WO2021007566A1 - Group searching - Google Patents

Abstract

A system and method for searching, listing, booking, and displaying lodging accommodations that is designed to be efficient and cost effective for customers traveling in groups. The search method uses a novel query structure that is based on bed types and desired privacy levels, rather than current technology using a generic total number of travelers. Query is processed by the search engine that applies extra filtering in addition to standard availability searches implemented by online travel agencies.

Description

INVENTION TITLE

GROUP SEARCHING

CROSS REFERENCE TO RELATED APPLICATIONS

[001] This Patent Cooperation Treaty (PCT) patent application claims priority to the co-pending United States patent provisional application having the Serial Number 62/872248, filed July 10, 2019, which is incorporated in its entirety by reference.

TECHNICAL FIELD

[002] The present disclosure relates generally to systems and methods for searching, listing, booking, and displaying lodging accommodations. More particularly, embodiments relate to systems and methods for using searching to book accommodations for individual and group travelers.

BACKGROUND

[003] Planning vacations for large groups can be problematic, slow, and frustrating. It is difficult to find multiple units or rooms, or blocks of hotel rooms, or housing accommodations using existing search methods.

[004] For example, a person planning a vacation for a group of eight can be searching for accommodations that require four rooms with eight beds and four bathrooms in a specified price range. The search quickly becomes tedious. The person exhausts time and energy, likely having to compromise comfort or budget.

[005] Today, accommodations within the group travel industry are based on individual listings such as hotel rooms, Airbnb listings, vacation homes, and individualized property listings. Those individual listings often do not meet the unique needs of group travelers nor is the process of searching for and booking individual listings designed with group travelers in mind. The current mapping of all accommodations worldwide only allows consumers to search and pay for single listings online. Special pricing for booking multiple listings can only be negotiated by contacting a decision maker by a manual laborious phone or email correspondence process requiring human intervention.

[006] Rather than having the ability to set preferred accommodation requirements, travelers in groups are often forced to compromise their needs or wants based on available inventory. For example, when searching and booking multiple hotel listings for a group, travelers do not have the option of booking neighboring rooms or even rooms on the same floor. When searching home rentals, travelers are not guaranteed to find an option that meets the needs of each group. Group travelers or renters either end up paying for bedrooms that will be unused or compromising their space and comfort by sharing beds or bunking up with extra people per room.

[007] Additionally, hotels are missing the opportunity to market and bundle prices to attract groups booking two or more rooms. Travelers are spending more money and buying more inventory yet there is not an easy way for them to book with special group rates. Tailoring a search engine to groups would allow hotels and short-term rental properties to compete in a more expansive, assertive, and precise manner.

[008] A prior approach tried to solve this problem by introduction of group traveling services that receive requests for group accommodations and then run auctions to collect bids from hotels that are willing to fulfill the specific request. The drawback of this solution is that it depends on human intervention in the process as hotel representatives review requests and place bids; resulting in bookings that cannot be performed in a single session and take several days to complete. The booking process in these systems are cumbersome and involve interactions with sales managers of the auction service and hotel representatives. Another problem is that travelers cannot pick and choose the combination of listings themselves and must consent with the combination of listings offered in the bid. Lastly, these systems are designed primarily for integration with hotels and do not provide options of staying in guesthouses, condominiums, and other properties therefore limiting choices available to group travelers.

[009] Accordingly, there is a need to improve the process of finding and booking housing and accommodations for groups. The embodiments described herein satisfies this critical need.

[0010] This section is intended to introduce the reader to various aspects of art, which may be associated with embodiments of the present invention. This discussion is believed to be helpful in providing the reader with information to facilitate a better understanding of techniques of the present invention. Accordingly, these statements are to be read in this light, and not necessarily as admissions of prior art.

SUMMARY

[0011] In one embodiment, a method is disclosed. The method comprises: determining the number of rooms; determining types of beds in each room; designating if there are private bathrooms; creating a listing that categorizes number of rooms, types of bed and indications if bathrooms are private; and searching the listing by number of rooms, type of bed, and number of private bathrooms.

[0012] In a second embodiment, a method is disclosed. The method comprises: providing a listing with multiple rooms available; searching the listing with multiple rooms available; displaying exact matches for combinations of listings that provide configuration of rooms, beds, and private bathrooms that are identical to number of rooms, bed types in each room, and designated number of private bathrooms in the search request; displaying label of floor number for combination of listings that are located on different floors of a single property; displaying label of physical addresses and distance for combination of listings that are located in different properties; and displaying additional filter relevance that allow customers to exclude combinations of listings with low compactness ratings.

[0013] In a third embodiment, a method is disclosed. The method comprises: providing a listing with multiple rooms available; setting discounts for travelers booking multiple listings, wherein pricing rules are based on total amount of transaction and total number of listings; allowing at least one person to package a plurality of listings; offering tiered pricing for booking two or more listings as a package; providing a user the ability to search with desired parameters selected form the group consisting of number of rooms, types of bed and indications if bathrooms are private, and combinations thereof; and completing in one online session a booking of multiple listings as a single package with group rates.

[0014] The foregoing summary is not intended to summarize each potential embodiment or every aspect of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The foregoing and other advantages of the present technique may become apparent upon reading the following detailed description and upon reference to the drawings in which:

[0016] FIG. l is a schematic view of a search box in current search engines;

[0017] FIG. 2 is a schematic view of search results in current search engines;

[0018] FIG. 3 is a workflow diagram of actions in an embodiment to analyze results of current search engines;

[0019] FIG. 4 is a workflow diagram of actions in an embodiment to select nearby rooms for group travelers with current search engines;

[0020] FIG. 5 is a workflow diagram of actions in an embodiment to select nearby rooms for group travelers with enhanced search engines based on the prior art;

[0021] FIG. 6 is a schematic view of a new search box along with display windows;

[0022] FIG. 7 is a flow chart block diagram of an embodiment of the proposed search algorithm; [0023] FIG. 8 is a comparison of existing search engines and an embodiment of the proposed search method;

[0024] FIG. 9 illustrates a data display in an embodiment of the proposed solution;

[0025] FIG. 10 illustrates an embodiment of tiered pricing by total price;

[0026] FIG. 11 illustrates an embodiment of tiered pricing based on the number of listings; and

[0027] FIG. 12 illustrates an embodiment of tiered pricing based on property structure.

[0028] The drawings are not necessarily to scale and that the disclosed embodiments are sometimes illustrated diagrammatically and in partial views. In certain instances, details which are not necessary for an understanding of the disclosed methods and apparatuses or which render other details difficult to perceive may have been omitted. It should be understood, of course, that this disclosure is not limited to the embodiments illustrated herein.

DETAILED DESCRIPTION

[0029] Below is a description of various embodiments of the invention. Before describing selected embodiments of the present disclosure in detail, it is to be understood that the present invention is not limited to the embodiments described herein. The disclosures and descriptions herein are illustrative and explanatory of one or more presently preferred embodiments and variations thereof. It will be appreciated by those skilled in the art that various changes in the design, organization, means of operation, structures and location, methodology, and use of equivalents may be made without departing from the spirit of the invention.

[0030] The drawings are intended to illustrate and plainly disclose presently preferred embodiments to one of skill in the art and are not intended to be renditions of final products. These may include simplified conceptual views to facilitate understanding or explanation. In addition, the relative size and arrangement of the components may differ from that shown and still operate within the spirit of the invention.

[0031] Various directions such as "upper", "lower", "bottom", "top", "left", "right", "first", "second" and so forth are made only with respect to explanation in conjunction with the drawings. The inventive components may be oriented differently, for instance, during data collection, processing, displaying and interpreting operations. Numerous variations and different embodiments and modifications may be made within the scope of the concept(s) embodiments herein taught and described. Therefore, it is to be understood that the details herein are to be interpreted as illustrative and non-limiting. For example, many embodiments and examples are used to describe image files and displaying the files. However, the invention can be used to handle any type of file format and/or display platform.

[0032] In one embodiment the "Group Travel Search Engine" allows travelers to easily search for and book travel accommodations that meet one or more of their preferred qualifications. In this embodiment relationships are mapped between multiple listings and offer the ability to bundle listings with group pricing. The system is a one click search process providing instant results online that display desired space requirements, privacy levels, proximity to others, and group rate pricing and combinations thereof.

[0033] Currently these types of searches and group rates are not available. They can only be achieved with human interaction on a per inquiry basis. The extra filtering step analyzes available bed sizes and listing structure in a property and selects a set of listings that are located close to each other and contain the requested number of beds and private bathrooms. This filtering can be expanded across multiple properties within a designated distance.

[0034] When booking travel, travelers may input the quantity of desired sleeping rooms, full size bathrooms and beds and bed types (king, queen, full, twin). The output of the search will allow travelers to find combinations of listings providing requested rooms, beds and bathrooms, compare options from multiple listings (such as, houses, partial houses, and combined hotel rooms) and easily book at least 1 reservation on behalf of the entire group. Payment can be easily split amongst the group and finding the best offer for their desired configuration is satisfied. This solution can alleviate the pain points group travelers experience. Travelers can search for and book accommodations that meet their unique needs without compromising comfort or cost; this is achieved with the new search method that removes the need to perform multiple manual steps that travelers have to take with the current search engines. In one embodiment the database can be searched for multiple rooms using multiple parameters with just one click. In another embodiment, multiple rooms can be booked or purchased with just one click.

[0035] The group search engine can function using an inventory of sharing economy properties. In addition, the same technology can be shared with multiple entities including the entire lodging industry to improve the group travel experience worldwide.

[0036] The present invention will now be described by referencing the appended figures, as describe below. Similar elements may be given the same reference numerals.

Current accommodation search

[0037] Today, accommodation search engines have inputs of destination, check in, check out, number of rooms, and number of people. These search engines do not consider whether you want to have your own bed or would like to share a bed. FIG. 1 depicts a schematic view 1 of a search box 2 in modern search engines. As shown in FIG. 1, there are limited numbers of search boxes in most searches. Unfortunately, simply adding additional search parameters will not solve the problem unless the search parameters are determined and cartelized.

Current Data Display of Search Results

[0038] In existing search engines results are displayed by listing or room type. Depending on the number of rooms you selected, it will multiply the room type or listing by the rate. The rate of the room is the same no matter if you selected one room or ten rooms. FIG. 2 is a schematic view 20 of search results 24 in modern search engines queries 23. In this embodiment, customers do not receive special pricing based on their cumulative buying power and have limited filtering or search engine query 23 options, as shown in. FIG. 2 No incentives are provided to buy multiple rooms as a group.

[0039] Customers must select a listing or room type to read the details on the next page and confirm how many beds there are in total, and the size of beds. FIG. 3 describes a series of actions 30 that a customer has to take to find lodging accommodation for a group of N travelers who need “M” beds (M ? N) and“K” rooms (K ? M) with the existing online search engines.

[0040] As shown in FIG. 3, first, the customer or user, enters query for“N” people and“K” rooms 31. Second, the Online Travel Agency’s (“OTA’s”) search engine finds“P” properties 32. Third, the OTA open first“P”’ properties in the OTA search result. Fourth, the user can open property detail web page on the room 34. The user can check whether combination of room and beds in the search result satisfies needs of all group members 35. Sixth, the user reviews alternative combinations of rooms in property. Seventh, the user moves on to next property on the search result.

[0041] The dashed box 32 describes the step that is automated with the current technology, all other steps are performed by the customer manually. For a large group (where N is larger than 8) customer needs to spend 3-5 minutes to review other possible combinations of rooms to accommodate all travelers in the group. As discussed above, FIG. 2 is a schematic view of search results in modern search engines and provides limited number of search and viewing options.

[0042] If a customer considers just the first 10 properties in the search result, they have to spend up to 1 hour to see whether they satisfy the needs of the group. This is based on the number of manual steps in the FIG. 3 flow chart that is required to analyze results of current search engines.

[0043] One of the most frequent requirements from group travelers is to have rooms close to each other, so that they do not feel disconnected. Existing search engines do not inform customers about proximity of rooms because existing systems either do not store locations of rooms in their databases or do not include them in the search result. As a result, customers need to take extra actions to obtain this information or risk not knowing. Specifically, they need to find contact information of the hotel reception or property owner, reach out to them by phone, email, messenger or other communication channel, describe rooms they would like to order and ask how these rooms are located in the building as shown in FIG. 4. Fig. 4 is a flow chart 40 illustrating actions to select nearby rooms for group travelers with current search engines.

[0044] As shown in FIG. 4, first, the customer or user views each candidate property 41. Second, the user finds contacts of reception or owner (42). Third, the user either: makes a phone call (43), send an email message 44; or sends a message via messaging applications (45). As shown in FIG. 4, these acts can typically take 5 to 10 minutes. Finally, the customer waists for a reply or answer (46) which may or may not happen.

[0045] This is a cumbersome process by itself, but a major problem is that communication over email or messengers might take several days to get a reply. During this time, the rooms could be booked by other people and become unavailable for the customer in question.

[0046] In the prior art (patent application WO2014140654A1) there were attempts to improve this deficiency by allowing customers to select individual rooms for their reservation when they were looking at accommodation options in an OTA system. This technology is not incorporated in existing search engines. WO2014140654A1 is incorporated in its entirety by reference.

[0047] In addition to process described in WO2014140654A1, there is a need for significant improvement in allowing individuals or couple travelers to eliminate the need for time-consuming communication with a property host. For group travelers this technology is not an optimal solution, because they will have to spend time manually looking at locations of rooms for each combination of rooms for each property they are considering for booking.

[0048] Fig. 5 illustrate embodiments of actions to select nearby rooms for group travelers with enhanced search engines that will provide room locations. As shown in FIG. 5, first, the customer or user views each candidate property 51. Second, the user finds for each candidate or result being displayed for at least one combination of rooms 32. The user can then review each room in the combination 53, or open floor plan 54, or estimate proximity to other rooms in the combination 55 or have the search algorithm do it for the user. The user can choose to: move on to the next candidate property 56 or move on to the next room combination 57 or move on to the next room in the combination 58. There is a need to give the user significant flexibility to review and choose room combinations quickly and effectively with only one click or on one display.

[0049] As shown in FIG. 8, for 10 candidate properties with 3 possible combinations of rooms in each of them customers will have to spend about 1 hour to manually review floor plans and analyze rooms proximity. The more properties customers want to consider, the more time they will need to spend on this analysis.

Group Accommodation Search

[0050] In an embodiment of this invention, primary search parameters are bed types per room with extra parameters of private bathroom and private unit that specifically search for desired accommodation options and combinations thereof. With these parameters user of the system can specify: What bed sizes they would like to have in each room; whether they want to have a private bathroom for their room that will not be shared with other visitors of a property; and whether they want their room to be in a private unit, separated from all other rooms in a property.

[0051] This combination of parameters, or ability to selected multiple parameters, did not exist in prior art. Embodiments that determine and categorize these parameters will allow the search algorithm to find accommodation options that are optimal for needs of the entire group as well as individual travelers. FIG. 6 is a schematic view 60 of proposed search box structure 61 and display results 62. Fig. 6 is a schematic view of only one embodiment of a search box in this invention. Persons skilled in the art with the benefit of the disclosures herein will recognize additional features that can be in the search and display views.

Group Search Algorithm [0052] The search engine will look up accommodation options in accordance with algorithms or method embodiments 1 and 2, as are described below.

[0053] Algorithm 1 or (group search method) has several steps. First, convert search requests to traditional search parameters supported by OTA search engines. Conversion is performed with algorithm 1.1, as described below. Second, process the converted search request with existing OTA search engines to find available properties. Third, for each property in the search results perform steps 4-7, as described below. Fourth, from room inventory of the current property select candidate rooms that can accommodate requested bed types that require option of private unit. Selection should be performed with algorithm 1.2, as described below. Fifth, from the remaining room inventory of the current property select all candidate rooms that can accommodate requested bed types that require the option of a private bathroom. Selection should be performed with algorithm 1.2, as described below. Sixth, from the remaining room inventory of the current property select all candidate rooms that can accommodate the remaining requested bed types. Selection should be performed with algorithm 1.2, as described below. Seventh, If room inventory in steps 4-6 could satisfy all requested bed types, then include the current property to group search results and proceed to the next one; otherwise - skip this property and proceed to the next one. Eight, for each result in the group search the engine calculates compactness rating - a metric that reflects how close to each other travelers will be located in the property. In one embodiment it can be calculated via formula ?_(i=l)^Au (t_i/T)^A2 where u is number of floors in the recommendation, t_i is number of travelers accommodated on floor i, T is the total number of travelers in the search request. Nine, sort results in group search results using lexicographical comparison by tuples consisting of compactness rating (sort in descending order) and price (sort in ascending order).

[0054] Fig. 7 illustrates a block diagram 70 of Algorithm 1, as described above. As shown in FIG. 7, first, the customer or user requests a search 71. Second, that search is executed in current Online Travel Agencies (OTAs) 72. Third, for each property in the Online Travel Agencies (OTAs) performs the next four steps 73 which are 74-77. These steps are: select candidate rooms for search request with private units 74, select candidates rooms for search request items with private bathrooms, select candidate rooms for other search request items 76 and determine if each search request items has candidate room 77. If the necessary conditions or parameters are meet the property is included in the group search results 78. If enough properties are found the property loop is completed 79 and the algorithm calculates compactness rating 79a and then sorts and displays the group search request 79b. If not, steps 73, 74, 75, 76, 77 and 78 are repeated, as needed.

[0055] If results of Algorithm 1 are empty, it means that no single property has enough available rooms to fit all travelers. In this case Algorithm 2 is applied can be used to find accommodation across multiple properties.

Algorithm 2 (group search across multiple properties)

[0056] Algorithm 2 (group search across multiple properties) has several steps. First, Split search request from step 1 of Algorithm 1 in two separate requests with smaller number of rooms. In one embodiment splitting can be implemented as sorting search request items by number of beds in them and then producing search sub -request 1 with 70% of first search request items and search sub-request 2 with 30% of last search request items. Second, apply Algorithm 1 to search sub request 1. If search results are empty, then recursively apply Algorithm 2 for search sub-request 1; otherwise proceed to step 3. Third, apply Algorithm 1 to search sub -request 2. Fourth, For each property from search results at step 2 find nearest (but not the same) property from search results at step 3. Add a combination of these properties to the joined search results. Fifth, apply steps 8 and 9 to the resulting joined search results.

Algorithm 1.1 (conversion to traditional search request)

[0057] Algorithm 1.1 (conversion to traditional search request) converts bed types to number of travelers with at least one rule. The rules include but are not limited to: Bed type "king" should be added to the search request as 2 travelers; bed type "queen" should be added to the search request as 2 travelers; bed type "full" should be added to the search request as 2 travelers; bed type "twin" should be added to the search request as 1 traveler; number of rooms is translated from new search request format to traditional search request "as is" without any changes; and combinations thereof.

Algorithm 1.2 (selection of rooms for bed types) [0058] Algorithm 1.2 (selection of rooms for bed types) first, sorts available rooms by price (provided with standard OTA rate system) in ascending order. The algorithm also considers several factors besides price. These factors include but are not limited to whether search request items have parameters of private bathroom and/or not parameter of private unit. Second, the algorithm filters out from available rooms those rooms that do not have a dedicated bathroom in mapping. Third, the algorithm sorts search request items in order of bed size (in lexicographical order from largest bed size to smallest bed size); fourth, for each search request item perform steps 5-7; Select the first room that has beds satisfying bed types in the search request item. Rule is: rooms must contain beds of sizes that are equal to or larger than beds in the search request item; If a room was not found, then stop processing this property, as it cannot fulfil current search requests. If the search request item has a parameter of private unit, then register the entire unit as recommended accommodation for current search request item and exclude this unit from the list of available rooms. Otherwise, register the room as recommended accommodation for current search request items and exclude this room from the list of available rooms.

[0059] Unlike prior art related to group accommodation problems with fine-tuned filters, embodiments of this invention are designed in the way to allow all advanced search and filtering to be performed by software and does not require human interaction. FIG. 7, as described above, illustrates a block diagram 70 of a proposed algorithm embodiment.

[0060] According to an embodiment, the technology component that implements the search procedure works on top of standard OTA functionality and augments it, rather than propose construction of a standalone software system that competes with and tries to replace existing OTA services. In one embodiment, this technology enables automatization of fine-tuned accommodation search with detailed parameters (bed types and preferred privacy level) that does not require this search to be handled manually by employees (like in bidding systems) or by consumer follow up (like in direct room booking systems).

[0061] The proposed algorithms deliver combinations of rooms and properties that are most cost- efficient for travelers. Prioritization logic used in these algorithms does not involve complex computations. Execution of these algorithms on modem hardware will take less than 10 seconds to provide results of at least a dozen, scores, hundreds or even thousands of properties. This significantly reduces time that group travelers need to spend to find accommodation options that satisfy bed and privacy requirements as well as proximity of rooms. With this embodiment, group travelers can find accommodation options satisfying these requirements in well under an hour or even a minute, typically within 10 seconds if not less than 5 seconds. In comparison, the current search engines they need to spend several hours to analyze rooms configurations (see FIG. 3) or several days to manually collect information about rooms locations (see FIG. 4). FIG. 8 illustrates a side-by-side comparison of existing search engines steps 80 and an embodiment of the proposed search method 81. As shown in FIG. 8, dashed boxes show automated steps.

[0062] Another important benefit reflected at FIG. 8 is that the proposed search method can provide customers with thousands of options in seconds whereas with the existing search engines it takes several hours (or days) to get just a few options. This means that customers will be able to analyze options based on larger set of options and, therefore, they have a higher chance to get a better price for their reservation than with narrow set of options they can realistically get with the current search engines. A comparison of existing search engines and the proposed search method is shown in FIG. 8.

[0063] As shown in FIG. 8, first, the customer or user, enters query for“N” people and“K” rooms 31. Second, the OTA’s search engine finds“P” properties 32. Third, the customer selects rooms with needed bed configurations 86. Fourth, the customer selects information about proximity of rooms. Fifth the customer chooses combinations of rooms that are close to each other 87. Fifth, the customer reviews options and makes multiple reservations 88. In this process only step 32 is automated the rest are manual. This process can take a few hours to several days for just 10 properties.

[0064] As shown in FIG. 8, an enmeshment allows a customer to perform the same process in under 10 seconds for at least 1,000 properties. First, the customer or user, enters query for“N” people and“K” rooms 31. Second, the search algorithm finds available rooms using OTA software. The search algorithm filters rooms with needed bed types. Fourth, the search algorithm prioritizes rooms based on compactness ratings and price 84. Finally, the customer reviews options and makes reservations. This process is significantly faster because steps 82, 83, 84 are automated and only steps 31 and 85 are manual. Therefore, the only manual steps are inputting the search data and making the room reservations. Since the room reservations can be made with a single or one-click the process or method is extremely fast and only requires two simple steps form, the user.

[0065] In an embodiment, the unique feature of the proposed search method is that it can find solutions for very large groups that cannot be accommodated in any single available property. Existing search engines are limiting the maximal number of people in a single search request. The specific numbers vary depending on OTA with an average limit of 20-30 travelers per search request. Existing search engines cannot serve customers who are organizing travel of groups of 50 or more people. Algorithm 2 utilized in the proposed search method solves this problem by automatically splitting a single search request into a series of two or more smaller search requests and finding accommodation options that cannot be fulfilled with the existing search engines. This not just overcomes limitation of existing search engines, but also enables customers to find accommodation options (potentially across several properties) for large groups even in the event that not a single property in the area could fit the entire group.

Group Search Results

[0066] As described above, the proposed search method introduces new filters and new mechanisms of accommodation options. Therefore, a new data display is required to present search results. FIG. 9 is a schematic of the proposed representation of search results showing a sample of search results 91 and the filter variable 92 in an embodiment of the proposed solution.

[0067] Unlike existing search result displays, this invention introduces a new type of accommodation option - a combined rental across multiple properties. This option enables group travelers to find accommodation options even if any single property in the area cannot fit the entire group. Furthermore, this embodiment allows customers to book several properties with a single online transaction. With existing search engines, customers would need to perform multiple searches for smaller search requests and book each property individually.

[0068] In a preferred embodiment, hotel rooms with the correct number of beds and bathrooms on the same floor and proximity, along with full vacation rental homes with the minimum specifications will be listed first in order of total price. Hotel rooms spread out on different floors or between sister properties will be in descending order. Partial vacation rental homes or combined rentals across multiple properties will also be in descending order.

[0069] The proposed search results display can include a new label“Exact Match” that is set when a property provides a combination of rooms that exactly matches the requested configuration of bed types, private bathrooms, and units. Having this label at the level of all search results saves customers from performing manual steps of reviewing each property details as needed with the current search engines, as shown in the FIG. 3 example.

[0070] The proposed search results display includes new labels“Different Floors and Different Properties” that are set for properties with worse compactness ratings. The former label is set in the event if all travelers are in a single property, but on different floors. The latter label“Different Properties” is set when travelers must be, or choose to be, distributed by multiple properties. These clear indicators allow customers to see which accommodation options will allow group members to stay together in nearby rooms and which will disperse them. Having this label at the level of all search results saves customers from performing manual steps of contacting property hosts or reviewing floor plans, as shown in FIG. 4 and FIG. 5.

[0071] The search result display provides a“Relevance” filter that allows customers to select only accommodation options that provide a certain level of proximity of group travelers. This simplifies analysis of results as customers will not need to check labels on all multiple options provided in the search results.

[0072] The“Property Distance” filter provides customers with ability to limit maximal distance between properties in Algorithm 2. Setting this parameter provides group travelers with ability to find cross-property accommodation options within desired distance from one another. With existing search engines, customers would have to perform multiple searches and manually review a map to analyze distances between the properties.

Bundle Pricing with tier discounts

[0073] Today many OTAs are partnering with other travel services and offer composite products that might include car rentals, airline tickets, cruises, tours, etc. in a single package. Such package offers a discount on included services that makes it more appealing to a traveler compared to purchasing each of these services independently.

[0074] This practice works well for individual travelers and small families, who need just a single item for each of these services. However, large groups of travelers who need multiple rooms for accommodation do not fit in this system and price for their bookings is calculated as standard rate per listings multiplied by number of booked listings without any discounts. The only way travelers can manage to get a discount is to contact property managers or hotel sales administrators, reach out to them and try to negotiate prices. The industry does not possess a method to price large bookings respecting size of the transaction and incenting customers to buy multiple rooms as a group.

[0075] In embodiment of this invention, we propose a method of calculating a bundled price for multiple listings and a method for booking multiple listing in a single transaction or click. Embodiments of the method relies on one of three different discount rules described below.

[0076] Push tier discount can be based on total reservation price in one embodiment. FIG. 10 shows tiered price by total price 100.

[0077] Alternatively, the price can be based on total listings booked whereas the push tier discount is based on total number of listings booked. FIG. 1 1 illustrates tiered pricing for number of listings 110 [0078] FIG. 12 shows tiered pricing 120 based on property structure applicable to Vacation Rental Homes. The lowest level of pricing is a room 121. The host assigns price to each room following standard existing processes. The next level is the floor of the house 122. The host can assign discounted prices for customers who want to rent all rooms at the floor, this price is smaller than sum of prices of these rooms. The topmost level is the entire property or house 123. The host can assign discounted price for customers who want to rent an entire house. A tiered price based on property structure is shown in Fig. 12.

[0079] These discounting rules are straightforward and well known in other business domains, so their application in the travel industry will be clear both to hosts, property providers and customers. Currently the industry does not possess any automated methods to discount prices for large reservations; hence, usage of familiar pricing rules is important to avoid confusion of property owners and customers.

[0080] The ability for Hotels to display blocks of rooms and offer bundle pricing will allow fair competition with Short Term Rental properties. The ability for Short Term Rentals to market rooms and combined rooms within their property, will allow fair competition with Hotels. In the end, the travelers benefit the most by having competitive pricing and an effective and prompt way to compare all accommodation options based on their true preferences.

Hypothetical Example

[0081] The following hypothetical example explains a step by step user experience of using an embodiment of the group search engine based on a specific need. In this example the user has been put in charge of booking their family reunion in Austin, TX. There will be a total of fifteen people attending of which there are a total of three couples, two families of four, and one young adult. After inputting the standard check in and check out dates along with location, the input shifts to the number of rooms. In this case it is six rooms needed of which all must have private bathrooms. Now the bed configuration must be entered. The requirements are four King beds and four Queen beds. An additional filter is then added for searching single properties only. Then in one step the customer clicks search (one-click).

[0082] In a few seconds (less than 5 seconds), the user receives ten results displayed on the first page. The first two results get the user’s immediate attention. The first result is a hotel with six rooms on the fourth floor at a total group rate price of $2,358.25 for the three nights including all taxes and fees. The second result is an entire house that has seven bedrooms and seven bathrooms and a total group rate price of $2,791.20 for the three nights needed. The hotel is four-stars with nice amenities and slightly less expensive. While the house has an additional bedroom, that will not be needed. However, the idea of having a kitchen for the best chefs in the family to cook and being under the same roof seems more ideal for this family reunion. The user clicks book and completes the transaction in one-click.

Claims

CLAIMS We claim:

1. A method of listing rooms comprising:

a. determining the number of rooms;

b. determining types of beds in each room;

c. designating if there are private bathrooms;

d. creating a listing that categorizes number of rooms, types of bed and indications if bathrooms are private; and

e. searching the listing by number of rooms, type of bed, and number of private bathrooms.

2. The method of claim 1, further comprising designating if at least one room is private.

3. The method of claim 1, further comprising designating proximity of at least one room to an exterior entrance.

4. The method of claim 1, further comprising designating a total amount of interior space in the room.

5. The method of claim 1, wherein multiple rooms are listed in a single building

6. The method of claim 1, wherein multiple rooms are designated and listed in a multiple building complex.

7. The method of claim 1, further comprising a user searching the listing

8. The method of claim 1, further comprising:

a. using a searching logic for searching the listing that selects rooms from available inventory with filters based on categorization of number of rooms, types of bed and indications if bathrooms are private, and combinations thereof;

b. processing ranking priority based on a compactness rating which quantifies proximity of guests in a property or across multiple properties; and

c. combining accommodations across multiple properties within a designated distance if an individual property cannot fulfill user’s filters.

9. The method of claim 8, further comprising a user booking a room based on at least one variable from the group selected from number of rooms, types of bed, indications if bathrooms are private, and combinations thereof.

10. A method of displaying accommodations, the method comprising:

a. providing a listing with multiple rooms available;

b. searching the listing with multiple rooms available;

c. displaying exact matches for combinations of listings that provide configuration of rooms, beds, and private bathrooms that are identical to number of rooms, bed types in each room, and designated number of private bathrooms in the search request.

d. displaying label of floor number for combination of listings that are located on different floors of a single property.

e. displaying label of physical addresses and distance for combination of listings that are in different properties; and

f. displaying additional filter relevance that allow customers to exclude combinations of listings with low compactness ratings.

11. The method of claim 10, further comprising designating if at least one room is private.

12. The method of claim 10, further comprising designating proximity of the room to an exterior entrance.

13. The method of claim 10, further comprising designating a total amount of interior space in the room.

14. The method of claim 10, wherein multiple rooms are listed in a single building

15. A method of bundle pricing model, the method comprising:

a. providing a listing with multiple rooms available;

b. setting discounts for travelers booking multiple listings, wherein pricing rules are based on total amount of transaction and total number of listings;

b. allowing at least one person to package a plurality of listings.

c. offering tiered pricing for booking two or more listings as a package; d. providing a user the ability to search with desired parameters selected form the group consisting of number of rooms, types of bed and indications if bathrooms are private, and combinations thereof; and

e. completing in one online session a booking of multiple listings as a single package with group rates.

16. The method of claim 15, wherein the user is a group of consumers.

17. The method of claim 15, wherein the user is a business booking multiple rooms.

18. The method of claim 15, wherein the user can compare rates in each package of listings.

19. The method of claim 15, wherein the booking of multiple listing with a group rate is performed with only one click.

20. The method of claim 15, further comprising designating a total amount of interior space in the room.