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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/27/2025, 11/05/2025, and 03/06/2026 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 1-20 were rejected in the Non-Final Office action mailed on 08/14/2025. Applicant’s amended claimset, entered on 11/14/2025, amended Claims 1-2, 4-5, 8-10, 12-13, 16-18, and 20, canceled Claims 3, 11, and 19, and added new Claims 21-23. Herein this Final Office Action, Claims 1-2, 4-10, 12-18, and 20-23 are rejected.
Response to Arguments
Applicant’s arguments filed 11/14/2025, with respect to Rejections under 35 U.S.C. 112(b) for Claims 1-2, 4-18, and 20, 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 In light of the entered amendments the rejection under 35 U.S.C. 112(b) has been withdrawn.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 11/14/2025, 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 9-12, Applicant argues that the claims are directed to statutory subject matter. Examiner does not agree, as discussed in greater detail below.
On Pages 10-11, Applicant argues that the claims reflect an improvement in the technical field of machine learning. Applicant further argues “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 of the ML models, is clearly an improvement of ML model technology. Further, the claims recite a technical solution (i.e., a unique arrangement of ML models). 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.” 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. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. . . [I]f the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
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.”
MPEP 2106.04(d)(1) states “While the courts usually evaluate "improvements" as part of the "directed to" inquiry in part one of the Alice/Mayo test (equivalent to Step 2A), they have also performed this evaluation in part two of the Alice/Mayo test (equivalent to Step 2B). See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349-50, 119 USPQ2d 1236, 1241-42 (Fed. Cir. 2016). However, the improvement analysis at Step 2A Prong Two differs in some respects from the improvements analysis at Step 2B. Specifically, the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity. That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. It should be noted that while this consideration is often referred to in an abbreviated manner as the "improvements consideration," the word "improvements" in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” (Emphasis added).
MPEP 2106.05.I.A states “Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include: . . . v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d)); . . .”
MPEP 2106.05(d)I states “2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). . .As such, an examiner should determine that an element (or combination of elements) is well-understood, routine, conventional activity only when the examiner can readily conclude, based on their expertise in the art, that the element is widely prevalent or in common use in the relevant industry. The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 ( Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph); . . .”
Examiner responds that Applicant’s assertion of improvement in machine learning (i.e. improved accuracy) is conclusory. The specification fails to provide the necessary technical explanation as to how Applicant’s disclosure provides an improvement to the technology (i.e. machine learning) itself beyond an improvement to an abstract idea.
Applicant’s specification notes that “cancellation probability is always estimated with some limited accuracy” in Paragraph 15. The only discussion in the disclosure related to improved accuracy is in Paragraph 54 (“For all trained predictive ML models disclosed above, one more of the models will be retrained in response to the passing of time when it is known how accurate the predictions were, such as the amount of new reservations received, the amount of reservations that are canceled in advance, etc.”), which merely alludes that retraining the model with known results could improve accuracy of the model. Instead, “embodiments account for the limited accuracy of the estimation by implementing a "robust" optimization approach to guarantee the optimal solution for the worst-case scenario.” (Emphasis added). Thus, Applicant addresses inaccuracy in predicting customer behavior by using a model geared towards “the worst-caste scenario,” i.e. hedging, which is not an improvement in machine learning itself.
As disclosed in Specification ¶16 and ¶¶134-36, the “two stages” represents two stages of business decisions related to making a reservation. First, determine whether to accept the reservation based on the model, then determine whether to check-in/upgrade based on the model. Although machine learning is used to develop the model, such multi-stage use, or use of multiple models, does not represent an improvement in machine learning, but merely the use of machine learning, in its ordinary capacity, to apply the abstract idea. Thus, the determination of “better,” e.g. more profitable, overbooking limits is an improvement in the abstract idea itself, and therefore the rejection remains.
On Pages 11-12, Applicant argues that the claims recite the practical application of automatically encoding a hotel room key. Applicant argues that the claims are analogous to PEG Example 45 Claim 2 and PEG Example 46 Claim 2. Applicant argues that the signals in the PEG Examples are analogous to the automatic encoding of a hotel room key.
PEG Example 45 Claim 2 Step 2A Prong Two states: “Limitation (d) does not merely link the judicial exceptions to a technical field, but instead adds a meaningful limitation in that it employs the information provided by the judicial exceptions (the calculated percentage of the extent of cure) to control the operation of the injection molding apparatus. As explained in the specification, because the claimed controller opens the mold and ejects the molded polyurethane at the time when the target percentage of cure is reached, the claimed controller avoids the technical problems associated with undercure and overcure, which would otherwise negatively affect the cured polyurethane’s strength and wear performance. Further, a person of ordinary skill in the art would recognize that limitation (d), in combination with the other claim limitations, reflects the technical advantages described in the specification. The claim as a whole thus improves upon previous controllers used in this technical field of injection molding. . . Practice note: As illustrated in the analysis of claim 2, the “improvements” consideration requires evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. Examiners are not expected to make a qualitative judgment on the merits of the asserted improvement except when a person of ordinary skill in the art, consulting the claims and specification, would clearly understand the invention does not improve technology as applicant asserts.” (Emphasis added).
PEG Example 46 Claim 2 Step 2A Prong Two states: “Thus, limitation (d) does not merely link the judicial exceptions to a technical field, but instead adds a meaningful limitation in that it can employ the information provided by the judicial exception (the mental analysis of whether the animal is exhibiting an aberrant behavioral pattern indicative of grass tetany) to operate the feed dispenser. As explained in the specification, automatically identifying aberrant behavioral patterns and operating farm equipment based on such identification avoids the need for the farmer to evaluate the behavior of each animal in the herd on a continual basis, and then manually take appropriate action for each animal exhibiting aberrant behaviors. Limitation (d) in combination with the feed dispenser enables the control of appropriate farm equipment based on the automatic detection of grass tetany, which goes beyond merely automating the abstract idea. Using the information obtained via the judicial exception to take corrective action such that the monitoring component is operable to control the feed dispenser in a particular way is an “other meaningful limitation” that integrates the judicial exception into the overall livestock management scheme and accordingly practically applies the exception, such that the claim is not directed to the judicial exception (Step 2A: NO).” (Emphasis added).
Examiner responds that the PEG Examples provide a technical improvement based on the required technical explanation in the specification. Applicant’s specification does not include such required technical explanation showing that encoding a hotel key is a technical improvement. Instead, the specification (¶4, ¶20, and ¶133) shows that permitting a customer access to a room during the check-in process improves the profit of the business by making better business decisions, which is an improvement in the abstract idea itself. This is distinguishable from the improvement in material manufacturing in PEG Example 45 Claim 2 and the automation of animal feeding based on automated observation of animal health in PEG Example 46 Claim 2. Therefore, the rejection remains.
On Pages 11-12, Applicant argues that “Newly added claims 21-23 add additional elements regarding a specific cloud infrastructure used to implement embodiments of the invention. These additional elements are not conventional elements, and consistent with Berkheimer v. HP Inc., 881 F.3d 1360, 1369 (Fed. Cir. 2018), should be considered another reason that the claims are subject matter eligible. For at least the foregoing reasons, the claims should be allowable over 35 U.S.C. §101.” Examiner does not agree.
MPEP 2106.05(b)II states “Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more.” (Emphasis added).
MPEP2106.05(h) states “Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.” (Emphasis added).
Examiner responds that the cloud infrastructure limitations are nearly unrelated to the rest of the claim limitations, e.g. the Claim 21 limitation of merely “using” cloud infrastructure. These further limitations merely limit the abstract idea to a field of use per MPEP 2106.05(h), and therefore do not provide significantly more. Thus, the rejections remain.
Applicant’s arguments filed 11/14/2025, with respect to Rejections under 35 U.S.C. 103 for Claims 1-2, 4-10, 12-18, and 20-23, have been fully considered and are persuasive.
On Pages 13-14, Applicant argues that the cited prior art does not teach the functionality of predicting future reservations and an average length of stay for every day from current day to the future hotel check in date. As discussed below, Examiner agrees.
Claim Interpretation
Claim 1 recites “A method of optimizing hotel room reservations for a hotel, the method comprising: [steps].” Dependent Claim 2 recites “The method of claim 1, the optimizing comprising [additional steps].” Dependent Claim 4 recites “The method of claim 1, the optimization further comprising [additional steps].” Dependent Claim 21 recites “The method of claim 1, wherein the optimizing hotel room reservations comprises using a cloud infrastructure . . .” The references to “the optimization” in the dependent claims have sufficient antecedent basis, and are interpreted as referring to the “method of optimization.”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 18 and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 18 recites the limitation "The system of claim 17, the optimizing comprising 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.” (Emphasis added). There is insufficient antecedent basis for this limitation in the claim. Depended upon Claim 17 recites “A cloud based hotel reservation system that optimizes hotel room reservations for a hotel, the system comprising: one or more processors adapted to: [perform operations].” (Emphasis added). For Examination purposes herein, Claim 18 will be interpreted as reciting “The system of claim 17, the one or more processors further adapted to generate 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.” (Emphasis added).
Claim 23 recites the limitation "The system of claim 17, wherein the optimizing hotel room reservations comprises using a cloud infrastructure comprising: [components].” (Emphasis added). There is insufficient antecedent basis for this limitation in the claim. Depended upon Claim 17 recites “A cloud based hotel reservation system that optimizes hotel room reservations for a hotel, the system comprising: one or more processors adapted to: [perform operations].” (Emphasis added). For Examination purposes herein, Claim 23 will be interpreted as reciting “The system of claim 17, wherein the system reservations further comprises a cloud infrastructure comprising: [components].” (Emphasis added).
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 optimizing hotel room reservations for a hotel, the method comprising:
for a future hotel room check in day, automatically determining, 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,
the automatically determining 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, wherein the overbooking limit is determined separately for each day from a current day to the future hotel check in day;
receiving a first reservation request for a first day for a first category room;
when the determined overbooking limit for the first day and the first category room has not been reached, accepting the first reservation request;
when the accepted first reservation request is being checked in to the hotel on the first day, automatically determining a check in decision, based on the objective function, 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) receiving a reservation request, and (3) upon check-in, using the objective function 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. reserving and checking in based on certain criteria 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 “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) “first trained machine learning (ML) model” and
(ii) “a second trained ML model.”
The additional elements of (i) first and (ii) 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) first and (ii) 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 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) first and (ii) 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.
Dependent Claims 2, 4-8, and 21 recite the 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.” (Claim 2)
“. . . generating a prediction of cancellations of existing reservations Claim 4)
“. . . transforming the two-stage optimization problem, the transforming comprising: generating a mixed integer optimization problem (MILP) that models the overbooking limit and models the check in decision; and converting the MILP into a linear optimization problem using a polyhedron uncertainty set for a total number of arriving customers to the hotel.” (Claim 5)
“. . . wherein the MILP comprises a plurality of random variables, the polyhedron uncertainty set eliminating the random variables.” (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 corresponding specialized data and transmitting the specialized data; in response to receiving the specialized data, . . .” (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 2-8 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. 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.
Independent Claim 9 recites the abstract idea of “. . . to optimize hotel room reservations for a hotel, the optimizing comprising:
for a future hotel room check in day, automatically determining, 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, the automatically determining 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, wherein the overbooking limit is determined separately for each day from a current day to the future hotel check in day;
receiving a first reservation request for a first day for a first category room;
when the determined overbooking limit for the first day and the first category room has not been reached, accepting the first reservation request;
when the accepted first reservation request is being checked in to the hotel on the first day, automatically determining a check in decision, based on the objective function, 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) receiving a reservation request, and (3) upon check-in, using the objective function 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. reserving and checking in based on certain criteria 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 “computer readable medium,” “one or more processors,” and the “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) “computer readable medium,”
(ii) “one or more processors,”
(iii) “first trained machine learning (ML) model,” and
(iv) “a second trained ML model.”
The additional elements of (i) computer readable medium (Fig. 2 and ¶28 shows “Memory 14 can be comprised of any combination of random access memory ("RAM"), read only memory ("ROM"), static storage such as a magnetic or optical disk, or any other type of computer readable media.” ¶29 shows “Computer readable media may be any available media that can be accessed by processor 22.”), (ii) processors (Fig. 2 and ¶28 shows “Processor 22 may be any type of general or specific purpose processor.”), and (iii) first and (iv) 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 show 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) computer readable medium, (ii) processors, and (iii) first and (iv) second trained machine learning model, when viewed as whole/ordered combination (Fig. 2, ¶¶28-29, and ¶¶49-54 show 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 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 show 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) computer readable medium, (ii) processors, and (iii) first and (iv) 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 show elements in combination.), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claim is ineligible.
Dependent Claims 10, 12-16, and 22 recite the 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.” (Claim 10)
“. . . generating a prediction of cancellations of existing reservations .” (Claim 12)
“. . . transforming the two-stage optimization problem, the transforming comprising: generating a mixed integer optimization problem (MILP) that models the overbooking limit and models the check in decision; and converting the MILP into a linear optimization problem using a polyhedron uncertainty set for a total number of arriving customers to the hotel.” (Claim 13)
“. . . wherein the MILP comprises a plurality of random variables, the polyhedron uncertainty set eliminating the random variables.” (Claim 14)
“. . . 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 15)
“. . . in response to the check in decision, generating corresponding specialized data and transmitting the specialized data . . .” (Claim 16)
Dependent Claims 10, 12-16, and 22, 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 10, 12-16, and 22 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. The further elements of Claims 10, 12-16, and 22 (i.e. “encoding a hotel room key” of Claim 16 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 22) 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 9 or generally link the abstract idea to a particular technology or field of use (i.e. the hotel computer system) just as in Claim 9. The organization of the further limitations of Claims 10, 12-16, and 22 fail to integrate an abstract idea into a practical application just as discussed above for Claim 9. Additionally, performing the abstract idea of Claim 9 as recited in each of the further limitations of Claims 10, 12-16, and 22, 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 9. Therefore, Claims 10, 12-16, and 22 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 10, 12-16, and 22 fail to establish that the claims provide an inventive concept, just as in Claim 9. Therefore, Claims 10, 12-16, and 22 fails the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
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, based on an objective function, an overbooking limit for each category of hotel rooms for the hotel, . . . the automatically determining 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). 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
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).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is as follows:
“The Evolution of Hotel Keys” (“Openkey” 12/21/2018 Openkey Press & Blog, https://www.openkey.co/2018/12/21/the-evolution-of-hotel-keys-2/) shows that most US hotels have magstripe door locks, which were developed in the 1980’s.
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
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
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