System and method for generating AI-driven integrated insights

Abstract

To provide computerized systems and methods for automated, AI-driven customer and vendor segmentation and personalized insight delivery that facilitate continuous optimization and adaptation, improving relevance and accuracy. [Solution] The method collects real-time data including purchasing behavior and market trends, analyzes it using AI / ML algorithms for segmentation, provides personalized insights through a user-friendly single-pane-of-glass user interface (SPoG UI), records transaction details for continuous improvement, monitors and refines the effectiveness of segmentation based on user feedback and evolving market dynamics, and integrates multiple data sources and analysis tools for comprehensive analysis. Users can customize segmentation parameters.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application is a Continued - in - Part (CIP) of U.S. Patent Application No. 18 / 341,714 filed on June 26, 2023; U.S. Patent Application No. 18 / 349,836 filed on July 10, 2023; U.S. Patent Application No. 18 / 424,193 filed on January 26, 2024; U.S. Patent Application No. 18 / 583,256 filed on February 21, 2024; U.S. Patent Application No. 18 / 583,337 filed on February 21, 2024; U.S. Patent Application No. 18 / 599,388 filed on March 8, 2024; U.S. Patent Application No. 18 / 614,517 filed on March 22, 2024; U.S. Patent Application No. 18 / 732,227 filed on June 3, 2024; U.S. Patent Application No. 18 / 768,998 filed on July 10, 2024; U.S. Patent Application No. 18 / 768,971 filed on July 10, 2024; U.S. Patent Application No. 18 / 789,602 filed on July 30, 2024; and U.S. Patent Application No. 18 / 793,346 filed on August 2, 2024. Each of these applications is hereby incorporated by reference in its entirety.

[0002] (Background) The previous order - processing procedures in distribution and supply - chain platforms have been plagued by inefficiencies, delays, and inaccuracies. In a traditional environment, it is common for multiple systems and vendors to perform each activity independently, from creating a parts list to registering a transaction, applying price settings, generating a quote, and issuing an order. This approach leads to increased business inefficiencies and the potential for errors.

[0003] Enterprise Resource Planning (ERP) systems have served as the cornerstone of managing business processes, including distribution and supply chain. These systems act as a central repository, allowing different departments such as finance, human resources, and inventory management to access and share real-time data. While ERP is comprehensive, it presents several challenges in today's complex distribution and supply chain environment. One of the main challenges is data fragmentation. Data silos across different departments or separate ERP systems make it difficult to achieve real-time visibility. Users lack a comprehensive understanding of key distribution and supply chain metrics, which negatively impacts decision-making processes.

[0004] Furthermore, ERP systems often fail to provide effective data integration capabilities. Existing ERP systems are not designed for efficient integration with external systems or between different modules within the same ERP suite. This design results in reliance on cumbersome and error-prone manual processes for transferring data between systems, negatively impacting the flow of information across the entire supply chain. When information exists in different formats across systems, data inconsistencies arise, hindering accurate data analysis and leading to uninformed decision-making.

[0005] Data inconsistencies present another challenge. When data exists in different formats or units across departments or ERP systems, standardizing this data for meaningful analysis becomes a laborious process. Businesses often rely on time-consuming manual processes for data transformation and validation, which further delays decision-making. In addition, existing ERP systems often lack the ability to effectively process large amounts of data. These systems struggle to provide timely insights for operational improvement, which is particularly problematic for businesses dealing with complex and expanding distribution and supply chain networks.

[0006] Data security is another concern, particularly given the sensitive nature of supply chain data, which includes customer details, pricing, and contracts. Ensuring compliance with global regulations on data security and governance adds another layer of complexity. Existing ERP systems often lack robust security features sufficient to adapt to the ever-evolving landscape of cybersecurity threats and compliance requirements. [Overview of the project]

[0007] The automated, AI-driven segmentation and insight generation process is designed to address shortcomings in technology 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, improving the flexibility and scalability of service options while ensuring data security and compliance. The platform integrates functionality for segmentation analysis, real-time market monitoring, and personalized insight delivery.

[0008] In the global distribution industry, challenges such as inefficient distribution management and the shift to direct-to-consumer (DTT) models necessitate innovative solutions. Traditional distribution methods are becoming increasingly inadequate, particularly due to shifts in consumer expectations and regulations. By integrating segmentation analysis, real-time market monitoring, and functionality for delivering personalized insights, the platform supports the transition to a flexible, AI-driven insight and recommendation engine that adapts to evolving market conditions and user preferences.

[0009] According to several embodiments, an AI-driven segmentation module can be configured to incorporate algorithms that optimize product and service selection based on real-time market data and user preferences. The system includes a segmentation module, which integrates with a real-time data mesh (RTDM) and a single-pane-of-glass user interface (SPoG UI) to provide users with AI-driven insights in real time. Using advanced algorithms, the system adapts offerings based on real-time market data and user behavior patterns to improve the relevance and value of service options.

[0010] In non-limiting examples, AI-driven segmentation and insight processes employ algorithms to provide users with dynamic, personalized insights and recommendations. The systems and processes may be configured to implement machine learning models, such as multivariable linear regression or random forests, to predict and adjust insights based on real-time market dynamics and user-specific factors.

[0011] In one embodiment, a segmentation management module and / or a real-time insights delivery module operably connected to RTDM and SPoG UI manages the delivery of AI-driven insights to users. The module can optimize service segments, insights, and deliveries based on real-time data using algorithms that dynamically adjust insights and segment configurations. The system includes an insights generation engine for predictive analytics that can adapt to variables such as user behavior patterns and market trends.

[0012] In some embodiments, the system allows users to receive personalized insights and recommendations with minimal required input via a SPoG UI. Embodiments may include one or more modules for validating user preferences and aggregating insight options based on real-time data, thereby facilitating the insight delivery process.

[0013] In addition, or alternatively, the system can employ verification algorithms such as support vector machines to ensure the accuracy of the insight configuration. Real-time data from various systems is synchronized to ensure consistent and up-to-date information across the insight delivery model. Embodiments disclosed herein integrate multiple systems, automate processes, and perform verification to automate the delivery of AI-driven insights to users. By implementing intelligent rules and verification, the system efficiently performs complex tasks, reducing time and errors. The system's adaptability ensures it remains up-to-date and evolves in line with market and user demands.

[0014] In some embodiments, the system uses data-driven methods to automate the creation and delivery of personalized insights based on user consumption patterns. This includes providing AI-driven insights aligned with individual user preferences and behavioral patterns, thereby improving user engagement and satisfaction. The system generates user profiles based on comprehensive data analysis encompassing aspects such as digital engagement and technology preferences, guiding the delivery of highly relevant insights.

[0015] In some embodiments, AI-driven segmentation analysis is automatically performed based on comprehensive market research and actual user data to identify user segments with distinct behavioral patterns and preferences. Identifying user segments enables a granular understanding of customer needs and allows for the provision of targeted insights that address specific user requirements for information in areas such as technology, software applications, cloud computing solutions, and hardware needs.

[0016] (Single pane of glass) Single Pane of Glass (SPoG) can provide a comprehensive solution aimed at addressing these multifaceted challenges. It can be configured to provide a holistic, user-friendly, and efficient platform that facilitates the distribution process.

[0017] According to several embodiments, SPoG can be configured to address supply chain and distribution management by enhancing visibility and control over supply chain processes. Through real-time tracking and analysis, SPoG can provide valuable insights into inventory levels and product status, ensuring that supply chain and distribution management processes are handled efficiently.

[0018] In some embodiments, SPoG can integrate multiple touchpoints into a single platform and emulate direct consumer channels within the distribution platform. This integration provides a unified direct channel for consumers to interact with distributors, significantly reducing supply chain complexity and improving the overall customer experience.

[0019] SPoG provides innovative solutions for improved inventory management through advanced predictive capabilities. These predictive analytics highlight demand trends and guide companies to manage inventory more efficiently, mitigating the risk of stockouts or excess inventory.

[0020] In some embodiments, SPoG can include a global compliance database. This database is updated in real time, enabling distributors to stay up-to-date with the latest international regulations. This feature significantly reduces the burden of manual tracking and ensures smooth, compliant cross-border transactions.

[0021] According to several embodiments, SPoG integrates data from various OEMs into a single platform to facilitate AI-driven segmentation and insight generation. This not only ensures data consistency but also significantly reduces the possibility of errors. Furthermore, it provides the ability to efficiently manage segmentation and insight generation, thereby aligning with the specific needs and requirements of the market.

[0022] According to several embodiments, SPoG is a highly configurable and user-friendly platform. Its intuitive interface allows users to easily access and purchase technology, thereby meeting the expectations of a new generation of technology buyers.

[0023] Furthermore, SPoG's advanced analytical capabilities provide valuable insights that can drive strategy and decision-making. It enables real-time tracking and analysis of trends, allowing companies to stay ahead and adapt to changing market conditions.

[0024] Thanks to its flexibility and scalability, SPoG is a future-proof solution. It can adapt to changing business needs, enabling the company to expand or scale down operations as required without significantly altering its infrastructure.

[0025] SPoG's innovative approach to solving distribution industry challenges is a highly valuable tool. By enhancing supply chain visibility, simplifying inventory management, ensuring compliance, performing AI-driven segmentation and insight generation, and delivering an excellent customer experience, it provides a comprehensive solution to the complex problems that have long plagued the distribution industry. Through its implementation, distributors can expect improved efficiency, reduced errors, and enhanced customer satisfaction, leading to sustainable growth in an ever-evolving global market.

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

[0027] The predictive analytics capabilities of RTDM provide a solution for efficient inventory control. By providing insights into demand trends, it supports the company's inventory management and reduces the risk of overstocking or stockouts.

[0028] The global compliance database of RTDM is updated in real time, ensuring that distributors are in a position to comply with international regulations. This significantly reduces the burden of manual tracking and enables cross-border transactions.

[0029] 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 aligns with specific market needs.

[0030] With its intuitive interface, RTDM enhances the customer experience, enables easy access to and purchase of technology, and meets the expectations of a new generation of technology buyers.

[0031] (Advantages of SPoG and RTDM Integration) Integrating the SPoG platform with RTDM offers several advantages. First, it provides an integrated solution to long-standing problems in the distribution industry. With the capabilities of RTDM, SPoG can enhance supply chain visibility, AI-driven segmentation, and insight generation.

[0032] The real-time tracking and analysis provided by RTDM improve SPoG's ability to effectively manage the supply chain and inventory. It provides accurate current information, enabling distributors to make informed decisions quickly.

[0033] Also, integrating SPoG with RTDM 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 supports alignment with market needs.

[0034] RTDM's global compliance database is integrated with SPoG, facilitating compliance-based cross-border transactions. It also reduces the burden of manual tracking, saving significant time and resources.

[0035] In some embodiments, the distribution platform incorporates SPoG and RTDM to provide an improved, comprehensive distribution system. This platform can leverage the advantages of the distribution model, address its existing challenges, and position itself for sustainable growth in the ever-evolving global market. [Brief explanation of the drawing]

[0036] [Figure 1] This embodiment illustrates one example of the operating environment of a distribution platform, referred to as the system in this embodiment. [Figure 2] Figure 1 illustrates one embodiment of the operating environment of a distribution platform constructed with the elements described. [Figure 3] An embodiment of a distribution management system is illustrated. [Figure 4] This document describes a system for an automated, AI-driven user segmentation and insight generation process according to one embodiment. [Figure 5] An RTDM module according to one embodiment is shown. [Figure 6] An illustration shows a SPoG UI according to one embodiment. [Figure 7] A system for automated, AI-driven user segmentation and insight generation, according to one embodiment, is illustrated. [Figure 8] This is a flowchart of a method for an automated AI-driven user segmentation process according to some embodiments of the present disclosure. [Figure 9] This is a flowchart of an automated AI-driven insight generation process according to some embodiments of the present disclosure. [Figure 10] This is a flowchart illustrating the automated delivery of AI-driven insights and recommendations in a system for automated AI-driven user segmentation and insight generation, according to some embodiments of the present disclosure. [Figure 11] This is a block diagram of exemplary components of a device according to some embodiments of the present disclosure. [Figures 12A-12Q] This document illustrates various screens and functionalities of the SPoG UI in several embodiments. [Modes for carrying out the invention]

[0037] This embodiment may be implemented in hardware, firmware, software, or any combination thereof. Alternatively, this embodiment may be implemented as instructions stored in a machine-readable medium, which can be read and executed by one or more processors. The machine-readable medium may include any mechanism for storing or transmitting information in a format readable by a machine (e.g., a computing device). For example, the machine-readable medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and others. Furthermore, firmware, software, routines, and instructions may be described herein as performing specific actions. However, such descriptions are merely for convenience, and it should be understood that such actions are actually the results obtained by a computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0038] The actions shown in the illustrative methods are not exhaustive, and it should be understood that other actions may similarly be performed before, after, or between any of the illustrated actions. In some embodiments of this disclosure, the actions may be performed in a different order and / or different order.

[0039] Figure 1 illustrates the operating environment 100 of a distribution platform called System 110 in this embodiment. System 110 operates within the context of an information technology (IT) distribution model and responds to the demands of various users, including customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. This operating environment encompasses a wide range of characteristics and dynamics that contribute to the success and efficiency of the distribution platform.

[0040] Customers 120 within the operating environment of System 110 represent businesses or individuals seeking IT solutions to meet specific needs. These customers may require a diverse range of IT products, such as hardware components, software applications, network equipment, or cloud-based services. System 110 provides customers with a user-friendly interface, enabling them to browse, search, and select the most suitable IT solutions based on their requirements. Furthermore, customers can access real-time data and analytics through System 110 to make informed decisions and optimize their IT infrastructure.

[0041] The end customer 130 can be the ultimate beneficiary of the IT solutions provided by System 110. The end customer may include businesses or individuals who use IT products and services to improve their operations, productivity, or daily activities. The end customer relies on System 110 to access a wide range of IT solutions and is ensured to have access to the latest technologies and innovations in the market. System 110 allows the end customer to track orders, receive delivery status updates, and access customer support services, thereby improving the overall customer experience.

[0042] Vendor 140 plays a crucial role within the operating environment of System 110. These vendors encompass manufacturers, distributors, and suppliers providing a diverse range of IT products and services. System 110 acts as a centralized platform for vendors to showcase their offerings, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage System 110 to simplify 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 improve overall visibility and competitiveness.

[0043] Resellers 150 can act as intermediaries within a distribution model, bridging the gap between vendors and customers. Resellers play a crucial role in the IT distribution ecosystem by connecting customers with appropriate IT solutions from various vendors. Resellers may include retailers, value-added resellers (VARs), system integrators, or managed service providers. System 110 enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipelines, and provide value-added services to customers. By leveraging System 110, resellers can improve customer relationships, optimize product offerings, and increase revenue streams.

[0044] Within the operating environment of System 110, various dynamics and characteristics may exist that 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 enables communication and interoperability, eliminates data silos, and provides end-to-end visibility.

[0045] System 110 can achieve scalability and flexibility. It can accommodate the growing demands of IT distribution models, whether through an expanding customer base, an increase in the number of vendors, or the involvement of a wide range of IT products and services. System 110 can be configured to handle large-scale data processing, storage, and analytics, ensuring it can support the evolving needs of distribution platforms. In addition, System 110 leverages a technology stack including .NET, Java, and other suitable technologies, providing a robust foundation for its operation.

[0046] In summary, the operating environment of System 110 within the IT distribution model encompasses customers 120, end customers 130, vendors 140, resellers 150, and other entities involved in the distribution process. System 110 functions as a centralized platform that facilitates efficient collaboration, communication, and transaction processes among these users. By leveraging real-time data exchange, integration, scalability, and flexibility, System 110 enables users to optimize their operations within the IT distribution ecosystem, improve customer experience, and drive business success.

[0047] Figure 2 illustrates the operating environment 200 of the distribution platform, extending the elements shown in Figure 1. This environment features an integration point 210, which enables data flow and connectivity between various systems, such as customer systems 220, vendor systems 240, reseller systems 260, and other entities within the AI-driven segmentation and insight generation process. Figure 2 illustrates the network interconnectivity and mechanisms that facilitate collaborative, data-driven decision-making for AI-driven segmentation and insight. The operating environment 200 is configured to automate the AI-driven segmentation and insight process using AI and ML technologies to process and analyze data for service transformation.

[0048] Several embodiments of the AI-driven segmentation and insights process involve a systematic approach to analyzing user behavior and preferences and tailoring services accordingly. This process encompasses several technical components: the collection of diverse data, including product specifications, user service interactions, and usage patterns. This data is aggregated from sources such as CRM systems and web analytics tools and fed into a real-time data mesh (RTDM). The RTDM processes and standardizes this data and functions as a centralized repository for real-time data updates and retrieval. The AAML module analyzes this aggregated data to identify the optimal strategy for service segmentation and insight generation. Services are segmented based on data-driven insights and predicted market preferences. The AI-driven segmentation and insights module, informed by the insights from the AAML module, tailors service offerings to each user or market segment. Predictive models and heuristic algorithms are applied to determine service offerings that align with specific user requirements. Users interact with these services through the SPoG UI, customizing and confirming their segmentation, insights, and offering selections. The system includes a feedback loop where responses to insights are collected and analyzed, continuously refining the service delivery.

[0049] AI algorithms in the AI-driven segmentation and insights process address inventory management, service customization, and user selection optimization. Machine learning models such as neural networks and decision trees refine service delivery. The AI-driven segmentation and insights process uses ML-based algorithms for real-time service configuration. Advanced analytics, such as ensemble learning or reinforcement learning, continuously optimize the AI-driven segmentation and insights process. AI and ML technologies within 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, and optimize decision-making paths through reinforcement learning. The ML component leverages predictive analytics, continuously refining its output by incorporating new data to improve segmentation accuracy and relevance.

[0050] The operating environment 200 includes system 110 as a central hub for managing AI-driven segmentation and insight processes. System 110 acts as a bridge between customer systems 220, vendor systems 240, reseller systems 260, and other entities, integrating communication, data exchange, and transaction processes to provide a consistent experience. Furthermore, environment 200 features an integration point 210 using a hybrid architecture combining RESTful APIs and WebSockets for real-time data exchange and synchronization. This architecture is protected by the SSL / TLS protocol to secure data in transit.

[0051] 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 enables the customer to access real-time information on AI-driven segmentation and insights, including personalized bundles, pricing details, order tracking, and other relevant data, improving their decision-making capabilities. This integration provides an automated, real-time solution for creating and managing the AI-driven segmentation and insights process, improving the customer's operational efficiency.

[0052] Data exchange between customer system 220, vendor system 240, and reseller system 260 is enabled by robust ETL (Extract, Transform, Load) operations, as described below, referencing a real-time data mesh architecture to ensure data consistency and reliability. This interaction can be governed by predefined business rules and logic that define data flow and processing methods. Advanced mapping and transformation tools are employed to harmonize heterogeneous data formats, enabling data integration and utilization across these systems. Orchestrated data exchange supports synchronized operations and enables efficient and informed decision-making across the distribution network.

[0053] Partner System Integration: Integration point 210 enables system 110 to connect to partner system 230, 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.

[0054] Vendor System Integration: Integration point 210 facilitates the connection between system 110 and vendor system 240. Vendor system 240 may include entities representing inventory management, pricing systems, and product catalogs, such as vendor system 241, vendor system 242, and vendor system 243. Integration with vendor system 240 ensures that vendors can efficiently update their product offerings, receive real-time notifications, and facilitate AI-driven segmentation and insight processes.

[0055] Reseller System Integration: Integration point 210 enables the reseller system 260 to connect with system 110. The reseller system 260 encompasses entities such as reseller system 261, reseller system 262, and reseller system 263, which handle sales, customer management, and service delivery. The integration allows resellers to access the latest product information and effectively manage customer relationships.

[0056] Other Entity System Integration: Integration Point 210 further connects with other entities involved in the distribution process, 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.

[0057] The configuration of System 110 includes advanced AI and ML capabilities to automate AI-driven segmentation and insight processing according to individual preferences, ensuring relevance and optimization of the distribution process.

[0058] Furthermore, integration point 210 enables connection with the record system 280 for additional data management and integration. The record system 280 can represent an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system, including both legacy ERP systems such as SAP, Impulse, META, and I-SCALA, as well as future systems. The record system can contain one or more storage repositories of critical legacy business data. This facilitates data exchange and synchronization integration between the distribution platform, system 110, and the ERP, enabling real-time updates and ensuring the availability of accurate and up-to-date information. Integration point 210 establishes a connection between the record system 280 and the distribution platform, enabling 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, vendors, and others.

[0059] Integration points 210 within the operating environment 200 can be facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and connected systems. System 110 adopts industry-standard protocols, such as RESTful API, SOAP, or GraphQL, to establish communication channels and enable data exchange.

[0060] In some embodiments, the system 110 can incorporate an authentication and authorization mechanism to ensure secure access and data protection. Technologies such as OAuth or JSON Web Token (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of exchanged information.

[0061] In some embodiments, the integration point 210 and the data flow within the operating environment 200 enable user interaction within a coordinated ecosystem. Data generated at various stages of the distribution process, including customer orders, inventory updates, shipping details, and sales analytics, flows between customer systems 220, vendor systems 240, reseller systems 260, and other entities. This data exchange facilitates real-time visibility, enables data-driven decision-making, and improves operational efficiency across the entire distribution platform.

[0062] In some embodiments, System 110 leverages advanced technologies such as Typescript, NodeJS, ReactJS, .NET Core, C#, and other preferred technologies to support the integration point 210 and enable communication within the operating environment 200. These technologies provide a robust foundation for System 110, ensuring scalability, flexibility, and efficient data processing capabilities. Furthermore, the integration point 210 can also employ algorithms, data analysis, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. The integration point 210 and the data flow within the operating environment 200 enable users to operate within a coordinated ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between different entities, systems, and components. The integrated data can be processed, harmonized, and made available in real time to relevant users through System 110. This real-time access to accurate and current information enables users to make informed decisions, optimize supply chain operations, and improve customer experiences.

[0063] Some elements of the operating environment shown in Figure 2 may include conventional, well-known elements that are only briefly described herein. For example, each of the customer systems, such as customer system 220, may include a desktop personal computer, workstation, laptop, PDA, mobile phone, or any Wireless Access Protocol (WAP) enabled device, or any other computing device that can interface directly or indirectly with the Internet or other network connectivity. Each of the customer systems may typically run an HTTP client such as Microsoft Edge, Google Chrome, Opera, or a WAP-enabled browser for mobile devices, and the customer systems may access, process, and display information, pages, and applications available from the distribution platform over the network.

[0064] Furthermore, each customer system may typically be equipped with user interface devices, such as a keyboard, mouse, trackball, touchpad, touchscreen, pen, or similar devices for interacting with a graphical user interface (GUI) provided by a browser. These user interface devices enable users of the customer system to navigate the GUI, interact with pages, forms, and applications, and access data and applications hosted by the distribution platform.

[0065] The customer system and its components can be configured by an operator using an application that includes a web browser running on a central processing unit such as an Intel Pentium processor or a similar processor. Similarly, the distribution platform (system 110) and its components can be configured by an operator using an application that runs on a central processing unit such as an Intel Pentium processor or a similar processor, and / or a processor system that may include multiple processor units.

[0066] Embodiments of a computer program product include a machine-readable storage medium containing instructions for programming a computer to perform the processes described herein. Computer code for operating and configuring distribution platforms and customer systems, vendor systems, reseller systems, and systems of other entities to communicate with each other and to process web pages, applications, and other data may be downloaded and stored on a hard disk or any other volatile or non-volatile storage medium or device, such as ROM, RAM, floppy disks, optical disks, DVDs, CDs, microdrives, magneto-optical disks, magnetic or optical cards, nanosystems, or any suitable medium for storing instructions and data.

[0067] Furthermore, computer code for implementing this embodiment can be transmitted 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 transmitted over an extranet, VPN, LAN, or other network and executed on a client system, server, or server system using a programming language such as C, C++, HTML, Java, JavaScript, ActiveX, VBScript, or others.

[0068] This embodiment can be implemented in various programming languages ​​running on a client system, server, or server system, and it will be understood that the choice of language may depend on the specific requirements and environment of the distribution platform.

[0069] This allows the operating environment 200 to connect the distribution platform with one or more integration points 210 and data flows, enabling efficient collaboration and a streamlined distribution process.

[0070] Figure 3 illustrates System 300 for supply chain and distribution management. System 300 (Figure 3) is a supply chain and distribution management solution configured to address the challenges faced by fragmented distribution ecosystems in the global distribution industry. System 300 can include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, improve collaboration, and drive business efficiency.

[0071] The Single Pane of Glass (SPoG) UI305 functions as a centralized user interface, providing users with a unified view of the entire supply chain. It aggregates information from various sources and presents real-time data, analytics, and functionality tailored to the user's specific role and responsibilities. By providing a customizable and intuitive dashboard-style layout, the SPoG UI enables users to access relevant information and tools to manage data-driven decision-making and efficient supply chain and distribution activities.

[0072] For example, logistics managers can use the SPoG UI to monitor shipment status, track delivery routes, and view real-time inventory levels across multiple warehouses. This data can be visualized through interactive charts, such as maps showing the current location of each shipment, or bar graphs showing inventory levels by product category. Having a unified view of the supply chain allows logistics managers to identify bottlenecks, optimize routes, and ensure timely product delivery.

[0073] SPoG UI305 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 UI305 ensures a smooth information flow and enables collaborative decision-making across the distribution ecosystem. Designed with a user-centric approach, SPoG UI305 features an intuitive and responsive layout. Leveraging front-end technologies, it provides dynamic and interactive data visualizations. Customizable dashboards allow users to tailor views based on specific roles and requirements. The UI supports drag-and-drop functionality for ease of use, and its adaptive design ensures compatibility across various devices and platforms. Advanced filtering and search capabilities allow users to efficiently navigate and access relevant supply chain data and insights.

[0074] For example, when a purchase order is generated in the SPoG UI, the system automatically updates inventory levels, sends notifications to the warehouse management system, and initiates the shipping process. This integration enables efficient order fulfillment, reduces manual errors, and improves overall supply chain visibility.

[0075] The Real-Time Data Mesh (RTDM) module 310 is another component of system 300 and is responsible for ensuring data flow within the distribution ecosystem. It collects and harmonizes data from multiple sources and ensures its real-time availability.

[0076] In a distribution network, the RTDM module collects data from various systems, including inventory management systems, point-of-sale (POS) terminals, and customer relationship management systems. This data is harmonized by aligning formats, standardizing units of measurement, and reconciling inconsistencies. The harmonized data can then be made available in real time, allowing users to access accurate and current information across the supply chain.

[0077] The RTDM module 310 can be configured to capture data changes across multiple transaction systems in real time. It employs an advanced Change Data Capture (CDC) mechanism that continuously monitors transaction systems to detect updates or modifications. The CDC component can be specifically configured to work with a variety of transaction systems, including legacy ERP systems, customer relationship management (CRM) systems, and other enterprise-scale systems, ensuring compatibility and flexibility for business operations in diverse environments.

[0078] Having access to real-time data allows users to make timely decisions and respond quickly to changing market conditions. For example, if the RTDM module detects a sudden surge in demand for a particular product, it can send an alert to the production team, allowing them to adjust the manufacturing schedule and prevent stockouts.

[0079] The RTDM module 310 simplifies data management within supply chain operations. It enables real-time harmonization of data from multiple sources, freeing vendors, resellers, customers, and end customers from the constraints imposed by legacy ERP systems. This increased flexibility supports improved efficiency, customer service, and innovation.

[0080] 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, enabling advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities.

[0081] For example, the AAML module can analyze sales data history to identify seasonal patterns and predict future demand. It generates demand forecasts, which can then be used to optimize inventory levels, ensuring sufficient stock during peak seasons and minimizing excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes.

[0082] In addition to forecasting demand, the AAML module can provide insights into customer behavior, enabling targeted marketing campaigns and personalized customer experiences. For example, by analyzing customer data, the module can identify cross-selling or upselling opportunities and recommend products relevant to individual customers.

[0083] Furthermore, the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of customer intentions and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations.

[0084] System 300 emphasizes integration and interoperability for connecting with existing enterprise systems, such as ERP systems, warehouse management systems, and customer relationship management systems. By establishing connectivity and data flow between these systems, System 300 enables seamless data exchange, process automation, and end-to-end visibility across the supply chain. Integrated protocols, APIs, and data connectors facilitate communication and interoperability between different modules and components, creating a comprehensive and interconnected distribution ecosystem.

[0085] The implementation and deployment of System 300 can be tailored to meet specific business needs. It can be deployed as a cloud-native solution using containerization technologies such as Docker and orchestration frameworks such as Kubernetes. This approach ensures scalability, ease of management, and efficient updates across different environments. The implementation process involves configuring the system to align with specific supply chain requirements, integrating it with existing systems, and customizing modules and components based on business needs and preferences.

[0086] System 300 for supply chain and distribution management is a comprehensive and innovative solution that addresses the challenges faced by fragmented distribution ecosystems. It combines the power of SPoG UI305, RTDM module 310, and AAML module 315 with integration with existing systems. By leveraging a diverse technology stack, scalable architecture, and robust integration capabilities, System 300 delivers end-to-end visibility, data-driven decision-making, and optimized supply chain operations. The examples and options provided herein are non-exclusive and can be customized to meet specific industry requirements, driving efficiency and success in supply chain and distribution management.

[0087] Figure 4 shows one embodiment of System 400 for automated, AI-driven segmentation and insights for customer and vendor segmentation, integrating SPoG UI, RTDM, and AI / ML technologies, and featuring interactions to realize a comprehensive insight generation system. System 400 is configured for integration with existing reseller systems, ensuring efficient data exchange and system synchronization.

[0088] SPoG UI405 functions as the primary user interface. Users interact with this interface to perform various tasks, and it provides simple, clear interaction and customization. It displays information and options related to the reseller's unique business model and customer attributes. It displays real-time data from data mesh 410 and provides control for initiating actions within system 400. For example, users can directly interact with tools for dynamic display of service options, interactive elements for segment and insight customization, and real-time feedback on user selections from SPoG UI405. It integrates with other system components to reflect accurate service information and user customization options. SPoG UI is developed using web-based technology, enabling access from various types of devices such as desktop computers, laptops, tablets, and smartphones. SPoG UI405 provides a comprehensive view of the entire distribution ecosystem, aggregating data and functionality from various modules into a centralized, easily navigable platform. SPoG UI405 simplifies the management of complex distribution tasks and provides resellers with a streamlined experience. In some embodiments, the SPoG405 includes a dynamic pricing tool that displays fluctuating costs based on individual users' consumption patterns.

[0089] The Data Mesh 410 is an advanced data management layer. It aggregates and harmonizes data from various sources, including ERP, vendor platforms, and third-party databases. This component ensures that all operational modules within System 400 have access to consistent and up-to-date information. System 400 can synchronize with existing reseller systems, ensuring efficient data exchange and system functionality.

[0090] Data Mesh 410 aggregates and reconciles data from various systems, including inventory management, point-of-sale (POS), and CRM, to ensure real-time availability. It employs Change Data Capture (CDC) to track real-time changes in transactional systems. This module standardizes data formats and units, ensuring data consistency and accuracy for decision-making processes related to service delivery.

[0091] AI Module 460 automates the generation of insights for customer and vendor segmentation using machine learning algorithms and predictive modeling. AI Module 460 dynamically adjusts insights by analyzing market trends, user preferences, and consumption data.

[0092] AI Module 460 includes a decision support system for personalized insights based on advanced data analysis. In some embodiments, AI Module 460 employs deep learning neural networks, specifically 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 decision trees analyze user data and market conditions and segment users into different categories based on service preferences. Random forests and gradient boosting algorithms, i.e., ensemble methods of decision trees, provide improved prediction accuracy and stability. In some embodiments, clustering, particularly K-means clustering and hierarchical clustering, is employed to segment the market and user base into heterogeneous groups. Market / user segmentation helps the AI ​​module 460 understand diverse user preferences and generate AI-driven insights for different market segments.

[0093] In some embodiments, the AI ​​module 460 can use reinforcement learning (RL) to adapt service delivery based on user feedback. RL algorithms, particularly Q-learning and policy gradient methods, can be used to tune the model to maximize user satisfaction and learn from each interaction to improve recommendation accuracy. This module integrates reinforcement learning algorithms to continuously adapt service delivery based on user feedback, improving the accuracy and relevance of customized segmentation and insights over time. Furthermore, NLP techniques can be employed to analyze user feedback and queries. Utilizing tokenization, sentiment analysis, and named entity recognition, the AI ​​module 460 interprets user feedback and improves the insight generation process.

[0094] Real-time processing based on the data mesh 410 enables the AI ​​module 460 to dynamically adjust service delivery based on current usage patterns and immediate feedback from the market. The data mesh 410 also allows for precise tracking of real-time usage data and the implementation of usage-based pricing strategies. The data mesh 410 includes collaborative filtering and content-based recommendation systems that can analyze user behavior and preferences, offering relevant insights by comparing them to similar user profiles or content characteristics.

[0095] In some embodiments, the AI ​​module 460 can integrate predictive analytics tools and employ time series forecasting methods (e.g., autoregressive integrated moving average, exponential smoothing, etc.) to predict future service demand. Optimization algorithms such as linear programming and genetic algorithms can consider various factors such as cost, user preferences, and resource availability to recommend the most effective service bundles and facilitate optimal segmentation and insight composition. The AI ​​module 460 can employ Monte Carlo simulation and scenario analysis for risk assessment and strategic planning, simulating different market scenarios and evaluating various insights generated under different conditions.

[0096] System 400 may include a service management module 420 to oversee the delivery of insights and ensure alignment with user expectations and contractual agreements. It incorporates tools for compliance monitoring, request processing, and service change processes to support efficient segmentation and personalized experiences.

[0097] The insights delivery module 408 functions as an interface within the SPoG UI 405 for users to access and interact with platform insights. It ensures that insights are delivered in real time and continuously within the same platform where transactions occur. The module allows for the customization of insights based on the user's role within the organization, ensuring relevant and actionable intelligence. For example, administrative users may receive different insights than procurement users. The module integrates with other system components to reflect accurate insights and user customization options, providing a streamlined experience for decision-makers.

[0098] The Insight Engine 430 is configured to aggregate and harmonize data from both internal and external sources via the Data Mesh 410. This enables the real-time availability of comprehensive, up-to-date information and facilitates integration with existing systems. By standardizing data formats and units, this module ensures data consistency and accuracy for decision-making processes related to insight delivery.

[0099] The Insight Engine 430 is configured to personalize insights for specific customer and vendor segments, taking into account roles and responsibilities. This engine enhances the decision-making process by providing personalized insights relevant to the user's specific needs and responsibilities. The Insight Engine 420 can employ AI and machine learning algorithms and be configured to process data in real time via the AI ​​Module 460. It ingests data from various sources, including market trends, competitive analysis, and predictive analytics, to generate actionable insights for the user. Unlike static insights typically provided periodically, for example monthly, this engine ensures that insights remain up-to-date, reflecting the latest market conditions and changes in internal data. The Insight Engine 420 can be configured to provide insights regardless of the technology stack used by the business. It enables integration with different technology solutions, providing flexibility and accessibility to the user. This module allows businesses to leverage insights regardless of whether they utilize cloud, hardware, or other technologies.

[0100] The Deployment and Integration Management Module 440 is configured to manage the deployment and integration of the AI-powered insights platform across different countries and systems. It focuses on enhancing and expanding the platform's capabilities, ensuring scalability and impact. This management module ensures the platform is deployed in a way that maximizes value for users while minimizing disruption to existing operations.

[0101] This enables System 400 to leverage real-time data and AI for customer and vendor segmentation. By integrating these modules into the underlying architecture, the platform provides a competitive advantage by enabling informed, data-driven decision-making within a unified transaction ecosystem. System 400 performs AI-driven segmentation and insight generation. System 400 leverages real-time data processing, AI-driven analytics, and user customization capabilities to deliver an insight-centric experience.

[0102] Figure 5 shows an embodiment of an advanced distribution platform including a system 500 for managing a complex distribution network, which can be an embodiment of system 300, providing a technology distribution platform for optimizing the management and operation of the distribution network. System 500 includes several interconnected modules, each performing a specific function and contributing to the overall efficiency of supply chain operations. In some embodiments, these modules may include a SPoG UI 505, a CIM 510, an RTDM module 515, an AI module 520, an interface display module 525, a personalized interaction module 530, a document hub 535, a catalog management module 540, a performance and insights indicator display 545, a predictive analytics module 550, a recommendation system module 555, a notification module 560, a self-onboarding module 565, and a communication module 570.

[0103] System 500, as an embodiment of System 300, can enable supply chain and distribution management using a wide range of technologies and algorithms. These technologies and algorithms facilitate efficient data processing, personalized interactions, real-time analysis, secure communication, and effective management of documents, catalogs, and performance metrics.

[0104] In some embodiments, SPoG UI505 functions as the central interface within System 500, providing users with a unified view of the entire distribution network. Frontend technologies such as ReactJS, TypeScript, and Node.js are used to create an interactive and responsive user interface. These technologies enable SPoG UI505 to deliver a user-friendly experience, allowing users to access relevant information, navigate between different modules, and perform tasks efficiently.

[0105] CIM510, or Customer Interaction Module, employs algorithms and technologies from Oracle Eloqua, Adobe Target, and Okta to manage customer relationships within the distribution network. These technologies enable the module to securely handle customer data, personalize the customer experience, and provide control over user access.

[0106] The RTDM module 515, or Real-Time Data Mesh module, is a component of System 500 and ensures 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. In addition, the module employs a Change Data Capture (CDC) mechanism to capture real-time data updates from various transaction systems, such as legacy ERP and CRM systems. This capability allows users to access accurate, current information and make informed decisions.

[0107] The AI ​​module 520 within system 500 can extract valuable insights from data using advanced analytical and machine learning algorithms, including Apache Spark, TensorFlow, and scikit-learn. 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 use predictive models to forecast demand, allowing users to optimize inventory management and minimize situations of stockouts or excess inventory.

[0108] 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 an interactive and responsive user interface. These technologies enable users to visualize data using various data visualization techniques, such as graphs, charts, and tables, facilitating efficient data understanding, comparison, and trend analysis.

[0109] The Personalized Interaction Module 530 utilizes customer data, behavioral history, and machine learning algorithms to generate personalized recommendations for products or services. It employs technologies such as Adobe Target, Apache Spark, and TensorFlow for data analysis, modeling, and the delivery of targeted recommendations. For example, the module can analyze customer preferences and purchase history to provide personalized product recommendations, improve customer satisfaction, and drive sales.

[0110] Document Hub 535 functions as a centralized repository for storing and managing documents within System 500. It utilizes technologies such as SeeBurger and Elastic Cloud for efficient document management, storage, and retrieval. For example, Document Hub 535 employs SeeBurger's document management capabilities to classify and organize documents based on type, such as contracts, invoices, product specifications, or compliance documents, allowing users to easily access and search for relevant documents when needed.

[0111] The catalog management module 540 enables the creation, management, and distribution of current product catalogs. This ensures that users have access to current product information, including specifications, pricing, availability, and promotions. Technologies such as Kentico and Akamai can be employed to facilitate catalog updates, content 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 geographical location.

[0112] The Performance and Insights Metrics Display 545 collects, analyzes, and visualizes real-time performance metrics and insights related to supply chain operations. Leveraging tools like Splunk and Datadog, it enables effective performance monitoring and provides actionable insights. For example, the module can use Splunk's log analysis capabilities to identify performance bottlenecks in the supply chain, enabling users to take proactive steps to optimize their operations.

[0113] 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 utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and forecasting. For example, the module leverages TensorFlow's deep learning capabilities to analyze sales history data and predict future demand, enabling users to optimize inventory levels and minimize costs.

[0114] The recommendation system module 555 focuses on providing intelligent recommendations to users within a distribution network. It generates personalized product or service recommendations based on customer data, behavioral history, and machine learning algorithms. Technologies such as Adobe Target and Apache Spark can be employed for data analysis, modeling, and the delivery of targeted recommendations. For example, the module can use Adobe Target's recommendation engine to analyze customer preferences and behavior, providing personalized product recommendations across various channels to improve customer engagement and drive sales.

[0115] The notification module 560 enables the delivery of real-time notifications to users regarding critical events, updates, or alerts within the supply chain. It utilizes technologies such as Apigee X and TIBCO for message queuing, an 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 the timely distribution of relevant information.

[0116] The Self-Onboarding Module 565 simplifies the onboarding process for new users entering the distribution network. It provides guided steps, tutorials, or documentation to help users become familiar with the system and its functionality. By employing technologies such as Okta and Kentico, it can ensure secure user authentication, access control, and self-learning resources. For example, the module can leverage Okta's identification and access management capabilities to securely onboard new users, provide appropriate access permissions, and guide them through the system's functionality.

[0117] The communication module 570 enables communication and collaboration within the system 500. It provides users with channels for interaction, message exchange, document sharing, and project collaboration. By employing technologies such as Apigee Edge and Adobe Launch, it facilitates secure and efficient communication, document sharing, and version control. For example, the module can leverage the API management capabilities of Apigee Edge to ensure secure and reliable communication between users, enabling effective collaboration.

[0118] This allows System 500 to incorporate a variety of modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management. These modules include 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 insights indicator display 545, predictive analytics module 550, recommendation system module 555, notification module 560, self-onboarding module 565, and communication module 570, which work together to provide end-to-end visibility, data-driven decision-making, personalized interaction, real-time analytics, and streamlined communication within the distribution network. By incorporating specific technologies and algorithms, efficient data management, secure communication, personalized experiences, and effective performance monitoring become possible, contributing to improved operational efficiency and success in supply chain and distribution management.

[0119] (Real-time data mesh) Figure 6 illustrates an RTDM module 600 according to one embodiment. The RTDM module 600 can be an embodiment of the RTDM module 310 and may include interconnected components, processes, and subsystems configured to enable real-time data management and analysis.

[0120] The RTDM module 600, as shown in Figure 5, represents an effective data mesh and change capture component within the overall system architecture. This module can be configured to provide real-time data management and standardization capabilities, enabling efficient operation within the supply chain and distribution management domain.

[0121] The RTDM module 600 may include an integration layer 610 (also called the “record system”) that integrates with various enterprise systems. These enterprise systems may include ERPs such as SAP, Impulse, META, and I-SCALA, as well as other data sources. The integration layer 610 can handle data exchange and synchronization between the RTDM module 600 and these systems. Data feeds can be established to retrieve relevant information from the record system, such as sales orders, purchase orders, inventory data, and customer information. These feeds enable real-time data updates, ensuring that the RTDM module operates with the most up-to-date and accurate data.

[0122] 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 mesh, i.e., a cloud-based infrastructure configured to provide scalable and fault-tolerant data storage capabilities. Within the data mesh, multiple purpose-specific data stores (PDSs) are deployed, which can 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 function as repositories of normalized and / or standardized data, ensuring data consistency and integrity across the system.

[0123] In some embodiments, the RTDM module 600 implements a data replication mechanism for capturing real-time changes from multiple data sources, including transactional systems such as ERP (e.g., SAP, Impulse, META, I-SCALA). The captured data can then be processed and standardized on the fly and converted into a standardized format suitable for analysis and integration. This process ensures that the data is readily available and current within the data mesh, facilitating real-time insights and decision-making.

[0124] 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 can encompass a highly scalable and robust data lake, which may be called the data lake 622, along with a set of purpose-specific data stores (PDSs) which may be denoted as PDS 624.1–624.N. These components are integrated to ensure efficient data management, standardization, and real-time availability.

[0125] Data Layer 620 includes Data Lake 622, a modern storage and processing infrastructure configured to handle the ever-increasing volume, diversity, and speed of data generated within the supply chain. Built on a scalable distributed file system, such as Apache Hadoop Distributed File System (HDFS) or Amazon S3, the data lake provides a unified, scalable platform for storing both structured and unstructured data. By leveraging the adaptability and fault tolerance of cloud-based storage, Data Lake 622 can accept data inflows from diverse sources.

[0126] In conjunction with Data Lake 622, a population of purpose-specific data stores PDS624.1–624.N can be employed. Each PDS624 can function as a dedicated repository optimized for storing and retrieving specific types of data related to the supply chain domain. In some non-limiting examples, PDS624.1 might be dedicated to customer data, storing information such as customer profiles, preferences, and transaction history. PDS624.2 might focus on product data, encompassing details such as SKU codes, descriptions, pricing, and inventory levels. These purpose-specific data stores enable efficient data retrieval, analysis, and processing, meeting the diverse needs of supply chain users.

[0127] 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 other enterprise-scale systems, in addition to transaction systems such as SAP, Impulse, META, and legacy ERPs like I-SCALA. CDC constantly monitors updates, modifications, or new transactions in these systems and captures them in real time. By capturing these changes, data layer 620 ensures that the data in data lake 622 and PDS 624 remains current, providing users with real-time insights into the distribution ecosystem.

[0128] In some embodiments, the data layer 620 can be implemented using one or more frameworks, such as .NET or Java, to facilitate integration with existing enterprise systems, ensuring broad compatibility with existing systems and providing flexibility for customization and scalability. For example, the data layer 620 can leverage a Java technology stack, including frameworks such as Spring and Hibernate, to facilitate integration with record systems that have a diverse population of ERP systems and other enterprise-scale solutions. This can facilitate smooth data exchange, process automation, and end-to-end visibility across the supply chain.

[0129] In terms of data processing and analysis, Data Layer 620 can, in some non-limiting examples, utilize the capabilities of distributed computing frameworks, such as Apache Spark or Apache Flink. These frameworks can enable parallel processing and distributed computing across large datasets stored in data lakes and PDSs. By using 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 forecasting models for demand forecasting, optimize inventory levels, and identify potential supply chain risks.

[0130] In some embodiments, the data layer 620 can incorporate robust data governance and security measures. Strict access control mechanisms and authentication protocols ensure that only authorized users can access and modify data in the data lake and PDS. Data encryption techniques protect sensitive supply chain information from unauthorized access, both at rest and in transit. In addition, the data layer 620 can implement data lineage and audit trail mechanisms to enable users to track the origin and history of data, ensuring data integrity and compliance with regulatory requirements.

[0131] In some embodiments, Data Layer 620 can be deployed in a cloud-native environment using containerization technologies such as Docker and orchestration 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 their managed services and scalable storage options. This enables demand-based resource scaling, minimizes operational overhead, and provides an adaptable infrastructure for managing supply chain data.

[0132] The RTDM module 600's data layer 620 can integrate the highly scalable data lake, data lake 622, along with application-specific PDSs, PDS624.1–624.N. By employing a CDC mechanism, data layer 620 ensures efficient data management, standardization, and real-time availability. In non-limiting examples, data layer 620 can be implemented using appropriate technologies, such as .NET or Java, and / or distributed computing frameworks like Apache Spark, enabling powerful data processing, advanced analytics, and machine learning capabilities. With robust data governance and security measures, data layer 620 ensures data integrity, confidentiality, and compliance. Through its scalable infrastructure and integration with existing systems, data layer 620 enables supply chain users to make data-driven decisions, optimize operations, and drive business success in dynamic and complex distribution environments.

[0133] The RTDM module 600 may include an AI module 630 configured to implement one or more algorithms and machine learning models and analyze data stored in the data layer 620 to derive meaningful insights. In some non-limiting examples, the AI ​​module 630 may apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI ​​module 630 can continuously learn from new data inputs and adapt its model to provide accurate, current insights. The AI ​​module 630 can generate predictions, recommendations, and alerts and publish such insights to a dedicated data feed.

[0134] The data engine layer 640 comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration. The data engine layer 640 of the RTDM module 600 may include a set of autonomously operating headless engines 640.1–640.N. These engines represent distinct functionalities within the system and may include, for example, one or more recommendation engines, insight engines, and subscription management engines. Engines 640.1–640.N can deliver specific business logic and services using standardized data stored in the data mesh. Each engine can be configured to be pluggable, allowing for flexibility and future expansion of the module's capabilities. Exemplary engines are shown in Figure 5, but these are not intended to be limiting. Any additional headless engines may be included in the data engine layer 640 or other exemplary layers of the disclosed system.

[0135] These systems can be configured to receive data from multiple sources, such as transaction systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and converting it into a standardized format. Data processing algorithms can be applied to cleanse, aggregate, and enhance the data, preparing it for further analysis and integration.

[0136] Furthermore, a data distribution mechanism can be employed to facilitate integration with and access to the RTDM module 600. The data distribution mechanism 645 can include one or more APIs and be configured to facilitate data distribution from the data mesh and engine to various endpoints, including user interfaces, micro-frontends, and external systems.

[0137] The Experience Layer 650 focuses on providing an intuitive, user-friendly interface for interacting with supply chain data. Experience Layer 650 can include data visualization tools, interactive dashboards, and user-centric functionality. Through this layer, users can search and analyze real-time data related to various supply chain metrics, such as inventory levels, sales performance, and customer demand. The User Experience Layer supports personalized data feeds, allowing users to customize their views based on their roles and responsibilities and receive relevant updates. Users can subscribe to specific data updates, such as inventory changes, pricing updates, or new SKU notifications, based on their preferences and roles.

[0138] This means that, in some embodiments, the RTDM module 600 for supply chain and distribution management can include integration with record systems and may include one or more data layers with a data mesh and purpose-specific data stores, AI components, a data engine layer, and a user experience layer. These components work together to provide users with intuitive access to real-time supply chain data, efficient data processing and analysis, and integration with existing enterprise systems. Technical feeds and search within the module ensure that users can find relevant current information and insights, make informed decisions, and optimize supply chain operations. Thus, the RTDM module 600 facilitates supply chain and distribution management by providing a scalable, real-time data management solution. Its innovative architecture enables rich integration of heterogeneous data sources, efficient data standardization, and advanced analytical capabilities. The module's ability to replicate and standardize data from diverse ERPs while maintaining auditable and repeatable transactions provides a clear advantage, enabling a unified view for vendors, resellers, customers, end customers, and other entities within the distribution system, including IT distribution systems.

[0139] (Automated AI-driven integrated insights) In one embodiment, Figure 7 shows a system 700 for generating AI-driven integrated insights for customer and vendor segmentation. The system 700 includes a real-time data mesh 710, a single-pane-of-glass user interface (SPoG UI) 705, an advanced analytics and machine learning (AAML) module 715, and a customer and vendor segmentation engine (CVSE) 720.

[0140] In some embodiments, SPoG UI705 can be an embodiment of the SPoG UI described above, which can be enhanced by access to an AI-driven integrated insights platform, allowing users to interact with the platform and access personalized, real-time, customizable insights tailored to their roles within the organization to improve the user experience.

[0141] RTDM710 aggregates and standardizes real-time data from various sources to generate insights within the AI-powered integrated insights platform. This includes internal data from the platform host and external data sources associated with users. RTDM710 builds a centralized, unified data hub for the AI-powered integrated insights platform, aggregating and standardizing data from multiple sources such as ERP, CRM systems, and market intelligence. It efficiently handles both structured and unstructured data by utilizing a hybrid of data warehouses and data lakes. RTDM710 employs ETL processes and data normalization techniques to ensure data uniformity and accessibility. This standardized data is essential for the CVSE720 and the AI-powered integrated insights platform 720 to function, providing the input required for accurate and effective AI-driven segmentation and insight generation. RTDM710 maintains data integrity and relevance, which are essential for the automated processes of user segmentation and insight generation. In some embodiments, the RTDM710 is configured to interface with an asset management system to support the ownership of a service provider's physical assets while simultaneously enabling users to access and utilize these assets under a comprehensive service agreement.

[0142] The AAML module 715 functions as the central processing unit for the AI-powered integrated insights platform 720. It incorporates dedicated rules and algorithms designed for tasks such as market data analysis, customer segmentation, and predictive analytics. The AAML module 715 employs analytical tools for big data processing and deep learning capabilities to generate actionable insights in real time. It performs sentiment analysis, trend forecasting, and behavioral analysis to understand and anticipate market and user demand. The AAML module 715 integrates and trains machine learning algorithms based on historical datasets to identify relevant insights. It adapts its algorithms based on a continuous feedback loop, refining accuracy over time to improve the precision and relevance of the insights provided. This module performs essential functions for the automated AI-driven insights process, ensuring that the service aligns with individual user preferences and market conditions.

[0143] In this embodiment, the Customer and Vendor Segmentation Engine (CVSE) 720 within the AI-powered integrated insights platform serves as a critical component for effectively segmenting customers and vendors based on various parameters extracted from the RTDM 710. Utilizing advanced algorithms, the CVSE 720 analyzes data streams and identifies 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 within the data, enabling precise targeting and personalized insights. For example, it can distinguish between high-value customers and general buyers, or segment vendors based on geographical location and product preferences.

[0144] The Personalization and Recommendation Engine (PRE) 730 leverages rich data from RTDM 710 and insights generated by the AAML module 715 to provide users with highly customized recommendations. PRE 730 employs a combination of collaborative filtering, content-based filtering, and matrix decomposition techniques to analyze user preferences, interaction history, and market trends. For example, it can recommend products or services based on past purchases, similar user profiles, or emerging market trends. Through continuous learning and adaptation, PRE 730 ensures that recommendations become increasingly relevant and valuable to the user.

[0145] The Real-Time Insights Delivery Module (RIDM) 740 plays a crucial role in ensuring the rapid and efficient delivery of insights to users within the SPoG UI 705. RIDM740 employs real-time data streaming technology and an event-driven architecture to deliver insights as soon as they become available. It supports a variety of delivery options, including push notifications, in-app messages, and email alerts, allowing users to receive insights in their preferred format and channel. For example, RIDM740 can notify procurement managers about sharp price drops in key product categories, enabling them to quickly capitalize on opportunities.

[0146] The Feedback and Adaptive Mechanism (FAM) 750 enables the AI-powered integrated insights platform to continuously evolve and improve based on user feedback and changing market conditions. FAM750 collects user feedback through interactive interfaces, sentiment analysis of user interactions, and direct input mechanisms within the SPoG UI 705. It monitors the effectiveness of delivered insights and measures key performance metrics such as engagement rate, conversion rate, and user satisfaction score. Based on this feedback, FAM750 dynamically adjusts algorithms and models within the AAML module 715, fine-tuning them to better meet user needs and preferences.

[0147] Regarding integration and deployment, the components of the AI-driven integrated insights platform are integrated into System 700, facilitating interoperability and scalability across different regions and user groups. Continuous development algorithms enhance the platform's model and performance through iterative feedback incorporation, updates, and additions. This ensures continuous improvement, guaranteeing the platform delivers personalized intelligence to users.

[0148] Figure 8 illustrates a flowchart of Method 800 for AI-driven customer and vendor segmentation, configured to initiate a segmentation process that implements an AI algorithm within System 700. This method outlines its operation, aimed at effectively segmenting customers and vendors based on real-time data, thereby improving the accuracy and relevance of the insights generated.

[0149] In some embodiments, Method 800 can be configured to effectively segment customers and vendors using one or more AI / ML algorithms. Specifically, in non-limiting examples, Method 800 can analyze real-time data such as purchasing behavior, demographic information, transaction history, and market trends, and the platform can identify meaningful segments in the data by employing one or more techniques such as clustering analysis, decision trees, and neural networks. These segmentation techniques can enable precise targeting and the delivery of personalized insights tailored to specific customer and vendor segments. This method improves the relevance and effectiveness of insights by ensuring that users receive information relevant to their segment, ultimately leading to improved decision-making and business outcomes.

[0150] In operation 801, real-time data encompassing elements such as purchasing behavior, demographic information, and transaction history is collected from various sources. Operation 801 may include operations performed by RTDM710, such as the collection of real-time data necessary for segmentation analysis. In some embodiments, RTDM710 utilizes a RESTful API to retrieve the latest transaction data, demographic information, market trend-related data, etc., to facilitate a segmentation process based on updated information.

[0151] In operation 802, the collected data is processed and analyzed using AI / ML algorithms, including clustering analysis, decision trees, and neural networks, to identify meaningful segments within the data. Operation 802 may include one or more segmentation processes that utilize information entered by one or more users interacting with the platform through the SPoG UI705. Users can provide inputs such as purchasing behavior, demographic information, and transaction history, which can be considered for segmentation analysis.

[0152] Operation 802 may include an AAML module 715 that performs a preliminary analysis to identify relevant parameters for segmentation. The AAML module 715 employs an algorithm to analyze user input and formulates an optimal segmentation strategy, taking into account factors such as past purchase patterns and current market trends.

[0153] Operation 802 may include CVSE720, which processes the collected data using techniques such as clustering analysis, decision trees, or neural networks. CVSE720 identifies meaningful segments within the data, facilitating precise targeting and personalized insights for users.

[0154] Operation 802 may include performing one or more processes to validate the accuracy and coherence of the segmentation results using error checking algorithms within the platform. This validation process ensures the integrity of the segmentation analysis, minimizes errors, and improves the quality of the insights generated. In another non-limiting example, Operation 802 may include CVSE module 720 which employs one or more machine learning models to analyze the effectiveness after the segmentation process has been performed. 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 thereof to validate the generated segments.

[0155] In operation 803, the platform generates personalized insights tailored to specific customer and vendor segments based on segmentation analysis. One or more PRE730 processes can be executed to generate segmented insights and recommendations for users, as described in more detail by method 900, which may be one embodiment of operation 803.

[0156] In operation 804, as described later in method 1000, personalized insights are provided to the user, improving the relevance and effectiveness of the decision-making process.

[0157] Optionally, additional actions, including action 805, can be performed to log transaction details related to the segmentation process within the platform, contributing to ongoing enhancement and optimization efforts. Action 805 may include capturing user interactions, segmentation results, feedback, etc., to facilitate iterative improvements to the segmentation method.

[0158] Optionally, in operation 806, CVSE720 can 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.

[0159] This workflow integrates SPoG UI705, RTDM710, AAML715, and AI-driven CVSE720. This allows Method 800 to automate and refine the segmentation process, improving the relevance and effectiveness of the generated insights. Alternative embodiments can incorporate variations in machine learning algorithms, data acquisition techniques, and user interfaces to further enhance adaptability and scalability.

[0160] Figure 9 illustrates a flowchart of Method 900 for a personalization and recommendation engine within System 700. This method describes how a highly personalized and segmented insight and recommendation engine provides users with highly personalized insights and recommendations. In some embodiments, Method 900 performs one or more processes of an AI / ML-driven personalization and recommendation engine to provide users with highly personalized insights. The engine analyzes real-time data, including insights generated by AI modules, and provides personalized recommendations using techniques such as collaborative filtering, content-based filtering, and matrix decomposition. By leveraging user preferences, interaction history, and market trends, the platform generates highly relevant recommendations aligned with user interests and needs. Continuous learning and adaptation ensure that recommendations remain up-to-date and valuable over time, further enhancing user engagement and driving business growth.

[0161] In operation 901, real-time data from various sources, including insights generated by the AI ​​module, is collected and processed. Operation 901 may be an embodiment of operation 801 described above. In some embodiments, RTDM710 collects real-time data necessary for recommendation analysis. Using a RESTful API, RTDM710 retrieves data that may include current transaction data, demographic information, and market trends to ensure that recommendations are based on up-to-date information.

[0162] In operation 902, the personalization and recommendation engine analyzes the collected data using techniques such as collaborative filtering, content-based filtering, and matrix decomposition, and generates relevant recommendations based on one or more segments generated by method 800.

[0163] Operation 902 may include one or more users interacting with the platform via SPoG UI705. In some non-limiting examples, users may provide input such as preferences, interaction history, and interest in products / services, which can be considered for recommendation / insight analysis.

[0164] In some embodiments, operation 902 may include the AAML module 715 performing a preliminary analysis to identify relevant parameters for personalized recommendations. Employing an algorithm, the AAML 715 analyzes user input, interaction history, and market trends to formulate an optimal recommendation strategy.

[0165] Operation 902 may include PRE730 processing the collected data using collaborative filtering, content-based filtering, and matrix decomposition techniques. PRE730 analyzes user preferences, past interactions, and market trends to generate highly personalized recommendations for the user.

[0166] Operation 902 may include using error checking algorithms within the platform to validate the accuracy and relevance of the generated recommendations. The validation process ensures the integrity of AI-driven insights, minimizes errors, and improves the quality of the user experience.

[0167] In operation 903, the platform provides users with personalized recommendations based on their preferences, past interactions, and market trends. Operation 903 can include providing insights that can be presented to the user via SPoG UI705, allowing the user to review and interact with personalized recommendations and insights. The intuitive interface enables users to browse recommendations aligned with their specific preferences and interests, improving overall satisfaction.

[0168] Action 903 may include providing users with an opportunity to provide feedback on the insights and recommendations presented by SPoG UI705. A feedback mechanism within the platform can allow users to express their satisfaction or dissatisfaction with the recommendations they receive, contributing to ongoing improvement efforts.

[0169] In operation 904, the feedback mechanism monitors the effectiveness of recommendations and dynamically adjusts the algorithms and models to better meet user needs and preferences, thereby fostering engagement and satisfaction. Operation 904 may include implementing user-provided feedback in FAM750 and dynamically adjusting the recommendation algorithms and models within AAML module 715, CVSE720, and / or PRE730. This continuous adaptation ensures that recommendations remain relevant and valuable to the user over time, improving user engagement and satisfaction.

[0170] This workflow integrates SPoG UI705, RTDM710, AAML715, PRE730, and FAM750 to automate and refine the insight / recommendation generation process. Alternative embodiments may incorporate variations in machine learning algorithms, data acquisition techniques, and user interface design to further enhance adaptability and scalability.

[0171] Figure 10 shows a flowchart of Method 1000 for providing AI-generated insights and recommendations to one or more users based on segmentation. This method focuses on delivering insights generated by the AI ​​module to users in real time through an intuitive interface and channels. Real-time data streaming technology and an event-driven architecture ensure that insights are delivered as soon as they become available. Various delivery options such as push notifications, in-app messages, and email alerts are supported, allowing users to receive insights in their preferred format and channel. A feedback mechanism plays a crucial role in continuously improving the platform based on user input and changing market conditions, ensuring that the platform remains responsive and adaptable and can deliver valuable intelligence to users.

[0172] In operation 1001, insights generated by the AI ​​module can be queued for real-time delivery to the user through an intuitive interface and channel. Operation 1001 may include integrating the AI-generated insights into the SPoG UI 705, which is a central hub for user interaction. In some embodiments, the system 700 can implement a protocol such as the Websocket protocol to establish a persistent full-duplex communication channel between the SPoG UI and the RIDM 740, facilitating the immediate delivery of insights without relying on the conventional HTTP polling mechanism. The system 700 comprehensively provides an AI and event-driven architecture, where AI-driven insights provide triggers that initiate the delivery process within the system. Events generated by the AI ​​module, including the CVSE 720 and PRE 730, signal the detection of new insights or significant data changes, which are captured by the SPoG UI via the RIDM 740 and trigger real-time delivery of updates to the user.

[0173] In some embodiments, to efficiently handle the ingestion and generation of vast amounts of real-time data, system 700 can leverage asynchronous message queuing technologies such as APACHE KAFKA or RABBITMQ, ensuring scalability and fault tolerance. SPoG UI 705 dynamically renders incoming insights on the client side and utilizes JavaScript frameworks such as React or Angular for updates that do not require page reloads. Content prioritization and filtering algorithms can be applied within SPoG UI to provide personalized insights based on user preferences and segmentation / relevance, mitigating information overload. Operation 1001 enables cross-platform compatibility, ensuring that users can access and receive updates to SPoG UI across various devices and operating systems, maintaining a consistent and responsive user experience.

[0174] In addition to leveraging asynchronous message queuing technology, System 700 can employ dynamic scaling techniques to adapt to fluctuating workloads. The auto-scaling mechanism can monitor system metrics such as CPU utilization and incoming message rates, automatically provisioning or deprovisioning 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, minimize bottlenecks, and improve scalability by distributing data across multiple nodes for parallel processing.

[0175] In some embodiments, to protect real-time data transmission, the system employs end-to-end encryption using industry-standard cryptographic protocols such as Transport Layer Security (TLS). Access control list (ACL) and role-based access control (RBAC) mechanisms are implemented to restrict access to sensitive data and functionality based on user roles and permissions. In addition, data integrity checks and message authentication mechanisms, such as digital signatures, are used to verify the authenticity and integrity of transmitted data and prevent tampering or unauthorized modifications. Regular security audits and penetration testing are conducted to identify and mitigate potential vulnerabilities and ensure compliance with industry regulations and standards.

[0176] In some embodiments, operation 1001 may include a system 700 that incorporates one or more fault handling mechanisms to manage failures and ensure operational continuity. Fault-tolerant architectures, such as microservices deployed in a containerized environment using a platform like Kubernetes, enable seamless failover and recovery even in the event of a node failure. A circuit breaker pattern can be employed to detect and isolate faulty components, preventing cascading failures and maintaining system stability. Automated monitoring and alerting systems proactively detect anomalies and performance degradations and trigger remedial actions such as automated rollback or resource scaling out. Furthermore, comprehensive disaster recovery plans and data backup strategies can be implemented to mitigate the impact of catastrophic events and ensure business continuity and data resilience.

[0177] In operation 1002, real-time data streaming technology and event-driven architecture facilitate the delivery of insights as soon as they become available. In some embodiments, system 700 can utilize real-time data streaming technology such as APACHE KAFKA or APACHE PULSAR to facilitate the delivery of insights as soon as they become available. System 700 can be configured to handle large amounts of data and efficiently deliver insights to users. SPoG UI 705 provides an efficient interface with the event-driven architecture and is employed to trigger the delivery of insights based on specific events or circumstances. For example, if there is a significant change in market conditions or user behavior, system 700 can generate relevant insights in real time and deliver them, for example, by push notification.

[0178] 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, the 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 PRE730 identifies new business insights or product recommendations for a user, an event is triggered, prompting RIDM740 to deliver recommendations to the user's mobile device via a push notification.

[0179] 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 fostering engagement and satisfaction. In some embodiments, the SPoG UI 705 can enable users to provide feedback on the relevance and usefulness of the insights they receive. These interfaces may include feedback forms or evaluation systems. In some embodiments, the SPoG UI 705 can employ one or more techniques via the AAML 715 to process sentiment analysis algorithms for analyzing user interactions. This allows operation 1001 to include a process for evaluating the sentiment of one or more users to the insights provided. The effectiveness of the insights may be assessed using positive or negative sentiment indicators.

[0180] In some embodiments, users can provide feedback or suggestions directly through a dedicated input mechanism integrated into the platform. This may include a text input field or voice commands for providing feedback. For example, after receiving a recommendation regarding an AI-driven business insight or product, a user can interact with the recommendation by clicking on it to view further details. The system tracks this interaction and interprets it as positive feedback indicating that the recommendation is relevant to the user's interests.

[0181] This allows Method 1000 to automatically deliver insights generated by the AI ​​module to users in real time through an intuitive interface and channels. Real-time data streaming technology and an event-driven architecture ensure that insights are delivered as soon as they become available. Various delivery options such as push notifications, in-app messages, and email alerts are supported, allowing users to receive insights in their preferred format and channel. A feedback mechanism plays a crucial role in continuously improving the platform based on user input and changing market conditions, ensuring that the platform remains responsive and adaptable, enabling it to deliver valuable intelligence to users.

[0182] Figure 11 is a block diagram of exemplary components of device 1100. One or more computer systems 1100 may be used, for example, to implement any of the embodiments described herein, as well as combinations and partial combinations thereof. Computer system 1100 may include one or more processors (also called central processing units or CPUs), for example, processor 1104. Processor 1104 may be connected to a communication infrastructure or bus 1106.

[0183] Furthermore, the computer system 1100 may include user input / output devices 1103 such as a monitor, keyboard, and pointing device, which can communicate with the communication infrastructure 1106 through a user input / output interface 1102.

[0184] One or more processors 1104 may be graphics processing units (GPUs). In one embodiment, the GPU may be a processor that is a special electronic circuit configured to process mathematically intensive applications. The GPU may have a parallel structure that can efficiently process large data blocks, such as mathematically intensive data common to computer graphics applications, images, videos, etc.

[0185] Furthermore, the computer system 1100 may include main or primary memory 1108, such as random access memory (RAM). The main memory 1108 may include one or more levels of cache. The main memory 1108 may have control logic (i.e., computer software) and / or data stored inside.

[0186] Furthermore, the computer system 1100 may include one or more secondary storage devices or memories 1110. The secondary memory 1110 may include, for example, a hard disk drive 1112 and / or a removable storage device or drive 1114.

[0187] The removable storage drive 1114 may interact with the removable storage unit 1118. The removable storage unit 1118 may include a computer-accessible or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 1118 may also include a program cartridge and cartridge interface (such as those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage units and associated interfaces. The removable storage drive 1114 may read from and / or write to the removable storage unit 1118.

[0188] The secondary memory 1110 may include other means, devices, components, mediators, or other approaches to enable computer programs and / or other instructions and / or data to be accessed by the computer system 1100. Such means, devices, components, mediators, or other approaches may include, for example, a removable storage unit 1122 and an interface 1120. Examples of the removable storage unit 1122 and interface 1120 may include a program cartridge and cartridge interface (such as those found in video game devices), a removable memory chip (such as an EPROM or PROM) and associated socket, a memory stick and USB port, a memory card and associated memory card slot, and / or other removable storage units and associated interfaces.

[0189] The computer system 1100 may further include a communication or network interface 1124. The communication interface 1124 may enable the computer system 1100 to communicate with and interact with a combination of external devices, external networks, external entities, etc. (referenced individually and collectively in reference number 1128). For example, the communication interface 1124 may enable the computer system 1100 to communicate with an external or remote device 1128 via a communication path 1126, which may be wired and / or wireless (or a combination thereof) and may include a combination of LAN, WAN, Internet, etc. Control logic and / or data may be transmitted to and from the computer system 1100 via the communication path 1126.

[0190] Furthermore, the computer system 1100 may be, to give some non-limiting examples, a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet, a smartphone, a smartwatch or other wearable, an appliance, part of the Internet of Things, and / or an embedded system, or a combination thereof.

[0191] The computer system 1100 may be a client or server that accesses or hosts applications and / or data through a delivery model, and may include, but is not limited to, remote or distributed cloud computing solutions, local or on-premises 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)), and / or hybrid models including combinations of the aforementioned examples or other services or delivery models.

[0192] The applicable data structures, file formats, and schemas in computer system 1100 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extended Markup Language (XML), Yet Another Markup Language (YAML), Extended Hypertext Markup Language (XHTML), Wireless Markup Language (WML), MessagePack, XML User Interface Language (XUL), or other functionally similar expressions, either alone or in combination. Alternatively, proprietary data structures, formats, or schemas may be used either exclusively or in combination with known or open standards.

[0193] In some embodiments, a tangible, non-temporary device or product comprising a tangible, non-temporary computer-readable or computer-compatible 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, the computer system 1100, the main memory 1108, the secondary memory 1110, and the removable storage units 1118 and 1122, as well as tangible products embodying the aforementioned combination. When such control logic is executed by one or more data processing devices (such as the computer system 1100), such data processing devices can be made to operate as described herein.

[0194] Figures 12A–12Q illustrate various screens and functionalities of the SPoG UI related to vendor onboarding, partner dashboard, customer cart, order summary, SKU generation, order tracking, shipping tracking, subscription history, and subscription changes. Detailed descriptions of each figure are provided below.

[0195] Figure 12A shows the vendor onboarding start screen, representing the first step in the vendor onboarding process. It provides a form or interface where vendors can express their interest in participating in the distribution ecosystem. Vendors can enter basic information such as company details, contact information, and product catalogs.

[0196] Figure 12B shows a vendor onboarding guide that displays a step-by-step guide or checklist for the vendor to follow during the onboarding process. It outlines the necessary tasks and requirements, ensuring that the vendor has a clear understanding of the onboarding process and can proceed smoothly.

[0197] Figure 12C shows a vendor onboarding call scheduler that facilitates scheduling calls or meetings between vendors and platform personnel or representatives responsible for guiding vendors through the onboarding process. Vendors can select a suitable time slot or request a call, ensuring effective communication and assistance throughout the onboarding process.

[0198] Figure 12D shows a vendor onboarding task list, which presents a comprehensive task list or dashboard outlining the specific steps and actions required for successful vendor onboarding. It provides an overview of pending tasks, completed tasks, and upcoming deadlines, helping vendors track progress and ensuring that each onboarding task is completed in a timely manner.

[0199] Figure 12E shows the vendor onboarding completion screen, confirming the successful completion of the vendor onboarding process. It may display a congratulatory message indicating that the vendor has been officially onboarded into the distribution ecosystem at this point, or a summary of completed tasks.

[0200] Figure 12F shows a partner dashboard that provides partners or users with an aggregated view of relevant information and metrics regarding their partnerships with the distribution ecosystem. It provides an overview of performance indicators, key data points, and actionable insights to facilitate effective collaboration and decision-making.

[0201] Figure 12G shows a customer product cart, where the customer can add items they wish to purchase. It displays a list of selected products, quantities, prices, and other relevant details. The customer can review and modify the contents of their cart before proceeding to the checkout process.

[0202] Figure 12H shows 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 confirming their selections.

[0203] Figure 12I shows a customer order summary, which provides a summary of the customer's order including details such as the purchased product or subscription, quantity, pricing, and any applied discounts or promotions. This allows the customer to review their order before confirming the purchase.

[0204] Figure 12J shows the vendor SKU generation screen for generating unique stock unit (SKU) codes for vendor products. It may include fields or options where the vendor can specify product details, attributes, and pricing, and the system automatically generates the corresponding SKU codes.

[0205] Figures 12K and 12L show dashboard order summaries for displaying summary information about orders placed within the distribution ecosystem. These present key order details, such as order number, customer name, product or subscription information, quantity, and order status. The dashboard provides an overview of order activity, enabling users to efficiently track and manage orders.

[0206] Figure 12M shows a customer subscription cart that allows customers to add, modify, or delete subscription plans. It can display a list of selected subscriptions, pricing, and renewal dates. Customers can manage their subscriptions and make changes according to their preferences and requirements.

[0207] Figure 12N shows a customer order tracking screen that allows customers to track the status and progress of their orders within the supply chain. It displays real-time updates on order fulfillment, including processing, packaging, and shipping. Customers can monitor the movement of their orders and predict delivery times.

[0208] Figure 12O shows customer shipment tracking, which provides customers with real-time tracking information about their shipments. This may include details such as carrier, tracking number, current location, and estimated delivery date. Customers can always get information about the whereabouts of their shipments.

[0209] Figure 12P shows the customer subscription history, which presents a historical record of the customer's subscription activity. It displays a list of previous subscriptions, including the subscription plan, duration, and status. Customers can review their subscription history, track past payments, and view details of previous subscriptions.

[0210] Figure 12Q shows the customer subscription modification dialog, which 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 according to their evolving needs or preferences.

[0211] The UI screens shown are not limited. In some embodiments, the UI screens in Figures 12A–12Q collectively represent the diverse functionality and features provided by the SPoG UI, offering users a comprehensive and user-friendly interface for vendor onboarding, partnership management, customer interaction, order management, subscription management, and tracking within the distribution ecosystem.

[0212] It should be understood that the detailed description section, rather than the abstract section, is intended to be used to interpret the claims. The abstract section may describe one or more, but not all, exemplary embodiments of the invention as intended by the inventors, and is therefore not intended to limit the invention and the appended claims in any way.

[0213] The present invention has been described above with the assistance of function-building blocks illustrating the implementation of specific functions and their relationships. The boundaries of these function-building blocks are arbitrarily defined herein for the sake of explanation. Alternative boundaries can be defined, as long as the specific functions and their relationships are adequately implemented.

[0214] The prior description relating to specific embodiments fully illustrates the general nature of the invention, and such specific embodiments can be readily modified and / or adapted to various uses without departing from the general concept of the invention, without requiring any unnecessary experimentation, by applying the knowledge of those skilled in the art. Such adaptations and modifications are therefore intended to be within the meaning and scope of equivalents of the disclosed embodiments, based on the teachings and guidance presented herein. The expressions or terms herein are for illustrative purposes only and not intended to limit, and therefore should be understood to those skilled in the art to be interpreted in light of the teachings and guidance.

[0215] The breadth and scope of the present invention should not be limited by any of the exemplary embodiments described above, but should be defined solely by the following claims and their equivalents.

Claims

1. A computerized method for automated artificial intelligence (AI)-driven customer and vendor segmentation using a system for generating integrated insights, Real-time data mesh (RTDM) collects real-time data from various sources, including purchasing behavior, demographic information, transaction history, and market trends. Analyzing data collected by customer and vendor segmentation engines (CVSEs) using AI / ML algorithms via advanced analysis and machine learning (AAML) modules, wherein the algorithms comprise one or more algorithms for performing clustering analysis, decision trees, or neural networks to identify meaningful segments within the data. Based on segmentation analysis, generate personalized insights tailored to specific customer and vendor segments, A method comprising providing the personalized insights to a user through a single-pane-of-glass user interface (SPoG UI) and enabling a decision-making process based on the relevance of the personalized insights.

2. The computerized method according to claim 1, further comprising logging transaction details related to the segmentation process within the platform for continuous enhancement and optimization efforts, and facilitating this through data logging mechanisms in the RTDM module and CVSE module within the system for generating integrated insights.

3. The computerized method according to claim 1, further comprising using machine learning models and predictive analytics to analyze the post-implementation effectiveness of a segmentation process to refine the segmentation strategy based on updated data and user feedback, and leveraging the feedback and adaptive mechanisms (FAM) within the system for generating integrated insights.

4. The computerized method according to claim 1, further comprising iteratively refining the segmentation analysis based on evolving market trends and user interactions, and ensuring continuous improvement and adaptation within the system to generate integrated insights.

5. The computerized method according to claim 1, further comprising dynamically adjusting segmentation algorithms and models based on real-time data streams and user feedback to improve the accuracy and relevance of insights generated within the system for generating integrated insights.

6. The computerized method according to claim 1, further comprising integrating multiple data sources and analytical tools within the system for generating integrated insights to facilitate comprehensive segmentation analysis and to ensure the generation of thorough and accurate insights.

7. The computerized method according to claim 1, further comprising providing the user with customizable segmentation parameters and criteria within the SPoG UI, and enabling the provision of segmentation analysis and insights tailored to the individual user's preferences within the system for generating integrated insights.

8. A system for automated, AI-driven, integrated insight generation and delivery, 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-powered integrated insights platform, Enhanced by access to the aforementioned AI-driven integrated insights platform, a single-pane-of-glass user interface (SPoG UI) facilitates user interaction and access to personalized, real-time, customizable insights tailored to the user's role within the organization, An advanced analytics and machine learning (AAML) module configured as a central processing unit for performing one or more market data analysis, customer segmentation, and / or predictive analytics by executing specialized rules and algorithms based on information obtained from the RTDM, operably connected to the RTDM, 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 clustering analysis, decision trees, or neural networks to identify meaningful segments within the data, enabling precise targeting and personalized insights.

9. The system according to claim 8, further comprising a Personalization and Recommendation Engine (PRE) module configured to provide users with highly tailored recommendations by utilizing data from the RTDM module and insights generated by the AAML module.

10. The system according to claim 9, wherein the PRE module employs a combination of collaborative filtering, content-based filtering, and matrix decomposition techniques to analyze user preferences, interaction history, and market trends, and to generate highly personalized recommendations for the user.

11. The system according to claim 8, further comprising a Real-Time Insights Delivery Module (RIDM) module configured to efficiently deliver insights to users within the SPoG UI by employing real-time data streaming technology and an event-driven architecture.

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

13. The system according to claim 8, further comprising a Feedback and Adaptive Mechanism (FAM) module that enables the continuous advancement and improvement of the AI-powered integrated insight platform based on user feedback and changing market conditions, wherein the FAM module collects user feedback through an interactive interface within the SPoG UI, sentiment analysis of user interactions, and a direct input mechanism, and dynamically adjusts the algorithms and models within the AAML module, CVSE, and / or PRE module.

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

15. The system according to claim 1, wherein the AAML module adapts the algorithm based on a continuous feedback loop, refining the accuracy of the AAML module process over time and increasing the relevance of the generated insights.

16. A computerized method for generating and providing personalized insights within a system utilizing an AI-driven engine, The collection of real-time data from various sources, wherein the real-time data includes one or more of the following: insights generated by an AI module, user interactions and behaviors, 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), and one or more relevant insights are generated based on one or more computerized algorithms and / or machine learning models. To provide personalized recommendations to a user via a single-pane-of-glass user interface (SPoG UI), wherein the SPoG UI is (i) To facilitate the rapid delivery of insights as they become available by utilizing real-time data streaming technology and event-driven architecture, (ii) To enable users to receive insights in their preferred format and channel, adopt multiple delivery options, including two or more of push notifications, in-app messages, and / or email alerts, (iii) Triggering the provision of insights based on specific events or circumstances such as significant changes in market conditions, user behavior, or the generation of the aforementioned real-time insights, (iv) A computerized method comprising: providing, configured to collect user input and generate computerized performance of sentiment analysis via a feedback mechanism to continuously optimize the computerized method.

17. The computerized method according to claim 16, wherein providing the above-mentioned information provides the user with an opportunity to provide feedback on the relevance of the presented insights.

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

19. The computerized method according to claim 16, further comprising refining the segmentation strategy based on updated data and evolving market trends, and enabling the continuous improvement and adaptation of the personalization and recommendation engine.

20. The computerized method according to claim 16, further comprising logging the details of a transaction relating to one or more insights generated within the platform, wherein the details of the transaction are logged to facilitate the optimization of the personalization and recommendation engine and / or the algorithm, and the details of the transaction include user interactions, segmentation results, and / or feedback.

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