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EP-4740109-A1 - A WEB APPLICATION SYSTEM FOR TEXT PROCESSING AND WEB PAGE NAVIGATION, AND A METHOD THEREOF

EP4740109A1EP 4740109 A1EP4740109 A1EP 4740109A1EP-4740109-A1

Abstract

Embodiments herein disclose a web application system comprising a system comprising a natural language processing unit (NLPU) and a graphical user interface (GUI) navigator. The user inputs their query via a conversational interface in the NLPU. An activity and context recognition engine (ACRE) identifies at least one past user activity, which is indicative of a context associated with the user. Based on the user query and the at least one past user activity, a core language engine (CLE) in the NLPU determines an intent associated with the user query, and generates a response. Based on the determined intent, the GUI navigator navigates the user to a web page having information pertaining to the determined intent associated with the user query.

Inventors

  • RAMASWAMY, SATYANARAYANAN
  • Chacko, Viju
  • GAJANANA, Prateek

Assignees

  • Air India Limited

Dates

Publication Date
20260513
Application Date
20240627

Claims (16)

  1. 1. A system, comprising: a conversational interface, configured to receive a query from a user that is input via a user device; a core language engine (CLE), configured to: receive the query from the conversational interface; and determine an intent associated with the user query; and a graphical user interface (GUI) navigator, configured to navigate the user to a desired web page based on the determined user query intent, wherein, the CLE determines the user query intent by: converting the user query into a mathematical vector, using a vector embedding model; converting a description of each intent, among a plurality of intents, to a mathematical vector, using the vector embedding model; comparing the user query vector with each intent description vector, based on a cosine similarity, to determine the distance between the user query vector and each intent description vector; selecting a subset of the intent description vectors, whose distance from the user query vector is below a threshold; and feeding to a first machine learning model (MLM) the following inputs: the user query; the intent and the description associated with each intent description vector in the subset of intent description vectors; and a set of instructions to be followed for determining the user query intent, wherein the first MLM is trained to determine the user query intent, based on the inputs, and wherein the determined user query intent is one of the intents associated with the subset of intent description vectors.
  2. 2. The system as claimed in claim, wherein: the CLE is configured to extract one or more named entities from the user query using the first MLM or a second MLM; and the GUI navigator is configured to pre-fill at least one input field on the desired web page based on the extracted one or more named entities.
  3. 3. The system as claimed in claim 1, further comprising: an activity and context recognition engine (ACRE), configured to identify at least one activity performed by the user prior to inputting the query, wherein the at least one activity is indicative of a context associated with the user, wherein the determination of the user query intent, by the first MLM, is further based on the identified at least one activity.
  4. 4. The system as claimed in claim 3, wherein the ACRE, utilizing a rule engine, identifies the at least one activity by: tracking a plurality of user actions across a plurality of web pages; mapping each user action, among the plurality of user actions, to a label, to generate at least one sequence of labels; and mapping the at least one sequence label to an activity, wherein the mapped-to activity is the identified at least one activity.
  5. 5. The system as claimed in claim 3, wherein the ACRE utilizes a third MLM for identifying the at least one activity, and wherein the trained machine learning model was trained by: receiving clickstream data indicative of past interactions of the user on one or more web pages, wherein each interaction is associated to a label; and receiving an association of a sequence of the labels to a predetermined activity.
  6. 6. The system as claimed in claim 2, wherein the first MLM is configured to generate a response to the user query based on the determined user query intent, the description of the determined user query intent, and the one or more named entities.
  7. 7. The system as claimed in claim 6, wherein a content of the desired web page substantiates the response generated by the first MLM.
  8. 8. The system as claimed in claim 1, wherein the user query is input through a text and/or a voice command.
  9. 9. A method, comprising: receiving, via a conversational interface, a query from a user; converting, using a vector embedding model, the user query into a mathematical vector; converting a description of each intent, among a plurality of intents, to a mathematical vector using the vector embedding model; comparing the user query vector with each intent description vector, based on a cosine similarity, to determine the distance between the user query vector and each intent description vector; selecting a subset of the intent description vectors, wherein the subset comprises those intent description vectors that are distanced below a threshold from the user query vector; feeding to a first machine learning model (MLM) the following inputs: the user query; the intent and the description associated with each intent description vector in the subset of intent description vectors; and a set of instructions to be followed for determining the user query intent, wherein the first MLM is trained to determine the user query intent based on the inputs, and wherein the determined user query intent is one of the intents associated with the subset of the intent description vectors; and navigating, by a graphical user interface (GUI) navigator, the user to a desired web page based on the determined user query intent.
  10. 10. The method as claimed in claim 9, comprising: extracting, using the first MLM or a second MLM, the one or more named entities from the user query; pre-filling, by the GUI navigator, at least one input field on the desired web page based on the extracted one or more named entities.
  11. 11. The method as claimed in claim 9, comprising: identifying, by an activity and context recognition engine (ACRE), at least one activity performed by the user prior to inputting the query, wherein the at least one is indicative of a context associated with the user, wherein the determination of the user query intent, by the first MLM, is further based on the identified at least one activity.
  12. 12. The method as claimed in 11, comprising: tracking, by a rule engine in the ACRE, a plurality of user actions across a plurality of web pages; mapping, by the rule engine in the ACRE, each user action, among the plurality of user actions, to a label, to generate at least one sequence of labels; and mapping, by the rule engine in the ACRE, the at least one sequence label to an activity, wherein the mapped-to activity is the identified at least one activity.
  13. 13. The method as claimed in claim 11, comprising: training a third MLM in the ACRE for identifying the at least one activity, wherein the training comprises: receiving clickstream data indicative of past interactions of the user on one or more web pages, wherein each interaction is associated to a label; and receiving an association of a sequence of the labels to a predetermined activity.
  14. 14. The method as claimed in claim 10, wherein the first MLM is configured to generate a response to the user query based on the determined user query intent, the description of the determined user query intent, and the one or more named entities.
  15. 15. The method as claimed in claim 14, wherein a content of the desired web page substantiates the response generated by the first MLM.
  16. 16. The method as claimed in claim 9, wherein the user query is input through a text and/or a voice command.

Description

A WEB APPLICATION SYSTEM FOR TEXT PROCESSING AND WEB PAGE NAVIGATION, AND A METHOD THEREOF Cross-Reference To Related Applications: [001] This application claims the benefit of and priority to Indian Provisional Application 202311045899, filed on July 7, 2023, which is hereby incorporated by reference in its entirety. Technical Field: [002] The present disclosure relates to the fields of computing and artificial intelligence, and more particularly relates to a web application system for text processing and web page navigation, and a method thereof. Background: [003] In today’s digital world, users are able to avail many services and products by accessing a web application system through their electronic device (e.g., a smartphone or a personal computer). Users can interact with a web application system through their device’s browser — by typing the website address directly or clicking on a link that leads to the desired page. Once the appropriate address is entered or the link is clicked, the application is rendered on the device’s browser. [004] Most web application systems are designed with multiple web pages that serve a dual purpose: presenting information and accepting user inputs. This interactive design enables users to engage with the website and perform various tasks as needed. To assist users in navigating through the web application, site maps are often provided as a visual representation of the website’s structure. However, despite their intended purpose, site maps can sometimes be challenging to use and may not effectively guide users to the correct application screen within the website. This has paved the way to leverage search- based approaches to find appropriate web pages that are relevant to the information requested or application. However, search-based approaches suffer from the limitation of requiring the user to input specific (or exact) keywords to retrieve the web pages that are associated with the requested information. [005] Nowadays, in order to quickly retrieve the information the user is looking out for, instead of having the user figure out the key terms to input into a search bar on the website, some web application systems utilize artificial intelligence -based chatbots for accepting a user’s input (in their own words) and providing a reply to the user’s input. In other words, the user’s query is formulated using their natural words, instead of specific terminology, and is input to the chatbot for processing. However, the reply from the chatbot may not be satisfactory to the user owing to the limitations in the input parameters considered by the chatbot for replying to the user, and the limitations in its output capabilities. Summary: [006] These and other problems are generally solved or circumvented, and technical advantages are generally achieved, by advantageous embodiments of the present disclosure. [007] A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. [008] According to a first embodiment, a system is disclosed. The system comprises a conversational interface configured to receive a query from a user that is input via a user device. The system comprises a core language engine (CLE) configured to receive the query from the conversational interface and determine an intent associated with the user query. The system comprises a graphical user interface (GUI) navigator, configured to navigate the user to a desired web page based on the determined user query intent. The CLE determines the user query intent by converting the user query into a mathematical vector, using a vector embedding model. The CLE converts a description of each intent, among a plurality of intents, to a mathematical vector, using the vector embedding model. The CLE compares the user query vector with each intent description vector, based on a cosine similarity, to determine the distance between the user query vector and each intent description vector. The cosine values can be indicative of those intent description vectors that are close to the user query vector. The CLE selects a subset of the intent description vectors, whose distance from the user query vector is below a threshold. The CLE feeds a first machine learning model (MLM) the following inputs: the user query; the intent and the description associated with each intent description vector in the subset of intent description vectors; and a set of instructions to be followed for determining the user query intent. The first LLM is trained to determine the user query intent, based on the inputs, and wherein the determined user query intent is one of the intents associated with the subset of intent description vectors. [00