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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application on 08/12/2026, after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/14/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/01/2026 was filed before the mailing of this office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for Domestic Benefit to provisional application #63/585735 filed on 09/27/2023.
Status of Claims
Claims 1-2, 4-10, 12-18, and 20-23 were rejected in the Final Office action mailed on 05/15/2026. Applicant’s amended claimset, filed on 07/14/2026 and entered on 08/12/2026, amended Claims 1-2, 4-6, 8-10, 12-14, 16-18, 20-21, and 23. Herein this Non-Final Office Action, Claims 1-2, 4-10, 12-18, and 20-23 are rejected.
Response to Arguments
Applicant’s arguments filed 07/14/2026, with respect to Rejections under 35 U.S.C. 112(b) for Claims 18 and 23, have been fully considered and are persuasive. In light of the entered amendments, the rejection under 35 U.S.C. 112(b) has been withdrawn.
Applicant’s arguments filed 07/14/2026, with respect to Rejections under 35 U.S.C. 101 for Claims 1-2, 4-10, 12-18, and 20-23, have been fully considered and are not persuasive.
On Pages 11-12, Applicant argues that “the pending claims are directed to a specific technological improvement in computerized hotel reservation systems that employ multiple trained machine-learning models, robust optimization, and automated operational control to optimize reservation acceptance and room assignment under uncertain future demand. The claims therefore are not directed merely to an abstract idea and, even assuming that they recite a judicial exception, integrate any such exception into a practical application and further recite significantly more than any alleged abstract idea.” Examiner does not agree. As discussed in greater detail below the amended claimset merely generally links the recited abstract idea to a computer environment by merely using the computer elements as a tool, used in its ordinary capacity, to apply the abstract idea.
On Pages 12-13, Applicant argues “The Pending Claims Are Not Directed Merely to an Abstract Idea.” Specifically, Applicant argues “The claims do not merely recite determining whether to accept reservations or whether to upgrade guests. Instead, the claims recite a specific computer-implemented architecture in which multiple specialized machine-learning models generate different categories of prediction data that are integrated into a robust optimization system to produce operational control data used during two distinct phases of hotel operation. As amended, independent claim 1 expressly requires: • using a first trained machine-learning model to predict future reservations for each room category; • using a second trained machine-learning model to predict average lengths of stay; • generating reservation-state data from the outputs of those models; • supplying the generated reservation-state data to an optimization system; • determining overbooking limits separately for each day from the current day through a future check-in day; and • automatically determining, during guest check-in, whether a reservation should be accepted, upgraded, or rejected based upon the generated reservation-state data. These limitations define a particular technological implementation employing multiple trained predictive models operating together with a specialized optimization system. The claims therefore are directed to a particular manner of computer operation rather than the generalized concept of improving hotel profitability. The Federal Circuit has repeatedly explained that claims directed to improvements in computerized technology are not rendered abstract merely because the technology ultimately benefits a business process. The relevant inquiry is whether the claimed invention improves the operation of computer technology itself or another technological field. Here, the claims improve computerized reservation optimization by introducing a specialized prediction-and-optimization architecture that did not previously exist.” Examiner does not agree.
MPEP 2106.05(a) states “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.”
MPEP 2106.05(a) states “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.”
Examiner responds that the instant claims do not provide an improvement to the functioning of a computer or another technology field. First, Applicant’s argument fails to point to a non-conclusory technical explanation of the asserted improvement to rebut Examiner’s determination that the specification fails to provide sufficient technical explanation. Second, the use of the computer components in the claims, as referenced in the argument above, merely claims the use of generic computer components (e.g. trained machine-learning model) to apply the abstract idea (i.e. predicting reservations to better administer reservations and check-ins while running a hotel).
Examiner’s determination is further supported by PEG Example 47. The instant claims are analogous to PEG Example 47 Claim 2, which is ineligible. Similar to the instant claims, which uses a machine learning model to predict hotel information, PEG Example 47 Claim 2 includes a trained artificial neural network used for a specific purpose, i.e. to detect, analyze, and output anomalous data. Thus, the amended claimset has failed to provide a patent eligible improvement to technology.
On Pages 13-15, Applicant argues “The Claims Integrate Any Alleged Judicial Exception into a Practical Application.” Specifically, Applicant argues “Even assuming that certain limitations could be characterized as involving mathematical concepts or economic practices, the claims as a whole integrate any such concepts into a practical application. The USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance explains that claims integrate an exception into a practical application when they apply the exception in a meaningful manner that imposes a real-world technological implementation. That is precisely what the present claims accomplish. The claimed machine-learning models are not employed merely to display predictions or assist human decision making. Rather, the outputs of multiple trained models are transformed into reservation-state data that is consumed by a specialized optimization system for generating operational decisions governing hotel reservation processing. The optimization system does not merely report predicted values. Instead, it automatically generates operational control decisions including: • category-specific overbooking limits, • reservation acceptance decisions, • room upgrade determinations, • room assignment determinations, and • check-in decisions executed during hotel operation. These operational outputs directly control subsequent computerized processing performed by the reservation system. Further, as recited in dependent claims and supported throughout the Specification, room assignment information generated by the optimization process is transmitted for use by additional computerized hotel systems, including hotel access control systems responsible for automatically encoding electronic room keys and programming room access devices. The optimization system therefore produces specialized operational control data that changes the operation of additional computer controlled hotel infrastructure rather than merely presenting information to a user.” Examiner does not agree.
MPEP 2106.04(d)I states “The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h).”
First, Examiner does not agree that “The USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance explains that claims integrate an exception into a practical application when they apply the exception in a meaningful manner that imposes a real-world technological implementation.” Without citations, Examiner does not know what portion the publication Applicant is referencing. However, the interpretation that “real-world technological implementation” results in the “claims integrat[ing the] exception into a practical application,” seems to completely void the analysis of at least MPEP 2106.05(b), 2106.05(f), and 2106.05(h).
Second, Examiner responds that the output of the model being used to perform the argued “operational control” does not integrate the recited abstract idea into a practical application. The use of a computer to perform subsequent portions of the recited abstract idea resultant to the predictions of the machine learning model are merely further use of a computer to apply the recited abstract idea.
Examiner’s determination is consistent with Vakhutinsky, Ex parte, Appeal 2026-000653 (PTAB June 10, 2026) (See pg. 23 quoting “We are not persuaded by Appellant's argument that ‘Dependent claims 7 and 15 include “practical application” limitations to the claims in that they recite “in response to receiving the specialized data, automatically encoding a hotel room key that corresponds to the first hotel room using the hotel room key machine.”’ Appeal Br. 12.” See also pg. 23 quoting “Generally, optimization, whether of sort sequence or pricing, and maximizing an objective function, which is also a form of optimization, are mathematical algorithms which themselves are abstract.”).
On Pages 15-17, Applicant argues “The Claims Reflect an Improvement in the Technical Field of a Specific Computer Implemented Optimization Technique.” Specifically, Applicant argues “The USPTO issued a memorandum on August 4, 2025 entitled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101." (hereinafter, the "Aug. 2025 memo"). The Aug. 2025 memo indicates that: "In computer-related technologies, examiners can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception." See Aug. 2025 memo, p. 4. "The examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement." See Aug. 2025 memo, p. 4. In connection with improvements, the claims reflect an improvement in the optimization of hotel room reservations in both determining an overbooking limit when a reservation is being made, and determining whether to upgrade a reservation during check in. Both stages of the process require accurate prediction to optimize revenue. Improving the accuracy of the predictions of the ML models, which is the primary function. The Office Action alleges that the Specification does not sufficiently explain how the claimed invention improves machine-learning technology. Applicant respectfully disagrees. The Specification provides extensive technical disclosure describing the architecture of the claimed system, including: • separate machine-learning models trained for different predictive tasks; • category-specific prediction models; • prediction-window-specific models; • Random Forest classifiers for cancellation prediction; • Random Forest regressors for future reservation prediction; • separate predictive models for average length of stay; • generation of uncertainty sets; • transformation of optimization problems into robust optimization formulations; • mixed-integer linear programming formulations; • generation of robust counterparts eliminating uncertainty variables; • continual retraining of predictive models using subsequently obtained reservation information. Rather than relying upon a single predictive model, embodiments construct an integrated prediction architecture that generates multiple forms of predictive information, each serving a different role within the optimization system. The resulting architecture enables robust optimization under uncertain future reservation demand while accounting for prediction uncertainty through explicitly modeled uncertainty sets. The Specification therefore provides considerably more than a conclusory statement that prediction accuracy is improved. Instead, it discloses a concrete computer architecture including multiple cooperating predictive models, specialized optimization techniques, uncertainty modeling, and continual model retraining that together improve computerized reservation optimization under uncertain operating conditions. The claimed invention therefore represents a technological improvement implemented through a specific arrangement of cooperating computational components, not merely an automation of economic principles. Therefore, the claims are similar to the claims in BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) where the inventive concept may be found in the non-conventional and non-generic arrangement of components that are individually well-known and conventional. See MPEP 2106.05. Therefore, the present claims recite a technical improvement on known overbooking limit determination systems, and technically novel arrangement of trained models that can not be considered "conventional" and known technology.” (Emphasis added). Examiner does not agree.
MPEP 2106.04.I states “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. For example, the mathematical formula in Flook, the laws of nature in Mayo, and the isolated DNA in Myriad were all novel or newly discovered, but nonetheless were considered by the Supreme Court to be judicial exceptions because they were "‘basic tools of scientific and technological work’ that lie beyond the domain of patent protection." Myriad, 569 U.S. 576, 589, 106 USPQ2d at 1976, 1978 (noting that Myriad discovered the BRCA1 and BRCA1 genes and quoting Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 ("the novelty of the mathematical algorithm is not a determining factor at all"); Mayo, 566 U.S. 73-74, 78, 101 USPQ2d 1966, 1968 (noting that the claims embody the researcher's discoveries of laws of nature). The Supreme Court’s cited rationale for considering even "just discovered" judicial exceptions as exceptions stems from the concern that "without this exception, there would be considerable danger that the grant of patents would ‘tie up’ the use of such tools and thereby ‘inhibit future innovation premised upon them.’" Myriad, 569 U.S. at 589, 106 USPQ2d at 1978-79 (quoting Mayo, 566 U.S. at 86, 101 USPQ2d at 1971). See also Myriad, 569 U.S. at 591, 106 USPQ2d at 1979 ("Groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry."). The Federal Circuit has also applied this principle, for example, when holding a concept of using advertising as an exchange or currency to be an abstract idea, despite the patentee’s arguments that the concept was "new". Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original).”
MPEP 2106.05(a)II states “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Examiner responds that the asserted improvements in accuracy of predictions, or optimizing revenue for hotel operations by making better decisions regarding reservation acceptance and check-in decisions, are improvements in the abstract idea itself. The “architecture” disclosed in the specification, that Applicant argues provides the technical explanation needed for the claims to provide a patent eligible improvement in technology, merely provides further explanation as to the abstract ideas used to make better decisions. Examiner concedes that the abstract idea disclosed may be novel, however, such improvement in an abstract idea itself does not integrate the recited abstract idea into a practical application by providing a patent eligible improvement in technology.
Examiner responds that the optimization and use of multiple models does not provide a technical architecture, but remains apart of the abstract idea that is merely applied using the generic computer components. Thus, the instant claims are distinguishable from BASCOM, because the configuration of the computer components themselves are generic. Examiner’s determination is consistent with Vakhutinsky, Ex parte, Appeal 2026-000653 (PTAB June 10, 2026) (See pg. 23 quoting “We are not persuaded by Appellant's argument that "Examiner has failed to analyze how the interaction between the three models (i.e., a logistic regression model, clustering models and MNL models) fail to integrate any alleged mental process or organizing human activity into a practical application." Appeal Br. 11. This analysis is detailed above under Step 2A, Prong 2. Such mathematical algorithms are themselves abstract ideas. See Benson above. Sequencing multiple abstract ideas together is insufficient to confer eligibility. "Adding one abstract idea ... to another abstract idea ... does not render the claim nonabstract." RecogniCorp, LLC v. Nintendo Co., Ltd., 855 F.3d 1322, 1327 (2017). Selecting particular statistical analysis models is a matter of matching algorithms to context. This is the epitome of conceptual ideas. Appellant does not contend it invented any of these models.” See also pg. 23 quoting “Generally, optimization, whether of sort sequence or pricing, and maximizing an objective function, which is also a form of optimization, are mathematical algorithms which themselves are abstract.”).
On Pages 17-18, Applicant argues “The Claims Recite The Practical Application of Automatically Encoding a Hotel Room Key.” Specifically, Applicant argues “Further, dependent claim 8 and 16 include further "practical application" limitations to the claims of "in response to the check in decision, generating specialized room-assignment data corresponding to a determined room assignment; transmitting the specialized room-assignment data; in response to receiving the specialized room assignment data, automatically encoding a hotel room key that corresponds to the determined room assignment". The Office Action characterizes this limitation as insignificant post-solution activity. Applicant respectfully disagrees. The automatic encoding of hotel room keys is not merely displaying or reporting calculated information. Rather, the computed optimized reservation and optimized reservation acceptance determination directly control operations of physical hotel access infrastructure by automatically initiating room-key encoding following reservation acceptance. This use of computed information to automatically control downstream physical operations is consistent with the principles illustrated in USPTO Subject Matter Eligibility Examples 45 and 46. The claims therefore extend beyond mere calculation and instead employ the calculated information to automatically control subsequent technological operations within the reservation system.” Examiner does not agree.
MPEP 2106.05(f) states “Use of a computer or other machinery in its ordinary capacity for economic 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., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.”
Examiner responds that the automatic encoding of a hotel key (i.e. memory storage device) is merely the use of “a computer or other machinery in its ordinary capacity” to apply the recited abstract idea under MPEP 2106.05(f). Such determination is consistent with Vakhutinsky, Ex parte, Appeal 2026-000653 (PTAB June 10, 2026) (See pg. 23 quoting “We are not persuaded by Appellant's argument that ‘Dependent claims 7 and 15 include “practical application” limitations to the claims in that they recite “in response to receiving the specialized data, automatically encoding a hotel room key that corresponds to the first hotel room using the hotel room key machine.”’ Appeal Br. 12.”).
Examiner further responds that the automatic encoding of a hotel key does not integrate the recited abstract idea into a practical application in reference to PEG Examples 45-46. This argument was addressed in the prior office action in Paragraphs 17-20. Further the prior office action in Paragraph 81 cites Openkey, which shows that most US hotels have magstripe door locks, which were developed in the 1980’s.
On Pages 18-19, Applicant argues “Additional Elements are Not Conventional.” Specifically, Applicant argues “Claims 21-23 further recite implementation within a cloud infrastructure including: • a Virtual Cloud Network (VCN); • a Local Peering Gateway (LPG); • a Secure Shell VCN; • a Control Plane VCN; and • a Data Plane VCN. The Office Action characterizes these limitations as generic networking components. However, this characterization improperly evaluates each element individually while ignoring the ordered combination recited by the claims. The claims specify a particular cloud deployment architecture for executing the claimed reservation optimization functionality. Even if each networking component were individually known, the Supreme Court has instructed that eligibility must consider the claims "as an ordered combination." Further, under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), whether particular combinations are well-understood, routine, and conventional is a factual inquiry. The Office Action cites no evidence establishing that the claimed combination of cloud infrastructure elements, operating together with the claimed optimization engine, was well-understood, routine, and conventional. Accordingly, the rejection fails to establish the absence of an inventive concept.” Examiner does not agree.
Examiner responds that “well-understood, routine, and conventional” (MPEP 2106.05(d)) is not the stand alone test for patent subject matter eligibility, and is not the authority for which the rejection is based. Examiner has addressed similar arguments in the previous office action in Paragraphs 21-24.
Examiner responds that the additional elements of Dependent Claims 21-23 do not integrate the recited abstract idea into a practical application or provide “significantly more” because the elements are merely used to apply the abstract idea. Specification paragraphs 27-29, 36, and 124, 133, and 170-71 demonstrates that the specific computer configuration claimed in Dependent Claims 21-23 is not necessary for the execution of the asserted invention. Thus, the use of the claimed computer components does not integrate the recited abstract idea into a practical application or provide “significantly more.”
Put plainly, the claims do not provide a meaningful nexus between the claimed configuration of computer components and the operations of the depended upon claims, and therefore, the use of the computer components of Claims 21-23 fails MPEP 2106.05(f) and (h).
On Pages 19-20, Applicant argues “The Claims Are Consistent with Recent Federal Circuit Eligibility Decisions.” Specifically, Applicant argues “When considered as a whole, the pending claims are consistent with Federal Circuit decisions recognizing patent eligibility for improvements to computer technology and computerized processing, including Enfish, McRO, BASCOM, DDR Holdings, Ancora Technologies, CardioNet, and SRI International. Like those cases, the present claims recite a specific technological architecture that changes how a computerized reservation system processes uncertain predictive information to generate operational control decisions. The claims do not merely automate human decision making, organize human activity, or perform mathematical calculations in the abstract. Instead, they define a concrete computer implementation employing multiple coordinated machine-learning models, robust optimization techniques, uncertainty modeling, and automated control of downstream computerized hotel-management systems. Viewed as an ordered combination, these claim limitations constitute a technological improvement to computerized reservation optimization systems and satisfy both Step 2A and Step 2B of the Alice/Mayo framework. For at least the foregoing reasons, the claims should be allowable over 35 U.S.C. §101.” Examiner does not agree.
Examiner responds that the generally cited case law is integrated into MPEP 2106, which was utilized as the basis for this rejection. The computer implementation claimed fails to provide a patent eligible improvement under MPEP 2106.05(a), merely uses the computer as a tool in its ordinary capacity to apply the recited abstract under MPEP 2106.05(f), and merely links the recited abstract idea to the technological field of computer under MPEP 2106.05(h). Therefore, the rejection remains.
Claim Interpretation
Claim 1 recites “A method of computerized constraint optimization for optimizing hotel room reservations for a hotel, the method comprising: for a future hotel room check in day, automatically determining, by one or more processors executing an optimization system and based on an objective function, an overbooking limit for each category of hotel rooms for the hotel, wherein the hotel includes a plurality of different room categories; . . .” (emphasis added) at the beginning of the claim. Because the “optimization system” is “execut[ed]” on the “one or more processors,” the “optimization system” could not be interpreted as a computer hardware system, e.g. “system 10” of Fig. 2 and Paragraphs 27-28. Instead, the broadest reasonable interpretation of the “optimization system” is analogous to an “optimization program/process/procedure/algorithm/ect.,” e.g. the “optimization system” is a “system” like how the “Dewey Decimal system” or the “metric system” is a “system” (i.e. not a physical system nor specifically linked to a computer).
Claims 2, 4-10, 12-18, and 20-23 provide similar recitations of the “optimization system” as discussed above regarding Claim 1, either via dependency or similar claim language. Therefore, the interpretation of the “optimization system” of Claim 1 is extended to the similar recitations of an “optimization system” in Claims 2, 4-10, 12-18, and 20-23.
Claim Objections
Claims 18, 20, and 23 are objected to because of the following informalities:
Claim 18 recites “The system of claim 17, . . .” Depended upon Claim 17 recites “A cloud based hotel reservation system” and an “optimization system.” Although the broadest reasonable interpretation of the claim language is definite in scope, as it would be unreasonable to interpret the “system of claim 17” as not referring to the “cloud based hotel reservation system,” the recitation of two prior “system[s]” in depended upon Claim 17 creates an informality in the explicit reading of the claim language.
Claim 20 recites “The system of claim 18, . . .” Thus, Claim 20 contains a similar informality to Claim 18, as discussed above.
Claim 23 recites “The system of claim 17, . . .” Thus, Claim 23 contains a similar informality to Claim 18, as discussed above.
Appropriate correction is required.
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 1-2, 4-10, 12-18, and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-2, 4-8, and 21 recite a method (i.e. a process), and Claims 9-10, 12-16 and 22 recite a computer readable medium (i.e. a machine or manufacture), Claims 17-18, 20 and 23 recite a system (i.e. a machine or manufacture). Therefore, Claims 1-2, 4-10, 12-18, and 20-23 all fall within the one of the four statutory categories of invention of 35 U.S.C. 101.
Step 2A, Prong One
Independent Claim 1 recites the abstract idea of “A method of . . . constraint optimization for optimizing hotel room reservations for a hotel, the method comprising:
for a future hotel room check in day, automatically determining, by . . . executing an optimization system and based on an objective function, an overbooking limit for each category of hotel rooms for the hotel, wherein the hotel includes a plurality of different room categories; wherein the automatically determining comprises:
using a first . . . model for each of the categories that predicts a number of future reservations for each of the categories; and
using a second . . . model for each of the categories that predicts an average length of stay for the future reservations
generating reservation-state data comprising the predicted future reservations and predicted average lengths of stay for each day from a current day through a future hotel room check-in day;
providing the generated reservation-state data as inputs to the optimization system;
wherein the optimization system determines the overbooking limit separately for each day from the current day through the future hotel room check-in day;
receiving a first reservation request for the future hotel room check-in day
when the determined overbooking limit for the first day and the first category room has not been reached, automatically accepting the first reservation request;
when the accepted first reservation request is being checked in to the hotel on the future hotel room check-in day , by the optimization system and the reservation-state data, whether to reject checking into a room associated with the accepted first reservation request, accept checking into a room associated with the first accepted reservation request, or upgrade the accepted first reservation request to a higher category room.”
The limitations stated above are processes/ functions that under broadest reasonable interpretation covers (1) determining an overbooking limit for each category of hotel rooms based on an objective function using two models that predict the number and length of future reservations, (2) inputting predicted future reservations and average length of stay into an optimization system that determines the overbooking limit for each day from the current day to the future check-in day, (3) receiving a reservation request, and (4) upon check-in, using the objective function, optimization system, and predicted reservations, to accept/reject/upgrade the check-in associated with the reservation, all of which are:
mathematical formulas or equations (i.e. objective function and prediction models that calculate a number and an average), which are mathematical concepts, an abstract idea, under MPEP 2106.04(a)(2)I and managing personal behavior by following rules and interacting between people (i.e. the reservation and check-in decision are “following rules or instructions”) and
commercial or legal interactions (i.e. use of overbooking in reserving and checking in a hotel room is at least “form of contracts” and “marketing or sales activities or behaviors”), which are certain methods of organizing human activity, an abstract idea, under MPEP 2106.04(a)(2)II.
The mere the recitation of generic computer components (i.e., the “one or more processors,” “trained machine learning model[s]”) implementing the identified abstract idea does not take the claim out of the mathematical concepts or certain methods of organizing human activity groupings. MPEP 2106.04(d). If a claim limitation, under its broadest reasonable interpretation, covers “mathematical formulas or equations,” “managing personal behavior or relationships or interactions between people,” and “commercial or legal interactions” but for the recitation of generic computer components, then it falls in the mathematical concepts and certain methods of organizing human activity groupings of abstract ideas. MPEP 2106.04. Therefore, Claim 1 recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent) and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claim recites the additional elements of:
(i) “one or more processors” (i.e. “computerized”),
(ii) “first trained machine learning (ML) model,” and
(iii) “a second trained ML model.”
The additional elements of (i) “one or more processors” (Fig. 2 and ¶28 shows “Processor 22 may be any type of general or specific purpose processor.”) and (ii) first and (iii) second trained machine learning model (¶¶49-54 shows use of “Random Forest” algorithm for training the models on the generic computer of Fig. 2 and ¶¶28-29), are recited at a high-level of generality, such that, when viewed as whole/ordered combination (Fig. 2, ¶¶28-29, and ¶¶49-54 shows elements in combination.), they amount to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
The (i) processors and (ii) first and (iii) second trained machine learning model, when viewed as whole/ordered combination (Fig. 2, ¶¶28-29, and ¶¶49-54 shows elements in combination.), does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. hotel machine learning computer environment) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (Fig. 2, ¶¶28-29, and ¶¶49-54 shows elements in combination.), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent) and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of (i) processors and (ii) first and (iii) second trained machine learning model, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (Fig. 2, ¶¶28-29, and ¶¶49-54 shows elements in combination.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Regarding Dependent Claims 2, 4-8, and 21, Dependent Claims 2 and 4-8 recite the further abstract idea of:
“. . . generating a two-stage optimization problem comprising decision variables comprising daily booking limits for each of the hotel room categories and assignment variables comprising an upgrading policy for each day; wherein the reservation-state data generated by the first and second . . . models is used by both stages of the optimization problem.” (Claim 2).
“. . . generating a prediction of cancellations of existing reservations; wherein the prediction of cancellations is incorporated into the reservation-state data used by the optimization system.” (Claim 4).
“. . . comprising transforming the two-stage optimization problem, the transforming comprising: generating a mixed integer optimization problem both the overbooking limit and models the check in decision; and converting the mixed integer optimization problem ; wherein the linear optimization problem is solved to generate the overbooking limit and a check-in decision.” (Claim 5).
“. . . wherein the mixed integer optimization problem Claim 6).
“. . . wherein the linear optimization problem comprises a plurality of constraints that comprises uncertainty, further comprising formulating a robust counterpart without random variables for each of the plurality of constraints.” (Claim 7).
“. . . in response to the check in decision, generating room- assignment data corresponding to a determined room assignment; room-assignment data; in response to receiving the specialized room-assignment data, automatically [granting access to a hotel room] that corresponds to the determined room assignment.” (Claim 8).
Dependent Claims 2, 4-8, and 21, have been given the full two-prong analysis including analyzing the further elements and limitations, both individually and in combination. When analyzed individually and in combination, these claims are also held to be patent ineligible under 35 U.S.C. 101. The further limitation of Claims 2, 4-8, and 21 fail to establish claims that are not directed to an abstract idea because the further limitations (1) generating a two stage optimization problem including certain variables, (2) generating prediction of new reservations and cancellations using learning models, (3) generating a MILP, comprising random variables, that models the overbooking limit and check in decision, (4) converting the MILP to linear optimization using polyhedron uncertainty set for total number of arriving customers that eliminates the random variables, (5) the linear optimization problem comprising uncertainty and a robust counterpart, and (6) transmitting data in response to the check-in decision, which are a part of the abstract idea. The further elements of Claims 2, 4-8, and 21 (i.e. “encoding a hotel room key” of Claim 8 and “a cloud infrastructure comprising: a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN” of Claim 21.) fails to establish claims that are not directed to an abstract idea because the elements merely recite additional generic computer components similar to the generic computer hardware of Claim 1 or generally link the abstract idea to a particular technology or field of use (i.e. the hotel computer system) just as in Claim 1. The organization of the further limitations of Claims 2, 4-8, and 21 fail to integrate an abstract idea into a practical application just as discussed above for Claim 1. Additionally, performing the abstract idea of Claim 1 as recited in each of the further limitations of Claims 2, 4-8, and 21, individually or in combination, does not (1) impose any meaningful limits on practicing the abstract ideas, or (2) provide improvements to the functioning of computing systems or to another technology or technical field, just as discussed above regarding Claim 1. Therefore, Claims 2, 4-8, and 21 amount to mere instructions to implement the abstract idea (1) using generic computer components—using the computer, in its ordinary capacity, as a tool to perform the abstract idea, and (2) generally linked to a particular technology or field of use. Because the claims merely use a computer, in its ordinary capacity in a particular field of use, as a tool to perform the abstract idea cannot provide an inventive concept, the elements and limitations of Claims 2, 4-8, and 21 fail to establish that the claims provide an inventive concept, just as in Claim 1. Therefore, Claims 2, 4-8, and 21 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claims 9-10, 12-16 and 22 recite elements and limitations that are substantially similar to Claims 1-2, 4-8, and 21. Therefore, Claims 9-10, 12-16 and 22 are rejected under 35 U.S.C. 101 just as Claims 1-2, 4-8, and 21 are rejected under 35 U.S.C. 101 as discussed above.
Claims 17-18, 20, and 23 recite elements and limitations that are substantially similar to Claims 9-10, 12, and 22. Therefore, Claims 17-18, 20, and 23 are rejected under 35 U.S.C. 101 just as Claims 9-12 and 22 are rejected under 35 U.S.C. 101 as discussed above.
Reasons for No Art Rejection
Claims 1-2, 4-10, 12-18, and 20-23 are not rejected over the prior art of record.
The Closest prior art of record is:
“When Your Hotel Is Overbooked, You Might Be ‘Walked’ to Another” (“Vora” 02/18/2019, The New York Times, https://www.nytimes.com/2019/02/18/business/hotels-overbooked-walking-travel.html);
US-20090307020-A1 (“Viale”);
US-20120053968-A1 (“Debarge”);
US-20180357574-A1 (“Pellerin”);
US-20080091480-A1 (“Geoghegan”);
US-20100153144-A1 (“Miller”);
US-20210125111-A1 (“Simon”);
US-20130096962-A1 (“Craig”);
US-20230244752-A1 (“Wasserkrug”);
US-20110270646-A1 (“Prasanna”);
CN-113408906-A (“Zhang”);
“Robust Capacity Control in Revenue Management: A literature Review” (“Jiang” 04/30/2018, Open Journal of Business and Management , 6, 488-497, https://doi.org/10.4236/ojbm.2018.62037);
“A stochastic approach to hotel revenue optimization” (“Lai” May 2005, Computers & Operations Research 32, 1059–1072, https://doi.org/10.1016/j.cor.2003.09.012);
US-20220197683-A1 (“Potlapally”);
US 20210117998 A1 (“Cho”);
“A simulation-Based Overbooking Approach For Hotel Revenue Management” (“Fouad” 03/02/2015, IEEE, https://ieeexplore.ieee.org/document/7050433); and
“Modeling and forecasting hotel room demand based on advance booking information” (“Lee” 12/01/2017, Tourism Management Vol. 66, Pgs. 62-71, https://www.sciencedirect.com/science/article/pii/S0261517717302431).
The Following is an examiner’s statement of reasons for no art rejection:
Vora shows that Hotels overbook in order to maximize profit based on predicted new reservations and cancelations. However, the practice of overbooking creates the risk of “walking” a customer with a reservation, i.e. upon check-in it is determined that the hotel does not have a room for that customer. Vora estimates that hotels overbook by 10 to 15 percent of their room capacity, e.g. a 500 to 700 room hotel is likely to walk five guests on high occupancy days. The probability of being walk depends generally on demand and supply of hotel rooms, with travelers checking in late having a higher probability of being walked. However, Vora does not provide the claimed specifics of how the overbooking limit is determined.
Viale shows determining an overbooking limit to maximize occupancy and generally discusses checking in on the first day of a reservation. The overbooking limit for each day is calculated based on estimated demand and forecasted quantities such as cancellations and no-shows. However, Viale does not address the mechanics of the predicted cancelations, and does not explicitly teach the use of an average length of stay of the forecasted reservations.
Debarge shows a floating inventory system which enables overbooking of hotel rooms. Although the floating inventory enables optimization of the average length of continuous stay at a single resource, i.e. minimizing having to switch rooms, and discusses the use of an overbooking limit, Debarge does not teach the prediction of future reservations and the average length of stay of those reservations.
Pellerin shows utilizing a margin of overbooking in hotel reservations. However, as most of the disclosed embodiments relate to airfare, Pellerin does not teach determining the overbooking limit by predicting average length of stay of future reservations.
Geoghegan shows determining overbooking limit on a per day basis based on machine learning and performing a check-in decision of whether to walk or check-in a reservation. Although Geoghegan shows the use of “stay-thrus” in the predicted capacity used for determining the overbooking limit, Geoghegan does not explicitly teach the use of an “average” stay for the predicted reservations.
Miller shows automatic check in process for overbooked hotel room that includes the possibility of an upgrade.
Simon shows automatic check in process that includes encoding a room key.
Craig shows a check-in process that includes presenting an room upgrade.
Wasserkrug shows a mathematical optimization model based on historical information that utilizes constraints and objective functions. The restrictions create an uncertain mixed integer linear program (MILP), that can be further transformed into a MILP robust counterpart, assuming a polyhedral uncertainty set is selected. However, Wasserkrug is not explicitly tied to hotel room demand forecasting, but used in various domains, including supply chain management, cloud computing operations, healthcare, environmental impact reduction, etc. Thus, the uncertainty set, and other variables, are not tied to a specific parameter, such as a total number of arriving customers to the hotel or length of stay of future reservations.
Prasanna shows inventory management optimization that uses a mixed integer optimization problem, a linear objective function, robust programing, random uncertain variables, and polyhedron probability distribution. However, Prasanna is not explicitly tied to hotel room demand forecasting. Thus, the uncertainty set is not tied to a specific parameter, such as a total number of arriving customers to the hotel.
Zhang shows a robust optimization model for predicting passenger flows (i.e. demand) for high speed trains. The optimization includes solving a mixed integer linear programming problem that uses a polyhedron uncertainty set to model future uncertainty of passenger travel demand. However, Zhang is limited to a train (not hotel) environment, and does not discuss check in decisions or predicted average length of stay.
Jiang shows that revenue management (i.e. revenue optimization) includes capacity control and overbooking. Demand modeling faces uncertainty usually characterized as random variables, and a robust linear counterpart can be created using polyhedral uncertainty sets. However, Jiang does not explicitly discuss what variables are replaces with a uncertainty sets or discuss check-in decisions.
Lai shows use of linear integer optimization to predict demand for overbooking hotel rooms and determining an overbooking limit for each day. However, Lai does not includes polyhedron uncertainty sets or check-in decisions. Additionally, Lai teaches predicting future demand, including length of stay. However, Lai utilizes a more complex stochastic method based on a distribution of probabilities for the possible lengths of stay for the predicted reservation demand, and does not rely on use of an “average” length of stay.
Potlapally shows functioning of a virtual cloud network with local peering gateway and secure shell VCN for general computing. Potlapally does not teach specific calculations or operation, but merely back-end cloud infrastructure.
Cho shows use of a predicted average length of stay for a hotel reservation. However, this parameter is determined as the result of a demand model used to determine a pricing policy, not an overbooking limit.
Fouad shows forecasting future hotel reservations to determine an overbooking limit. However, Fouad utilizes a probability distribution for the length of stay for each of the predicted future reservations, and does not teach the prediction of an “average” length of stay to determine the overbooking limit.
Lee teaches away from using an “average” length of stay of the anticipated reservations in forecasting hotel room demand by stating that best forecasting methods would vary by length of stay.
Generally, the closest prior art teaches (1) overbooking hotel rooms to optimize hotel reservations (Vora, Viale, Debarge, Pellerin, Geoghegan, and Miller), (2) forecasting future reservations (Viale, Geoghegan, Zhang, Jiang, Lai, Cho, Fouad, and Lee), (3) check-in process (Geoghegan, Miller, Simon, and Craig), (4) linear optimization in demand forecasting (Geoghegan, Wasserkrug, Prasanna, Zhang, Jiang, Lai, and Cho), and (5) cloud computing environment (Potlapally).
With respect to independent Claims 1, 9, and 17, the closest prior art, taken individually and in an ordered combination, does not explicitly or implicitly disclose the specific ordered combination of elements that include “automatically determining, by one or more processors executing an optimization system and based on an objective function, an overbooking limit for each category of hotel rooms for the hotel; wherein the automatically determining comprises: using a first trained machine learning (ML) model for each of the categories that predicts a number of future reservations for each of the categories and using a second trained ML model for each of the categories that predicts an average length of stay for the future reservations” (Emphasis added) as claimed in representative Claim 1. The broadest reasonable interpretation of this limitation is limited to the plain meaning of the word “average,” e.g. a sum divided by a quantity. Although colloquially, in a different context, “average” can be used as a synonym to “expected” or “typical,” A person of ordinary skill in the art, in light of the extensive mathematical nature of the instant disclosure, would interpret the claimed “average length of stay” as a mathematical “average,” e.g. a sum divided by a quantity. The art of record does not teach the use of a trained ML model that predicts an “average” length of stay for the predicted future reservations in determining an overbooking limit. Thus, the independent claims are held to be novel and non-obvious over the prior art.
Dependent Claims 2, 4-8, 10, 12-16, 18, and 20-23 depend on independent Claims 1, 9, and 17, and therefore are also not rejected over the prior art via dependency.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW PARKER GOODMAN whose telephone number is (571) 272-5698. The examiner can normally be reached on Monday-Thursday from 9:30 AM ET to 6:00 PM ET. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman, can be reached at telephone number (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW PARKER GOODMAN/Examiner, Art Unit 3628
/DANIEL VETTER/Primary Examiner, Art Unit 3628
