An AI-based vendor- and customer-independent framework for improving legacy system integration.

Abstract

This provides a system and method for integrating legacy systems into a unified platform. [Solution] System 100 takes in data using a data layer 110, normalizes and stores the data, analyzes and transforms the data using a processing layer 120, and provides data visualization and a dynamic user interface using a presentation layer 130. Computing devices receive data such as email orders, vendor catalogs, and CRM data, extract data from the content, process the data using natural language processing techniques, analyze the data using machine learning models, map the data to a unified platform schema, manage integrated workflows using a workflow orchestration engine, provide real-time data insights, and resolve data errors or anomalies using an exception management system.

Description

Technical Field

[0001] (Background) Integrating the legacy systems and data of various vendors and customers into a unified platform presents a major challenge. Conventionally, legacy systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, configure - price - quote (CPQ) systems, etc. are operated separately. Each system uses different data formats, protocols, and operation standards, resulting in a heterogeneous technical environment.

[0002] The main challenges are the time and cost associated with integrating these heterogeneous systems. Usually, integration projects require extensive manual coding, data mapping, and conversion efforts, and in some cases, it often takes months to years to complete. Also, these projects require specialized personnel and resources, thus demanding a large economic investment. Furthermore, the maintenance of these integrations is cumbersome because updates and changes to legacy systems require additional adjustments and reconfigurations.

[0003] Another problem is the lack of interoperability between different systems. Legacy systems are often built on outdated technologies that do not support the latest communication standards, making it difficult to facilitate data exchange. As a result, data silos are fragmented, and information is confined to individual systems, hindering comprehensive data analysis and decision - making.

[0004] Furthermore, the integration process is often accompanied by significant friction. For example, organizations have to deal with multiple vendor contracts, different service - level agreements (SLAs), and a diverse range of support structures. This complexity also extends to order management, and often hardware, software, and cloud services are procured and managed through separate channels, leading to inefficiencies and increased operational overhead.

[0005] Existing systems often impose significant operational burdens on vendors and customers by requiring changes to existing formats, workflows, or frameworks. This rigidity creates friction and limits the adoption of new platforms. Traditional approaches to integrating legacy systems face complexity, resulting in lengthy timelines, soaring costs, interoperability issues, and operational friction. These problems hinder an organization's ability to achieve an integrated, efficient, and agile technology ecosystem. [Overview of the Initiative]

[0006] The embodiments described herein provide an AI-based, vendor- and customer-independent framework designed to improve the integration of legacy systems. It provides systems and methods for dynamically adapting to and integrating with customer-specific formats and frameworks while maintaining the integrity of a unique unified schema, which is crucial for reducing barriers and facilitating seamless business operations. This framework leverages advanced artificial intelligence algorithms, including natural language processing and machine learning, to interpret data from various legacy systems and convert it 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 existing systems, the platform ensures seamless integration. The system takes in external data structures and converts them into canonical forms while preserving the customer's original schema for continued interoperability. This capability allows customers to maintain their business operations "as is" without interruption, while providing the benefits of a unified platform for streamlined data management and advanced analytics.

[0007] The framework significantly reduces the time and cost associated with traditional integrations by automating the data transformation process, facilitating rapid integration within weeks rather than months or years. The Generative AI (Gen-AI) framework can support a unified order management system that brings hardware, software, and cloud services together into a single interface, minimizing operational friction. The framework's self-learning component can analyze transactional data to provide actionable insights and recommendations, improving decision-making capabilities. Being vendor and customer independent, the framework ensures compatibility with a wide range of systems without significant changes, providing a comprehensive solution to the long-standing challenges of integrating diverse legacy systems.

[0008] For example, a computing device can use an AI-based framework to parse and map data from a vendor's legacy ERP / CRM system or other data sources. The device employs natural language processing (NLP) algorithms to interpret unstructured data and machine learning (ML) models to learn the data structures and formats used by the legacy system. Once parsed, the computing device transforms this data into a common format compatible with the unified platform. This process can include schema mapping, data cleansing, and format transformation, 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, allowing the computing device to complete the process in weeks rather than months, without the need for extensive manual coding and data mapping.

[0009] In some embodiments, a computing device can leverage an AI-based framework to facilitate integration with a customer's legacy system. The computing device employs NLP methods to understand and extract relevant data fields from the legacy system. Using ML models, the device can identify patterns and relationships within the data, enabling it to create accurate mappings to the data schema of the unified platform. This automated process may include tasks such as data normalization, deduplication, and transformation, ensuring that 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 an AI-based framework to handle diverse data types and structures, significantly reducing the complexity and duration of integration projects.

[0010] In some embodiments, the computing device can also employ an AI-based framework to continuously monitor and optimize the integration process. First, the device collects data from the customer's legacy systems and applies 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 inquiries and frequent problems and generate a data schema that aligns with the structure of the unified platform.

[0011] Computing devices perform data normalization to ensure consistency in data format, units, and naming conventions. They also run deduplication routines to identify and merge duplicate records, maintaining data integrity and accuracy. Transformation algorithms convert legacy data into a format that can be efficiently ingested and utilized by a unified platform.

[0012] Once the initial integration is complete, the computing devices continuously monitor the data flow between legacy systems and the unified platform. Using machine learning models, they identify inconsistencies and anomalies in real time, triggering automated corrective actions to resolve these issues immediately. Furthermore, the devices can adapt to changes in legacy systems, such as software updates and data structure modifications, ensuring continuous compatibility without requiring large-scale reconfiguration.

[0013] Furthermore, computing devices can leverage AI-based frameworks to provide actionable insights from integrated data. They can analyze customer interaction data to identify emerging customer needs and declining satisfaction trends, generating recommendations for targeted marketing campaigns and product improvements. Additionally, devices can evaluate the performance of integrated systems and provide suggestions 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, configured by a data layer to execute instructions for ingesting, normalizing, and storing data from multiple sources. The system may further 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, supporting multiple data formats and communication protocols. The data layer may include a preprocessing unit that cleans and standardizes data by employing techniques such as tokenization, stemming, lemmatization, scaling, and transformation. The processing layer may include a workflow orchestration engine that can manage integrated workflows to facilitate data flow between legacy systems and a unified platform. The processing layer may include AI and analytics modules with a self-learning AI engine that continuously adapts the integrated process based on new data and user interactions. The presentation layer may include customizable dashboards 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, a secure login mechanism, and data encryption both in transit and at rest.

[0016] In some embodiments, a computerized method for processing email orders may include a computing device receiving emails containing order information. The method may further include the computing device extracting data from the email content and attachments, and processing the extracted data using natural language processing techniques to parse and structure the information. The method may also include the computing device analyzing the extracted data using machine learning models to identify patterns and relationships within the data, facilitating accurate data mapping and transformation. The method may also include the computing device mapping the structured data into a unified platform schema and performing normalization, deduplication, and transformation tasks. The method may also include the computing device managing integrated workflows using a workflow orchestration engine to facilitate data flow to the order management system. The processed order data may be analyzed by a predictive analytics module to provide insights and recommendations such as inventory needs, supply chain disruptions, and optimal delivery methods. The method may also include the computing device visualizing the processed order data in a customizable dashboard displaying real-time data insights and actionable items tailored to the user's role and preferences. This method may include using an exception management system by the computing device to identify and resolve errors or anomalies in the data during processing in order to maintain the accuracy and reliability of the data.

[0017] In some embodiments, a computerized method for processing vendor catalogs may include a computing device receiving the vendor catalogs in various formats. The method may further include the computing device extracting data from the catalogs using a data ingestion engine and processing the extracted data using natural language processing techniques to parse and structure the information. The method may also include the computing device analyzing the extracted data using machine learning models to identify patterns and relationships within the catalog data, facilitating accurate data mapping and transformation. The method may also include the computing device mapping the structured catalog data into a unified platform schema and performing normalization, deduplication, and transformation tasks. The method may also include the computing device managing integrated workflows using a workflow orchestration engine to facilitate data flow to a product management system. The processed catalog data may be analyzed by a predictive analytics module to provide insights and recommendations such as product demand forecasts, vendor pricing trends, and optimal inventory levels. This method may include using a computing device to visualize processed catalog data on a customizable dashboard that displays real-time data insights and actionable items tailored to the user's role and preferences, and using a computing device to resolve data errors or anomalies using an exception management system. [Brief explanation of the drawing]

[0018] [Figure 1] This is a diagram of a system that provides an AI-based, vendor- and customer-independent framework for integrating legacy systems into a distribution platform, in several embodiments. [Figure 2]This is a diagram of a system that provides an enhanced AI-based vendor and customer-independent framework for improving the integration of legacy systems, through several embodiments. [Figure 3] This is a flowchart of a system that supports real-time AI-driven integration within an enhanced AI-based, vendor- and customer-independent framework to improve the integration of legacy systems, through several embodiments. [Figure 4] This diagram shows a system configured as an advanced IT distribution platform to enhance data handling, processing, and presentation capabilities through integrated subcomponents, in several different embodiments. [Figure 5] This is a flowchart of a method for performing the conversion of email orders to a structured data format according to some embodiments of the present disclosure. [Figure 6] This is a flowchart of a method for processing vendor catalogs according to some embodiments of the present disclosure. [Figure 7] This is a block diagram of exemplary components of a device illustrating the processor, memory, and communication interface used to implement the system in several embodiments. [Modes for carrying out the invention]

[0019] 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.

[0020] 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.

[0021] Figure 1 illustrates System 100, in several 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.

[0022] The data layer 110 can be configured to manage the ingestion, normalization, and storage of diverse data types from multiple sources. This configuration enables robust data processing capabilities and ensures that the platform operates with the high levels of accuracy and efficiency required in the IT distribution sector. The data layer 110 can be configured to automate the ingestion of data from various sources, including direct uploads, APIs, databases, and external systems such as ERP platforms, CRM platforms, CPQ platforms, etc. This layer utilizes a series of data adapters and connectors that can interface with diverse data formats such as XML files, JSON files, CSV files, and unstructured data such as emails and documents. These adapters can be designed to be highly modular and configurable, allowing for quick adaptation to changes in new data sources and data structures without significant redevelopment.

[0023] Once the data is ingested, the data layer 110 can include one or more preprocessing units that utilize algorithms to clean and standardize the data. These units address common data issues such as missing values, inconsistent formats, and errors in data entries. The normalization process involves techniques such as tokenization, stemming, and lemmatization, particularly when processing text data. In the case of numerical data, normalization can involve scaling and transformation processes to fit the data to a uniform scale required for subsequent analysis tasks. In conjunction with normalization, the data layer 110 can include a validation mechanism that applies a set of predefined rules and machine learning models to ensure data integrity and accuracy. These validation processes maintain the data quality within the platform by detecting anomalies and potential inaccuracies that could affect the overall system performance. The validation algorithms can include integrity checks such as checksum validation and data type confirmation, ensuring that the incoming data matches the expected patterns and formats.

[0024] Also, the data layer 110 can be configured with transformation tools that convert raw data into a more useful form. This can include aggregating data points, calculating new metrics based on raw data inputs, parsing, and restructuring the data into dimensions suitable for analysis and reporting. These transformations can be addressed by a combination of procedural code and a configuration-driven framework, allowing flexibility in how data can be manipulated and stored. With regard to storage, the data layer 110 can be configured to integrate both SQL database and NoSQL database technologies. SQL databases can be used for structured data that requires complex queries with high precision and consistency, such as financial records and transaction logs. NoSQL databases, due to their flexibility, can be adopted for handling semi-structured and unstructured data, as well as situations that require high write speeds and horizontal scalability. This dual database approach enables the data layer 110 to efficiently manage diverse data needs and query requirements, optimizing both performance and scalability.

[0025] The data layer 110 can 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 all components of the data layer to protect sensitive information from external threats and internal vulnerabilities. The data layer 110 can include automated backup and disaster recovery solutions to protect data from loss due to system failures, cyberattacks, or other unforeseen events. These solutions can perform regular backups of all data stored in the system and be configured to enable rapid recovery in the event of data loss, minimizing downtime and ensuring data availability and continuity. The data layer 110 can directly incorporate AI and machine learning algorithms into the data management process via the processing layer 120. These algorithms can be used for predictive analytics, such as demand forecasting based on historical data or identifying potential market trends. Machine learning models can be trained and deployed within the data layer 110 to continuously improve data handling and processing based on incoming data streams, making the system progressively smarter and more efficient.

[0026] Thus, the data layer 110 of system 100 can be configured to address the complex data management needs of the IT distribution platform. This configuration enables efficient management of a wide range of data types and sources, ensuring high data quality and security. This layer functions as the foundation of the platform, supporting advanced data processing capabilities that enable effective decision-making and operational efficiency in IT distribution. The processing layer 120 can provide the core analysis engine of system 100, further analyzing the vast amounts of data collected and pre-processed by the data layer 110 and transforming them into actionable insights. This layer can be configured to leverage LLM, AI, and ML algorithms to enhance data processing and decision-making capabilities.

[0027] The processing layer 120 can be configured to run machine learning algorithms that handle a wide variety of analytical tasks. These algorithms can be configured to perform customer segmentation by analyzing customer data and grouping customers into clear categories based on their behavior, preferences, and purchase history. This segmentation enables targeted marketing and personalized service delivery. Demand forecasting algorithms analyze historical sales data, as well as external variables such as market trends and seasonal factors, to predict future product demand and assist in inventory planning and management. Inventory management algorithms optimize inventory levels based on real-time sales data and forecasts to reduce excess inventory and stockouts, thereby improving operational efficiency. In some embodiments, the processing layer 120 can incorporate robotic process automation (RPA) to automate routine and repetitive tasks that may be time-consuming to perform manually. This automation can be extended to data entry, report generation, and the execution of 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 and ensures more accurate and reliable output.

[0028] The 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 behavior based on new data and feedback loops. For example, predictive models within the layer can be continuously refined as they receive new sales data and customer interactions, improving accuracy and relevance over time. This capability ensures the system remains effective in dynamic market conditions and can anticipate changes before they occur. The processing layer 120 can be configured to communicate data between various layers and components of the system 100. Processed data can be returned to the data layer 110 for storage and integrated, and synchronized with the presentation layer 130 to display the processed information. This integration can be managed through a set of APIs and data management protocols that ensure data consistency and integrity across the platform.

[0029] Processing Layer 120 can be configured with advanced analytical capabilities that enable the execution of complex data analyses and the generation of comprehensive reports. These analyses include multivariate testing, correlation analysis, and regression modeling, helping to uncover deeper insights into business operations and customer behavior. Furthermore, this layer can support real-time analysis, providing immediate feedback and insights that can be put into action right away. Due to the nature of the tasks performed within Processing Layer 120, security measures to protect data integrity and privacy can be embedded within the layer. This may include implementing secure data access protocols, encrypting sensitive data, and conducting regular audits to ensure compliance with industry regulations and standards.

[0030] The processing layer 120 can be designed for scalability, enabling it to handle increasing data volumes without performance degradation. It uses a distributed computing model that allows processing tasks to be scaled across multiple servers as needed. In addition, this flexibility facilitates integration as new algorithms, models, and techniques evolve. The presentation layer 130 can be configured as an interface to the system 100, visualizing the processed data from the processing layer 120 so that it is accessible to users in a meaningful and actionable format. This layer can be provided to address a diverse range of user needs and entities, ensuring that data presentations are presented intuitively, up-to-date, and effectively, improving user decision-making across various roles within the organization.

[0031] Presentation Layer 130 can be configured with a dynamic user interface that adapts to the specific requirements of different users, such as vendors, distributors, and resellers. It can be configured to provide a customizable dashboard that displays information tailored to the user's role and preferences. This customization capability allows the interface to automatically adjust its layout, content, and complexity depending on whether the user is a trading partner requiring detailed customer analytics, a vendor checking inventory levels, or a reseller reviewing cost metrics. Presentation Layer 130 incorporates data visualization tools that transform complex datasets into graphical representations such as charts, graphs, and heatmaps, in addition to push notifications and insights. These tools enable users to quickly and accurately understand large amounts of data, making it easy to grasp detailed insights without digging deep into raw data. This layer can support interactive visualizations that allow users to explore data points in depth, such as digging deeper into sales trend charts or adjusting financial forecast parameters.

[0032] This layer can be configured to continuously retrieve data from processing layer 120 and update the display in real time. 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 decision-making, such as adjusting pricing based on inventory levels or responding to customer service inquiries. Presentation layer 130 can be configured to be accessible from multiple devices, including desktops, tablets, and smartphones, ensuring that users can access information anytime, anywhere. A responsive design adapts the display to suit different screen sizes and resolutions, providing a consistent user experience across all devices.

[0033] Furthermore, this layer can include features that enhance collaboration among team members. Tools such as shared dashboards, real-time annotation capabilities, and an integrated communication platform (e.g., chat functionality) allow users to directly discuss data-driven insights within the platform, fostering a collaborative decision-making environment. Considering the confidentiality of the data being handled, 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 related to their role, secure login mechanisms, and data encryption both in transit and at rest.

[0034] To extend functionality and user utility, the presentation layer 130 can be designed to efficiently integrate with external applications such as CRM systems, financial software, and marketing tools. This integration can be facilitated through APIs that enable a smooth data flow between system 100 and other business systems, allowing users to leverage platform insights within other operational contexts. The presentation layer 130 can be optimized for performance, ensuring fast loading times and smooth interaction even with large datasets and complex visualizations. This architecture supports scalability, allowing it to handle increasing user numbers and more complex data visualizations without compromising performance. The presentation layer 130 can provide a persona-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, facilitating informed decision-making and action. This allows system 100 to 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 a user interface that can be customized to the specific needs of the user.

[0035] System 100 incorporates an AI-based, vendor- and customer-independent framework through a Generative AI (Gen-AI) engine within the processing layer 120. This engine can automate the adaptation of the platform to various external systems provided by different vendors and customers, regardless of the underlying technology or data structure. The Gen-AI engine can provide improvements in reducing the time required for complex processes. For example, a task that would normally take 5-10 days can be completed in less than 4 minutes, even with a large codebase. The Gen-AI engine removes platform complexity and minimizes friction by integrating various services such as hardware, cloud subscriptions, and services into a single order book.

[0036] Furthermore, the Gen-AI framework facilitates the integration of vendor and customer legacy systems into the platform with minimal effort. This dependency-independent framework transforms data formats, facilitating integration in weeks rather than years, thus avoiding the significant costs and delays previously associated with such integrations. This capability can be demonstrated through the smooth integration with a security vendor's complex legacy system. Gen-AI technology is an artificial intelligence framework designed to streamline and simplify complex processes in the technology and distribution industries. Processing times are drastically reduced, transforming tasks that typically take days into operations that can be completed in seconds or minutes. This technology removes platform complexity, allowing various services such as hardware, cloud subscriptions, and services to be integrated into a single order book, minimizing operational friction. It features a self-learning component capable of analyzing vast amounts of transactional data to provide insights and recommendations, enabling users to understand performance metrics and identify profitable areas. The framework can be customer and vendor-independent, enabling integration with various legacy systems through AI-driven data transformation, thus reducing the time and costs typically associated with such integrations.

[0037] For example, the Gen-AI engine can include NLP and ML algorithms to interpret data from various legacy systems and transform it into a common format. NLP algorithms can facilitate the understanding and processing of unstructured data, while ML models are trained on historical data to improve transformation accuracy. The data transformation and integration layer incorporates AI models to map data from different vendor and customer systems and transform it into a unified format compatible with the Gen-AI platform. This can include schema mapping, data cleansing, and transformation algorithms. The unified order management system can integrate multiple services into a single interface, employing database management systems and API integrations to handle hardware, software, and cloud subscriptions within a single platform. The self-learning analysis engine analyzes transactional data using advanced machine learning algorithms such as neural networks and decision trees. This can provide insights and forecasts regarding performance metrics, market trends, and profitability.

[0038] The Gen-AI framework is delivered on a scalable infrastructure and can handle large amounts of data in some cases, leveraging cloud computing resources to ensure fast processing times. This infrastructure can support the real-time processing capabilities of Gen-AI technology. In addition, one or more security and compliance modules can ensure that data is handled securely and complies with relevant regulations. These protect sensitive information using encryption algorithms, access control, and auditing mechanisms. The engine can automate data ingestion and integration into distribution platforms for various vendor and customer systems. More specifically, the Gen-AI engine at processing layer 120 can automate the ingestion of data from multiple sources, using artificial intelligence to automatically handle and process different data formats, regardless of whether they are structured data such as spreadsheets and databases, or unstructured data such as emails and documents. It can be configured to run across various vendor and customer platforms without requiring manual configuration. This capability stems from the engine's use of AI algorithms that can quickly learn and adapt to new data formats and operational protocols, facilitating integration regardless of the architecture of external systems.

[0039] This engine can process and normalize ingested data, ensuring it conforms to the formats and standards required for accurate analysis and reporting within the platform. This process can include cleaning the data, resolving inconsistencies, and transforming it into a usable state. By leveraging AI, the Gen-AI engine promotes self-service capabilities, enabling non-expert users to configure and manage integrated settings and data processing rules. This allows users to tailor the system to specific operational needs without deep 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 requirements evolve.

[0040] The Gen-AI engine begins by analyzing data arriving from diverse sources, including ERP systems, direct uploads, and external databases. It employs classification algorithms to recognize the data's format and structure. This process relies not on predefined rules, but on the engine's ability to learn and recognize patterns over time, operating across a wide range of vendor and customer interfaces without manual reconfiguration. Once the data format and structure are identified, the transformation algorithm converts the data into a uniform format compatible with the system. This ensures consistency across data processed within the system, guaranteeing efficient subsequent data processing, storage, and analysis. These transformation processes can be dynamic and responsive in real time, allowing the system to quickly adapt to new data without human intervention.

[0041] Furthermore, the system incorporates predictive analytics capabilities within the Gen-AI engine to anticipate potential problems and needs based on the data being processed. This capability allows system 100 to not only adapt to the current data structure but also anticipate future changes in vendor and customer data streams, improving the system's proactive management capabilities. Regarding user interaction, the presentation layer 130 dynamically adjusts based on the processed data, ensuring that each user, whether a vendor manager, finance officer, or sales representative, can access information and controls tailored to their specific needs and roles. This personalization extends to data visualization and user interface customization, facilitated by an AI-driven backend that adjusts information presentation and accessibility based on user preferences learned over time.

[0042] The configuration of System 100 reduces the manual workload typically associated with integrating new vendor systems and adjusting to changes in customer data requirements. By automating these processes, System 100 can reduce operational delays and errors, improve data integrity, and enhance user engagement by providing a more responsive and intuitive platform.

[0043] Figure 2 illustrates System 200 for providing an enhanced AI-based vendor- and customer-independent framework to improve the integration of legacy systems, in several embodiments. System 200 may include a data layer 210, a data transformation and mapping layer 220, an integration and orchestration layer 230, an AI and analysis layer 240, and a presentation layer 250, each configured to support specific functionalities within the platform.

[0044] The data layer 210, also known as the real-time data mesh (RTDM) layer, can be configured to manage the ingestion, normalization, and storage of vast amounts of data types from multiple sources. It can include connectors and APIs to facilitate data extraction from different legacy systems such as ERP, CRM, and CPQ. The connectors support multiple data formats and communication protocols, including XML, JSON, CSV, and unstructured data formats such as email and documents. The data ingestion engine within this layer collects data in real-time or batch mode, depending on the system's capabilities and requirements.

[0045] Data Layer 210 can include data adapters and connectors capable of interfacing with diverse data formats. Once data is ingested, the preprocessing unit within this layer uses algorithms to clean and standardize the data, addressing issues such as missing values, format inconsistencies, and data entry errors. The normalization process involves techniques such as tokenization, stemming, and lemmatization for text data, and scaling and transformation processes for numerical data. The validation mechanism applies predefined rules and machine learning models to ensure data integrity and accuracy. Data transformation tools within this layer transform raw data into more useful formats, including data point aggregation, calculation of new metrics, and data reconstruction for analysis and reporting. Regarding storage, Data Layer 210 can integrate both SQL and NoSQL database technologies. To ensure data security and availability, it can further include comprehensive security measures, automated backups, and disaster recovery solutions.

[0046] The data transformation and mapping layer 220 may include an NLP module that uses NLP to understand and extract relevant information from unstructured data sources such as customer notes and emails within legacy systems. ML models analyze historical data to identify patterns and relationships, facilitating accurate data mapping and transformation. The schema mapping engine automatically maps data schemas from legacy systems to schemas in the unified platform, addressing data normalization, deduplication, and transformation tasks.

[0047] The integration and orchestration layer 230 may include a workflow orchestration engine that manages end-to-end integrated workflows and facilitates data flow between legacy systems and the unified platform. The API gateway provides a centralized interface for accessing transformed data and can facilitate interaction between different systems and the unified platform.

[0048] 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 to improve efficiency and accuracy over time. In addition, the predictive analytics module can provide insights and recommendations based on integrated data, helping users make informed decisions. This layer supports advanced analytical capabilities, including multivariate testing, correlation analysis, and regression models, as well as real-time analysis to provide immediate feedback and insights.

[0049] The Presentation Layer 250, also known as the Single Pane of Glass (SPoG) user interface, can be configured as the interface for System 200 and can visualize processed data from the AI ​​and Analysis Layer 240, making it accessible to users in a meaningful and actionable format. This layer can feature a dynamic user interface that adapts to the specific requirements of different users, such as vendors, distributors, and resellers. It can provide a customizable dashboard that displays information tailored to the user's role and preferences. This 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.

[0050] Presentation Layer 250 can be configured to be accessible from multiple devices, including desktops, tablets, and smartphones, ensuring users can access information anytime, anywhere. Its responsive design adapts the display to different screen sizes and resolutions. It can include features that enhance collaboration among team members, such as shared dashboards, real-time annotation capabilities, and an integrated communication platform. Robust security features protect user information and maintain data confidentiality through role-based access control, secure login mechanisms, and data encryption.

[0051] Furthermore, System 200 can include customizable integration settings that allow users to specify authorized domains, input sources, and business rules for data processing. A robust exception management system identifies and addresses data errors, system errors, and other anomalies, ensuring data integrity and smooth operation. The system is designed for scalability, capable of handling increasing data volumes and new integration requirements without compromising performance.

[0052] Specific implementation examples of System 200 include processing vendor catalogs, converting them from various formats to the platform's standard format, and converting email orders to a system-readable format. The system handles bid data from vendors, converting and integrating it into the platform to streamline bid management and decision-making.

[0053] System 200 can integrate both vendor and customer systems, providing a comprehensive use case that ensures real-time data synchronization between the integrated systems. This versatility eliminates the need for separate solutions for different stakeholders and ensures that information remains up-to-date across the platform.

[0054] Figure 3 illustrates an exemplary embodiment of System 300 for supporting real-time AI-driven integration within an enhanced AI-based vendor and customer-independent framework to improve legacy system integration. 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 analysis layer 340, a presentation layer 350, and an exception management layer 360.

[0055] The real-time data ingestion layer 310 can be configured to handle the ingestion of data from various formats, such as email, PDF, spreadsheets, and other documents, into a standardized platform format. This layer includes connectors and APIs for extracting data from diverse legacy systems and can support multiple data formats and communication protocols. The data ingestion engine within this layer collects data in real-time or batch mode, depending on the system's capabilities and requirements, ensuring a continuous and efficient data flow to the platform.

[0056] The data transformation and mapping layer 320 may include an NLP module that utilizes NLP techniques to understand and extract relevant information from unstructured data sources. ML models within this layer analyze historical data to identify patterns and relationships, facilitating accurate data mapping and transformation. The schema mapping engine automatically maps data schemas from legacy systems to schemas in the 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 handling of new integrations.

[0057] The Integration and Orchestration Layer 330 may include a workflow orchestration engine that manages end-to-end integration workflows and facilitates data flow between legacy systems and the unified platform. API gateways within this layer provide a centralized interface for accessing transformed data and can facilitate interaction between different systems and the unified platform. This layer emphasizes compatibility with general-purpose data formats across systems, enabling integration with any vendor or customer system regardless of the data format used, thereby eliminating the need for bespoke solutions and significantly reducing integration time and costs.

[0058] 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 to improve efficiency and accuracy over time. The predictive analytics module within this layer can provide insights and recommendations based on integrated data, helping users make informed decisions. In addition to advanced analytical capabilities including multivariate testing, correlation analysis, and regression models, this layer can support real-time analysis to provide immediate feedback and insights.

[0059] Presentation Layer 350, referred to as the Single Pane of Glass (SPoG) user interface, can be configured to visualize processed data from AI and Analysis Layer 340, making it accessible in a meaningful and actionable format. This layer can provide a dynamic user interface that adapts to the specific requirements of different users, such as vendors, distributors, and resellers. It offers customizable dashboards that display information tailored to the user's role 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.

[0060] 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, maintaining the reliability and accuracy of integrated data. This allows system 300 to manage anomalies that could otherwise disrupt workflows and impact operational efficiency.

[0061] In a non-limiting example, email orders can be converted into a structured data format that the system can process automatically. The real-time data ingestion layer 310 can extract data from email content and attachments, which can then be processed by the NLP module and ML model of the data transformation and mapping layer 320. These components parse unstructured email data and convert it into a standardized format. The workflow orchestration engine of the integration and orchestration layer 330 can manage the integration workflow and ensure that the transformed data can be efficiently aggregated into the order management system. The AI ​​and analytics layer 340 can provide predictive insights and recommendations based on the processed order data, and the presentation layer 350 visualizes this information in a user-friendly dashboard. The exception management layer 360 addresses data errors or anomalies during the process, ensuring accuracy and reliability.

[0062] For example, the real-time data ingestion layer 310 initiates data extraction from the email content and attachments. This layer may include connectors and APIs to facilitate data extraction from various sources and supports multiple data formats and communication protocols. Upon receiving an email order, the data ingestion engine captures the email and its attachments in real-time or batch mode, depending on the system configuration. The engine then initiates the data extraction process, identifying relevant data fields such as order details, customer information, product description, and quantity.

[0063] Once ingested, the data can be pushed to the data transformation and mapping layer 320, which may include NLP modules and ML models that process and parse unstructured email data. The NLP module parses the email content to understand and extract relevant information from text such as customer notes, order instructions, and special requests. Simultaneously, the ML model analyzes historical data to identify patterns and relationships, facilitating accurate data mapping and transformation. The schema mapping engine within this layer automatically maps the extracted data to the schema of the unified platform and performs the necessary normalization, deduplication, and transformation tasks. This transformation converts the unstructured email data into a standardized format that the system can process.

[0064] The transformed data can be pushed to the integration and orchestration layer 330, where the workflow orchestration engine can manage the end-to-end integration workflow. This engine ensures that the transformed data can be efficiently aggregated into the order management system and coordinates the various tasks and processes required for integration. An API gateway within this layer can provide a centralized interface for accessing the transformed data, enabling interaction between different systems and a unified platform. The orchestration engine handles the sequence of integration tasks, including data validation, error handling, and system synchronization, ensuring a smooth and efficient workflow.

[0065] As data flows through the system, the AI ​​and analytics layer 340 can provide additional processing and analysis. This layer incorporates a self-learning AI engine that continuously learns from new data and user interactions, adapting the integration process to improve efficiency and accuracy over time. The predictive analytics module within this layer can analyze processed order data to provide insights and recommendations. For example, it can predict inventory needs based on order trends, identify potential disruptions in the supply chain, and recommend optimal delivery methods. These predictive insights help users make informed decisions, increasing the efficiency and effectiveness of the entire order management process.

[0066] The processed data can then be visualized in Presentation Layer 350, also known as the SPoG user interface. This layer can provide a dynamic user interface that adapts to the specific requirements of different users. It offers customizable dashboards that display real-time data, insights, and actionable items tailored to the user's role and preferences. Advanced data visualization tools within this layer transform complex datasets into graphical representations such as charts, graphs, and heatmaps, making it easier for users to understand the information and act upon it. For example, sales managers can view real-time order status and inventory levels, while customer service representatives can track order fulfillment and respond quickly to customer inquiries.

[0067] Throughout the entire process, the Exception Management Layer 360 maintains data integrity and efficient operation. This layer can include exception management capabilities to 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 flags the issue and prompts corrective action, either through an automated process or manual intervention. This proactive approach helps maintain the accuracy and reliability of integrated data, prevents workflow disruptions, and provides an up-to-date and persistent order management process.

[0068] System 300 achieves end-to-end process automation through the integration of these layers, automating processes from data ingestion and transformation to order creation and management. This approach ensures efficient workflows and reduces manual intervention at every stage. The system's cross-functional utility can provide customized interfaces and workflows for various functions and roles, improving user experience and operational efficiency across vendors, customers, resellers, stakeholders, and internal employees.

[0069] System 300 can be configured to scale to meet business requirements, handling increasing data volumes and new integration requirements without compromising performance. In another non-limiting example, a real-world implementation could include processing vendor catalogs, converting them from various formats to the platform's standard format, and processing bid data from vendors. The system's performance in converting email orders into structured data exemplifies its ability to address real-world scenarios and improve operational efficiency.

[0070] 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. This engine initiates the data extraction process by identifying and capturing relevant data fields such as product descriptions, prices, inventory levels, and vendor details. Once ingested, the data can be processed by the data transformation and mapping layer 320. This layer may include NLP modules and ML models that parse unstructured catalog data and transform it into a standardized format. The NLP module parses the content and extracts relevant information, while the ML model identifies patterns and relationships within the data to facilitate accurate mapping and transformation. The schema mapping engine automatically maps the extracted data to the schema of the unified platform and performs normalization, deduplication, and transformation tasks. This ensures that the catalog data is consistent and usable within the platform.

[0071] Subsequently, the converted catalog data is moved to the integration and orchestration layer 330. Within this layer, the workflow orchestration engine can manage end-to-end integrated workflows, facilitating data flow to the product management system. The API gateway provides a centralized interface for accessing the converted 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.

[0072] The AI ​​and analytics layer 340 can provide additional processing and analysis of catalog data. The 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 within this layer can analyze catalog data to provide insights and recommendations. For example, it can forecast demand for specific products, identify vendor pricing trends, and recommend optimal inventory levels. These insights help users make informed decisions and improve the efficiency of product management and procurement processes.

[0073] The processed catalog data can be visualized in Presentation Layer 350. This layer can provide a dynamic user interface that adapts to the specific requirements of different users, such as procurement managers, product experts, and vendors. It provides a customizable dashboard that displays real-time data, insights, and actionable items tailored to the user's role 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 the information and act upon it.

[0074] Throughout the entire process, the Exception Management Layer 360 ensures data integrity and efficient 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 issue and prompts corrective action. This proactive approach maintains the accuracy and reliability of integrated data, prevents confusion, and simplifies catalog management.

[0075] In these exemplary use cases, System 300 can provide both vendor and customer systems with guaranteed real-time data synchronization and ensure that information remains up-to-date across platforms. This scalable aggregation and standardization eliminates the need for separate solutions for different entities, significantly improving operational efficiency and reducing integration time and costs.

[0076] Figure 4 illustrates System 400, an advanced IT distribution platform configuration designed to enhance data handling, processing, and presentation capabilities through integrated subcomponents. In some embodiments, System 400 may include a Real-Time Data Mesh (RTDM) 410, an Advanced Analytics and Machine Learning (AAML) module 420, a Single Pane of Glass (SPoG) user interface (UI) 430, and cross-layer services 440. The structure and capabilities of System 400 can be aligned with the Real-Time AI-Driven Integration Framework illustrated in Figure 3, ensuring a comprehensive and efficient integration process.

[0077] According to several 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. The 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, crowdsources, and traditional databases. The normalization and cleansing module 412 can be configured to employ advanced algorithms and machine learning models to clean and standardize incoming data, ensuring quality and consistency. The data transformation module 413 can be configured to support real-time data streaming transformations, facilitating immediate analysis and decision-making processes. The metadata management module 414 can be configured to effectively manage metadata, enhancing data governance and discoverability. The storage optimization module 415 can be configured to optimize data storage and retrieval, adjusting how and how data is stored based on usage patterns and access frequency. RTDM410 can be one embodiment of the real-time data acquisition layer 310, which is capable of acquiring, aggregating, and normalizing various data sources and formats in real time.

[0078] RTDM410 can be configured to dynamically capture and adapt data formats and workflows provided by customers and vendors without requiring changes to existing systems. The capture 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 transformations are performed without altering the integrity of the customer's original data format, enabling bidirectional compatibility. This configuration allows customers to continue their operations without adjustment while ensuring compatibility with the platform's unified data handling processes.

[0079] The AAML module 420 of system 400 can be configured as a core analytics engine capable of performing complex data processing and analysis. The advanced analytics engine 421 can be configured to perform complex data analysis, including predictive and prescription analysis, using state-of-the-art artificial intelligence models. The process automation hub 422 can be configured to integrate complex workflows across various components and external systems to improve operational efficiency. The learning and adaptation module 423 can be configured with self-learning algorithms that adapt 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 function within a larger ecosystem of business tools. This layer incorporates the AI ​​and analytics layer 340 shown in Figure 3, which leverages a self-learning AI engine and predictive analytics to continuously enhance integrated processes and decision-making capabilities.

[0080] The SPoG UI 430 of System 400 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 the user's role and individual 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 scenario visualization, enabling users to interact with data in innovative ways. The real-time collaboration framework 433 can be configured to support advanced collaboration tools, including virtual workspaces and real-time data manipulation, to facilitate 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. The SPoG UI 430 can be one embodiment of the presentation layer 350 in Figure 3, and can be configured to give users real-time, interactive, and secure access to data visualization and collaboration tools.

[0081] The cross-layer services 440 of 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 record 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 communication portal 443 can be configured to integrate communication tools across the platform and enable users to interact within the system environment through voice, video, and text. The cross-layer services 440 can be an embodiment of the exception management layer 360 and can be configured to identify and resolve data errors, system anomalies, and other issues in real time to maintain data integrity and reliability.

[0082] 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.

[0083] Figure 5 is a flowchart of Method 500 for performing the conversion of email orders to a structured data format, according to some embodiments of the present disclosure. In some embodiments, Method 500 provides operational steps for automating the extraction and processing of email data. In some embodiments, Method 500 performs real-time data conversion and integration. Based on the disclosure herein, the operations of Method 500 may be performed in different and / or different orders.

[0084] In operation 505, the computing device may receive an email containing order information. This email may include attachments such as PDFs or spreadsheets containing additional order details.

[0085] In operation 510, the computing device can use the real-time data ingestion layer 310 to extract data from the content and attachments of emails. This involves identifying relevant data fields such as order number, product description, quantity, and customer information. The data ingestion engine leverages connectors and APIs that support multiple data formats and communication protocols to capture emails and their attachments in real time.

[0086] In operation 515, the computing device can process the extracted data using the NLP module in the data transformation and mapping layer 320. The NLP module parses the email content to extract structured information from unstructured text. This may include identifying key phrases and entities related to order details within the email body and attachments.

[0087] In operation 520, the ML models in the data transformation and mapping layer 320 analyze historical data to identify patterns and relationships, facilitating accurate data mapping and transformation. These models improve accuracy over time using training data, learning from past email orders to better predict and extract relevant data fields.

[0088] In operation 525, the schema mapping engine within the data transformation and mapping layer 320 automatically maps the extracted data to the schema of the unified platform and performs normalization, deduplication, and transformation tasks. This ensures that the data is in a correctly consistent format and ready for integration into the system.

[0089] In operation 530, the workflow orchestration engine of the integration and orchestration layer 330 can manage the integrated workflow and ensure that the transformed data can be efficiently aggregated into the order management system. This engine coordinates various tasks such as data validation, error handling, and system synchronization to maintain a smooth workflow.

[0090] In operation 535, the AI ​​and analytics layer 340 can analyze processed order data to provide predictive insights and recommendations. For example, the predictive analytics module may predict inventory needs based on order trends, identify optimal shipping methods, or suggest adjustments to pricing strategies based on real-time data.

[0091] In operation 540, processed order data can be visualized in presentation layer 350, providing a customizable dashboard that displays real-time data, insights, and actionable items tailored to the user's role and preferences. The dashboard can support interactive visualizations, allowing users to delve into specific data points for more detailed analysis.

[0092] In operation 545, the exception management layer 360 identifies and resolves errors and anomalies in the data during the process, ensuring data accuracy and reliability. This layer employs robust exception handling mechanisms, such as automated alerts and error logs, to quickly address and correct problems as they occur.

[0093] Figure 6 is a flowchart of Method 600 for processing vendor catalogs according to some embodiments of the present disclosure. In some embodiments, Method 600 provides operational steps for automating the extraction, transformation, and integration of catalog data. In some embodiments, Method 600 performs real-time data transformation and integration of diverse vendor formats into a standardized platform format. Based on the disclosure herein, the operations of Method 600 may be performed in different and / or different orders.

[0094] 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 through connectors and APIs, enabling it to capture data from multiple vendor systems and formats.

[0095] In operation 610, computing devices can extract data from these catalogs using a real-time data acquisition layer 310. This involves the use of data adapters and connectors capable of interfacing with a variety of data formats and communication protocols. The data acquisition engine initiates the extraction process by identifying and capturing relevant fields such as product description, price, inventory level, and vendor details. This extraction can be performed in real-time or batch mode, depending on the system configuration and performance.

[0096] In operation 615, the NLP module in data transformation and mapping layer 320 parses catalog content to extract structured information from unstructured text. The NLP module processes text data using techniques such as tokenization, stemming, and lemmatization to extract key information such as product names, specifications, and vendor notes from unstructured text within PDFs and documents.

[0097] In operation 620, the ML models in the data transformation and mapping layer 320 analyze historical data to identify patterns and relationships within the catalog data. These models facilitate accurate data mapping and transformation by recognizing patterns in product descriptions, pricing structures, and inventory levels, enabling the system to predict and automate the mapping of new catalog data based on learned patterns.

[0098] In operation 625, the schema mapping engine within the data transformation and mapping layer 320 automatically maps the extracted catalog data to the schema of the unified platform. This process may include performing normalization to ensure consistency in data format, deduplication to remove redundant entries, and transformation tasks to align the data with the platform's standardized structure. The schema mapping engine uses predefined rules and learned patterns to accurately map data fields from the vendor catalog to the platform schema.

[0099] In operation 630, the workflow orchestration engine of the integration and orchestration layer 330 can manage the integration workflow. This involves coordinating various tasks and processes required to integrate the converted catalog data into the product management system. The workflow orchestration engine ensures that data validation, error handling, and system synchronization tasks can be executed sequentially and efficiently, facilitating the data flow to the product management system.

[0100] In operation 635, the AI ​​and analytics layer 340 can analyze processed catalog data to provide predictive insights and recommendations. The self-learning AI engine within 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 vendor pricing trends, and recommend optimal inventory levels, helping users to perform informed procurement and inventory management.

[0101] In operation 640, processed catalog data can be visualized in presentation layer 350. This layer provides a customizable dashboard that displays real-time data, insights, and actionable items tailored to the user's role 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 the information and act upon it. The single-pane-of-glass (SPoG) user interface ensures a consistent and intuitive user experience across multiple devices.

[0102] In operation 645, the exception management layer 360 identifies and resolves errors and anomalies in the data during the process. This layer can include robust exception management capabilities that proactively flag data inconsistencies, system anomalies, and other issues, prompting corrective actions to maintain data integrity and reliability. The exception management layer ensures that errors in vendor catalog data can be addressed promptly, preventing workflow disruptions and ensuring smooth operation.

[0103] Figure 7 is a block diagram of exemplary components of device 700. One or more computer systems 700 may be used, for example, to implement any of the embodiments described herein, as well as combinations thereof and combinations of some of them. Computer system 700 may include one or more processors (also called central processing units or CPUs), for example, processor 704. Processor 704 may be connected to a communication infrastructure or bus 706.

[0104] Furthermore, the computer system 700 may include user input / output devices 703 such as a monitor, keyboard, and pointing device, which can communicate with the communication infrastructure 706 through a user input / output interface 702.

[0105] One or more processors 704 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.

[0106] Furthermore, the computer system 700 may include main or primary memory 708, such as random access memory (RAM). The main memory 708 may include one or more levels of cache. The main memory 708 may have control logic (i.e., computer software) and / or data stored internally.

[0107] Furthermore, the computer system 700 may include one or more secondary storage devices or memories 710. The secondary memory 710 may include, for example, a hard disk drive 712 and / or a removable storage device or drive 714.

[0108] The removable storage drive 714 may interact with the removable storage unit 718. The removable storage unit 718 may include a computer-accessible or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 718 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 714 may read from and / or write to the removable storage unit 718.

[0109] The secondary memory 710 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 700. Such means, devices, components, mediators, or other approaches may include, for example, a removable storage unit 722 and an interface 720. Examples of removable storage units 722 and interfaces 720 may include program cartridges and cartridge interfaces (such as those found in video game devices), removable memory chips (such as EPROMs or PROMs) and associated sockets, memory sticks and USB ports, memory cards and associated memory card slots, and / or other removable storage units and associated interfaces.

[0110] The computer system 700 may further include a communication or network interface 724. The communication interface 724 may enable the computer system 700 to communicate with and interact with a combination of external devices, external networks, external entities, etc. (referenced individually and collectively in reference number 728). For example, the communication interface 724 may enable the computer system 700 to communicate with an external or remote device 728 via a communication path 726, 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 700 via the communication path 726.

[0111] Furthermore, the computer system 700 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.

[0112] The computer system 700 may be a client or server that accesses or hosts applications and / or data through a delivery method, 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 methods.

[0113] The applicable data structures, file formats, and schemas in computer system 700 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.

[0114] In some embodiments, a tangible, non-temporary device or product comprising a tangible, non-temporary computer-readable medium on which control logic (software) is stored may also be referred to herein as a computer program product or program storage device. This includes, but is not limited to, the computer system 700, the main memory 708, the secondary memory 710, and the removable storage units 718 and 722, 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 700), such data processing devices can be operated as described herein.

[0115] It should be understood that the detailed description section, rather than the summary and abstract section, is intended to be used to interpret the claims. The summary and 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.

[0116] 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.

[0117] 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.

[0118] 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 system for integrating legacy systems into a unified platform, A server connected to a processor, The data layer takes in data from multiple sources, normalizes it, and stores it. The processing layer uses AI algorithms to analyze and transform the data. A system comprising a presentation layer, with a server configured to execute commands that provide a dynamic user interface for data visualization and interaction.

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

3. The system according to claim 1, wherein the data layer comprises a preprocessing unit that cleans and standardizes data by employing techniques such as tokenization, stemming, lemmatization, scaling, and transformation.

4. The system according to claim 1, wherein the processing layer comprises a workflow orchestration engine that manages an integrated workflow that facilitates data flow between the legacy system and the unified platform.

5. The system according to claim 1, wherein the processing layer comprises an AI and analysis module equipped with a self-learning AI engine that continuously adapts the integration process based on new data and user interactions.

6. The system according to claim 1, wherein the presentation layer comprises a customizable dashboard that provides 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, a secure login mechanism, and data encryption both in transit and at rest.

8. A computerized method for processing email orders, The computing device receives emails containing order information, The computing device extracts data from the content and attachments of the email, A computerized method comprising processing the extracted data using natural language processing techniques in order to parse and structure the information using the computing device.

9. The computerized method according to claim 8, further comprising using a computing device to analyze the extracted data using a machine learning model to identify patterns and relationships within 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 schema of a unified platform and performs normalization, deduplication, and transformation tasks.

11. The computerized method according to claim 8, further comprising using a workflow orchestration engine to manage the integrated workflow in order to facilitate the flow of data to the order management system via the computing device.

12. The computerized method according to claim 8, wherein the processed order data is analyzed by a predictive analytics module using the computing device to provide insights and recommendations such as inventory needs, supply chain disruptions, and optimal delivery methods.

13. The computerized method according to claim 8, further comprising using the computing device to visualize the processed order data on a customizable dashboard that displays real-time data insights and actionable items tailored to the user's role and preferences.

14. The computerized method according to claim 8, further comprising using an exception management system to identify and resolve errors or anomalies in the data during processing in order to maintain the accuracy and reliability of the data by the computing device.

15. A computerized method for processing vendor catalogs, Computing devices can receive vendor catalogs in various formats, The computing device extracts data from the catalog using a data acquisition engine, A computerized method comprising processing the extracted data using natural language processing techniques to parse and structure the information using the computing device.

16. The computerized method according to claim 15, further comprising using a computing device to analyze the extracted data using a machine learning model 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 catalog data to a schema of a unified platform and performs normalization, deduplication, and transformation tasks.

18. The computerized method according to claim 15, further comprising using a workflow orchestration engine to manage the integrated workflow in order to facilitate the flow of data to the product management system by the computing device.

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

20. The computerized method according to claim 15, further comprising: using the computing device to visualize the processed catalog data on a customizable dashboard that displays real-time data insights and actionable items tailored to the user's role and preferences; and using the computing device to resolve data errors or anomalies using an exception management system.

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