, to enable users to receive insights in their preferred format and channel; (iii) trigger delivery of insights based on specific events or conditions, such as market conditions, significant changes in user behavior, or generation of real-time insights; (iv) collect user input and continuously optimize computerized performance of sentiment analysis against computerized methods via a feedback mechanism.

17. The computerized method of claim 16, wherein, The delivery includes providing users with an opportunity to provide feedback on the relevance of the presented insights.

18. The computerized method of claim 16, wherein, The computerized method further includes monitoring the effectiveness of the recommendations and dynamically adjusting the algorithms and models based on user feedback.

19. The computerized method of claim 16, wherein, The computerized method further includes refining segmentation strategies based on updated data and evolving market dynamics to enable continuous improvement and adaptation of the personalization and recommendation engine.

20. The computerized method of claim 16, wherein, The computerized method further includes recording transaction details related to one or more insights generated within the platform, wherein the transaction details are recorded to facilitate optimization of the personalization and recommendation engine and / or the algorithms, wherein the transaction details include user interactions, segmentation results, and / or feedback.

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