Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The present invention is not generally linked to the field of machine learning but includes machine learning to improve the technological field of search engines and, more specifically, the information retrieval process associated with search engines. Accordingly, the Applicant maintains that the present invention is directed to the technological field of search engines. As even specifically described in the Applicant's Specification with respect to providing search results, a search engine typically only provides premade search result bundles, "...the current process only includes bundles that are premade." Applicant's Specification, para. [0038].,” Examiner respectfully disagrees. The present independent claims recite several limitations for consideration under Step 2A, Prong 1. Furthermore, the additional elements of the claims, for consideration under Step 2A, Prong 2 and Step 2B, do not improve the field of computer search engines or the field of machine learning. The present claims are merely generally linking to the field of machine learning. Furthermore, the claims do not explicitly recite the use of a “search engine.” The claims merely recite “using… a procurement engine,” which is nothing more than use of a computer as a tool. Therefore, the present claims do not recite any particular improvements to the additional elements of the claims.
Regarding Applicant’s assertion of “However, again, the Applicant respectfully disagrees with the Office Action's reasoning of what constitutes "Certain Methods of Organizing Human Activity" even under broadest reasonable interpretation. As previously presented in previous claim amendments, the present invention uses a trained machine learning model to improve the retrieval of search results (which include generated object bundles) that are relevant to a determined intent of a user, and the trained machine learning model and procurement engine may prioritize and sort the retrieved search results based on that intent. The claimed invention does not include any type sales, ,” Examiner respectfully disagrees. The present claims do not improve the functioning of the machine learning model itself. Rather, the machine learning model is merely generally linking to the claimed invention. The purpose of the claimed invention is to improve the prioritized search results, which is not an improvement to the machine learning model itself. This type of an improvement, which is an improvement to the search results, is an improvement to the abstract idea identified in the independent claims. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “To the contrary, again, the presently claimed invention does not include any such commercial interactions including advertising or marketing, sales activities, or behaviors, but specifically includes improvements to a search engine's generation and order of search results that include bundles based on a likely intent of a user's query. Specifically, the present invention provides a system, computer-implemented method, and computer program product that dynamically generates and displays search results that may include bundles with recommended attributes to a user "based on the likely intent of the user 102 's reason for a query." As listed above, example cases such as OIP Techs., Inc. vs, Amazon.com involve cases that were dismissed as patent ineligible because they involved some type of business relation/transaction and/or a direct marketing relation/transaction. The present invention does not deal with any commercial interactions including advertising or marketing sales activities or behaviors whatsoever and, again, is directed to a search engine that incorporates the claimed machine learning model for generating specific search results (i.e. bundles) that are recommend and include specific and different attributes based on a determined intent for the object query. Again, independent claims 1, 8, and 15 are in no way directed to such sales and marketing activity.,” Examiner respectfully disagrees. The present claims recite receiving a query and generating an optimized object bundle based on a recommendation. The broadest reasonable interpretation of the claimed invention is inclusive of that of the disclosure, which includes in at least [0030-0031], disclose booking a hotel room based on the attributes and likely user intent. Therefore, Examiner respectfully disagrees with Applicant’s assertion and maintains that the present claims recite an abstract idea.
Regarding Applicant’s assertion of “The additional elements in the claims are not simply generally linking the use of the judicial exception to the field of machine learning. Again, the claims require the use of the specifically claimed machine learning model which is incorporated with a procurement (search) engine to generate and prioritize search results for an object query by specifically generating search results (i.e. bundles) that uses the machine learning model to recommend and incorporate specific and different attributes associated with a determined intent for the object query.,” Examiner respectfully disagrees with Applicant’s assertion. These additional elements, as drafted, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Regarding Applicant’s assertion of “Furthermore, and as previously stated, under Step 2A Prong 2, an improvement in the functioning of a computer or other technology or technological field may render a claim patent eligible at Step 2A Prong Two." Applicant has asserted that the presently claimed invention is indeed an improvement to a technological field in that of computer search engines and, more specifically, is directed to a search engine that uses unique algorithms for generating specific search results (i.e. bundles) that uses machine learning to recommend and incorporate specific and different attributes associated with a determined intent for the object query.,” Examiner respectfully disagrees with Applicant’s assertion that the present claims improve the search engine. First, the present claims do not recite the use of a “search engine.” Furthermore, the present claims do not improve the claimed “procurement engine” itself. Rather, the present claims utilize the machine learning model and procurement engine to generate one or more object bundles. There are no improvements to the “procurement engine” of the claim itself because it is nothing more than a tool to perform the step of generating the bundles. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Accordingly, Yin fails to specifically teach the above features that are now more clearly captured by the independent claims which require…,” Examiner has introduced the combination of Sutcliffe and Belgaied Hassine to cure the deficiencies of Yin. Therefore, the 35 USC 102 rejection has been withdrawn; however, the present claims are rejected under 35 USC 103. This new grounds of rejection was necessitated by amendment. See the detailed rejection below.
Accordingly, the 35 USC 102 rejection has been withdrawn; however, the present claims are rejected under 35 USC 103.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 07/03/2025 has been fully considered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-12, and 14-19 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-5 and 7 are directed to a system, claims 8-12 and 14 are directed to a method, and claims 15-19 are directed to a computer program product comprising a computer readable medium. Paragraph [0094] of the instant specification defines the computer readable medium through the following statement: “A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.” Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 8, and 15 recite generating one or more object bundles based on a user object query, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors. Claim 1 recites limitations, similarly recited in claims 8 and 15, including “receiving an object query, wherein the object query comprises at least one leading object and at least one object attribute; wherein the intent is further determined based on a specific type of day that is further distinguished between a weekday and a weekend; based on the determined user intent, generating, one or more object bundles, wherein each of the one or more object bundles are not predetermined, and wherein generating the one or more object bundles further comprises identifying and recommending one or more object attributes associated with the determined intent for the object query, sorting and including the identified and recommended one or more object attributes in the one or more object bundles, and prioritizing the one or more object bundles based on the at least one attribute and the identified and recommended one or more object attributes; wherein the identifying and recommending the one or more object attributes associated with the determined intent for the object query is further based on the specific type of day and further comprises results that are distinguished between the weekday and the weekend, prioritizing the one or more object bundles based on a predicted procurement propensity by a user based on nested logit estimation; and outputting the one or more object bundles to the user, wherein outputting the one or more object bundles further comprises displaying the one or more object bundles in a prioritized sort order according to the prioritization of the one or more object bundles based on the at least one attribute and the identified and recommended one or more object attributes,” that, as drafted, is a process that, under its broadest reasonable interpretation covers an abstract idea but for the recitation of generic computer components. For example, with the exception of the language of the preamble, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 4, 11, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 2-3, 5, 7, 9-10, 12, 14, 16-17, and 19 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Claims 1, 8, and 15 do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “a memory; and a processor in communication with the memory, the processor being configured to perform operations comprising” that are configured to perform the steps of the abstract idea. Claim 8 is a “computer-implemented method,” which is recited in the preamble of the claim. Claim 15 is a “computer program product for determining search order and rate in an attribute-based environment comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations, the operations comprising,” which is configured to perform the steps of the abstract idea. The independent claims further recite “receiving input data.” The independent claims further recite “a processor prioritizing…” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Independent claims 1, 8, and 15 further recite the additional element of “training, using one or more algorithms, a machine learning model to determine a user intent for the object query based on the at least one leading object and the at least one object attribute,” and “generating, in real-time and using the trained machine learning model and a procurement engine, one or more object bundles.” The claim further recites generating one or more object bundles “in real-time and using the trained machine learning model.” These additional elements, as drafted, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2, 9, and 16 introduce the additional element of “receiving prior user data.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claims 3, 10, and 17 introduce the additional element of “generating, based on the object query and the machine learning model, an attribute rate model.” Dependent claims 5, 12, and 19 introduce the additional element of “generating, based on the machine learning model and the attribute rate model, one or more procurement predictions.” Dependent claims 7 and 14 introduce the additional element of “wherein the machine learning model includes a nested logit estimation model.” These additional elements, as drafted, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not integrate the judicial exception into a practical application because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 8, and 15 do not comprise anything significantly more than the judicial exception. Claim 1 is a system comprising “a memory; and a processor in communication with the memory, the processor being configured to perform operations comprising” that are configured to perform the steps of the abstract idea. Claim 8 is a “computer-implemented method,” which is recited in the preamble of the claim. Claim 15 is a “computer program product for determining search order and rate in an attribute-based environment comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations, the operations comprising,” which is configured to perform the steps of the abstract idea. The independent claims further recite “receiving input data.” The independent claims further recite “a processor prioritizing..” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Independent claims 1, 8, and 15 further recite the additional element of “training, using one or more algorithms, a machine learning model to determine a user intent for the object query based on the at least one leading object and the at least one object attribute,” and “generating, in real-time and using the trained machine learning model and a procurement engine, one or more object bundles.” The claim further recites generating one or more object bundles “in real-time and using the trained machine learning model.” These additional elements, as drafted, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations do not comprise anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 4, 11, and 18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 2, 9, and 16 introduce the additional element of “receiving prior user data.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claims 3, 10, and 17 introduce the additional element of “generating, based on the object query and the machine learning model, an attribute rate model.” Dependent claims 5, 12, and 19 introduce the additional element of “generating, based on the machine learning model and the attribute rate model, one or more procurement predictions. Dependent claims 7 and 14 introduce the additional element of “wherein the machine learning model includes a nested logit estimation model.” These additional elements, as drafted, are nothing more than generally linking the use of the judicial exception to the field of machine learning. The claims employ generic computer functions to execute an abstract idea, even when limiting the use of the idea to the field of machine learning. These limitations are not anything significantly more than the judicial exception because they are nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-5, 7-12, and 14-19 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (US 20230368084 A1) in view of Sutcliffe et al. (US 20140095222 A1) in view of Belgaied Hassine et al. (US 20090234710 A1).
Regarding claim 1, Yin teaches a system for determining search order and rate in an attribute- based environment (Figs. 10 & 11), the system comprising:
a memory (Fig. 11 and [0156-0157] teach a computing system including a memory and processor; see also: Fig. 10, [0151-0153, 0158]);
and a processor in communication with the memory (Fig. 11 and [0156-0158] teach a computing system including a memory and processor, wherein the memory stores instructions that can be executed by the processor; see also: Fig. 10, [0151-0153]), the processor being configured to perform operations comprising:
receiving an object query (Fig. 9A and [0133] teaches the combined listing search system receives, from a searching end-user, a listing request for one or more listings posted to the network site, the listing requesting specifying a multiple-day length of stay parameter, as well as in [0037] teaches receiving a listing request including a specified duration of stay and a date range, wherein the search criteria can include location, destination, type of accommodation, price range, and more, wherein [0025] teaches the client device includes reservation applications for temporary stays or experiences at hotels, motels, or residences managed by other users; see also: [0031, 0038]),
wherein the object query comprises at least one leading object and at least one object attribute (Fig. 9A and [0133] teaches the combined listing search system receives, from a searching end-user, a listing request for one or more listings posted to the network site, the listing requesting specifying a multiple-day length of stay parameter, as well as in [0037] teaches receiving a listing request including a specified duration of stay and a date range, wherein the search criteria can include location, destination, type of accommodation, price range, and more, wherein [0025] teaches the client device includes reservation applications for temporary stays or experiences at hotels, motels, or residences managed by other users, and wherein [0031] teaches a combined listing encapsulates two or more individual listings that are consumed together as part of the same accommodation on different dates or days to fulfill an entire length of the requested accommodation; see also: [0038]);
training, using one or more algorithms, a machine learning model to determine a user intent for the object query based on the at least one leading object and the at least one object attribute ([0061] teaches the indexing engine can generate training data including multiple listings and their corresponding listing quality signals, wherein the training data includes ground truth quality estimations, wherein the indexing engine can apply a machine learning model to the training data to generate prediction of the quality estimation, wherein the output of the machine learning model can be compared with the ground truth quality estimation of each listing to compute a deviation and update parameters of the machine learning model, wherein this process for training the model to estimate quality for new listings, wherein the quality includes the most inspiring tier, high tier, acceptable tier, or low quality, wherein listings with relatively high quality estimations can be prioritized and selected for human review, as well as in [0077] teaches the machine learning tools may learn from existing data in order to make predictions about new data, wherein the machine-learning tools operate by building a model from example training data including combined listings data, wherein the machine learning tools include neural networks, random forest, and other tools in order to generate a combined listing, a ranked order, a combined listing comparison, and more, wherein [0078] teaches the machine learning algorithms utilize features, such as various combinations of previously combined listings and rankings, for analyzing the data to generate assessments in determining whether or not to generate a combined listing, a ranked order, a combined listing comparison and ranking, and to select whether presentation of a given individual listing is to form part of a given combined listing, wherein [0079] teaches the machine learning algorithms utilize the training data to find correlations among the identified features that affect the assessment, such as the known or ground truth attributes or features of combined listings and their rankings and the attributes of individual listings, wherein the training data includes labeled data, which is known for one or more identified features and outcomes, wherein [0080] teaches once the training data is collected and processed, the machine learning model can be built using machine learning techniques, as well as in [0121] teaches the indexing engine trains a machine learning model to predict the likelihood of a user booking a stay in a listing associated with each category; see also: [0031, 0038, 0081-0086, 0122-0123]),
based on the determined user intent ([0077] teaches the machine learning tools may learn from existing data in order to make predictions about new data, wherein the machine-learning tools operate by building a model from example training data including combined listings data, wherein the machine learning tools include neural networks, random forest, and other tools in order to generate a combined listing, a ranked order, a combined listing comparison, and more, wherein [0078] teaches the machine learning algorithms utilize features, such as various combinations of previously combined listings and rankings, for analyzing the data to generate assessments in determining whether or not to generate a combined listing, a ranked order, a combined listing comparison and ranking, and to select whether presentation of a given individual listing is to form part of a given combined listing, wherein [0079] teaches the machine learning algorithms utilize the training data to find correlations among the identified features that affect the assessment, such as the known or ground truth attributes or features of combined listings and their rankings and the attributes of individual listings, wherein the training data includes labeled data, which is known for one or more identified features and outcomes, wherein [0080] teaches once the training data is collected and processed, the machine learning model can be built using machine learning techniques, as well as in [0121] teaches the indexing engine trains a machine learning model to predict the likelihood of a user booking a stay in a listing associated with each category; see also: [0031, 0038, 0061, 0081-0086, 0122-0123]), generating, in real-time and using the trained machine learning model and a procurement engine, one or more object bundles (Fig. 9A and [0135] teach the combined listing search system, in response to determining that the multiple-day length of stay parameter of the listing request transgresses the minimum length of stay threshold, generates a combined listing comprising a first listing of the plurality of listings associated with a first portion of the multiple-day length of stay parameter and a second listing of the plurality of listings associated with a second portion of the multiple-day length of stay parameter, wherein [0066] teaches generating one or more combined listings in real-time in response to receiving listing requests, wherein the indexing engine generates one or more combined listings by performing a set of operations including determining a combined listing based on the first and second listings, wherein [0077] teaches the trained machine learning tools can build a model from training data including listing data, combined listing data, and more in order to make data-driven predictions as outputs, wherein the machine learning model can generate a combined listing, a ranked order, a combined listing comparison and ranking, and selecting whether a given listing is to form part of a given combined listing, as well as in [0122-0123] teach in response to receiving the search criteria, the search parameters and user history are converted into a set of features and fed into the trained machine learning model, wherein the trained machine learning model generates a prediction for each category indicating a likelihood that a listing in the category will be booked, wherein [0124] teaches the rankings for each categories determined by the indexing engine (i.e. procurement engine) can then be used to select a category from a list of categories associated with a theme that is assigned a particular slot, which can then be presented in a corresponding slot for the theme; see also: Fig. 8, [0031, 0125-0127]),
wherein each of the one or more object bundles are not predetermined (Fig. 9A and [0135] teach the combined listing search system, in response to determining that the multiple-day length of stay parameter of the listing request transgresses the minimum length of stay threshold, generates a combined listing comprising a first listing of the plurality of listings associated with a first portion of the multiple-day length of stay parameter and a second listing of the plurality of listings associated with a second portion of the multiple-day length of stay parameter, wherein [0066] teaches generating one or more combined listings in real-time in response to receiving listing requests, wherein the indexing engine generates one or more combined listings by performing a set of operations including determining a combined listing based on the first and second listings, wherein [0077] teaches the trained machine learning tools can build a model from training data including listing data, combined listing data, and more in order to make data-driven predictions as outputs, wherein the machine learning model can generate a combined listing, a ranked order, a combined listing comparison and ranking, and selecting whether a given listing is to form part of a given combined listing; see also: Fig. 8, [0031, 0122-0127]),
and wherein generating the one or more object bundles further comprises identifying and recommending one or more object attributes associated with the determined intent for the object query (Fig. 9A and [0135] teach the combined listing search system, in response to determining that the multiple-day length of stay parameter of the listing request transgresses the minimum length of stay threshold, generates a combined listing comprising a first listing of the plurality of listings associated with a first portion of the multiple-day length of stay parameter and a second listing of the plurality of listings associated with a second portion of the multiple-day length of stay parameter, wherein [0066] teaches generating one or more combined listings in real-time in response to receiving listing requests, wherein the indexing engine generates one or more combined listings by performing a set of operations including determining a combined listing based on the first and second listings, wherein [0016] teaches the listing platform can search for result listings that are available for a specified date range, price range, and other attributes, which can be specified in a given query, wherein some listing attributes can be static and other attributes may be dynamic, wherein [0030] teaches the system provides functionality to recommend one or more combined listing that include one or more listings as part of a single offering, as well as in [0122-0123] teach in response to receiving the search criteria, the search parameters and user history are converted into a set of features and fed into the trained machine learning model, wherein the trained machine learning model generates a prediction for each category indicating a likelihood that a listing in the category will be booked, wherein [0124] teaches the rankings for each categories determined by the indexing engine can then be used to select a category from a list of categories associated with a theme that is assigned a particular slot, which can then be presented in a corresponding slot for the theme, as well as in Fig. 6 and [0125] teach a user interface for providing results in response to a listing request, or search criteria or search request, wherein the user interface includes recommending a combined listing of two individual listings matching a listing criterion, wherein the combined listing can provide the available dates, pricing, and more, as well as in Fig.7 teaches the user interface can be provided in a combined listing that includes individual listings matching a same search criterion is presented, wherein the user interface includes a message identifying the combined listing and indicates the attribute common to the individual listings that was used to form a combined listing that results in the combined listing having a certain high rank; see also: Fig. 8, [0031, 0070, 0077-0079, 0082, 0107-0108, 0126-0127]),
sorting and including the identified and recommended one or more object attributes in the one or more object bundles ([0121] teaches training a machine learning model to predict bookings or likelihood of a user booking a stay in a listing associated with each category, wherein the machine learning model receives signals from the search criteria in order to process the signals and predict a likelihood that the user will select a search result in a particular category and book a stay associated with the listing, wherein [0122-0123] teach in response to receiving the search criteria, the search parameters and user history are converted into a set of features and fed into the trained machine learning model, wherein the trained machine learning model generates a prediction for each category indicating a likelihood that a listing in the category will be booked, wherein [0124] teaches the rankings for each categories determined by the indexing engine can then be used to select a category from a list of categories associated with a theme that is assigned a particular slot, which can then be presented in a corresponding slot for the theme, as well as in Fig. 6 and [0125] teach a user interface for providing results in response to a listing request, or search criteria or search request, wherein the user interface includes recommending a combined listing of two individual listings matching a listing criterion, wherein the combined listing can provide the available dates, pricing, and more, as well as in Fig.7 teaches the user interface can be provided in a combined listing that includes individual listings matching a same search criterion is presented, wherein the user interface includes a message identifying the combined listing and indicates the attribute common to the individual listings that was used to form a combined listing that results in the combined listing having a certain high rank, wherein the combined listings include recommendations for travel, wherein [0089] teaches the search engine searches all previously identified listings for those having availability during the collection of dates, wherein this filtering can be performed and various collections of individual listings that match criteria input by a user and are available during the starting or ending portion of the travel dates, as well as in [0091] teaches the search engine can filter the combined listings by criteria, such as user profile, price similarity, individual listing rank, distance, and more, wherein the searching engine can generate a combined listing rank for each combined listing, wherein [0092] teaches the search engine conditions the generation and display of combined listing on determining that a listing request includes a particular parameter or criterion, wherein the search engine can generate the combined listing rank, wherein [0096] teaches the search engine can identify various listings through the flexible search option to identify high quality listings beyond the preferred distance, wherein the searching engine can sort the listings based on whether they are within the box computed as relevant to the location, wherein [0098] teaches the search engine can sort the listings based on whether they are within the relevant area of the location of the user, which can be used to demote or reduce a rank of the listings; see also: [0075, 0099]),
and prioritizing the one or more object bundles based on the at least one attribute and the identified and recommended one or more object attributes ([0121] teaches training a machine learning model to predict bookings or likelihood of a user booking a stay in a listing associated with each category, wherein the machine learning model receives signals from the search criteria in order to process the signals and predict a likelihood that the user will select a search result in a particular category and book a stay associated with the listing, wherein [0122-0123] teach in response to receiving the search criteria, the search parameters and user history are converted into a set of features and fed into the trained machine learning model, wherein the trained machine learning model generates a prediction for each category indicating a likelihood that a listing in the category will be booked, wherein [0124] teaches the rankings for each categories determined by the indexing engine can then be used to select a category from a list of categories associated with a theme that is assigned a particular slot, which can then be presented in a corresponding slot for the theme, as well as in Fig. 6 and [0125] teach a user interface for providing results in response to a listing request, or search criteria or search request, wherein the user interface includes recommending a combined listing of two individual listings matching a listing criterion, wherein the combined listing can provide the available dates, pricing, and more, as well as in Fig.7 teaches the user interface can be provided in a combined listing that includes individual listings matching a same search criterion is presented, wherein the user interface includes a message identifying the combined listing and indicates the attribute common to the individual listings that was used to form a combined listing that results in the combined listing having a certain high rank, wherein the combined listings include recommendations for travel, wherein [0089] teaches the search engine searches all previously identified listings for those having availability during the collection of dates, wherein this filtering can be performed and various collections of individual listings that match criteria input by a user and are available during the starting or ending portion of the travel dates, as well as in [0091] teaches the search engine can filter the combined listings by criteria, such as user profile, price similarity, individual listing rank, distance, and more, wherein the searching engine can generate a combined listing rank for each combined listing, wherein [0092] teaches the search engine conditions the generation and display of combined listing on determining that a listing request includes a particular parameter or criterion, wherein the search engine can generate the combined listing rank, wherein [0096] teaches the search engine can identify various listings through the flexible search option to identify high quality listings beyond the preferred distance, wherein the searching engine can sort the listings based on whether they are within the box computed as relevant to the location, wherein [0098] teaches the search engine can sort the listings based on whether they are within the relevant area of the location of the user, which can be used to demote or reduce a rank of the listings; see also: [0075, 0099]),
and outputting the one or more object bundles to the user (Fig. 9A and [0136] teach the combined listing search system causes, on a user device of the searching end-user, presentation of the combined listing together with one or more other listings that match the listing request in the ranked order, as well as in Fig. 6 and [0125] teach a user interface for providing results in response to a listing request, or search criteria or search request, wherein the user interface includes a combined listing of two individual listings matching a listing criterion, wherein the combined listing can provide the available dates, pricing, and more, as well as in Fig. 7 and [0126] teach the user interface can be provided in a combined listing that includes individual listings matching a same search criterion is presented, wherein the user interface includes a message identifying the combined listing and indicates the attribute common to the individual listings that was used to form a combined listing that results in the combined listing having a certain high rank; see also: [0031, 0082, 0121-0124, 0127-0130]),
wherein outputting the one or more object bundles further comprises displaying the one or more object bundles in a prioritized sort order according to the prioritization of the one or more object bundles based on the at least one attribute and the identified and recommended one or more object attributes (Fig. 9A and [0136] teach the combined listing search system causes, on a user device of the searching end-user, presentation of the combined listing together with one or more other listings that match the listing request in the ranked order, as well as in Fig. 6 and [0125] teach a user interface for providing results in response to a listing request, or search criteria or search request, wherein the user interface includes a combined listing of two individual listings matching a listing criterion, wherein the combined listing can provide the available dates, pricing, and more, wherein each combined listing can include a recommendation message to a user to split their time between two different cities associated with the listing criterion, as well as in Fig. 7 and [0126] teach the user interface can be provided in a combined listing that includes individual listings matching a same search criterion is presented, wherein the user interface includes a message identifying the combined listing and indicates the attribute common to the individual listings that was used to form a combined listing that results in the combined listing having a certain high rank, as well as in [0030] teaches the combined listing search system provides functionality to recommend one or more combined listings that include one or more individual listings as part of a single offering, wherein [0066] teaches generating one or more combined listings in real-time in response to receiving listing requests, wherein the indexing engine generates one or more combined listings by performing a set of operations including determining a combined listing based on the first and second listings, wherein [0016] teaches the listing platform can search for result listings that are available for a specified date range, price range, and other attributes, which can be specified in a given query, wherein some listing attributes can be static and other attributes may be dynamic, as well as in [0122-0123] teach in response to receiving the search criteria, the search parameters and user history are converted into a set of features and fed into the trained machine learning model, wherein the trained machine learning model generates a prediction for each category indicating a likelihood that a listing in the category will be booked, wherein [0124] teaches the rankings for each categories determined by the indexing engine can then be used to select a category from a list of categories associated with a theme that is assigned a particular slot, which can then be presented in a corresponding slot for the theme, wherein [0082] teaches the machine learning techniques train models to accurately make predictions on data fed into the models, wherein the models are developed against a training data of inputs to optimize the models to correctly predict the output for a given input; see also: [0031, 0121, 0127-0130]).
However, Yin does not explicitly teach wherein the intent is further determined based on a specific type of day that is further distinguished between a weekday and a weekend; wherein the identifying and recommending the one or more object attributes associated with the determined intent for the object query is further based on the specific type of day and further comprises results that are distinguished between the weekday and the weekend, and wherein the prioritizing further comprises a processor prioritizing the one or more object bundles based on a predicted procurement propensity by a user based on nested logit estimation.
From the same or similar field of endeavor, Sutcliffe teaches wherein the intent is further determined based on a specific type of day that is further distinguished between a weekday and a weekend ([0047] teaches the hotel may allow a premium service provider to bundle rooms with meals, wherein the bundling engine can create bundles based on hotel provided rules, wherein [0048] teaches the different rooms have different prices and the same room may have different prices depending upon the day of the week, as well as in [0049] teaches the various rules may be applied regarding the total price, the discount, and the days a bundle may apply to, wherein [0084] teaches various rules may be provided to the bundling engine including rules based on rules by particular providers, wherein a rule may be that all bundled room and meal voucher prices are discounted by $10 from the standard price on weekdays and $20 on weekends, wherein the bundled price can be discounted during weekdays and weekends to different prices, wherein the bundled prices may not be discounted on weekends, wherein [0085] teaches the bundled services can be accessed on the website including goods and services offered by the hotel, as well as in [0004] teaches hotel data includes rates for particular days including rates for weekends being higher than for weekdays; see also: [0023, 0033, 0056]);
wherein the identifying and recommending the one or more object attributes associated with the determined intent for the object query is further based on the specific type of day and further comprises results that are distinguished between the weekday and the weekend ([0033] teaches the lodging CRS server is configured to support a hotel room reservation website, wherein the user computing device can display various information regarding the hotel and rooms which are available for reservation based on the requested dates, number of guests, room type, and more, wherein [0047] teaches the hotel may allow a premium service provider to bundle rooms with meals, wherein the bundling engine can create bundles based on hotel provided rules, wherein [0048] teaches the different rooms have different prices and the same room may have different prices depending upon the day of the week, as well as in [0049] teaches the various rules may be applied regarding the total price, the discount, and the days a bundle may apply to, wherein [0084] teaches various rules may be provided to the bundling engine including rules based on rules by particular providers, wherein a rule may be that all bundled room and meal voucher prices are discounted by $10 from the standard price on weekdays and $20 on weekends, wherein the bundled price can be discounted during weekdays and weekends to different prices, wherein the bundled prices may not be discounted on weekends, wherein [0085] teaches the bundled services can be accessed on the website including goods and services offered by the hotel, as well as in [0004] teaches hotel data includes rates for particular days including rates for weekends being higher than for weekdays; see also: [0023, 0056]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Yin to incorporate the teachings of Sutcliffe to include wherein the intent is further determined based on a specific type of day that is further distinguished between a weekday and a weekend; wherein the identifying and recommending the one or more object attributes associated with the determined intent for the object query is further based on the specific type of day and further comprises results that are distinguished between the weekday and the weekend. One would have been motivated to do so in order to reflect the lodging provider’s own room information by accessing and utilizing the room information directly from the lodging provider including different prices depending upon the day of the week (Sutcliffe, [0048]). By incorporating the teachings of Sutcliffe, one would have been able to generate new and unique marketing opportunities based on hotel room and food bundles (Sutcliffe, [0095]).
However, the combination of Yin and Sutcliffe does not explicitly teach and wherein the prioritizing further comprises a processor prioritizing the one or more object bundles based on a predicted procurement propensity by a user based on nested logit estimation.
From the same or similar field of endeavor, Belgaied Hassine teaches and wherein the prioritizing further comprises a processor prioritizing the one or more object bundles based on a predicted procurement propensity by a user based on nested logit estimation ([0346-0352] teach generating an ordered list of recommended offers including room type and more, wherein the ranked offers include a score that indicates the choice probability, wherein [0442-0452] teach the choice model is activated for a given customer segment in order to generate the choice probabilities, wherein the model that generates the probabilities is a nested logit, wherein [0951] teaches the options available include a limited number of alternatives based on probability of being chosen, wherein [1045-1048] teach the probability of choice is calculated using a nested logit or “nested” model, wherein [0032] teaches evaluating the revenue that can be received based on variations in price, day of week, and more for a hotel; see also: [1051-1066, 1081]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yin and Sutcliffe to incorporate the teachings of Belgaied Hassine to include and wherein the prioritizing further comprises a processor prioritizing the one or more object bundles based on a predicted procurement propensity by a user based on nested logit estimation. One would have been motivated to do so in order to display an optimal order of offers based on the choice probability of the customer selecting the option (Belgaied Hassine, [0346-0352]). By incorporating the teachings of Belgaied Hassine, one would have been able to utilize a nested logit model in order to optimize the offerings of a rich product catalog (Belgaied Hassine, [0367-0371]).
Regarding claims 8 and 15, the claims recite limitations that are already addressed by the rejection of claim 1. Regarding claim 8, Yin teaches a computer-implemented method for determining search order and rate in an attribute-based environment (Figs. 2 & 4), the method comprising. Regarding claim 15, Yin teaches a computer program product for determining search order and rate in an attribute-based environment comprising a computer readable storage medium having program instructions embodied therewith (Fig. 11 and [0156-0157] teach a computing system storing instructions in the form of a software, program, application, or other executable code, wherein [0163-0166] teach the memories may store sets of instructions that cause various operations, wherein the instructions are stored on the computer storage medium; see also: [0151-0153, 0158]), the program instructions executable by a processor to cause the processor to perform operations, the operations comprising (Fig. 11 and [0156-0157] teach a computing system storing instructions in the form of a software, program, application, or other executable code, wherein [0163-0166] teach the memories may store sets of instructions that cause various operations, wherein the instructions are stored on the computer storage medium; see also: [0151-0153, 0158]). Accordingly, claims 8 and 15 are rejected as being unpatentable over Yin in view of Sutcliffe in view of Belgaied Hassine.
Claim(s) 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (US 20230368084 A1) in view of Sutcliffe et al. (US 20140095222 A1) in view of Belgaied Hassine et al. (US 20090234710 A1) in view of Thompson et al. (US 20220335513 A1).
Regarding claims, 2, 9, and 16, the combination of Yin, Sutcliffe, and Belgaied Hassine teaches all the limitations of claims 1, 8, and 15 above.
Yin further teaches wherein the processor is further configured to perform operations comprising: receiving prior user data ([0075] teaches the search engine obtains past interactions a searching end-user had with the network site, wherein the past interactions can include filtering criteria, types of destinations searched for in a past threshold interval, price point of searched listings, or family status, as well as in [0114] teaches considering a user’s previous or current destination searches including determining the price of past bookings provided by the user; see also: [0066, 0087-0089, 0111]).
However, Yin does not explicitly teach wherein the prior user data includes, at least a unique identifier for a prior user, and procurement information that includes time series data associated with the procurement of the at least one leading object together with the one or more object attributes.
From the same or similar field of endeavor, Thompson teaches wherein the prior user data includes, at least a unique identifier for a prior user (Fig. 3 and [0145] teach building a profile for a user using a machine learning model to provide better preference lists and recommendations to the traveler, wherein after booking, the user profile can be updated in order to refine the traveler’s profile, as well as in [0102] teaches traveler profiles may be built with travel preferences and travel history, wherein the users may submit information related to travel and booking, as well as in [0157] teaches the system appends guest data to the request showing the likelihood to book, the historical data of past vacations by the guest, and other information, wherein [0134] teaches the hospitality platform maintains information to specifically determine each traveler’s likelihood to book based on their history of travel and searching, wherein [0136] teaches the system can determine a user’s likelihood to book a particular accommodation, such as hotels over private rentals, as well as their likelihood to book specific deals based on their preferences to select options with meal packages or less likely to select options based on discounts, as well as their preferences related to brand loyalty, such as a particular hotel chain, and wherein [0161] teaches each guest booking can be stored using a transaction ID; see also: Figs. 1A-1B, 6, [0018, 0143, 0150, 0154]),
and procurement information that includes time series data associated with the procurement of the at least one leading object together with the one or more object attributes ([0149-0150] teach a machine learning model that receives empirical data as input, such as guest selections and activities, in order to recognize complex patterns in the user input data including user preferences and likelihood to book, wherein the machine learning model can model time series data as input data, wherein Fig. 3 and [0145] teach building a profile for a user using a machine learning model to provide better preference lists and recommendations to the traveler, wherein after booking, the user profile can be updated in order to refine the traveler’s profile, wherein [0102] teaches traveler profiles may be built with travel preferences and travel history, wherein the users may submit information related to travel and booking, as well as in [0157] teaches the system appends guest data to the request showing the likelihood to book, the historical data of past vacations by the guest, and other information, wherein [0134] teaches the hospitality platform maintains information to specifically determine each traveler’s likelihood to book based on their history of travel and searching, wherein [0136] teaches the system can determine a user’s likelihood to book a particular accommodation, such as hotels over private rentals, as well as their likelihood to book specific deals based on their preferences to select options with meal packages or less likely to select options based on discounts, as well as their preferences related to brand loyalty, such as a particular hotel chain; see also: Figs. 1A-1B, 6, [0135, 0143, 0146, 0154]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yin, Sutcliffe, and Belgaied Hassine to incorporate the teachings of Thompson to include wherein the prior user data includes, at least a unique identifier for a prior user, and procurement information that includes time series data associated with the procurement of the at least one leading object together with the one or more object attributes. One would have been motivated to do so in order to enhance hospitality platforms by connecting travelers to their ideal experiences by intelligently coordinating the unique needs of individual travelers with unique offerings that provide better likelihood of booking and the overall experience (Thompson, [0162]). By incorporating the teachings of Thompson, one would have been able to provide better preference recommendations to travelers using machine learning models that identify and refine a traveler’s profile through their actual bookings (Thompson, [0145]).
Claim(s) 3-5, 10-12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (US 20230368084 A1) in view of Sutcliffe et al. (US 20140095222 A1) in view of Belgaied Hassine et al. (US 20090234710 A1) in view of Thompson et al. (US 20220335513 A1) in view of Coulthurst et al. (US 20230186411 A1).
Regarding claims 3, 10, and 17, the combination of Yin, Sutcliffe, Belgaied Hassine, and Thompson teaches all the limitations of claims 2, 9, and 16 above.
However, Yin does not explicitly teach wherein the processor is further configured to perform operations comprising: generating, based on the object query and the machine learning model, an attribute rate model, wherein the attribute rate model indicates respective rates for the one or more object attributes.
From the same or similar field of endeavor, Coulthurst teaches wherein the processor is further configured to perform operations comprising: generating, based on the object query and the machine learning model, an attribute rate model ([0033] teaches a predictive model generates estimated model coefficients, wherein the predictive model is a customer behavior model that determines the probability of booking each room-rate combination based on its order in the list, price, and other factors including the customer persona, wherein [0034-0035] teach the estimated model coefficients are input into an offer optimization model that generates the optimized ordering and display of hotel rooms, wherein given the estimated coefficient values, the optimization model finds the model variable values that maximize the revenue, wherein the offer optimization model uses decision variables of the prices and positions of the room options offered to the customer, wherein the variables include which room options to offer, how to price the room options, and how to arrange the room options, wherein [0037] teaches the offer optimization model provides a personalized display of the hotel booking options in real time with the objective to maximize the expected revenue using the probability computed from a multinomial logit discrete-choice predictive model trained on historical observations, wherein the model uses soft clustering of the customer population by assuming that a customer belongs to each cluster with some probability that is predicted by the soft clustering model, wherein [0041] teaches on a per guest/customer basis, the offer optimization model receives a request for reserving a hotel room and provides an optimized response, wherein [0042] teaches the offer optimization model clusters the guest based on the request attributes including channel, arrival date, length of stay, number of people, and more, wherein the offer optimization model retrieves the computed optimal order solution and reorders the offer array and assembles the optimized response; see also: Fig. 4, [0017, 0040]),
wherein the attribute rate model indicates respective rates for the one or more object attributes ([0033] teaches a predictive model generates estimated model coefficients, wherein the predictive model is a customer behavior model that determines the probability of booking each room-rate combination based on its order in the list, price, and other factors including the customer persona, wherein [0034-0035] teach the estimated model coefficients are input into an offer optimization model that generates the optimized ordering and display of hotel rooms, wherein given the estimated coefficient values, the optimization model finds the model variable values that maximize the revenue, wherein the offer optimization model uses decision variables of the prices and positions of the room options offered to the customer, wherein the variables include which room options to offer, how to price the room options, and how to arrange the room options, wherein [0037] teaches the offer optimization model provides a personalized display of the hotel booking options in real-time with the objective to maximize the expected revenue using the probability computed from the multinomial logit discrete choice predictive model; see also: [0017, 0040-0041, 0071-0072]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of the combination of Yin, Sutcliffe, Belgaied Hassine, and Thompson to incorporate the teachings of Coulthurst to include wherein the processor is further configured to perform operations comprising: generating, based on the object query and the machine learning model, an attribute rate model, wherein the attribute rate model indicates respective rates for the one or more object attributes. One would have been motivated to do so in order to recommend the best experience for hotel customers by personalizing the offer optimization problem using a more accurate and sophisticated demand model that considers customer’s heterogeneity (Coulthurst, [0074]). By incorporating the teachings of Coulthurst, one would have been able to provide a personalized solution based on the customer’s characteristics known at the time of hotel booking based on the customer’s characteristics known at the time of hotel booking (Coulthurst, [0017]).
Regarding claims 4, 11, and 18, t the combination of Yin, Sutcliffe, Belgaied Hassine, Thompson, and Coulthurst teaches all the limitations of claims 3, 10, and 17 above.
However, Yin does not explicitly teach wherein the attribute rate model captures dependencies across the one or more object attributes using one or more of rate elasticity, attraction value, and a dissimilarity index, and wherein the processor is further configured to perform operations comprising: refining the attribute rate model by utilizing a fixed-point iteration.
From the same or similar field of endeavor, Coulthurst further teaches wherein the attribute rate model captures dependencies across the one or more object attributes using a dissimilarity index ([0074] teaches the system provides the optimized personalized hotel room offers and an optimized display order of the room-rate pairs, wherein the offer optimization problem uses more accurate and sophisticated demand modelling which considers the customer’s heterogeneity with significantly different patterns of purchase behavior, wherein [0090] teaches the problem of predicting demand for multiple hotel room categories and service type combinations is solved based on customer attributes, room category and service type features, and the order in which the rooms are presented to the user, wherein the system assumes that the customer population includes several clusters to allow for customer characteristics and choice patterns to be heterogenous across the clusters, wherein in addition to predicting the demand of these heterogenous customers, the system can iteratively identify each cluster and the centroid of each cluster, wherein [0037] teaches the offer optimization model provides a personalized display of the hotel booking options in real time with the objective to maximize the expected revenue using the probability computed from a multinomial logit discrete-choice predictive model trained on historical observations, wherein the model uses soft clustering of the customer population by assuming that a customer belongs to each cluster with some probability that is predicted by the soft clustering model, wherein [0041] teaches on a per guest/customer basis, the offer optimization model receives a request for reserving a hotel room and provides an optimized response, wherein [0042] teaches the offer optimization model clusters the guest based on the request attributes including channel, arrival date, length of stay, number of people, and more, wherein the offer optimization model retrieves the computed optimal order solution and reorders the offer array and assembles the optimized response; see also: Fig. 4, [0017, 0034-0035, 0040, 0075, 0091-0092]),
and wherein the processor is further configured to perform operations comprising: refining the attribute rate model by utilizing a fixed-point iteration ([0033] teaches a predictive model generates estimated model coefficients, wherein the predictive model is a customer behavior model that determines the probability of booking each room-rate combination based on its order in the list, price, and other factors including the customer persona, wherein [0034-0035] teach the estimated model coefficients are input into an offer optimization model that generates the optimized ordering and display of hotel rooms, wherein given the estimated coefficient values, the optimization model finds the model variable values that maximize the revenue, wherein the offer optimization model uses decision variables of the prices and positions of the room options offered to the customer, wherein the variables include which room options to offer, how to price the room options, and how to arrange the room options, wherein [0037] teaches the offer optimization model provides a personalized display of the hotel booking options in real time with the objective to maximize the expected revenue using the probability computed from a multinomial logit discrete-choice predictive model trained on historical observations, wherein the model uses soft clustering of the customer population by assuming that a customer belongs to each cluster with some probability that is predicted by the soft clustering model, wherein [0041] teaches on a per guest/customer basis, the offer optimization model receives a request for reserving a hotel room and provides an optimized response, wherein [0042] teaches the offer optimization model clusters the guest based on the request attributes including channel, arrival date, length of stay, number of people, and more, wherein the offer optimization model retrieves the computed optimal order solution and reorders the offer array and assembles the optimized response, wherein the guest can either provide a booking request upon receiving the optimized response display or they may not provide a booking request, wherein the selection of the guest can be stored in the historic database and is provided to the prediction model which uses the selection as an additional iteration to further train the prediction model, wherein [0093-0094] teach the model may be iterated until a convergence criterion is met, wherein after the convergence is met, the final estimates of the model parameters can be identified, wherein [0113-0119] teach the iteratively reconfigurable clustering algorithm is iteratively adjusted and updated until a convergence criterion is met, as well as in [0088] teaches the iteratively reconfigurable dynamic clustering is based on a semi-parametric mixture of discrete choice models to fully reflect the customer’s behavior; see also: Fig. 4, [0017, 0034-0035, 0037, 0040, 0075, 0128]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yin, Sutcliffe, Belgaied Hassine, Thompson, and Coulthurst to incorporate the further teachings of Coulthurst to include wherein the attribute rate model captures dependencies across the one or more object attributes using one or more of rate elasticity, attraction value, and a dissimilarity index, and wherein the processor is further configured to perform operations comprising: refining the attribute rate model by utilizing a fixed-point iteration. One would have been motivated to do so in order to recommend the best experience for hotel customers by personalizing the offer optimization problem using a more accurate and sophisticated demand model that considers customer’s heterogeneity (Coulthurst, [0074]). By incorporating the teachings of Coulthurst, one would have been able to provide a personalized solution based on the customer’s characteristics known at the time of hotel booking based on the customer’s characteristics known at the time of hotel booking (Coulthurst, [0017]).
Regarding claims 5, 12, and 19, the combination of Yin, Sutcliffe, Belgaied Hassine, Thompson, and Coulthurst all the limitations of claims 4, 11, and 18 above.
However, Yin does not explicitly teach wherein the processor is further configured to perform operations comprising: generating, based on the machine learning model and the attribute rate model, one or more procurement predictions, wherein the one or more procurement predictions are respectively associated with the one or more object bundles.
From the same or similar field of endeavor, Coulthurst further teaches wherein the processor is further configured to perform operations comprising: generating, based on the machine learning model and the attribute rate model, one or more procurement predictions ([0033] teaches a predictive model generates estimated model coefficients, wherein the predictive model is a customer behavior model that determines the probability of booking each room-rate combination based on its order in the list, price, and other factors including the customer persona, wherein [0034-0035] teach the estimated model coefficients are input into an offer optimization model that generates the optimized ordering and display of hotel rooms, wherein given the estimated coefficient values, the optimization model finds the model variable values that maximize the revenue, wherein the offer optimization model uses decision variables of the prices and positions of the room options offered to the customer, wherein the variables include which room options to offer, how to price the room options, and how to arrange the room options, wherein [0037] teaches the offer optimization model provides a personalized display of the hotel booking options in real-time with the objective to maximize the expected revenue using the probability computed from the multinomial logit discrete choice predictive model, wherein [0090] teaches solving the problem of predicting demand for multiple hotel room categories and service type combinations based on the hotel customer attributes, room category, and service feature types, offered price, and order in which the room-rate pairs are presented to the customer, wherein the principal output of the problem is the probability of each individual customer booking a room in a specific room categories-service type combination, as well as in [0092] teaches calculating choice probabilities for multiple hotel room categories-service type combinations for each customer based on the modelled values, wherein [0094] teaches predicting choice probabilities for the new customer after estimating their association with each cluster, wherein the final model parameters are estimated in order to predict the choice probabilities associated with the customer based on customer characteristics, orders of the room-service pairs, and room category features including price; see also: [0017, 0040-0042, 0071-0072, 0088, 0129, 0135]),
wherein the one or more procurement predictions are respectively associated with the one or more object bundles ([0033] teaches a predictive model generates estimated model coefficients, wherein the predictive model is a customer behavior model that determines the probability of booking each room-rate combination based on its order in the list, price, and other factors including the customer persona, wherein [0034-0035] teach the estimated model coefficients are input into an offer optimization model that generates the optimized ordering and display of hotel rooms, wherein given the estimated coefficient values, the optimization model finds the model variable values that maximize the revenue, wherein the offer optimization model uses decision variables of the prices and positions of the room options offered to the customer, wherein the variables include which room options to offer, how to price the room options, and how to arrange the room options, wherein [0037] teaches the offer optimization model provides a personalized display of the hotel booking options in real-time with the objective to maximize the expected revenue using the probability computed from the multinomial logit discrete choice predictive model, wherein [0090] teaches solving the problem of predicting demand for multiple hotel room categories and service type combinations based on the hotel customer attributes, room category, and service feature types, offered price, and order in which the room-rate pairs are presented to the customer, wherein the principal output of the problem is the probability of each individual customer booking a room in a specific room categories-service type combination, as well as in [0092] teaches calculating choice probabilities for multiple hotel room categories-service type combinations for each customer based on the modelled values, wherein [0094] teaches predicting choice probabilities for the new customer after estimating their association with each cluster, wherein the final model parameters are estimated in order to predict the choice probabilities associated with the customer based on customer characteristics, orders of the room-service pairs, and room category features including price; see also: [0017, 0040-0042, 0071-0072, 0088, 0129, 0135]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yin, Sutcliffe, Belgaied Hassine, Thompson, and Coulthurst to incorporate the further teachings of Coulthurst to include wherein the processor is further configured to perform operations comprising: generating, based on the machine learning model and the attribute rate model, one or more procurement predictions, wherein the one or more procurement predictions are respectively associated with the one or more object bundles. One would have been motivated to do so in order to recommend the best experience for hotel customers by personalizing the offer optimization problem using a more accurate and sophisticated demand model that considers customer’s heterogeneity (Coulthurst, [0074]). By incorporating the teachings of Coulthurst, one would have been able to provide a personalized solution based on the customer’s characteristics known at the time of hotel booking based on the customer’s characteristics known at the time of hotel booking (Coulthurst, [0017]).
Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (US 20230368084 A1) in view of Sutcliffe et al. (US 20140095222 A1) in view of Belgaied Hassine et al. (US 20090234710 A1) in view of Cho et al. (US 20210117998 A1).
Regarding claims 7 and 14, the combination of Yin, Sutcliffe, and Belgaied Hassine teaches all the limitations of claims 1 and 8 above.
However, Yin does not explicitly teach wherein the machine learning model includes a nested logit estimation model.
From the same or similar field of endeavor, Cho teaches wherein the machine learning model includes a nested logit estimation model (Fig. 5 and [0053-0054] teach applying a mixture MNL model to each cluster to estimate the demand, wherein the choice model follows a MNL for each cluster separately (e.g. MNL cluster 1 for cluster 1, MNL 2 for cluster 2), as well as in Fig. 6 and [0075-0076] tech the personalized demand model can be used for developing personalized pricing policies to maximize the hotel revenue, which changes as a function of the price of each room type with various features; see also: [0054, 0058, 0061, 0073]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Yin, Sutcliffe, and Belgaied Hassine to incorporate the teachings of Cho to include wherein the machine learning model includes a nested logit estimation model. One would have been motivated to do so in order to improve upon traditional demand forecasting tools currently used by the hotel industry by generating a personalized demand and price optimization model that considers heterogenous guests with significantly different willingness to pay (Cho, [0019]). By incorporating the teachings of Cho, one would have been able to solve the optimization problem to determine the optimal price by fitting a multinomial choice to each guest cluster, thus maximizing the expected revenue to each room type for each guest (Cho, [0081]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Katsuki et al. (US 20180033059 A1) discloses generating the price of a hotel room based on the booking being during weekdays versus weekends
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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