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 .
Status of the Claims
Claims 1, 4, 6, 8-10, 13, 15, 17-19, and 21-29 are pending. Claims 1, 9-10, 18-19 and 27 are amended. Claims 2-3, 5, 7, 11-12, 14, 16 and 20 are cancelled. Claims 28-29 are new.
Response to Arguments
Applicant’s arguments, filed 02/28/2026, with respect to the 101 rejection have been considered but are not persuasive.
Applicant argues, on pages 12-13, that the claims recite technical details to the alleged abstract ideas of "organizing human activity" and "mathematical concepts" that solve technical problems with the prior art and therefore should be considered a practical application. Specifically, the technical details include specific types of machine learning models (first and second) uniquely suited to determine an overbooking limit using "individual reservations" and "group reservations".
Examiner respectfully disagrees. The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as determining an overbooking limit based on predicted cancellations. (see specification, Par. 0002-0005). The additional elements (ML models, See specification par. 0060-0062 and 0097) are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.
Applicant argues, on pages 13-14, that similar to Desjardin, the present claims recite the use of two different ML models. Applicant argues that improving the accuracy of the overbooking limit predictions of a reservation system, via improved training of ML models, and the use of different ML models for individual reservations and group reservations, is clearly an improvement of ML model technology.
Examiner respectfully disagrees. Desjardin was considered eligible because it provided an improvement to the training of the machine learning model itself. In contrast, here the alleged improvement is to the accuracy of the overbooking limit predictions and not the technology or technical field of machine learning.
Applicant argues, on pages 15-16, that the claims are eligible because they are similar to example 39 instead of example 47.
Examiner respectfully disagrees. Example 39 was considered eligible because the claim did not recite an abstract idea because it was collecting digital images and applying transformations to each image and the neural network for identifying human faces in digital images. In contrast, as mentioned above, the claims do recite a judicial exception.
Applicant argues, on page 16, that the claims recite a technical solution and a technical improvement on known overbooking limit determination systems, and technically novel arrangement of trained models that cannot be considered "conventional" and known technology. Applicant argues that the claims reflect an improvement in the technical field of machine learning.
Examiner respectfully disagrees. As mentioned above, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. Furthermore, 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. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Here, the alleged improvement to overbooking limit determination systems or the accuracy of predictions is to the sales activity of booking and not a technology or technical field.
Applicant argues, on pages 17, that the claims are similar to the claims of the PTAB decision of Ex Parte Jere Armas.
Examiner respectfully disagrees. The claims were considered eligible because the claims were not directed to an abstract idea. Here, as mentioned above, the claims are directed to certain method of organizing human activity. The additional elements of the machine learning models when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use.
Applicant argues, on pages 17-18, that the encoding of a hotel room key integrates the judicial exception into a practical application because it is similar to eligible examples of automatically sending signals.
Examiner respectfully disagrees. At this level of breadth, this is also considered part of the abstract idea of certain methods of organizing human activity as it is part of the hotel booking process. Furthermore, Examiner notes that the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. (See MPEP 2106.04(a)(2))
Applicant argues, on page 19, that the additional elements of new claims 21-25 are not conventional elements and therefore the claims should be considered eligible.
Examiner respectfully disagrees. The cloud computer elements recited in the claims are recited at a high level of generality (See specification, par. 0111-0112) and therefore, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. Additionally, the additional elements, when considered separately and in combination, do not add significantly more to the exception. Therefore, the claims are considered ineligible.
Novelty/Non-obviousness
The closest prior art of record was included in the action mailed on 01/16/2025. The claims would be considered allowable if re-written or amended to overcome the rejections in this office action.
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, 4, 6, 8-10, 13, 15, 17-19, and 21-29 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, 4, 6, 8-9, 21-22 are directed to a series of steps, and therefore is a process.
Claims 10, 13, 15, 17-18, 23-24 are directed to a non-transitory computer readable media and therefore are an article of manufacture.
Claims 19 and 25-29 are directed to a system with multiple components, and therefore is a machine
Independent Claims
Step 2A Prong One
The limitation of Claim 1 recites:
A method of optimizing hotel room reservations for hotel rooms of a hotel, the method comprising:
receiving pending hotel room reservations, the pending hotel room reservations comprising individual reservations and group reservations;
…
…, predicting a first cancellation probability for each of the individual reservations, …;
…
…, predicting a second cancellation probability for each of the group reservations, …;
based on the first cancellation probabilities and the second cancellation probabilities, building a probability distribution for the pending hotel room reservations;
based on an occupancy forecast for the hotel and the probability distribution, determining overbooking limits for each of one or more categories of the hotel rooms;
based on the overbooking limits, accepting additional reservations for each of the categories up to the overbooking limits; and
based to an outcome of the additional reservations, retraining the first ML model and the second ML model, wherein the outcome comprises, for each additional reservation, a cancellation or a check-in.
The limitation of Claim 10 recites:
receiving pending hotel room reservations, the pending hotel room reservations comprising individual reservations and group reservations;
…
…, predicting a first cancellation probability for each of the individual reservations, …;
…
…, predicting a second cancellation probability for each of the group reservations,…;
based on the first cancellation probabilities and the second cancellation probabilities, building a probability distribution for the pending hotel room reservations;
based on an occupancy forecast for the hotel and the probability distribution, determining overbooking limits for each of one or more categories of the hotel rooms;
based on the overbooking limits, accepting additional reservations for each of the categories up to the overbooking limits; and
based to an outcome of the additional reservations, retraining the first ML model and the second ML model, wherein the outcome comprises, for each additional reservation, a cancellation or a check-in.
The limitation of Claim 19 recites:
receive pending hotel room reservations, the pending hotel room reservations comprising individual reservations and group reservations;
…
…, predicting a first cancellation probability for each of the individual reservations, …;
…
…, predicting a second cancellation probability for each of the group reservations, …;
based on the first cancellation probabilities and the second cancellation probabilities, build a probability distribution for the pending hotel room reservations;
based on an occupancy forecast for the hotel and the probability distribution, determine overbooking limits for each of one or more categories of the hotel rooms;
based on the overbooking limits, accept additional reservations for each of the categories up to the overbooking limits; and
based to an outcome of the additional reservations, retrain the first ML model and the second ML model, wherein the outcome comprises, for each additional reservation, a cancellation or a check-in.
The claim limitations as drafted, recite a concept, that, under broadest reasonable interpretation, is a certain method of organizing human activity. The limitations are analogous to managing personal behavior or interactions between people (interactions between people), or a commercial or legal interaction (sales activity) such as determining an overbooking limit based on predicted cancellations. The generic computer implementations (see below) do not change the character of the limitations. Accordingly, the claims recite an abstract idea.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
Claim 1:
training a first machine learning (ML) model for the individual reservations, the training the first ML model comprising generating a first training sample by replicating each existing individual reservation for each number of days before a check-in in a prediction horizon range;
using the first trained machine learning (ML) model… wherein the first trained ML model comprises a classification decision tree ensemble, wherein each leaf of a decision tree in the ensemble predicts whether an individual reservation will be canceled;
training a second ML model for the individual reservations, the training the second ML model comprising generating a second training sample by replicating each existing group reservation for each number of days before the check-in in the prediction horizon range;
using the second trained ML model… wherein the second trained ML model comprises a regression tree ensemble, wherein each leaf of a decision tree in the ensemble predicts a number of reservations for each group reservation that will be canceled;
Claim 10:
A non-transitory computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to optimize hotel room reservations for hotel rooms of a hotel, the optimizing comprising:
training a first machine learning (ML) model for the individual reservations, the training the first ML model comprising generating a first training sample by replicating each existing individual reservation for each number of days before a check-in in a prediction horizon range;
using the first trained machine learning (ML) model… wherein the first trained ML model comprises a classification decision tree ensemble, wherein each leaf of a decision tree in the ensemble predicts whether an individual reservation will be canceled;
training a second ML model for the individual reservations, the training the second ML model comprising generating a second training sample by replicating each existing group reservation for each number of days before the check-in in the prediction horizon range;
using the second trained ML model… wherein the second trained ML model comprises a regression tree ensemble, wherein each leaf of a decision tree in the ensemble predicts a number of reservations for each group reservation that will be canceled;
Claim 19:
A cloud based hotel reservation system that optimizes hotel room reservations for hotel rooms of a hotel, the system comprising:
one or more processors adapted to:
train a first machine learning (ML) model for the individual reservations, the training the first ML model comprising generating a first training sample by replicating each existing individual reservation for each number of days before a check-in in a prediction horizon range;
using the first trained machine learning (ML) model… wherein the first trained ML model comprises a classification decision tree ensemble, wherein each leaf of a decision tree in the ensemble predicts whether an individual reservation will be canceled;
training a second ML model for the individual reservations, the training the second ML model comprising generating a second training sample by replicating each existing group reservation for each number of days before the check-in in the prediction horizon range;
using the second trained ML model… wherein the first trained ML model comprises a classification decision tree ensemble, wherein each leaf of a decision tree in the ensemble predicts whether and individual reservation will be canceled;
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h))
Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
Dependent Claims
Dependent claims 4, 6, 8-9, 13, 15, 17-18, and 21-29 further narrow the same abstract ideas recited in Claims 1, 10 and 19, respectively. Therefore, claims 4, 6, 8-9, 13, 15, 17-18, and 21-29 are directed to an abstract idea for the reasons given above.
Step 2A Prong Two
The judicial exception is not integrated into a practical application. In particular, the dependent claims recite the following additional elements:
Claim 9:
N-window summary statistics model
Second model
Third model
Claim 18:
N-window summary statistics model
Second model
Third model
Claim 21:
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.
Claim 22:
the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN
Claim 23:
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.
Claim 24:
the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN.
Claim 25:
the 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.
Claim 27
N-window summary statistics model
Second model
Third model
These additional elements are recited at a high-level of generality such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Accordingly, the additional elements, when viewed individually and in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Therefore, the claims recite an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B. The additional elements, when considered separately and in combination, do not add significantly more to the exception. They are generally linking the use of a judicial exception to a particular technological environment or field of use and cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims are ineligible.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISMAIL A MANEJWALA whose telephone number is (571)272-8904. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISMAIL A MANEJWALA/Primary Examiner, Art Unit 3628
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