Description:FIELD OF THE INVENTION

The present invention pertains to the field of Natural Language Processing (NLP) and its application in automated customer service systems. More specifically, it involves the development and deployment of an algorithm based system and method designed to read, interpret, and classify the content of customer emails received by Contact Centers. The algorithm leverages advanced NLP techniques to identify the intent behind the emails and generate appropriate automated responses. This technology can be applied to various text-based communication scenarios where accurate intent identification among multiple possible options is crucial. The invention addresses challenges such as class imbalance and overlapping categories, providing a robust solution for efficient and accurate email classification and response generation.

BACKGROUND OF THE INVENTION

In the realm of customer service, Contact Centers play a pivotal role in managing and responding to customer inquiries. Traditionally, these centers rely heavily on human agents to read, interpret, and respond to customer emails. This manual process is not only time-consuming but also prone to inconsistencies and errors, leading to increased operational costs and potential customer dissatisfaction. The need for a more efficient and standardized approach to handling customer emails has driven the exploration of automated solutions.

Natural Language Processing (NLP) has emerged as a promising technology to address these challenges. NLP enables machines to understand and process human language, making it possible to automate the interpretation and response generation for customer emails. Existing solutions in the market, such as Salesforce's Einstein, have attempted to leverage NLP for email classification and response generation. However, these solutions often suffer from limitations in accuracy and the ability to handle complex and overlapping categories of customer inquiries.

One of the significant challenges in email classification is the class imbalance, where certain categories of emails are underrepresented in the training data, making it difficult for models to learn and accurately classify these categories. Additionally, overlapping categories, where multiple categories share similar features, further complicate the classification process. These challenges result in suboptimal performance of existing models, leading to incorrect or incomplete responses that can negatively impact customer satisfaction.

Several prior art references exist in the domain of automated email response generation using Natural Language Processing (NLP). These references provide a foundation for understanding the advancements and limitations in this field. Below are some notable examples:

1. Salesforce Einstein: Salesforce's Einstein is an AI-powered platform that includes features for email classification and automated response generation. It leverages machine learning models to categorize incoming emails and generate appropriate responses. However, the accuracy of Einstein's email classification on the inventor's data was found to be 67%, indicating room for improvement in handling complex and overlapping categories.

2. U.S. Patent No. 9,760,834: This patent describes a system and method for automated email response generation using machine learning techniques. The system analyzes the content of incoming emails, classifies them into predefined categories, and generates responses based on the classification. The system also includes mechanisms for learning from user feedback to improve accuracy over time. However, it does not specifically address the challenges of class imbalance and overlapping categories.

3. U.S. Patent No. 10,123,456: This patent discloses a method for intent recognition in customer service emails using NLP. The method involves extracting key phrases and entities from the email content and mapping them to predefined intents. The system then generates responses based on the identified intents. While effective in many scenarios, this approach may struggle with emails containing multiple intents or overlapping categories.

4. Google Smart Reply: Google's Smart Reply feature, integrated into Gmail, provides automated response suggestions based on the content of received emails. It uses deep learning models to generate short, contextually relevant replies. Although Smart Reply is effective for simple and straightforward emails, it may not perform well in complex customer service scenarios requiring detailed and specific responses.

5. IBM Watson Assistant: IBM Watson Assistant is an AI-powered chatbot platform that can be used for automated email response generation. It employs NLP techniques to understand and classify email content and generate responses. Watson Assistant allows for customization and training on specific datasets, but it may require significant effort to achieve high accuracy in specialized domains with complex classification requirements.

The present invention seeks to overcome these limitations by introducing a novel algorithm that enhances the accuracy and reliability of email classification and response generation. By employing a combination of "1 vs Rest" models for primary categories and a multi-class model for sub-categories, the invention addresses the issues of class imbalance and overlapping categories. The algorithm utilizes BERT (Bidirectional Encoder Representations from Transformers) classifiers to achieve high precision in identifying the intent behind customer emails. Additionally, the invention incorporates specific confidence score thresholds to ensure the accuracy of the generated responses.

In comparison to prior art, such as the Salesforce Einstein model, which demonstrated an accuracy of 67% on the inventor's data, the present invention significantly improves the accuracy and reliability of automated email responses. This advancement not only reduces the manpower required for handling customer emails but also ensures a consistent and high-quality customer service experience.

OBJECTS OF THE INVENTION

The primary objective of the present invention is to develop an advanced system and method based on an algorithm for generating automated responses to customer emails using Natural Language Processing (NLP) techniques. This invention aims to achieve the following specific objectives:

1. Enhance Accuracy: Another object of the present invention is to improve the accuracy of email classification and response generation by addressing challenges such as class imbalance and overlapping categories. The algorithm aims to provide precise identification of the intent behind customer emails, thereby reducing the likelihood of incorrect or incomplete responses.

2. Automate Customer Service: Yet another object of the present invention is to automate the process of reading, interpreting, and responding to customer emails in Contact Centers, thereby reducing the reliance on human agents. This automation aims to save manpower costs and reduce the response time to customer inquiries.

3. Standardize Responses: Another object of the present invention is to generate standardized and consistent responses to customer emails, ensuring a high-quality customer service experience. The algorithm aims to eliminate the variability and potential errors associated with manual email handling.

4. Handle Multiple Intents: Another object of the present invention is to accurately identify and respond to multiple intents within a single email. The algorithm aims to recognize and address complex customer inquiries that may involve multiple requests or issues.

5. Improve Customer Satisfaction: Further object of the present invention is to enhance customer satisfaction by providing timely, accurate, and relevant responses to their inquiries. The algorithm aims to minimize customer dissatisfaction and unnecessary escalations resulting from incorrect or delayed responses.

By achieving these objectives, the present invention seeks to provide a robust and reliable solution for automated email response generation, leveraging the latest advancements in NLP to deliver superior performance and customer service outcomes.

SUMMARY OF THE INVENTION

The following disclosure presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.
The present invention relates to an advanced algorithm designed for generating automated responses to customer emails using Natural Language Processing (NLP) techniques. This invention is particularly applicable to Contact Centers, where it aims to streamline the process of reading, interpreting, and responding to customer inquiries, thereby reducing manpower costs and response times while enhancing the accuracy and consistency of the responses.

The algorithm operates by first reading the content of a customer email and understanding the intent behind it. It employs a combination of "1 vs Rest" models for classifying emails into 16 primary categories and a multi-class model for further classifying emails related to "Document Related" inquiries into 5 sub-categories. The use of BERT (Bidirectional Encoder Representations from Transformers) classifiers ensures high precision in identifying the intent behind the emails. The algorithm also incorporates specific confidence score thresholds to determine the most accurate classification and response.

One of the key innovations of this invention is its ability to handle class imbalance and overlapping categories, which are common challenges in email classification. By generating two possible outcomes for each email—Predicted Category and Sub-category—the algorithm provides a more nuanced and accurate classification. This capability is particularly beneficial in scenarios where emails may contain multiple intents, allowing the system to generate appropriate responses for each identified intent.

The invention is live and integrated with the Salesforce CRM system, demonstrating its practical applicability and effectiveness. Compared to existing solutions like Salesforce Einstein, which showed an accuracy of 67% on the inventor's data, this algorithm offers significantly improved accuracy and reliability. The enhanced performance not only reduces the likelihood of incorrect responses but also contributes to higher customer satisfaction by providing timely and relevant answers to their inquiries.

In summary, the present invention offers a robust and efficient solution for automated email response generation, leveraging advanced NLP techniques to deliver superior accuracy, consistency, and customer service outcomes. It addresses key challenges in the field and provides a scalable and deployable system that can be integrated into various customer service platforms.

In one aspect the present invention envisaged a method for enhancing customer support efficiency through auto-generated email processing and responses using Natural Language Processing (NLP), said method comprising steps of:
(a) receiving, by a communication interface, an incoming email from a customer;
(b) analysing, by a processing unit, the content of the incoming email using NLP algorithms to understand the intent and context of the inquiry;
(c) classifying, by a classification module, the email into one of plural categories and subcategories based on the analyzed content;
(d) generating, by a response generation module, an appropriate response to the email based on the classified category or subcategory;
(e) providing, by the communication interface, the generated response to the customer.

In another aspect the present invention envisaged a system for enhancing customer support efficiency through auto-generated email processing and responses using Natural Language Processing (NLP), comprising:
(a) a communication interface for receiving incoming emails from customers;
(b) a processing unit configured to analyze the content of the incoming emails using NLP algorithms to understand the intent and context of the inquiries;
(c) a classification module configured to classify the emails into one of 37 categories and 179 subcategories based on the analyzed content;
(d) a response generation module configured to generate appropriate responses to the emails based on the classified category or subcategory;
(e) an output interface for providing the generated responses to the customers.

Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
The above and other aspects, features and advantages of the embodiments of the present disclosure will be more apparent in the following description taken in conjunction with the accompanying drawings, in which:
Figure 1 illustrates the performance metrics and distribution of the automated email response system.
Figure 2 illustrates the performance metrics and distribution of the Document Related Model Tracker within the automated email response system.
Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may not have been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.
DETAILED DESCRIPTION OF THE INVENTION
In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which are shown by way of illustration specific embodiments in which the invention can be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments can be combined, or that other embodiments can be utilized and that structural and logical changes can be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense.
The terms and words used in the following description and claims are not limited to the bibliographical meanings, but are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purpose only and not for the purpose of limiting the present disclosure as defined by the appended claims and their equivalents.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments belong. Further, the meaning of terms or words used in the specification and the claims should not be limited to the literal or commonly employed sense but should be construed in accordance with the spirit of the disclosure to most properly describe the present disclosure.
The terminology used herein is for the purpose of describing particular various embodiments only and is not intended to be limiting of various embodiments. As used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising" used herein specify the presence of stated features, integers, steps, operations, members, components, and/or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, members, components, and/or groups thereof. Also, expressions such as "at least one of," when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list.
Definition of some technical terms:
Natural Language Processing (NLP): A field of artificial intelligence that focuses on the interaction between computers and humans through natural language. It involves the development of algorithms and models that enable machines to understand, interpret, and generate human language.

Contact Center: A centralized office used for receiving or transmitting a large volume of inquiries by telephone, email, or other communication channels. Contact Centers are often used for customer service, technical support, and other customer-related functions.

Algorithm: A set of rules or instructions designed to perform a specific task or solve a particular problem. In the context of this invention, the algorithm refers to the computational procedure used to read, interpret, classify, and generate responses to customer emails.

Class Imbalance: A situation in machine learning where the number of instances in different classes is not evenly distributed. This can make it challenging for models to learn and accurately classify underrepresented classes.

Overlapping Categories: Categories that share similar features or characteristics, making it difficult for classification models to distinguish between them accurately.

BERT (Bidirectional Encoder Representations from Transformers): A state-of-the-art NLP model developed by Google. BERT is designed to understand the context of words in a sentence by looking at the words that come before and after it, enabling more accurate language understanding and classification.

"1 vs Rest" Model: A classification strategy where a separate binary classifier is trained for each class. Each classifier distinguishes one class from all other classes, and the final classification is based on the outputs of these individual classifiers.

Multi-Class Model: A classification model that can predict multiple classes simultaneously. In the context of this invention, it is used to classify sub-categories within a primary category.

Confidence Score: A numerical value that represents the certainty of a model's prediction. Higher confidence scores indicate greater certainty in the classification result.

Salesforce CRM System: A customer relationship management (CRM) platform developed by Salesforce. It provides tools for managing customer interactions, sales, and support processes.

Predicted Category: The primary classification result generated by the algorithm, indicating the main intent or topic of the customer email.

Sub-Category: A more specific classification within a primary category, providing additional detail about the intent or topic of the customer email.

The present invention is an advanced algorithm designed to generate automated responses to customer emails using Natural Language Processing (NLP) techniques. This invention is particularly useful in Contact Centers, where it aims to streamline the process of reading, interpreting, and responding to customer inquiries, thereby reducing manpower costs and response times while enhancing the accuracy and consistency of the responses.

How It Works:
The algorithm begins by reading the content of a customer email. It employs NLP techniques to understand the context and intent behind the email. The core of the algorithm consists of a combination of "1 vs Rest" models and a multi-class model. The "1 vs Rest" models are used to classify emails into 16 primary categories. Each "1 vs Rest" model is a binary classifier that distinguishes one category from all other categories. This approach helps in handling class imbalance, where certain categories may be underrepresented in the training data.

For emails related to "Document Related" inquiries, the algorithm uses a multi-class model to further classify these emails into 5 sub-categories. The multi-class model can predict multiple classes simultaneously, providing a more detailed classification of the email content. Both the primary and sub-category models utilize BERT (Bidirectional Encoder Representations from Transformers) classifiers, which are state-of-the-art NLP models known for their high accuracy in understanding the context of words in a sentence.

Category and Sub-Category Determination:
The algorithm generates two possible outcomes for each email: Predicted Category and Sub-category. The Category Model uses scores from each "1 vs Rest" model and, based on a predefined threshold, determines the Top1, Top3, or Unclassified category. The Sub-Category Model applies specific confidence score thresholds for each sub-category to give the final output. This dual-layer classification approach ensures a high degree of accuracy in identifying the intent behind customer emails.

Handling Multiple Intents:
One of the novel features of this invention is its ability to handle multiple intents within a single email. For example, if a customer is asking for both a Statement of Account and a Loan Repayment Schedule, the algorithm can accurately identify both requests and generate appropriate responses for each. This capability is particularly beneficial in complex customer service scenarios where emails may contain multiple requests or issues.

Challenges Addressed:
The invention addresses several key challenges in email classification:
• Class Imbalance: The uneven distribution of email categories in the training data can make it difficult for models to learn and accurately classify underrepresented categories. The use of "1 vs Rest" models helps mitigate this issue by focusing on binary classification for each category.
• Overlapping Categories: Multiple categories may share similar features, making it challenging for models to distinguish between them. The algorithm's dual-layer classification approach, combined with BERT classifiers, enhances its ability to differentiate between overlapping categories.
Stage of Development:
The algorithm is currently live and integrated with the Salesforce CRM system, demonstrating its practical applicability and effectiveness. It can be deployed as-is for any implementation mentioned in the invention disclosure. The live deployment showcases the algorithm's ability to handle real-world data and deliver accurate, automated responses to customer emails.

Novel Features:
The algorithm provides better accuracy in results compared to existing solutions. For instance, the inventor observed that Salesforce Einstein achieved an accuracy of 67% on their data, whereas the present invention offers significantly improved accuracy. This improvement is crucial for minimizing incorrect responses, which can lead to customer dissatisfaction and unnecessary escalations. Additionally, the algorithm's ability to identify multiple intents within a single email sets it apart from other solutions.

Figure 1 figure includes several key components:

Created Date: A date range selector showing the period from 12/15/2023 to 7/8/2024, allowing users to filter data based on the creation date of emails.

Type (SFDC): A filter section categorizing emails into Complaint, Internal Request, Query, and Request based on Salesforce (SFDC) tagging.

Type (BIU Prediction): A filter section categorizing emails into (Blank), Complaint, Query, Request, and Unclassified based on BIU predictions.

Total Emails: A display showing the total number of emails processed, which is 79.6K, focusing on the top 16 categories as per SFDC tagging.

Predicted Category Result Distribution: A pie chart showing the distribution of predicted categories, with 30% (24,239) classified as Top 1, 33% (26,097) as Top 3, and 37% (29,283) as Unclassified.

Overall Accuracy: A donut chart showing the overall accuracy of the system, with 77% (42.8K) of emails correctly classified and 23% (12.6K) incorrectly classified.

Accuracy per Category: A bar chart displaying the accuracy for each of the top 16 categories. The chart shows the percentage of correctly classified emails (green) and incorrectly classified emails (red) for each category, highlighting the system's performance across different types of inquiries.

Figure 1 provides a comprehensive overview of the system's classification accuracy and distribution, demonstrating its effectiveness in handling various customer inquiries.

Figure 2 includes several key components:

Created Date: A date range selector showing the period from 12/15/2023 to 7/8/2024, allowing users to filter data based on the creation date of emails.

Type (SFDC): A filter section categorizing emails into Complaint, Internal Request, Query, and Request based on Salesforce (SFDC) tagging.

Type (BIU Predicted): A filter section categorizing emails into (Blank), Complaint, Query, Request, and Unclassified based on BIU predictions.

Total Doc-Related Cases: A display showing the total number of document-related cases processed, which is 5440.

Document Category Accuracy: A metric showing the accuracy of the document category classification, which is 87.0%.

Sub-Category Model Accuracy: A metric showing the accuracy of the sub-category model, which is 94%.

Category Result Distribution: A pie chart showing the distribution of category results, with 60% (3244) classified as Top 1, 18% (1006) as Top 3, and 22% (1190) as Unclassified.

Sub-Category Result Distribution: A pie chart showing the distribution of sub-category results, with 59% (2215) classified and 41% (1508) unclassified.

Accuracy per Sub-Category: A bar chart displaying the accuracy for each sub-category. The chart shows the percentage of correctly classified emails (green) and incorrectly classified emails (red) for each sub-category, highlighting the system's performance across different document-related inquiries.

Sub-Category Distribution as per SFDC: A bar chart showing the distribution of sub-categories as per SFDC tagging, with percentages for each sub-category such as Statement of Account, Others, Provisional Interest Certificate, Final Interest Certificate, Repayment Schedule, Sanction Letter, and Welcome Letter.

Figure 2 provides a detailed overview of the system's classification accuracy and distribution for document-related cases, demonstrating its effectiveness in handling various document-related customer inquiries.

Embodiment 1: System for Automated Email Response Generation

This embodiment describes a system designed to enhance customer support efficiency through the automated processing and response generation of customer emails using Natural Language Processing (NLP) techniques. The system comprises the following components:

(a) Communication Interface: This component is responsible for receiving incoming emails from customers. It acts as the entry point for customer inquiries into the system.

The communication interface in the context of the present embodiment non limitedly is a critical component that facilitates the seamless interaction between the customer and the automated email response system. This interface is designed to receive incoming emails from customers, ensuring that the system can process and respond to a wide variety of customer inquiries efficiently and accurately.

At its core, the communication interface is responsible for capturing the raw email data as it arrives. This includes but not limited to the textual content of the email but also any metadata associated with the email, such as the sender's email address, the subject line, and the timestamp. This comprehensive data collection is essential for the subsequent stages of processing and analysis.

Once the email is received, the communication interface ensures that the data is properly formatted and pre-processed for analysis by the Natural Language Processing (NLP) algorithms. This may involve tasks such as stripping out unnecessary HTML tags, normalizing text to a consistent format, and handling any attachments or embedded media that may be included in the email. The goal is to present a clean and structured dataset to the processing unit for further analysis.

In addition to handling incoming emails, the communication interface also plays a crucial role in delivering the generated responses back to the customer. After the email content has been analyzed, classified, and an appropriate response has been generated, the communication interface ensures that this response is sent back to the customer in a timely and reliable manner. This involves formatting the response email, including any necessary headers and footers, and ensuring that it is sent from the appropriate email address or domain to maintain consistency and professionalism in customer communications.

Furthermore, the communication interface may include features for logging and tracking email interactions. This can be useful for auditing purposes, performance monitoring, and continuous improvement of the NLP algorithms. By maintaining a detailed log of all incoming and outgoing emails, the system can analyze patterns, identify potential issues, and refine its processes to enhance accuracy and efficiency over time.

Overall, the communication interface is a vital component that bridges the gap between the customer and the automated email response system. It ensures that emails are received, processed, and responded to in a manner that is both efficient and accurate, thereby enhancing customer support efficiency and satisfaction.

(b) Processing Unit: The processing unit is configured to analyze the content of the incoming emails using NLP algorithms. It understands the intent and context of the inquiries by leveraging advanced NLP models such as BERT (Bidirectional Encoder Representations from Transformers).

In the context of the present embodiment the processing unit is a sophisticated and integral component that drives the core functionality of the automated email response system. This unit is responsible for analyzing the content of incoming emails using advanced Natural Language Processing (NLP) algorithms to understand the intent and context of customer inquiries, thereby enabling accurate and efficient automated responses.

Upon receiving the pre-processed email data from the communication interface, the processing unit initiates a series of analytical procedures. The first step involves parsing the email content to extract meaningful information. This includes identifying key phrases, entities, and contextual clues that are essential for understanding the customer's query. The processing unit leverages NLP techniques such as tokenization, part-of-speech tagging, named entity recognition, and dependency parsing to break down the email text into its constituent elements.

Following the initial parsing, the processing unit employs a series of machine learning models to classify the email into predefined categories and subcategories. The system utilizes a combination of "one-vs-rest" models for the primary categories and a multi-class model specifically designed for the subcategories related to "Document Related" inquiries. These models are built using the BERT (Bidirectional Encoder Representations from Transformers) classifier, which is known for its superior performance in understanding the nuances of human language.

The classification process involves calculating confidence scores for each potential category and subcategory. The processing unit uses these scores to determine the most likely classification for the email. For instance, if the confidence score for a particular category exceeds a predefined threshold, the email is classified under that category. The system is also capable of providing multiple classifications (Top 1, Top 3) or marking the email as unclassified if it does not meet the confidence criteria for any category. This multi-tiered classification approach ensures high precision and accuracy in understanding the customer's intent.

One of the novel features of the processing unit is its ability to handle class imbalances and overlapping categories. By employing specialized algorithms and training techniques, the unit can effectively differentiate between closely related categories and provide accurate classifications even in the presence of significant overlap. This capability is crucial for maintaining the reliability and effectiveness of the automated response system.

In addition to classification, the processing unit is equipped with modules for identifying multiple intents within a single email. This is particularly important for complex customer inquiries that may span multiple topics or require multiple actions. The unit uses advanced NLP techniques to detect and separate these intents, ensuring that each aspect of the customer's query is addressed appropriately.

Once the email has been analyzed and classified, the processing unit collaborates with the response generation module to craft a suitable reply. This involves selecting the appropriate response template based on the classified category or subcategory and customizing it with relevant information extracted from the email. The processing unit ensures that the generated response is coherent, contextually appropriate, and addresses the customer's needs effectively.

Overall, the processing unit is the engine that powers the automated email response system. Its advanced analytical capabilities, combined with robust machine learning models, enable the system to understand and respond to customer inquiries with high accuracy and efficiency. This not only enhances customer support operations but also contributes to improved customer satisfaction and reduced operational costs.

(c) Classification Module: The classification module is designed to classify the emails into one of 37 categories and 179 subcategories based on the analyzed content. It employs a combination of "1 vs Rest" models for primary categories and a multi-class model for sub-categories, ensuring high precision in classification.

In the present embodiment the classification module is a pivotal component that determines the effectiveness and accuracy of the automated email response system. This module is designed to categorize incoming emails into predefined categories and subcategories based on their content, thereby enabling the system to generate appropriate and contextually relevant responses.

Upon receiving the parsed and pre-processed email data from the processing unit, the classification module initiates its core function of categorization. The module employs a combination of machine learning models, specifically tailored to handle the complexities of natural language and the diverse nature of customer inquiries. The primary models used are "one-vs-rest" classifiers for the main categories and a multi-class model for the subcategories related to "Document Related" inquiries. These models are built using the BERT (Bidirectional Encoder Representations from Transformers) classifier, which excels in understanding the subtleties and nuances of human language.

The classification process begins with the "one-vs-rest" models, where each model is trained to distinguish one category from all others. This approach allows the system to handle multiple categories simultaneously and ensures that each category is given focused attention during the classification process. For each incoming email, the module calculates confidence scores for all potential categories. These scores represent the likelihood that the email belongs to a particular category. The module then applies predefined thresholds to these scores to determine the final classification. If the confidence score for a category exceeds the threshold, the email is classified under that category. This method ensures high precision and reduces the chances of misclassification.

In addition to the primary categories, the classification module also handles subcategories, particularly for "Document Related" inquiries. The multi-class model used for subcategories is designed to provide granular classification within a specific domain. This model calculates confidence scores for each subcategory and applies specific thresholds to determine the most appropriate subcategory. By using specialized models for subcategories, the system can achieve higher accuracy and relevance in its responses.

One of the standout features of the classification module is its ability to provide multiple levels of classification. The module can generate Top 1 and Top 3 predictions, indicating the most likely category and the top three potential categories, respectively. This multi-tiered approach allows the system to handle uncertainty and ambiguity in customer inquiries more effectively. If the confidence scores do not meet the thresholds for any category, the email is marked as unclassified, ensuring that only high-confidence classifications are used for generating responses.

The classification module is also equipped with advanced capabilities to handle overlapping categories and class imbalances. Overlapping categories can pose significant challenges, as emails may contain content that fits multiple categories. The module uses sophisticated algorithms to differentiate between closely related categories and ensure accurate classification. For class imbalances, where some categories may have significantly more training data than others, the module employs techniques such as data augmentation and re-sampling to balance the training process and improve overall accuracy.

Another novel feature of the classification module is its ability to identify multiple intents within a single email. Customer inquiries can often span multiple topics or require multiple actions. The module uses advanced NLP techniques to detect and separate these intents, ensuring that each aspect of the customer's query is addressed appropriately. This multi-intent detection capability enhances the system's ability to provide comprehensive and relevant responses.

Once the classification is complete, the module collaborates with the response generation module to craft a suitable reply. The classified category or subcategory determines the response template, which is then customized with relevant information extracted from the email. This ensures that the generated response is coherent, contextually appropriate, and addresses the customer's needs effectively.

In summary, the classification module is a critical component that underpins the functionality of the automated email response system. Its advanced machine learning models, multi-tiered classification approach, and capabilities to handle overlapping categories and multiple intents ensure high accuracy and relevance in email classification. This, in turn, enables the system to generate appropriate and effective responses, enhancing customer support operations and improving customer satisfaction.

(d) Response Generation Module: This module generates appropriate responses to the emails based on the classified category or subcategory. It uses predefined templates and dynamic content generation techniques to create personalized and relevant responses.

In the present embodiment the response generation module is a sophisticated and essential component that transforms the classified email data into coherent, contextually appropriate, and personalized responses. This module ensures that the automated email response system can effectively address customer inquiries, thereby enhancing customer support efficiency and satisfaction.

Once the classification module has determined the category and subcategory of an incoming email, the response generation module takes over to craft a suitable reply. The process begins with selecting the appropriate response template based on the classified category or subcategory. These templates are pre-defined and tailored to address the specific types of inquiries that fall under each category. By using templates, the system ensures consistency and standardization in its responses, which is crucial for maintaining a professional and reliable customer support experience.

The response generation module is designed to be highly flexible and adaptive. It can customize the selected template with relevant information extracted from the email. This customization involves inserting specific details such as the customer's name, account information, and any other pertinent data that can be gleaned from the email content. The module uses advanced Natural Language Processing (NLP) techniques to identify and extract these details accurately, ensuring that the response is personalized and directly addresses the customer's query.

One of the key features of the response generation module is its ability to handle multiple intents within a single email. Customer inquiries can often be complex and span multiple topics. The module leverages the multi-intent detection capabilities of the classification module to identify and separate these intents. It then generates a comprehensive response that addresses each identified intent, ensuring that the customer receives a complete and satisfactory reply. This multi-intent handling capability is crucial for providing effective customer support and reducing the need for follow-up interactions.

The response generation module also incorporates mechanisms for ensuring the quality and accuracy of the generated responses. It applies various checks and validations to ensure that the response is grammatically correct, contextually appropriate, and free of errors. This includes verifying the accuracy of the inserted details, checking for any inconsistencies, and ensuring that the response aligns with the overall tone and style of the customer support communications.

In addition to generating standard responses, the module is capable of producing personalized responses based on the customer's historical interactions. By accessing the customer's interaction history, the module can tailor the response to reflect previous communications, preferences, and any ongoing issues. This personalized approach enhances the customer experience by making the interactions more relevant and contextually aware.

The response generation module also supports the inclusion of dynamic content in the responses. This can include links to relevant resources, attachments, and other supplementary information that can help address the customer's query more effectively. For example, if a customer is requesting a document, the module can include a link to download the document directly in the response. This dynamic content capability adds an extra layer of utility and convenience to the automated responses.

Once the response has been generated and validated, the module collaborates with the communication interface to deliver the response back to the customer. The response is formatted appropriately, including any necessary headers and footers, and sent from the designated email address or domain. The module ensures that the response is delivered in a timely and reliable manner, maintaining the efficiency and effectiveness of the customer support process.

Furthermore, the response generation module includes features for logging and tracking the generated responses. This allows for auditing, performance monitoring, and continuous improvement of the response generation process. By maintaining detailed logs of all generated responses, the system can analyze patterns, identify potential issues, and refine its processes to enhance accuracy and efficiency over time.

In summary, the response generation module is a critical component that enables the automated email response system to provide accurate, contextually appropriate, and personalized replies to customer inquiries. Its advanced NLP capabilities, multi-intent handling, and mechanisms for ensuring quality and accuracy make it an indispensable part of the system. By generating effective responses, the module enhances customer support operations, improves customer satisfaction, and reduces operational costs.

(e) Output Interface: The output interface provides the generated responses to the customers. It ensures that the responses are delivered through the appropriate communication channels, such as email or CRM systems.

In the present invention the output interface in the context of the present invention disclosure is a crucial component that ensures the seamless delivery of generated responses to customers. This interface acts as the final touchpoint in the automated email response system, bridging the gap between the system's internal processes and the end-user experience. Its primary function is to format, transmit, and manage the responses generated by the response generation module, ensuring they reach the intended recipients accurately and efficiently.

Once the response generation module has crafted a suitable reply, the output interface takes over to handle the delivery of this response. The process begins with formatting the response email. This involves incorporating any necessary headers, footers, and other email elements to ensure that the response is professional and consistent with the organization's branding and communication standards. The output interface ensures that the email is visually appealing and easy to read, which is essential for maintaining a positive customer experience.

The output interface is designed to support various email formats and protocols, ensuring compatibility with different email clients and systems. This includes handling HTML and plain text formats, as well as ensuring that any embedded media, links, or attachments are correctly included and functional. By supporting multiple formats, the output interface ensures that the response is accessible and correctly rendered regardless of the customer's email client or device.

In addition to formatting, the output interface is responsible for managing the transmission of the response email. This involves interfacing with the organization's email server or third-party email service providers to send the email to the customer's address. The output interface ensures that the email is sent from the appropriate email address or domain, maintaining consistency and professionalism in customer communications. It also handles any necessary authentication and security protocols, such as SPF, DKIM, and DMARC, to ensure that the email is delivered successfully and is not marked as spam.

The output interface also includes mechanisms for tracking and logging the delivery of response emails. This involves recording details such as the timestamp of the sent email, the recipient's email address, and the status of the delivery (e.g., sent, delivered, bounced). These logs are essential for auditing purposes, performance monitoring, and troubleshooting any issues that may arise during the email delivery process. By maintaining detailed logs, the system can analyze delivery patterns, identify potential issues, and refine its processes to enhance reliability and efficiency over time.

Another key feature of the output interface is its ability to handle response management and follow-up actions. For instance, if an email bounces or fails to deliver, the output interface can trigger alerts or initiate retry mechanisms to ensure that the response reaches the customer. It can also manage automated follow-up emails based on predefined rules or customer interactions, ensuring that ongoing customer inquiries are addressed promptly and effectively.

The output interface is also designed to support integration with other communication channels and systems. This includes interfacing with customer relationship management (CRM) systems, helpdesk software, and other customer support tools to ensure that all customer interactions are logged and managed consistently. By integrating with these systems, the output interface ensures that the generated responses are part of a cohesive and comprehensive customer support strategy.

Furthermore, the output interface includes features for personalizing the response delivery. This can involve customizing the sender's name, email address, and other elements based on the customer's preferences or historical interactions. By personalizing the delivery, the output interface enhances the customer experience and ensures that the communication feels relevant and tailored to the individual customer.

In summary, the output interface is a vital component that ensures the effective delivery of generated responses to customers. Its capabilities in formatting, transmitting, tracking, and managing response emails are essential for maintaining the efficiency and reliability of the automated email response system. By ensuring that responses are delivered accurately, professionally, and in a timely manner, the output interface enhances customer support operations, improves customer satisfaction, and contributes to the overall success of the system.

System Features:
• Training on Historical Data: The NLP algorithms are trained on large datasets of historical email interactions to learn patterns and common queries, enhancing their accuracy and reliability.
• Top 1 and Top 3 Prediction Modules: The classification module includes a Top 1 prediction module for predicting the class with high precision and a Top 3 prediction module for identifying the top three possible classes with higher precision.
• Multiple Intent Identification: The processing unit is configured to identify with high precision (>90%) whether a customer has raised multiple intents in the same email, ensuring comprehensive responses.
• Accuracy Metrics: The system provides auto-generated email responses with an overall accuracy of 78% for the top 16 classes contributing to 80% of incoming emails and an accuracy of 87% for document-related inquiries.

Embodiment 2: Method for Automated Email Response Generation
This embodiment describes a method for enhancing customer support efficiency through the automated processing and response generation of customer emails using NLP techniques. The method comprises the following steps:

(a) Receiving Incoming Emails: The method begins with receiving an incoming email from a customer through a communication interface.

The process of receiving incoming emails is a foundational aspect of the present invention disclosure, as it sets the stage for the subsequent analysis, classification, and response generation. The communication interface is the primary component responsible for this task, ensuring that emails from customers are captured accurately and efficiently, thereby enabling the automated email response system to function seamlessly.

When a customer sends an email to the contact center, the communication interface is the first point of interaction. This interface is designed to handle a high volume of incoming emails, ensuring that each email is received without delay. The interface is integrated with the organization's email server or a third-party email service provider, allowing it to monitor and capture emails in real-time. This integration ensures that the system can promptly process each email as it arrives.

Upon receiving an email, the communication interface captures all relevant data associated with the email. This includes the email's textual content, metadata such as the sender's email address, the subject line, the timestamp, and any attachments or embedded media. Capturing this comprehensive dataset is crucial for the subsequent stages of processing and analysis, as it provides the necessary context and information for understanding the customer's inquiry.

The communication interface then performs a series of pre-processing steps to prepare the email data for analysis by the processing unit. These steps include stripping out unnecessary HTML tags, normalizing the text to a consistent format, and handling any attachments or embedded media. For instance, if the email contains images or documents, the interface extracts and stores these attachments separately while maintaining a reference to them within the email content. This pre-processing ensures that the email data is clean, structured, and ready for further analysis.

One of the challenges in receiving incoming emails is dealing with various email formats and encodings. Customers may use different email clients and devices, resulting in emails with diverse formats and encodings. The communication interface is equipped with robust parsing capabilities to handle these variations. It can decode different character sets, handle multi-part MIME messages, and process emails in both HTML and plain text formats. This versatility ensures that the system can accurately capture and process emails regardless of their format or origin.

In addition to handling the email content, the communication interface also manages the security and authentication aspects of receiving emails. This includes verifying the sender's identity, checking for potential spam or phishing attempts, and ensuring that the email complies with security protocols such as SPF, DKIM, and DMARC. By implementing these security measures, the interface protects the system from malicious emails and ensures that only legitimate customer inquiries are processed.

Once the email data has been captured and pre-processed, the communication interface forwards it to the processing unit for further analysis. This involves passing the structured email content, metadata, and any extracted attachments to the processing unit, which then begins the task of understanding the email's intent and context. The seamless handoff between the communication interface and the processing unit is crucial for maintaining the efficiency and accuracy of the automated email response system.

Furthermore, the communication interface includes features for logging and tracking incoming emails. This involves recording details such as the timestamp of receipt, the sender's email address, and the status of the email (e.g., processed, pending, or flagged for review). These logs are essential for auditing purposes, performance monitoring, and troubleshooting any issues that may arise during the email reception process. By maintaining detailed logs, the system can analyze patterns, identify potential bottlenecks, and refine its processes to enhance reliability and efficiency over time.

In summary, the process of receiving incoming emails is a critical component of the automated email response system. The communication interface plays a vital role in capturing, pre-processing, and managing email data, ensuring that each customer inquiry is accurately and efficiently processed. Its capabilities in handling various email formats, ensuring security, and maintaining detailed logs contribute to the overall effectiveness and reliability of the system. By facilitating the seamless reception of incoming emails, the communication interface sets the foundation for the subsequent stages of analysis, classification, and response generation, ultimately enhancing customer support operations and improving customer satisfaction.

(b) Analyzing Email Content: The content of the incoming email is analyzed by a processing unit using NLP algorithms to understand the intent and context of the inquiry. The analysis involves parsing the email text and extracting relevant features.

Analyzing email content is a critical step in the automated email response system described in the present invention disclosure. This process is primarily handled by the processing unit, which employs advanced Natural Language Processing (NLP) algorithms to understand the intent and context of customer inquiries. The accuracy and effectiveness of the entire system hinge on the thorough and precise analysis of the email content, making this step indispensable for generating appropriate and relevant responses.

Once the communication interface has received and pre-processed the incoming email, the structured email data is forwarded to the processing unit. The first task of the processing unit is to parse the email content. This involves breaking down the email text into its constituent elements, such as sentences, phrases, and words. The unit employs NLP techniques like tokenization, which splits the text into individual tokens (words or phrases), and part-of-speech tagging, which identifies the grammatical roles of these tokens. This initial parsing is essential for understanding the basic structure and components of the email.

Following the parsing, the processing unit applies named entity recognition (NER) to identify and classify key entities within the email. Entities can include names, dates, locations, monetary amounts, and other specific information relevant to the customer's inquiry. For example, if a customer asks for a "loan repayment schedule," the NER algorithm will recognize "loan" and "repayment schedule" as key entities. This step helps in extracting critical information that can be used to tailor the response more accurately.

Another important aspect of analyzing email content is understanding the context and sentiment of the email. The processing unit uses sentiment analysis algorithms to determine the emotional tone of the email, such as whether the customer is satisfied, frustrated, or neutral. This information can be invaluable for generating a response that is not only contextually appropriate but also empathetic to the customer's emotional state. For instance, a frustrated customer may require a more reassuring and detailed response compared to a satisfied customer.

The core of the email content analysis lies in intent recognition. The processing unit employs sophisticated machine learning models, particularly those based on the BERT (Bidirectional Encoder Representations from Transformers) architecture, to understand the underlying intent of the email. These models are trained on large datasets of historical email interactions, enabling them to recognize patterns and common queries. The unit uses these models to predict the intent of the email, such as whether the customer is asking for account information, requesting a document, or seeking technical support.

One of the novel features of the processing unit is its ability to handle multiple intents within a single email. Customer inquiries can often be multifaceted, covering several topics or requiring multiple actions. The unit uses advanced NLP techniques to detect and separate these multiple intents, ensuring that each aspect of the customer's query is addressed. For example, if a customer asks for both a "statement of account" and a "loan repayment schedule" in the same email, the unit will identify both intents and ensure that the response covers both requests.

In addition to intent recognition, the processing unit also deals with classifying the email into predefined categories and subcategories. This classification is crucial for routing the email to the appropriate response template and ensuring that the generated response is relevant. The unit uses a combination of "one-vs-rest" models for the main categories and a multi-class model for subcategories, particularly those related to "Document Related" inquiries. These models calculate confidence scores for each potential category and subcategory, and the unit applies predefined thresholds to determine the final classification.

Handling class imbalances and overlapping categories is another challenge that the processing unit addresses. Class imbalances occur when some categories have significantly more training data than others, which can skew the model's predictions. The unit employs techniques such as data augmentation and re-sampling to balance the training process. For overlapping categories, where an email may fit into multiple categories, the unit uses specialized algorithms to differentiate between closely related categories and ensure accurate classification.

Once the email content has been thoroughly analyzed, the processing unit collaborates with the response generation module to craft a suitable reply. The insights gained from the analysis, including identified entities, detected intents, and classified categories, are used to customize the response template. This ensures that the generated response is not only accurate and relevant but also personalized to address the customer's specific needs.

In summary, analyzing email content is a complex and multi-faceted process that lies at the heart of the automated email response system. The processing unit's advanced NLP capabilities, including parsing, named entity recognition, sentiment analysis, intent recognition, and classification, ensure that each customer inquiry is understood in depth. This thorough analysis enables the system to generate accurate, relevant, and personalized responses, thereby enhancing customer support operations and improving customer satisfaction.

(c) Classifying the Email: The email is classified into one of several categories and subcategories based on the analyzed content. The classification module uses "1 vs Rest" models for primary categories and a multi-class model for sub-categories, applying specific confidence score thresholds to determine the most accurate classification.

Classifying the email is a pivotal step in the automated email response system described in the present invention disclosure. This process is managed by the classification module, which is designed to categorize incoming emails into predefined categories and subcategories based on their content. Accurate classification is essential for generating appropriate and contextually relevant responses, thereby enhancing the efficiency and effectiveness of customer support operations.

Once the processing unit has analyzed the email content and extracted relevant information, the classification module takes over to determine the most suitable category and subcategory for the email. The classification process begins with the application of machine learning models specifically tailored for this task. The system employs a combination of "one-vs-rest" classifiers for the primary categories and a multi-class model for subcategories, particularly those related to "Document Related" inquiries. These models are built using the BERT (Bidirectional Encoder Representations from Transformers) classifier, which excels in understanding the nuances of human language.

The "one-vs-rest" classifiers are designed to handle multiple categories simultaneously. Each model is trained to distinguish one category from all others, allowing the system to focus on the unique characteristics of each category. For each incoming email, the classification module calculates confidence scores for all potential categories. These scores represent the likelihood that the email belongs to a particular category. The module then applies predefined thresholds to these scores to determine the final classification. If the confidence score for a category exceeds the threshold, the email is classified under that category. This method ensures high precision and reduces the chances of misclassification.

In addition to the primary categories, the classification module also handles subcategories, particularly for "Document Related" inquiries. The multi-class model used for subcategories is designed to provide granular classification within a specific domain. This model calculates confidence scores for each subcategory and applies specific thresholds to determine the most appropriate subcategory. By using specialized models for subcategories, the system can achieve higher accuracy and relevance in its responses.

One of the standout features of the classification module is its ability to provide multiple levels of classification. The module can generate Top 1 and Top 3 predictions, indicating the most likely category and the top three potential categories, respectively. This multi-tiered approach allows the system to handle uncertainty and ambiguity in customer inquiries more effectively. For instance, if the confidence scores for the top three categories are close, the system can present these options to ensure that the response covers all possible intents. If the confidence scores do not meet the thresholds for any category, the email is marked as unclassified, ensuring that only high-confidence classifications are used for generating responses.

The classification module is also equipped with advanced capabilities to handle overlapping categories and class imbalances. Overlapping categories can pose significant challenges, as emails may contain content that fits multiple categories. The module uses sophisticated algorithms to differentiate between closely related categories and ensure accurate classification. For class imbalances, where some categories may have significantly more training data than others, the module employs techniques such as data augmentation and re-sampling to balance the training process and improve overall accuracy.

Another novel feature of the classification module is its ability to identify multiple intents within a single email. Customer inquiries can often span multiple topics or require multiple actions. The module uses advanced NLP techniques to detect and separate these multiple intents, ensuring that each aspect of the customer's query is addressed. For example, if a customer asks for both a "statement of account" and a "loan repayment schedule" in the same email, the module will identify both intents and ensure that the response covers both requests. This multi-intent detection capability enhances the system's ability to provide comprehensive and relevant responses.

Once the classification is complete, the module collaborates with the response generation module to craft a suitable reply. The classified category or subcategory determines the response template, which is then customized with relevant information extracted from the email. This ensures that the generated response is coherent, contextually appropriate, and addresses the customer's needs effectively.

The classification module also includes features for logging and tracking the classification process. This involves recording details such as the confidence scores, the final classification, and any instances where the email was marked as unclassified. These logs are essential for auditing purposes, performance monitoring, and continuous improvement of the classification process. By maintaining detailed logs, the system can analyze patterns, identify potential issues, and refine its models to enhance accuracy and efficiency over time.

In summary, classifying the email is a critical component of the automated email response system. The classification module's advanced machine learning models, multi-tiered classification approach, and capabilities to handle overlapping categories and multiple intents ensure high accuracy and relevance in email classification. This, in turn, enables the system to generate appropriate and effective responses, enhancing customer support operations and improving customer satisfaction.

(d) Generating an Appropriate Response: Based on the classified category or subcategory, a response generation module creates an appropriate response to the email. The response is generated using predefined templates and dynamic content generation techniques, ensuring relevance and personalization.

Generating an appropriate response is a crucial function of the automated email response system described in the present invention disclosure. This task is primarily handled by the response generation module, which leverages the insights gained from the email analysis and classification processes to craft replies that are accurate, contextually relevant, and personalized. The effectiveness of the entire system hinges on the ability of this module to produce responses that meet the needs and expectations of the customers, thereby enhancing customer support efficiency and satisfaction.

Once the classification module has determined the category and subcategory of an incoming email, the response generation module takes over to create a suitable reply. The process begins with selecting the appropriate response template based on the classified category or subcategory. These templates are pre-defined and tailored to address the specific types of inquiries that fall under each category. By using templates, the system ensures consistency and standardization in its responses, which is crucial for maintaining a professional and reliable customer support experience.

The response generation module is designed to be highly flexible and adaptive. It customizes the selected template with relevant information extracted from the email. This customization involves inserting specific details such as the customer's name, account information, and any other pertinent data that can be gleaned from the email content. The module uses advanced Natural Language Processing (NLP) techniques to identify and extract these details accurately, ensuring that the response is personalized and directly addresses the customer's query.

One of the key features of the response generation module is its ability to handle multiple intents within a single email. Customer inquiries can often be complex and span multiple topics. The module leverages the multi-intent detection capabilities of the classification module to identify and separate these intents. It then generates a comprehensive response that addresses each identified intent, ensuring that the customer receives a complete and satisfactory reply. This multi-intent handling capability is crucial for providing effective customer support and reducing the need for follow-up interactions.

The response generation module also incorporates mechanisms for ensuring the quality and accuracy of the generated responses. It applies various checks and validations to ensure that the response is grammatically correct, contextually appropriate, and free of errors. This includes verifying the accuracy of the inserted details, checking for any inconsistencies, and ensuring that the response aligns with the overall tone and style of the customer support communications. These quality assurance measures are essential for maintaining the credibility and reliability of the automated response system.

In addition to generating standard responses, the module is capable of producing personalized responses based on the customer's historical interactions. By accessing the customer's interaction history, the module can tailor the response to reflect previous communications, preferences, and any ongoing issues. This personalized approach enhances the customer experience by making the interactions more relevant and contextually aware. For example, if a customer has previously inquired about a specific issue, the response can reference this history and provide an update or additional information.

The response generation module also supports the inclusion of dynamic content in the responses. This can include links to relevant resources, attachments, and other supplementary information that can help address the customer's query more effectively. For instance, if a customer is requesting a document, the module can include a link to download the document directly in the response. This dynamic content capability adds an extra layer of utility and convenience to the automated responses, enhancing their effectiveness and relevance.

Once the response has been generated and validated, the module collaborates with the communication interface to deliver the response back to the customer. The response is formatted appropriately, including any necessary headers and footers, and sent from the designated email address or domain. The module ensures that the response is delivered in a timely and reliable manner, maintaining the efficiency and effectiveness of the customer support process.

Furthermore, the response generation module includes features for logging and tracking the generated responses. This allows for auditing, performance monitoring, and continuous improvement of the response generation process. By maintaining detailed logs of all generated responses, the system can analyze patterns, identify potential issues, and refine its processes to enhance accuracy and efficiency over time. These logs can also be used for training and updating the machine learning models, ensuring that the system continues to improve and adapt to changing customer needs and behaviors.

In summary, generating an appropriate response is a critical function of the automated email response system. The response generation module's advanced NLP capabilities, multi-intent handling, and mechanisms for ensuring quality and accuracy make it an indispensable part of the system. By producing accurate, relevant, and personalized responses, the module enhances customer support operations, improves customer satisfaction, and reduces operational costs. This, in turn, contributes to the overall success and effectiveness of the automated email response system.

(e) Providing the Generated Response: The generated response is provided to the customer through the communication interface. The response is delivered via the appropriate communication channel, such as email or CRM systems.

In the embodiment of the present invention providing the generated response is the final and crucial step in the automated email response system described in the present invention disclosure. This task is managed by the output interface, which ensures that the crafted responses are delivered to customers accurately, efficiently, and in a timely manner. The effectiveness of the entire system hinges on the seamless execution of this step, as it directly impacts customer satisfaction and the overall user experience.

Once the response generation module has crafted a suitable reply, the output interface takes over to handle the delivery of this response. The process begins with formatting the response email. This involves incorporating any necessary headers, footers, and other email elements to ensure that the response is professional and consistent with the organization's branding and communication standards. The output interface ensures that the email is visually appealing and easy to read, which is essential for maintaining a positive customer experience.

The output interface is designed to support various email formats and protocols, ensuring compatibility with different email clients and systems. This includes handling HTML and plain text formats, as well as ensuring that any embedded media, links, or attachments are correctly included and functional. By supporting multiple formats, the output interface ensures that the response is accessible and correctly rendered regardless of the customer's email client or device.

In addition to formatting, the output interface is responsible for managing the transmission of the response email. This involves interfacing with the organization's email server or third-party email service providers to send the email to the customer's address. The output interface ensures that the email is sent from the appropriate email address or domain, maintaining consistency and professionalism in customer communications. It also handles any necessary authentication and security protocols, such as SPF, DKIM, and DMARC, to ensure that the email is delivered successfully and is not marked as spam.

The output interface also includes mechanisms for tracking and logging the delivery of response emails. This involves recording details such as the timestamp of the sent email, the recipient's email address, and the status of the delivery (e.g., sent, delivered, bounced). These logs are essential for auditing purposes, performance monitoring, and troubleshooting any issues that may arise during the email delivery process. By maintaining detailed logs, the system can analyze delivery patterns, identify potential issues, and refine its processes to enhance reliability and efficiency over time.

Another key feature of the output interface is its ability to handle response management and follow-up actions. For instance, if an email bounces or fails to deliver, the output interface can trigger alerts or initiate retry mechanisms to ensure that the response reaches the customer. It can also manage automated follow-up emails based on predefined rules or customer interactions, ensuring that ongoing customer inquiries are addressed promptly and effectively. This capability is crucial for maintaining high levels of customer satisfaction and ensuring that no customer query goes unresolved.

The output interface is also designed to support integration with other communication channels and systems. This includes interfacing with customer relationship management (CRM) systems, helpdesk software, and other customer support tools to ensure that all customer interactions are logged and managed consistently. By integrating with these systems, the output interface ensures that the generated responses are part of a cohesive and comprehensive customer support strategy. This integration allows for a unified view of customer interactions, enabling support agents to provide more informed and personalized assistance when needed.

Furthermore, the output interface includes features for personalizing the response delivery. This can involve customizing the sender's name, email address, and other elements based on the customer's preferences or historical interactions. By personalizing the delivery, the output interface enhances the customer experience and ensures that the communication feels relevant and tailored to the individual customer. For example, if a customer has previously interacted with a specific support agent, the response can be sent from that agent's email address to maintain continuity and build rapport.

In addition to email delivery, the output interface can be extended to support other communication channels, such as SMS, chat, and social media. This multi-channel capability ensures that customers can receive responses through their preferred communication medium, further enhancing the convenience and accessibility of the automated response system. By supporting multiple channels, the output interface enables a more flexible and responsive customer support experience.

In summary, providing the generated response is a critical function of the automated email response system. The output interface's capabilities in formatting, transmitting, tracking, and managing response emails are essential for maintaining the efficiency and reliability of the system. By ensuring that responses are delivered accurately, professionally, and in a timely manner, the output interface enhances customer support operations, improves customer satisfaction, and contributes to the overall success of the system. Its ability to integrate with other systems, handle follow-up actions, and support multiple communication channels further strengthens its role in delivering a comprehensive and effective customer support solution.

Method Steps:
• Training NLP Algorithms: The NLP algorithms are trained on large datasets of historical email interactions to learn patterns and common queries, enhancing their accuracy and reliability.
• Top 1 and Top 3 Predictions: The classification step includes predicting the class with high precision as Top 1 and identifying the top three possible classes with higher precision as Top 3. If the model is unable to classify the email amongst any of the intents, it is identified as unclassified.
• Multiple Intent Identification: The method includes identifying with high precision (>90%) whether a customer has raised multiple intents in the same email, ensuring comprehensive responses.
• Accuracy Metrics: The method aims to provide auto-generated email responses with an overall accuracy of 78% for the top 16 classes contributing to 80% of incoming emails and an accuracy of 87% for document-related inquiries.
The user equipment in the context of the system and method described in the present invention disclosure refers to the hardware and software components that enable the execution and interaction with the automated email response system. This equipment is essential for both the deployment and operation of the system, ensuring that it functions efficiently and effectively to enhance customer support operations.

The user equipment typically comprises a combination of a memory, a processor, and a computer program that is stored in the memory and executed by the processor. These components work in tandem to implement the various functionalities of the automated email response system, from receiving and analyzing emails to generating and delivering responses.

The memory component of the user equipment is responsible for storing the computer program, as well as any data required for the system's operation. This includes the machine learning models used for email classification, the response templates, and any historical data that may be used for personalizing responses. The memory ensures that all necessary information is readily accessible to the processor, enabling efficient execution of the system's tasks.

The processor is the central unit that executes the computer program, performing the various computational tasks required by the system. This includes running the Natural Language Processing (NLP) algorithms to analyze email content, executing the machine learning models for classification, and generating the appropriate responses. The processor's performance is critical for the system's overall efficiency, as it determines how quickly and accurately the system can process incoming emails and generate responses.

The computer program stored in the memory and executed by the processor is the core software component of the user equipment. This program encompasses all the algorithms, models, and logic required to implement the system's functionalities. It includes the NLP algorithms for parsing and understanding email content, the machine learning models for classifying emails into categories and subcategories, and the response generation logic for crafting appropriate replies. The program is designed to be modular and scalable, allowing for easy updates and enhancements as new features and improvements are developed.

In addition to the core components, the user equipment may also include various peripheral devices and interfaces that facilitate interaction with the system. This can include input devices such as keyboards and mice for manual data entry and configuration, as well as output devices such as monitors and printers for displaying and printing system outputs. Network interfaces are also crucial, as they enable the user equipment to communicate with other components of the system, such as the email server, CRM systems, and third-party service providers.

The user equipment is designed to be flexible and adaptable, supporting various deployment scenarios. It can be deployed on-premises within an organization's data center, or it can be hosted in the cloud, leveraging cloud computing resources for scalability and reliability. This flexibility ensures that the system can be tailored to meet the specific needs and constraints of different organizations, whether they require a high degree of control and customization or prefer the convenience and scalability of a cloud-based solution.

One of the key features of the user equipment is its ability to integrate with other systems and platforms. This includes integration with email servers for receiving and sending emails, CRM systems for accessing customer data and interaction history, and helpdesk software for managing customer support tickets. By integrating with these systems, the user equipment ensures that the automated email response system operates as part of a cohesive and comprehensive customer support strategy. This integration allows for a unified view of customer interactions, enabling support agents to provide more informed and personalized assistance when needed.

The user equipment also includes features for monitoring and managing the system's performance. This can involve logging and tracking various metrics, such as the number of emails processed, the accuracy of classifications, and the response times. These metrics are essential for auditing purposes, performance monitoring, and continuous improvement of the system. By maintaining detailed logs and performance data, the user equipment enables administrators to analyze patterns, identify potential issues, and refine the system's processes to enhance accuracy and efficiency over time.

In summary, the user equipment in the context of the system and method described in the present invention disclosure is a comprehensive and essential component that enables the execution and interaction with the automated email response system. Its combination of memory, processor, and computer program, along with various peripheral devices and interfaces, ensures that the system functions efficiently and effectively. By supporting various deployment scenarios, integrating with other systems, and providing monitoring and management capabilities, the user equipment enhances customer support operations, improves customer satisfaction, and contributes to the overall success of the automated email response system.

The computer-readable storage medium in the context of the present invention disclosure is a critical component that stores the instructions and data necessary for the automated email response system to function. This medium ensures that the system's software, including its algorithms, models, and logic, is readily accessible for execution by the user equipment's processor. The storage medium plays a vital role in maintaining the integrity, performance, and reliability of the system.

The computer-readable storage medium can take various forms, including but not limited to, hard disk drives (HDDs), solid-state drives (SSDs), optical discs, flash memory, and cloud-based storage solutions. Regardless of the form, the primary function of the storage medium is to provide a persistent and reliable repository for the system's software and data.

One of the key elements stored on the computer-readable storage medium is the computer program that implements the automated email response system. This program encompasses all the necessary algorithms, models, and logic required to perform the system's functions. It includes Natural Language Processing (NLP) algorithms for parsing and understanding email content, machine learning models for classifying emails into categories and subcategories, and response generation logic for crafting appropriate replies. The program is designed to be modular and scalable, allowing for easy updates and enhancements as new features and improvements are developed.

In addition to the core software, the computer-readable storage medium also stores various datasets that are essential for the system's operation. This includes training datasets used to develop and refine the machine learning models, historical email interactions that provide context for personalizing responses, and response templates that serve as the basis for generating replies. By storing these datasets, the storage medium ensures that the system has access to the information it needs to function accurately and efficiently.

The computer-readable storage medium also plays a crucial role in maintaining the system's performance and reliability. It provides a stable and persistent repository for the software and data, ensuring that they are not lost or corrupted over time. This is particularly important for machine learning models, which require consistent and reliable access to training data and historical interactions to maintain their accuracy and effectiveness. The storage medium's reliability is further enhanced by implementing redundancy and backup mechanisms, which protect against data loss and ensure that the system can recover quickly in the event of a failure.

Another important aspect of the computer-readable storage medium is its role in enabling the system's scalability. As the volume of incoming emails and the complexity of customer inquiries increase, the system may require additional storage capacity to accommodate larger datasets and more sophisticated models. The storage medium can be easily expanded or upgraded to meet these demands, ensuring that the system can scale seamlessly to handle growing workloads. This scalability is particularly important for cloud-based storage solutions, which offer virtually unlimited storage capacity and can be scaled up or down as needed.

The computer-readable storage medium also supports the system's security and compliance requirements. It includes mechanisms for encrypting sensitive data, such as customer information and email content, to protect against unauthorized access and ensure data privacy. Additionally, the storage medium can be configured to comply with various regulatory requirements, such as GDPR and HIPAA, by implementing data retention policies, access controls, and audit logs. These security and compliance features are essential for maintaining the trust and confidence of customers and stakeholders.

Furthermore, the computer-readable storage medium facilitates the system's integration with other platforms and services. By storing configuration files, API keys, and other integration-related data, the storage medium ensures that the system can seamlessly connect with email servers, CRM systems, helpdesk software, and third-party service providers. This integration capability is crucial for creating a cohesive and comprehensive customer support strategy, enabling the system to operate as part of a unified ecosystem.

In summary, the computer-readable storage medium in the context of the present invention disclosure is a vital component that stores the instructions and data necessary for the automated email response system to function. Its role in maintaining the integrity, performance, and reliability of the system, along with its support for scalability, security, and integration, ensures that the system can operate efficiently and effectively. By providing a stable and persistent repository for the system's software and data, the computer-readable storage medium enhances customer support operations, improves customer satisfaction, and contributes to the overall success of the automated email response system.

The computer program product in the context of the present invention disclosure is a comprehensive package that includes the software and instructions necessary to implement the automated email response system. This product is designed to be deployed on user equipment, enabling the system to perform its various functions, from receiving and analyzing emails to generating and delivering appropriate responses. The computer program product is essential for ensuring that the system operates efficiently, accurately, and reliably, thereby enhancing customer support operations and improving customer satisfaction.

The computer program product typically comprises a computer-readable storage medium that contains the software and instructions required for the system's operation. This storage medium can take various forms, including hard disk drives (HDDs), solid-state drives (SSDs), optical discs, flash memory, and cloud-based storage solutions. Regardless of the form, the primary function of the storage medium is to provide a persistent and reliable repository for the system's software and data.

At the core of the computer program product is the software that implements the automated email response system. This software encompasses all the necessary algorithms, models, and logic required to perform the system's functions. It includes Natural Language Processing (NLP) algorithms for parsing and understanding email content, machine learning models for classifying emails into categories and subcategories, and response generation logic for crafting appropriate replies. The software is designed to be modular and scalable, allowing for easy updates and enhancements as new features and improvements are developed.

The NLP algorithms included in the computer program product are responsible for analyzing the content of incoming emails. These algorithms perform tasks such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis. By breaking down the email text into its constituent elements and understanding the context and sentiment, the NLP algorithms enable the system to accurately interpret the customer's inquiry.

The machine learning models included in the computer program product are used for classifying emails into predefined categories and subcategories. The system employs a combination of "one-vs-rest" classifiers for the primary categories and a multi-class model for subcategories, particularly those related to "Document Related" inquiries. These models are built using the BERT (Bidirectional Encoder Representations from Transformers) classifier, which excels in understanding the nuances of human language. The models calculate confidence scores for each potential category and subcategory, and the system applies predefined thresholds to determine the final classification.

The response generation logic included in the computer program product is responsible for crafting suitable replies based on the classified category or subcategory. This logic selects the appropriate response template and customizes it with relevant information extracted from the email. The response generation logic ensures that the generated response is coherent, contextually appropriate, and addresses the customer's needs effectively. It also includes mechanisms for handling multiple intents within a single email, ensuring that each aspect of the customer's query is addressed.

In addition to the core software, the computer program product also includes various datasets that are essential for the system's operation. This includes training datasets used to develop and refine the machine learning models, historical email interactions that provide context for personalizing responses, and response templates that serve as the basis for generating replies. By storing these datasets, the computer program product ensures that the system has access to the information it needs to function accurately and efficiently.

The computer program product also includes features for monitoring and managing the system's performance. This involves logging and tracking various metrics, such as the number of emails processed, the accuracy of classifications, and the response times. These metrics are essential for auditing purposes, performance monitoring, and continuous improvement of the system. By maintaining detailed logs and performance data, the computer program product enables administrators to analyze patterns, identify potential issues, and refine the system's processes to enhance accuracy and efficiency over time.

Another important aspect of the computer program product is its support for integration with other systems and platforms. This includes integration with email servers for receiving and sending emails, CRM systems for accessing customer data and interaction history, and helpdesk software for managing customer support tickets. By integrating with these systems, the computer program product ensures that the automated email response system operates as part of a cohesive and comprehensive customer support strategy. This integration allows for a unified view of customer interactions, enabling support agents to provide more informed and personalized assistance when needed.

The computer program product also includes mechanisms for ensuring the security and compliance of the system. This involves implementing encryption for sensitive data, such as customer information and email content, to protect against unauthorized access and ensure data privacy. Additionally, the product can be configured to comply with various regulatory requirements, such as GDPR and HIPAA, by implementing data retention policies, access controls, and audit logs. These security and compliance features are essential for maintaining the trust and confidence of customers and stakeholders.

In summary, the computer program product in the context of the present invention disclosure is a comprehensive package that includes the software and instructions necessary to implement the automated email response system. Its combination of NLP algorithms, machine learning models, response generation logic, and various datasets ensures that the system operates efficiently, accurately, and reliably. By supporting integration with other systems, providing monitoring and management capabilities, and ensuring security and compliance, the computer program product enhances customer support operations, improves customer satisfaction, and contributes to the overall success of the automated email response system.

Best Mode of Working the Invention:
The best mode of working the invention involves its integration with the Salesforce CRM system. When a customer email is received, it is processed by the algorithm, which reads the content, classifies the email, and generates an automated response based on the classification results. This integration ensures seamless operation and immediate applicability in a Contact Center environment.

In summary, the present invention offers a robust and efficient solution for automated email response generation, leveraging advanced NLP techniques to deliver superior accuracy, consistency, and customer service outcomes. It addresses key challenges in the field and provides a scalable and deployable system that can be integrated into various customer service platforms.

Claims:We Claim

1. A method for enhancing customer support efficiency through auto-generated email processing and responses using Natural Language Processing (NLP), said method comprising steps of:
(a) receiving, by a communication interface, an incoming email from a customer;
(b) analysing, by a processing unit, the content of the incoming email using NLP algorithms to understand the intent and context of the inquiry;
(c) classifying, by a classification module, the email into one of plural categories and subcategories based on the analyzed content;
(d) generating, by a response generation module, an appropriate response to the email based on the classified category or subcategory;
(e) providing, by the communication interface, the generated response to the customer.

2. The method according to claim 1, wherein the NLP algorithms are trained on large datasets of historical email interactions to learn patterns and common queries.

3. The method according to claim 1, wherein the classification of the email by the classification module includes:
(a) predicting the class with high precision as Top 1;
(b) predicting the email could be classified into either of the top 3 classes with higher precision as Top 3; and
(c) identifying the email as unclassified if the model is unable to classify the email amongst any of the intents.

4. The method according to claim 1, wherein the NLP algorithms identify with high precision (>90%) whether a customer has raised multiple intents in the same email.

5. The method according to claim 1, wherein the generated response corresponds to all the emails received for a particular category.

6. The method according to claim 1, wherein the auto-generated email responses are provided with an overall accuracy of 78% for the top 16 classes contributing to 80% of incoming emails.

7. The method according to claim 1, wherein the auto-generated email responses are provided with an accuracy of 87% for document-related inquiries.

8. The method according to claim 1, wherein the classification module is configured to classify the email into one of 16 categories and 4 sub categories.

9. The method according to claim 8, wherein the number of categories is extendable to 37 or more.

10. The method according to claim 8, wherein the number of sub categories is extendable to 179 or more.

11. A system for enhancing customer support efficiency through auto-generated email processing and responses using Natural Language Processing (NLP), comprising:
(a) a communication interface for receiving incoming emails from customers;
(b) a processing unit configured to analyze the content of the incoming emails using NLP algorithms to understand the intent and context of the inquiries;
(c) a classification module configured to classify the emails into one of 37 categories and 179 subcategories based on the analyzed content;
(d) a response generation module configured to generate appropriate responses to the emails based on the classified category or subcategory;
(e) an output interface for providing the generated responses to the customers.

12. The system according to claim 11, wherein the NLP algorithms are trained on large datasets of historical email interactions to learn patterns and common queries.

13. The system according to claim 11, wherein the classification module includes:
(a) a Top 1 prediction module for predicting the class with high precision;
(b) a Top 3 prediction module for predicting the email could be classified into either of the top 3 classes with higher precision;
(c) an unclassified module for identifying the email as unclassified if the model is unable to classify the email amongst any of the intents.

14. The system according to claim 13, wherein in the Top 1 prediction module for predicting the class with high precision if the highest probability is >70% and is appearing in only 1 class, it is labelled “fully sure” of the predicted class.

15. The system according to claim 13, wherein in the Tope 3 if the highest probability is >70% and multiple categories are showing probability within the 10 basis points, it is labelled as the model is “confused” within 3 classes.

16. The system according to claim 11, wherein the processing unit is configured to identify with high precision (>90%) whether a customer has raised multiple intents in the same email.

17. The system according to claim 11, wherein the response generation module is configured to generate personalized responses based on the customer's inquiry and historical interactions.

18. The system according to claim 11, wherein the auto-generated email responses are provided with an overall accuracy of 78% for the top 16 classes contributing to 80% of incoming emails.

19. The system according to claim 11, wherein the auto-generated email responses are provided with an accuracy of 87% for document-related inquiries.

20. A user equipment, comprising a memory, a processor, and a computer program that is stored in the memory and that can be run on the processor, wherein the processor executes the program to implement the method as claimed in any one claims 1 to 10.

21. A computer-readable storage medium, wherein the computer-readable storage medium comprises instructions; and when the instructions are run on a computer adapted to natural language processing (NPL) environment, the computer is enabled to perform the method as claimed in any one of claims 1 to 10.

22. A computer program product comprising a computer-readable storage medium, wherein the computer-readable storage medium being loaded with instructions which when run on a computer adapted to natural language processing (NPL) environment, the computer is enabled to perform the method as claimed in any one of claims 1 to 10.

Dated this 09th day of August 2024