System and method for automating email orders using generative artificial intelligence (AI)

Abstract

This invention provides a system and method for automatically converting email orders into structured order entries using generative AI. [Solution] A system 100 that leverages 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 to improve the efficiency, accuracy, and scalability of order processing, enabling businesses to process email orders with greater accuracy with minimal manual effort, and including an email parser that extracts order information from emails, an order generation engine that converts order information into structured entries, and an integrated gateway that synchronizes entries with an external system. RTDM manages data flow and transformation, and AAML provides predictive analytics and process automation. The SPoG UI also performs real-time data visualization and user interaction.

Description

Technical Field

[0001] (Background) Traditional order processing relies on manual processing to handle orders received via various communication channels, especially emails. Typically, when an order is sent via email, staff need to manually review the content of the email to identify relevant order information such as product description, quantity, price, customer details, etc., and then input this data into the company's order management system. This manual process is not only labor-intensive but also poses a significant risk of human error. Misinterpretation of unstructured text, typos, or omission of important details can lead to inaccurate order fulfillment, customer dissatisfaction, and ultimately business losses.

[0002] Furthermore, existing systems often struggle with the variability in email formats and attached files. Orders arrive in various formats, including plain text, Portable Document Format (PDF), spreadsheets, or other document formats, each requiring different procedures for handling. Without a standardized process or advanced tools for automating data extraction, employees are burdened with manually parsing these documents. This lack of uniformity leads to delays and inefficiencies, especially when dealing with a large volume of orders or when the system receives orders in non-standard formats that it cannot easily process.

[0003] Traditional systems present additional challenges in data standardization and normalization. Manually entered order data often lacks consistency, particularly when dealing with product codes, units of measurement, or currency conversions. For example, product codes used by customers may not match the internal codes used by the company's system, requiring manual cross-referencing and adjustment. Similarly, if customers use different units of measurement or currencies, these must be manually converted to the standard units or currencies used by the company. Such tasks are prone to errors and can lead to inconsistencies in order processing, inventory management, and billing.

[0004] Furthermore, integration between traditional order processing systems and external platforms such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, or supply chain management tools is typically limited and often requires manual intervention. Data synchronization between these systems is usually not automated and requires additional steps to ensure that order details are accurately reflected across all platforms. As a result, this lack of integration can lead to delays, data inconsistencies, and increased administrative overhead, all of which hinder the scalability and efficiency of the order processing workflow. [Overview of the project]

[0005] The embodiments described herein provide an automated solution for transforming email orders into structured order entries using generative AI, significantly improving the efficiency, accuracy, and scalability of order processing workflows. The invention addresses the challenges associated with manual data entry, data format inconsistencies, and lack of integration with external systems. By automating the extraction, transformation, and synchronization of order information, the described system reduces human error and minimizes manual intervention.

[0006] In some embodiments, a system is provided that automates the conversion of email orders into structured entries. This system includes a server connected to a processor that receives emails and executes instructions to parse their contents 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 integrated gateway. The system manages data flow to ensure data consistency across processes and improves overall operational efficiency.

[0007] For example, a computing device can perform a method that includes receiving an email containing order details, parsing the email content to extract relevant information, and converting the extracted data into structured order entries. This method may further include steps to standardize and cleanse the data, optimize order generation using predictive analytics, and synchronize orders with external systems. The method also provides, through a user interface, the presentation of orders to the user for review and confirmation, and the generation of notifications to relevant parties once the order is finalized.

[0008] In some embodiments, the computing device can execute instructions stored on a non-temporary computer-readable medium to perform operations similar to those described in the claims of the System and Method. These operations include receiving and parsing email content, converting the parsed data into structured order entries, and synchronizing orders with external systems. In addition, the computing device can manage data flow, apply business rules and predictive analytics, and present final orders for user confirmation to ensure accurate and efficient order processing across various business environments. [Brief explanation of the drawing]

[0009] [Figure 1] This is a diagram of a system for automating the conversion of email orders into structured order entries using generative AI, according to several embodiments. [Figure 2] This diagram shows a real-time data mesh (RTDM) within a system architecture configured to manage the ingestion, transformation, and synchronization of data related to email orders, according to several embodiments. [Figure 3] This is a diagram of an Advanced Analytics and Machine Learning (AAML) module within a system architecture, configured to perform process automation for data analysis, predictive modeling, and generating structured order entries, in several embodiments. [Figure 4] This diagram shows a single-pane-of-glass (SPoG) user interface (UI) within a system architecture configured to present and manage structured order entries generated from email orders, according to several embodiments. [Figure 5] This is a diagram of cross-layer services within a system architecture, configured to improve performance, security, compliance, and communication across the system, in several embodiments. [Figure 6] This is a flowchart of a method for automating the conversion of email orders into structured order entries using generative AI, based on several embodiments. [Modes for carrying out the invention]

[0010] 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.

[0011] 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.

[0012] Figure 1 illustrates System 100, a platform configured to automate the conversion of email orders into structured order entries using generative AI. System 100 is configured to operate under a comprehensive architecture comprising a Real-Time Data Mesh (RTDM) 110, an Advanced Analytics and Machine Learning (AAML) module 120, and a Single Pane of Glass (SPoG) user interface 130. This integrated system performs data capture, processing, and presentation related to email orders, enabling efficient and automated order management.

[0013] System 100 may include multiple core components that interact within a broader architecture. At the heart of the email-to-order functionality is the email parser 111. The email parser 111 is configured to receive and parse incoming emails and extract relevant order information, such as product descriptions, quantities, customer details, and attachments such as PDFs or spreadsheets. In some embodiments, the email parser 111 employs large-scale language modeling (LLM) techniques, such as natural language processing (NLP), transformer-based architectures, context embedding generation, and other related methods, to interpret unstructured text within email bodies and identify data points even when emails lack a standardized format. The parser can integrate with RTDM 110, which provides a data layer, to cross-reference the extracted information with existing data records, ensuring consistency and accuracy.

[0014] The email parser 111 can be configured to process various types of email formats and attachments, including plain text, HTML, PDF, and spreadsheets. In some embodiments, the email parser can employ LLM techniques such as NLP, transformer-based architecture, context embedding generation, and other related methods to interpret unstructured text within the email body and extract key data points such as product names, quantities, customer details, and special instructions. The email parser can also process attachments using optical character recognition (OCR) for text-based images within PDFs or spreadsheets. For example, if an email contains a PDF invoice attachment, the email parser can extract relevant order information such as items and prices and convert it into a structured format. This information can then be sent to a 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 to facilitate accurate order processing.

[0015] The email parser 111 can interpret and extract information from the content of unstructured emails by leveraging generative AI models such as GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). These models can be fine-tuned to datasets specific to the types of emails typically received by the system, enabling the parser to handle diverse forms and representations. For example, the generative AI can be trained to understand variations in product descriptions, customer requests, and delivery instructions that may be expressed differently across emails. In some embodiments, the AI ​​can generate possible interpretations of ambiguous text within an email and select the most likely interpretation based on context, such as when a customer uses non-standard terminology about a product. The generative AI can also be configured to recognize patterns indicating implicit requests or preferences within an email, for example, by detecting a customer's usual delivery method even if it is not explicitly stated in the order.

[0016] Generative AI models, such as GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), can be specifically trained and fine-tuned using datasets related to the types of emails typically received by the system. This training process may involve the use of large, annotated datasets, including email order history, product descriptions, customer interactions, and other relevant data. Fine-tuning these models involves adjusting parameters to optimize performance on specific tasks, such as accurately extracting order information from unstructured text.

[0017] 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 can undergo supervised learning, which learns from labeled samples, or unsupervised learning, which detects patterns from unlabeled data. The fine-tuning process may involve iterative testing and validation, where the model's ability to correctly interpret diverse email formats and product descriptions is evaluated.

[0018] Furthermore, generative AI models can be configured to adapt to new data formats or changes in customer behavior through a continuous learning mechanism. For example, if the system encounters a new email format or an unfamiliar product description, the AI ​​can analyze this new data and update its internal model accordingly. This adaptability ensures that the system remains robust and effective in a dynamically changing environment. In some embodiments, reinforcement learning techniques can be employed, allowing the AI ​​to receive feedback based on the accuracy of its predictions and adjust its strategy to improve future performance.

[0019] The order generation engine 112 is configured to transform parsed data into structured order entries. This engine can interact with AAML 120 to apply complex rules and algorithms that determine how the extracted data should be structured. For example, as a non-limiting example, the order generation engine 112 could map customer-specific product codes in emails to standardized product identifiers used within the system. The engine could also be configured to handle various data formats, such as converting free text quantities into standardized units or converting currency values ​​based on real-time exchange rates. AAML 120 can support these processes by providing predictive analytics that optimize order generation based on historical data, customer preferences, and operational patterns.

[0020] 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 prices, apply discounts based on customer loyalty, or calculate taxes and shipping costs. In some embodiments, the engine can also normalize data formats, for example, converting different units of measurement or currency values ​​into a standardized format used by the system. For example, if a customer email specifies quantities in different units (e.g., dozens, pieces), the order generation engine can convert them to a single standard unit (e.g., individual items) before generating the final order entry.

[0021] The order generation engine 112 can automatically generate structured order entries from parsed data using a generation AI. The AI ​​employs 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, the AI ​​can automatically generate order line items by combining product information, quantity, and price, taking into account business rules configured within the system. The generation AI can also handle cases where data is incomplete or ambiguous by generating forecasts based on historical data or similar past orders. For instance, if a customer omits a shipping method, the AI ​​can suggest or automatically select a default option based on the customer's previous orders. In other use cases, the AI ​​can dynamically create custom order templates for different customers, enabling fast and accurate order processing.

[0022] RTDM110 functions as the data layer of system 100 and is configured to manage the data flow across the entire system. RTDM110 can include one or more application programming interfaces (APIs) that facilitate communication and integration with external systems such as ERP, CRM, and supply chain management platforms. These APIs enable the system to capture, transform, and synchronize data in real time, ensuring that current information is available across the components of the system. By leveraging these APIs, RTDM110 improves the interoperability and scalability of the system and supports efficient and accurate order processing.

[0023] RTDM110 can include a change data capture (CDC) mechanism 113 and is configured to detect updates and modifications to related data when an order is processed. For example, if a customer updates order details or there is a change in product availability, 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 and reduces the risk of errors or inconsistencies in order processing.

[0024] Within the data layer, the data conversion module 114 can be configured to perform processes that convert raw unstructured email data into a format suitable for analysis and integration into the system. This module can utilize machine learning models to perform data cleansing, normalization, and enrichment processes, improving the quality and consistency of the data. For example, in some embodiments, the data conversion module 114 can normalize product names and categories extracted from emails to match standardized terms used within the system, facilitating accurate inventory management and reporting.

[0025] AAML120 functions as the processing layer of system 100 and is configured to process analytical tasks related to the conversion from email to order. The AI learning module 121 within AAML120 can be configured to improve the accuracy of the email parser 111 and the order generation engine 112. This module can refine the algorithm using feedback from previous orders, learn from past mistakes, and adapt to new types of emails or order formats. For example, when the system encounters a new email format that it has never processed before, the AI learning module 121 can analyze the new data and update the model to improve its ability to process future similar emails.

[0026] In some embodiments, the AAML module 120 can be configured to perform complex data analysis and apply machine learning models to optimize the order processing workflow. This module can include an AI learning module that continuously refines the parsing process and the order generation process by analyzing feedback from past orders. For example, when the system encounters a new email format or unique product description, the AI learning module can adjust the parsing rules and improve the accuracy of future orders. Additionally, AAML can include a predictive analytics engine that anticipates demand, proposes alternative products when specific items are out of stock, or recommends preferred delivery options based on customer preferences and historical data.

[0027] In some embodiments, the AAML module 120 can optimize the order processing workflow by integrating a generative AI that continuously learns from new data and adapts to changing patterns. One approach may involve the use of a reinforcement learning (RL) algorithm, where the AI ​​model is trained to maximize the efficiency of the order generation process by receiving performance feedback. The AI ​​can be rewarded for correctly predicting customer preferences or for generating accurate and complete order entries with minimal human intervention. Over time, the system becomes more proficient in handling complex or unusual orders, reducing the need for manual adjustments.

[0028] In some embodiments, AAML120 can use variational autoencoders (VAEs) or generative adversarial networks (GANs) to simulate and predict possible scenarios in order fulfillment. For example, VAEs can be used to model the distribution of order quantities and delivery times, enabling the system to generate realistic scenarios for inventory management or delivery logistics. By employing GANs to generate artificial but realistic email orders that the system must process, the robustness of the order generation process can be tested, thereby identifying potential weaknesses or areas for improvement. These simulations are particularly useful in stress testing the system's ability to handle large volumes of orders during peak times and in identifying exceptional cases that may not be covered by standard operating procedures.

[0029] In some embodiments, alternative generative AI techniques may include the use of long short-term memory (LSTM) networks for tasks requiring the processing of sequential data, such as understanding the sequence of instructions within a customer email. LSTMs can be particularly useful in scenarios where order details are scattered across multiple sentences or paragraphs within an email, and the AI ​​needs to maintain context across a long range of text.

[0030] As a non-limiting example, a customer might send an email containing a partial or vague order, such as, "Please add a few things to my usual order." The generative AI could interpret this request by analyzing the customer's order history, generating a complete and accurate order entry based on previous patterns, and adding suggested additions based on similar customer profiles. In another example, when a new product is introduced, the generative AI could automatically learn its details from the first few orders and begin recognizing it in subsequent emails, ensuring the product is handled correctly without manual system updates.

[0031] The integration gateway 122 is configured to facilitate data integration between the email ordering system and external platforms such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, or other supply chain management tools. The gateway can be configured to ensure that generated order entries are integrated into a broader range of enterprise systems, enabling end-to-end automation of the order processing workflow. In a non-limiting example, the integration gateway 122 can automatically feed generated order entries into an ERP system for further processing such as inventory allocation, shipping, and billing.

[0032] The integrated gateway 122 can be configured to facilitate data exchange between the email-based ordering system and external enterprise platforms such as ERP, CRM, or supply chain management systems. This gateway can support various communication protocols and data formats, enabling integration with a wide range of external systems. For example, the integrated gateway can automatically send generated order entries to the ERP system for inventory allocation and update the CRM system with the latest customer order information. In another use case, the gateway can synchronize order data in real time, ensuring all connected systems have access to the latest information, thereby reducing the risk of data inconsistencies or delays in order fulfillment.

[0033] Furthermore, the integration gateway 122 can benefit from the generative AI by automating the creation of integration scripts or API calls required to synchronize data with external systems. For example, the AI ​​can generate and test API requests in real time to ensure that structured order data is correctly transmitted to the ERP system or CRM platform. In a non-limiting example, if a schema change occurs in an external system, the generative AI can dynamically adapt the integration script to fit the new structure, thereby preventing disruptions to the data flow.

[0034] The system 100 also includes a SPoG UI 130 configured to present generated order entries and associated data to the end user. A dynamic user interface engine 131 can be configured to provide a customizable interface, allowing the user to view 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 dashboards, order tracking maps, and predictive scenario modeling. These visualizations can help the user monitor order processing in real time, identify potential problems, and make informed decisions.

[0035] In addition, SPoG UI130 can be configured to support a real-time collaborative framework 133, enabling multiple users to collaborate on 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 they are finalized, ensuring that the order is accurate and aligned with customer needs.

[0036] The architecture of System 100 is based on RTDM110, AAML120, and SPoG UI130, and is configured to provide a comprehensive solution for automating the email-to-order process. By leveraging advanced data management, analytics, and user interface capabilities, System 100 can significantly reduce the manual work involved in processing email orders and improve efficiency, accuracy, and scalability across various business processes. In a non-limiting example, a customer may send an email with a PDF file containing an order list attached, and System 100 will automatically parse the email to generate structured order entries, which will be presented to the user in real time and quickly for final review. This automated approach streamlines the order processing workflow, enabling businesses to process large volumes of orders with greater accuracy and speed.

[0037] Figure 2 illustrates System 200, which includes RTDM210 configured to manage the data flow related to the order process from email. System 200 is configured to handle real-time data capture, transformation, and synchronization, ensuring that the data used to generate and process orders remains up-to-date and accurate across various interconnected systems.

[0038] System 200 may include an email parser 211 configured to capture and process incoming emails. The email parser 211 can be configured to interface directly with RTDM 210, providing a backbone for managing the data flow within the system. In some embodiments, the email parser 211 can work in conjunction with a data ingestion module 212 within RTDM 210. The data ingestion module 212 can be configured to handle the ingestion of data from various sources, including email servers, cloud storage, and external databases. For example, upon receiving an email containing an order, the data ingestion module 212 can capture the email and attachments, ensuring that the relevant data is available for processing.

[0039] Once data is captured, the Change Data Capture (CDC) mechanism 213 within the RTDM210 is configured to monitor for updates or modifications to the data as it is being processed. The CDC mechanism 213 can be 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 change in order quantity or a request for additional products, the CDC mechanism 213 can detect these changes in real time and update the relevant data in the system. This real-time update capability ensures that the order generation process reflects accurate and up-to-date information.

[0040] Furthermore, system 200 may include a data transformation module 214 within RTDM 210, configured to convert raw, unstructured email data into a format that can be easily integrated into a wider range of systems. The data transformation module 214 can be configured to perform functions including data cleansing, normalization, and enrichment. In a non-limiting example, the module could standardize product names and categories extracted from emails to align with a classification system used within the system, ensuring consistency in how the data is stored and retrieved. The module can also be configured to validate the extracted data against predefined business rules, such as ensuring that product quantities are within acceptable limits and that customer details are complete and accurate.

[0041] Furthermore, RTDM210 can incorporate a data harmonization layer 215, which is configured to ensure that data from various sources across the system is consistent and coherent. The data harmonization layer 215 can be particularly beneficial in environments where data is supplied from multiple systems, such as legacy ERP systems, CRM platforms, and third-party data providers. By harmonizing data from these separate sources, RTDM210 ensures that the system operates in a single, unified view, reducing the potential for errors or inconsistencies in order processing.

[0042] System 200 can be integrated with external enterprise systems through a system integration interface 216. This interface can be configured to facilitate data exchange between RTDM 210 and external systems such as ERP, CRM, and supply chain management platforms. In some embodiments, the system integration interface 216 can be configured to support multiple communication protocols, 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 in real time between RTDM 210 and an ERP system, ensuring that inventory levels and order status are always up-to-date.

[0043] Furthermore, the architecture of system 200 supports advanced data replication and redundancy mechanisms through the data replication engine 217. The data replication engine 217 can be configured to replicate data across multiple nodes within RTDM 210, ensuring high availability and fault tolerance. This enables uninterrupted access to data during periods of high order processing volume. In a non-limiting example, if one node within RTDM 210 becomes unavailable, the data replication engine 217 can ensure that the data remains accessible from other nodes, minimizing the risk of downtime or data loss.

[0044] The metadata management module 218 can be configured to manage and maintain metadata related to the ingested and processed data. The metadata management module 218 can be configured to store information about the data's source, structure, and history, enabling enhanced data governance and traceability. For example, in some embodiments, the module can track the origin of data as it flows through the system, providing a detailed audit trail that can be used for compliance purposes or troubleshooting issues.

[0045] The RTDM210's integration with a broad system architecture, including AAML and SPoG UI, facilitates data flow and processing. The RTDM210 can be configured to feed processed and harmonized data into AAML for further analysis, applying advanced algorithms and machine learning models to generate insights and optimize order processing workflows. The data can then be presented to users through the SPoG UI, enabling real-time monitoring and interaction with order data.

[0046] System 200's comprehensive data management capabilities are driven by RTDM210, enabling the automation and optimization of 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, harmonize it with existing data, and generate a structured order entry that is ready for user review and confirmation. This level of automation reduces manual work, minimizes errors, ensures orders are processed quickly and accurately, and improves overall operational efficiency. Figure 3 shows System 300, including the AAML module 320, which provides the core processing layer of the email-to-order system. System 300 is configured to perform complex data analysis, predictive modeling, and process automation to transform purged email data into structured, actionable order entries.

[0047] System 300 may include an order generation engine 321 configured to process data extracted by an email parser and convert it into structured order entries. The order generation engine 321 can leverage advanced machine learning algorithms and business logic built into AAML 320 to determine the optimal way to structure the data. For example, in a non-limiting example, if an email contains a product list in a variable format, such as different units of measurement or currencies, the order generation engine 321 can be configured to normalize these details according to the system's predefined standards, ensuring that the resulting orders are consistent and accurate.

[0048] Furthermore, AAML320 may include an AI learning module 322 configured to continuously improve the accuracy and efficiency of the system's processes. The AI ​​learning module 322 can 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 can analyze patterns in email order history and predict potential problems or errors in new orders, and actively adjust the order generation engine 321 to avoid these pitfalls. For example, if the AI ​​learning module 322 detects that incorrect phrases and formatting in past emails led to incorrect order entries, it can modify purging and processing rules to better handle similar situations in the future.

[0049] The integration gateway 323 within AAML320 is configured to facilitate data exchange between the email-based ordering 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, enabling adaptation to different enterprise environments. For example, in a non-limiting example, the gateway can automatically feed generated order data into an ERP system for inventory allocation or a CRM system for customer tracking without manual intervention.

[0050] Furthermore, system 300 may include a process automation hub 324 configured to automate various workflows within the order system from email. The process automation hub 324 can be used to orchestrate complex processes such as order verification, price adjustment, and inventory checks by integrating with both other system components such as AAML 320, RTDM 310, and SPoG UI 330. In some embodiments, the process automation hub 324 can be configured to perform conditional logic, such as automatically applying discounts based on customer loyalty and adjusting delivery schedules based on current inventory levels. This automation reduces the need for manual oversight and ensures that orders are processed quickly and accurately.

[0051] The predictive analytics engine 325 can be configured to perform advanced data analysis and generate predictive insights. By analyzing order history data, customer behavior patterns, and external market factors, the predictive analytics engine 325 can forecast future trends and enable informed decision-making in the order generation process. For example, in a non-limiting scenario, the predictive analytics engine 325 could predict a surge in demand for a particular product based on recent trends, automatically adjusting inventory levels or recommending alternative products if inventory levels are low. This functionality helps the system optimize order fulfillment and improve customer satisfaction.

[0052] Furthermore, AAML320 can integrate a learning and adaptive module 326 configured to adapt the system's processing strategy based on real-time data and operational feedback. This module can monitor the performance of the order generation engine 321, the integration gateway 323, and other components, and make adjustments as needed to optimize system efficiency. For example, if the system detects that it is taking too long to process email formats, the learning and adaptive module 326 can improve processing speed by refining the parsing algorithm or reconfiguring the data transformation workflow.

[0053] In addition to these core components, system 300 may include an anomaly detection engine 327 configured to identify and flag potential problems during the order processing workflow. The anomaly detection engine 327 can analyze data streams from RTDM 310, AAML 320, and external systems to detect anomalies such as discrepancies in order details, price inconsistencies, and unusual patterns of customer behavior. When an anomaly is detected, the engine can trigger an alert or automated response, such as pausing the order processing workflow for further review or applying corrective actions to resolve the problem.

[0054] The output generated by AAML320, including structured order entries, predictive insights, and process automation results, can be supplied to SPoG UI330 for presentation to the user. SPoG UI330 is configured to provide a dynamic and customizable interface, enabling users to interact with order data, monitor the status of ongoing processes, and make informed decisions based on insights generated by AAML320.

[0055] System 300 is configured to optimize the email-to-order process by automating data processing, improving decision-making, and enhancing overall efficiency through advanced analytics and machine learning capabilities. In a non-limiting example, when a customer places an order via email, the system can automatically parse the email, generate structured order entries, apply predictive analytics to optimize inventory allocation, synchronize order data with external enterprise systems, and continuously learn and adapt to improve future performance. This comprehensive approach enables businesses to process email orders more efficiently, reduce manual work, minimize errors, and accelerate order fulfillment. Figure 4 shows System 400, focusing on SPoG UI430, which is configured as the presentation layer of the email-to-order system. System 400 integrates outputs from RTDM and AAML modules and provides users with a dynamic, interactive, and customizable interface for managing and monitoring the order process generated from email orders.

[0056] 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 results, and system notifications in a format tailored to each user's specific needs and roles. For example, in a non-limiting example, a sales representative might view a dashboard focused on order status and customer interactions, while a supply chain manager could view real-time inventory levels and shipping schedules. This flexibility ensures that each user can quickly and efficiently access relevant information, improving overall decision-making and operational efficiency.

[0057] The interactive visualization toolkit 432 within the SPoG UI 430 is configured to provide users with advanced data visualization options. This toolkit can support a wide 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 forecasting scenarios generated by AAML, such as predicting demand for a particular product or visualizing potential disruptions in the supply chain. For example, in a non-limiting example, a user could use a 3D model to simulate the impact of a sudden surge in orders on inventory levels and delivery timelines, helping the user make informed adjustments before problems occur.

[0058] Furthermore, the system 400 may include a real-time collaboration framework 433 configured to facilitate collaborative work across different departments and roles. The real-time collaboration framework 433 can enable 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, a member of the sales team may flag potential issues with an order so that they are immediately visible to the logistics manager, who can then adjust the shipping plan accordingly. This collaborative work environment reduces the need for back-and-forth communication and helps ensure that stakeholders are informed throughout the entire ordering process.

[0059] The security and compliance module 434 within the SPoG UI 430 is configured to manage the security and regulatory aspects of the data presented through the interface. This module includes features such as biometric authentication, role-based access control, and advanced encryption standards, enabling it to protect sensitive information and ensure that only authorized users can access or modify the data. The security and compliance module 434 can also be configured to monitor user activity within the SPoG UI 430, maintain detailed logs for auditing purposes, and ensure compliance with relevant data protection regulations. For example, in a non-limiting example, the module could automatically flag or generate alerts for attempts to access restricted data if unusual access patterns are detected.

[0060] The architecture of system 400 supports advanced customization through a widget management engine 435, which is configured to allow users to personalize the 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 based on user needs. In some embodiments, the widget management engine 435 can be configured to learn user interactions and automatically suggest the widgets or layouts that may be most useful based on the user's past behavior. This adaptability helps users maintain an efficient workspace and focus on relevant information.

[0061] The adaptive user experience module 436 can be configured to dynamically modify the interface based on usage 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, and the urgency of the task. For example, in a non-limiting example, if a user is handling a high-priority order, the adaptive user experience module 436 may automatically highlight information, streamline the order approval process, and temporarily hide less relevant data to reduce distractions.

[0062] The notification and alert system 437 within the SPoG UI 430 is configured to ensure that users continue to receive information about important events and updates related to the ordering process via email. This system can generate real-time notifications for events such as the submission of new orders, 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 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 a supply chain issue, the system can immediately send an alert to the relevant parties, enabling them to take swift corrective action.

[0063] Furthermore, System 400 integrates with a broader architecture of the email-to-order system, enabling interaction with other system components such as RTDM and AAML. Data processed and harmonized by RTDM 410 can be displayed through SPoG UI 430, allowing users to monitor the real-time status of the data ingestion and transformation process. Similarly, insights and predictive analytics generated by AAML 420 can be visualized within SPoG UI 430 and made actionable based on them, ensuring that users have access to the latest relevant information.

[0064] Overall, System 400 is configured to provide a comprehensive, user-friendly interface for managing the order process from email through the SPoG UI 430. By integrating advanced data visibility, real-time collaboration, security features, and an adaptive user experience, System 400 enables users to efficiently monitor and control the entire order processing workflow. In non-limiting examples, users can receive notifications of new email orders, review automatically generated order entries, collaborate with colleagues to confirm details, and approve order processing within a single, unified interface. This streamlined approach improves user productivity, reduces the potential for errors, and supports faster, more informed decision-making.

[0065] Figure 5 illustrates System 500, which includes cross-layer services within the order system, from email to the ordering system, providing support functions across RTDM, AAML modules, and the SPoG user interface. System 500 is configured to improve overall system performance, security, compliance, and communication capabilities, ensuring a cohesive and efficient operating environment.

[0066] System 500 may include an audit and compliance tracker 541 configured to monitor and record operations within the ordering system from email and ensure compliance with internal policies and external regulations. The audit and compliance tracker 541 may be configured to capture detailed logs of user activity, data access, and system modifications. These logs can be stored in a secure, tamper-proof environment within RTDM and can be easily retrieved and reviewed during audits. In some embodiments, the audit and compliance tracker 541 may also generate compliance reports highlighting deviations from standard operating procedures or potential regulatory violations. For example, in a non-limiting example, if a user attempts to circumvent security protocols or access restricted data, the system may log the incident and alert the compliance team for further investigation.

[0067] The performance optimization engine 542 is configured to dynamically adjust system resources and processing parameters to optimize performance across architectural layers. It can monitor system metrics such as processing speed, data throughput, and server load in real time and automatically adjust to maintain optimal performance. For example, in a non-limiting scenario, 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 to ensure the increased workload is processed efficiently without delay. Furthermore, the engine can integrate with AAML to predict future performance bottlenecks and proactively adjust system settings to avoid them.

[0068] Furthermore, System 500 may include a unified communications portal 543 configured to integrate various communications tools across platforms, enabling interaction between users, teams, and external stakeholders. The unified communications portal 543 can support multiple communications channels, including voice, video, and text, and can be configured to facilitate both real-time and asynchronous communications. In some embodiments, the portal can integrate with popular communications platforms such as Microsoft Teams or Slack, allowing users to interact directly with the ordering system from their preferred communications tool, such as email. For example, in a non-limiting example, a user could receive an alert about a new email order in Slack, review the order details, and confirm the order in collaboration with team members without leaving the Slack interface. This integration enhances user convenience and maintains smooth and efficient communication across the entire system.

[0069] 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 features such as multi-factor authentication, encryption of data in quiescent 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 unusual login patterns that suggest a potential breach and automatically trigger additional security measures, such as requiring re-authentication or temporarily locking affected user accounts.

[0070] Furthermore, system 500 may include a data synchronization module 545 configured to ensure data consistency and up-to-dateness across systems. The data synchronization module 545 can be particularly important in environments where data is distributed across multiple locations or systems, such as when the 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 the relevant systems, minimizing the risk of data inconsistencies 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 this update is immediately reflected in the connected ERP system, maintaining consistency across platforms.

[0071] The event logging and monitoring system 546 within system 500 is configured to provide comprehensive visibility into system operation and events. This system can capture and store detailed logs 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 breaches and to 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 in parallel with 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 and identify trends that could be useful for future system improvements.

[0072] Furthermore, System 500 supports an advanced redundancy and failover mechanism 547 configured to ensure high availability and resilience in the event of system failure or interruption. The redundancy and failover mechanism 547 may include functions 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 major system failure. In a non-limiting example, if the server handling the order generation engine fails, the system can automatically switch to a backup server without interrupting the order processing workflow, ensuring that system operation continues and no data is lost.

[0073] Finally, System 500 may include a scalability management framework 548 configured to enable the email-to-order system to scale efficiently as the volume of email orders or the complexity of processing requirements increases. The scalability management framework 548 can be configured to dynamically allocate resources based on current system demand, ensuring that the system can handle changing workloads without performance degradation. In some embodiments, the framework can integrate with cloud-based resources, enabling the system to quickly scale up during peak periods and scale down during periods of low activity, optimizing resource utilization and cost efficiency.

[0074] System 500 provides foundational cross-layer services that support robust operation, security, and scalability of email-to-order systems. By integrating components such as audit and compliance trackers, performance optimization engines, a unified communications portal, and a security management engine, System 500 ensures that the system operates efficiently and securely and complies with relevant regulations. In non-limiting examples, these cross-layer services work together to enable order processing, real-time data synchronization, and comprehensive monitoring, providing a fault-tolerant and adaptable platform for managing email orders in complex enterprise environments.

[0075] 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.

[0076] Figure 6 is a flowchart of Method 600 for automating the email-to-order process using Generative AI, according to several embodiments of the present disclosure. Method 600 provides a detailed process flow outlining the operational steps involved in receiving, parsing, generating, and finalizing orders from email within an integrated system architecture comprising RTDM, AAML modules, and a SPoG user interface.

[0077] In some embodiments, method 600 can begin with operation 605, in which the system receives an email containing an order. At this stage, the email capture module can capture the email, including 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 captured, it is passed on to the next stage for analysis.

[0078] In operation 610, the email parser analyzes the content of the ingested email and extracts relevant order information. This may include extracting product names, quantities, customer information, shipping information, and additional order-related data. In some embodiments, the email parser may use LLM techniques such as NLP, transformer-based architecture, context-embedded generation, and other related methods to interpret unstructured text and identify relevant data points, even if the email does not conform to a standardized format. The parser may also process attachments, such as extracting order information from a PDF invoice. The extracted data is then transferred to a data processing layer for further action.

[0079] In operation 615, the extracted data undergoes transformation and normalization within the RTDM data transformation module. Here, the system is configured to standardize and cleanse the data to ensure it conforms to the system's internal data format. For example, product names may be standardized to conform to the naming convention used within the organization, and quantities may be normalized to the standard units used in inventory management. At this stage, the Change Data Capture (CDC) mechanism can be enabled to monitor updates or changes to the data being processed. This ensures that accurate and up-to-date information is used throughout the entire order generation process.

[0080] After data transformation, in operation 620, the order generation engine within AAML is configured to convert the normalized data into structured order entries. This engine applies complex business logic and rules to determine the best way to structure the order. For example, it can cross-reference extracted customer information with existing customer records in the system to ensure the accuracy of billing and shipping information. In addition, it can calculate prices, apply discounts, and generate the necessary order details based on products and quantities extracted from emails.

[0081] In operation 625, the predictive analytics engine within AAML can be configured to perform analysis of generated orders. Based on historical data, this engine can provide insights and recommendations, such as suggesting alternative products if an item is out of stock, or recommending expedited delivery if the customer has a history of urgent orders. An AI learning module can be involved in improving these recommendations by learning from past orders and adjusting the algorithm accordingly. As a result, order entries are well optimized to meet customer needs and operational constraints.

[0082] In operation 630, the order entry is finalized and integrated into a broader enterprise system through 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 are immediately actionable across relevant departments. For example, in a non-limiting example, the integration gateway could automatically feed the finalized order into the ERP system for inventory allocation or update the CRM system with the latest customer order history. This integration ensures that the systems reflect the current state of the order and that downstream processes such as inventory management and shipping can begin without delay.

[0083] In operation 635, the SPoG UI is configured to present the finalized order to the user for review and confirmation. The dynamic user interface engine can provide order details in a customizable view, allowing the user to review the order details, including product details, pricing, and shipping information. If discrepancies are found, the user can make adjustments directly within the SPoG UI, which triggers updates to the underlying data layer via RTDM and AAML. Once the order is confirmed, the user can finalize the transaction and initiate the fulfillment process with a single click.

[0084] In operation 640, the system generates confirmations and notifications through a notification and alert system. The system is configured to send a confirmation email to the customer summarizing the order details and providing an estimated delivery timeline. In addition, the system can generate internal notifications to alert relevant teams, such as the warehousing or shipping departments, that a new order has been finalized and is ready for processing. These notifications ensure that stakeholders are informed and that the order fulfillment process can begin promptly.

[0085] Finally, in operation 645, the system activates an audit and compliance tracker to log the entire process for future reference. This tracker is configured to record the steps of the order generation process from email ingestion to final confirmation, creating a detailed audit trail that can be used for compliance purposes and troubleshooting any issues that may arise later. The data captured by the audit and compliance tracker can also be used to generate reports and conduct performance reviews, providing valuable insights into the efficiency and accuracy of the order process from email to order.

[0086] In summary, Method 600 outlines a comprehensive process flow for automatically converting email orders into structured order entries using generative AI. By integrating advanced data processing, predictive analytics, and a user-friendly interface, the system can process email orders efficiently and accurately, reduce manual work, and improve operational throughput. This automated approach not only streamlines order processing but also ensures that relevant data is synchronized across the enterprise, enabling faster and more informed decision-making.

[0087] It should be understood that the detailed description section, rather than the abstract section, is intended to be used to interpret the claims. The abstract section may describe one or more, but not all, exemplary embodiments of the invention as intended by the inventors, and is therefore not intended to limit the invention and the appended claims in any way.

[0088] 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.

[0089] 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 requiring excessive experimentation and without departing from the general concept of the invention, 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.

[0090] 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. This is a system for automating the conversion of emails into orders. It is coupled to the processor and the following instructions, namely, Receiving an email containing order details, wherein the email is processed by an email parser configured to extract relevant order information, and the extraction is based on at least one of a Large-Scale Language Model (LLM) algorithm, a Natural Language Processing (NLP) algorithm, attachment processing, and cross-referencing with a data layer. The extracted order information is converted into structured order entries using an order generation engine, wherein the conversion is based on at least one of customer-specific product codes, data normalization, and real-time currency conversion. A system comprising a server configured to synchronize the structured order entries with an external system via an integrated gateway, wherein the synchronization is based on at least one of data format conversion, communication protocol adaptation, and real-time data updating.

2. The aforementioned email parser, The system according to claim 1, further configured to process various email formats and attachments, including PDFs and spreadsheets, by employing a machine learning model to dynamically adapt parsing rules based on at least one of past email formats and new email patterns.

3. The aforementioned order generation engine, The system according to claim 1, wherein predictive analytics provided by a predictive analytics engine are applied to optimize order generation, the optimization being further configured to be applied based on at least one of customer order history, inventory levels, and market trends.

4. The aforementioned integrated gateway The system according to claim 1, further configured to integrate with one or more enterprise resource planning (ERP) systems and / or customer relationship management (CRM) systems based on at least one of real-time data synchronization, API-based communication, or system-specific data mapping.

5. The system according to claim 1, configured to manage data exchange between the email parser and the order generation engine, wherein the management further comprises a real-time data mesh (RTDM) based on at least one of a change data capture (CDC) mechanism, data transformation, and data harmonization process.

6. The aforementioned RTDM, The system according to claim 5, further comprising a data transformation module configured to standardize and cleanse extracted order data, wherein the standardization is based on at least one of predefined business rules, data normalization techniques, and metadata management.

7. The system according to claim 1, wherein the structured order entries are configured to be presented to the user for review and confirmation, and the presentation further comprises a single pane of glass (SPoG) user interface (UI) based on at least one of dynamic user interface customization, real-time data visualization, and collaborative order management tools.

8. A computer implementation method, The email parser will receive an email containing the order details, Using natural language processing (NLP) algorithms and attachment processing techniques, relevant order information is extracted from the email. Using an order generation engine, the extracted order information is converted into structured order entries, wherein the conversion includes customer-specific product codes and data normalization. A method comprising synchronizing the structured order entries with an external system via an integrated gateway, wherein the synchronization includes real-time data updates and adaptation of communication protocols.

9. The method according to claim 8, further comprising dynamically adapting parsing rules based on past email formats and new email patterns using the email parser.

10. The method according to claim 8, further comprising optimizing order generation using predictive analytics, wherein the optimization includes analyzing customer order history, inventory levels, and market trends using a predictive analytics engine.

11. The method according to claim 8, further comprising integrating the structured order entries with an Enterprise Resource Planning (ERP) system and a Customer Relationship Management (CRM) system via the integration gateway.

12. The method according to claim 8, further comprising managing data exchange between the email parser and the order generation engine by a real-time data mesh (RTDM), wherein the management includes a change data capture (CDC) mechanism, data transformation, and data harmonization.

13. The method according to claim 12, further comprising using a data transformation module to standardize and cleanse extracted order data, wherein the standardization applies predefined business rules and data normalization techniques.

14. The method according to claim 8, further comprising presenting the structured order entries to the user for review and confirmation through a single-pane-of-glass (SPoG) user interface (UI), wherein the presentation includes dynamic customization of the user interface and real-time data visualization.

15. A tangible, non-temporary computer-readable device in which, when a stored instruction is executed by a computer device, The email parser will receive an email containing the order details, Using one or more Large-Scale Language Model (LLM) algorithms and attachment processing techniques, relevant order information is extracted from the email. Using an order generation engine, the extracted order information is converted into structured order entries, wherein the conversion includes customer-specific product codes and data normalization. A computer-readable device that causes the computer device to perform an operation including synchronizing the structured order entries with an external system via an integrated gateway, wherein the synchronization includes real-time data updates and adaptation of communication protocols.

16. The tangible non-temporary computer-readable device according to claim 15, further comprising instructions causing the computing device to perform an operation including dynamically adapting one or more parsing rules based on at least one of past email formats and new email patterns.

17. The tangible, non-temporary, computer-readable device according to claim 15, further comprising instructions for causing the computing device to perform an operation including optimizing order generation using predictive analytics, wherein the optimization includes analyzing customer order history, inventory levels, and market trends by a predictive analytics engine.

18. The tangible non-temporary computer-readable device according to claim 15, further comprising instructions causing the computing device to perform an operation including integrating the structured order entries with an Enterprise Resource Planning (ERP) system and a Customer Relationship Management (CRM) system, via the integration gateway.

19. The tangible, non-temporary computer-readable device according to claim 15, further comprising instructions for causing the computing device to perform operations including managing data exchange between the email parser and the order generation engine by real-time data mesh (RTDM), wherein the management includes a change data capture (CDC) mechanism, data transformation, and data harmonization.

20. The tangible, non-temporary, computer-readable device according to claim 19, further comprising instructions for causing the computing device to perform an operation including standardizing and cleansing the extracted order data using a data transformation module, wherein the standardization includes applying predefined business rules and data normalization techniques.

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