System and method for automating conversion of emails into orders using generative artificial intelligence

Abstract

The present invention provides a system and method for automating the conversion of emails into structured order items using generative AI, which utilizes an integrated architecture comprising a real-time data mesh (RTDM), advanced analytics and machine learning (AAML) module, and a single pane of glass (SPoG) user interface. The system includes an email parser that extracts order information from emails, an order generation engine that converts the information into structured items, and an integration gateway that synchronizes the items with external systems. The RTDM manages data flow and transformation, while the AAML provides predictive analytics and process automation. The SPoG UI performs real-time data visualization and user interaction. The system improves order processing efficiency, accuracy, and scalability, enabling businesses to handle email orders with minimal human effort and higher precision.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and more specifically to a system and method for automatically converting emails into structured order entries using generative AI. Background Technology

[0002] Routine order processing relies on manual procedures to handle orders received through various communication channels, particularly email. Typically, when an order is submitted via email, staff manually review the email content, verifying relevant order information such as product descriptions, quantities, pricing, and customer details before inputting this data into the company's order management system. This manual process is not only labor-intensive but also carries a significant risk of human error. Misunderstandings of unstructured text, input errors, or omissions of important details can lead to order fulfillment failures, decreased customer satisfaction, and ultimately, business losses.

[0003] Furthermore, traditional systems often face the challenge of diverse email formats and attachments. Orders may take various forms, including plain text, Portable Document Format (PDF), spreadsheets, or other document types, each requiring different processing procedures. Without standardized processes or advanced tools to automate data extraction, employees must manually parse these documents. This inconsistency leads to delays and inefficiencies, especially when processing large volumes of orders or receiving orders in non-standard formats that the system is incompatible with.

[0004] Data standardization and normalization present additional challenges to conventional systems. Manually entered order data often lacks consistency (especially when dealing with product codes, units of measurement, or currency conversions). For example, a customer's product code may not match the internal code used in the company system, requiring manual verification and adjustment. Similarly, if a customer uses different units of measurement or currencies, manual conversion to the company's standard units or currencies is necessary. Such tasks are error-prone and can lead to discrepancies in order processing, inventory management, and billing.

[0005] Furthermore, integration between conventional order processing systems and external platforms such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, or supply chain management tools is often limited and requires manual intervention. Data synchronization between these systems typically cannot be automated, necessitating additional steps to ensure order details are accurately reflected across all platforms. This lack of integration can lead to delays, data inconsistencies, and increased administrative overhead, hindering the scalability and efficiency of order processing workflows. Summary of the Invention

[0006] The embodiments described herein provide an automated technical solution for converting email orders into structured order entries using generative AI, significantly improving the efficiency, accuracy, and scalability of the order processing workflow. This invention addresses challenges such as manual data input, inconsistent data formats, and insufficient integration with external systems. By automating the extraction, transformation, and synchronization of order information, the system reduces human error and minimizes manual intervention.

[0007] In some embodiments, the present invention provides a system for automating the conversion of email orders into structured entries. The system includes a server coupled to a processor that executes instructions to receive and analyze email content using an email parser. The parser extracts order details and converts them into a standardized format. An order generation engine then converts this data into structured order entries, which are synchronized with external systems such as ERP and CRM platforms via an integration gateway. The system manages the data flow and ensures data consistency across processes, thereby improving overall operational efficiency.

[0008] For example, a computing device could execute a method including: receiving an email containing order details; parsing the email content to extract relevant information; and converting the extracted data into structured order entries. The method could also include the steps of: standardizing and cleaning the data; optimizing order generation using predictive analytics; and synchronizing the orders with external systems. The method would also provide: presenting the orders to users via a user interface for review and confirmation; and generating notifications to relevant stakeholders after the order is finalized.

[0009] In some embodiments, the computing device can execute instructions stored on a non-transient computer-readable medium to perform similar operations as described in the systems and methods of the present invention. These operations include: receiving and parsing email content; converting the parsed data into structured order entries; and synchronizing the orders with external systems. Furthermore, the computing device can manage data streams, apply business rules and predictive analytics, and present the final order for user confirmation, thereby ensuring accurate and efficient order processing across various business environments. Attached Figure Description

[0010] Figure 1 A system is shown, according to some embodiments, for automating the conversion of email orders into structured order entries using generative AI.

[0011] Figure 2 A Real-Time Data Grid (RTDM) within a system architecture according to some embodiments is shown, configured to manage data ingestion, transformation, and synchronization related to email orders.

[0012] Figure 3 The diagram illustrates an Advanced Analytics and Machine Learning (AAML) module within a system architecture according to some embodiments, configured to perform data analysis, predictive modeling, and process automation to generate structured order entries.

[0013] Figure 4 The Single View Window (SPoG) user interface (UI) within a system architecture according to some embodiments is shown, configured to present and manage structured order entries generated from email orders.

[0014] Figure 5 Cross-layer services within a system architecture, according to some embodiments, are illustrated, configured to enhance the performance, security, compliance, and communication of the entire system.

[0015] Figure 6 A flowchart is shown, according to some embodiments, of a method for automating the conversion of email orders into structured order entries using generative AI. Detailed Implementation

[0016] Embodiments of this disclosure can be implemented through hardware, firmware, software, or any combination thereof. Embodiments of this disclosure can also be implemented as instructions stored on a machine-readable medium, readable and executable by one or more processors. A machine-readable medium can include any mechanism that stores or transmits information in a machine-readable form (e.g., a computing device). For example, a machine-readable medium can 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 herein can be described as performing certain actions. However, it should be understood that such descriptions are for ease of description only, and such actions actually arise from the execution of firmware, software, routines, instructions, etc., by a computing device, processor, controller, or other device.

[0017] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or in between any of the shown operations. In some embodiments of this disclosure, operations may be performed in different orders and / or in different ways.

[0018] Figure 1System 100 is illustrated, a platform configured to automatically convert email orders into structured order entries using generative AI. System 100 is configured to operate within an overall architecture that includes a Real-Time Data Mesh (RTDM) 110, an Advanced Analytic and Machine Learning (AAML) module 120, and a Single Pane of Glass (SPoG) user interface 130. This integrated system can perform the ingestion, processing, and presentation of email order-related data, thereby enabling efficient and automated order management.

[0019] System 100 may include multiple core components that interact within a broader architecture. At the heart of the email-to-order conversion function is the email parser 111. The email parser 111 is configured to receive and analyze incoming emails, extracting relevant order information such as product descriptions, quantities, customer details, and attachments such as PDFs or spreadsheets. In some embodiments, the email parser 111 may employ Large Language Model (LLM) techniques, such as Natural Language Processing (NLP), Transformer-based architectures, context embedding generation, and other related methods, to interpret unstructured text in the email body, thereby identifying data points even when emails lack standardized formatting. The parser may be integrated with the RTDM 110, which provides the data layer, to cross-reference the extracted information with existing data records, ensuring consistency and accuracy.

[0020] Email parser 111 can be configured to handle various types of email formats and attachments, including plain text, HTML, PDF, and spreadsheets. In some embodiments, the email parser can employ LLM technologies, such as NLP, Transformer-based architectures, context embedding generation, and other related methods, to interpret unstructured text in the email body and extract key data points, such as product names, quantities, customer details, and special notes. For text-based images within PDFs or spreadsheets, the email parser can also use Optical Character Recognition (OCR) to process attachments. For example, when an email contains a PDF invoice attachment, the email parser can extract relevant order information, such as details and pricing, and convert it into a structured format. This information can then be sent to the data layer for further processing. In another example, the email parser can identify customer-specific product codes and map them to standardized product identifiers used by the system, thereby facilitating accurate order processing.

[0021] Email parser 111 can utilize generative AI models such as Generative Pre-trained Transformer (GPT) or Bidirectional Encoder Representations from Transformer (BERT) to interpret and extract information from unstructured email content. These models can be fine-tuned with respect to email type-specific datasets typically received by the system, enabling the parser to handle diverse formats and wording. For example, the generative AI can be trained to understand potential differences in product descriptions, customer requests, and shipping instructions expressed differently across emails. In some embodiments, the AI ​​can generate potential interpretations of ambiguous text in emails and select the most likely interpretation based on context (such as when a customer uses non-standard terminology for a product). The generative AI can also be configured to identify patterns in emails that indicate implicit requests or preferences, such as detecting a customer's preferred delivery method even if not explicitly stated in the order.

[0022] Generative AI models such as GPT (Generative Pre-trained Transformer) and BERT (Transformer-based Bidirectional Encoder Representation) can be specifically trained and fine-tuned on datasets relevant to the types of emails typically received by the system. This training process may involve using large, annotated datasets containing historical email orders, product descriptions, customer interactions, and other relevant data. Fine-tuning these models involves adjusting their parameters to optimize performance for specific tasks, such as accurately extracting order information from unstructured text.

[0023] In some embodiments, the training dataset may include a combination of publicly available datasets and proprietary data collected from the system’s previous order processing history. The AI ​​model may undergo both supervised learning (i.e., learning from labeled examples) and unsupervised learning (i.e., detecting patterns from unlabeled data). The fine-tuning process may involve iterative testing and validation, i.e., evaluating the model’s ability to correctly interpret different email formats and product descriptions.

[0024] Furthermore, generative AI models can be configured to adapt to new data formats or changes in customer behavior through continuous learning mechanisms. For example, when the system encounters a new email format or an unfamiliar product description, the AI ​​can analyze the new data and update its internal model accordingly. This adaptability ensures that the system remains robust and effective in dynamically evolving environments. In some embodiments, reinforcement learning techniques can be employed, where the AI ​​receives feedback based on the accuracy of its predictions and adjusts its strategies to improve future performance.

[0025] The order generation engine 112 is configured to transform parsed data into structured order entries. This engine can interact with AAML 120 to apply sophisticated rules and algorithms to determine how the extracted data should be structured. For example, in a non-restricted example, the order generation engine 112 can map customer-specific product codes from emails to standardized product identifiers used within the system. The engine can also be configured to handle different data formats, such as transforming free text into standardized units or converting currency values ​​based on real-time exchange rates. AAML 120 supports these processes by providing predictive analytics that optimizes order generation based on historical data, customer preferences, and operational patterns.

[0026] The order generation engine 112 can be configured to transform extracted data into structured order entries by applying business logic and data normalization techniques. This engine can interact with AAML 120 to apply predictive analytics and decision-making algorithms. For example, the order generation engine can determine appropriate pricing, apply discounts based on customer loyalty, or calculate taxes and shipping costs. In some embodiments, the engine can also normalize data formats, such as converting different units of measurement or currency values ​​into a standardized format used by the system. For example, if a customer email specifies different units of quantity (e.g., “dozens”, “pieces”), the order generation engine can convert these quantities into a uniform standardized unit (e.g., “pieces”) before generating the final order entries.

[0027] The order generation engine 112 can use generative AI to automatically generate structured order entries from parsed data. The AI ​​can employ algorithms such as sequence-to-sequence models or Transformer networks to map unstructured data extracted from emails into a structured format compatible with the company's internal order management system. For example, taking into account the business rules configured within the system, the AI ​​can automatically generate order details by combining product information, quantity, and pricing. Generative AI can also handle incomplete or unclear data by generating possible values ​​based on historical data or similar past orders. For example, if a customer omits the shipping method, the AI ​​can suggest options based on the customer's previous orders or automatically select the default option. In other use cases, the AI ​​can dynamically create custom order templates for different customers, allowing for fast and accurate order processing.

[0028] RTDM 110, serving as the data layer of System 100, is configured to manage data flow across the entire system. RTDM 110 may include one or more Application Programming Interfaces (APIs) that facilitate communication and integration with external systems such as ERP, CRM, or supply chain management platforms. These APIs enable the system to ingest, transform, and synchronize data in real time, ensuring the availability of current information among system components. By leveraging these APIs, RTDM 110 enhances the system's interoperability and scalability, thereby supporting efficient and accurate order processing.

[0029] RTDM 110 may include a Change Data Capture (CDC) mechanism 113, configured to detect any updates or modifications to order-related data while an order is being processed. For example, if a customer updates their order details, or if available products change, the CDC mechanism 113 can capture these changes in real time and update the order data accordingly. This real-time data synchronization ensures that the components of system 100 operate with current information, thereby reducing the risk of errors or discrepancies in order processing.

[0030] Within the data layer, the data transformation module 114 can be configured to perform the following processes: converting raw, unstructured email data into a format suitable for analysis and integrating it into the system. This module can perform data cleaning, normalization, and enrichment processes, thereby leveraging machine learning models to improve data quality and consistency. For example, in some embodiments, the data transformation module 114 can normalize product names and categories extracted from emails to match them with standardized terminology used within the system, thereby facilitating accurate inventory management and reporting.

[0031] AAML 120 serves as the processing layer of system 100, configured to handle analytical tasks related to email-to-order conversion. The AI ​​learning module 121 within AAML 120 can be configured to improve the accuracy of the email parser 111 and the order generation engine 112. This module can use feedback from previous orders to refine its algorithms, learn from past errors, and adapt to new types of emails or order formats. For example, if the system encounters a new email format it has not previously processed, the AI ​​learning module 121 can analyze the new data, update its model, and improve its ability to process similar emails in the future.

[0032] In some embodiments, the AAML module 120 can be configured to perform sophisticated data analysis and apply machine learning models to optimize the order processing workflow. This module may include an AI learning module that continuously refines the parsing and order generation process by analyzing feedback from previous orders. For example, if the system encounters a new email format or a unique product description, the AI ​​learning module can adjust the parsing rules and improve its accuracy for future orders. AAML may also include a predictive analytics engine that forecasts demand, suggests alternative products if certain items are out of stock, or recommends expedited shipping options based on customer preferences and historical data.

[0033] In some embodiments, the AAML module 120 can integrate generative AI to optimize order processing workflows by continuously learning from new data and adapting to changing patterns. One approach may involve using reinforcement learning (RL) algorithms, where an AI model is trained to maximize the efficiency of the order generation process by receiving feedback on its performance. The AI ​​is rewarded for correctly predicting customer preferences or generating accurate and complete order entries with minimal human intervention. Over time, the system becomes better at handling complex or unusual orders, thereby reducing the need for manual adjustments.

[0034] In some embodiments, the AAML 120 may use a Variational Autoencoder (VAE) or a Generative Adversarial Network (GAN) to simulate and predict potential scenarios in order fulfillment. For example, a VAE can be used to model the distribution of order quantity and delivery time, allowing the system to generate realistic and feasible scenarios for inventory management or freight logistics. A GAN can be used to test the robustness of the order generation process by generating synthetic but realistically feasible email orders that the system must process, thereby identifying potential weaknesses or areas for improvement. These simulations are particularly valuable in stress testing the system's ability to handle a large number of orders during peak periods or in identifying edge cases that may not be covered by standard operating procedures.

[0035] In some embodiments, alternative generative AI techniques may include using Long Short-Term Memory (LSTM) networks for sequences of data that require processing sequential data, such as understanding instructions in a customer's email. LSTMs are particularly useful in scenarios where order details are distributed across multiple sentences or paragraphs in an email, requiring AI to maintain context over long text spans.

[0036] In some non-restricted examples, a customer might send an email with a partial or vague order, such as "Sending the usual order, but with some extra parts." Generative AI can interpret this request by analyzing the customer's order history, generating a complete and accurate order entry based on previous patterns, and adding suggested extra parts based on similar customer profiles. In another example, if a new product is introduced, generative AI can automatically learn its details from the first few orders and begin recognizing it in subsequent emails, ensuring the product is handled correctly without requiring manual system updates.

[0037] Integration Gateway 122 is configured to facilitate data integration between the email-to-order system and external platforms such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, or other supply chain management tools. This gateway can be configured to ensure that generated order entries are integrated into the broader enterprise system, thereby enabling end-to-end automation of the order processing workflow. In a non-limiting example, Integration Gateway 122 can automatically push generated order entries to the ERP system for further processing, such as inventory allocation, shipping, and invoicing.

[0038] Integration Gateway 122 can be configured to facilitate data exchange between email-to-order systems and external enterprise platforms such as ERP, CRM, or supply chain management systems. This gateway supports various communication protocols and data formats, enabling it to integrate with a wide range of external systems. For example, the integration gateway can automatically push generated order entries to an ERP system for inventory allocation or update a CRM system with the latest customer order information. In another use case, the gateway can synchronize order data in real time, ensuring that all connected systems have access to the most current information, thereby reducing the risk of data discrepancies or order fulfillment delays.

[0039] Integration Gateway 122 can also benefit from generative AI by automating the creation of integration scripts or API calls—which are needed to synchronize data with external systems. For example, AI can generate and test API requests in real time, ensuring that structured order data is correctly transmitted to an ERP system or CRM platform. In a non-limiting example, if an external system undergoes a schema change, generative AI can dynamically adjust integration scripts to adapt to the new structure, thus preventing data flow disruptions.

[0040] System 100 also includes an SPoG UI 130, configured to present generated order entries and related data to end users. A dynamic user interface engine 131 can be configured to provide a customizable interface, allowing users to review and interact with order data according to their preferences. The SPoG UI 130 may also include an interactive visualization toolkit 132, which can be configured to provide various data visualization options, such as real-time summary tables, order tracking graphs, and predictive scenario modeling. These visualizations can help users monitor order processing in real time, identify potential problems, and make informed decisions.

[0041] Furthermore, the SPoG UI 130 can be configured to support a real-time collaboration framework 133, enabling multiple users to collaboratively perform order management tasks within the system. For example, in some embodiments, sales representatives and logistics managers can collaborate in real time to review and adjust orders before finalization, ensuring that all aspects of the order are accurate and aligned with customer needs.

[0042] Based on RTDM 110, AAML 120, and SPoG UI 130, System 100's architecture is configured to provide a comprehensive technology solution for automating the email-to-order process. By leveraging advanced data management, analytics, and user interface capabilities, System 100 significantly reduces the manual work involved in processing email orders, thereby improving efficiency, accuracy, and scalability across various business operations. In a non-limiting example, a customer might send an email with a PDF attachment containing a list of orders. System 100 will automatically parse the email, generate structured order entries, and present them to the user for final review (even in real-time, quickly). This automation streamlines the order processing workflow and enables businesses to process larger volumes of orders with greater accuracy and speed. Figure 2 System 200, including RTDM 210, is shown and configured to manage data flows associated with the email-to-order process. System 200 is configured to handle real-time data ingestion, transformation, and synchronization to ensure that the data used in generating and processing orders remains current and accurate across various interconnected systems.

[0043] System 200 may include an email parser 211 configured to ingest and process incoming emails. Email parser 211 may be configured to interface directly with RTDM 210, which provides a streaming backbone for managing data within the system. In some embodiments, email parser 211 may work in conjunction with a data ingestion module 212 within RTDM 210. Data ingestion module 212 may be configured to process the ingestion of data from various sources, including email servers, cloud storage, and external databases. For example, when an email containing an order is received, data ingestion module 212 may capture the email and any attachments to ensure that relevant data is available for processing.

[0044] Once the data is ingested, the Change Data Capture (CDC) mechanism 213 within the RTDM 210 is configured to monitor any updates or modifications to the data as it is processed. The CDC mechanism 213 is particularly useful in scenarios where order details may change after the initial email has been processed. For example, if a customer sends a follow-up email with a revised order quantity or an additional product request, the CDC mechanism 213 can detect these changes in real time and update the relevant data within the system. This real-time updating capability ensures that the order generation process reflects accurate and current information.

[0045] System 200 may also include a data transformation module 214 within RTDM 210, configured to transform raw, unstructured email data into a format that can be easily integrated into a wider system. Data transformation module 214 may be configured to perform functions including data cleaning, normalization, and enrichment. In one non-limiting example, the module may normalize product names and category information extracted from emails to match the taxonomy used within the system, thereby ensuring consistency in data storage and retrieval. The module may also be configured to validate the extracted data against predefined business rules, such as ensuring product quantities are within permissible limits or ensuring customer details are complete and accurate.

[0046] RTDM 210 may also include a data harmonization layer 215, configured to ensure that data from various sources remains consistent and aligned throughout the system. The data harmonization layer 215 can be particularly valuable in environments where data originates from multiple systems, such as traditional ERP systems, CRM platforms, and third-party data providers. By harmonizing data from these diverse sources, RTDM 210 ensures that the system operates with a single, unified view of data, thereby reducing the likelihood of errors or discrepancies in order processing.

[0047] System 200 can integrate with external enterprise systems via system integration interface 216. This interface can be configured to facilitate data exchange between RTDM 210 and external systems such as ERP, CRM, or supply chain management platforms. In some embodiments, system integration interface 216 can be configured to support multiple communication protocols, thereby enabling integration with a wide range of external systems. For example, in a non-limiting example, the interface can be configured to synchronize order data between RTDM 210 and an ERP system in real time, thereby ensuring that inventory levels and order status are always current.

[0048] The architecture of System 200 also supports advanced data replication and redundancy mechanisms through Data Replication Engine 217. Data Replication Engine 217 can be configured to replicate data across multiple nodes within RTDM 210, thereby ensuring high availability and fault tolerance. This facilitates uninterrupted data access during periods of high-volume order processing. In a non-limiting example, if one node in RTDM 210 becomes unavailable, Data Replication Engine 217 can ensure that data remains accessible from other nodes, minimizing the risk of downtime or data loss.

[0049] Metadata management module 218 can be configured to manage and maintain metadata associated with the ingested and processed data. Metadata management module 218 can be configured to store information about the source, structure, and history of the data, thereby enabling enhanced data management and traceability. For example, in some embodiments, the module can track the flow of data through the system, providing a detailed audit trail that can be used for compliance purposes or to resolve issues.

[0050] The integration of the RTDM 210 with a broader system architecture, including AAML and SPoG UI, facilitates data flow and processing. The RTDM 210 can be configured to feed processed and coordinated data into AAML for further analysis, where advanced algorithms and machine learning models can be applied to generate insights and optimize order processing workflows. The data can then be presented to users via SPoGUI, allowing for real-time monitoring and interaction with order data.

[0051] Driven by the RTDM 210, System 200's comprehensive data management capabilities automate and optimize the email-to-order process. In a non-limiting example, when a customer sends an email order with a product list, the system can automatically ingest the email, process the data in real time, reconcile it with existing data, and generate a structured order entry ready for user review and confirmation. This level of automation reduces manual work, minimizes errors, and ensures orders are processed quickly and accurately, thereby improving overall operational efficiency. Figure 3 System 300 is shown, including AAML module 320, which provides the core processing layer for the email-to-order system. System 300 is configured to perform sophisticated data analysis, predictive modeling, and process automation to transform parsed email data into structured and actionable order entries.

[0052] System 300 may include an order generation engine 321 configured to process data extracted by an email parser and transform it into structured order entries. Order generation engine 321 may leverage advanced machine learning algorithms and business logic contained in AAML 320 to determine how best to structure the data. For example, in a non-restricted example, if an email contains a list of products with different formats (such as different units of measurement or currencies), order generation engine 321 may be configured to normalize these details according to predefined criteria of the system, thereby ensuring that the resulting orders are consistent and accurate.

[0053] AAML 320 may also include an AI learning module 322 configured to continuously improve the accuracy and efficiency of the system's processes. The AI ​​learning module 322 may employ techniques such as supervised learning, reinforcement learning, and deep learning to refine its model based on feedback from previous order processing tasks. In some embodiments, the AI ​​learning module 322 may analyze patterns in historical email orders to predict potential problems or errors in new orders, thereby proactively adjusting the order generation engine 321 to avoid these pitfalls. For example, if the AI ​​learning module 322 detects that phrases or formats in past emails have resulted in incorrect order entries, it can modify its parsing and processing rules to better handle similar situations in the future.

[0054] The integration gateway 323 within AAML 320 is configured to facilitate data exchange between the email-to-order system and external enterprise platforms. This gateway can be configured to integrate with various systems, such as ERP, CRM, or supply chain management platforms, ensuring that structured order entries generated by the order generation engine 321 are automatically synchronized with these external systems. The integration gateway 323 can support multiple data formats and communication protocols, making it adaptable to different enterprise environments. For example, in a non-limiting example, the gateway can automatically push generated order data to an ERP system for inventory allocation or to a CRM system for customer tracking, without any manual intervention.

[0055] System 300 may also include a process automation hub 324, configured to automate various workflows within the email-to-order system. Through integration with AAML 320 and other system components such as RTDM 310 and SPoG UI 330, the process automation hub 324 can be used to orchestrate complex processes such as order validation, pricing adjustments, and inventory checks. In some embodiments, the process automation hub 324 may be configured to execute conditional logic, such as automatically applying discounts based on customer loyalty or adjusting delivery timelines based on current inventory levels. This automation reduces the need for human oversight and ensures that orders are processed quickly and accurately.

[0056] The Predictive Analytics Engine 325 can be configured to perform advanced data analytics and generate predictive insights. It can analyze historical order data, customer behavior patterns, and external market factors to predict future trends and make informed decisions during the order fulfillment process. For example, in a non-restricted example, the Predictive Analytics Engine 325 can predict a surge in demand for a specific product based on current trends and automatically adjust inventory levels or recommend alternative products when inventory levels are low. This capability helps the system optimize order fulfillment and improve customer satisfaction.

[0057] AAML 320 can also integrate a learning and adaptation module 326, configured to adjust the system's processing strategies based on real-time data and operational feedback. This module can monitor the performance of the order generation engine 321, integration gateway 323, and other components, making adjustments as needed to optimize system efficiency. For example, if the system detects that an email format takes longer to process, the learning and adaptation module 326 can refine the parsing algorithm or reconfigure the data transformation workflow to improve processing speed.

[0058] In addition to these core components, system 300 may also include an anomaly detection engine 327, configured to identify and flag potential issues during the order processing workflow. The anomaly detection engine 327 can analyze data streams from RTDM 310, AAML 320, and external systems to detect irregularities such as discrepancies in order details, pricing inconsistencies, or unusual patterns in customer behavior. When an anomaly is detected, the engine can trigger alerts or automatic responses, such as pausing the order processing workflow for further review or applying corrective actions to resolve the issue.

[0059] Output generated by AAML 320, including structured order entries, predictive insights, and process automation results, can be fed into SPoG UI 330 for presentation to the user. SPoG UI 330 is configured to provide a dynamic and customizable interface that allows users to interact with order data, monitor the status of ongoing processes, and make informed decisions based on insights generated by AAML 320.

[0060] System 300, equipped with advanced analytics and machine learning capabilities, is configured to optimize the email-to-order process by automating data processing, enhancing decision-making, and improving overall efficiency. In a non-restricted example, when a customer sends an email order, the system can automatically parse the email, generate structured order entries, apply predictive analytics to optimize inventory allocation, and synchronize order data with external enterprise systems, while continuously learning and adapting to improve future performance. This comprehensive approach enables businesses to process email orders more effectively, reducing manual intervention, minimizing errors, and accelerating order fulfillment. Figure 4 System 400 is shown, which focuses on SPoG UI 430, configured as the presentation layer for an email-to-order system. System 400 integrates outputs from RTDM and AAML modules to provide users with a dynamic, interactive, and customizable interface for managing and monitoring the order process generated from email orders.

[0061] System 400 may include a dynamic user interface engine 431, a core component configured to provide a highly customizable and user-centric experience. The dynamic user interface engine 431 can present order data, analytics, and system notifications in formats tailored to each user's specific needs and role. For example, in a non-limiting example, a sales representative might review a summary table focusing on order status and customer interactions, while a supply chain manager might view real-time inventory levels and shipping schedules. This flexibility ensures that each user can quickly and effectively access relevant information, thereby improving overall decision-making and operational efficiency.

[0062] The interactive visualization toolkit 432 within the SPoG UI 430 is configured to provide users with advanced data visualization options. This toolkit supports a variety of visualization formats, including graphs, charts, heatmaps, and 3D models, enabling users to interactively explore and analyze data. In some embodiments, the interactive visualization toolkit 432 can be configured to present predictive scenarios generated by AAML, such as forecasting demand for specific products or visualizing potential supply chain disruptions. For example, in a non-limiting example, users can use 3D models to simulate the impact of a sudden surge in orders on inventory levels and delivery timelines, helping them make informed adjustments before problems arise.

[0063] System 400 may also have a real-time collaboration framework 433 configured to facilitate collaborative work across different departments and roles. The real-time collaboration framework 433 enables multiple users to interact with the same dataset simultaneously, allowing for real-time updates, shared annotations, and collaborative decision-making. For example, in some embodiments, sales team members can flag potential problems with an order, which logistics managers can immediately see and adjust freight schedules accordingly. This collaborative environment reduces the need for back-and-forth communication and helps ensure stakeholders are informed throughout the order process.

[0064] The Security and Compliance module 434 within the SPoG UI 430 is configured to manage the security and regulatory aspects of data presented through the interface. This module may include features such as biometric authentication, role-based access control, and advanced encryption standards to protect sensitive information and ensure that only authorized users can access or modify data. The Security and Compliance module 434 can also be configured to monitor user activity within the SPoG UI 430, thereby maintaining detailed records for auditing purposes and ensuring compliance with relevant data protection regulations. For example, in a non-restrictive example, if an unusual access pattern is detected, the module may automatically flag or generate an alert for any attempt to access restricted data.

[0065] The architecture of System 400 supports high customization through a widget management engine 435, which is configured to allow users to personalize their interface by selecting and arranging widgets according to their preferences. Widgets can represent various data elements, such as order summaries, customer details, inventory snapshots, or real-time alerts, and can be added, removed, or resized according to user needs. In some embodiments, the widget management engine 435 can be configured to learn from user interactions, thereby automatically suggesting the most potentially useful widgets or layouts based on past user behavior. This adaptability helps users maintain an efficient workspace, focusing on relevant information.

[0066] The Adaptive User Experience Module 436 can be configured to dynamically modify the interface based on the usage context and the specific task being performed. The Adaptive User Experience Module 436 can adjust the layout, functionality, and available tools within the SPoG UI 430 based on factors such as the user's role, the type of order being processed, or the urgency of the task. For example, in a non-limiting example, if the user is processing a high-priority order, the Adaptive User Experience Module 436 can automatically highlight information, streamline the order approval process, and temporarily hide less relevant data to reduce distractions.

[0067] The notification and alert system 437 within the SPoG UI 430 is configured to keep users informed of important events and updates related to the email-to-order process. The system can generate real-time notifications for events such as new order submissions, changes to existing orders, or potential problems detected by the system's anomaly detection algorithm. In some embodiments, the notification and alert system 437 can be configured to deliver alerts through multiple channels, including in-app notifications, email, SMS, or even integration with external communication platforms such as Slack or Microsoft Teams. For example, in a non-limiting example, if an order is delayed due to supply chain issues, the system can send immediate alerts to relevant stakeholders, enabling them to take immediate corrective action.

[0068] System 400 also integrates with the broader architecture of email-to-order systems, enabling interaction with other system components such as RTDM and AAML. Data processed and coordinated by RTDM 410 can be displayed through SPoG UI 430, allowing users to monitor the real-time status of data ingestion and change processes. Similarly, insights and predictive analytics generated by AAML 420 can be visualized and acted upon within SPoG UI 430, ensuring users have access to current and relevant information.

[0069] Overall, System 400, configured with SPoG UI 430, provides a comprehensive and user-friendly interface for managing the email-to-order process. By integrating advanced data visualization, real-time collaboration, security features, and an adaptive user experience, System 400 enables users to effectively monitor and control the entire order processing workflow. In a non-restricted example, a user can receive notifications of new email orders, review automatically generated order entries, collaborate with colleagues to confirm details, and approve orders for processing within a single, unified interface. This streamlined approach increases user productivity, reduces the likelihood of errors, and supports faster, more informed decision-making.

[0070] Figure 5 System 500 is illustrated, which includes cross-tier services within an email-to-order system, providing support across RTDM, AAML modules, and the SPoG user interface. System 500 is configured to enhance overall system performance, security, compliance, and communication capabilities, thereby ensuring a cohesive and efficient operating environment.

[0071] System 500 may include an audit and compliance tracker 541 configured to monitor and log email activity within the order system to ensure compliance with internal policies and external regulations. The audit and compliance tracker 541 may be configured to capture detailed records of user activity, data access, and system modifications. These records may be stored in a secure, tamper-proof environment within the RTDM, allowing for easy retrieval and review during audits. In some embodiments, the audit and compliance tracker 541 may also generate compliance reports highlighting any deviations from standard operating procedures or potential regulatory violations. For example, in a non-limiting example, if a user attempts to bypass security protocols or access restricted data, the system may log the incident and alert the compliance team for further investigation.

[0072] The Performance Optimization Engine 542 is configured to dynamically adjust system resources and processing parameters to optimize performance across layers of the architecture. It can monitor real-time system metrics such as processing speed, data throughput, and server load, and automatically adjust to maintain optimal performance. For example, in a non-limiting example, if the system detects a sudden surge in email orders, the Performance Optimization Engine 542 can allocate additional computing resources to the order generation engine and data transformation module, ensuring the increased workload is handled effectively without latency. The engine can also integrate with AAML to predict future performance bottlenecks and proactively adjust system settings to prevent them.

[0073] System 500 may also feature a unified communications portal 543, configured to integrate various communication tools across the platform, enabling interaction between users, teams, and external stakeholders. The unified communications portal 543 may support multiple communication channels, including voice, video, and text, and can be configured to facilitate real-time and asynchronous communication. In some embodiments, the portal may integrate with popular communication platforms such as Microsoft Teams or Slack, allowing users to interact directly with the email-to-order system from their preferred communication tools. For example, in a non-limiting example, a user could receive alerts about new email orders, review order details, and collaborate with team members to confirm orders within Slack without leaving the Slack interface. This integration enhances user convenience and ensures smooth and efficient communication across the system.

[0074] The security management engine 544 is configured to provide advanced security features that protect the system from unauthorized access, data breaches, and other security threats. The security management engine 544 may include capabilities such as multi-factor authentication, data encryption at rest and in transit, and continuous monitoring of system vulnerabilities. In some embodiments, the security management engine 544 may also implement machine learning algorithms to detect and respond to potential security threats in real time. For example, in a non-limiting example, the engine may identify anomalous login patterns that indicate potential breaches and automatically trigger additional security measures, such as requiring re-authentication or temporarily locking affected user accounts.

[0075] System 500 may also include a data synchronization module 545, configured to ensure that data across the system remains consistent and current. The data synchronization module 545 is particularly important in environments where data is distributed across multiple locations or systems (such as when an email-to-order system is integrated with an external ERP or CRM platform). This module can be configured to monitor data changes in real time and synchronize these changes across relevant systems, thereby minimizing the risk of data discrepancies or conflicts. For example, in a non-limiting example, if an order is updated in the email-to-order system, the data synchronization module 545 can ensure that the update is immediately reflected in the connected ERP system, thus maintaining cross-platform consistency.

[0076] The event logging and monitoring system 546 within system 500 is configured to provide comprehensive visibility into system operations and events. The system can capture and store detailed records of system activity, including data processing events, user interactions, system errors, and performance metrics. The event logging and monitoring system 546 can be configured to generate real-time alerts for events such as system failures or security vulnerabilities and provide detailed reports for post-event analysis. In some embodiments, the system can also be integrated with external monitoring tools, enabling organizations to track system performance and security metrics alongside other enterprise systems. For example, in a non-limiting example, an organization could use the event logging and monitoring system 546 to track the frequency and nature of email order processing errors, thereby identifying trends that could inform future system improvements.

[0077] System 500 also supports advanced redundancy and failover mechanisms 547, configured to ensure high availability and resilience in the event of system failure or interruption. Redundancy and failover mechanisms 547 may include features such as automatic data backup, real-time replication of system processes across multiple servers, and failover to a backup system in the event of a primary system failure. In a non-limiting example, if the server processing the order generation engine fails, the system can automatically switch to a backup server without interrupting the order processing workflow, thus ensuring the system remains operational and data is not lost.

[0078] Finally, system 500 may include a scalability management framework 548 configured to allow the email-to-order system to scale efficiently as email order volume or processing complexity increases. The scalability management framework 548 may be configured to dynamically allocate resources based on current system needs, ensuring the system can handle varying workloads without performance degradation. In some embodiments, the framework may be integrated with cloud-based resources, enabling the system to scale rapidly during peak periods or scale down during off-peak periods, thereby optimizing resource utilization and cost-effectiveness.

[0079] Overall, System 500 provides foundational cross-tier services that support robust operation, security, and scalability of email-to-order systems. By integrating components such as an audit and compliance tracker, performance optimization engine, unified communications portal, and security management engine, System 500 ensures efficient, secure, and compliant operation. In a non-restrictive example, these cross-tier services work together to enable order processing, real-time data synchronization, and comprehensive monitoring, providing a resilient and adaptable platform for managing email orders in complex enterprise environments.

[0080] It should be understood that the operations shown in the exemplary methods are not exhaustive, and other operations may be performed before, after, or in between any of the shown operations. In some embodiments of this disclosure, operations may be performed in different orders and / or in different ways.

[0081] Figure 6 This is a flowchart of a method 600 for customer and end-customer login using an SPoG UI, according to some embodiments of this disclosure. Method 600 provides a detailed process flow outlining the operational steps involved in receiving, parsing, generating, and finally determining an order from an email address within an integrated system architecture including RTDM, AAML modules, and the SPoG user interface.

[0082] In some embodiments, method 600 may begin with operation 605, whereby the system receives an email containing an order. At this stage, the email ingestion module can capture the email, including any attachments such as PDFs, spreadsheets, or other documents. This module can be configured to interface with various email servers or platforms to ensure secure email retrieval. Once the email is ingested, it is passed to the next stage for analysis.

[0083] In operation 610, the email parser analyzes the content of the ingested email to extract relevant order information. This operation may include extracting product names, quantities, customer details, shipping information, and any additional order-related data. In some embodiments, even if the email does not follow a standardized format, the email parser may use LLM techniques, such as NLP, Transformer-based architectures, context embedding generation, and other related methods, to interpret unstructured text and identify relevant data points. The parser may also process attachments, such as extracting order information from PDF invoices. The extracted data is then forwarded to the data processing layer for further action.

[0084] Operation 615 transforms and normalizes the extracted data within the RTDM's data transformation module. Here, the system is configured to standardize and clean the data to ensure consistency with the system's internal data format. For example, product names can be standardized to match terminology used within the organization, and quantities can be normalized to standard units used in inventory management. At this stage, a Change Data Capture (CDC) mechanism can be activated to monitor any updates or changes during data processing. This ensures that accurate current information is used throughout the order generation process.

[0085] Following data transformation, in operation 620, the order generation engine within AAML is configured to convert normalized data into structured order entries. This engine can apply complex business logic and rules to determine the best way to structure orders. For example, the engine can compare extracted customer details with existing customer records in the system to ensure accurate billing and shipping information. Furthermore, the engine can calculate pricing, apply discounts, and generate necessary order details based on products and quantities extracted from emails.

[0086] Operation 625 allows the predictive analytics engine within AAML to be configured to analyze generated orders. This engine can provide insights and recommendations based on historical data, such as suggesting alternative products if an item is out of stock or expedited shipping if the customer has a history of urgent orders. The AI ​​learning module can work to improve these recommendations by learning from past orders and adjusting its algorithms accordingly. The result is fully optimized order entries tailored to customer needs and operational constraints.

[0087] Operation 630 finalizes the order entry and integrates it into the broader enterprise system via an integration gateway. This gateway is configured to synchronize order data with external platforms such as ERP, CRM, or supply chain management systems, ensuring that orders can be executed immediately across relevant departments. For example, in a non-limiting example, the integration gateway could automatically push the finalized order to the ERP system for inventory allocation or update the CRM system with the latest customer order history. This integration ensures that the system reflects the current status of the order and ensures that downstream processes, such as inventory management and shipping, start without delay.

[0088] Operation 635 configures the SPoG UI to present the finalized order to the user for review and confirmation. The dynamic user interface engine provides a customizable view of the order details, allowing users to verify various aspects of the order, including product details, pricing, and shipping information. If any discrepancies are found, the user can make adjustments directly within the SPoG UI, triggering updates to the underlying data layer via RTDM and AAML. Once the order is confirmed, the user completes the transaction with a single click, initiating the fulfillment process.

[0089] In operation 640, the system generates confirmations and notifications via a notification and alert system. The system is configured to send confirmation emails to customers, summarize order details, and provide expected delivery timelines. Additionally, the system can generate internal notifications to alert relevant teams, such as warehouse or freight departments, that new orders have been finalized and are ready for processing. These notifications ensure that relevant stakeholders are informed and that order fulfillment can begin immediately.

[0090] Finally, in operation 645, the system employs an audit and compliance tracker to document the entire process for future reference. This tracker is configured to record each step of the order generation process, from email ingestion to final confirmation, thereby creating a detailed audit trail that can be used for compliance purposes or to resolve any issues that may arise later. The data captured by the audit and compliance tracker can also be used to generate reports or conduct performance reviews, providing valuable insights into the efficiency and accuracy of the email-to-order conversion process.

[0091] In summary, Method 600 outlines a comprehensive process for automating the conversion of email orders into structured order entries using generative AI. By integrating advanced data processing, predictive analytics, and a user-friendly interface, the system processes email orders efficiently and accurately, reducing manual labor and increasing operational throughput. This automated approach streamlines order processing while ensuring relevant data is synchronized across the enterprise, enabling faster and more informed decision-making.

[0092] 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 conceived by the inventors, and is therefore not intended to limit the invention or the appended claims in any way.

[0093] The invention has been described above by way of illustrating the functional building blocks that implement specified functions and their relationships. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. Alternative boundaries can be defined as long as the specified functions and their relationships are properly performed.

[0094] The specific embodiments described above will fully reveal the overall nature of the invention. Without departing from the overall concept of the invention, others can easily modify and / or adapt them for various applications (such as the specific embodiments) by applying the common knowledge of those skilled in the art, without excessive experimentation. Therefore, based on the teachings and guidance presented herein, such modifications and adaptations are intended to fall within the meaning and scope of equivalents of the embodiments disclosed herein. It should be understood that the wording or terminology herein is for descriptive purposes and not restrictive, and thus the terminology or terminology in this specification should be interpreted by those skilled in the art based on the teachings and guidance.

[0095] The scope and extent of this invention should not be limited to any of the exemplary embodiments described above, but should be defined solely by the appended claims and their equivalents.

Claims

1. A system for automating the conversion of emails into orders, comprising a server coupled to a processor and configured to execute the following instructions: Receive an email containing order details, the email being processed by an email parser configured to extract relevant order information, the extraction being based on at least one of the following: large language model algorithm, natural language processing algorithm, attachment processing, and data layer comparison checklist; The extracted order information is converted into structured order entries using an order generation engine, the conversion being based on at least one of the following: customer-specific product codes, data normalization, and real-time currency conversion; The structured order entries are synchronized with external systems via an integrated gateway, the synchronization being based on at least one of the following: data format conversion, communication protocol adaptation, and real-time data updates.

2. The system according to claim 1, wherein, The email parser is also configured to: It processes various email formats and attachments, including PDFs and spreadsheets, by dynamically adjusting parsing rules based on at least one of historical email formats and new email patterns using a machine learning model.

3. The system according to claim 1, wherein, The order generation engine is also configured to: The application uses predictive analytics provided by a predictive analytics engine to optimize order generation, the optimization being based on at least one of the following: customer order history, inventory levels, and market trends.

4. The system according to claim 1, wherein, The integrated gateway is also configured to: Integration with one or more enterprise resource planning systems and / or customer relationship management systems is based on at least one of the following: real-time data synchronization, API-based communication, and system-specific data mapping.

5. The system according to claim 1, further comprising: A real-time data grid configured to manage data exchange between the email parser and the order generation engine, the management being based on at least one of the following: a change in data capture mechanism, data transformation, and data reconciliation processes.

6. The system according to claim 5, wherein, The real-time data grid also includes: The data transformation module is configured to standardize and clean the extracted order data. The standardization is based on at least one of the following: predefined business rules, data normalization technology and metadata management.

7. The system according to claim 1, further comprising: A single-view window user interface configured to present the structured order entries to a user for review and confirmation, the presentation being based on at least one of the following: dynamic user interface customization, real-time data visualization, and collaborative order management tools.

8. A computer-implemented method, comprising: Receive emails containing order details using an email parser; Relevant order information was extracted from the email using natural language processing algorithms and attachment processing technology. The extracted order information is converted into structured order entries using an order generation engine, wherein the conversion includes customer-specific product codes and data normalization; The structured order entries are synchronized with external systems via an integrated gateway, wherein the synchronization includes real-time data updates and communication protocol adaptation.

9. The method according to claim 8, further comprising: The email parser dynamically adjusts parsing rules based on historical email formats and new email patterns.

10. The method of claim 8, further comprising: Predictive analytics is used to optimize order generation, wherein the optimization includes analyzing customer order history, inventory levels, and market trends through a predictive analytics engine.

11. The method of claim 8, further comprising: The structured order entries are integrated with the enterprise resource planning system and customer relationship management system through an integration gateway.

12. The method according to claim 8, further comprising: The data exchange between the email parser and the order generation engine is managed through a real-time data grid, wherein the management includes changing the data capture mechanism, data transformation, and data coordination.

13. The method of claim 12, further comprising: The extracted order data is standardized and cleaned using a data transformation module. The standardization includes applying predefined business rules and data normalization techniques.

14. The method of claim 8, further comprising: The structured order entries are presented to the user through a single view window user interface for review and confirmation, wherein the presentation includes dynamic user interface customization and real-time data visualization.

15. A non-transient tangible computer-readable device, wherein instructions stored thereon, when executed by a computing device, cause the computing device to perform the following operations: Receive emails containing order details using an email parser; The relevant order information was extracted from the email using one of the large language model algorithms and attachment processing techniques. The extracted order information is converted into structured order entries using an order generation engine, wherein the conversion includes customer-specific product codes and data normalization; The structured order entries are synchronized with external systems via an integrated gateway, wherein the synchronization includes real-time data updates and communication protocol adaptation.

16. The non-transient tangible computer-readable device of claim 15, further comprising instructions for causing the computing device to perform the following operations: The email parser dynamically adjusts one or more parsing rules based on historical email formats and new email patterns.

17. The non-transient tangible computer-readable device of claim 15, further comprising instructions for causing the computing device to perform the following operations: Predictive analytics is used to optimize order generation, where... The optimizations include analyzing customer order history, inventory levels, and market trends using a predictive analytics engine.

18. The non-transient tangible computer-readable device of claim 15, further comprising instructions for causing the computing device to perform the following operations: The structured order entries are integrated with the enterprise resource planning system and customer relationship management system through an integration gateway.

19. The non-transient tangible computer-readable device of claim 15, further comprising instructions for causing the computing device to perform the following operations: The data exchange between the email parser and the order generation engine is managed through a real-time data grid, wherein... The management includes changing the data capture mechanism, data transformation, and data coordination.

20. The non-transient tangible computer-readable device of claim 19, further comprising instructions for causing the computing device to perform the following operations: The extracted order data is standardized and cleaned using the data transformation module. The standardization includes applying predefined business rules and data normalization techniques.

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