DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Status of the Claims
Claims 1-20 were previously pending and subject to a final office action mailed 12/08/2025. Claims 1, 15, 18 and 20 were amended; no claim was cancelled, or added in a reply filed 02/06/2026 that was not entered. Claims 1 and 20 were further amended in a reply filed 02/24/2026. Therefore claims 1-20 are currently pending and subject to the non-final office action below.
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 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/24/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/21/2026 and 02/25/2026 were considered by the examiner.
Response to Arguments
Applicant's arguments filed 02/24/2026 in regards to 101 rejection have been fully considered but they are not persuasive.
Applicant argues “The Examiner rejects claims 1-20 under 35 U.S.C. § 101 as allegedly being directed to a judicial exception without significantly more. Applicant respectfully disagrees, but to expedite prosecution, Applicant amends certain claims. Applicant amends the independent claims to further clarify how the tuning is integrated with the other elements of the claimed invention. Applicant clarifies that the expectation maximization algorithm triggers the tuning of the files to allow real time improvement to the data with less bottlenecks. As stated in paragraph 0048, the claimed system includes "triggering downstream systems and processes." Moreover, as stated in paragraph 0077, "Unobscuring module 146 and/or method 200 may be utilized on any suitable time schedule, as desired. For example, unobscuring module 146 may be operable on a weekly basis, on a daily basis, on an hourly basis, on a "triggered" basis (for example, each time a trigger event occurs, such as a booking or series of bookings), after collection of data associated with a DCP, and/or the like." Similarly, paragraph 0105 states "Unconstraining module 147 and/or method 300 may be utilized on any suitable time schedule, as desired. For example, unconstraining module 147 may be operable on a weekly basis, on a daily basis, on an hourly basis, on a "triggered" basis (for example, each time a trigger event occurs, such as a booking or series of bookings), after collection of data associated with a DCP, and/or the like. In various embodiments, unconstraining module 147 is operable subsequent to operation of unobscuring module 146." Paragraph 0145 states "PCR module 149 and/or method 500 may be utilized on any suitable time schedule, as desired. For example, PCR module 149 may be operable after collection of data associated with a DCP, on a weekly basis, on a daily basis, on an hourly basis, on a "triggered" basis (for example, each time a trigger event occurs, such as a prime class booking), and/or the like ."” (remarks p. 7).
Examiner respectfully notes that the amendment does not change the basic character of the claims. The claims still recite organizing booking information, determining unobscured demand using an expectation maximization algorithm in iterative fashion, identifying price-oriented bookings, performing proportional and loss related calculations, and generating booking instructions. These limitations remain directed to mathematical concepts and commercial activity.
Furthermore, although the amendment adds language regarding tuning database files and reducing bottlenecks, the claim does not recite a specific technological improvement to database functionality itself. Instead, the tuning language is appended to the abstract demand estimation and booking decision process. The specification does mention generic database tuning and reducing bottlenecks, but it does not describe the expectation maximization algorithm itself as improving database architecture in specific technical manner.
Accordingly, the amendment does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea.
First, the cited “triggering downstream system and process” language along with “initiating other software modules” in paragraph 48 describes conventional functional interaction among software modules in a generic computerized environment. It does not disclose a specific technological mechanism by which the expectation maximization algorithm improves computer technology or database functionality. Generic triggering of downstream software processes is not sufficient to integrate the abstract idea into a practical application.
Second, the cited disclosure in paragraph 77 shows only that unobscuring may be run on various schedules, including a triggered basis. Paragraph 77 states that unobscuring module 146 may operate weekly, daily, hourly, or when triggered by a booking or series of bookings. This passage concerns when the module may run. It does not disclose that the expectation maximization algorithm itself causes a specific technical improvement in database operation, indexing, or filesystem design. Event driven or scheduled invocation of analytics remains a conventional computer implementation feature and does not convert the underlying mathematical and booking analysis limitations into patent eligible subject matter.
Similarly paragraph 105 discloses only that unconstraining may be performed on a suitable schedule, including after a trigger event, and may be run subsequent to unobscuring. This disclosure again related to the timing and sequencing of module operation. It does not disclose a particular technological improvement to database performance, processor functionality or another technical field. the mere fact that one analytics module may run after another does not integrate the abstract idea into a practical application.
Applicant argues that Paragraph 145 states that PCR module 149 may be utilized on a suitable time schedule, including on a triggered basis such as when a prime class booking occurs. As with the unobscuring and unconstraining modules, this passage concerns only module scheduling and invocation. It does not disclose a specific technical solution to a computer centric problem. Triggered execution of a fare related business analysis module remains conventional automation of abstract analytical activity.
Applicant argues “The recent USPTO comments and guidance have recalibrated how Examiners review software, modeling and algorithm related inventions. The USPTO emphasizes that when claim elements encompass software, modeling and algorithms in a way that cannot be practically performed in the human mind, then the claim falls outside of the mental process grouping. In particular, the claimed invention involves significant data volumes, processing speed and algorithmic complexity with computer executed operations and technical aspects at nonhuman implementation and scale.” (remarks p. 8).
Examiner respectfully disagrees. Even assuming that certain claimed operations are impractical to perform mentally at the claimed scale, the claims still expressly recite an expectation maximization algorithm, iterative statistical estimation, and loss based calculations. These are mathematical concepts. In addition, the claims are directed to airline booking, fare classes, price oriented customers, and booking instructions, which are commercial concepts. Thus, the claims remain directed to abstract ideas even if Applicant contends that some steps are not mentally performable in practice.
Furthermore, implementation at larger scale, greater speed, or with more data does not by itself establish eligibility. The claims must recite a specific technological improvement, not merely automation of abstract analysis on a processor and database. The specification frames the invention as forecasting, yield management, inventory control, and revenue management.
Applicant argues “Applicant asserts that at least the following steps cannot be practically performed in the human mind, and fall outside of the mental process grouping, namely, "triggering, by the processor using the expectation maximization algorithm in the iterative fashion and based on the booking information, tuning of the database to optimize performance of the database, wherein the tuning includes placing frequently used of the bookings table files as indexes on separate file systems to reduce in and out bottlenecks;" and "repeating, by the processor and responsive to the triggering based on the booking information, at discrete intervals and in real-time, the determining for each parent fare class of a plurality of fare classes until the unobscured demand for a highest fare class is obtained, thereby reducing forecasting errors," as recited by independent claim 1.” (remarks p. 8).
Examiner respectfully disagrees. The recited limitations do not remove the claim from the abstract idea analysis. The claim still recites use of an expectation maximization algorithm to estimate unobscured demand across fare classes, repeated iteratively, using booking information. That is mathematical and forecasting related analysis.
The database tuning limitation does not recite a particular inventive database technology. The specification does disclose generic database tuning and placement of indexes on separate file systems to reduce I/O bottlenecks. However, the specification does not disclose that the expectation maximization algorithm itself technically improves database functionality, nor does it describe a specific technical mechanism by which the EM processing triggers or controls a specialized indexing arrangement. Instead, the cited tuning disclosure appears in the generic database discussion. Thus, the limitation amounts to use of conventional database organization in connection with the abstract forecasting process, rather than a specific technological improvement. Similarly, performing the determinations “at discrete intervals and in real time” merely recites timing of execution. The specification presents such operation as optional scheduling of the modules. Routine timing or automation of abstract analysis does not render the claims patent eligible.
Applicant argues “Applicant also asserts that at least the following steps cannot be practically performed in the human mind, and fall outside of the mental process grouping, namely, "triggering, by the processor using the unconstraining algorithm in the iterative fashion and based on the unconstrained demand information, tuning of the database to optimize performance of the database, wherein the tuning includes placing frequently used of the bookings table files as indexes on separate file systems to reduce in and out bottlenecks;" and "identifying, by the processor responsive to the triggering and based on the unconstrained demand information, any of the seat bookings in the bookings table file that are associated with a price oriented customer to determine the proportion of the seat bookings that are price oriented." (remarks p. 8-9).
Examiner respectfully disagrees. These limitations remain directed to abstract analysis. They recite use of unconstrained demand information, iterative algorithmic processing, identification of price-oriented bookings, and determination of a proportion of bookings that are price oriented. Those are mathematical and commercial determinations. The added database tuning language does not transform those determination into a technological improvement. As noted above, the specification discloses generic database tuning practices, but not a specific technical solution in which the unconstraining algorithm improves database technology itself. Therefore, these limitations do not integrate the abstract idea into a practical application.
Applicant argues “The claimed invention also provides improvements to "other technologies" (e.g., generating booking instructions, optimizing performance of a database, etc.) such that the improvements provide the practical application under Step 2A, Prong 2. As stated in the USPTO Guidelines, in Step 2A, Prong Two, Examiners "should ensure that they give weight to all additional elements, whether or not they are conventional, when evaluating whether a judicial exception has been integrated into a practical application." Additionally, the claimed invention recites specific steps and data flows that improve the functionality of the systems, so Applicant respectfully asserts that the claimed invention cannot be oversimplified into the "apply it" rationale.” (remarks p. 9).
Examiner respectfully disagrees. “generating booking instructions” is not an improvement to another technology. It is the business output of the fare analysis process. The specification describes the invention as enabling airlines to improve decisions about booking, opening/closing of fare classes and revenue outcomes. That is a business objective and not a technological one. Similarly, “optimizing performance of a database” is recited at a high level and supported only by generic database tuning disclosure. The claim does not recite a specific database structure, indexing method, file system algorithm or other concrete technological solutions. Thus, the claim does not improve another technical field in the sense required for eligibility.
All additional elements have also been considered, including the processor, the database, booking table files, tuning, indexes, separate file systems, triggered operation, real time operation and booking instructions. Even when considered together, these elements do not meaningfully limit the judicial exception. Rather, they amount to generic computer and database implementation of the underlying mathematical and commercial analysis.
The recited specific steps and data flows still amount to receiving booking data, storing it, performing iterative statistical estimation, identifying particular categories of bookings, performing loss calculations, and generating booking instructions. Those steps are directed to abstract analytical processing in the revenue management context and the additional elements, alone or in combination, do not integrate the abstract idea nor provide significantly more.
Applicant’s arguments, see remarks p.9-11, filed 02/24/2026, with respect to 103 rejection have been fully considered and are persuasive. The 103 rejection of claims 1-20 has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1/20 recites “triggering, by the processor using the expectation maximization algorithm in the iterative fashion and based on the booking information, tuning of the database to optimize performance of the database, wherein the tuning includes placing frequently used of the bookings table files as indexes on separate file systems to reduce in and out bottlenecks; repeating, by the processor and responsive to the triggering and based on the booking information, at discrete intervals and in real- time, the determining for each parent fare class of a plurality of fare classes until the unobscured demand for a highest fare class is obtained, thereby reducing forecasting errors.” The underlined limitations are new matter because Examiner is unable to find support for it in the specification. The closest support Examiner could find are paragraph 78-83 which describe converting booking information into top-down format, applying an EM algorithm in an iterative fashion to calculate unobscured demand for the current class and repeating the process for parent classes until the highest fare class is reached. However, these paragraphs do not describe how the EM algorithm triggers the tuning of the databases or how the determining of each parent fare class is done in response to the triggering. Paragraph 60 describes generic database tuning such as placing indexes on separate file system to reduce I/O bottlenecks but it also does not disclose how the EM triggers such tuning of the database. As such, the limitation is new matter.
The independent claims 2-19 are also rejected for failing to cure the deficiency above.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “converting the booking information for a flight into a top-down format, storing the bookings table files containing the booking information as part of data tables; determining, by the processor in communication with the unobscuring system and in an iterative fashion, the unobscured demand for a current fare class using an expectation maximization algorithm and the booking information from the bookings table file, in response to the current fare class being obscured; repeating, and responsive to the triggering and based on the booking information, at discrete intervals and in real- time, the determining for each parent fare class of a plurality of fare classes until the unobscured demand for a highest fare class is obtained, thereby reducing forecasting errors; identifying any seat bookings in the bookings table files that have been modified as a result of an unobscuring algorithm based on the unobscured demand for the highest fare class; identifying any of the seat bookings in the bookings table file that are associated with a price oriented customer to determine the proportion of the seat bookings that are price oriented; multiplying the proportion of the seat bookings that are price oriented by a loss associated with a seat booking in a closed class; and generating booking instructions based on the unobscured demand for a highest fare class.”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method to determine a demand for a fare class which is a mathematical concepts and certain method of organizing human activity. That is, the method allows for a fundamental economic and business practice and mathematical formula.
This judicial exception is not integrated into a practical application. In particular, the claim recites a processor, database, “triggering, by the processor using the expectation maximization algorithm in the iterative fashion and based on the booking information, tuning of the database to optimize performance of the database, wherein the tuning includes placing frequently used of the bookings table files as indexes on separate file systems to reduce in and out bottlenecks”, “unobscuring system”. These limitations are recited at a high level of generality and amounts to apply it instructions. The “triggering” functions triggers a generic database tuning process and does not define a bottleneck condition, a specific indexing or file placement rule, or a specific reconfiguration of storage layout. Accordingly, these additional elements, alone or 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 claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are nothing more than mere instructions to apply the exception on a general computer.
Dependent claims 2-10 and 13-14, 16-19 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claims 11-12 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (the unconstraining module is recited at a high level of generality which amounts to simple instructions of applying the abstract idea into a computer environment) or providing significantly more limitations.
Claim 15 is also directed to an abstract idea without significantly more because it narrows the abstract idea described in relation to claim 13 without successfully integrating the exception into a practical application. In particular, the claim recites as additional elements “storing, by a processor and in a database, a booking table files containing booking information as part of data tables in the database; creating, by the processor, a linked series of data fields to form a data structure that contains the booking table files; designating, by the processor, a key field in the booking table files to speed searching; and sorting, by the processor, records in the booking table files in a known order to simplify lookup.”. these steps are recited at a high level of generality and amounts to insignificant extra solution activity. Accordingly, these additional elements, alone or 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 claim is directed to an abstract idea.
In addition, the specification of the application as filed (paragraph 60) does not provide any indication that the additional elements described above are anything other than generic, off the shelf computer components. Accordingly, a conclusion that the storing, creating and tuning steps are well-understood, routine and conventional activities are supported under Berkheimer.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “receiving, by a processor in communication with an unobscuring system, an unobscured booking table in bookings table files comprising an unobscured demand for a flight; storing the bookings table files containing the unobscured booking table as part of data tables in a database; identifying constrained fare classes; converting the unobscured demand for each of the constrained fare classes to unconstrained demand information based on statistical information about a cohort of flights containing the flight; identifying, in real time and in iterative fashion, any seat bookings in the bookings table files that have been modified as a result of an unconstraining algorithm based on the unconstrained demand information for the highest fare class; identifying, responsive to the triggering and based on the unconstrained demand information, any of the seat bookings in the bookings table file that are associated with a price oriented customer to determine the proportion of the seat bookings that are price oriented; multiplying the proportion of the seat bookings that are price oriented by a loss associated with a seat booking in a closed class; and generating booking instructions based on the unconstrained demand information.”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method to determine a demand for a fare class which is a mathematical concepts and certain method of organizing human activity. That is, the method allows for a fundamental economic and business practice and mathematical formula.
This judicial exception is not integrated into a practical application. In particular, the claim recites “a processor in communication with an unobscuring system”, “database”, and “triggering, by the processor using the unconstraining algorithm in the iterative fashion and based on the unconstrained demand information, tuning of the database to optimize performance of the database, wherein the tuning includes placing frequently used of the bookings table files as indexes on separate file systems to reduce in and out bottlenecks”. These limitations are recited at a high level of generality and amounts to apply it instructions. The “triggering” functions triggers a generic database tuning process and does not define a bottleneck condition, a specific indexing or file placement rule, or a specific reconfiguration of storage layout. Accordingly, these additional elements, alone or 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 claim is directed to an abstract idea.
The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are nothing more than mere instructions to apply the exception on a general computer.
Novelty and Non-obviousness
The closest prior art is:
Hornick (US 5255184)
Queenan at al, “A comparison of Unconstrianing Methods to Improve Revenue management systems”, published by Production and Operations Management in 2007, hereinafter “Queenan”.
Cereghini (US 6615205).
Boyd and Kallesen, “The Science of Revenue Management When Passengers Purchase the Lowest Available Fare”, published by Journal of Revenue and Pricing Management in 2004, Hereinafter “Boyd”.
Fiig et al, “Optimization of Mixed Fare Structures: Theory and Applications”, published by Journal of Revenue and Pricing Management in 2010, hereinafter “Fiig”.
Foote, “Separate Indexes from Tables, some thoughts Part I (everything in its right place)”, published by Richardfoote.wordpress.com in 2008, hereinafter “Foote”.
In regards to the “triggering, by the processor using the expectation maximization algorithm in the iterative fashion and based on the booking information, tuning of the database” operation, no reference of record teaches this relationship. Foote teaches placing indexes on separate file systems as a static, one time database administrator configuration decision made prior to system operation, it is pre-planned architectural choice, not a dynamic event triggered by an executing algorithm. Foote does not suggest any algorithm, let alone an EM algorithm operating on airline booking data, triggers the index placement as part of its iterative execution.
Cereghini teaches that the EM algorithm triggers SQL table and index creation and deletion operations, but these are internal computational operations of the EM algorithm itself and are not a database tuning step directed at optimizing the performance of the booking database for the purpose described by Foote. The gap between “EM algorithm running iteratively” in Cereghini and Queenan and the “database tuning including index placement on separate file system” in Foote is not bridged by any reference of record and a person of ordinary skill in the art would not have understood the EM algorithm’s iterative execution as the trigger for the Foote type tuning.
No reference teaches the real time, iterative repetition of EM unobscuring responsive to the database tuning trigger. This limitation requires that the repetition of the unobscuring determination is responsive to the triggering. No reference of record, individually or in combination, discloses a system where a database tuning step triggers or enables subsequent real time repetition o EM based demand unobscuring. Hornick discloses periodic batch optimization, not real time processing responsive to a database event and Queenan describes EM as an unconstraining methodology without specifying real time operational conditions or a triggering relationship from a database tuning step.
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3628