CA3148108A1 - System and method for automatically generating customised respose to an email - Google Patents

Abstract

A method and a system for automatically generating a draft response to an email communication received by a user on an electronic device are disclosed. A server is connected communicatively connected to a database and a frontend module in a communication network. The frontend module is configured to interact with the user to receive user inputs. A rule engine is provided to generate one or more rules based on at least one input from the user. An AI module (artificial intelligence-based module) is configured to identify one or more keywords from the email communication received by the user, to analyze context of the email. Subsequently, the draft response is generated and displayed to the user, based on the analysed context of the email and the one or more rules.

Description

DETAILED DESCRIPTION OF THE INVENTION
[1] This section is intended to provide explanation and description of various possible embodiments of the present invention. The embodiments used herein, and various features and advantageous details thereof are explained more fully with reference to non-limiting embodiments illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended only to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable the person skilled in the art to practice the embodiments used herein. Also, the examples/embodiments described herein should not be construed as limiting the scope of the embodiments herein. Corresponding reference numerals indicate corresponding parts throughout the drawings.

[2] The present invention discloses automated generation or creation of a draft response to an email communication received by a user on an electronic device. A rule engine is provided to generate one or more rules based on at least one input from the user. An AT
module (artificial intelligence-based module) is configured to identify one or more keywords from the email communication received by the user, to analyze context of the email.
Subsequently, the draft response is generated and displayed to the user, based on the analysed context of the email and the one or more rules.

[3] As used herein, 'processing unit' is an intelligent device or module, that is capable of processing digital logics and also possesses analytical skills for analyzing and processing various rental management related data or information, according to the embodiments of the present invention.

[4] As used herein, 'database' refers to a local or remote memory device;
docket systems;
storage units; each capable to store information including, data pertaining to email processing, user data, user email account data, user profiles, location data, predefined rules, categories and types of emails, emails received by a user, draft responses to be sent as email replies by the user, and other data and related information. In an embodiment, the storage unit may be a database server, a cloud storage, a remote database, a local database.

[5] As used herein, 'user device' or 'electronic device' is a smart electronic device capable of communicating with various other electronic devices and applications via one or more communication networks. Examples of said user device include, but not limited to, a wireless Date Recue/Date Received 2022-02-09 communication device, a smart phone, a tablet, a desktop, a laptop, etcetera.
The user device comprises: an input unit to receive one or more input data; an operating system to enable the user device to operate; a processing unit to process various data and information; a memory unit to store initial data, intermediary data and final data pertaining to claims data; and an output unit having a graphical user interface (GUI).

[6] As used herein, 'module' or 'unit' refers to a device, a system, a hardware, a computer application configured to execute specific functions or instructions according to the embodiments of the present invention. The module or unit may include a single device or multiple devices configured to perform specific functions according to the present invention disclosed herein.

[7] As used herein, 'communication network' includes a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a virtual private network (VPN), an enterprise private network (EPN), Internet, and a global area network (GAN).

[8] Terms such as 'connect', 'integrate', 'configure', and other similar terms include a physical connection, a wireless connection, a logical connection or a combination of such connections including electrical, optical, RF, infrared, Bluetooth, or other transmission media, and include configuration of software applications to execute computer program instructions, as specific to the presently disclosed embodiments, or as may be obvious to a person skilled in the art.

[9] Terms such as 'send', 'transfer', 'transmit', 'receive', 'collect', 'obtain', 'access' and other similar terms refers to transmission of data between various modules and units via wired or wireless connections across a communication network.

[10] Figure 1 illustrates architecture of a system 100 for automatically generating a draft response to an email communication received by a user on an electronic device, according to an exemplary embodiment of the present invention. The system 100 comprises a frontend module 102, a backend module 104, at least one electronic device 106 (user device 106), an API
(Application Programming Interface) module, a rule engine, an Al (artificial intelligence) module, a database 112 and a template generating module.

[11] The system also comprises a server that is communicatively connected to the database 112 and various other modules in a communication network for providing remote management of data. The frontend module 102 is connected in the network and configured to interact with Date Recue/Date Received 2022-02-09 the user for receiving at least one user input. The frontend module 102 is configured for the user to interact and add email accounts to be automated. The rule engine is a processing unit that is configured to generate one or more rules based on the at least one user input. The user inputs are the data entered by the users as per their requirements. The user enters or feeds the data/user input by using corresponding user device 106 or electronic device 106. The AT
module 118 is an artificial intelligence-based module with machine learning capabilities. The AT module 118 is configured to identify one or more keywords from the email communication received by the user, in order to analyze context of the email. Thereafter, based on the analysed context of the email and the generated one or more rules, the AT module 118 generates the draft response based for the user.

[12] According to the embodiments of the present subject matter, the one or more rules include, but is not limited to 'to ignore emails based on the sender'; to ignore emails based on the content'; 'forwarding emails using any specific content'; etc.
Additionally, templates being selected can also be included as user rules. Further, in an event if a sender of an email sends an inquiry, then rules may be set to respond with a given text by the user.

[13] According to an embodiment of the present disclosure, the AT module 118 reads content of the received email to identify the one or more keywords. Further, each rule of the one or more rules is applied to a corresponding template, wherein the corresponding template is being personalized based on the content of the received email. The AT module 118 categorizes the received email into one or more types to thereby generate the draft response for each type of category. The API (Application Programming Interface) module that sends relevant information to the AT module 118, the relevant information pertaining to the added email accounts.

[14] According to an embodiment of the present disclosure, the AT module 118 executes specific enhanced algorithm to provide customized responses to users who receive emails and wish to respond to the received emails automatically. The AT module 118 is associated with machine learning capabilities that facilitate in constantly learning new information to incorporate into the email communication for thereby generating personalized email content for each of the received emails which are to be delivered to each of the recipients. The AT module 118 analyses the received email and in order to prepare a reply, it analyses the context of the email. The reply as prepared or generated by the AT module 118 is customized according to the user requirements. For example, if an email is received by a user and a simple Date Recue/Date Received 2022-02-09 acknowledgement has to be sent to the sender, then the AT module 118 will generate an acknowledgement email wherein a template for the acknowledgement message will be used by the AT module 118. On the other hand, if the AT module 118 analyses that any specific information has to be added to the email content, it will search for the content from the database 112 and add the same in the email body to accordingly prepare a suitable response. The Al module 118 also takes as input a user's rules, which are set by the rule engine 116 and displayed to the users on their dashboard. The rules are used to reply to emails by reading the content and sending across the reply based on the keywords in the email. Initially, the AT
module 118 iterates through all rules as set or inputted by the user via the electronic device 106. Each rule is applied to specific templates which are then personalized as per the content of the opposite party's email.

[15] This way, the AT module 118 is configured to analyse all requirements of the user, to understand context of the email message/communication, and accordingly generate a suitable reply to the email communication. The users do not have to prepare the response manually and are able to save a lot of time. As per the embodiments of the subject matter as disclosed herein, web-based applications or mobile-based applications may be provided to implement the AT
module 118 to generate response drafts. This is different to the conventional methods of predicting the response sentences, words, and giving options. The emails written by AT module 118 are a lot more elaborate and personalized for respective users. Further, the drafted email responses are longer, elaborate, and can also schedule meetings based on the draft.
Furthermore, the response generated is a lot more personalized due to the artificial intelligence learning information about the recipient based on previous or historical correspondences.

[16] In various embodiments of the present invention, the AT module 118 is configured to categorize the type of email received and picks an appropriate response using the pre-assigned templates/ information. This acts as an email plugin, where after initially connecting it to email applications on the respective electronic devices 106, a user need not require to make any further connection or configuration. The user may send the email draft as soon as it is generated or go to the site to adjust the rules or edit as per specific requirements.

[17] The frontend module 102 may be configured as a web application which connects to a system with an SQL database 112 for storing and querying user records and information along with user authentication. The AT module 118 connects to the database 112 to access the saved Date Recue/Date Received 2022-02-09 emails. The required information is passed to the machine learning model. The machine learning model is pre-trained using custom data, which tokenize strings (split words into vectors) and build a method of classification to ensure when emails are read, they are categorized correctly.

[18] The frontend module 102 is configured for the user to interact and add email accounts to be automated. The frontend module 102 is connected to backend module 104 and database 112 which stores user information and has API paths 114 for signing the users sign in. In addition, the API module 108 that will connect to email accounts and pass information to the machine learning model which will categorize the email received and respond appropriately using the information provided. The machine learning model is trained using one or more mechanisms.
Using the learning model of the AT module 118, custom data and labels are passed in so the model can be fine-tuned to categorize emails based on the wanted labels or types. Along with this, the general neural network is customized to ensure the model is trained appropriately to the data wanted, by customizing the amount of data passed in, number of iteration and such details. The user is also facilitated to automate several other tasks associated with email management. This helps in reducing time spent in reading and writing emails.
The AT module 118 with machine learning capabilities and pre-assigned templates, as generated by the template generating module 110, are used to generate draft responses of emails and are also used in other tasks, such as scheduling meetings in a way that reduces the amount of back and forth.

[19] Figure 2 illustrates working of a transformer encoder 200 for automatically generating a draft response to an email communication received by a user on an electronic device 106, according to an exemplary embodiment of the present invention disclosure. The figure describes working of the transformer to transform the words to numbers. Once the email is received, the content of the email is analysed to identify words and keywords.
Words are then passed in and they are converted to the numbers using the information inside the transformer.
Thereafter, using the knowledge inside of the transformer, weights ("w" and "V") are assigned for which words connect which others. The reason words are converted into numbers is because that way words can be matched with the words previously learned in the model or match it with similar words. Additionally, this makes it possible for the AT module to learn the context of words by seeing what comes before and after. Further, the weight of words on the outcome is also checked by the process of tokenization. For example, a sentence "Hi, I am Jas" would go Date Recue/Date Received 2022-02-09 into the model and be treated as numbers such as [1,0,0,0], [0,1,0,0].[0,0,1,0], [0Ø0,1].
Assuming, the model only knows 4 words, (the more words, the more zeros are necessary), this way the model can easily know what is being referred and can apply the necessary math on these value in predictions. Outputting the information that the machine learning model being trained can use to figure out the importance of words in a sentence and how to classify them.
The transformer is a pre-trained model, that may be trained on a large dataset to provide a wider understanding. The transformer may b provided with custom data and can be trained to predict according to custom data, by changing the weights of what it learns and improving itself to fit the use case.

[20] Figure 3 illustrates the method for automatically generating a draft response to an email communication received by a user on an electronic device 106. The structural elements performing these method steps have been described in detail in description of Fig. 1 and Fig.2.
Given below are the steps of the method according to the embodiments of the present subject matter.

[21] At step 302, at least one user input is received from a user via corresponding user device 106 or electronic device 106. An AT module 118, via the server, may be communicatively connected to the database 112 in the communication network; A frontend module 102 or device may be connected in the communication network to interact with the user for receiving at least one user input via the electronic device 106.

[22] At step 304, a rule engine 116 configured to generate one or more rules based on the at least one user input. The user rules sets are pulled in the backend. The rule engine 116 is in communication with the backend module 104, so when the user sets up the rules, it gets saved there and can be pulled in, along with automating calls.

[23]

[24] At step 306, the AT module 118 (artificial intelligence-based module) is configured to identify one or more keywords from the email communication received by the user, to analyze context of the email.

[25] At step 308, the draft response is generated based on the analysed context of the email and the generated one or more rules.

[26] According to an embodiment of the present disclosure, the AT module 118 reads content of the received email to identify the one or more keywords.
Date Recue/Date Received 2022-02-09

[27] According to an embodiment of the present disclosure, each rule of the one or more rules is applied to a corresponding template, the corresponding template being personalized based on the content of the received email. The frontend module 102 is configured for the user to interact and add email accounts to be automated. Further, the API
(Application Programming Interface) module is configured that sends relevant information to the Al module 118, the relevant information pertaining to the added email accounts. The Al module 118 categorizes the received email into one or more types to thereby generate the draft response for each type of category.

[28] It will be understood by those skilled in the art that the figures are only a representation of the structural components and process steps that are deployed to provide an environment for the solution of the present invention disclosure discussed above, and does not constitute any limitation. The specific components and method steps may include various other combinations and arrangements than those shown in the figures.

[29] The term exemplary is used herein to mean serving as an example. Any embodiment or implementation described as exemplary is not necessarily to be construed as preferred or advantageous over other embodiments or implementations. Further, the use of terms such as including, comprising, having, containing and variations thereof, is meant to encompass the items/components/process listed thereafter and equivalents thereof as well as additional items/components/process.

[30] Although the subject matter is described in language specific to structural features and/or acts, it is to be understood that the subject matter defined in the claims is not necessarily limited to the specific features or process as described above. In fact, the specific features and acts described above are disclosed as mere examples of implementing the claims and other equivalent features and processes which are intended to be within the scope of the claims.
Date Recue/Date Received 2022-02-09 Date Recue/Date Received 2022-02-09 SUMMARY
[1] In order to provide a holistic solution to the above-mentioned limitations, it is necessary to deploy a solution for automatically generating email responses for users.
[2] An object of the present disclosure is to prepare drafts of customized responses to emails received by the users.
[3] Another object of the present disclosure is to provide artificial intelligence capability to facilitate constant learning of new information to incorporate into the communication to further send personalized emails to each recipient.
[4] According to an embodiment of the present disclosure, there is provided a computer-implemented system for automatically generating a draft response to an email communication received by a user on an electronic device, the system comprising: a server communicatively connected to a database in a communication network; a frontend module connected in the network and configured to interact with the user for receiving at least one user input; a rule engine configured to generate one or more rules based on the at least one user input; an AT module (artificial intelligence-based module) configured to:
identify one or more keywords from the email communication received by the user, to analyze context of the email;
generate the draft response based on the analysed context of the email and the generated one or more rules.
[5] According to an embodiment of the present disclosure, the AT module reads content of the received email to identify the one or more keywords.
[6] According to an embodiment of the present disclosure, each rule of the one or more rules is applied to a corresponding template, the corresponding template being personalized based on the content of the received email.
[7] According to an embodiment of the present disclosure, the frontend module is configured for the user to interact and add email accounts to be automated.
[8] According to an embodiment of the present disclosure, an API
(Application Programming Interface) module that sends relevant information to the AT
module, the relevant information pertaining to the added email accounts.
Date Recue/Date Received 2022-02-09 [9]
According to an embodiment of the present disclosure, the AT module categorizes the received email into one or more types to thereby generate the draft response for each type of category.
[10] According to an embodiment of the present disclosure, a computer-implemented method is provided for automatically generating a draft response to an email communication received by a user on an electronic device. The method comprises: configuring a server communicatively connected to a database in a communication network;
configuring a frontend module connected in the communication network to interact with the user for receiving at least one user input; a rule engine configured to generate one or more rules based on the at least one user input; configuring an AT module (artificial intelligence-based module) to: identify one or more keywords from the email communication received by the user, to analyze context of the email; generate the draft response based on the analysed context of the email and the generated one or more rules.
[11] The afore-mentioned objectives and additional aspects of the embodiments herein will be better understood when read in conjunction with the following description and accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. This section is intended only to introduce certain objects and aspects of the present invention, and is therefore, not intended to define key features or scope of the subject matter of the present invention.

Date Recue/Date Received 2022-02-09 BRIEF DESCRIPTION OF THE DRAWINGS
[1] The figures mentioned in this section are intended to disclose exemplary embodiments of the claimed system and method. Further, the components/modules and steps of a process are assigned reference numerals that are used throughout the description to indicate the respective components and steps. Other objects, features, and advantages of the present invention will be apparent from the following description when read with reference to the accompanying drawings:
[2] Figure 1 illustrates a system architecture, according to an exemplary embodiment of the invention disclosure;
[3] Figure 2 illustrates working of a transformer encoder implemented for automatically generating a draft response to an email communication received by a user on an electronic device, according to an exemplary embodiment of the present invention disclosure; and [4] Figure 3 illustrates the method for automatically generating a draft response to an email communication received by a user on an electronic device, according to an exemplary embodiment of the present invention disclosure.
[5] Like reference numerals refer to like parts throughout the description of several views of the drawings.

Date Recue/Date Received 2022-02-09

Claims (12)

What claimed is:

1. A computer-implemented system for automatically generating a draft response to an email communication received by a user on an electronic device, the system comprising:
an AI module (artificial intelligence-based module) communicatively connected to a database in a communication network;
a frontend module connected in the network and configured to interact with the user for receiving at least one user input;
a rule engine configured to generate one or more rules based on the at least one user input;
wherein the AI module is configured to:
identify one or more keywords from the email communication received by the user, to analyze context of the email;
generate the draft response based on the analysed context of the email and the generated one or more rules.

2. A computer-implemented system of claim 1, wherein the AI module reads content of the received email to identify the one or more keywords.

3. A computer-implemented system of claim 1, wherein each rule of the one or more rules is applied to a corresponding template, the corresponding template being personalized based on the content of the received email.

4. A computer-implemented system of claim 1, wherein the frontend module is configured for the user to interact and add email accounts to be automated.

5. A computer-implemented system of claim 4, further comprises an API
(Application Programming Interface) module that sends relevant information to the AI
module, the relevant information pertaining to the added email accounts.

6. A computer-implemented system of claim 1, wherein the AI module categorizes the received email into one or more types to thereby generate the draft response for each type of category.

7. A computer-implemented method for automatically generating a draft response to an email communication received by a user on an electronic device, the method comprising:
configuring an AI module (artificial intelligence-based module) communicatively connected to a database in a communication network;
configuring a frontend module connected in the communication network to interact with the user for receiving at least one user input;
a rule engine configured to generate one or more rules based on the at least one user input;
wherein the AI module is configured to:
identify one or more keywords from the email communication received by the user, to analyze context of the email;
generate the draft response based on the analysed context of the email and the generated one or more rules.

8. A computer-implemented method of claim 7, wherein the AI module reads content of the received email to identify the one or more keywords.

9. A computer-implemented method of claim 7, wherein each rule of the one or more rules is applied to a corresponding template, the corresponding template being personalized based on the content of the received email.

10. A computer-implemented method of claim 7, wherein the frontend module is configured for the user to interact and add email accounts to be automated.

11. A computer-implemented method of claim 10, further comprises an API
(Application Programming Interface) module that sends relevant information to the AI
module, the relevant information pertaining to the added email accounts.

12. A computer-implemented method of claim 7, wherein the AI module categorizes the received email into one or more types to thereby generate the draft response for each type of category.

FIELD OF THE DISCLOSURE
[1] The present invention relates to email management, and more particularly to systems and methods for automatically generating email responses for users.
BACKGROUND OF THE DISCLOSURE
[2] Communication via emails (or E-Mails) have become a vital mode of exchanging messages between users over a communication network. Email messages generally contain text messages, images and/or document attachments and are delivered using various available web-based services. Communications via emails help business organizations and individual users to expand their network for the purpose of doing businesses.
[3] The business organizations and institutions spend a large amount of time networking in order to find new customers and partners for business collaborations.
Networking and providing services entail scheduling, email exchanges, account maintenance, engagement analysis, and other clerical tasks. Most businesses must either visit numerous websites or hire a virtual assistant to undertake the repetitive tasks so that personnel may focus on providing services to clients. Both of these methods necessitate a lot of time and money that could be devoted to more productive activities.
[4] Various solutions are known in the art for predicting a user response to the email content. For example, prediction modelling process may be configured to analyse email interaction data associated with a particular member and content of the email being sent by the member. The content data describing a particular email content item is accessed and later encoded into one or more feature vectors. Thereafter, a prediction modeling process is performed, based on such feature vectors and a trained prediction model, to predict a likelihood of the particular member performing a particular user action on the particular email content item.
[5] However, such prediction modelling process are designed to attend a particular type of email structure and may not be effective to be used for all. Further, such existing models merely predicts options to the users which the users may or may not select while drafting their email messages. The predictions of such systems or models do not provide elaborated responses of the emails. Furthermore, the predicted text as suggested in prior solutions, is not personalized according to users' requirement. This is due to the fact that such solutions lack the ability to precisely understand content of an email and/or email trails to accordingly draft suitable responses.
[6] In view of the above, the present subject matter as disclosed herein, aims to provide a novel system and method for automatically generating email responses for users. Further, a novel platform for providing a digital assistant is required that can handle the emailing and scheduling, while users/employees can devote more of their time to process other job activities.