Automated travel planning system and method

Abstract

The present disclosure discloses the automated travel planning system and method. The system includes an input data module (102) to receive real-time user inputs, a data fetching module (104) to collect travel-related data, and a data aggregation module (106) to generate an aggregate database. A preprocessing module (108) processes data, while a filtering module (109) refines travel plans based on predefined criteria. A recommendation module (110) generates a ranked list of plans, and a booking module (112) facilitates reservations. A notification module (114) ensures real-time updates, while a feedback module (116) collects user reviews. Additionally, a subscription module (118) manages subscriptions, a payment module (120) enables secure transactions, and a schedule organizer module (122) assists in managing daily activities. The system (100) ensures efficient, user-centric travel planning.

Description

[0001] AUTOMATED TRAVEL PLANNING SYSTEM AND METHOD

[0002] FIELD

[0003] The present disclosure generally relates to the field of systems for travel planning. More particularly, the present disclosure relates to an automated travel planning system and method.

[0004] DEFINITIONS

[0005] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicates otherwise.

[0006] The term “Application Framework” is a software platform that provides a structured foundation for developing applications. It includes pre-built components, libraries, tools, and APIs that simplify common development tasks such as user authentication, database management, and UI design.

[0007] The term “API (Application Programming Interface)” is a set of rules and protocols that allow different software applications to communicate with each other. It defines how requests and responses should be structured, enabling seamless data exchange and functionality integration between systems, services, or devices.

[0008] The term “API calls” are requests made by a client application to an API (Application Programming Interface) to retrieve, send, or modify data on a server or another system. These calls enable communication between different software components, allowing applications to access external services, databases, or functionalities in real time.

[0009] The term “Augmented Reality (AR)” refers to technology that overlays digital information — such as images, audio, text, or interactive elements — onto the real-world environment, typically viewed through a compatible device like a smartphone, tablet, smart glasses, or AR headset.

[0010] The term “Biometric authentication services” refers to security mechanisms that use unique biological characteristics, such as fingerprints, facial recognition, iris scans, or voice patterns, to verify a user's identity. These services enhance security by providing a more reliable and fraud-resistant method of authentication compared to traditional passwords or PINs.

[0011] The term “Backend system” refers to the server-side infrastructure that processes data, manages business logic, and supports communication between the application’s user interface and databases or external services.

[0012] The term “Caching Mechanism” is a technique used to store frequently accessed data in a temporary storage layer, allowing for faster retrieval and reducing the need to repeatedly fetch the same data from the primary source. This improves system performance, reduces latency, and optimizes resource usage in applications such as databases, web services, and content delivery networks.

[0013] The term “Calendar applications” refers to software programs designed to help users schedule, manage, and track events, appointments, and tasks. These applications often include features such as event reminders, notifications, time zone support, and integration with other productivity tools.

[0014] The term “Collaborative filtering” is a recommendation technique used in machine learning and data analysis that predicts a user's preferences based on the behavior and preferences of similar users. It works by identifying patterns in user interactions, such as ratings, purchases, or browsing history, to provide personalized recommendations. Collaborative filtering is commonly used in applications like e-commerce, streaming services, and online content recommendations.

[0015] The term “Customer Relationship Management (CRM) Unit” is a system or module designed to manage interactions with customers, track user engagement, and store relevant customer data. It helps businesses analyze customer behavior, personalize services, automate communications, and improve overall customer experience through data-driven insights.

[0016] The term “Fraud detection mechanisms” are automated processes or tools used to identify, prevent, and respond to unauthorized or suspicious activities in financial or data transactions.

[0017] The term “GPS (Global Positioning System)” refers to a satellite -based navigation system that provides real-time location, timing, and velocity information to GPS-enabled devices anywhere on Earth. The term “Intuitive UI (User Interface)” is a design that allows users to navigate and interact with a system effortlessly, without requiring extensive instructions or prior experience. It ensures clarity, consistency, and ease of use by leveraging familiar layouts, logical workflows, and responsive feedback, enabling users to complete tasks efficiently with minimal confusion.

[0018] The term “Machine learning techniques” are a set of methods and algorithms that enable computers to learn patterns from data and make predictions or decisions without being explicitly programmed. These techniques include supervised learning (training with labeled data), unsupervised learning (finding patterns in unlabeled data), reinforcement learning (learning through rewards and penalties), and deep learning (using neural networks to process complex data). Machine learning techniques are widely used in applications such as image recognition, natural language processing, recommendation systems, and fraud detection.

[0019] The term “Natural Language Processing (NLP)” is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language. It involves techniques such as text analysis, sentiment detection, language translation, and speech recognition to facilitate human-computer interactions in a natural and meaningful way.

[0020] The term “Parallel processing techniques” refers to methods used to divide a computational task into smaller sub-tasks that can be executed simultaneously across multiple processors or computing units. These techniques improve processing speed, efficiency, and performance in complex computations, such as large-scale data analysis, machine learning, and real-time processing applications.

[0021] The term “Payment gateways” are services that securely process and authorize online payment transactions between users and financial institutions.

[0022] The term “Pre-Trained Machine-Learning Model” is a model that has been previously trained on a large dataset to recognize patterns, make predictions, or perform specific tasks. Instead of training a model from scratch, it can be fine-tuned or directly used for similar applications, saving time and computational resources while improving accuracy and efficiency.

[0023] The term “PCI-DSS (Payment Card Industry Data Security Standard)” is a global standard that ensures the secure handling of credit card information by businesses. The term “Sentiment Analysis Techniques” refers to methods used to analyze and interpret the emotional tone, opinions, or attitudes expressed in text data. These techniques leverage natural language processing (NLP), machine learning, and computational linguistics to classify sentiments as positive, negative, or neutral, helping businesses and applications understand user feedback, reviews, or social media interactions.

[0024] The term “SMS (Short Message Service)” is a text messaging service that allows users to send and receive short text messages over mobile networks. It is a widely used communication method that supports up to 160 characters per message in standard format. SMS operates independently of internet connectivity and is commonly used for personal communication, notifications, alerts, and authentication purposes.

[0025] The term “Thematic audio content” refers to audio material created around specific topics, themes, or experiences to enhance user engagement, such as travel guides, cultural insights, or location-based storytelling.

[0026] The above definitions are in addition to those expressed in the art.

[0027] BACKGROUND

[0028] The background information herein below relates to the present disclosure but is not necessarily prior art.

[0029] In the current scenario, travel is typically planned by travel planners and agencies, which generally provide users with fixed, pre-configured plans (preplans) that offer limited flexibility and personalization. Conventional travel planning systems, including digital platforms and mobile applications, are commonly utilized to assist users in organizing trips. These existing systems typically provide pre-configured itineraries and generic recommendations relating to destinations, accommodations, activities, and dining options. While such systems offer a foundational framework for travel planning, they are generally characterized by static architecture and limited adaptability. In particular, traditional systems predominantly employ predefined templates with minimal scope for user input, resulting in low levels of personalization and the provision of generalized content that does not align with individual user preferences or dynamic travel scenarios. A significant limitation of existing systems resides in their inability to respond to real-time conditions. Such systems commonly fail to deliver live updates pertaining to traffic congestion, weather variations, or localized disruptions, such as road closures, public gatherings, or unforeseen venue unavailability. For instance, a user may plan an itinerary involving a specific route or attraction without receiving timely alerts regarding traffic congestion or venue closure, thereby leading to potential delays or suboptimal experiences. Moreover, existing systems typically do not notify users about the current operational status of intended destinations — such as whether a hotel, park, historical site, or restaurant is closed, under renovation, or otherwise unavailable — until the user physically arrives at the location, thereby impairing the overall travel experience.

[0030] From a technical perspective, current travel planning systems generally lack integration with live data sources, including traffic application programming interfaces (APIs), weather data feeds, and real-time venue information from official sources. These systems do not facilitate dynamic itinerary generation in response to evolving travel conditions or based on personalized user constraints, such as individual interests, mobility speed, or financial budget. Furthermore, conventional systems exhibit limited interoperability, often lacking integration with auxiliary services such as transportation bookings, event ticketing, or sustainable travel options. Enhanced features, including virtual exploration, artificial intelligence-based route optimization, or user engagement mechanisms such as referral incentives, are also typically absent. Additionally, these systems are prone to data obsolescence and inaccuracy due to insufficient synchronization with authoritative data sources. Consequently, users are frequently left with inflexible, outdated, and impersonal travel plans that necessitate manual modifications and lack contextual responsiveness.

[0031] Therefore, there is felt a need for an automated travel planning system and method that alleviates the aforementioned drawbacks.

[0032] OBJECTS

[0033] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:

[0034] It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative. An object of the present disclosure is to provide a system for planning and managing travel- related activities.

[0035] Another object of the present disclosure is to provide a system for facilitating seamless booking and secure transactions.

[0036] Still another object of the present disclosure is to provide a system for generating personalized travel recommendations based on user preferences.

[0037] Yet another object of the present disclosure is to provide a system for sending automated notifications related to travel plans.

[0038] An object of the present disclosure is to provide a system for collecting and analyzing user feedback to enhance future recommendations.

[0039] Another object of the present disclosure is to provide a system for organizing and managing travel schedules effectively.

[0040] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0041] SUMMARY

[0042] The present disclosure envisages a system for automated travel planning.

[0043] The system comprises an input data module, a data fetching module, a data aggregation module, a preprocessing module, a recommendation module, a booking module, a notification module, a feedback module, a subscription module, a payment module, and a schedule organizer module.

[0044] The input data module is configured to cooperate with a user interface to receive real-time preference inputs from a user upon a successful login.

[0045] The data fetching module is configured to cooperate with various data sources to collect travel-related data based on the received real-time preference input data. The data aggregation module is configured to cooperate with a pre-trained machine learning model to generate an aggregate database by collecting travel-related data and providing information related to emergency services and helplines during trips.

[0046] The preprocessing module is configured to cooperate with the aggregate database to process the aggregated data and generate one or more travel plans using a pre-trained machinelearning model.

[0047] The filtering module is configured to cooperate with the preprocessing module to filter travel plans based on predefined criteria, including user preferences, budget constraints, travel time, and availability;

[0048] The recommendation module is configured to cooperate with the preprocessing module to generate a recommendation list of travel plans based on ratings, feedback, budget, user’s previous trips, or a wish list.

[0049] The booking module is configured to cooperate with various service providers to facilitate bookings based on user selection from the recommendation list of travel plans, wherein the booking module further enables pre-booking and on-spot booking, confirms reservations, and provides real-time confirmation to the user.

[0050] The notification module is configured to cooperate with the system components to send notifications to the user for each activity of the travel plan until the trip is completed.

[0051] The feedback module is configured to cooperate with a feedback database to receive user feedback data, wherein the user feedback data is analyzed using a pre-trained machine learning model.

[0052] The subscription module is configured to cooperate with the application framework to manage user subscriptions, including plan selection, upgrades, downgrades, billing management, subscription status viewing, and cancellations.

[0053] The payment module is configured to cooperate with various payment gateways to facilitate secure transactions via at least one of multiple payment modes, wherein the payment module transmits successful transaction data. The schedule organizer module is configured to cooperate with the user interface to assist in planning and managing daily activities during the travel period based on the selected travel plan.

[0054] In an aspect, the input data module is further configured to cooperate with biometric authentication services to enhance security during user login.

[0055] In an aspect, the data fetching module is further configured to cooperate with weather forecasting services to provide real-time weather updates for the selected travel destination.

[0056] In an aspect, the data aggregation module is further configured to cooperate with governmental and private emergency response databases to ensure updated emergency service information is available to users.

[0057] In an aspect, the preprocessing module is further configured to cooperate with natural language processing techniques to interpret user preferences and travel history more accurately.

[0058] In an aspect, the recommendation module is further configured to cooperate with sentiment analysis techniques to refine recommendations based on user feedback and online travel reviews.

[0059] In an aspect, the booking module is further configured to cooperate with airline, hotel, and local transport APIs to provide real-time availability and pricing information.

[0060] In an aspect, the notification module is further configured to cooperate with email, SMS, and in-app messaging services to deliver timely travel alerts and reminders to the user.

[0061] In an aspect, the feedback module is further configured to cooperate with the machine learning technique to analyze feedback trends and improve future travel recommendations.

[0062] In an aspect, the subscription module is further configured to cooperate with a customer relationship management (CRM) unit to provide personalized travel offers based on user preferences.

[0063] In an aspect, the payment module is further configured to cooperate with fraud detection mechanisms to ensure secure and verified transactions. In an aspect, the schedule organizer module is further configured to cooperate with calendar applications and productivity tools to synchronize travel itineraries with the user's daily schedule.

[0064] In an aspect, the preprocessing module is further configured to cooperate with the data aggregation module to generate a localised lifestyle plan based on user geolocation, preference inputs, and duration constraints, such as a short-term itinerary for dining and shopping in the user’s current city.

[0065] In an aspect, the input data module is further configured to operate in an unauthenticated exploration mode based on device location permissions, allowing users to browse potential travel or lifestyle plans on an interactive map without requiring user login.

[0066] In an aspect, the data fetching module is further configured to cooperate with traffic analytics services to collect real-time congestion and route data for optimizing travel and lifestyle plans.

[0067] In an aspect, the data aggregation module is further configured to cooperate with authoritative third-party data sources, including encyclopedias, wikis, and government heritage databases, to retrieve contextual information about places of interest.

[0068] In an aspect, the notification module is further configured to present the contextual information retrieved from authoritative sources in real time as an interactive script within the application or through immersive display technologies such as augmented reality headsets.

[0069] The present disclosure also envisages a method for automated travel planning. The method comprises the following steps:

[0070] • receiving, by an input data module, real-time preference inputs from a user upon successful login;

[0071] • collecting, by a data fetching module, travel-related data based on the received real-time preference inputs data;

[0072] • generating, by a data aggregation module, an aggregate database by collecting travel-related data and providing information related to emergency services and helplines during trips;

[0073] • processing, by a preprocessing module, the aggregated data; • generating, by the preprocessing module, one or more travel plans using a pretrained machine learning model based on user preference input;

[0074] • filtering, by a filtering module, travel plans based on predefined criteria, including user preferences, budget constraints, travel time, and availability;

[0075] • generating, by a recommendation module, a recommendation list of travel plans based on ratings, feedback, budget, user’s previous trips, or wish list;

[0076] • facilitating, by a booking module, bookings based on user selection from the recommendation list of travel plans, enabling pre-booking and on-spot booking, confirming reservations, and providing real-time confirmation to the user;

[0077] • sending a notification module, notifications to the user for each activity of the travel plan until the trip is completed;

[0078] • receiving, by a feedback module, user feedback data, wherein the user feedback data is analyzed using a pre-trained machine learning model;

[0079] • managing, by a subscription module, user subscriptions, including plan selection, upgrades, downgrades, billing management, subscription status viewing, and cancellations;

[0080] • facilitating, by a payment module, gateways to secure transactions via multiple payment modes transmitting successful transaction data; and

[0081] • assisting, by a schedule organizer module, in planning and managing daily activities during the travel period based on the selected travel plan.

[0082] In an aspect, the method comprises the step of authenticating, by the input data module, the user through biometric verification prior to receiving real-time preference inputs.

[0083] In an aspect, the method further comprises the step of fetching, by the data fetching module, real-time weather updates from weather forecasting services corresponding to the travel destinations selected in the travel plan.

[0084] In an aspect, the method further comprises the step of integrating, by the data aggregation module, emergency services and helpline information from governmental and private emergency response databases for each travel destination. In an aspect, the method further comprises the step of processing of the aggregated data by the preprocessing module further comprises interpreting user travel preferences and history using natural language processing techniques.

[0085] In an aspect, the method further comprises the step of generating the recommendation list by the recommendation module further comprises applying sentiment analysis techniques to refine travel recommendations based on user feedback and external travel reviews.

[0086] In an aspect, the method further comprises the step of facilitating bookings by the booking module further comprises fetching real-time availability and pricing information by cooperating with airline, hotel, and local transport APIs.

[0087] In an aspect, the method further comprises the step of sending notifications by the notification module further comprising delivering alerts and reminders via email, SMS, and in-app messaging services for each travel activity.

[0088] BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

[0089] The automated travel planning system and method of the present disclosure will now be described with the help of the accompanying drawing in which:

[0090] Figure 1 illustrates a block diagram of the automated travel planning system in accordance with the present disclosure;

[0091] Figure 2A and Figure 2C illustrate a flow chart depicting steps involved in the method for automated travel planning in accordance with an embodiment of the present disclosure; and

[0092] Figure 3 illustrates a flow chart for automated travel planning, designed to assist users in generating optimized travel itineraries in accordance with an embodiment of the present disclosure.

[0093] LIST OF REFERENCE NUMERALS

[0094] 100 - System

[0095] 102 - Input Data Module 104 - Data Fetching Module

[0096] 106 - Data Aggregation Module

[0097] 108 - Preprocessing Module

[0098] 109 - Filtering Module

[0099] 110 - Recommendation Module

[0100] 112 - Booking Module

[0101] 114 - Notification Module

[0102] 116 - Feedback Module

[0103] 118 - Subscription Module

[0104] 120 - Payment Module

[0105] 122 - Schedule Organizer Module

[0106] DETAILED DESCRIPTION

[0107] Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.

[0108] Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.

[0109] The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.

[0110] When an element is referred to as being “engaged to,” “connected to,” or “coupled to” another element, it may be directly engaged, connected, or coupled to the other element. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed elements.

[0111] In the current scenario, travel is typically planned by travel planners and agencies, which generally provide users with fixed, pre-configured plans (preplans) that offer limited flexibility and personalization. Conventional travel planning systems, including digital platforms and mobile applications, are commonly utilized to assist users in organizing trips. These existing systems typically provide pre-configured itineraries and generic recommendations relating to destinations, accommodations, activities, and dining options. While such systems offer a foundational framework for travel planning, they are generally characterized by static architecture and limited adaptability. In particular, traditional systems predominantly employ predefined templates with minimal scope for user input, resulting in low levels of personalization and the provision of generalized content that does not align with individual user preferences or dynamic travel scenarios.

[0112] A significant limitation of existing systems resides in their inability to respond to real-time conditions. Such systems commonly fail to deliver live updates pertaining to traffic congestion, weather variations, or localized disruptions, such as road closures, public gatherings, or unforeseen venue unavailability. For instance, a user may plan an itinerary involving a specific route or attraction without receiving timely alerts regarding traffic congestion or venue closure, thereby leading to potential delays or suboptimal experiences. Moreover, existing systems typically do not notify users about the current operational status of intended destinations — such as whether a hotel, park, historical site, or restaurant is closed, under renovation, or otherwise unavailable — until the user physically arrives at the location, thereby impairing the overall travel experience.

[0113] From a technical perspective, current travel planning systems generally lack integration with live data sources, including traffic application programming interfaces (APIs), weather data feeds, and real-time venue information from official sources. These systems do not facilitate dynamic itinerary generation in response to evolving travel conditions or based on personalized user constraints, such as individual interests, mobility speed, or financial budget. Furthermore, conventional systems exhibit limited interoperability, often lacking integration with auxiliary services such as transportation bookings, event ticketing, or sustainable travel options. Enhanced features, including virtual exploration, artificial intelligence-based route optimization, or user engagement mechanisms such as referral incentives, are also typically absent. Additionally, these systems are prone to data obsolescence and inaccuracy due to insufficient synchronization with authoritative data sources. Consequently, users are frequently left with inflexible, outdated, and impersonal travel plans that necessitate manual modifications and lack contextual responsiveness. To address the issues of the existing systems and methods, the present disclosure envisages a system(hereinafter referred to as “system 100”) for automated travel planning and a method (hereinafter referred to as “method 200”) for automated travel planning. The system 100 will now be described with reference to Figure 1 and the method 200 will be described with reference to Figures 2a-2c.

[0114] Referring to Figure 1, the system 100 comprises an input data module 102, a data fetching module 104, a data aggregation module 106, a preprocessing module 108, a recommendation module 110, a booking module 112, a notification module 114, a feedback module 116, a subscription module 118, a payment module 120, and a schedule organizer module 122.

[0115] The input data module 102 is configured to cooperate with a user interface to receive realtime preference inputs from a user upon a successful login.

[0116] In an aspect, the input data module 102 is further configured to cooperate with biometric authentication services to enhance security during user login.

[0117] In an aspect, the input data module 102 is further configured to operate in an unauthenticated exploration mode based on device location permissions, allowing users to browse potential travel or lifestyle plans on an interactive map without requiring user login. In another embodiment, the input data module 102 is integrated with an intuitive UI that enhances user experience. The interface allows users to enter travel preferences seamlessly, with features such as predictive text input, autocomplete suggestions, and a dynamic recommendation engine powered by the recommendation module 110. The UI is designed for cross-platform compatibility, ensuring smooth access via web and mobile applications.

[0118] In one embodiment, the input data module 102 receives real-time user preferences through a user interface. The input data may include destination, travel dates, budget, preferred travel style (adventure, leisure, cultural), and accommodation type.

[0119] The data fetching module 104 is configured to cooperate with various data sources to collect travel-related data based on the received real-time preference inputs data.

[0120] In an aspect, the data fetching module 104 is further configured to cooperate with weather forecasting services to provide real-time weather updates for the selected travel destination.

[0121] In an aspect, the data fetching module 104 is further configured to cooperate with traffic analytics services to collect real-time congestion and route data for optimizing travel and lifestyle plans.

[0122] In one embodiment, the data fetching module 104 interacts with various travel-related APIs, extracting up-to-date information on flight schedules, hotel availability, transport options, and weather conditions. The backend efficiently processes data, storing relevant information in the aggregate database for seamless retrieval.

[0123] In another embodiment, the data fetching module 104 is configured to cooperate with various verified and authoritative sources to fetch real-time data related to travel venues and services. Such real-time data may include, but is not limited to, information about complete or partial closures of venues, access restrictions, traffic conditions, transport service status, weather conditions, dining availability, and live event updates. The sources of such information comprise venue-specific websites, social media announcements verified by the venue or authority, brochures, and local governmental authority platforms, without limitation to dedicated heritage offices. The data fetching module 104 is further configured to resolve the fragmented nature of these data sources by enabling centralized access to real-time information that would otherwise require manual user efforts for cross-verification and consolidation. In another embodiment, the data fetching module 104 collects dynamic travel-related data from multiple sources, including transport APIs, accommodation services, local weather platforms, event calendars, and real-time pricing feeds. This module ensures that the fetched data is contextually relevant to the preferences received via the input data module 102.

[0124] The data aggregation module 106 is configured to cooperate with a pre-trained machine learning model to generate an aggregate database by collecting travel-related data and providing information related to emergency services and helplines during trips.

[0125] In an aspect, the data aggregation module 106 is further configured to cooperate with governmental and private emergency response databases to ensure updated emergency service information is available to users.

[0126] In an aspect, the data aggregation module 106 is further configured to cooperate with authoritative third-party data sources, including encyclopedias, wikis, and government heritage databases to retrieve contextual information about places of interest.

[0127] In another embodiment, the data aggregation module 106 is configured to cooperate with the data fetching module 104 and a pre-trained machine learning model to generate an aggregate database that consolidates data fetched from disparate and disconnected sources. The aggregate database includes encyclopedic data and lifestyle-related information such as, but not limited to, historical and cultural details of venues, architecture overviews, dining and shopping tips, sightseeing guidelines, safety advisories, and localized customs. The data aggregation module 106 collects such data from a variety of trusted sources, including travel guide platforms, municipal or tourism department websites, lifestyle content providers, and community-rated forums, in addition to traditional encyclopedic databases.

[0128] In one embodiment, the data aggregation module 106 uses a pre-trained machine learning model to merge fetched data into a unified format. The data aggregation module 106 additionally fetches or receives information regarding emergency services, such as helpline numbers, hospitals, and police stations, depending on the user’s selected destination.

[0129] The preprocessing module 108 is configured to cooperate with the aggregate database to process the aggregated data and generate one or more travel plans using a pre-trained machine -learning model. In an aspect, the preprocessing module 108 is further configured to cooperate with natural language processing techniques to interpret user preferences and travel history more accurately.

[0130] In an aspect, the preprocessing module 108 is further configured to cooperate with the data aggregation module 106 to generate a localised lifestyle plan based on user geolocation, preference inputs, and duration constraints, such as a short-term itinerary for dining and shopping in the user’s current city.

[0131] In another embodiment, the preprocessing module 108 is optimized to handle large volumes of travel-related data using parallel processing techniques. The preprocessing module 108 allows for faster generation of travel plans by leveraging a pre-trained machine learning model. Additionally, a caching mechanism is implemented to enhance response time and reduce redundant API calls.

[0132] In another embodiment, the preprocessing module 108 cleans and organizes the aggregated data to generate potential travel plans. The machine learning model within this module ranks the generated plans based on past user behavior, preference weights, and optimized itinerary generation rules.

[0133] The fdtering module 109 is configured to cooperate with the preprocessing module 108 to filter travel plans based on predefined criteria, including user preferences, budget constraints, travel time, and availability.

[0134] In one embodiment, the filtering module 109 refines the generated travel plans using filters such as user-defined budget limits, travel duration, availability of experiences or services, and preferred activity types. The filtering process ensures that only relevant and feasible travel plans are displayed.

[0135] The recommendation module 110 is configured to cooperate with the preprocessing module 108 to generate a recommendation list of travel plans based on ratings, feedback, budget, user’s previous trips, or wish list.

[0136] In an aspect, the recommendation module 110 is further configured to cooperate with sentiment analysis techniques to refine recommendations based on user feedback and online travel reviews. In yet another embodiment, the recommendation module 110 employs advanced Al techniques such as sentiment analysis and collaborative filtering. The module analyzes user preferences, travel history, and ratings to generate highly personalized recommendations. The continuous model updates are facilitated through backend improvements, ensuring that recommendations evolve based on real-time feedback trends.

[0137] In another embodiment, the recommendation module 110 analyzes filtered travel plans and user data (including feedback, past trips, and wish-listed places) to curate a ranked list of recommendations. The recommendation module 110 considers user-specific factors like travel history, social behavior patterns, and reviews to enhance recommendation accuracy.

[0138] The booking module 112 is configured to cooperate with various service providers to facilitate bookings based on user selection from the recommendation list of travel plans, wherein the booking module 112 further enables pre-booking and on-spot booking, confirms reservations, and provides real-time confirmation to the user.

[0139] In an aspect, the booking module 112 is further configured to cooperate with airline, hotel, and local transport APIs to provide real-time availability and pricing information.

[0140] In one embodiment, the booking module 112 enables direct interaction with airline, hotel, and local transport service providers. The Booking Module 112 supports both pre-booking and on-spot booking functionalities.

[0141] In another embodiment, the booking module 112 is further configured to facilitate native bookings of attractions, transportation, accommodation, dining, and other travel elements within the system 100 without external linking. The booking module 112 cooperates with the payment module 120 to process transactions natively within the application framework, based on the user’s budget, browsing location, and availability of the desired resource.

[0142] In another embodiment, the booking module 112 and the subscription module 118 are configured to cooperate with external service providers to enable a dual-mode booking capability. The system 100 supports both:

[0143] • Native in-app bookings, wherein users can complete bookings entirely within the application interface. This includes bookings for accommodations (such as hotels or hostels), dining (such as restaurant reservations), transport (such as flights, trains, buses, or boats), and experience-based services (such as guided tours, adventure activities, amusement parks, and cultural events).

[0144] • Third-party integrated or linked bookings, wherein the system 100 is configured to initiate browser-based sessions or integrate third-party application interfaces within the app to facilitate external purchases. Said integrations may be facilitated through deep-linking, embedded APIs, affiliate frameworks, or other business partnerships. Upon confirmation, booking and reservation data obtained from third-party services are incorporated into the user’s itinerary within the app through confirmation identifiers or reservation tracking IDs.

[0145] In one embodiment, the booking module 112 and subscription module 118 are further configured to support in-app purchases and allow hosting, facilitating, and executing transactions related to travel itineraries. The system 100 incorporate affiliate or partnershipbased booking widgets or external website frames for services, including but not limited to accommodations, dining, transport, and experiences. The system 100 thereby ensures flexibility between executing native transactions within the application and enabling transactions with third-party platforms while maintaining a unified itinerary interface.

[0146] In one embodiment, the booking module 112 integrates with third-party booking services to facilitate reservations for flights, accommodations, local transport, and activities. The booking module 112 also supports both pre-booking (ahead of travel) and on-spot booking (during travel), providing real-time confirmation updates to the user interface.

[0147] The notification module 114 is configured to cooperate with the system components to send notifications to the user for each activity of the travel plan until the trip is completed.

[0148] In an aspect, the notification module 114 is further configured to cooperate with email, SMS, and in-app messaging services to deliver timely travel alerts and reminders to the user.

[0149] In an aspect, the notification module 114 is further configured to present the contextual information retrieved from authoritative sources in real time as an interactive script within the application or through immersive display technologies such as augmented reality headsets.

[0150] In another embodiment, the notification module 114 is designed to keep users informed throughout their journey. The notification module 114 integrates with SMS, email, and in-app notification services, ensuring users receive timely reminders about flight schedules, hotel check-ins, itinerary changes, and travel alerts. The backend optimizes message delivery based on user preferences and notification priority levels.

[0151] In yet another embodiment, the notification module 114 is configured to send proactive alerts and status updates to the user in response to various system activities. These notifications include, but are not limited to, confirmations of travel bookings, changes in itinerary events, emergency advisories, venue closures, and updates derived from real-time data feeds. The notification module 114 is further configured to cooperate with the schedule organizer module 122 to provide scheduled alerts for daily travel activities in accordance with the selected travel plan.

[0152] In another embodiment, the notification module 114 continuously monitors the trip progress and system events to send reminders, alerts, confirmations, or updates to the user, such as check-in reminders, local traffic warnings, or activity start times.

[0153] The feedback module 116 is configured to cooperate with a feedback database to receive user feedback data, wherein the user feedback data is analyzed using a pre-trained machine learning model.

[0154] In an aspect, the feedback module 116 is further configured to cooperate with machine learning techniques to analyze feedback trends and improve future travel recommendations.

[0155] In one embodiment, the feedback module 116 collects structured and unstructured feedback from users. The machine learning models analyze data to identify patterns, refine travel recommendations, and improve system performance. The backend improvements ensure feedback is processed in real time, contributing to the continuous enhancement of travel plans and services.

[0156] In one embodiment, the feedback module 116 collects textual, rating -based, or voice feedback from users post-trip or during specific stages. The collected data is analyzed using a machine learning model to extract sentiment, identify improvement areas, and refine future plan generation. The subscription module 118 is configured to cooperate with the application framework to manage user subscriptions, including plan selection, upgrades, downgrades, billing management, subscription status viewing, and cancellations.

[0157] In an aspect, the subscription module 118 is further configured to cooperate with a customer relationship management (CRM) unit to provide personalized travel offers based on user preferences.

[0158] In another embodiment, the subscription module 118 provides a seamless experience for users to manage their subscription plans. The backend system ensure smooth plan upgrades, downgrades, and cancellations, with integration into a customer relationship management (CRM) system for personalized offers. The UI displays clear subscription details, billing history, and renewal notifications.

[0159] In another embodiment, the subscription module 118 handles user subscription management. The subscription module 118 includes plan upgrades or downgrades, billing cycles, payment status, and the ability to pause or cancel subscriptions directly from the system 100 interface.

[0160] The payment module 120 is configured to cooperate with various payment gateways to facilitate secure transactions via at least one of multiple payment modes, wherein the payment module 120 transmits successful transaction data.

[0161] In an aspect, the payment module 120 is further configured to cooperate with fraud detection mechanisms to ensure secure and verified transactions.

[0162] In another embodiment, the payment module 120 ensures secure transactions by integrating with multiple payment gateways and fraud detection systems. The backend enhancements ensure transaction logs are securely stored and instantly verified for user confirmation.

[0163] In one embodiment, the payment module 120 supports secure payment processing through integration with popular gateways (e.g., Stripe, PayPal, UPI). The payment module 120 ensures compliance with industry standards (e.g., PCI-DSS) and supports multiple payment methods, including credit / debit cards, net banking, and mobile wallets. The payment module 120 stores transaction IDs and returns success or failure statuses. The schedule organizer module 122 is configured to cooperate with the user interface to assist in planning and managing daily activities during the travel period based on the selected travel plan.

[0164] In an aspect, the schedule organizer module 122 is further configured to cooperate with calendar applications and productivity tools to synchronize travel itineraries with the user's daily schedule.

[0165] In yet another embodiment, the schedule organizer module 122 assists users in planning their daily activities by synchronizing with third-party calendar applications and productivity tools. The backend improvements ensure real-time updates, while UI enhancements allow users to customize their itineraries with drag-and-drop functionality and automated time optimization features. In another embodiment, the schedule organizer module 122 offers a personalized day-by-day travel calendar. The schedule organizer module 122 automatically adds booked activities, suggests nearby restaurants or attractions based on the current location, and allows manual schedule editing. Integration with third-party calendar apps (e.g., Google Calendar, Apple Calendar) is optionally provided.

[0166] In another embodiment, the system 100 further comprises a continuous assessment module configured to cooperate with the data fetching module 104 and the data aggregation module 106 to continuously assess and reassess travel itineraries and lifestyle information. The continuous assessment module retrieves data from the internet at large and from specific databases and websites to monitor and process travel trends, popularity indices, information consumption patterns, weather conditions, traffic updates, disaster notifications, and the opening and closing times of places of interest.

[0167] In one embodiment, the system 100 further comprises a synchronization module configured to cooperate with the user interface and the schedule organizer module 122 to enable itinerary synchronization between multiple mobile devices having the application installed. The synchronization module allows itinerary alignment between multiple users or family user groups, wherein the individual itineraries of users travelling together in the same geographic region are dynamically adjusted and synchronized for efficiency. The synchronization module further enables the addition, removal, or modification of travel elements by each user before or during the execution of the travel plan. In yet another embodiment, the system 100 further comprises a health monitoring module configured to cooperate with external health applications and wearable technology, including but not limited to bracelets, smartwatches, anklets, or connected footwear. The health monitoring module is adapted to track physical activity and health parameters during the execution of the travel plan and to generate suggestions for travel guide planning for differently abled users.

[0168] In another embodiment, the system 100 further comprises an augmented reality (AR) module configured to cooperate with the device’s camera, navigation, and orientation sensors, as well as with external wearable AR devices, to provide interactive multimedia guidance for monuments and places of interest. The AR module is adapted to generate interactive maps and virtual information layers by embedding data from external sources or affiliate databases, including but not limited to museums, amusement parks, and historical sites.

[0169] In one embodiment, the system 100 further comprises an audio integration module configured to cooperate with music, podcast, and audiobook platforms through native hosting, external linking, or account syncing with third-party services. The audio integration module provides thematic audio content relevant to the travel guide, including curated playlists, audiobooks, or podcasts that enhance the user’s travel experience based on the destination, event, or point of interest.

[0170] In another embodiment, the system 100 determines a user’s current operational context based on the “user’s current location” rather than a fixed “city” to provide location-specific services. The term “user’s current location” is used in an encompassing manner to refer to the real-time geospatial position of the user obtained via GPS, cellular, or other network-based location detection technologies. The use of “location” instead of “city” enables the system to provide more granular recommendations within varying radii, from a few meters to several kilometers, tailored to the user’s travel context.

[0171] In an aspect, the system 100 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the microprocessor may fetch and execute computer- readable instructions stored in a memory. The functions of the microprocessor may be provided through the use of dedicated hardware as well as hardware capable of executing machine-readable instructions. In other examples, the microprocessor may be implemented by electronic circuitry or printed circuit board. The microprocessor may be configured to execute functions of various modules of the system 100 such as the input data module 102, the data fetching module 104, the data aggregation module 106, the preprocessing module 108, the recommendation module 110, the booking module 112, the notification module 114, the feedback module 116, the subscription module 118, the payment module 120, and the schedule organizer module 122.

[0172] Figure 2A and Figure 2C illustrate a flow chart depicting the steps involved in the method for automated travel planning in accordance with an embodiment of the present disclosure. The order in which method 200 is described is not intended to be construed as a limitation, and any number of the described method steps may be combined in any order to implement method 200 or an alternative method. Furthermore, method 200 may be implemented by processing resources or computing device(s) through any suitable hardware, non-transitory machine-readable medium / instructions, or a combination thereof. The method 200 comprises the following steps:

[0173] At step 202, the method 200 includes receiving, by an input data module 102, realtime preference inputs from a user upon successful login.

[0174] At step 204, the method 200 includes collecting, by a data fetching module 104, travel-related data based on the received real-time preference inputs data.

[0175] At step 206, the method 200 includes generating, by a data aggregation module 106, an aggregate database by collecting travel-related data and providing information related to emergency services and helplines during trips.

[0176] At step 208, the method 200 includes processing, by a preprocessing module 108, the aggregated data.

[0177] At step 210, the method 200 includes generating, by the preprocessing module 108, one or more travel plans using a pre-trained machine learning model based on user preference input. At step 212, the method 200 includes filtering by a filtering module 109, travel plans based on predefined criteria, including user preferences, budget constraints, travel time, and availability.

[0178] At step 214, the method 200 includes generating, by a recommendation module 110, a recommendation list of travel plans based on ratings, feedback, budget, user’s previous trips, or wish list.

[0179] At step 216, the method 200 includes facilitating, by a booking module 112, bookings based on user selection from the recommendation list of travel plans, enabling prebooking and on-spot booking, confirming reservations, and providing real-time confirmation to the user.

[0180] At step 218, the method 200 includes sending a notification module 114, notifications to the user for each activity of the travel plan until the trip is completed.

[0181] At step 220, the method 200 includes receiving, by a feedback module 116, user feedback data, wherein the user feedback data is analyzed using a pre-trained machine learning model.

[0182] At step 222, the method 200 includes managing, by a subscription module 118, user subscriptions, including plan selection, upgrades, downgrades, billing management, subscription status viewing, and cancellations.

[0183] At step 224, the method 200 includes facilitating, by a payment module 120, gateways to secure transactions via multiple payment modes transmitting successful transaction data.

[0184] At step 226, the method 200 includes assisting, by a schedule organizer module 122, in planning and managing daily activities during the travel period based on the selected travel plan.

[0185] In an aspect, the method 200 comprises the step of authenticating, by the input data module 102, the user through biometric verification prior to receiving real-time preference inputs. In an aspect, the method 200 further comprises the step of fetching, by the data fetching module 104, real-time weather updates from weather forecasting services corresponding to the travel destinations selected in the travel plan.

[0186] In an aspect, the method 200 further comprises the step of integrating, by the data aggregation module 106, emergency services and helpline information from governmental and private emergency response databases for each travel destination.

[0187] In an aspect, the method 200 further comprises the step of processing the aggregated data by the preprocessing module 108 further comprises interpreting user travel preferences and history using natural language processing techniques.

[0188] In an aspect, the method 200 further comprises the step of generating the recommendation list by the recommendation module 110 further comprises applying sentiment analysis techniques to refine travel recommendations based on user feedback and external travel reviews.

[0189] In an aspect, the method 200 further comprises the step of facilitating bookings by the booking module 112 further comprises fetching real-time availability and pricing information by cooperating with airline, hotel, and local transport APIs.

[0190] In an aspect, the method 200 further comprises the step of sending notifications by the notification module 114 further comprising delivering alerts and reminders via email, SMS, and in-app messaging services for each travel activity.

[0191] Figure 3 illustrates a flow chart for automated travel planning, designed to assist users in generating optimized travel itineraries in accordance with an embodiment of the present disclosure. The system 100 integrates multiple modules that interact systematically to facilitate data collection, travel plan generation, booking management, and user engagement. The system 100 begins when the user initiates a login request via a user interface. The system 100 verifies the authentication credentials, allowing access upon successful validation. If the authentication fails, the system prompts the user to re-enter valid credentials. Upon successful login, the input data module 102 cooperates with the user interface to receive travel-related inputs, including destination, travel dates, budget preferences, and specific constraints. The received data is transmitted to the data fetching module 104, which interacts with multiple data sources, including transportation services, accommodation providers, and external travel databases, to collect relevant travel data. The fetched data is aggregated and stored in an aggregate database, where the data aggregation module 106 systematically processes and structures the collected information. The aggregated data is then subjected to preprocessing operations via the preprocessing module 108, which utilizes a pre-trained machine learning model to generate one or more travel plans based on predefined parameters.

[0192] The filtering module 109 refines the generated travel plans by analyzing user preferences, such as cost-effectiveness, shortest routes, or highest-rated services, ensuring a customized recommendation experience. The recommendation module 110 subsequently generates a ranked list of travel plans, enabling the user to make an informed selection. Upon selection of a preferred travel plan, the system 100 determines whether the booking is required. If booking is necessary, booking module 112 facilitates reservations by interacting with service providers to confirm bookings. The system 100 triggers the notification module 114, which dispatches real-time updates to the user regarding booking confirmations, itinerary modifications, and reminders. As the user progresses through the travel plan, the system 100 continuously monitors the trip completion status. If the trip is ongoing, the notification module 114 continues to send updates. Once the trip is completed, the system 100 prompts the user to provide feedback, which is collected and analyzed via the feedback module 116.

[0193] Additionally, the system 100 includes a subscription module 118 that manages user subscriptions, enabling upgrades, downgrades, billing management, and cancellations. The payment module 120 ensures secure transactions via multiple payment gateways. Further, the schedule organizer module 122 assists users in planning and managing daily activities throughout the travel period, ensuring an optimized and seamless experience.

[0194] In an operative configuration, the system 100 functions by integrating multiple modules to facilitate seamless travel planning, booking, and management. Upon user login, the input data module 102 receives real-time travel preferences, which are processed by the data fetching module 104 to collect relevant travel-related data from various sources. The data aggregation module 106 compiles this data into an aggregate database, which is further processed by the preprocessing module 108 to generate potential travel plans.

[0195] The filtering module 109 refines these plans based on predefined criteria, such as user preferences, budget, and travel time, ensuring optimized recommendations. The recommendation module 110 ranks and suggests suitable travel plans, allowing the user to make informed selections. The booking module 112 facilitates reservations, while the notification module 114 sends real-time updates regarding the travel itinerary. During and after the trip, the feedback module 116 collects user reviews for continuous system improvement. The subscription module 118 manages user subscriptions, while the payment module 120 enables secure transactions. Additionally, the schedule organizer module 122 assists users in managing their daily travel activities, ensuring an efficient and user-centric travel planning experience.

[0196] Advantageously, the system 100 facilitates an efficient and automated travel planning experience by integrating multiple functional modules that streamline the process from itinerary generation to trip completion. The filtering module 109 optimizes travel plans based on user preferences, budget, and availability, ensuring personalized recommendations. The recommendation module 110 enhances decision-making by ranking travel options based on ratings, past trips, and feedback. The booking module 112 enables seamless reservations with real-time confirmations, while the notification module 114 ensures timely updates regarding bookings, schedule changes, and trip progress. The feedback module 116 aids in continuous system improvement by analyzing user reviews through machine learning. The subscription module 118 and payment module 120 provide flexible management of subscription plans and secure transactions.

[0197] Further, the schedule organizer module 122 assists in structuring daily activities, making the system 100 a comprehensive, user-centric, and resource-efficient travel planning solution.

[0198] An exemplary pseudo-code depicting the working of an automated travel planning system and method in accordance with the embodiment of the disclosure is under. - class TravelPlanningSystem: def init (self): self.user_data = None self.travel_data = None self, recommendations = [] self.filtered_plans = [] self.bookings = [] self.notifications = [] self.feedback = None self.subscription_status = None self.payment_status = None self, schedule = None self.commute_data = None self.dining data = [] sclf.sccrct guidc data = [] self.tour_time_info = { } self.bookmarks = [] self.itineraries = [] def receive_input(self, user_credentials, travel_preferences): if authenticate_user(user_credentials) : self.user_data = travel_preferences return "User authenticated and input received" return "Authentication failed" def fetch_travel_data(self): self.travel_data = query_data_sources(self.user_data) return self.travel_data def aggregate_data(self): self.travel_data = preprocess_data(self.travel_data) self.travel_data["emergency_info"] = get_emergency_services() self.travel_data["dining"] = scrape_and_validate_dining() self. travcl_data| "sccrct giiidcs" ] = fctch_sccrct_guidc_data() self.travel_data["tour_time"] = get_tour_time_estimations() return "Data aggregation completed" def generate_travel_plans(self): return machine_leaming_model .generate _plans(sclf. travcl data) def fdter_travel_plans(self): plans = self.generate_travel_plans() self.filtered_plans = [ plan for plan in plans if plan["budget"] <= self.user_data["budget"] and plan["travel_time"] <= self.user_data["max_travel_time"] and plan["availability"] == "Available"

[0199] ] return self.filtered_plans def add_commute_data(self, commute data): self.commute data = preprocess commute data(commute data) self.travel_data["commute"] = self.commute_data return "Commute data integrated into travel plan" def generate_recommendations(self) : self. recommendations = rank_recommendations(self.fdtered_plans, self.commute_data) return self, recommendations def book_travel(self, selected_plan): booking_status = process_booking(selected_plan) self, bookings . append(booking_status) return booking status def send_notifications(self): for event in self.travel_data.get("events", []): self.notifications.append(notify_user(event)) return "Notifications sent' def receive_feedback(self, feedback_data): self.feedback = analyze_feedback(feedback_data) return "Feedback received and analyzed" def manage_subscription(self, subscription_action): self.subscription status = update subscription(subscription action) return "Subscription updated" def process_payment(self, payment_details): self.payment_status = process_transaction(payment_details) return "Payment processed" def organize_schedule(self): self, schedule = sync_with_calendar(self.travel_data) return "Schedule organized" def manage_bookmarks(self, place, action): if action == "add" and place not in self.bookmarks: self.bookmarks.append(place) elif action == "remove" and place in self.bookmarks: self.bookmarks. remove(place) return self.bookmarks def fetch_itineraries(self, user_id): self.itineraries = get_user_itineraries(user_id) return self.itineraries

[0200] # Supporting Enhanced Functions def authenticate_user(credentials) : return True def query_data_sources(user_input) : return {"routes": [], "weather": {}, "prices": {}, "events": []} def preprocess_data(data): return data def get_emergency_services(): return {"police": "999", "ambulance": " 112"} def preprocess commute data(commute data): return { "start_time": commute_data[" Start Time"],

[0201] "arrival_time": commute_data[" Arrival Time"],

[0202] "total duration": commute_data["Total Duration"],

[0203] "details" : commute_data["Details"]

[0204] } def rank_recommendations(plans, commute data): for plan in plans: plan["adjusted_rating"] = calculate_rating(plan, commute data) return sorted(plans, key=lambda x: x["adjusted_rating"], reverse=True) def calculate_rating(plan, commute data): return plan["rating"] - (int(commute_data["total_duration"].split()[0]) / 10) def process_booking(plan): return {"status": "Confirmed", "details": plan} def notify_user(event): return f 'Notification sent for {event}" def analyze_feedback(feedback_data) : return {"sentiment": "Positive", "suggestions": []} def update subscription(action): return f Subscription {action} successful" def process_transaction(payment_details): return {"status": "Success", "transaction_id" : "XYZ123"} def sync_with_calendar(travel_data) : return {"status": "Synced"} def scrape_and_validate_dining() : return get_validated_dining_data() def fctch_sccrct_guidc_data(): return gct_sccrct_guidcs() def get_tour_time_estimations(): return fetch_tour_times() def get_user_itineraries(user_id) : return [" Itinerary 1", "Itinerary2"] def get_validated_dining_data(): return ["Dining Place A", "Dining Place B"] def gct_sccrct_guidcs(): return ["Guide A", "Guide B"] def fetch_tour_times(): return {"Place A": "45 mins", "Place B": " 1 hour"}

[0205] # Usage Example travel_system = TravelPlanningSystem() travel_system.receive_input("user_credentials", {"budget": 1000, "max_travel_time": 5}) travel_system . fetch_travel_data() travel_system . aggregate_data() travel_system.fdter_travel_plans() travel_system . add_commute_data( {

[0206] "Start Time": "07: 14 AM",

[0207] "Arrival Time": "08:40 AM",

[0208] Total Duration": "86 minutes", Details": [{"mode": "Bus", "duration": " 15 minutes"}]

[0209] }) travel_system.generate_recommendations() travel_system.book_travel(" Selected Plan") travel_system . send_notifications() travel_system.receive_feedback("Feedback Data") travel_system.manage_subscription("Upgrade") travel_system.process_payment("Payment Details") travel_system.organize_schedule() travel_system.manage_bookmarks("Dining Place A", "add") travel_system.fetch_itineraries("user_001")The foregoing description of the embodiments has been provided for purposes of illustration and is not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment but are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.

[0210] TECHNICAL ADVANCEMENTS

[0211] The present disclosure described herein above has several technical advantages, including, but not limited to automated travel planning system and method that:

[0212] • provides seamless travel planning by integrating multiple data sources and services;

[0213] • provides real-time travel recommendations using a pre-trained machine learning model;

[0214] • provides secure user authentication through biometric authentication services;

[0215] • provides real-time weather updates for better trip planning; • provides updated emergency service information by integrating governmental and private databases;

[0216] • provides accurate travel suggestions using natural language processing and sentiment analysis;

[0217] • provides real-time availability and pricing for flights, hotels, and transport services;

[0218] • provides timely notifications through multiple communication channels;

[0219] • provides enhanced travel recommendations by analyzing user feedback trends;

[0220] • provides personalized offers through CRM integration; and

[0221] • provides secure transactions with fraud detection mechanisms.

[0222] The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0223] The foregoing description of the specific embodiments so fully reveals the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

[0224] The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results. While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

Claims

AMENDED CLAIMS received by the International Bureau on 02 September 2025 (02.09.2025)WE CLAIM:

1. A system (100) for automated travel planning, comprising:• an input data module (102) configured to cooperate with a user interface to receive real-time preference inputs from a user upon successfid login, wherein said input data module (102) is integrated with an intuitive user interface (UI);• a data fetching module (104) configured to cooperate with various data sources to collect travel-related data based on the received real-time preference inputs data and configured to cooperate with weather forecasting services and traffic analytics services to provide real-time weather updates, real-time congestion, and route data;• a data aggregation module (106) configured to cooperate with a pretrained machine learning model to generate an aggregate database by collecting travel-related data, consolidating data fetched from disparate and disconnected sources, and providing information related to emergency services and helplines during trips;• a preprocessing module (108) configured to cooperate with the aggregate database to process the aggregated data and generate one or more travel plans using a pre-trained machine learning model based on user preference input wherein said preprocessing module (108) is further configured to cooperate with a natural language processing technique to interpret user preferences and travel history more accurately;• a filtering module (109) configured to cooperate with the preprocessing module (108) to filter travel plans and refine said travel plans based on predefined criteria, including user preferences, budget constraints, travel time, availability;• a recommendation module (110) configured to cooperate with the preprocessing module (108) to generate a recommendation list of travel plans based on ratings, feedback, budget, user’s previous trips, or wish list , wherein said recommendation module (110) is further configured to cooperate with sentiment analysis techniques to refine recommendations based on user feedback and online travel reviews;• a booking module (112) configured to cooperate with various service providers to facilitate bookings based on user selection from the recommendation list of travel plan, wherein said booking module (112) further enables pre-booking and on-spot booking, confirms reservations, and provides real-time confirmation to the user;• a notification module (114) configured to cooperate with the system components to send notifications to the user for each activity of the travel plan until the trip is completed, wherein said notification module (114) further integrates with email, SMS, and in-app messaging services to deliver timely travel alerts and reminders to the user;• a feedback module (116) configured to cooperate with a feedback database to receive user feedback data, wherein the user feedback data is analyzed using a pre-trained machine learning model to improve future recommendations and detect feedback trends;• a subscription module (118) configured to cooperate with the application framework to manage user subscriptions, including plan selection, upgrades, downgrades, billing management, subscription status viewing, and cancellations, and further integrates with a customer relationship management (CRM) unit to provide personalized offers;• a payment module (120) configured to cooperate with various payment gateways to facilitate secure transactions via at least one of multiple payment modes, wherein said payment module (120) transmits successful transaction data and ensures secure transactions by integrating with multiple gateways and a fraud detection system; and• a schedule organizer module (122) configured to cooperate with the user interface to assist in planning and managing daily activities during the travel period based on the selected travel plan to integrate with calendar applications and productivity tools to synchronize itineraries with the user's daily schedule.

2. The system (100) as claimed in claim 1, wherein said input data module (102) is further configured to cooperate with biometric authentication services to enhance security during user login.

3. The system (100) as claimed in claim 1, wherein said data aggregation module (106) is further configured to cooperate with governmental and private emergency response databases to ensure updated emergency service information is available to users.

4. The system (100) as claimed in claim 1, wherein said booking module (112) is further configured to cooperate with airline, hotel, and local transport APIs to provide real-time availability and pricing information.

5. The system (100) as claimed in claim 1, wherein said payment module (120) is further configured to cooperate with fraud detection mechanisms to ensure secure and verified transactions.

6. The system (100) as claimed in claim 1, wherein said preprocessing module (108) is further configured to cooperate with the data aggregation module (106) to generate a localised lifestyle plan based on user geolocation, preference inputs, and duration constraints, such as a short-term itinerary for dining and shopping in the user’s current city.

7. The system (100) as claimed in claim 1, wherein said input data module (102) is further configured to operate in an unauthenticated exploration mode based on device location permissions, allowing users to browse potential travel or lifestyle plans on an interactive map without requiring user login.

8. The system (100) as claimed in claim 1, wherein said data aggregation module (106) is further configured to cooperate with authoritative third-party data sources including encyclopedias, wikis, and government heritage databases to retrieve contextual information about places of interest.

9. The system (100) as claimed in claim 1, wherein said notification module (114) is further configured to present the contextual information retrieved from authoritative sources in real-time as an interactive script within the application or through immersive display technologies such as augmented reality headsets.

10. A method (200) for automated travel planning, said method (200) comprises the steps of:• receiving, by an input data module (102), real-time preference inputs from a user upon successful login;• collecting, by a data fetching module (104), travel-related data based on the received real-time preference inputs data;• generating, by a data aggregation module (106), an aggregate database by collecting travel-related data and providing information related to emergency services and helplines during trips;• processing, by a preprocessing module (108), the aggregated data;• generating, by said preprocessing module (108), one or more travel plans using a pre-trained machine learning model based on user preference input;• filtering, by a filtering module (109), travel plans based on predefined criteria, including user preferences, budget constraints, travel time, and availability;• generating, by a recommendation module (110), a recommendation list of travel plans based on ratings, feedback, budget, user’s previous trips, or wish list;• facilitating, by a booking module (112), bookings based on user selection from the recommendation list of travel plans, enabling pre-booking and on-spot booking, confirming reservations, and providing real-time confirmation to the user;• sending, a notification module (114), notifications to the user for each activity of the travel plan until the trip is completed;• receiving, by a feedback module (116), user feedback data, wherein the user feedback data is analyzed using a pre-trained machine learning model;• managing, by a subscription module (118), user subscriptions, including plan selection, upgrades, downgrades, billing management, subscription status viewing, and cancellations;• facilitating, by a payment module (120), gateways to secure transactions via multiple payment modes, transmitting successful transaction data; and• assisting, by a schedule organizer module (122), in planning and managing daily activities during the travel period based on the selected travel plan.

11. The method (200) as claimed in claim 10, the method (200) comprising the step of authenticating, by the input data module (102), the user through biometric verification prior to receiving real-time preference inputs.

12. The method (200) as claimed in claim 10, further comprising the step of fetching, by the data fetching module (104), real-time weather updates from weather forecasting services corresponding to the travel destinations selected in the travel plan.

13. The method (200) as claimed in claim 10, further comprising the step of integrating, by the data aggregation module (106), emergency services and helpline information from governmental and private emergency response databases for each travel destination.

14. The method (200) as claimed in claim 10, wherein processing the aggregated data by the preprocessing module (108) further comprises interpreting user travel preferences and history using natural language processing techniques.

15. The method (200) as claimed in claim 10, wherein generating the recommendation list by the recommendation module (110) further comprises applying sentiment analysis techniques to refine travel recommendations based on user feedback and external travel reviews.

16. The method (200) as claimed in claim 10, wherein facilitating bookings by the booking module (112) further comprises fetching real-time availability and pricing information by cooperating with airline, hotel, and local transport APIs.

17. The method (200) as claimed in claim 10, wherein sending notifications by the notification module (114) further comprises delivering alerts and reminders via email, SMS, and in-app messaging services for each travel activity.

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