An AI-based, vendor- and customer-independent framework for improving the integration of legacy systems.

Abstract

Systems and methods are provided for integrating legacy systems into a unified platform. Implementations include a system configured to: ingest, normalize, and store data from multiple sources using a data layer; analyze and transform the data using AI algorithms by a processing layer; and provide a data visualization and interactive, dynamic user interface by a presentation layer. Methods are provided for receiving data (such as email orders, vendor catalogs, CRM data, etc.) from computing devices; extracting data from the content; processing the data using natural language processing techniques; analyzing the data using machine learning models; mapping the data to patterns on a unified platform; managing integration workflows using a workflow orchestration engine; providing real-time data insights; and resolving data errors or anomalies using an exception management system.

Description

Background Technology

[0001] Integrating legacy systems and data from different vendors and customers into a unified platform presents significant challenges. Typically, legacy systems, such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and Configuration, Pricing, and Quotation (CPQ) systems, operate independently. Each system uses different data formats, protocols, and operating standards, resulting in a heterogeneous technological landscape.

[0002] The main challenges lie in the time and cost associated with integrating these disparate systems. Integration projects typically require extensive manual coding, data mapping, and transformation work, often taking months to years to complete. They also demand significant financial investment due to the need for specialists and resources. Furthermore, maintaining these integrations is cumbersome, as any updates or changes to the legacy systems require additional tweaking and reconfiguration.

[0003] Another problem is the lack of interoperability between different systems. Legacy systems are often built on outdated technologies that do not support modern communication standards, making it difficult to facilitate data exchange. This results in fragmented data silos, where information is trapped in single systems, hindering comprehensive data analysis and decision-making.

[0004] Furthermore, the integration process often involves considerable friction. For example, organizations must manage multiple vendor contracts, different service level agreements (SLAs), and disparate support structures. This complexity extends to order management, where hardware, software, and cloud services are typically procured and managed through separate channels, leading to inefficiencies and increased operating costs.

[0005] Existing systems often require changes to their existing formats, workflows, or frameworks, imposing significant operational burdens on vendors and customers. This rigidity generates friction and limits the adoption of new platforms. Conventional approaches to integrating legacy systems encounter complexity, leading to extended timelines, high costs, interoperability issues, and operational friction. These problems hinder organizations' ability to achieve a unified, efficient, and agile technology ecosystem. Summary of the Invention

[0006] The embodiments described herein provide an AI-based, vendor- and customer-independent framework designed to improve the integration of legacy systems. Systems and methods are provided for dynamically adapting and integrating customer-specific formats and frameworks while maintaining the integrity of their own unified schema, which is crucial for reducing these barriers and facilitating seamless business operations. The framework leverages advanced artificial intelligence algorithms, including natural language processing and machine learning, to interpret and transform data from various legacy systems into a common format compatible with the unified platform. The embodiments described herein enable the unified platform to dynamically adapt to vendor- and customer-specific formats, frameworks, and workflows. By eliminating the need for customers to modify their existing systems, the platform ensures seamless integration. The system takes external data structures and transforms them into a canonical form while preserving the customer's original schema for continued interoperability. This capability allows customers to keep their business operations "as is" without disruption, while providing the benefits of the unified platform in terms of simplified data management and advanced analytics.

[0007] The framework significantly reduces the time and cost traditionally associated with integration by automating the data transformation process, facilitating rapid integration within weeks rather than months or years. The generative AI (Gen-AI) framework enables a unified order management system that integrates hardware, software, and cloud services into a single interface, minimizing operational friction. The framework's self-learning components analyze transaction data to provide actionable insights and recommendations, enhancing decision-making capabilities. By being independent of vendors and customers, the framework ensures compatibility with a wide range of systems without requiring significant changes, providing a comprehensive solution to the long-standing challenge of integrating various legacy systems.

[0008] For example, computing devices can use an AI-based framework to analyze and map data from legacy ERP / CRM systems or other data sources from retailers. The devices employ Natural Language Processing (NLP) algorithms to interpret unstructured data and use Machine Learning (ML) models to learn the data structures and formats used by the legacy systems. Once the analysis is complete, the computing device transforms this data into a common format compatible with a unified platform. This process may include schema mapping, data cleaning, and format conversion (all performed automatically by AI algorithms). The transformed data can then be integrated into the unified platform, facilitating interaction and interoperability with other systems. This automation significantly reduces the time and effort required for integration, enabling computing devices to complete the process in weeks rather than months, without the need for extensive manual coding or data mapping.

[0009] In some embodiments, computing devices can leverage AI-based frameworks to facilitate the integration of a customer's legacy systems. The computing device employs NLP methods to understand and extract relevant data fields from the legacy system. Using ML models, the device identifies patterns and relationships in the data, enabling it to create an accurate mapping to a unified platform's data schema. This automated process may include tasks such as data normalization, deduplication, and transformation to ensure data from the CRM system is compatible with the unified platform. Once the data is transformed, the computing device can integrate it into the unified platform, facilitating data exchange and interaction with other integrated systems. This embodiment demonstrates the ability of AI-based frameworks to manipulate various data types and structures, significantly reducing the complexity and duration of integration projects.

[0010] In some embodiments, the computing device may also employ an AI-based framework to continuously monitor and optimize the integration process. Initially, the device collects data from the customer's legacy systems, applying NLP to extract meaningful information from unstructured text fields such as customer notes or communication logs. Then, ML algorithms analyze this data to detect patterns, such as common customer queries or frequent questions, and generate data patterns consistent with the architecture of the unified platform.

[0011] The computing device performs data normalization to ensure consistency in data format, units, and naming conventions. It can also perform deduplication routines to identify and merge duplicate records, thus maintaining data integrity and accuracy. Transformation algorithms convert legacy data into a format that a unified platform can effectively ingest and utilize.

[0012] Once the initial integration is complete, the computing device continuously monitors the data flow between the legacy system and the unified platform. It uses machine learning models to identify any discrepancies or anomalies in real time, triggering automatic corrective actions to quickly resolve these issues. The device can also adapt to changes in the legacy system, such as software updates or modifications to data structures, ensuring continued compatibility without requiring extensive reconfiguration.

[0013] Furthermore, the computing device can leverage AI-based frameworks to provide actionable insights derived from integrated data. It can analyze customer interaction data to identify trends, such as emerging customer needs or declining satisfaction levels, and generate recommendations for targeted marketing campaigns or product improvements. The device can also evaluate the performance of the integrated system, thereby providing recommendations for improving operational efficiency and customer service quality.

[0014] In some embodiments, a system for integrating legacy systems into a unified platform may include a server coupled to a processor and configured to execute instructions to ingest, normalize, and store data from multiple sources by a data layer. The system may also include a processing layer configured to analyze and transform data using AI algorithms, and a presentation layer configured to provide a dynamic user interface for data visualization and interaction.

[0015] In some embodiments, the data layer may include connectors and APIs to facilitate data extraction from legacy systems such as ERP, CRM, and CPQ, thereby supporting multiple data formats and communication protocols. The data layer may include preprocessing units that cleanse and standardize data using techniques such as tokenization, stemming, lemmatization, scaling, and transformation. The processing layer may include a workflow orchestration engine that manages integration workflows, thereby facilitating data flow between legacy systems and a unified platform. The processing layer may include an AI and analytics module featuring a self-learning AI engine that continuously adjusts the integration process based on new data and user interactions. The presentation layer may include customizable summary tables that provide real-time data insights and interactive visualizations such as charts, graphs, and heatmaps. The presentation layer may include security features such as role-based access control, secure login mechanisms, and data encryption in transit and at rest.

[0016] In some embodiments, a computerized method for processing email orders may include receiving an email containing order information by a computing device. The method may also include extracting data from the email content and attachments by the computing device and processing the extracted data using natural language processing techniques to parse and structure the information. The method may include using machine learning models by the computing device to analyze the extracted data to identify patterns and relationships within the data, thereby facilitating accurate data mapping and transformation. The method may include mapping structured data to patterns on a unified platform by the computing device, thereby performing normalization, deduplication, and transformation tasks. The method may include using a workflow orchestration engine by the computing device to manage integrated workflows to facilitate data flow into an order management system. The processed order data may be analyzed by the computing device through a predictive analytics module to provide insights and recommendations, such as inventory demand, supply chain disruptions, and optimal shipping methods. The method may include visualizing the processed order data in a customizable summary table by the computing device, which displays real-time data insights and actionable items tailored to user roles and preferences. The method may include using an exception management system by the computing device to identify and resolve data errors or anomalies during the process to maintain data accuracy and reliability.

[0017] In some embodiments, a computerized method for processing a retailer catalog may include receiving a retailer catalog in various formats by a computing device. The method may further include the computing device extracting data from the catalog using a data ingestion engine and processing the extracted data using natural language processing techniques to parse and structure the information. The method may include the computing device using a machine learning model to analyze the extracted data to identify patterns and relationships in the catalog data, thereby facilitating accurate data mapping and transformation. The method may include the computing device mapping structured catalog data to patterns on a unified platform, thereby performing normalization, deduplication, and transformation tasks. The method may include the computing device using a workflow orchestration engine to manage integrated workflows to facilitate data flow into a product management system. The computing device may analyze the processed catalog data via a predictive analytics module to provide insights and recommendations, such as product demand forecasts, retailer pricing trends, and optimal inventory levels. The method may include: the computing device visualizing the processed catalog data in a customizable summary table displaying real-time data insights and actionable items tailored to user roles and preferences; and the computing device using an exception management system to resolve data errors or anomalies. Attached Figure Description

[0018] Figure 1 This is an illustration of a system, according to some embodiments, for providing an AI-based framework independent of vendors and customers to integrate legacy systems into a distribution platform.

[0019] Figure 2 This is an illustration of a system, according to some embodiments, for providing an enhanced, vendor- and customer-independent, AI-based framework to improve legacy system integration.

[0020] Figure 3 This is a flowchart of a system, based on some embodiments, that supports real-time AI-driven integration within an enhanced, vendor- and customer-independent framework to improve the integration of legacy systems.

[0021] Figure 4 This is an illustration of a system configured as an advanced IT distribution platform according to some embodiments to enhance data manipulation, processing, and presentation capabilities through integrated sub-components.

[0022] Figure 5 This is a flowchart of a method for performing an email order to a structured data format, according to some embodiments of the present disclosure.

[0023] Figure 6 This is a flowchart of a method for processing a retailer catalog according to some embodiments of the present disclosure.

[0024] Figure 7This is a block diagram of example components of a device according to some embodiments, illustrating a processor, memory, and communication interface for implementing the system. Detailed Implementation

[0025] Implementations may be carried out in hardware, firmware, software, or any combination thereof. Implementations may also be carried out as instructions stored on a machine-readable medium that can be read and executed by one or more processors. Machine-readable media may include any mechanism for storing or transmitting information in a machine-readable form (e.g., a computing device). For example, machine-readable media may include read-only memory (ROM); random access memory (RAM); disk storage media; optical storage media; flash memory devices, etc. Furthermore, firmware, software, routines, and instructions may be described herein as performing certain actions. However, it should be understood that such descriptions are merely for convenience, and such actions are actually generated by the computing device, processor, controller, or other device executing the firmware, software, routines, instructions, etc.

[0026] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the shown operations. In some embodiments of this disclosure, the operations may be performed in different orders and / or varied.

[0027] Figure 1 The figure illustrates a system 100, according to some embodiments, for providing an AI-based, vendor- and customer-independent framework for integrating legacy systems into a distribution platform. System 100 may include a data layer 110, a processing layer 120, and a presentation layer 130, each configured to support specific functionalities within the platform.

[0028] Data Layer 110 can be configured to manage the ingestion, normalization, and storage of massive amounts of data types from multiple sources. Its configuration allows for powerful data manipulation capabilities to ensure the platform operates with a high level of accuracy and efficiency, essential for the IT distribution industry. Data Layer 110 can be configured to automate the ingestion of data from various sources, including direct uploads of content, APIs, databases, and external systems such as ERP, CRM, and CPQ platforms. This layer utilizes a suite of data adapters and connectors capable of interfacing with various data formats, such as XML, JSON, CSV files, and unstructured data (emails and documents). These adapters can be designed to be highly modular and configurable, enabling rapid adaptation to new data sources or changes in data structures without significant redevelopment.

[0029] Once data is ingested, data layer 110 may include one or more preprocessing units that utilize algorithms to clean and standardize the data. These units address common data problems such as missing values, inconsistent formatting, and data entry errors. Specifically, when processing text data, the normalization process involves techniques such as tokenization, stemming, and lemmatization. For numerical data, normalization may involve scaling and transformation processes to convert the data to a uniform scale necessary for subsequent analytical tasks. In conjunction with normalization, data layer 110 may include validation mechanisms that apply a set of predefined rules and machine learning models to ensure the integrity and correctness of the data. These validation processes maintain the quality of data within the platform by detecting anomalies and potential inaccuracies that may affect overall system performance. Validation algorithms may include integrity checks, such as checksum validation and data type validation, to ensure that incoming data matches the expected patterns and formats.

[0030] Data tier 110 can also be configured with transformation tools to convert raw data into more useful formats. This can include summarizing data points, calculating new metrics based on raw data input, and reorganizing data into dimensions suitable for analysis and reporting. These transformations can be manipulated through a combination of program code and a configuration-driven framework, allowing for flexibility in how data is manipulated and stored. For storage, data tier 110 can be configured to integrate both SQL and NoSQL database technologies. SQL databases can be used for structured data requiring complex queries with high precision and integrity, such as financial records and transaction logs. NoSQL databases can be used to flexibly manipulate semi-structured or unstructured data, and can also be used in situations requiring high write speeds and horizontal scalability. This dual-database approach allows data tier 110 to efficiently manage various data needs and query requirements, thereby optimizing performance and scalability.

[0031] Data layer 110 may include comprehensive security measures such as encryption at rest and in transit, strict access control, and continuous monitoring of unauthorized access attempts. Security protocols can be integrated into each component of the data layer to protect sensitive information from external threats and internal vulnerabilities. Data layer 110 may include automated backup and disaster recovery solutions to protect data from loss due to system failures, cyberattacks, or other unforeseen events. These solutions can be configured to regularly back up all data stored in the system and enable rapid recovery in the event of data loss, thereby minimizing downtime and ensuring data availability and continuity. Data layer 110 can directly incorporate AI and machine learning algorithms into the data management process via processing layer 120. These algorithms can be used for predictive analytics, such as forecasting demand based on historical data or identifying potential market trends. Machine learning models can be trained and deployed within data layer 110 to continuously improve data manipulation and processing based on incoming data streams, making the system increasingly intelligent and efficient.

[0032] Therefore, the data layer 110 of system 100 can be configured to handle the complex data management needs of the IT distribution platform. Its configuration allows for efficient management of a wide range of data types and sources, ensuring high data quality and security. As the foundation of the platform, this layer supports advanced data processing capabilities, enabling effective decision-making and operational efficiency in IT distribution. The processing layer 120 provides the core analytics engine of system 100, where the large amounts of data collected and preprocessed by the data layer 110 can be further analyzed and transformed into actionable insights. This layer can be configured to leverage LLM, AI, and ML algorithms to enhance data processing and decision-making capabilities.

[0033] Processing layer 120 can be configured to execute machine learning algorithms that manipulate various analytical tasks. These algorithms can be configured to perform customer segmentation by analyzing customer data and grouping customers into different categories based on their behavior, preferences, and purchase history. This segmentation enables targeted marketing and personalized service offerings. Demand forecasting algorithms analyze historical sales data as well as external variables such as market trends and seasonal factors to predict future product demand, thereby assisting inventory planning and management. Inventory management algorithms optimize inventory levels based on real-time sales data and forecasts to reduce backlogs and downtime, thereby improving operational efficiency. In some embodiments, processing layer 120 can incorporate Robotic Process Automation (RPA) to automate routine and repetitive tasks that might be time-consuming when performed manually. This automation can extend to the execution of data entry, report generation, and predefined data queries and transfers, significantly reducing the need for manual intervention. By automating these tasks, RPA not only accelerates data processing workflows but also minimizes human error, ensuring more accurate and reliable output.

[0034] Processing layer 120 can perform AI-driven automation, enabling the system to continuously adapt and improve. This aspect of the layer can include adaptive learning algorithms that adjust and optimize their operation based on new data and feedback loops. For example, predictive models in this layer can be continuously refined as they encounter new sales data and customer interactions, thereby improving their accuracy and relevance over time. This capability ensures the system remains effective under dynamic market conditions and can predict changes before they occur. Processing layer 120 can be configured to transfer data between various layers and components of system 100. It can integrate processed data back into data layer 110 for storage and synchronize it with presentation layer 130 to display processed information. This integration can be managed through a series of APIs and data management protocols that ensure cross-platform data consistency and integrity.

[0035] Processing layer 120 can be configured with advanced analytics capabilities, enabling it to perform complex data analyses and generate comprehensive reports. These analyses include multivariate testing, correlation analysis, and regression models, helping to uncover deeper insights into business operations and customer behavior. The layer can also support real-time analytics, providing immediate feedback and insights that can be used to take swift action. Given the nature of the tasks performed within processing layer 120, security measures can be embedded within the layer to protect data integrity and privacy. This can include implementing secure data access protocols, encrypting sensitive data, and conducting regular audits to comply with industry regulations and standards.

[0036] Processing layer 120 can be designed for scalability, capable of handling ever-increasing data volumes without performance degradation. It employs a distributed computing model, allowing processing tasks to be scaled across multiple servers as needed. Furthermore, this flexibility facilitates the integration of new algorithms, models, and technologies as they evolve. Presentation layer 130 can be configured as an interface to system 100, where processed data from processing layer 120 can be visualized and made accessible to users in a meaningful and executable format. This layer can be provided to accommodate a wide range of user needs and entities, ensuring that data presentation is intuitive, up-to-date, and effective, enhancing user decision-making across diverse organizational roles.

[0037] The presentation layer 130 can be configured with a dynamic user interface that adapts to the specific requirements of different users, such as retailers, distributors, and dealers. It can be configured to provide customizable summary tables displaying tailored information based on user roles and preferences. This customization capability allows the interface to automatically adjust its layout, content, and complexity depending on whether the user is an employee requiring detailed customer analysis, a retailer reviewing inventory levels, or a dealer reviewing cost metrics. The presentation layer 130 incorporates data visualization tools to transform complex datasets into graphical representations, such as push notifications and insights, as well as charts, graphs, heatmaps, etc. These tools enable users to quickly and accurately understand large amounts of data, gaining detailed insights without delving into the raw data. The layer can support interactive visualizations that users can manipulate to explore data points in depth, such as digging into sales trend charts or adjusting parameters in financial forecasts.

[0038] The presentation layer can be configured to update its display in real time, continuously retrieving data from the processing layer 120. This ensures that all presented information is up-to-date, reflecting the latest business conditions and enabling immediate action when needed. Real-time capabilities can influence decisions such as adjusting pricing in response to inventory levels or responding to customer service inquiries. The presentation layer 130 can be configured to be accessible on multiple devices, including desktops, tablets, and smartphones, ensuring users can access information anytime, anywhere. Responsive design adapts displays to accommodate different screen sizes and resolutions, providing a consistent user experience across devices.

[0039] The layer may also include features that enhance collaboration among team members. Tools such as shared summary tables, real-time annotation capabilities, and integrated communication platforms (e.g., chat functionality) allow users to discuss data-driven insights directly within the platform, creating a collaborative decision-making environment. Given the sensitivity of the data being manipulated, the presentation layer 130 can be configured with robust security features to protect user information and maintain data confidentiality. These features include role-based access control that ensures users can only view data relevant to their roles, secure login mechanisms, and data encryption both in transit and at rest.

[0040] To expand functionality and user usability, the presentation layer 130 can be designed to integrate efficiently with external applications such as CRM systems, financial software, and marketing tools. This integration can be facilitated through APIs that allow seamless data flow between system 100 and other business systems, enabling users to leverage the platform's insights in other operational environments. The presentation layer 130 can be optimized for performance, ensuring fast loading times and smooth interaction even with large datasets and complex visualizations. The architecture can support scalability, accommodating increasing numbers of users and more complex data visualizations without compromising performance. The presentation layer 130 provides a role-based, customizable, and secure interface that enhances the overall user experience. By transforming processed data into an easily understandable and interactive format, it enables effective communication of insights across various organizational levels, driving informed decision-making and action. Therefore, system 100 can integrate the data layer 110, processing layer 120, and presentation layer 130 into a unified platform configured to improve the efficiency of IT distribution by automating key processes and providing customizable user interfaces tailored to the specific needs of its users.

[0041] System 100 incorporates an AI-based, vendor- and customer-independent framework through a generative AI (Gen-AI) engine within processing layer 120. This engine automatically adapts the platform to a variety of external systems brought in by different vendors and customers, regardless of their underlying technology or data structure. The Gen-AI engine offers improvements in reducing the time required for complex processes. For example, even for large codebases, tasks that typically take five to ten days can be completed in less than four minutes. The Gen-AI engine eliminates platform complexity, integrating various services such as hardware, cloud subscriptions, and services into a single order and minimizing friction.

[0042] Furthermore, the Gen-AI framework facilitates the easy integration of legacy systems from resellers and customers into the platform with minimal effort. This independent framework transforms data formats, enabling integration in weeks rather than years, thus avoiding the high costs and delays traditionally associated with such integrations. This capability is demonstrated by successful integration with the complex legacy systems of security resellers. Gen-AI technology can be an artificial intelligence framework designed to streamline and simplify complex processes in the technology and distribution industries. It significantly reduces processing time, transforming tasks that typically take days into operations completed in seconds or minutes. This technology eliminates platform complexity, integrating various services such as hardware, cloud subscriptions, and services into a single order and minimizing operational friction. It features a self-learning component that analyzes large amounts of transactional data to provide insights and recommendations, allowing users to understand performance metrics and identify profitable areas. The framework can be customer- and reseller-independent, capable of integrating with various legacy systems through AI-driven data transformation, thereby reducing the time and costs typically associated with such integrations.

[0043] For example, the Gen-AI engine can include NLP and ML algorithms to interpret and transform data from various legacy systems into a common format. NLP algorithms facilitate the understanding and processing of unstructured data, while ML models are trained from past data to improve transformation accuracy. The data transformation and integration layer incorporates AI models to map and transform data from different vendor and customer systems into a unified format compatible with the Gen-AI platform. This can include pattern mapping, data cleaning, and transformation algorithms. The unified order management system integrates multiple services into a single interface, leveraging database management systems and API integration to control hardware, software, and cloud subscriptions within a single platform. The self-learning and analytics engine uses advanced machine learning algorithms, such as neural networks and decision trees, to analyze transaction data. It can provide insights and predictions about performance metrics, market trends, and profitability.

[0044] The Gen-AI framework can be provided on a scalable infrastructure, potentially leveraging cloud computing resources to manipulate large volumes of data and ensure rapid processing times. This infrastructure can support the real-time processing capabilities of Gen-AI technologies. Furthermore, one or more security and compliance modules ensure secure data manipulation and compliance with relevant regulations. They use encryption algorithms, access controls, and auditing mechanisms to protect sensitive information. The engine can automate data ingestion and the integration of various reseller and customer systems into the distribution platform. More specifically, the Gen-AI engine in processing layer 120 can automatically ingest data from multiple sources using artificial intelligence to automatically manipulate and process different data formats, whether they are structured data like spreadsheets and databases or unstructured data such as emails and documents. It can be configured to work across various reseller and customer platforms without manual configuration. This capability comes from the engine's use of AI algorithms that can quickly learn and adapt to new data formats and operational protocols, thereby facilitating integration regardless of the architecture of external systems.

[0045] The engine processes and normalizes the ingested data, ensuring it conforms to the formats and standards required for accurate analysis and reporting within the platform. This process may include data cleaning, resolving inconsistencies, and transforming it into a usable state. By leveraging AI, the Gen-AI engine facilitates self-service capabilities, allowing non-technical users to configure and manage integration settings and data processing rules. This enables users to autonomously tailor solutions to their specific operational needs without requiring deep technical expertise. The AI-driven nature of the Gen-AI engine supports scalability and flexibility, allowing System 100 to expand its capacity and functionality as new types of data sources are added or as customer and vendor needs evolve.

[0046] The Gen-AI engine begins by analyzing incoming data from diverse sources, such as ERP systems, directly uploaded content, or external databases. It employs classification algorithms to identify data formats and structures. This process does not rely on predefined rules but rather on the engine's ability to learn and recognize patterns over time, enabling it to operate across a wide range of vendors and customers without manual reconfiguration. Once the data format and structure are identified, transformation algorithms convert the data into a uniform format compatible with the system 100%. This ensures consistency of data processed across systems, thereby guaranteeing efficient subsequent data manipulation, storage, and analysis. These transformation processes can be dynamic and customizable in real-time, allowing the system to quickly adapt to new data without human intervention.

[0047] The system also incorporates predictive analytics into its Gen-AI engine to anticipate potential problems or needs based on the data being processed. This capability allows System 100 to not only react to current data configurations but also predict future changes in vendor or customer data flows, thereby enhancing the system's proactive management capabilities. Regarding user interaction, the presentation layer 130 is dynamically adjusted based on processed data to ensure that each user, whether a vendor manager, financial professional, or sales assistant, sees information and control relevant to their specific needs and role. This personalization extends to data visualization and user interface customization facilitated by an AI-driven backend that tailors the presentation and accessibility of information based on user preferences learned over time.

[0048] The configuration of System 100 enables a reduction in the manual work typically associated with integrating new vendor systems or adapting to changes in customer data requirements. By automating these processes, System 100 reduces operational delays and errors, improves data integrity, and enhances user engagement by providing a more responsive and intuitive platform.

[0049] Figure 2 The figure illustrates system 200, according to some embodiments, for providing an enhanced, vendor- and customer-independent AI-based framework to improve integration of legacy systems. System 200 may include a data layer 210, a data transformation and mapping layer 220, an integration and orchestration layer 230, an AI and analytics layer 240, and a presentation layer 250, each configured to support specific functionalities within the platform.

[0050] Data Layer 210, also known as the Real-Time Data Grid (RTDM) layer, can be configured to manage the ingestion, normalization, and storage of large amounts of data from multiple sources. It can include connectors and APIs that facilitate the extraction of data from various legacy systems such as ERP, CRM, and CPQ. Connectors support a variety of data formats and communication protocols, including XML, JSON, CSV, and unstructured data formats such as email and documents. The data ingestion engine in this layer collects data in real-time or batch mode, depending on the system's capabilities and requirements.

[0051] Data layer 210 may include data adapters and connectors capable of interfacing with various data formats. Once data is ingested, preprocessing units in this layer utilize algorithms to clean and standardize the data, addressing issues such as missing values, format inconsistencies, and errors in the data input. Normalization processes involve techniques such as tokenization, stemming, and lemmatization for text data, as well as scaling and transformation processes for numerical data. Validation mechanisms apply predefined rules and machine learning models to ensure data integrity and accuracy. Data transformation tools in this layer transform raw data into more useful formats, including data point summarization, calculation of new metrics, and data reorganization for analysis and reporting. For storage, data layer 210 can integrate both SQL and NoSQL database technologies. It may also include comprehensive security measures and automated backup and disaster recovery solutions to ensure data security and availability.

[0052] The data transformation and mapping layer 220 may include an NLP module that leverages NLP to understand and extract relevant information from unstructured data sources, such as customer notes or emails from legacy systems. ML models analyze historical data to identify patterns and relationships, thereby facilitating accurate data mapping and transformation. A schema mapping engine automatically maps data schemas from legacy systems to schemas on a unified platform, thus manipulating data normalization, deduplication, and transformation tasks.

[0053] The integration and orchestration layer 230 may include a workflow orchestration engine that manages end-to-end integration workflows, thereby facilitating data flow between legacy systems and the unified platform. An API gateway can provide a centralized interface for accessing transformed data, thereby facilitating interaction between different systems and the unified platform.

[0054] The AI ​​and analytics layer 240 features a self-learning AI engine that continuously learns from new data and user interactions, adapting the integration process over time to improve efficiency and accuracy. Furthermore, a predictive analytics module can provide insights and recommendations based on the integrated data, helping users make informed decisions. This layer can support advanced analytics capabilities, including multivariate testing, correlation analysis, and regression models, as well as real-time analytics to provide immediate feedback and insights.

[0055] Presentation layer 250, also known as the Single Glass Pane (SPoG) user interface, can be configured as the interface of system 200, where processed data from AI and analytics layer 240 can be visualized and accessed by the user in a meaningful and actionable format. This layer can be equipped with a dynamic user interface that adapts to the specific requirements of different users, such as resellers, distributors, and dealers. It can provide customizable summary tables displaying tailored information based on user roles and preferences. The layer incorporates advanced data visualization tools designed to transform complex datasets into graphical representations such as charts, graphs, and heatmaps. Real-time data updates ensure that all presented information is up-to-date, enabling immediate action when needed.

[0056] The presentation layer 250 can be configured to be accessible on multiple devices, including desktops, tablets, and smartphones, ensuring users can access information anytime, anywhere. Responsive design adapts to displays to fit different screen sizes and resolutions. Features that enhance collaboration among team members can be included, such as sharing summary tables, real-time annotation capabilities, and integrated communication platforms. Robust security features protect user information and maintain data confidentiality through role-based access control, secure login mechanisms, and data encryption.

[0057] System 200 may also include customizable integration settings that allow users to specify authorization domains, input sources, and business rules for data processing. A robust exception management system can identify and resolve data errors, system errors, and other anomalies, thereby maintaining data integrity and ensuring smooth operation. The system is designed for scalability, enabling it to handle increasing data volumes and new integration requirements without compromising performance.

[0058] A practical implementation example of System 200 includes processing vendor directories, converting them from various formats to the platform's standard format, and converting email orders into a system-readable format. The system manipulates bidding data from vendors, transforming and integrating it into the platform to streamline bidding management and decision-making.

[0059] System 200 offers a comprehensive range of use cases, integrating both vendor and customer systems and ensuring real-time data synchronization between the integrated systems. This versatility eliminates the need for separate solutions for different stakeholders and keeps information up-to-date across platforms.

[0060] Figure 3The figure illustrates a system 300 in an exemplary embodiment for supporting real-time AI-driven integration within an enhanced, vendor- and customer-independent framework to improve the integration of legacy systems. System 300 may include a real-time data ingestion layer 310, a data transformation and mapping layer 320, an integration and orchestration layer 330, an AI and analytics layer 340, a presentation layer 350, and an exception management layer 360.

[0061] The real-time data ingestion layer 310 can be configured to manipulate the ingestion of data from various formats, such as email, PDF, spreadsheets, and other documents, into a standardized platform format. This layer may include connectors and APIs for extracting data from various legacy systems, thereby supporting multiple data formats and communication protocols. The data ingestion engine in this layer collects data in real-time or batch mode, depending on the system's capabilities and requirements, ensuring continuous and efficient data ingestion into the platform.

[0062] The data transformation and mapping layer 320 may include an NLP module that leverages NLP techniques to understand and extract relevant information from unstructured data sources. The ML model in this layer analyzes historical data to identify patterns and relationships, thereby facilitating accurate data mapping and transformation. A schema mapping engine automatically maps data schemas from legacy systems to schemas on a unified platform, performing necessary normalization, deduplication, and transformation tasks. This layer emphasizes the use of self-learning AI models that continuously adapt to new data formats and vendor-specific requirements, reducing the need for ongoing manual reconfiguration and enabling more efficient and accurate manipulation of new integrations.

[0063] The integration and orchestration layer 330 may include a workflow orchestration engine that manages end-to-end integration workflows, thereby facilitating data flow between legacy systems and the unified platform. An API gateway in this layer provides a centralized interface for accessing transformed data, facilitating interaction between different systems and the unified platform. The improvement layer highlights the system's compatibility with common data formats, allowing it to integrate with any vendor or customer system, regardless of the data format they use, thus eliminating the need for custom solutions and significantly reducing integration time and costs.

[0064] The AI ​​and analytics layer 340 features a self-learning AI engine that continuously learns from new data and user interactions, adapting the integration process over time to improve efficiency and accuracy. The predictive analytics module in this layer can provide insights and recommendations based on the integrated data, helping users make informed decisions. This layer can support advanced analytics capabilities, including multivariate testing, correlation analysis, and regression models, as well as real-time analytics to provide immediate feedback and insights.

[0065] The presentation layer 350, known as the Single Glass Pane (SPoG) user interface, can be configured to visualize and access processed data from the AI ​​and analytics layer 340 in a meaningful and actionable format. This layer provides a dynamic user interface that adapts to the specific requirements of different users, such as resellers, distributors, and dealers. It offers customizable summary tables that display tailored information based on user roles and preferences. Advanced data visualization tools within this layer transform complex datasets into graphical representations such as charts, graphs, and heatmaps. Real-time data updates ensure that all presented information is up-to-date, enabling immediate action when needed.

[0066] The exception management layer 360 can be an advanced system that identifies and resolves data errors, system anomalies, and other issues in real time. This layer can include robust exception management capabilities that ensure data integrity and smooth operation, thereby maintaining the reliability and accuracy of integrated data. This enables System 300 to manage anomalies that could otherwise disrupt workflows and impact operational efficiency.

[0067] In a non-restricted example, email orders can be transformed into a structured data format that the system can process automatically. The real-time data ingestion layer 310 extracts data from email content and attachments, which is then processed by NLP modules and ML models in the data transformation and mapping layer 320. These components analyze unstructured email data and convert it into a standardized format. The workflow orchestration engine in the integration and orchestration layer 330 manages the integration workflow, ensuring that the transformed data is efficiently aggregated into the order management system. The AI ​​and analytics layer 340 provides predictive insights and recommendations based on the processed order data, and the presentation layer 350 visualizes this information in a user-friendly summary table. The exception management layer 360 addresses any data errors or anomalies during the process, ensuring accuracy and reliability.

[0068] For example, the real-time data ingestion layer 310 initiates the extraction of data from email content and attachments. This layer may include connectors and APIs that facilitate data extraction from various sources, thereby supporting multiple data formats and communication protocols. When an email order is received, the data ingestion engine, depending on the system configuration, captures the email and its attachments in real-time or batch mode. The engine then initiates the data extraction process, identifying relevant data fields such as order details, customer information, product descriptions, and quantities.

[0069] Once the data is ingested, it can be pushed to the data transformation and mapping layer 320, which may include NLP modules and ML models for processing and analyzing unstructured email data. The NLP module parses the email content to understand the text and extract relevant information such as customer notes, order descriptions, and special requests. Simultaneously, the ML model analyzes historical data to identify patterns and relationships, thereby facilitating accurate data mapping and transformation. The schema mapping engine in this layer automatically maps the extracted data to a unified platform schema, performing necessary normalization, deduplication, and transformation tasks. This transformation converts the unstructured email data into a standardized format that the system can process.

[0070] The transformed data can be pushed to the integration and orchestration layer 330, where the workflow orchestration engine manages the end-to-end integration workflow. This engine ensures that the transformed data is efficiently aggregated into the order management system, thereby coordinating the various tasks and processes required for integration. The API gateway in this layer provides a centralized interface for accessing the transformed data, enabling interaction between different systems and the unified platform. The orchestration engine manipulates the sequencing of integration tasks (including data validation, error handling, and system synchronization) to ensure a smooth and efficient workflow.

[0071] As data flows through the system, the AI ​​and analytics layer 340 provides additional processing and analysis. This layer may incorporate a self-learning AI engine that continuously learns from new data and user interactions, adapting the integration process over time to improve efficiency and accuracy. The predictive analytics module within this layer can analyze processed order data to provide insights and recommendations. For example, it can predict inventory demand based on order trends, identify potential supply chain disruptions, or recommend optimal shipping methods. These predictive insights help users make informed decisions, thereby improving the overall efficiency and effectiveness of the order management process.

[0072] The processed data can then be visualized in presentation layer 350 (i.e., the SPoG user interface). This layer provides a dynamic user interface that adapts to the specific needs of different users. It offers customizable summary tables that display real-time data, insights, and actionable projects tailored to user roles and preferences. Advanced data visualization tools in this layer transform complex datasets into graphical representations such as charts, graphs, and heatmaps, making it easier for users to understand and act upon the information. For example, a sales manager can view real-time order status and inventory levels, while a customer service representative can track order fulfillment and address customer inquiries promptly.

[0073] Throughout the entire process, the exception management layer maintains data integrity and efficient operation. This layer can include exception management capabilities, which identify and resolve data errors, system anomalies, and other issues in real time. For example, if an email order contains incomplete or inconsistent data, the exception management system will flag the issue and prompt corrective action through automated processes or manual intervention. This proactive approach helps maintain the accuracy and reliability of integrated data, preventing workflow disruptions and providing up-to-date, persistent order management processes.

[0074] System 300 achieves end-to-end process automation through the integration of these layers, thereby automating processes from data ingestion and transformation to order creation and management. This approach ensures efficient workflows, reducing human intervention at all stages. The system's cross-functional usability allows for customized interfaces and workflows for various functions and roles, enhancing user experience and operational efficiency across retailers, customers, distributors, partners, and internal staff.

[0075] System 300 can be configured to scale according to business needs, thereby managing increasing data volumes and new integration requirements without compromising performance. In another non-limiting example, practical implementation could include processing vendor catalogs to convert them from various formats to the platform's standard format, and manipulating bidding data from vendors. The system's ability to transform email orders into structured data demonstrates its capacity to manipulate real-world scenarios and improve operational efficiency.

[0076] For example, the real-time data ingestion layer 310 can initiate data extraction from vendor catalogs. Vendor catalogs can be provided in various formats, such as PDFs, spreadsheets, and documents. The data ingestion engine captures these catalogs through connectors and APIs that support multiple data formats and communication protocols. The engine initiates the data extraction process by identifying and capturing relevant data fields such as product descriptions, prices, inventory levels, and vendor details. Once the data is ingested, it can be processed by the data transformation and mapping layer 320. This layer can include NLP modules and ML models that analyze unstructured catalog data and transform it into a standardized format. The NLP modules parse the content to extract relevant information, while the ML models identify patterns and relationships in the data, facilitating accurate mapping and transformation. The schema mapping engine automatically maps the extracted data to a unified platform schema, performing normalization, deduplication, and transformation tasks. This transformation ensures the consistency and availability of catalog data within the platform.

[0077] The transformed catalog data is then moved to the integration and orchestration layer 330. The workflow orchestration engine in this layer manages end-to-end integrated workflows, facilitating data flow into the product management system. The API gateway provides a centralized interface for accessing the transformed data, enabling interaction between different systems and the unified platform. The orchestration engine coordinates tasks such as data validation, error handling, and system synchronization, ensuring a smooth and efficient workflow.

[0078] The AI ​​and Analytics layer 340 provides additional processing and analysis of catalog data. A self-learning AI engine continuously learns from new data and user interactions, adapting the integration process to improve efficiency and accuracy. The predictive analytics module in this layer analyzes catalog data to provide insights and recommendations. For example, it can predict demand for specific products, identify trends in vendor pricing, or recommend optimal inventory levels. These insights help users make informed decisions, thereby improving the efficiency of product management and procurement processes.

[0079] Processed catalog data can be visualized in presentation layer 350. This layer provides a dynamic user interface that adapts to the specific requirements of different users, such as purchasing managers, product specialists, and salespeople. It offers customizable summary tables that display real-time data, insights, and actionable projects tailored to user roles and preferences. Advanced data visualization tools transform complex datasets into graphical representations such as charts, graphs, and heatmaps, making it easier for users to understand and act upon the information.

[0080] Throughout the process, the Exception management layer ensures data integrity and smooth operation by identifying and resolving data errors, system anomalies, and other issues in real time. For example, if a vendor catalog contains inconsistent pricing data, the Exception management system flags the problem and prompts corrective action. This proactive approach maintains the accuracy and reliability of integrated data, preventing disruptions and facilitating catalog management.

[0081] In these exemplary use cases, System 300 can provide a vendor and customer system that ensures real-time data synchronization and maintains up-to-date information across platforms. This scalable aggregation and standardization eliminates the need for separate solutions for different entities, thereby significantly improving operational efficiency and reducing integration time and costs.

[0082] Figure 4The diagram illustrates System 400, an advanced configuration of an IT distribution platform designed to enhance data manipulation, processing, and presentation capabilities through integrated sub-components. In some embodiments, System 400 may include a Real-Time Data Grid (RTDM) 410, an Advanced Analytics and Machine Learning (AAML) module 420, a Single Glass Pane (SPoG) user interface (UI) 430, and cross-layer services 440. The architecture and capabilities of System 400 can be integrated with… Figure 3 The illustrated real-time, AI-driven integration framework ensures a comprehensive and efficient integration process.

[0083] According to some embodiments, the RTDM 410 of system 400 can be configured as an AI-based, vendor- and customer-independent framework for integrating legacy systems into a distribution platform. RTDM 410 can be configured to efficiently manage complex data workflows. This layer may include an ingestion module 411, which can be configured to automate the data ingestion process from various sources, such as IoT devices, cloud resources, and traditional databases. A normalization and cleansing module 412 can be configured to cleanse and normalize incoming data using advanced algorithms and machine learning models to ensure quality and consistency. A data transformation module 413 can be configured to support real-time streaming data transformation, thereby facilitating immediate analytics and decision-making processes. A metadata management module 414 can be configured to effectively manage metadata, thereby enhancing data governance and discoverability. A storage optimization module 415 can be configured to optimize data storage and retrieval, thereby adjusting data storage methods and structures based on usage patterns and access frequencies. RTDM 410 may be an embodiment of a real-time data ingestion layer 310 capable of real-time ingestion, aggregation, and normalization of various data sources and formats.

[0084] RTDM 410 can be configured to dynamically ingest and adapt data formats and workflows provided by customers and vendors without requiring any modifications to their existing systems. The ingestion module 411 identifies the data structures and formats used by the customer's legacy systems and maps them to the platform's internal schema. The normalization and cleansing module 412 ensures that any transformations are performed without altering the integrity of the customer's original data format, achieving bidirectional compatibility. This configuration allows customers to continue their operations without adjustments, while ensuring compatibility with the platform's unified data manipulation processes.

[0085] The AAML module 420 of System 400 can be configured as the core analytics engine, where complex data processing and analysis can be performed. The advanced analytics engine 421 can be configured to perform complex data analysis using cutting-edge artificial intelligence models, including predictive and prescriptive analysis. The process automation hub 422 can be configured to integrate complex workflows across various components and external systems, thereby improving operational efficiency. The learning and adaptation module 423 can be configured with a self-learning algorithm that adapts processing strategies based on new data insights and operational feedback. The integration gateway 424 can be configured to facilitate data integration with external platforms, enabling System 400 to operate within a larger ecosystem of business tools. This layer merges... Figure 3 The AI ​​and analytics layer 340 in the middle utilizes a self-learning AI engine and predictive analytics to continuously improve the integration process and decision-making capabilities.

[0086] System 400's SPoG UI 430 can be configured to provide dynamic and customizable user interaction capabilities. The dynamic user interface engine 431 can be configured to provide a highly customizable interface tailored to user roles and personal preferences, thereby enhancing user engagement. The interactive visualization toolkit 432 can be configured to include a wide range of data visualization options, such as 3D modeling and predictive scene visualization, allowing users to interact with data in innovative ways. The real-time collaboration framework 433 can be configured to support enhanced collaboration tools, including virtual workspaces and real-time data manipulation, thereby promoting effective teamwork. The security and compliance module 434 can be configured to implement advanced security features, such as biometric access control and advanced encryption standards, to ensure data integrity and compliance with global data protection regulations. SPoG UI 430 can be... Figure 3 One embodiment of the presentation layer 350 is configured to enable users to access real-time, interactive, and secure data visualization and collaboration tools.

[0087] The cross-layer service 440 in system 400 can be configured to provide services across the data management layer, processing layer, and presentation layer. The audit and compliance tracker 441 can be configured to monitor and log all operations within the system to ensure compliance with internal and external regulations. The performance optimization engine 442 can be configured to dynamically adjust system resources and processing parameters to optimize performance across all layers. The unified communications portal 443 can be configured to integrate cross-platform communication tools, enabling users to interact via voice, video, and text within the system environment. The cross-layer service 440 can be an implementation of the exception management layer 360 and is configured to identify and resolve data errors, system anomalies, and other issues in real time to maintain data integrity and reliability.

[0088] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or between any of the illustrated operations. In some embodiments of this disclosure, the operations may be performed in different orders and / or varied.

[0089] Figure 5 This is a flowchart of a method 500 for performing an email order conversion into a structured data format, according to some embodiments of the present disclosure. In some embodiments, method 500 provides operational steps to automate the extraction and processing of email data. In some embodiments, method 500 performs real-time data transformation and integration. Based on the disclosure herein, the operations in method 500 may be performed in different orders and / or varied.

[0090] In Operation 505, the computing device can receive an email containing order information. This email may include attachments such as a PDF or spreadsheet containing further order details.

[0091] In operation 510, the computing device can use the real-time data ingestion layer 310 to extract data from email content and attachments. This involves identifying relevant data fields such as order numbers, product descriptions, quantities, and customer information. The data ingestion engine captures emails and their attachments in real time, thereby utilizing connectors and APIs that support multiple data formats and communication protocols.

[0092] In operation 515, the computing device can use the NLP module in the data transformation and mapping layer 320 to process the extracted data. The NLP module parses the email content to extract structured information from unstructured text. This can include identifying keyword groups and entities related to order details in the email body and attachments.

[0093] In Operation 520, the ML models in the data transformation and mapping layer 320 analyze historical data to identify patterns and relationships, thereby facilitating accurate data mapping and transformation. These models use training data to improve their accuracy, learning from previous email orders to better predict and extract relevant data fields.

[0094] In operation 525, the schema mapping engine within the data transformation and mapping layer 320 automatically maps the extracted data to a unified platform schema, thereby performing normalization, deduplication, and transformation tasks. This ensures that the data is formatted correctly and consistently, thus preparing it for integration into the system.

[0095] In operation 530, the workflow orchestration engine in the integration and orchestration layer 330 manages the integrated workflow, thereby ensuring that the transformed data can be efficiently aggregated into the order management system. The engine coordinates various tasks, such as data validation, error handling, and system synchronization, to maintain a smooth workflow.

[0096] In operation 535, the AI ​​and analytics layer 340 can analyze the processed order data to provide predictive insights and recommendations. For example, the predictive analytics module can predict inventory demand based on order trends, identify the best shipping methods, or suggest adjustments to pricing strategies based on real-time data.

[0097] In operation 540, the processed order data can be visualized in presentation layer 350, providing a customizable summary table that displays real-time data, insights, and actionable items tailored to user roles and preferences. The summary table can support interactive visualizations, allowing users to delve deeper into specific data points for detailed analysis.

[0098] In Operation 545, the exception management layer 360 identifies and resolves any data errors or anomalies during the process, ensuring data accuracy and reliability. This layer employs robust exception handling mechanisms, such as automatic alerts and error logs, to quickly resolve and correct any issues that arise.

[0099] Figure 6 This is a flowchart of a method 600 for processing a reseller catalog according to some embodiments of the present disclosure. In some embodiments, method 600 provides operational steps to automate the extraction, transformation, and integration of catalog data. In some embodiments, method 600 performs real-time data transformation and integrates various reseller formats into a standardized platform format. Based on the disclosure herein, the operations in method 600 may be performed in different orders and / or varied.

[0100] In Operation 605, the computing device can receive vendor catalogs in various formats, such as PDFs, spreadsheets, and documents. The computing device interfaces with various data sources via connectors and APIs, enabling it to capture data from multiple vendor systems and formats.

[0101] In operation 610, the computing device can extract data from these catalogs using the real-time data ingestion layer 310. This involves utilizing data adapters and connectors capable of interfacing with various data formats and communication protocols. The data ingestion engine initiates the extraction process by identifying and capturing relevant fields such as product descriptions, prices, inventory levels, and vendor details. Depending on the system configuration and capabilities, this extraction can be performed in real-time or batch mode.

[0102] In operation 615, the NLP module in data transformation and mapping layer 320 parses the catalog contents to extract structured information from unstructured text. The NLP module uses techniques such as tokenization, stemming, and lemmatization to process the text data, thereby extracting key information such as project names, specifications, and vendor notes from unstructured text in PDFs or documents.

[0103] In operation 620, the ML models in the data transformation and mapping layer 320 analyze historical data to identify patterns and relationships in the catalog data. These models facilitate accurate data mapping and transformation by identifying patterns in product descriptions, pricing structures, and inventory levels, enabling the system to predict and automate the mapping of new catalog data based on the learned patterns.

[0104] In operation 625, the schema mapping engine within the data transformation and mapping layer 320 automatically maps the extracted catalog data to the unified platform's schema. This process may include performing normalization to ensure data format consistency, performing deduplication to remove redundant entries, and performing transformation tasks to align the data with the platform's standardized structure. The schema mapping engine uses predefined rules and learned schemas to accurately map data fields from the vendor catalog to the platform schema.

[0105] In operation 630, the workflow orchestration engine in the integration and orchestration layer 330 manages the integration workflow. This involves coordinating the various tasks and processes required to integrate the transformed catalog data into the product management system. The workflow orchestration engine ensures that data validation, error handling, and system synchronization tasks are performed sequentially and efficiently, thereby facilitating the flow of data into the product management system.

[0106] In Operation 635, the AI ​​and analytics layer 340 analyzes the processed catalog data to provide predictive insights and recommendations. The self-learning AI engine in this layer continuously learns from new data and user interactions, improving the integration process over time. The predictive analytics module can forecast product demand based on historical sales data, identify retailer pricing trends, and recommend optimal inventory levels, helping users make informed purchasing and inventory management decisions.

[0107] In operation 640, the processed catalog data can be visualized in presentation layer 350. This layer provides customizable summary tables that display real-time data, insights, and actionable projects tailored to user roles and preferences. Advanced data visualization tools transform complex datasets into graphical representations such as charts, graphs, and heatmaps, making it easier for users to understand and act upon the information. The Single Glass Pane (SPoG) user interface ensures a consistent and intuitive user experience across multiple devices.

[0108] In Operation 645, the exception management layer 360 identifies and resolves any data errors or anomalies during the process. This layer can include robust exception management capabilities, proactively flagging data inconsistencies, system anomalies, and other issues to prompt corrective action to maintain data integrity and reliability. The exception management layer ensures that any errors in the vendor catalog data are resolved promptly, preventing workflow disruptions and ensuring smooth operation.

[0109] Figure 7 A block diagram depicting example components of device 700 is provided. One or more computer systems 700 can be used, for example, to implement any of the embodiments discussed herein, as well as combinations and sub-combinations thereof. Computer system 700 may include one or more processors (also referred to as central processing units or CPUs), such as processor 704. Processor 704 may be connected to communication infrastructure or bus 706.

[0110] The computer system 700 may also include multiple user input / output devices 703, such as monitors, keyboards, pointing devices, etc., which can communicate with the communication infrastructure 706 through multiple user input / output interfaces 702.

[0111] One or more processors 704 may be graphics processing units (GPUs). In one embodiment, the GPU may be a processor that can function as a dedicated electronic circuit configured to process mathematically intensive applications. The GPU may have a parallel architecture capable of efficiently performing parallel processing of large blocks of data, such as mathematically intensive data commonly found in computer graphics applications, images, and videos.

[0112] Computer system 700 may also include main memory or main memory 708, such as random access memory (RAM). Main memory 708 may include one or more levels of cache. Control logic (i.e., computer software) and / or data may be stored in main memory 708.

[0113] The computer system 700 may also include one or more auxiliary storage devices or memories 710. The auxiliary storage 710 may include, for example, a hard disk drive 712 and / or a removable storage device or drive 714.

[0114] The removable storage drive 714 can interact with the removable storage unit 718. The removable storage unit 718 may include a computer-usable or readable storage device on which computer software (control logic) and / or data are stored. The removable storage unit 718 may be a program box and box interface (such as those present in video game devices), a removable memory chip (such as EPROM or PROM) and associated slots, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface. The removable storage drive 714 can read from and / or write to the removable storage unit 718.

[0115] Auxiliary storage 710 may include other means, devices, components, tools, or other paths for allowing computer system 700 to access computer programs and / or other instructions and / or data. Such means, devices, components, tools, or other paths may include, for example, removable storage unit 722 and interface 720. Examples of removable storage unit 722 and interface 720 may include a program box and box interface (such as those present in video game devices), a removable memory chip (such as EPROM or PROM) and associated slots, a memory stick and USB port, a memory card and associated memory card slot, and / or any other removable storage unit and associated interface.

[0116] Computer system 700 may also include a communication or network interface 724. The communication interface 724 enables computer system 700 to communicate and interact with any combination of external devices, external networks, external entities, etc. (individually and collectively referred to as reference numeral 728). For example, communication interface 724 may allow computer system 700 to communicate with external or remote devices 728 via communication path 726, which may be wired and / or wireless (or a combination thereof), and may include any combination of LAN, WAN, Internet, etc. Control logic and / or data may be transmitted to and from computer system 700 via communication path 726.

[0117] The computer system 700 may also be any one of a personal digital assistant (PDA), a desktop workstation, a laptop or notebook computer, a netbook, a tablet computer, a smartphone, a smartwatch or other wearable device, an appliance, part of the Internet of Things and / or an embedded system (to name just a few non-limiting examples), or any combination thereof.

[0118] Computer system 700 can be a client or server that accesses or manages any application and / or data through any delivery paradigm, including but not limited to remote or distributed cloud computing solutions; on-premises or pre-installed software (“on-premises” cloud-based solutions); “as-a-service” models (e.g., Content as a Service (CaaS), Digital Content as a Service (DCaaS), Software as a Service (SaaS), Managed Software as a Service (MSaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Framework as a Service (FaaS), Backend as a Service (BaaS), Mobile Backend as a Service (MBaaS), Infrastructure as a Service (IaaS), etc.); and / or hybrid models that include any combination of the foregoing examples or other services or delivery paradigms.

[0119] Any available data structures, file formats, and schemas in the computer system 700 may be derived from standards including, but not limited to, JavaScript Object Notation (JSON), Extensible Markup Language (XML), YAML, Extensible Hypertext Markup Language (XHTML), Wireless Markup Language (WML), message packets, XML User Interface Language (XUL), or any other functionally similar representation (alone or in combination). Alternatively, proprietary data structures, formats, or schemas may be used exclusively or in combination with known or open standards.

[0120] In some embodiments, a tangible non-transitory device or article of manufacture, including a tangible non-transitory computer-usable or readable medium on which control logic (software) is stored, may also be referred to herein as a computer program product or program storage device. This may include, but is not limited to, computer system 700, main memory 708, secondary memory 710 and removable storage units 718 and 722, and tangible articles of manufacture implementing any combination thereof. When executed by one or more data processing devices, such as computer system 700, such control logic can cause such data processing devices to operate as described herein.

[0121] It should be understood that the Detailed Description section, rather than the Summary and Abstract section, is intended to interpret the claims. The Summary and Abstract section may set forth one or more, but not all, exemplary embodiments of the invention as conceived by the inventors(s), and is therefore not intended to limit the invention and the appended claims in any way.

[0122] The invention has been described above using functional building blocks that illustrate implementations of specified functions and their relationships. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and their relationships are properly performed.

[0123] The foregoing description of specific embodiments so fully reveals the general nature of the invention that, without departing from the overall conception of the invention, others can easily make modifications and / or adjustments for various applications (such as the specific embodiments) by applying the knowledge of those skilled in the art without excessive experimentation. Therefore, based on the teachings and guidance presented herein, such adjustments and modifications are intended to be within the meaning and scope of equivalents of the disclosed embodiments. It should be understood that the wording or terminology herein is for descriptive and not limiting purposes, and that the terminology or terminology of this specification should be interpreted by those skilled in the art based on the teachings and guidance.

[0124] The breadth and scope of this invention should not be limited by any of the exemplary embodiments described above, but should be defined only by the appended claims and their equivalents.

Claims

1. A system for integrating legacy systems into a unified platform, wherein, The system includes: A server, coupled to a processor, is configured to execute the following instructions: The data layer ingests, normalizes, and stores data from multiple sources. The processing layer uses AI algorithms to analyze and transform data; and A dynamic user interface for data visualization and interaction is presented by the presentation layer.

2. The system according to claim 1, wherein, The data layer includes connectors and APIs to facilitate the extraction of data from legacy systems such as ERP, CRM, and CPQ that support multiple data formats and communication protocols.

3. The system according to claim 1, wherein, The data layer includes a preprocessing unit that uses techniques such as tokenization, stemming, lemmatization, scaling, and transformation to clean and standardize the data.

4. The system according to claim 1, wherein, The processing layer includes a workflow orchestration engine that manages and facilitates the integration of data flows between legacy systems and the unified platform.

5. The system according to claim 1, wherein, The processing layer includes AI and analysis modules with self-learning AI engine features, which continuously adjusts the integration process based on new data and user interactions.

6. The system according to claim 1, wherein, The presentation layer includes customizable summary tables that provide real-time data insights and interactive visualizations such as charts, graphs, and heatmaps.

7. The system according to claim 1, wherein, The presentation layer includes security features such as role-based access control, secure login mechanisms, and data encryption during transmission and at rest.

8. A computerized method for processing email orders, wherein, This computerized approach includes: The computing device receives an email containing order information; The computing device extracts data from the email content and attachments; The computing device uses natural language processing technology to process the extracted data in order to parse and structure the information.

9. The computerized method according to claim 8, wherein, The computerized method also includes analyzing the extracted data using a machine learning model by the computing device to identify patterns and relationships in the data, thereby facilitating accurate data mapping and transformation.

10. The computerized method according to claim 8, wherein, The computing device maps the structured data to a unified platform schema, thereby performing normalization, deduplication, and transformation tasks.

11. The computerized method according to claim 8, wherein, The computerized method also includes the computing device using a workflow orchestration engine to manage integrated workflows to facilitate the flow of data into the order management system.

12. The computerized method according to claim 8, wherein, The computing device analyzes the processed order data through a predictive analytics module to provide insights and recommendations, such as inventory demand, supply chain disruptions, and optimal transportation methods.

13. The computerized method according to claim 8, wherein, The computerized method also includes visualization of the processed order data by the computing device in a customizable summary table, which displays real-time data insights and actionable items tailored to user roles and preferences.

14. The computerized method according to claim 8, wherein, The computerized method also includes the use of an exception management system by the computing device to identify and resolve data errors or anomalies during the process in order to maintain data accuracy and reliability.

15. A computerized method for processing a retailer's catalog, wherein, This computerized approach includes: The computer device receives vendor catalogs in various formats; The computing device uses a data ingestion engine to extract data from the catalog; The computing device uses natural language processing technology to process the extracted data in order to parse and structure the information.

16. The computerized method according to claim 15, wherein, The computerized method also includes using a machine learning model by the computing device to analyze the extracted data to identify patterns and relationships within the catalog data, thereby facilitating accurate data mapping and transformation.

17. The computerized method according to claim 15, wherein, The computing device maps the structured directory data to a unified platform schema, thereby performing normalization, deduplication, and transformation tasks.

18. The computerized method according to claim 15, wherein, The computerized method also includes the computing device using a workflow orchestration engine to manage the integrated workflow to facilitate the flow of data into the product management system.

19. The computerized method according to claim 15, wherein, The computing device analyzes the processed catalog data through a predictive analytics module to provide insights and recommendations, such as product demand forecasts, retailer pricing trends, and optimal inventory levels.

20. The computerized method according to claim 15, wherein, The computerized method also includes the computing device visualizing the processed catalog data in a customizable summary table that displays real-time data insights and actionable items tailored to user roles and preferences, and the computing device resolving data errors or anomalies using an exception management system.

Images