Integrated online travel booking system with enhanced geographic coverage and service aggregation

Abstract

A computer-implemented system for optimizing travel itineraries in real time includes a failover-capable API gateway, a search and recommendation engine, and a machine learning model trained on historical travel data. The system expands itinerary options using an Alternative Logic Service that incorporates flexible dates and nearby airports based on geographic metadata and pricing trends. Structured offer requests are transmitted to external APIs and evaluated using predictive scoring. A client-side graphical radius filter reduces request volume and latency. Updated itineraries are reranked dynamically and propagated across devices via a pub-sub notification system.

Description

[0001] INTEGRATED ONLINE TRAVEL BOOKING SYSTEM WITH ENHANCED GEOGRAPHIC COVERAGE AND SERVICE AGGREGATION

[0002] REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to and the benefit of United States Provisional Patent Application No. 63 / 647,232, filed on May 14, 2024, the contents of which are incorporated by reference in their entirety as if fully set forth herein.

[0004] TECHNICAL FIELD

[0005] The present disclosure pertains to the technical field of digital booking systems, specifically to systems that facilitate the integrated booking of travel-related services. More particularly, the disclosure relates to an online travel agency platform that utilizes advanced computational algorithms and data integration techniques to optimize travel planning. This includes the aggregation of flight options, ground transportation, and accommodation services, providing a comprehensive and user-centric travel booking experience. The disclosure addresses the needs for cost-efficiency, extensive geographic coverage including smaller regional airports, and enhanced user convenience by seamlessly combining multiple travel services into a singular, efficient digital interface. The system is designed to cater to various types of travelers seeking a streamlined and adaptive approach to arranging travel across multiple transportation modes and accommodation options, tailored to individual preferences and real-time conditions.

[0006] BACKGROUND

[0007] The history of online travel websites reflects a significant shift in how consumers plan and book travel, transitioning from traditional travel agency services to a digital-first approach. The genesis of this shift can be traced back to the early 1990s with the advent of the internet. Initially, airline reservation systems, which had previously been available only to travel agencies, began to be accessible to consumers directly through internet technology. This change was marked by the launch of several key platforms that allowed users to view and book flights without the need for a travel agent. These platforms quickly expanded to include hotel bookings, car rentals, and other travel services, providing a comprehensive travel planning experience.

[0008] By the late 1990s and early 2000s, online travel agencies (OTAs) began to emerge as powerful players in the travel industry. Websites such as Expedia, Priceline, and Travelocity revolutionized the travel booking process by aggregating data from multiple service providers, offering consumers a one-stop-shop for comparing and booking travel. These websites provided tools that allowed users to easily compare prices and options across a wide range of travel sendees, significantly enhancing user autonomy in making travel arrangements.

[0009] The rise of OTAs coincided with advancements in internet technology' and increased consumer confidence in online transactions. The ability to instantly confirm reservations and receive electronic tickets reduced the uncertainties previously associated with phone-based or in-person bookings. Moreover, the user-friendly interfaces and the convenience of 24 / 7 access to travel booking services further attracted consumers to online platforms. This shift was also supported by the development of secure payment systems, which helped alleviate concerns over the safety of online financial transactions.

[0010] The benefits of using online travel websites over traditional travel agents are manifold. Firstly, OTAs provide greater transparency in pricing and availability'. Consumers have the ability to access a global database of flights, hotels, and car rentals, enabling them to make informed decisions based on comprehensive information and competitive pricing. This level of transparency was difficult to achieve through traditional travel agents, who had limited access to or control over airline and hotel databases.

[0011] Another significant advantage is the customization and flexibility offered by online platforms. Travelers can tailor their plans according to specific preferences and requirements, such as travel dates, destinations, and budget constraints. This personalized planning is facilitated by sophisticated algorithms that filter and recommend options best suited to the traveler’s criteria. In contrast, traditional travel agents often provided more generic solutions that might not fully align with individual customer needs.

[0012] Online travel websites offer additional functionalities and resources that enhance the travel planning experience. These include customer reviews, virtual tours of accommodations, and detailed descriptions of destinations and amenities. Such features provide a depth of insight that traditional travel agencies could seldom offer. The integration of these elements into the booking process empowers consumers, providing them with a holistic view of their travel options and contributing to more satisfying travel experiences.

[0013] Online travel agencies (OTAs) have brought numerous benefits to the travel industry, but they also present several drawbacks, particularly in their tendency to focus on major airport hubs, which can limit options for travelers and skew market dynamics. This focus is primarily driven by the business models of OTAs. which aim to maximize bookings through streamlined logistics and partnerships with major airlines that predominantly serve these hubs. Despite their advantages, one significant issue with the concentration on major hubs is the underrepresentation of smaller, regional airports, which are often closer to travelers' homes or destinations. This limitation can lead to increased travel time and additional costs for consumers, who might have to drive longer distances to reach major hubs or require additional transportation from these hubs to their final destinations. Additionally, the focus on major hubs often excludes smaller airlines that might offer more competitive rates or direct routes from smaller airports, thus reducing the overall diversity of travel options available to the consumer.

[0014] Furthermore, the algorithmic optimization used by OTAs to populate flight options often prioritizes routes based on agreements with specific airlines and the profitability of certain routes. This can result in a lack of comprehensive offerings that might be available through direct bookings with airlines or regional travel services. Consequently, while OTAs simplify' the booking process, they can inadvertently obscure potentially more convenient or cheaper alternatives that do not align with their commercial interests.

[0015] The dominance of OTAs in major markets also poses competitive challenges for local travel agents and smaller online platforms. The extensive marketing capabilities and larger customer base of big OTAs allow them to secure preferential deals with airlines and hotels, which smaller entities cannot match. This dominance not only affects competition but also limits consumer exposure to a variety of travel services and packages that might be more tailored or specialized than those offered by major OTAs.

[0016] Additionally, the reliance on OTAs for travel planning has conditioned consumer behavior towards convenience over quality. While OTAs offer a variety of options, the depth of these options is often limited to what is most lucrative for the platform. This can lead to a homogenization of travel experiences, where unique, niche, or culturally rich alternatives are sidelined in favor of mainstream, profitable choices. This shift can dilute the cultural and experiential richness of travel.

[0017] While online travel websites offer undeniable advantages in terms of convenience and access to global travel options, their focus on major airport hubs presents considerable drawbacks. This emphasis often results in reduced access to smaller markets, less competitive pricing, and a narrowed scope of travel experiences. Such limitations highlight the need for more balanced offerings that include diverse travel opportunities and support for regional travel providers, which can enrich the travel experience for consumers and maintain healthy market competition.

[0018] The current landscape of online travel agencies predominantly centers around major airport hubs, often overlooking smaller, regional airports and limiting the breadth of travel options available to consumers. An online travel platform that comprehensively facilitates travel planning by including these smaller hubs, alongside integrated sendees such as car rental and hotel reservations, addresses a significant unmet need in the art. Such a platform would not only enhance the convenience and efficiency of the booking process but also prioritize cost-effectiveness, enabling travelers to reach their destinations more inexpensively. This would represent a notable advancement in the travel industry, offering a more inclusive and diverse range of travel opportunities and potentially reshaping market dynamics to better cater to the varied needs and preferences of modem travelers.

[0019] SUMMARY

[0020] In general, the present invention relates to an online travel agency platform designed to revolutionize the way travelers plan and book their trips by emphasizing cost-efficiency, comprehensive service integration, and the inclusion of smaller regional airports. This platform extends beyond the traditional scope of major airport hubs and incorporates a wider network of travel options, ensuring that users can access the most direct and cost-effective travel routes available.

[0021] The platform intelligently combines data from various travel sendees, including, but not limited to flights, car rentals, and hotel reservations, to provide a seamless travel planning experience. For instance, consider a traveler planning a trip from New York City to Rockford, Illinois. While a direct flight from LaGuardia Airport to Rockford Airport might seem the most straightforward option, it may not always be the most economical. In this example, the platform performs a holistic search that includes not only direct flight options but also alternative routes, such as flying from LaGuardia to Chicago O'Hare International Airport.

[0022] Upon identifying a potentially less expensive or more convenient flight to O'Hare, the platform further enhances the travel plan by calculating the cost and logistics of renting a car and driving from Chicago to Rockford. It also assesses the need for an overnight stay, checking for hotel availability and rates in the vicinity of both airports if necessary. This integrative approach ensures that the traveler is presented with a variety of travel paths, providing both cost-effectiveness, convenience, and, if requested, the option for sightseeing in the local area.

[0023] Moreover, the platform is equipped with advanced algorithms, which can be driven by artificial intelligence (Al) that factors in real-time traffic conditions, weather forecasts, and local events, which may impact travel times and costs. This dynamic approach to travel planning not only saves money but also adapts to the personal preferences and requirements of the user, making the travel experience as efficient and enjoyable as possible.

[0024] The platform fills a critical gap in the current online travel agency market by offering a more holistic, flexible, and cost-effective way to plan travel. It caters specifically to the needs of travelers looking to optimize their journeys not just through major hubs but through any accessible route, thereby democratizing the availability of travel options and empowering users with unprecedented control over their travel plans. This innovation holds significant potential to impact the travel industry by introducing a new standard of personalized, integrated travel planning.

[0025] In a first general aspect, a computer-implemented travel itinerary optimization system includes a user interface module configured to receive travel parameters from a user, the travel parameters including an origin location, a destination location, one or more travel dates, and user-defined preferences; an API gateway communicatively coupled to external travel service providers, the API gateway configured to transmit offer requests to one or more remote services using structured parameters including maximum connection count, fare class, and sort criteria, and further including a failover mechanism configured to detect degraded responses from the external services and, in response, reroute a request to an alternate provider or mark the corresponding travel data as stale; an Alternative Logic Service configured to expand the travel parameters to include a set of additional airport codes within a geographic radius of the origin and destination, and a range of travel dates within a specified window' centered on the user’s selected dates; a search and recommendation engine configured to generate candidate travel itineraries based on the travel parameters and expanded data provided by the Alternative Logic Service; a machine learning engine configured to receive, for each candidate itinerary, a feature vector including at least fare variance, connection count, load factor, departure time classification, routing distance, weather deviation score, and carbon emissions data, and to output a cost-savings probability; wherein the search and recommendation engine is further configured to dynamically re-rank the candidate travel itineraries based on the cost-savings probabilities and on updates in API data availability; and wherein the user interface module includes a client-side geographic radius filter operable to pre-filter candidate airports prior to query submission, thereby reducing bandwidth and round-trip latency.

[0026] In some embodiments, the Alternative Logic Service calculates additional airport codes using the Haversine formula and metadata obtained from a travel service API. In certain implementations, the API gatew ay transmits offer requests to a remote service using one or more endpoints, such as / offer_requests and / offers. The machine learning engine can include a gradient-boosted tree model trained on historical global travel booking data including more than one billion records. A metadata cache can be included and configured to store frequently accessed travel data and to prevent redundant API calls during repeated queries. In some embodiments, the search and recommendation engine annotates each itinerary with a label, such as “Best Value,’" “Cheapest Nearby Date,” and “Eco-Friendly Route,” based on a percentile ranking or cost differential from a baseline.

[0027] In another embodiment, stale-state indicators assigned by the API gateway prevent itineraries relying on incomplete data from being prioritized in the reranking process. A notification subsystem can be configured to propagate itinerary updates across multiple user devices using a publish-subscribe messaging architecture.

[0028] In a second general aspect, a non-transitory computer-readable medium stores instructions that, when executed by one or more processors, cause a computing system to perform operations including receiving, via a user interface module, travel parameters from a user, the travel parameters including an origin, a destination, one or more travel dates, and user preferences; expanding the travel parameters by identifying additional airport codes within a geographic radius of the origin and destination, and additional dates within a temporal window centered on the specified travel dates, based on metadata retrieved from a remote service; constructing and transmitting offer requests to one or more external travel data providers using structured parameters including connection constraints, fare class, and sorting preferences; receiving offer data from the external travel data providers and generating a set of candidate itineraries based on the expanded travel parameters; for each candidate itinerary, computing a feature vector including at least routing distance, fare variance, departure time classification, connection count, load factor, weather deviation score, and emissions data; processing each feature vector using a trained machine learning model to generate a cost-savings probability for each itinerary; dynamically reprioritizing the candidate itineraries based on the cost-savings probabilities and the availability of underlying data; and presenting the reprioritized itineraries to the user, wherein the user interface includes a client-side airport radius filter configured to reduce network request volume prior to submission.

[0029] In some embodiments, the instructions further cause the system to retry failed API requests up to a threshold and to mark associated itinerary data as stale when the threshold is exceeded. In such cases, stale-state designations can suppress prioritization of itineraries based on incomplete or outdated data. In other embodiments, the cost-savings probabilities are used to assign labels to itineraries, such as “Best Value,” “Cheapest Nearby Date.” and "Eco-Friendly Route.” These labels can be assigned based on percentile thresholds applied to the output of the machine learning model. The instructions can also cause the system to propagate itinerary updates caused by changes in underlying data across user devices via a publish-subscribe messaging service.

[0030] In a third general aspect, a server system is configured to generate and update travel itineraries in real time, including a processor and memory storing executable instructions to implement an input module configured to receive travel parameters from a user device, the travel parameters including origin and destination locations, travel dates, and user-defined preferences; an Alternative Logic Service configured to expand the travel parameters to include a plurality of alternative airports within a specified radius and a range of flexible dates around the input dates, using geographic metadata and historical pricing trends; an API gateway module communicatively coupled to one or more external travel data sources, the API gateway configured to construct and transmit structured offer requests to one or more endpoints, such as an / offer requests endpoint, with parameters including maximum connection count, fare class, and sort order, and to apply retry and failover logic when external responses are delayed or invalid; a data aggregation and normalization module configured to combine data from multiple sources into structured itinerary candidates; a machine learning inference module configured to generate a cost-savings score for each itinerary candidate based on a feature vector including routing distance, fare variance, connection count, load factor, weather deviation, emissions data, and time classification; a recommendation module configured to re-rank itinerary' candidates based on the cost-savings score and to suppress itineraries associated with stale data; and a client interface handler configured to transmit the reranked itineraries to the user device, wherein the user device is operable to define a geographic radius constraint using a local graphical filter prior to query submission.

[0031] In some embodiments, the Alternative Logic Service applies the Haversine formula to identify proximate airports based on Duffel airport metadata. In other configurations, the travel parameters are expanded to include a window of dates, e.g., plus-or-minus three days, around the user-specified travel dates. Labels such as “Best Value,” “Cheapest Nearby Date,” and “Eco-Friendly Route” can be assigned to itineraries based on the cost-savings score or emissions estimates. These labels can be determined using predefined percentile thresholds relative to a baseline itinerary. Itinerary state changes can be propagated to user devices via a publish-subscribe messaging system that ensures eventual consistency across asynchronous user sessions. The platform provides numerous advantages that significantly advance the field of online travel booking by offering a solution that not only prioritizes cost and convenience but also enhances the breadth of travel options and overall user satisfaction. This comprehensive approach addresses the limitations of current platforms and sets a new benchmark in the travel industry. For example. A core advantage of the invented platform is its capability to reduce travel costs significantly. By considering not only direct routes but also alternative travel paths involving other major or regional airports, the platform ensures that the user has access to the most economical options available. For example, if a direct flight is more expensive, the platform will suggest alternative flights to nearby airports coupled with ground transportation options. This holistic view of travel planning allows users to make cost-effective decisions without compromising on the overall travel experience.

[0032] The platform simplifies the travel planning process by aggregating various travel- related services into a single, user-friendly interface. This includes flights, car rentals, and accommodations, which can be booked simultaneously. The integration of these services reduces the time and effort required by the user to plan multiple aspects of a trip separately. For example, a traveler can book a flight to a major airport, reserve a rental car, and arrange accommodation in one seamless process, ensuring that each component of the travel plan is well-coordinated.

[0033] Unlike traditional OTAs that primarily focus on major hubs, this invention expands the reach to include smaller, regional airports. This not only provides more travel options but also supports local economies by diverting some travel traffic to less frequented airports. The inclusion of these smaller airports can be particularly beneficial for travelers living near these airports or those visiting destinations closer to them.

[0034] The platform is designed to adapt to the individual preferences and needs of users. Advanced algorithms analyze a wide range of factors, including pricing, timing, and user preferences, to tailor travel recommendations. Additionally, the platform can adjust recommendations based on real-time data such as weather conditions, traffic updates, and special events, ensuring that the travel plan remains optimal even as external conditions change.

[0035] By providing detailed comparisons of various travel options and incorporating user reviews and ratings for services like hotels and car rentals, the platform enhances the overall travel planning experience. Users gain access to a wealth of information that aids in making informed decisions, thereby improving satisfaction with the travel arrangements.

[0036] The platform can also contribute to reduced environmental impact by suggesting more efficient travel routes and modes of transportation. For example, combining flights with ground transportation options in a strategic manner can lower the carbon footprint compared to less optimized travel routes.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of any described embodiment, suitable methods and materials are described below. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting. In case of conflict with terms used in the art, the present specification, including definitions, will control.

[0038] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description and claims.

[0039] BRIEF DESCRIPTION OF DRAWINGS

[0040] The present embodiments are illustrated by way of the figures of the accompanying drawings, which may not necessarily be to scale, in which like references indicate similar elements, and in which:

[0041] FIG. 1 is an online travel agency platform 100 according to one embodiment;

[0042] FIG. 2 is an exemplary flowchart showing steps of the platform according to one embodiment;

[0043] FIG. 3 is an exemplary screen snapshot of a user interface of the platform, showing options for creating a travel itinerary, according to one embodiment;

[0044] FIG. 4 is an exemplary screen snapshot of a user interface of the platform, showing itinerary options generated by the platform, according to one embodiment;

[0045] FIG. 5 illustrates a geographical search area utilized to search for alternative travel itineraries.

[0046] DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0047] The present invention operates on a modem computing architecture designed to handle complex data integration and real-time processing, ensuring an efficient and responsive online travel booking system. The platform can be hosted on a scalable cloud infrastructure, which allows for the robust handling of large volumes of user data and travel sendee information. This architecture can be critical in supporting the seamless operation of the platform, accommodating spikes in user activity, and expanding storage needs without compromising performance. In one embodiment, the core of the platform's architecture is a distributed database system that stores and manages comprehensive travel data, including flight schedules, hotel availability7, car rental services, and user preferences. This database is structured to facilitate quick retrieval of information, enabling the platform to perform fast searches and update records in real-time. The use of a distributed database also enhances the system's resilience and availability, ensuring that the service remains operational even in the event of localized failures.

[0048] The platform can utilize a modular architecture to manage the various components of the travel booking process. In such an approach, each module is dedicated to a specific function such as user management, payment processing, travel recommendations, and booking confirmations. This modular approach not only simplifies the development and maintenance of the system but also improves scalability7by allowing individual services to be updated or scaled independently based on demand. Integration with external travel and accommodation providers is achieved through a series of application program interfaces (APIs) that allow the platform to retrieve up-to-date information and conduct transactions with these services. The API gateway can serve as the intermediary, routing requests and responses between the platform's microservices and external systems. This gateway can manage the complexities of interacting with multiple external APIs, providing a unified interface for the platform's services to access external data efficiently.

[0049] The platform can incorporate advanced algorithms that analyze both static data, such as standard travel schedules, and dynamic, real-time, or stored data, such as current flight prices and traffic conditions. These algorithms can employ machine learning techniques to predict the best travel options based on historical data and real-time inputs. The system's ability to adapt to changing conditions and learn from user behavior patterns significantly enhances the personalization of travel recommendations, ensuring that users receive the most relevant and cost-effective travel options.

[0050] The platform can incorporate artificial intelligence (Al) in any embodiment of the invention, providing significant enhancements to functionality7and user experience. Al technologies can be deployed to automate complex decision-making processes, enabling the platform to offer more accurate and personalized travel suggestions based on user preferences and past behavior. For instance, Al-driven predictive analytics can be used to forecast price trends, suggest optimal travel times, and identify cost-saving opportunities by analyzing vast datasets that would be impractical to process manually. Machine learning models can further refine the user experience by continuously learning from interactions, thereby improving the relevance of search results and recommendations over time. Additionally, natural language processing (NLP) capabilities can be integrated to allow users to interact with the platform through conversational interfaces, making travel planning more intuitive and accessible. By leveraging Al across various aspects of the platform, the invention not only enhances operational efficiencies but also delivers a more engaging and responsive service to its users.

[0051] Referring now to FIG. 1, an online travel agency platform (hereinafter ‘platform’) 100 is described according to one embodiment. In this embodiment, the platform 100 relies on bidirectional communication through the Internet 107 between an end user and the platform’s servers 106. facilitating interactive and real-time exchanges of information. This communication primarily relies on the client-server model where the user's device 104 serves as the client and the platform’s servers 106 act as the central hub that processes and responds to user requests. This model is fundamental to web applications, allowing users to interact with the platform via a web browser or a mobile application.

[0052] In this embodiment, the platform 100 primarily uses the Hypertext Transfer Protocol (HTTP) or its secure variant, HTTPS, to facilitate communication. HTTPS is essential for ensuring data security, incorporating encry ption through Secure Socket Layer (SSL) or Transport Layer Security (TLS) protocols, which protect data integrity and privacy during transmission. Each user interaction, whether entering search criteria, making a selection, or finalizing a booking, initiates a request to the server using these protocols. The server 106 then processes the request, accessing databases or external APIs as necessary, and sends back a response to the user's device 104.

[0053] These interactions are not just limited to data retrieval but also include real-time data input from users, which the system processes to update or modify travel plans. For instance, when a user modifies a booking or updates a preference, this information is sent to the server 106 where it is processed in real-time to reflect the changes across the system. The server’s response confirms the successful update, ensuring the user interface reflects the current status of the user’s itinerary or preferences.

[0054] Additionally, this bidirectional communication is enhanced by AJAX (Asynchronous JavaScript and XML) technology', which allows the web pages to update dynamically by exchanging small amounts of data with the server behind the scenes. This means that it is possible to update parts of a web page, without reloading the whole page, creating a smoother and more responsive user experience. This is particularly useful in a travel booking platform where flight prices, availability, and other details may change frequently and need to be updated promptly to provide users with the most current information. In this embodiment, the platform 100 utilizes a central processing unit (CPU) 105. The CPU 105 executes the program instructions and manages the operations of various modules of the platform 100. The CPU serves as the primary processor for the platform, handling the execution of all computational tasks required by the system. It interprets and processes commands from the software applications and orchestrates the interactions between other modules such as the User Interface, Booking Management, Search and Recommendation Engine, and Payment Processing, among others. The CPU performs critical functions such as data processing, e.g., handling data computations, from user inputs in the User Interface to complex algorithms in the Search and Recommendation Engine. It processes search queries, filters results based on preferences, calculates pricing comparisons, etc. The CPU schedules and manages tasks across different modules, ensuring efficient resource allocation and parallel processing where possible to enhance system performance and responsiveness, as well as coordinating the activities between internal modules and external APIs, ensuring seamless data flow and synchronization across the platform. This includes managing data transfers between databases and the front-end, as well as handling requests and responses from external sendee providers via the API Gateway.

[0055] The CPU is also responsible for executing encryption algorithms and securityprotocols, ensuring that all user data and transactions are securely processed to prevent unauthorized access and data breaches. With integrated Al and machine learning capabilities, the CPU processes real-time data for dynamic pricing, personalized recommendations, and predictive analytics, adjusting the system's outputs based on current data inputs and learned patterns.

[0056] In this embodiment, the platform 100 includes a user interface module 110. The User Interface (UI) module 110 serves as the primary point of interaction between the user and the platform 100. Its primary- function is to provide a clear, intuitive, and responsive interface through which users can access and utilize the various features offered by the platform, such as searching for flights, booking accommodations, renting cars, and managing travel plans.

[0057] In this embodiment, the UI module 110 accommodates the needs of a diverse user base, ensuring that the interface is accessible to users with varying levels of technical expertise and from different cultural and linguistic backgrounds. This includes the implementation of user-friendly design principles, such as a clean layout, legible typography, and intuitive navigation paths. The module typically uses responsive design techniques to ensure that the platform is equally functional and visually appealing across different devices, including desktops, tablets, and smartphones. This responsiveness is crucial given the varied environments in which users may plan travel. Within the platform 100. the UI module 110 interacts closely with other backend modules to deliver a seamless user experience. For instance, when a user enters travel details, the UI module captures this input and communicates with the Search and Recommendation Engine to fetch and display available travel options that match the user's criteria. This interaction often involves dynamically updating content on the web page without needing to reload it entirely, leveraging technologies like AJAX to enhance interactivity and speed. Additionally, the UI module plays a vital role in facilitating a secure and straightforward transactional process. This includes forms for entering payment information, which are designed to be secure and compliant with data protection regulations. The UI ensures that sensitive data entered by the user is encrypted and securely transmitted to the Payment Processing Module.

[0058] The UI module can additionally incorporate elements of personalization, adapting its displays and suggestions based on user preferences and previous interactions. This could involve presenting tailored travel recommendations or highlighting promotions and special offers that align with the user's travel patterns.

[0059] In this embodiment, the platform 100 includes an Authentication and Authorization Module 120. This module is responsible for managing the security' aspects of user access and interactions within the system. The module 120 ensures that only legitimate users can access the platform and perform actions according to their permissions, thereby safeguarding sensitive user information and transactional data.

[0060] The functionality7of the Authentication and Authorization Module 120 includes handling user authentication, which is the process of verifying the identify of a user attempting to access the platform 100. This typically involves the user providing credentials, such as a username and password, which the module checks against stored data in the platform's database. Advanced platforms may also employ multifactor authentication (MFA), requiring users to provide additional verification such as a code sent to their mobile device or biometric data, enhancing security against unauthorized access. Once authenticated, the user enters the authorization phase managed by the same module. Authorization involves determining the resources and actions that an authenticated user is permitted to access and perform. This can be important in a travel booking platform where different levels of user access might be necessary. For example, a regular user may only have the ability to view and book travel, while an administrative user could have permission to modify pricing or access detailed user analytics.

[0061] The module 120 also handles session management, which ensures that a user's authentication state is maintained securely as they navigate through the platform 100, without needing to re-authenticate for each action or page request. This is typically achieved through the generation of a temporary session token post-authentication, which carries the authentication and authorization information in a secure manner throughout the user's session. Additionally, the Authentication and Authorization Module 120 plays a critical role in data security. It helps in implementing security policies that protect user data privacy and compliance with legal standards such as GDPR or HIPAA, depending on the geographical operation and scope of the platform. This includes ensuring that sensitive data, such as payment information and personal identification details, are accessed only by authorized personnel and that all data transmissions are encrypted.

[0062] In this embodiment, the platform 100 includes a Search and Recommendation Engine 130. The Search and Recommendation Engine 130 is a central component of the platform 100, designed to enhance the user experience by providing efficient, accurate, and personalized travel options. This engine 130 is responsible for handling complex queries from users, processing vast amounts of data, and delivering relevant travel solutions tailored to the individual preferences and requirements of each user.

[0063] The engine utilizes sophisticated algorithms to ‘sift through’ and analyze large datasets that include flight schedules, accommodation availability, car rental options, and pricing information. It cross-references this data with user input, such as travel dates, destination preferences, budget constraints, and previous booking history, to find matches to the user’s desired travel plans, including, but not limited to preferences regarding expense, preferred driving distance, whether or not to utilize a rental car and / or hotel during travel, and other considerations. This capability ensures that users are presented with the most relevant options, saving them time and enhancing their decision-making process.

[0064] The recommendation aspect of the engine 130 can apply machine learning techniques to predict and suggest travel options that align with the user's preferences and past behavior. Over time, as the system gathers more data on a user's choices and preferences, these recommendations can become increasingly precise, providing a highly personalized user experience. This not only improves user satisfaction but also increases the likelihood of bookings by suggesting highly relevant travel packages and promotions.

[0065] Moreover, the Search and Recommendation Engine 130 can dynamically adjust its outputs based on real-time factors such as changes in pricing, availability, special promotions, and even external factors like weather conditions or local events. This adaptability ensures that the recommendations are not only personalized but also contextually appropriate, providing users with the most practical and advantageous travel options at any given moment. To facilitate these functions, the engine integrates with various external APIs from airlines, hotels, and rental services 135, allowing it to pull in up-to-date information and reflect the most current offerings and prices. The engine 130 also interacts with the platform's own database to retrieve user profdes and historical data, which supports the personalization of search results and recommendations.

[0066] In this embodiment, the platform 100 includes a Booking Management Module 140. The Booking Management Module 140 facilitates and manages aspects of the travel booking (or ‘‘ticketing”) process. This module 140 ensures that reservations for flights, hotels, car rentals, and other travel services are handled efficiently, securely, and without error, from initial booking to final confirmation. The Booking Management Module 140 communicates with the Search and Recommendation Engine 130 to receive user selections. Once a user finalizes their travel choices, the module 140 processes these selections by coordinating with external APIs linked to airlines, hotels, and rental services 135. This integration allows the platform 100 to access real-time availability and pricing information, ensuring that the bookings are made based on the most current data. The module 140 also manages the transaction process, including the collection and processing of payment information. It ensures that all financial transactions are conducted securely, employing encry ption and compliance protocols to protect user data and prevent fraud. This includes interfacing with the Payment Processing Module 150 to handle charges, refunds, and the secure storage of payment details, adhering to international standards such as PCI DSS for payment security.

[0067] Once a booking is confirmed, the Booking Management Module 140 sends detailed confirmation to the user, typically via email or through the platform's user interface. This confirmation includes all pertinent details such as booking numbers, travel dates, locations, and instructions for check-in and use of the booked services. It may also provide additional information such as weather forecasts, travel advisories, and tips related to the destination.

[0068] Moreover, the module 140 supports modifications and cancellations, allowing users to change or cancel their bookings through the platform. This involves not only updating the booking details but also managing any financial adjustments such as issuing refunds or processing cancellation fees. The module ensures that these changes are reflected in real-time across the platform and communicated effectively to the user and sendee providers.

[0069] In this embodiment, the platform 100 includes a Payment Processing Module 150. The Payment Processing Module 150 handles all aspects of financial transactions associated with travel bookings on the platform 100. This module 150 ensures that payments are processed securely, efficiently, and in compliance with international financial standards and regulations, such as the Payment Card Industry' Data Security Standard (PCI DSS). The payment module 150 facilitates the collection, processing, and management of payment information when users book flights, hotels, car rentals, or any other travel-related sendees through the platform. It supports a variety of payment methods, including credit cards, debit cards, bank transfers, and emerging digital payment systems like e-wallets, to accommodate the preferences and needs of a diverse user base.

[0070] The module can and should employ advanced encryption methods to safeguard sensitive payment information during transmission between the user’s device and the platform’s servers. Additionally, the module can implement secure tokenization of card details, ensuring that actual card numbers are never stored on the platform’s servers, further minimizing the risk of data breaches. In this embodiment, the module 150 is also responsible for verifying transaction details to prevent fraudulent activities. This includes verifying the authenticity of payment information, checking for unusual transaction patterns, and implementing anti-fraud measures such as two-factor authentication during the payment process. These security measures are vital in building and maintaining user trust, especially when handling financial transactions online. Once a payment is successfully processed, the Payment Processing Module 150 communicates with the Booking Management Module 140 to confirm the booking and trigger the issuance of tickets, reservations, or other confirmable services. In cases of booking modifications or cancellations, the module handles the necessary refunds or additional charges, managing these financial adjustments in real-time to ensure accurate billing and user satisfaction.

[0071] Additionally, the module 150 can generate financial reports and analytics, which can be used for administration and financial management of the platform. These reports can assist in tracking revenue, understanding user spending patterns, and optimizing pricing strategies based on real-time financial data.

[0072] In this embodiment, the platform includes a Customer Support Module 160. The Customer Support Module 160 exists to ensure that users receive timely assistance and support for any issues or inquiries they may have regarding the services offered by the platform 100. This module 160 plays a role in maintaining user satisfaction and loyalty, addressing concerns ranging from booking difficulties to post-travel service feedback.

[0073] The Customer Support Module 160 can provide multiple channels of communication, including live chat, email, and telephone support, allowing users to choose their preferred method of contact based on the urgency and nature of their queries. This multichannel approach ensures accessibility and convenience, accommodating different user preferences and enhancing the overall sendee experience. Within the platform 100, the module 160 can be integrated with a sophisticated ticketing system that efficiently manages incoming queries. Each inquiry can be logged as a ticket, which is then assigned to the appropriate customer sendee representative based on the issue type and priority. This system helps streamline the handling of support requests, ensuring that each issue is addressed promptly and by the most qualified personnel.

[0074] Moreover, the Customer Support Module 160 can be equipped with an automated response system capable of providing instant answers to common queries. This system uses a database of frequently asked questions and standard responses to offer immediate assistance around the clock. For more complex or unique issues, the automated system can escalate the query to human support agents who provide personalized assistance. The module 160 can also incorporate feedback mechanisms, allowing users to rate their support experience and provide comments on their interactions. This feedback is invaluable for continuous improvement, enabling the platform to regularly assess and enhance its customer service practices.

[0075] In this embodiment, the platform 100 includes a Data Analytics Module 170. The Data Analytics Module 170 captures, processes, and analyzes data generated from user interactions and operational processes. This module 170 transforms raw data into valuable insights, which can drive strategic decision-making and enhance the platform's overall performance and user experience. The module 170 can collect data across various points of interaction within the platform, such as user search queries, booking patterns, payment transactions, and customer service interactions. By aggregating this data, the module 170 provides a comprehensive view of user behavior and platform performance. Advanced analytical tools and algorithms are employed to dissect this large volume of data, identifying trends, anomalies, and opportunities for improvement.

[0076] The Data Analytics Module 170 can perform real-time analytics. This capability allows the platform 100 to dynamically adjust offerings and sendees based on current user behavior and market conditions. For example, if there is a sudden increase in searches for flights to a particular destination, the platform can immediately respond by highlighting relevant promotions or travel tips for that location. Moreover, the module 170 supports predictive analytics, using historical data and machine learning models to forecast future trends. This can include predicting peak travel times, anticipating demand for certain destinations, or identifying the likelihood of user conversion based on their activity patterns. These predictions help the platform to proactively adjust its marketing strategies and inventory management, ensuring optimal resource utilization and enhanced user satisfaction

[0077] The Data Analytics Module 170 also plays a critical role in enhancing the user experience through personalization. By analyzing individual user data, the platform can tailor its search results and recommendations to better match each user’s preferences and previous interactions. This personalized approach not only improves user engagement but also increases the chances of repeat bookings. Insights generated by the Data Analytics Module 170 can be used for operational reporting and strategic planning. The module 170 can provide detailed reports on key performance indicators such as user acquisition costs, conversion rates, average booking values, and customer lifetime value. These metrics can be useful for assessing the health of the business and guiding future growth strategies.

[0078] In this embodiment, the platform 100 includes a Notification System 180. The Notification System 180 is configured to communicate information to users. This module 180 can enhance user engagement and ensure that travelers are well-informed about their travel plans and any related updates or changes. In one embodiment, the Notification System 180 can send alerts and notifications to users through various channels, including email, SMS, and mobile push notifications to ensure that users receive important information in a manner that is most convenient for them, enhancing the likelihood of timely and effective communication.

[0079] For example, in this embodiment, the Notification System 180 is responsible for sending booking confirmations immediately after a user completes a transaction on the platform 100. The module 180 also sends reminders about upcoming travel dates, notifications about check-in times, and alerts regarding any changes or delays in travel schedules. This keeps users informed at every step of their travel journey, reducing the stress and uncertainty7that often accompany travel planning. Additionally, the Notification System 180 can be used for promotional communication. It can send targeted offers and deals to users based on their travel history and preferences. This personalized approach not only improves the relevance of the promotions but also enhances user engagement by providing offers that are likely to be of interest to the recipient. The module 180 can also integrate with the platform’s customer support services to alert users about responses to their inquiries or updates on any issues they have reported. This immediate feedback loop helps build trust and satisfaction, as users feel their concerns are promptly addressed. Moreover, the Notification System 180 can incorporate advanced algorithms to optimize the timing and frequency of notifications, ensuring that messages are neither too intrusive nor too sparse. This balance is crucial in maintaining user interest without causing notification fatigue.

[0080] In this embodiment, the platform 100 includes an API Gateway 190. The API Gateway 190 orchestrates the complex network of interactions between the travel booking platform 100 and external service providers. The API Gateway 190 enhances the functionality of the platform 100 by enabling efficient data integration, ensures security' across all data exchanges, and supports performance optimization through effective load management and real-time monitoring. This module 190 not only supports operational efficiency but also plays a critical role in safeguarding user data and maintaining trust in the platform’s capabilities.

[0081] The API Gateway 190 streamlines and manages all communication and data exchanges between the platform and various third-party systems, such as airline booking systems, hotel reservation systems, and car rental sendees, ensuring that these interactions are seamless, secure, and efficient. The API Gateway 190 handles incoming and outgoing API calls. For the user interfacing with the platform, this means that when they perform actions like searching for flights or booking a hotel, these requests are routed through the API Gateway 190. The gateway 190 then directs these requests to the appropriate external APIs, retrieves the data, and returns it to the platform where it can be processed and displayed to the user. This process can be crucial for providing real-time information on availability, pricing, and other details essential for making informed travel decisions.

[0082] Furthermore, the API Gateway 190 can implement various security measures to protect the integrity and confidentiality of the data being exchanged, including, but not limited to authentication mechanisms that ensure only authorized users and services can access sensitive data, as well as encryption protocols to secure data in transit. By centralizing these security functions, the API Gateway 190 maintains a consistent and robust security posture across all external interactions. The module 190 also manages load balancing and API throttling, which are critical for maintaining the performance and stability of the platform. Load balancing ensures that no single service or server bears too much demand, which can prevent slowdowns and crashes during peak times. Throttling controls the rate at which the API requests are made to external services, preventing overloads and ensuring that all users receive timely responses.

[0083] The API Gateway 190 can furthermore provide detailed logging and monitoring of all API traffic. This visibility allows platform administrators to track and analyze how APIs are used, monitor for any unusual patterns that might indicate security issues, and optimize the system’s overall performance.

[0084] In this embodiment, the platform 100 includes an Infrastructure Management Module 200. The Infrastructure Management Module 200 monitors and controls the underlying physical and virtual resources that support the platform 100. This module 200 ensures that the platform's infrastructure is robust, scalable, and capable of handling varying levels of user demand without compromising performance or security7. The Infrastructure Management Module 200 manages the hardware and software environments where the platform operates. This includes servers, storage systems, network devices, and the associated software stack that includes operating systems, databases, and application servers. By continuously monitoring these resources, the module ensures that they are functioning optimally and are not overloaded.

[0085] The Infrastructure Management Module 200 provides scalability, in that it can dynamically allocate resources based on real-time demand. For example, during periods of high user activity, such as during holiday seasons when travel bookings surge, the module 200 can automatically scale up server capacity to handle increased traffic and data loads. Conversely, it can scale down resources during off-peak times to optimize costs and energy consumption. The module 200 can also implement disaster recovery and data backup strategies to protect critical platform data and ensure continuity of service in case of hardware failure or other disruptions. This includes regular backups of user data and system configurations, as well as the establishment of failover mechanisms that allow the system to quickly switch to standby resources in the event of a primary system failure.

[0086] Furthermore, the Infrastructure Management Module 200 can implement security protocols at the infrastructure level. This can include, e.g., configuring and deploying firewalls, intrusion detection systems, and other network security measures to protect against external threats. It also manages the installation of security patches and updates to keep all system components up to date with the latest protection measures. Additionally, the module 200 can provide comprehensive reporting and analytics on infrastructure usage and performance. These insights can be useful for future capacity7planning and operational improvements. By analyzing trends in resource usage, administrators of the platform 100 can make informed decisions about when to upgrade or modify the infrastructure.

[0087] The functionality of the platform 100 is highlighted by an example. In this example, a user named Alex wishes to travel from New Y ork City, New Y ork to Rockford, Illinois. Alex is primarily concerned with finding a travel option that balances cost and convenience. Initially, Alex accesses the online travel booking platform from a desktop computer, initiating the process via the User Interface (UI) Module. The UI is designed for ease of use, allowing Alex to input travel dates and destinations quickly. Alex starts by accessing the platform 100 through its website on his laptop. He enters his travel dates and both his departure city (New York City) and destination (Rockford, Illinois) into the search fields. The User Interface Module facilitates this interaction, displaying the input fields, and later, the search results in a clear and structured layout. Alex specifies a desire to travel to Rockford but is open to suggestions for less expensive alternatives. Upon receiving the travel query, the UI Module communicates with the Search and Recommendation Engine. This engine, utilizing advanced algorithms, begins searching for available flights, car rentals, and other relevant travel services. The platform 100 accesses its own internal databases, and external databases through the API Gateway, which securely connects to various external APTs, e.g., airline and car rental APIs, pulling real-time data on availability and pricing from these external service providers.

[0088] In this example, the direct flight from New York's LaGuardia Airport to Rockford Airport is priced at $500. Noticing that direct flights are relatively expensive, the engine uses its algorithms to explore alternative routes. Aiming to find the most cost-effective options, the Search and Recommendation Engine also looks for flights landing at nearby airports. It finds a flight to Chicago's O'Hare International Airport for just $150 on the same day. Realizing that the distance between Rockford, IL and O’Hare International Airport is almost 100 miles, the Search and Recommendation module searches for car rental options and finds a suitable car rental from O'Hare to Rockford for $90.

[0089] The total cost for the alternative route (flight to O'Hare plus car rental) amounts to $240, significantly less than the direct flight. The Booking Management Module then processes this information and presents both options to Alex via the UI Module. Alex reviews the choices and, in this example, selects the more economical option. Once Alex decides, the Payment Processing Module facilitates the financial transaction. Alex enters payment details, which are encry pted and securely transmitted for processing. The Payment Processing Module verifies the transaction against potential fraud and confirms the booking by interfacing again with the Booking Management Module, which finalizes the flight and car rental reservations.

[0090] Post-booking, the Notification System sends Alex a detailed confirmation via email and SMS, including itinerary' details, booking references, and instructions for car rental pickup at O'Hare. This system ensures Alex is well-informed and prepared for the trip. Throughout this process, the Customer Support Module remains available to Alex, providing assistance and answering any queries regarding the booking. If Alex encounters any issues or requires changes, this module facilitates communication with support staff through live chat and ticketing systems. Additionally, the Data Analytics Module collects and analyzes data from Alex's interaction with the platform. This data helps in understanding user behavior and preferences, contributing to the continuous improvement of service offerings and user experience.

[0091] In this example, the Infrastructure Management Module plays a silent yet crucial role in ensuring that all platform components operate smoothly without technical hitches. It manages server load dynamically, ensuring that during Alex’s interaction and subsequent transactions, the platform remains responsive and efficient.

[0092] In another example, the platform 100 can suggest lodging for the traveler, for example, if flights depart or arrive late at night. Continuing with the preceding example, in this extended scenario, after securing a flight to O'Hare Airport and arranging the car rental, the platform's Search and Recommendation Engine detects that Alex's flight arrives late in the evening. Anticipating that driving to Rockford immediately after a late flight might not be ideal, the engine proactively searches for nearby accommodation options to ensure Alex can rest before continuing his journey.

[0093] The platform 100, through its API Gateway, communicates with various hotel APIs to check for available accommodation near O'Hare Airport on the night of Alex's arrival. The gateway facilitates the fetching of real-time data concerning hotel rates, availability7, and user ratings. Leveraging this information, the Search and Recommendation Engine assesses several hotels, focusing on those offering the best balance of price, convenience, and traveler reviews. It finds a well-rated hotel for $90 per night, which is added to the list of travel options presented to Alex. The recommended hotel option is displayed to Alex via the User Interface Module, which updates dynamically to include a detailed breakdown of his new itinerary options. The module presents the flight, car rental, and hotel stay clearly, allowing Alex to review the details and total cost before deciding.

[0094] Continuing this example, once Alex chooses to add the hotel to his itinerary7, the Booking Management Module processes the addition. It handles the reservation details, ensuring that the hotel booking is secured for the right date and that Alex receives immediate confirmation of his overnight stay. Simultaneously, the Payment Processing Module manages the transaction for the hotel booking. It securely processes an additional $90 charge, bringing Alex's total expenditure to $330, which still remains $170 less than the direct flight option to Rockford.

[0095] With the new booking confirmed, the Notification System updates Alex's travel itinerary in the confirmation email. It includes details about the hotel reservation, check-in times, and other relevant information. The system also adjusts the reminders it sends to Alex, ensuring he receives timely notifications about his flight, hotel check-in, and car rental pickup. If Alex has any questions about his updated itinerary or needs further assistance, the Customer Support Module is readily available. Whether he needs guidance on hotel policies or adjustments to his booking, the support team is equipped to provide swift and efficient service. This example demonstrates the platform’s ability to provide convenience and safety to late-night arrivals, or arrivals where adverse weather is predicted; however, the costeffectiveness of the overall travel plan is maintained. In this and other embodiments, each module of the platform 100 collaborates and communicates to adapt to changing circumstances, providing a comprehensive and responsive service that caters to the user's needs while still prioritizing affordability.

[0096] In another functional example of the platform 100, Alex has previously configured his user profile on the platform 100 to include various interests such as, but not limited to historical sites, local cuisine, and nature parks. This personalized data is stored and managed by the platform 100 to enhance his travel experience by offering tailored recommendations. When Alex books his trip, the platform accesses his profile to review his saved interests. This information can be used to customize his journey to align with his preferences, enhancing the overall travel experience.

[0097] As Alex's itinerary includes driving from O'Hare to Rockford the following morning, the Search and Recommendation Engine uses his interests to identify potential stops along the route. The engine queries local databases and travel APIs through the API Gateway to gather information on attractions that align with his preferences. Through the API Gateway, the platform pulls in detailed information about potential points of interest along the drive from O'Hare to Rockford. This can include, e.g., historical landmarks, recommended local eateries famous for their unique dishes, and serene parks that offer a respite from travel.

[0098] The engine processes this data and formulates a customized travel route that includes suggested stops at a renowned local diner that serves award-winning regional specialties, a historic site noted for its architectural beauty and cultural significance, and a small natural reserve that offers short scenic trails perfect for stretching legs after a long drive. Such personalized recommendations are then presented to Alex through the User Interface Module. The module displays a map with the route highlighted, including pins for each recommended stop. Each pin provides detailed information about the site, including visitor reviews, opening hours, and the estimated time he w ould spend there.

[0099] If any of the suggested stops require booking — for example, if a historical site offers a guided tour that needs to be reserved — the Booking Management Module handles this process. It allows Alex to add these activities to his itinerary directly through the platform, ensuring all details are synchronized and included in his travel plan.

[0100] Once Alex finalizes his itinerary, including his chosen stops, the Notification System updates to send him reminders and notifications pertinent to his new' stops. This might include weather updates for the day of his drive, reservation confirmations, or time-sensitive alerts like road closures or changes in opening hours at the places he intends to visit. Throughout his trip planning and execution, Alex has access to the Customer Support Module for any on-the-fly adjustments or queries related to his itinerary’ stops. Should he decide to skip a stop or add a new one, customer support can facilitate these changes smoothly.

[0101] In this extended scenario, each module of the platform collaborates to not only accommodate Alex's basic travel needs but also enrich his journey with personalized experiences that align with his interests. This level of customization not only maximizes his satisfaction but also encourages deeper engagement with the platform for future travel planning.

[0102] Referring now to FIG. 2, an exemplary7system flowchart 300 is shown that describes a method sequence from the perspective of the online travel booking platform 100.

[0103] In this example, the process begins at step 310 when a user interacts with the User Interface Module. Here, travelers input their travel details, including origin, destination, travel dates, and preferences. This module serves as the primary7interaction point for users, presenting a user-friendly interface that collects and forwards data to the system. The module ensures the input data is complete and valid before it is sent to the next stage for processing.

[0104] Next, at step 320. once the user input is received, the Authentication and Authorization Module verifies the identity of the user and ensures they have the appropriate permissions to proceed. This step is crucial for maintaining the security7of the system and protecting sensitive user information. It checks the user credentials and, if authenticated, grants access to the system's features according to the user's access level.

[0105] With user credentials authenticated, the system uses the API Gateway at step 330 to fetch real-time data relevant to the user’s query. The API gateway interfaces with external travel service providers, retrieving up-to-date information on flights, car rentals, and accommodations. The API Gateway ensures that data exchange between the platform and external APIs is efficient, secure, and up to date, providing a solid foundation for making travel recommendations.

[0106] At step 340, the Search and Recommendation Engine takes the user input and realtime data obtained from the API Gateway to process and analyze various travel options. This engine uses algorithms to compare costs, schedules, and compliance with user preferences to generate a list of optimal travel itineraries. This step 340 is where the platform determines the cost of one or more complete travel plans or “packages” by considering various factors like cost, convenience, and user preferences to suggest the most suitable travel options, according to the user’s input. The processed travel options are then displayed back to the user at step 350 through the User Interface Module. Users can review the suggested itineraries, compare them, and choose the one that best suits their needs. This step is interactive, allowing users to fine-tune their choices based on further preferences or changed criteria.

[0107] Next, at step 360. once the user selects an itinerary, the Booking Management Module facilitates the booking of flights, hotels, and car rentals as per the chosen itinerary. This module coordinates with the Payment Processing Module to handle financial transactions, ensuring that all bookings are secured and paid for. It confirms availability', locks in prices, and processes payments while adhering to security’ standards for transaction processing.

[0108] After the bookings are confirmed, the Notification System sends a detailed confirmation to the user, step 370. This includes all travel details, such as ticket numbers, reservation codes, and schedules. Additionally, this system is responsible for sending any updates or alerts related to the booked travel, such as changes in flight times or reminders for check-ins, enhancing the user experience by keeping them informed throughout their journey.

[0109] At step 380, post-trip, the platform may solicit feedback from the user, which is processed by the Data Analytics Module. This module analyzes all collected data, including user feedback and booking patterns, to refine and enhance the functionality of the Search and Recommendation Engine. This continuous improvement loop helps the platform to adapt and evolve based on user interactions and preferences, ensuring relevance and precision in future travel recommendations.

[0110] Turning now to FIG. 3. an exemplary’ display screen 400 is shown. In this example, the screen 400 is that of an end user utilizing, e g., a laptop computer, mobile computing device, or other electronic device connected to the platform 100 through the UI module 310 who has already passed through the login screen and has been authenticated as a user. In this example, the screen includes an origin input field 410. into which the user can enter a city, nearby airport or other information that will be used by the system to determine a travel origin. In this example, the screen 400 includes a destination input field 420, yvhere a user can enter a destination city, town, area, airport or other geographical information that the system 100 can use as the destination in determining a travel itinerary as described herein.

[0111] In this example, the screen 400 further includes checkboxes to search nearby (430). include a hotel or other accommodations (440) and include a car rental (450). Each checkbox can be checked independently of the others, alloyving the user to broaden the parameters used to determine the most cost-effective itinerary to travel between the origin and destination. In this example, the "‘Search Nearby” checkbox 430 can allow the system 100 to search for destination airports other than major airport hubs. In some embodiments, clicking the checkbox 430 can bring up, e.g., a modal dialog box, allowing the user to input additional information, such as a maximum distance from the major airport hub that they are willing to travel to as alternative landing destinations. The “Include Hotel” and “Include Car Rental” checkboxes (440, 450), if checked, instruct the system 100 to determine the cost of vehicle rental and accommodations in an overall travel itinerary, as previously discussed.

[0112] In this example, the screen includes input fields for departure (460) and return (470) dates, which are used by the system 100 to determine airfare, vehicle rental and accommodation price and availability as previously discussed. The “Include Attractions” input field 490 can be, for example, and without limitations, a dropdow n list of attractions or events that may be populated, e.g., when the user selects or inputs a destination in the destination field 420. For example, if the user selects Chicago, Illinois as their destination, the UI interface can immediately access external API’s that store information on events happening in Chicago. If the user selects an attraction, the system 100 can access details related to the attraction such as dates, times, cost, location, and other information that the user may be interested in.

[0113] Referring now to FIG. 4, continuing the present example, a ‘results’ screen 500 is shown. The results screen 500 shows suggested itineraries (two, in this example, however there can be multiple itineraries) from which the user can select. In this example, the first itinerary 501 shows an airfare-only price from New7York City’s LaGuardia Airport to Rockford Regional Airport (RFD) in Rockford, IL, the user’s destination. In this example, the Search and Recommendation Engine 130 has found a flight for a price of $800.00. Within the first itinerary 501 , the user can click on the “Book it!” link, which would lead the user to a subsequent page to complete the itinerary7purchase.

[0114] In this example, a second itinerary 502 is presented as an option to the first itinerary7501. In this example, the Search and Recommendation Engine 130 has found a flight from LaGuardia airport (LGA) to Chicago’s O’Hare International airport (ORD) for $220; significantly less expensive than the direct flight to Rockford (RFD). Determining that O’Hare airport is 75 miles from Rockford, IL, the platform 100 has suggested rental of a vehicle. The system 100 has searched for available vehicles for rent from O’Hare airport and retrieved a quote of $75.00 one-way to Rockford. The system has also suggested accommodation at a hotel and found lodging for a price of $75.00. The total cost of the second itinerary7502 is $370.00, which is $430.00 less expensive than the direct route from LGA to RFD. In this example, the page 500 includes selectable “remove” buttons 503, 504, that, when selected remove the associated itinerary element. For example, if the user did not want lodging accommodations, he could select the remove button 504 which would remove the element and update the itinerary price accordingly.

[0115] It should be understood that the foregoing example illustrates basic use of the system 100 and is non-limiting. In particular, in practice, the system 100 can generate many itinerary options, each with different airport / car rental / hotel / event combinations so that the user may select one that best fits his or her needs.

[0116] Referring now to FIG. 5, in one embodiment, a user can specify an adjustable area feature 600 around their selected destination (in this case, Rockford, IL). This feature 600 allows the platform to display, in real time, price options for alternative routes (in this example, to Chicago, as with the previous example) and transportation modes within the area of the feature 600, adding flexibility and potentially additional cost-effective options for the user. In this example, if the user increased the area of the feature 600 to include Madison, WI and / or Milwaukee, WI. the platform would create a pop-up modal box similar to the modal box shown for the alternative itinerary to Chicago 610.

[0117] In this embodiment, the User Interface Module 110 provides a feature where users can input a desired radius on an interactive map around their destination airport or location. The area feature 600 can be input using a numerical value on an input field or, in another example, input using a slidable graphic feature that allows the user to slide the feature 600 to encompass a desired area. The feature 600 can be, e.g., a circle, square, or freeform object. This interface allow s users to specify' the radius in miles or kilometers, providing a more tailored search parameter that interacts dynamically with the map and listing services. This user input is then captured and sent to the Search and Recommendation Engine 130 for processing.

[0118] In this embodiment, the API Gateway 190, which facilitates data exchange between the platform and external APIs, is configured to handle requests that involve geographical data queries. The gateway 190 interacts with geolocation services to determine airports, hotels, car rental agencies, and other travel services within the specified radius. The gateway 190 ensures that these queries are efficiently managed and that the data retrieved is up-to- date and accurate.

[0119] In this embodiment, once the Search and Recommendation Engine 130 receives the destination and radius from the User Interface Module, the engine 130 calculates available routes and associated travel services within that area. The engine 130 employs algorithms that factor in various travel options, including alternative nearby airports, local transportation methods, and direct versus indirect routes. The engine can assess the total travel cost implications of each option, optimizing for factors like cost, travel time, and user preferences. To support real-time interaction and display of price options, the Search and Recommendation Engine 130 can be configured to process data in “real time”. This can include, e.g., quickly fetching and recalculating available travel options as the user adjusts the radius or modifies other travel parameters. This dynamic processing can rely on the efficient performance of the API Gateway 190 and robust computational resources to handle the high volume of data and calculations.

[0120] In this embodiment, the dynamically processed data is displayed to the user in an interactive format via the User Interface Module 110, which is configured to provide users with a map, allowing visualization of the different travel options within the selected radius and a comparative list detailing the prices and travel times associated with each option. This feature enhances user interaction, allowing exploration of various scenarios and the ability to choose the most suitable itinerary' based on real-time data.

[0121] In this embodiment, once the user selects an optimal travel route and associated services, the Booking Management Module 140 handles the reservation and booking processes across multiple sendee providers. This module 140, in conjunction with the Payment Processing Module 150, ensures that all aspects of the booking are finalized, from seat reservations to accommodation bookings, and processes payments securely. Upon successful booking, the Notification System 180 can generate and send detailed confirmations and travel plans to the user. This system will also be responsible for real-time alerts related to any changes or updates in the booked travel itinerary'.

[0122] In an enhanced embodiment, the platform incorporates a modular microservice architecture that enables greater fault tolerance, scalability, and responsiveness under realtime operating conditions. This architecture organizes key' platform functionalities into independently deployable and horizontally scalable services. These sendees include, but are not limited to, an input processing layer, a search and recommendation engine, a machine learning inference engine, a customer session handler, a notification manager, a caching and metadata store, and a gateway layer that interfaces with multiple third-party7travel APIs. Each microservice is connected via asynchronous message queues that support event-driven communication, reducing bottlenecks and improving failover resilience.

[0123] The API gateway is responsible for brokering requests between the internal platform sendees and external data providers, such as airline and hotel APIs. The gateway performs real-time monitoring of request outcomes and incorporates an adaptive failover strategy. In the event that a request to a third-party' service fails or exceeds a timeout threshold, the gateway retries the call up to a configurable maximum. Upon continued failure, the affected data stream is annotated with a stale-state tag and either re-routed to a backup provider or flagged for downstream exclusion, depending on context. This logic prevents systemic degradation by isolating third- party outages and ensures that user-facing components remain responsive, albeit with gracefully degraded data completeness.

[0124] A key feature of the platform is a real-time itinerary reprioritization mechanism driven by a machine learning model hosted in a dedicated inference microservice. This model, in one embodiment, comprises a gradient-boosted decision tree architecture trained on a corpus of historical booking data from a global distribution system (GDS) comprising over one billion rows. Feature vectors ingested by the model include fare volatility, historic load factor, weather forecast score, airport reh abi lity index, day-of-week and time-of-day encodings, routing distance, user preference embeddings, connection counts, layover airport quality scores, and estimated carbon emissions per passenger. The output of the model is a cost-saving probability score which is used to rank travel itineraries for optimality.

[0125] Whenever a third-party API response alters the availability or price of an itinerary component — such as when a fare is updated, or a service is marked stale due to gateway failover — the affected itineraries are automatically rescored and reprioritized in the recommendation engine. This enables the platform to adaptively present the most relevant and cost-effective options to the user, based on the latest reliable data. The reranking occurs asynchronously and is propagated to the user interface via a pub-sub notification system, which ensures that interface elements are updated in real time without requiring user refresh.

[0126] In an alternative embodiment, the platform integrates the Duffel API for travel data sourcing, particularly to support flexible date and alternate airport logic. This embodiment includes an Alternative Logic Service microservice that expands user queries both spatially and temporally. For spatial flexibility, the service calculates alternative airport candidates using Duffel’s / airports endpoint and geospatial logic based on the Haversine formula, applying a radius constraint around the user-defined origin and destination. For temporal flexibility, the system constructs a ±3 day travel window centered on the user's preferred travel dates.

[0127] The Alternative Logic Sen-ice submits these expanded parameters to Duffel’s / offer_requests endpoint, specifying parameters such as max_connections, cabin_class, and sort=total_amount to balance cost, speed, and comfort. Once flight offers are retrieved via Duffel’s / offers endpoint, the results are transformed into itinerary candidates and evaluated using the same ML-based scoring engine described above. These itineraries are then passed through the recommendation pipeline and may be presented with annotated labels, such as "Cheapest Nearby Date” or “Best Value;’ based on their score percentile and delta to baseline options.

[0128] The platform caches Duffel metadata and frequently accessed airport and airline descriptors using Redis, and persists session state and itinerary identifiers in PostgreSQL. Booking confirmations are executed via Duffel’s / orders endpoint, and payments are processed using secure integrations such as Stripe, with support for real-time fraud prevention and tokenized card storage. The system automatically synchronizes order status updates with the user interface via the notification subsystem.

[0129] The complete itinerary generation and presentation process can span multiple services. The flow begins when a user submits a query using the client-side user interface, potentially utilizing the radius-based geographic filter to restrict airport selection. The request is dispatched to the input handling layer, which validates parameters and forw ards them to the Alternative Logic Service. After expansion of airports and dates, requests are routed through the API gateway to the Duffel API or alternate providers. The response data is cached and scored, and reranked itinerary sets are transmitted back to the interface. Any updates to service availability are pushed to subscribed user sessions to ensure interface consistency.

[0130] This enhanced architecture offers measurable technical benefits: the radius tool reduces unnecessary API calls by filtering invalid endpoints client-side; the failover-aware gateway reduces user-facing failures; the machine learning reprioritization engine adapts to live conditions; and the modular containerized deployment model supports edge execution for low-latency regional access. Together, these features enable a robust, scalable, and user- responsive platform architecture that substantially improves over traditional travel booking systems.

[0131] A number of illustrative embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the various embodiments presented herein. Accordingly, other embodiments are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented travel itinerary optimization system comprising: a user interface module configured to receive travel parameters from a user, the travel parameters comprising an origin location, a destination location, one or more travel dates, and one or more user-defined preferences: an API gateway communicatively coupled to at least one external travel service provider, the API gateway configured to transmit offer requests to one or more remote services using structured parameters including maximum connection count, fare class, and sort criteria, and further comprising a failover mechanism configured to detect degraded responses from the external services and, in response, reroute a request to an alternate provider or mark the corresponding travel data as stale: an Alternative Logic Service module configured to expand the travel parameters to include a set of additional airport codes within a geographic radius of the origin and destination, and a range of travel dates within a specified window centered on the user’s selected dates: a search and recommendation engine configured to generate candidate travel itineraries based on the travel parameters and expanded data provided by the Alternative Logic Service; a machine learning engine configured to receive, for each candidate itinerary, a feature vector comprising one or more of: fare variance, connection count, load factor, departure time classification, routing distance, weather deviation score, and carbon emissions data, and to output a cost-savings probability; wherein the search and recommendation engine is further configured to dynamically re-rank the candidate travel itineraries based on the cost-savings probabilities and on updates in API data availability; and wherein the user interface module includes a client-side geographic radius filter operable to pre-filter candidate airport destinations prior to query submission, thereby reducing bandwidth and round-trip latency.

2. The system of claim 1, wherein the Alternative Logic Service module calculates additional airport codes using the Haversine formula and metadata obtained from a travel service API.

3. The system of claim 1. wherein the API gateway transmits offer requests to a remote sendee utilizing at least “offer_requests” and “offers” endpoints.

4. The system of claim 1 , wherein the machine learning engine comprises a gradient- boosted tree model trained on historical global travel booking data.

5. The system of claim 1. further comprising a metadata cache configured to store frequently accessed travel data and prevent redundant API calls during repeated queries.

6. The system of claim 1 , wherein the search and recommendation engine annotates each itinerary' with a label selected from a group consisting of: ‘‘Best Value,’' “Cheapest Nearby Date,” and “Eco-Friendly Route.” based on a percentile ranking or cost differential from a baseline.

7. The system of claim 1, wherein the stale-state indicators assigned by the API gateway prevent itineraries relying on incomplete data from being prioritized in the re-ranking process.

8. The system of claim 1, further comprising a notification subsystem configured to propagate itinerary updates across multiple user devices using a publish-subscribe messaging architecture.

9. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a computing system to perform operations comprising: receiving, via a user interface module, travel parameters from a user, the travel parameters including an origin, a destination, one or more travel dates, and user preferences; expanding the travel parameters by identifying additional airport codes within a geographic radius of the origin and destination, and additional dates within a temporal window centered on the specified travel dates, based on metadata retrieved from a remote service; constructing and transmitting offer requests to one or more external travel data providers using one or more connection constraints, fare class, or sorting preference structured parameters; receiving offer data from the external travel data providers and generating a set of candidate itineraries based on the expanded travel parameters; for each candidate itinerary, computing a feature vector comprising at least one of routing distance, fare variance, departure time classification, connection count, load factor, weather deviation score, or emissions data; processing each feature vector using a trained machine learning model to generate a cost-savings probability for each itinerary; dynamically reprioritizing the candidate itineraries based on the cost-savings probabilities and the availability of underlying data; and presenting the reprioritized itineraries to the user, wherein the user interfaceincludes a client-side airport radius filter configured to reduce network request volume prior to submission.

10. The computer-readable medium of claim 9, wherein the instructions further cause the system to retry failed API requests up to a threshold and to mark associated itinerary data as stale when the threshold is exceeded.

11. The computer-readable medium of claim 10, wherein the stale-state designation suppresses prioritization of itineraries based on incomplete or outdated data.

12. The computer-readable medium of claim 9, wherein the cost-savings probabilities are used to assign labels to itineraries selected from the group consisting of: ‘"Best Value." "Cheapest Nearby Date,” and “Eco-Friendly Route.”13. The computer-readable medium of claim 12, wherein the label assignment is based on percentile thresholds applied to the output of the machine learning model.

14. The computer-readable medium of claim 9, wherein itinerary updates caused by changes in underlying data are propagated across user devices via a publish-subscribe messaging sen-ice.

15. A server system configured to generate and update travel itineraries in real time, comprising: a processor and memory storing executable instructions to implement: an input module configured to receive travel parameters from a user device, the travel parameters comprising origin and destination locations, travel dates, and one or more user-defined preferences; an Alternative Logic Sendee module configured to expand the travel parameters to include a plurality of alternative airports within a specified radius and a range of flexible dates around the input dates, using geographic metadata and historical pricing trends; an API gateway module communicatively coupled to one or more external travel data sources, the API gateway configured to construct and transmit structured offer requests to at least an “offer requests” endpoint, with parameters comprising maximum connection count, fare class, and sort order, and to apply retry and failover logic when external responses are delayed or invalid; a data aggregation and normalization module configured to combine data from multiple sources into structured itinerary candidates: a machine learning inference module configured to generate a cost-savings score for each itinerary7candidate based on a feature vector comprising routing distance, fare variance, connection count, load factor, weather deviation, emissions data, and timeclassification; a recommendation module configured to re-rank itinerary candidates based on the cost-savings score and to suppress itineraries associated with stale data; and a client interface handler configured to transmit the reranked itineraries to the user device, wherein the user device is operable to define a geographic radius constraint using a local graphical filter prior to query submission.

16. The server system of claim 15, wherein the Alternative Logic Service module applies the Haversine formula to identify proximate airports based on Duffel airport metadata.

17. The server system of claim 16, wherein the travel parameters are expanded to include a window of days around the user-specified travel dates.

18. The server system of claim 15, wherein itinerary labels selected from the group consisting of: “Best Value,” “Cheapest Nearby Date,” and “Eco-Friendly Route” are assigned based on the cost-savings score or emissions estimates.

19. The server system of claim 18. wherein the labels are applied based on predefined percentile thresholds relative to a baseline itinerary.

20. The server system of claim 15, wherein itinerary' state changes are propagated to user devices via a publish-subscribe messaging system that ensures eventual consistency across asynchronous user sessions.

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