Traveler pattern recognition and recommendation system

Abstract

The techniques herein are directed generally to a traveler pattern recognition and recommendation system. In one embodiment, artificial intelligence (AI) and machine learning (ML) techniques learn general and individualized traveler behaviors and preferences, and apply them to a behavior-based traveler recommendation engine to provide an intelligent and personalized traveler search and recommendation experience prior to booking travel. For instance, a travel-based application may use a recommendation engine to establish traveler-specific search results regarding locations, hotel brands, travel dates, costs, and so forth based on understanding travel patterns in general, and particularly those of the searching traveler. Search results may be based on amenities, discounts, incentives, events, activities, previous searches, previous travel, and many other metrics and / or observations, and may thus be adapted to the particular searcher. In one embodiment, the behavior-based traveler recommendation engine may also send relevant traveler preference information to travel-related businesses, further personally tailoring the traveler's experience.

Description

[0001] TRAVELER PATTERN RECOGNITION

[0002] AND RECOMMENDATION SYSTEM

[0003] RELATED APPLICATION

[0004] This application claims priority to U.S. Provisional Application No. 63 / 610,933, filed December 15, 2023, entitled TRAVELER PATTERN RECOGNITION AND RECOMMENDATION SYSTEM, by Kevin Bidner, et al., the contents of which are incorporated herein by reference.

[0005] TECHNICAL FIELD

[0006] The present disclosure relates generally to integrated hotel management systems, and, more particularly, to a traveler pattern recognition and recommendation system.

[0007] BACKGROUND

[0008] Guest satisfaction is paramount to hotel operations. Sophisticated travelers expect top-notch service and flawless execution of operations during their stay. That is, in the realm of modem travel, hotel guests increasingly demand an unparalleled level of ease and comfort during their stays. Today’s travelers seek not just a place to rest their heads, but an immersive experience that caters to their individual needs and preferences. The concept of comfort extends beyond the physical aspects of a hotel room; it encompasses the entire guest journey from booking to check-out.

[0009] The contemporary traveler seeks a harmonious blend of technology, thoughtful design, and personalized services to create an environment that caters to their specific needs and desires. Hotels that prioritize and deliver on these expectations are not only meeting the demands of the present but are also positioning themselves as leaders in shaping the future of hospitality.

[0010] At the forefront of guest expectations, in particular, is the seamless integration of technology. Travelers desire a frictionless experience, starting with an intuitive and user- friendly online booking process. Mobile check-ins, keyless room entry, and smart room controls have become essential features that empower guests with the ability to customize their environment at the touch of a button. Automation not only streamlines the check-in process but also allows guests to focus on enjoying their stay rather than dealing with administrative hassles.

[0011] At the same time, much of the success in the guest accommodation industry is also based on the ability to efficiently provide for personalized traveler communications and associated travel options.

[0012] SUMMARY

[0013] The techniques herein are directed generally to a traveler pattern recognition and recommendation system. Specifically, the systems and methods described herein use artificial intelligence (Al) and machine learning (ML) techniques to learn general and individualized traveler behaviors and preferences (e.g., transactional behavior monitoring). By then applying the learned behaviors and preferences to a behavior-based traveler recommendation engine, the techniques herein may then provide an intelligent and personalized traveler search and recommendation experience prior to booking travel. For instance, a travel-based application (e.g., associated with a hotel or else a general travel search engine) may use a recommendation engine to establish traveler-specific search results regarding locations, hotel brands, travel dates, costs, and so forth based on understanding travel patterns in general, and particularly those of the searching traveler. Search results may be based on amenities, discounts, incentives, events, activities, previous searches, previous travel, and many other metrics and / or observations, and may thus be adapted to the particular searcher, thus enhancing the personalized travel experience. In addition, in one embodiment, the behavior-based traveler recommendation engine may also send relevant traveler preference information to any booked hotel, airline, car rental company, excursion guides, or other travel -related business so that the traveler’s experience may be further personally tailored, accordingly.

[0014] In the present disclosure, in one embodiment, certain aspects of the techniques herein may be implemented within a hotel by providing a communication terminal in each guest's assigned room. Such devices may communicate with a server and software system to provide on-demand access to both static and dynamic information and provided content. An interactive interface on the communication terminals provides coordination between the guest and the hotel to thus manage guest communication, scheduling, and overall satisfaction, accordingly. Alternatively, or in addition, a travel-based application (e.g., hotel specific or otherwise) executing on a user’s personal device may be configured to operate in accordance with the techniques described herein.

[0015] Other embodiments of the present disclosure may be discussed in the detailed description below, and the summary above is not meant to be limiting to the scope of the invention herein.

[0016] BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

[0018] FIG. 1 illustrates an example system in accordance with one embodiment;

[0019] FIG. 2 illustrates an example system in accordance with one embodiment;

[0020] FIG. 3 illustrates an example method in accordance with one embodiment;

[0021] FIG. 4 illustrates an example system in accordance with one embodiment;

[0022] FIG. 5 illustrates an example system in accordance with one embodiment;

[0023] FIGS. 6A-6B illustrate an example interactive interface in accordance with one embodiment;

[0024] FIG. 7 illustrates an example system in accordance with one embodiment;

[0025] FIG. 8 illustrates another example interactive interface in accordance with one embodiment; FIG. 9 illustrates an example simplified procedure for managing room-based hotel communication terminal content based on guest information in accordance with one or more embodiments described herein, particularly from the perspective of a hotel server;

[0026] FIG. 10 illustrates another example simplified procedure for managing roombased hotel communication terminal content based on guest information in accordance with one or more embodiments described herein, particularly from the perspective of a communication terminal;

[0027] FIG. 11 illustrates an example implementation of an in-room hotel communication terminal; and

[0028] FIG. 12 illustrates an example simplified procedure for a traveler pattern recognition and recommendation system in accordance with one or more embodiments described herein.

[0029] DESCRIPTION OF EXAMPLE EMBODIMENTS

[0030] As noted above, sophisticated travelers expect top-notch service and flawless execution of operations during their stay. Savvy travelers, in particular, expect seamless integration of technology into their lives on the go. This disclosure leverages a multilayered architecture where server-side processing, dynamic data retrieval, and distributed client devices collaboratively provide guests with tailored, real-time services.

[0031] In the quest for their next travel adventure, today's discerning travelers increasingly seek personally tailored search results, especially when exploring new and unfamiliar destinations. The desire for familiarity often guides their decision-making process, as they tend to gravitate towards the known rather than the unknown. Personalization in travel searches goes beyond generic recommendations, encompassing individual preferences, interests, and travel styles. Travelers appreciate platforms and services that employ advanced algorithms to deliver suggestions aligned with their unique tastes, whether it be culinary experiences, cultural attractions, or outdoor adventures. The comfort derived from personalized search results not only streamlines the decision-making process but also fosters a sense of trust in the travel planning journey, making the exploration of new places a more enjoyable and rewarding experience. As technology continues to evolve, the intersection of personalization and travel planning is becoming increasingly pivotal, transforming the way individuals approach and engage with the vast landscape of the unknown.

[0032] The techniques herein, therefore, provide a system and method for a traveler pattern recognition and recommendation system. According to one or more embodiments of the disclosure as described in greater detail below, the systems and methods described herein use artificial intelligence (Al) and machine learning (ML) techniques to learn general and individualized traveler behaviors and preferences (e.g., transactional behavior monitoring). By then applying the learned behaviors and preferences to a behavior-based traveler recommendation engine, the techniques herein may then provide an intelligent and personalized traveler search and recommendation experience prior to booking travel, as described below. For instance, a travel-based application (e.g., associated with a hotel or else a general travel search engine) may use a recommendation engine to establish traveler-specific search results regarding locations, hotel brands, travel dates, costs, and so forth based on understanding travel patterns in general, and particularly those of the searching traveler. Search results may be based on amenities, discounts, incentives, events, activities, previous searches, previous travel, and many other metrics and / or observations, and may thus be adapted to the particular searcher, thus enhancing the personalized travel experience. In addition, in one embodiment, the behavior-based traveler recommendation engine may also send relevant traveler preference information to any booked hotel, airline, car rental company, excursion guides, or other travel -related business so that the traveler’s experience may be further personally tailored, accordingly.

[0033] As will be understood by those skilled in the art, a "LAN" refers to a local area network, which is a computer network that interconnects computers within a limited area, such as a residence, a business, or an office building. Also, "PMS" refers to a property management system, which is a platform that enables a hotel or group of hotels to manage front-office capabilities, such as booking reservations, guest check-in / check-out, room assignment, managing room rates, billing, food and beverage operations, housekeeping and maintenance management, sales and catering, and revenue management.

[0034] One medium a hotel may use to deliver on-demand, customized content to hotel guests is a hotel-wide communications network based on placing communication terminals in every hotel room. These communication terminals may be a tablet computing device owned, rented, leased or licensed by the hotel, dedicated to an assigned room and configured with applications specific to guest interaction, as well as basic tablet computing functionality such as a touch screen, speakers and microphones, and network connectivity. Communication terminals may be off-the-shelf tablet computers, custom communications consoles, touch-screen televisions with wireless connectivity, or even systems similar to smart home controllers where a small computing module allows a guest to control audiovisual and other connected devices in their assigned room (e.g., speakers and televisions, lights, curtains, etc.) via the interface and / or voice recognition, optionally with various additional machine learning / artificial intelligence capabilities.

[0035] Alternatively, in place of or in addition to the hotel-supplied terminals, a user (hotel guest) may use his or her own personal device, such as a smart phone, tablet, laptop, or other device with the capability of executing a hotel-based application, such as described herein.

[0036] To send customized content to specific communication terminals (or apps operating on user devices), a hotel may install a centralized control system for all communication terminals, in the form of a hotel server. The hotel server may be a dedicated server, programmed specifically for the task of determining information for a specific guest and providing appropriate configuration commands to the communication terminals. The hotel server is programmed to interface with multiple data repositories, including a PMS, loyalty rewards databases, and third-party content repositories. Through structured query execution and API-based data retrieval, the server consolidates and processes guest-specific data to generate real-time, context-aware service recommendations. Based on inputs received, the hotel server may configure communication terminals throughout the hotel to load and display on-demand the content specifically targeted toward a guest accessing the communication terminal associated with their assigned room.

[0037] In one specific embodiment, the hotel server utilizes a structured data schema to manage communication terminal configurations across the hotel. Each terminal accesses pre-defined memory blocks within its boot directory, as instructed by the server. These memory blocks are dynamically updated based on guest profile data retrieved from centralized databases, ensuring the delivery of personalized content and services. The boot directory may contain all of the content specific to a hotel, divided into boot blocks associated with appropriate selection criteria. In this manner, boot code in the different sections of the boot directory may configure the communication terminals to load and display on-demand the content specifically targeted toward a guest accessing the communication terminal in their assigned room. In an embodiment, the communication terminals boot up and acquire content from the hotel server, content management system, or third party systems over the Internet. In certain embodiments, the terminals (in-room and / or user devices) may be further customized with user-specific and / or room-specific information, options, etc.

[0038] This disclosure relates to a method and system that implement the solution described above. One of skill in the art will realize that the methods and systems of this disclosure describe proscribed functionality associated with a specific, structured communications network interface, wherein a specially programmed server is configured to manage dedicated communication terminals or instances of an associated application on user devices. Specifically, the methods and systems, among other things, are directed to a traveler pattern recognition and recommendation system. One of skill in the art will realize that these methods are significantly more than abstract data collection and manipulation, nor are they merely software-implemented human behavior.

[0039] Further, the methods provide a technological solution to a technological problem, and do not merely state the outcome or results of the solution. As an example, one technological hurdle to providing customized, on-demand content is the disparity of input sources the control software must interpret and act upon, and the variety of devices the hotel and / or user may employ as communication terminals. The hotel server described in this solution must be more than a simple router. Instead, the hotel server and communication terminal software that provides the solution disclosed herein is specifically designed to address the technical challenges described herein.

[0040] Referring to FIG. 1, the system 100 in one embodiment comprises a PMS 102, a hotel server 104, one or more guest information databases 110 (e.g., a loyalty rewards program database, a guest profile database, etc.), a content repository 112, an Internet 114, communication terminals 116 (e.g., located in each hotel room) interconnected on a hotel LAN 118, a guest services entity 120, and an automated scheduler 122. In such an embodiment, the hotel server 104 may include a processor 106 and a memory 108 configured to implement the method disclosed herein. The communication terminals 116 may alternatively or additionally include a processor 106 and memory 108 configured to implement the method disclosed herein, though not shown so as to not obfuscate the drawings.

[0041] The PMS 102 may be the administration and accounting system used by the hotel to identify guests, assign rooms at check-in, and store a record of a guest's registration information. The PMS 102 may be a server or database that resides within the hotel or at a remote location. The PMS 102 may have a wired or wireless communications connection to the hotel server 104. Through the communications connection, the PMS 102 may notify the hotel server 104 that a guest has been registered, and transmit pertinent registration information, such as the guest's name and assigned room.

[0042] The hotel server 104 may be a server installed in the hotel or remotely in the Cloud that manages the configuration of communication terminals 116 to load customized content. The hotel server 104 may have a wired or wireless connection to the Internet 114, allowing communication with a guest information database 110 and a content repository 112. The hotel server 104 may connect to the communication terminals 116 through a hotel LAN 118, or if the hotel server 104 is remotely located, through the Internet 114. The hotel server 104 may configure the communication terminals 116 to provide on-demand, customized content based on the guest information (e.g., loyalty tier level, personal information, other groupings of guests, and so on) of each individual guest.

[0043] The guest profde database 110 may store information pertaining to guests who have visited the hotel or affiliated hotels in the past. This information may include a guest's preference settings (e.g., wakeup call times, room temperature, and housekeeping services), as well as other information such as personal information (e.g., name, address, demographics, etc.). Access to this database, for example, may allow the interactive interface created for a guest to offer a guest's previous selections as default options in selection menus, among other things.

[0044] The guest information database 110 comprises one or more data stores for guest information, such as personal information (e g., name, address, demographics, etc.), hotel or other loyalty databases (e.g., hotel rewards profiles and / or points, airline memberships, etc.), or other groupings of guests (e.g., conference attendee information, wedding guests, other events, and so on). The database(s) 110 may be public or private databases residing at the hotel or at a remote location. In some embodiments, one or more of the databases 110 may also be integrated with the PMS 102, allowing the hotel to send guest information directly to the hotel server 104, with no database query needed.

[0045] For an example, in one embodiment, one particular type of database 110 is a loyalty rewards program database that may store information pertaining to guests who hold membership in the hotel's customer loyalty program. Customer loyalty information may comprise the guest's name and contact information, a customer loyalty program ID number, and categorization within one or more loyalty tiers (e.g., gold membership or platinum membership). Customer loyalty information may also include details about the guest's history with the hotel, hotel chain, and any other participating partners. For example, a loyalty rewards program database may store a record of nights stayed at the hotel, miles traveled on a partnered airline, or money spent on a loyalty credit card, and the rewards thereby earned. The content repository 112 may store a body of media curated by a hotel administrator, by various advertisers, various group / event organizers, or by a third-party content specialist. The stored content may be tagged for applicability to different guests based on their inclusion within one or more identified groups, such as whether they are participating in a particular event, belong to a particular group, or have a particular loyalty tier level, and so on. The content repository 112 may be databases or libraries available online, a dedicated media storage server networked directly to the hotel server 104, or a partition or folder structure within hotel server 104 memory. The content repository 112 may also be a plurality of these sources, in any combination.

[0046] In some embodiments, the content repository 112 may reside within a content management system (CMS), located on a centrally located server, and accessible to hotel administration. The CMS may implement a graphical user interface (GUI) allowing content to be uploaded, edited, categorized (tagged), formatted using stored templates, routed for approval before publication, and published for access by the communication terminals 116. In such an embodiment, content curation and routing to more than one hotel's communication terminals may provide a solution scaled to multiple hotels participating in the same distribution of content, such as a distributed customer loyalty program.

[0047] The communication terminals 116 may be located in each hotel room and may incorporate real-time voice and data communications, with the capability for both unidirectional (one-way) broadcast and bidirectional (two-way) interaction with the guest. As noted above, these devices may be off-the-shelf tablet computers or custom communications consoles, or they may also be smart home-style systems.

[0048] The communication terminals 116 may receive guest-based content from the content repository 112 over an Internet 114 connection with the hotel LAN 118. Communication terminals 116 may format and store the content and generate interfaces for the content appropriate to the type of devices used. The communication terminals 116 may accept a control signal from the hotel server 104 indicating which content they may load (e.g., format and / or information displayed), such as through initiating a real-time download of the content (activating a download from, or upload to, the terminals) or else through dictating how the terminals may boot up and load the content.

[0049] The hotel LAN 118 may be a closed hotel intranet carried over wired or wireless data channels, or any other communications channel in place throughout the hotel. The hotel LAN 118 provides connection between the hotel server 104 and the communication terminals 116.

[0050] The communication terminals 116 transmit structured response signals back to the hotel server 104, encapsulating guest interactions and preferences. These signals are processed by the server’s logic layer to update guest profiles in real time and trigger automated actions, such as scheduling housekeeping or ordering services, when they desire to check out, or other selections, as described further below. The hotel server 104 may communicate the guest response to a guest services entity 120. This may be an automated scheduler 122 that receives a signal containing the pertinent information and automatically updates a scheduling database or similar application. Alternately, the communication may be in the form of an email to a person or entity, such as those responsible for staffing and planning housekeeping services each day, review platforms, and so on. In some embodiments, the hotel server 104 may update the guest profile database 110 with this information.

[0051] Referring to FIG. 2, the system 200 in one embodiment comprises a PMS 102, a hotel server 104, a guest information database 110, an optional loyalty rewards program database 210, a content repository 112, and a communication terminal in an assigned room, hereinafter “communication terminal 212”. The hotel server 104 may contain logic for a query engine 202, a device address lookup table 204, and a user response handler 208. The communication terminal 212 may include a question gate 206, a communications terminal configuration engine 214, a formatting engine 216, a content selector 218, an interface builder 220, a GUI engine 222, and a device display / user interface 224. The query engine 202 in the hotel server 104 may receive a guest's name (or other identification, such as a username, account number, or group identification, etc.) from the PMS 102 upon check-in and may send queries to the guest profile database 110 and loyalty rewards program database 210. Results of these queries may include the guest's loyalty tier level, loyalty rewards points accrued, and purchase history related to the loyalty rewards program, as well as a guest's preferences indicated and / or learned during previous stays at the hotel or its affiliated properties or other pertinent information. Query results may be used to generate a guest identification signal that may contain guest and loyalty data in an assigned room to select customized content as indicated by the query results, as described herein.

[0052] The hotel server 104 may also receive the guest's assigned room number from the PMS 102. This assigned room number may be sent to a device address lookup table 204 that may provide addressing information needed to signal the correct device (in this case, the communication terminal 212). The device address may be an IP address assigned by the hotel LAN, a MAC address programmed into a network adapter installed in the communication terminal, or some other unique address by which the device can be distinguished over the hotel LAN 118. The device address is sent over the hotel LAN 118 to configure the addressed communication terminal device to boot and configure its interactive interface as directed.

[0053] The communications terminal configuration engine 214 may receive the guest identification signal from the hotel server 104. The communications terminal configuration engine 214 may execute a set of commands instructing the communication terminal 212 in the assigned room to start up. The communications terminal configuration engine 214 may also configure the communication terminal 212 in the assigned room to display the interactive interface based the guest information provided to it by the hotel server. All communication terminals 116 may have the same content from which to choose, or else may be based on other formatting / content criteria.

[0054] Guest-facing content is curated and stored in a centralized content repository 112 (e.g., a content database), accessible via the hotel’s server infrastructure. The repository employs a relational database model, with metadata tagging to facilitate efficient retrieval and dynamic content delivery. Content is streamed to communication terminals or mobile devices over secure channels, ensuring real-time updates and contextual relevance. Updates to content in the content repository 112 may be automatically pushed down to communication terminals, or the communication terminals may be programmed to periodically monitor the content repository 112 for added, removed, or modified content. Content may also be updated multiple times in a draft state or "sandbox" area and be sent to communication terminals only when it is approved and published through a content management platform.

[0055] The communication terminal 212 may import content from the content repository 112. Content transmitted to the communication terminal 212 may be sent to a formatting engine 216. The formatting engine 216 may recognize content tagged as being already compatible with interactive interface requirements. Compatible content may be sent directly through with no modification. Files sent with no tagging or tagging that indicates a possible incompatibility may be ignored or may be modified by the formatting engine 216 to generate files that may be displayed and otherwise manipulated within the interactive interface.

[0056] Formatted content may be customized for a particular guest by passing through a content selector 218. A content selection signal generated by the communications terminal configuration engine 214 based on the content received from the communication terminal 212 may be used to filter the content and construct an interactive interface appropriate for each individual guest.

[0057] Selected content may be sent to an interface builder 220. The interface builder 220 may generate a graphical user interface (GUI) using markup code, such as HTML or XML. The GUI may also be implemented as machine logic, such as JavaScript, that may be integrated into a browser or other web-based application installed on the communication terminals, or as a non-transitory machine logic configuration for operating a computer processor. This interface, when instantiated by the processor may comprise links, buttons, text interface, and graphics. The interactive interface may also provide instructions and functionality allowing the guest to access a Web page or complete a simple form in order to take advantage of an offered (e g., loyalty-based) reward.

[0058] A GUI engine 222 may be used to transmit the interactive interface to a device display / user interface 224. In a preferred embodiment, the device display / user interface 224 may be a touch screen capable of passing a user input signal back to the GUI engine 222. The GUI engine 222 may interpret user input as a menu selection, such as described herein.

[0059] Referring to FIG. 3, in block 302, a routine 300 receives registration information when a guest checks into a hotel, wherein the registration information includes the guest's name, assigned room, and stay duration. In block 306, routine 300 identifies a communication terminal installed in the assigned room (or else prepares a virtual terminal for use on a personal user device). In block 308, routine 300 constructs interactive interfaces, wherein the interactive interfaces are graphical user interfaces (GUIs) for display on the communication terminal in the assigned room (or on the personal user device). In block 310, routine 300 configures the communication terminal to display the interactive interface indicated for the assigned room.

[0060] Referring to FIG. 4, the system 400 comprises the addition of third-party database(s) 402 accessed by the hotel server 104 through the Internet 114.

[0061] A number of databases may be available online for access by the public or by paying subscribers. Such databases may contain, for example, purchase histories recorded by online retailers or credit card companies, restaurant menus and prices, events or activities, and so on. The databases may also record instances of social media check-in or geotagging at various businesses. Where the hotel has licensed access to such database(s), the communication terminals 116 may receive information from these databases (e.g., directly or via the hotel server 104). In some embodiments, a content management system, which manages content in the content repository 112, may also perform these database queries and provide appropriately tagged content to the communication terminals 116. In another embodiment, the hotel server 104 may query the third-party database(s) 402, either before, after, or concurrent with a query of the guest information database 110.

[0062] Notably, a guest may be linked to a large number of categories based on information returned by the third-party database query. The configuration engine in the communication terminal or the query engine in the hotel server may incorporate logic allowing categories to be prioritized and limited to a reasonable number. Prioritization logic may also be part of a third-party marketing service or be implicit in the third-party database records accessed.

[0063] Referring to FIG. 5, a system 500 comprises the addition of guest mobile devices 504, in communication with the hotel server 104 through a cellular / Wi-Fi network 502.

[0064] Guest mobile devices 504 may typically comprise cellular phones (smart phones) and tablet computers with communications enabled through either data connections provided by the guest's cellular service provider, or connection to the Internet over a local public or private Wi-Fi network, administered by a business or service at or near the guest's geographic location.

[0065] Guests may register their mobile devices as part of check-in at the hotel or, in certain embodiments, when they sign-up with a customer loyalty program. An invitation to download a mobile application may be sent to these mobile devices, or the communication terminals 116 in the guest's assigned room may display instructions for locating and downloading a mobile application over the Internet. Once installed, the mobile application may allow the guest mobile devices 504 to mirror the communication terminals 116 provided by the hotel, such as through an associated application (app).

[0066] Referring to FIG. 6A, the sample interactive interface 600 shows elements that may be included in the interactive interface generated for a typical communication terminal 602 located in the room of a guest who is a member of a particular grouping, such as a member of a loyalty rewards program. A template used by the interface builder 220 module in the communication terminals 116 may collect content for display or linked access via a number of screen modules. For example, the top of the interface may comprise a branded task bar 604. Beneath that, there may be left sidebar menu 606, center content viewing pane 610, and right sidebar content 616 areas.

[0067] Customized content may be allocated to one or more of these screen areas. For example, general hotel information may be linked from the left sidebar menu 606 and displayed in the center content viewing pane 610. This information may include operating hours for hotel restaurants and services, among other things.

[0068] As an example, an offer at a local store 612 may be shown as part of a loyalty rewards the guest is eligible for. The offer may appear in the right sidebar content 616 and remain visible as the guest navigates other customized content in the center content viewing pane 610. Additionally, the sidebar content may be linked to display a larger format of the content in the center content viewing pane 610 when clicked in the sidebar. Other examples of customer loyalty program benefits may include discounted admission fees to local sporting events, VIP room access at local clubs, or a free drink at the hotel bar, and so on.

[0069] As another example, a particular local attraction 614 may be displayed if the guest's personal online data indicates an interest. Again, the content may appear in the right sidebar content 616 and remain visible as the guest navigates other customized content in the center content viewing pane 610. Additionally, the sidebar content may be linked to display a larger format of the content in the center content viewing pane 610 when clicked in the sidebar. For example, if access to a public third-party database provides the information that the guest has previously checked-in on social media while on a dinner cruise, an advertisement for a local business that also runs dinner cruises may be displayed. If a recent golf club purchase is indicated, advertisement for the hotel's golf course may be featured. This display may be the end result of the method and system disclosed herein. Such a display represents a cost-effective and scalable solution to the problem of providing targeted, on demand content directly to a hotel guest's assigned room. The content displayed may be optimized based on the guest's interests, groups, events, and standing in a customer loyalty program, in order to maximize guest use of and response to the content provided.

[0070] As an alternative, FIG. 6B illustrates an example of the sample interactive interface 600 that shows how elements included in the interactive interface generated for a communication terminal 602 may differ from FIG. 6A if located in the room of a guest who is not a member of any particular grouping, such as not being a member of a loyalty rewards program. Though in one embodiment the template may be same and only the content differs, as shown in FIG. 6B, the template itself may also change, such as removing the entire right sidebar content 616 area. Other variants of changes may be made, and the ones shown and discussed herein are merely examples for illustration.

[0071] Referring to FIG. 7, an embodiment of a computer system 700 useful in implementing the methods disclosed herein comprises a central processing unit 702, memory 704, a bus 706, user VO 708, a network interface 710, an operating system 712, applications 714, and a database 716.

[0072] In various embodiments, system 700 may include a desktop PC, server, workstation, mobile phone, laptop, tablet, set-top box, appliance, or other computing device that is capable of performing operations such as those described herein. In some embodiments, system 700 may include more or fewer components than those shown in FIG. 7. However, it is not necessary that all of these generally conventional components be shown in order to disclose an illustrative embodiment. Collectively, the various tangible components or a subset of the tangible components may be referred to herein as "logic" configured or adapted in a particular way, for example as logic configured or adapted with particular software or firmware. In various embodiments, system 700 may comprise one or more physical and / or logical devices that collectively provide the functionalities described herein. In some embodiments, system 700 may comprise one or more replicated and / or distributed physical or logical devices.

[0073] In some embodiments, system 700 may comprise one or more computing resources provisioned from a cloud computing provider, as will be understood by those skilled in the art.

[0074] System 700 includes a bus 706 interconnecting several components including a network interface 710, user I / O 708, a central processing unit 702, and a memory 704.

[0075] Memory 704 generally comprises a random access memory ("RAM") and permanent non-transitory mass storage device, such as a hard disk drive or solid-state drive. Memory 704 stores an operating system 712, and may also store applications 714 used for both general operation and for implementing the methods disclosed herein. Memory 704 may also include a database 716. In some embodiments, system 700 may communicate with database 716 via network interface 710, a storage area network ("SAN"), a high-speed serial bus, and / or via the other suitable communication technology.

[0076] In some embodiments, database 716 may comprise one or more storage resources provisioned from a cloud storage provider, as will be understood by those skilled in the art.

[0077] User I / O 708 may comprise one or more components allowing human interaction with the computing system 700, such as a monitor, a touch screen display, a mouse, a keyboard, a stylus, or other, similar devices.

[0078] Terms used herein should be accorded their ordinary meaning in the relevant arts, or the meaning indicated by their use in context, but if an express definition is provided, that meaning controls.

[0079] "Circuitry" in this context refers to electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes or devices described herein), circuitry forming a memory device (e.g., forms of random access memory), or circuitry forming a communications device (e.g., a modem, communications switch, or optical- electrical equipment).

[0080] "Firmware" in this context refers to software logic embodied as processorexecutable instructions stored in read-only memories or media.

[0081] "Hardware" in this context refers to logic embodied as analog or digital circuitry.

[0082] "Logic" in this context refers to machine memory circuits, non transitory machine-readable media, and / or circuitry which by way of its material and / or materialenergy configuration comprises control and / or procedural signals, and / or settings and values (such as resistance, impedance, capacitance, inductance, current / voltage ratings, etc.), that may be applied to influence the operation of a device. Magnetic media, electronic circuits, electrical and optical memory (both volatile and nonvolatile), and firmware are examples of logic. Logic specifically excludes pure signals or software per se (however does not exclude machine memories comprising software and thereby forming configurations of matter).

[0083] "Software" in this context refers to logic implemented as processor-executable instructions in a machine memory (e.g. read / write volatile or nonvolatile memory or media).

[0084] Herein, references to "one embodiment" or "an embodiment" do not necessarily refer to the same embodiment, although they may. Unless the context clearly requires otherwise, throughout the description and the claims, the words "comprise," "comprising," and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in the sense of "including, but not limited to." Words using the singular or plural number also include the plural or singular number respectively, unless expressly limited to a single one or multiple ones. Additionally, the words "herein," "above," "below" and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. When the claims use the word "or" in reference to a list of two or more items, that word covers all of the following interpretations of the word: any of the items in the list, all of the items in the list and any combination of the items in the list, unless expressly limited to one or the other. Any terms not expressly defined herein have their conventional meaning as commonly understood by those having skill in the relevant art(s).

[0085] Various logic functional operations described herein may be implemented in logic that is referred to using a noun or noun phrase reflecting said operation or function. For example, an association operation may be carried out by an "associator" or "correlator". Likewise, switching may be carried out by a "switch", selection by a "selector", and so on.

[0086] Additional and / or alternative embodiments may also be contemplated in accordance with the present disclosure and the techniques described herein. For instance, in one embodiment, the groupings may be based on a guest’s language preference (e.g., groups for English, French, Chinese, Japanese, etc.), and as such, the information (or perhaps even the format) of the customized interface may be established based on the appropriate language group.

[0087] Also, in one embodiment, a guest may be associated with a plurality of groups at the same time. For example, a guest may be a member of a loyalty program (a first group), and may be staying at the hotel for a friend’s wedding for the weekend (a second group). Another guest may be a member of the same loyalty program, but may be attending a work conference at the hotel or at a convention center in the same city as the hotel (a third group). FIG. 8 illustrates an example interactive interface 800 similar to FIGS. 6A-6B, showing elements that may be included in the interactive interface generated for a typical communication terminal 602 located in the room of a guest who is a member of a plurality of grouping, such as a member of a loyalty rewards program and an attendee of an on-site wedding. For example, the top of the interface may still comprise a branded task bar 604, a left sidebar menu 606 general hotel information, and center content viewing pane 610 to display the details of the current selection. However, in this embodiment, right sidebar content 616 now has a plurality of sub-content areas 816a and 816b, with the first content in 816a corresponding to the first grouping (e.g., loyalty rewards program based information) and the second content in 816b corresponding to the second grouping (e.g., wedding information).

[0088] Additional and / or alternative embodiments may also be contemplated in accordance with the present disclosure and the techniques described herein. For instance, in one embodiment, tiered checkout times may be shown on the display with various incentives for each (e.g., 8-10a for an incentive / reward, 10-12a no reward, 12-2p with a cost / penalty, etc.). Additionally, a one-touch checkout button may allow a guest to leave the room, checkout, and optionally schedule services at the same time, such as valet, a bellhop for bags, and so on. This may have the option of being at an advanced time (e.g., 15 minutes or other value that may be set or selected by the guest). Also, this checkout button may reset the room to a default through controlled systems, such as shutting off lights, resetting HVAC settings, and so on, either immediately, after a timer, or after sensors detect that no one is in the room any longer. In still further embodiments, the system herein may detect if a guest is still in the room after the scheduled checkout time, and if so may notify the guest (e.g., an alarm) and may offer an update to the late checkout options. Furthermore, in certain embodiments, the incentives and costs mentioned herein may be dynamic, meaning they change based on demand (e.g., higher incentives and / or costs when the hotel is particularly busy and needs the rooms). Still further embodiments may be contemplated herein, and those mentioned are merely examples.

[0089] Additionally, FIG. 9 illustrates an example simplified procedure for managing room-based hotel communication terminal content based on guest information in accordance with one or more embodiments described herein, particularly from the perspective of a system of a hotel server 104. For example, a non-generic, specifically configured device (e.g., hotel server 104) may perform procedure 900 by executing stored instructions. The procedure 900 may start at step 905, and continues to step 910, where, as described in greater detail above, the server receives registration information when a guest checks into a hotel, the registration information including an identification of the guest and a particular assigned room of a plurality of hotel rooms of the hotel. In step 915, the server identifies a particular communication terminal of a plurality of communication terminals in communication with the server that is associated with the particular assigned room, wherein each of the communication terminals has a graphical user interface (GUI) and is associated with a respective room of the plurality of hotel rooms of the hotel.

[0090] Then, in one embodiment, in step 920 the server queries a guest information database using the identification of the guest to determine a particular grouping associated with the guest (or other guest-specific information / display), and in step 925 generates an interactive interface comprising customized content specific to the particular grouping associated with the guest. In an alternative embodiment, the server informs the particular communication terminal of the identification of the guest, and the terminal may perform these steps, accordingly. In still another alternative embodiment, the server performs step 920, then informs the particular communication terminal of the particular grouping, such that the terminal may perform step 925 based on the grouping.

[0091] In step 930, the server causes the particular communication terminal to present the interactive interface comprising customized content specific to the particular grouping associated with the guest on a corresponding GUI of the particular communication terminal.

[0092] Note that other steps may be included within procedure 900, such as generating an interactive interface incorporating customized content specific to each of a plurality of groupings associated with the guest, and causing the particular communication terminal to present the interactive interface incorporating customized content specific to each of the plurality of groupings associated with the guest on the corresponding GUI of the particular communication terminal.

[0093] Optionally, in step 935, the server may also identify a mobile device associated with the guest as described above, and may cause a mobile application of the mobile device to mirror the particular communication terminal while the guest is staying at the hotel.

[0094] The illustrative simplified procedure 900 may then end in step 940, notably with the option to update the content, or to return the terminal to a default or other action upon checkout of the guest, accordingly.

[0095] Moreover, FIG. 10 illustrates another example simplified procedure for managing room-based hotel communication terminal content based on guest information in accordance with one or more embodiments described herein, particularly from the perspective of a communication terminal 116. For example, a non-generic, specifically configured device (e.g., communication terminal 116) may perform procedure 1000 by executing stored instructions. The procedure 1000 may start at step 1005, and continues to step 1010, where, as described in greater detail above, a particular communication terminal of a plurality of communication terminals receives identification of a guest, wherein the particular communication terminal is associated with a particular room of a plurality of hotel rooms of a hotel, and wherein the guest has checked into the hotel and has been assigned to the particular room.

[0096] As such, in step 1015, the particular communication terminal may query a guest information database using the identification of the guest to determine a particular grouping (or other information / display options) associated with the guest, and in step 1020 stores an interactive interface comprising customized content specific to the particular grouping associated with the guest. As noted above, one or both of steps 1015- 1020 may be performed by the server, and the illustrative procedure 1000 is merely one example embodiment. In step 1025, the particular communication terminal presents the interactive interface comprising customized content specific to the particular grouping associated with the guest on a corresponding graphical user interface (GUI), and the procedure ends in step 1030 (e.g., until content is updated or until the guest checks out, as noted above).

[0097] FIG. 11 illustrates an example implementation of an in-room hotel communication terminal 1100.

[0098] Operationally, and as noted above, the techniques herein provide for a traveler pattern recognition and recommendation system. The following description describe such systems and methods specifically, and may, though need not, be implemented by, or in conjunction with, certain features of the embodiments described above.

[0099] In particular, according to one or more embodiments of the present disclosure, a traveler pattern recognition and recommendation system described herein redefines personalized travel experiences. This innovative approach utilizes advanced Artificial Intelligence (Al) and Machine Learning (ML) techniques to comprehend both general and individualized traveler behaviors and preferences, primarily through transactional behavior monitoring. By applying these learned behaviors to a sophisticated behaviorbased traveler recommendation engine, this system transforms the travel planning experience into an intelligent and personalized journey, allowing travelers to search for, find, and book their best travel plans.

[0100] The traveler pattern recognition system includes behavior and preference learning of users. That is, by leveraging Al and ML techniques, the system comprehensively learns general and individual traveler behaviors and preferences through transactional behavior monitoring. The learned patterns are then applied to a behavior-based traveler recommendation engine, shaping an intelligent and personalized search and recommendation experience for travelers before they book their journey.

[0101] Personalized travel search results may then be provided by travel -based applications (e g., associated with hotels or general travel search engines) that use the recommendation engine to tailor search results based on traveler-specific metrics. These may include locations, hotel brands, travel dates, costs, and more, finely tuned to the individual traveler's preferences and patterns.

[0102] Search results consider various factors such as amenities, discounts, incentives, events, activities, previous searches, and travel history, adapting the recommendations to the particular searcher and their traveler preferences, thus enhancing the overall personalized travel experience.

[0103] In one embodiment, the behavior-based traveler recommendation engine communicates relevant traveler preference information to booked hotels, airlines, car rental companies, excursion guides, and other travel-related businesses. This ensures that the entire travel experience is personally tailored to the individual traveler.

[0104] This traveler pattern recognition and recommendation system not only revolutionize the pre-booking travel experience but also ensures that the entire journey is uniquely crafted to align with the individual preferences and behaviors of each traveler, fostering a new era of truly personalized travel.

[0105] Additionally, FIG. 12 illustrates an example simplified procedure for a traveler pattern recognition and recommendation system, implemented through an advanced, multi-layered architecture in accordance with one or more embodiments described herein. The procedure 1200, executed by one or more non-generic, specifically configured devices such as servers, machine learning (ML) engines, and user-facing applications, begins at step 1205 and proceeds with the following operations, leveraging secure data handling protocols, Al algorithms, and dynamic content generation as described in greater detail above:

[0106] Operation 1210: Behavior and Preference Data Collection: The system employs artificial intelligence (Al) and machine learning (ML) models to collect and analyze extensive traveler behavior and preference data. This includes transactional behavior monitoring, historical search patterns, booking data, and demographic attributes. Data is aggregated from multiple sources, such as hotel property management systems (PMS), online travel agencies (OTAs), loyalty programs, and social media interactions. Advanced feature extraction techniques and clustering algorithms are used to identify meaningful patterns and insights, which are securely stored in a distributed data repository for real-time access.

[0107] Operation 1215: Application to Recommendation Engine: The learned patterns and behavioral insights are applied to a behavior-based traveler recommendation engine, which utilizes a predictive modeling framework to generate personalized travel suggestions. The engine incorporates natural language processing (NLP) for interpreting user queries and adaptive algorithms to dynamically rank and prioritize recommendations based on traveler-specific metrics.

[0108] Operation 1220: Travel-Based Application Access: Travelers access the system through associated hotel applications or general travel search platforms. Upon access, the system authenticates the user and retrieves their profile data, leveraging secure communication protocols such as OAuth or tokenized authentication. The personalized search experience is initiated by dynamically configuring the application’s user interface (UI) based on traveler preferences and previously observed patterns.

[0109] Operation 1225: Intelligent Search Result Generation: The recommendation engine dynamically generates search results by applying advanced filtering and ranking algorithms. Inputs such as traveler-specific metrics (e.g., preferred destinations, favorite hotel brands, travel dates, and budget constraints) are combined with contextual data (e.g., real-time availability, promotions, and weather conditions). The results are rendered in a responsive graphical user interface (GUI), optimized for both desktop and mobile devices.

[0110] Operation 1230: Adaptive Recommendations: Search results are refined in real time through iterative feedback loops and contextual updates. The system evaluates factors such as preferred amenities, discounts, incentives, events, and past travel history. Using reinforcement learning, the engine adapts its recommendations to provide highly targeted and dynamic suggestions that align with the traveler’s evolving preferences. Operation 1235: Information Sharing with Travel Businesses: The system securely communicates traveler preference data and booking details to partnered travel- related businesses, such as airlines, car rental companies, and event organizers. This data exchange utilizes structured APIs and encrypted channels to ensure privacy and security. Partner businesses use this information to deliver personalized services, creating a seamless and consistent experience throughout the traveler’s journey.

[0111] The illustrative simplified procedure 1200 may then end in step 1240, notably with the option to update the content, or to return the terminal to a default or other action upon checkout of the guest, accordingly.

[0112] It should be noted that while certain steps within procedures above may be optional as described above, the steps shown herein are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein. Moreover, while procedures may be described separately, certain steps from each procedure may be incorporated into each other procedure, and the procedures are not meant to be mutually exclusive.

[0113] Advantageously, the techniques described herein thus provide for a traveler pattern recognition and recommendation system. In particular, a solution tailored to the evolving preferences of today's travelers offers an array of advantages in the realm of travel planning. By prioritizing personally tailored search results, the solution caters to the discerning traveler's desire for a more customized and familiar journey, especially in new and unfamiliar destinations. Going beyond generic recommendations, this solution integrates advanced algorithms that align with individual preferences, interests, and travel styles. As a result, the decision-making process becomes more streamlined, empowering travelers with curated suggestions ranging from culinary experiences to cultural attractions. The inherent comfort derived from personalized search results not only enhances the overall travel planning journey but also fosters a deeper sense of trust between the traveler and the platform or service. In an era where technology continues to evolve, this intersection of personalization and travel planning becomes pivotal, revolutionizing how individuals approach and engage with the vast and often unknown landscape of their next adventure. The complete solution is economically viable and is scalable to serve hotel businesses of all sizes, from hotels that are independently owned and run to international hotel chains.

[0114] Said differently, the techniques herein provide a centralized, hotel-agnostic application that offers unparalleled observability into travelers' transactions across multiple hotel chains and third-party service providers. By analyzing both hotel-specific and hotel-agnostic patterns, the application identifies what travelers value, their spending habits, and their preferences, including what they are willing to pay for, when, and where. Beyond hotel services, the application captures a holistic view of traveler behavior, encompassing external activities such as dining, events, and excursions. This capability enables the centralized server to communicate with various hotel property management systems and external providers, offering tailored recommendations and personalized experiences. The techniques herein, and their ability to synthesize data from diverse sources, establish a comprehensive profile of user behavior, driving smarter, more effective recommendations while enhancing the travel experience across all aspects of a journey, from searching, to booking, to experiencing.

[0115] In closing, according to one or more embodiments herein, a system for traveler pattern recognition and personalized recommendations may comprise: a data collection module configured to: i) collect traveler behavior and preference data from multiple sources, including property management systems (PMS), online travel agencies (OTAs), loyalty programs, and social media platforms; and ii) process the data using machine learning (ML) algorithms to extract patterns and generate behavioral insights; a recommendation engine configured to: i) apply the behavioral insights to generate personalized travel recommendations; ii) dynamically rank and prioritize recommendations based on traveler-specific metrics, including destinations, hotel brands, travel dates, and budgets; and iii) adapt recommendations in real time based on traveler interactions and feedback; a user interface module configured to: i) provide travelers with access to personalized search platforms via hotel applications or travel search engines; and ii) dynamically render search results in a responsive graphical user interface (GUI) optimized for various device types; a communication module configured to securely share traveler preference data with travel -related businesses via structured APIs and encrypted communication channels; and a data storage module configured to store aggregated traveler data and behavioral insights in a distributed repository for real-time retrieval and system optimization. In one embodiment, the data collection module employs natural language processing (NLP) to analyze traveler interactions and extract sentiment and intent. In one embodiment, the communication module uses blockchain technology to log traveler data exchanges and ensure transactional integrity. In one embodiment, the recommendation engine integrates with external booking systems to enable one-click reservations for hotels, flights, and activities. In one embodiment, the user interface module supports voice-activated search and recommendations using AL powered virtual assistants.

[0116] In addition, according to one or more embodiments herein, a method for traveler pattern recognition and personalized recommendations may comprise: collecting traveler behavior and preference data from multiple sources, including PMS, OTAs, loyalty programs, and social media platforms; processing the data using artificial intelligence (Al) and machine learning (ML) algorithms to identify behavioral patterns and generate insights; applying the insights to a behavior-based traveler recommendation engine to create personalized travel suggestions; authenticating travelers on a search platform via secure protocols, retrieving their profile data for a personalized experience; generating search results dynamically using advanced filtering and ranking algorithms, based on traveler-specific metrics such as destinations, budgets, and travel dates; adapting recommendations in real time through iterative feedback loops, incorporating preferences for amenities, discounts, events, and travel history; and communicating traveler preference data and booking details to partnered travel -related businesses to enable seamless and personalized service delivery. In one embodiment, the recommendation engine incorporates reinforcement learning algorithms to refine recommendations over time based on user feedback. In one embodiment, the method further comprises displaying localized travel options, such as dining, events, and activities, based on the traveler’s geolocation. In one embodiment, the recommendation engine incorporates dynamic pricing algorithms.

[0117] Moreover, according to one or more embodiments herein, an apparatus for traveler pattern recognition and personalized recommendations may comprise: a processor configured to execute stored instructions; a memory storing instructions that, when executed by the processor, cause the apparatus to: collect traveler behavior and preference data from external systems and platforms; apply machine learning algorithms to analyze the data and generate behavioral insights; generate and display personalized recommendations using a predictive modeling framework; and adapt recommendations dynamically based on real-time traveler interactions and external contextual data; and a network interface configured to securely transmit traveler preference data to third-party travel -related businesses and retrieve real-time contextual data for recommendation updates. In one embodiment, the memory stores encrypted traveler profiles to ensure compliance with data privacy regulations. In one embodiment, the network interface supports real-time data synchronization across multiple devices, including mobile phones, tablets, and desktop computers.

[0118] Furthermore, according to one or more embodiments herein, a method for traveler pattern recognition and personalized travel recommendations may comprise: collecting and analyzing traveler behavior and preference data using artificial intelligence (Al) and machine learning (ML) techniques, including transactional behavior monitoring, historical search patterns, and booking data aggregated from multiple sources; applying the collected data to a behavior-based traveler recommendation engine, the engine generating personalized travel suggestions using predictive modeling frameworks and dynamically ranking recommendations based on traveler-specific metrics; enabling travelers to access personalized search experiences through hotel applications or general travel search platforms, authenticated via secure communication protocols; generating search results dynamically using filtering and ranking algorithms, incorporating traveler- specific metrics such as preferred destinations, hotel brands, travel dates, and budget constraints; refining search results in real time through iterative feedback loops, incorporating contextual updates such as preferred amenities, discounts, events, and past travel history; and securely sharing traveler preference data and booking details with partnered travel -related businesses via structured APIs and encrypted channels, enabling personalized services throughout the traveler’s journey. In one embodiment, the traveler behavior and preference data collected includes demographic attributes and social media interaction data, analyzed using clustering algorithms for pattern recognition. In one embodiment, the predictive modeling framework incorporates natural language processing (NLP) to interpret user queries and provide context-aware recommendations. In one embodiment, the secure communication protocols include OAuth-based authentication and tokenized session management to ensure traveler data security. In one embodiment, the filtering and ranking algorithms incorporate real-time contextual data such as weather conditions, promotions, and availability to enhance recommendation accuracy. In one embodiment, the iterative feedback loops use reinforcement learning to adapt recommendations dynamically based on evolving traveler preferences and interactions. In one embodiment, the search results are displayed in a responsive graphical user interface (GUI) optimized for both desktop and mobile devices. In one embodiment, the data sharing ensures compliance with data privacy regulations by anonymizing sensitive traveler information prior to transmission. In one embodiment, the partnered travel -related businesses include airlines, car rental companies, hotels, and event organizers, which use the shared traveler data to deliver tailored services. In one embodiment, the method further comprises generating detailed analytics reports for travel businesses based on aggregated traveler behavior data to optimize service offerings and enhance customer satisfaction.

[0119] The techniques may also be embodied as a tangible, non-transitory, computer- readable medium having program instructions stored thereon, which when executed by a processor on a computer are configured to perform a process as described above. While there have been shown and described illustrative embodiments, it is to be understood that various other adaptations and modifications may be made within the scope of the embodiments herein. For example, though the disclosure was often described with respect to hotels, those skilled in the art should understand that this was done only for illustrative purpose and without limitations, and the techniques herein may be used with any temporary accommodation such as for house rentals, apartment rentals, condo rentals, room rentals, office rentals, and so on. Furthermore, while the embodiments may have been demonstrated with respect to certain communication environments, physical environments, or device form factors, other configurations may be conceived by those skilled in the art that would remain within the contemplated subject matter of the description above. Also, each component of the system described herein may operate independently or in conjunction with one another, and any hotel system need not implement all of the systems and methods described herein.

[0120] Specifically, according to a particular embodiment of the present disclosure, a method for traveler pattern recognition and recommendation may comprise: collecting, by a centralized hotel-agnostic server, traveler behavior data associated with a plurality of hotels and third-party external services through transactional behavior monitoring conducted via a centralized application; analyzing, by the centralized hotel-agnostic server, the traveler behavior data to identify both hotel-specific patterns and hotelagnostic patterns using artificial intelligence (Al) and machine learning (ML) algorithms, the hotel-specific patterns and hotel-agnostic patterns comprising traveler preferences, spending habits, and usage trends across the plurality of hotels and third-party services; generating, by the centralized hotel-agnostic server, tailored travel-based recommendations for a particular traveler based on a profile of the particular traveler as compared to the hotel -specific patterns and hotel-agnostic patterns; and providing, by the centralized hotel-agnostic server, a personalized user interface within the centralized application for the particular traveler, the personalized user interface configured to display the tailored travel-based recommendations and to facilitate travel -related bookings. In one embodiment, collecting traveler behavior data includes aggregating transactional data from a plurality of sources selected from a group consisting of hotel property management systems, loyalty programs, online travel agencies, and third-party service providers.

[0121] In one embodiment, analyzing traveler behavior data includes identifying patterns based on factors selected from a group consisting of: preferred travel destinations, preferred hotel brands, seasonal spending habits, activity preferences, and historical search and booking trends.

[0122] In one embodiment, the tailored travel-based recommendations are selected from a group consisting of: destinations, hotel brands, events, room upgrades, exclusive promotions, dining options, transportation services, curated excursions, and calendar dates.

[0123] In one embodiment, providing the personalized user interface includes dynamically generating search results ranked according to traveler-specific metrics selected from a group consisting of: spending thresholds, location preferences, and past interaction history.

[0124] In one embodiment, the traveler behavior data includes insights derived from both in-hotel data and external activity data.

[0125] In one embodiment, the centralized application includes one or more feedback mechanisms to allow travelers to refine their preferences, enabling continuous improvement of tailored recommendations through reinforcement learning.

[0126] In one embodiment, the method further comprises: providing insights based on the hotel-specific patterns and hotel -agnostic patterns to one or more partnered hotels or one or more third-party providers to allow optimization of respective travel-based offerings.

[0127] In one embodiment, the method further comprises: updating the tailored travelbased recommendations in real time based on new traveler behavior data collected through the centralized application. In one embodiment, the method further comprises: updating the tailored travelbased recommendations in real time based on contextual changes selected from a group consisting of: availability of services, traveler location updates, and newly added preferences.

[0128] Additionally, an apparatus for traveler pattern recognition and recommendation may comprise: a processor configured to execute a process; a network interface to communicate on a computer network; and a memory configured to store a process that, when executed by the processor, causes the apparatus to: collect traveler behavior data associated with a plurality of hotels and third-party external services through transactional behavior monitoring conducted via a centralized application; analyze the traveler behavior data to identify both hotel-specific patterns and hotel -agnostic patterns using artificial intelligence (Al) and machine learning (ML) algorithms, the hotel-specific patterns and hotel-agnostic patterns comprising traveler preferences, spending habits, and usage trends across the plurality of hotels and third-party services; generate tailored travel-based recommendations for a particular traveler based on a profile of the particular traveler as compared to the hotel-specific patterns and hotel-agnostic patterns; and provide a personalized user interface within the centralized application for the particular traveler, the personalized user interface configured to display the tailored travel -based recommendations and to facilitate travel -related bookings. The memory may also be further configured to store a process that, when executed by the processor, causes the apparatus to perform additional steps as described above.

[0129] The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that certain components and / or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks / CDs / RAM / EEPROM / etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true intent and scope of the embodiments herein.

Claims

CLAIMSWhat is claimed is:

1. A method for traveler pattern recognition and recommendation, the method comprising: collecting, by a centralized hotel-agnostic server, traveler behavior data associated with a plurality of hotels and third-party external services through transactional behavior monitoring conducted via a centralized application; analyzing, by the centralized hotel-agnostic server, the traveler behavior data to identify both hotel-specific patterns and hotel-agnostic patterns using artificial intelligence (Al) and machine learning (ML) algorithms, the hotel-specific patterns and hotel-agnostic patterns comprising traveler preferences, spending habits, and usage trends across the plurality of hotels and third-party services; generating, by the centralized hotel-agnostic server, tailored travel -based recommendations for a particular traveler based on a profile of the particular traveler as compared to the hotel-specific patterns and hotel-agnostic patterns; and providing, by the centralized hotel-agnostic server, a personalized user interface within the centralized application for the particular traveler, the personalized user interface configured to display the tailored travel-based recommendations and to facilitate travel -related bookings.

2. The method of claim 1, wherein collecting traveler behavior data includes aggregating transactional data from a plurality of sources selected from a group consisting of: hotel property management systems, loyalty programs, online travel agencies, and third-party service providers.

3. The method of claim 1, wherein analyzing traveler behavior data includes identifying patterns based on factors selected from a group consisting of: preferred travel destinations, preferred hotel brands, seasonal spending habits, activity preferences, and historical search and booking trends.

4. The method of claim 1, wherein the tailored travel -based recommendations are selected from a group consisting of: destinations, hotel brands, events, room upgrades, exclusive promotions, dining options, transportation services, curated excursions, and calendar dates.

5. The method of claim 1, wherein providing the personalized user interface includes dynamically generating search results ranked according to traveler-specific metrics selected from a group consisting of: spending thresholds, location preferences, and past interaction history.

6. The method of claim 1, wherein the traveler behavior data includes insights derived from both in-hotel data and external activity data.

7. The method of claim 1, wherein the centralized application includes one or more feedback mechanisms to allow travelers to refine their preferences, enabling continuous improvement of tailored recommendations through reinforcement learning.

8. The method of claim 1, further comprising: providing insights based on the hotel-specific patterns and hotel-agnostic patterns to one or more partnered hotels or one or more third-party providers to allow optimization of respective travel-based offerings.

9. The method of claim 1, further comprising: updating the tailored travel-based recommendations in real time based on new traveler behavior data collected through the centralized application.

10. The method of claim 1, further comprising: updating the tailored travel-based recommendations in real time based on contextual changes selected from a group consisting of: availability of services, traveler location updates, and newly added preferences.

11. An apparatus for traveler pattern recognition and recommendation, the apparatus comprising: a processor configured to execute a process; a network interface to communicate on a computer network; and a memory configured to store a process that, when executed by the processor, causes the apparatus to: collect traveler behavior data associated with a plurality of hotels and third-party external services through transactional behavior monitoring conducted via a centralized application; analyze the traveler behavior data to identify both hotel-specific patterns and hotel-agnostic patterns using artificial intelligence (Al) and machine learning (ML) algorithms, the hotel-specific patterns and hotel-agnostic patterns comprising traveler preferences, spending habits, and usage trends across the plurality of hotels and third-party services;generate tailored travel-based recommendations for a particular traveler based on a profile of the particular traveler as compared to the hotel-specific patterns and hotel-agnostic patterns; and provide a personalized user interface within the centralized application for the particular traveler, the personalized user interface configured to display the tailored travel-based recommendations and to facilitate travel -related bookings.

12. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: collect traveler behavior data by aggregating transactional data from a plurality of sources selected from a group consisting of: hotel property management systems, loyalty programs, online travel agencies, and third-party service providers.

13. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: analyze traveler behavior data to identify patterns based on factors selected from a group consisting of: preferred travel destinations, preferred hotel brands, seasonal spending habits, activity preferences, and historical search and booking trends.

14. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: generate tailored travel -based recommendations selected from a group consisting of: destinations, hotel brands, events, room upgrades, exclusive promotions, dining options, transportation services, curated excursions, and calendar dates.

15. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: provide the personalized user interface by dynamically generating search results ranked according to traveler-specific metrics selected from a group consisting of: spending thresholds, location preferences, and past interaction history.

16. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: collect traveler behavior data including insights derived from both in-hotel data and external activity data.

17. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: incorporate one or more feedback mechanisms within the centralized application to allow travelers to refine their preferences, enabling continuous improvement of tailored recommendations through reinforcement learning.

18. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: provide insights based on the hotel-specific patterns and hotel -agnostic patterns to one or more partnered hotels or one or more third-party providers to allow optimization of respective travel-based offerings.

19. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to:update the tailored travel-based recommendations in real time based on new traveler behavior data collected through the centralized application.

20. The apparatus of claim 11, wherein the process, when executed by the processor, further causes the apparatus to: update the tailored travel-based recommendations in real time based on contextual changes selected from a group consisting of: availability of services, traveler location updates, and newly added preferences.

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