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 non-final office action mailed 04/07/2026. Claims 1 and 20 were amended; no claim was cancelled or added in a reply filed 06/18/2026. Therefore claims 1-20 are currently pending and subject to the final office action below.
Response to Arguments
Applicant’s arguments, see remarks p. 9, filed 06/18/2026, with respect to 112a rejection have been fully considered and are persuasive. The 112a rejection has been withdrawn. However, a new 112a rejection on different grounds is issued below.
Applicant's arguments filed 06/18/2026 in regards to 101 rejection have been fully considered but they are not persuasive.
Applicant notes “Applicant adds new paragraphs to the specification to further explain the functioning and benefits of expectation maximization (EM) algorithm 235. Moreover, to avoid a written description rejection, Applicant amends the specification to provide specification support for the new claim amendments.” (remarks p. 9)
Examiner respectfully notes Written description support must exist in the application as originally filed. Applicant cannot avoid a written description rejection by adding disclosure after the filing date. New matter added to the specification is objected to under 35 USC 132(a), while claim limitations depending on that new matter are rejected under 35 USC 112a (please see MPEP 608.04 and 2163)
The originally filed disclosure describes an EM algorithm applied iteratively to booking information to calculate unobscured demand for an obscured fare class. Paragraph 87 further states that an exemplary iterative algorithm may be applied until a convergent solution is reached. Those disclosure do not reasonably convey possession of: transforming booking table files into structured probabilistic models; compacting a database using EM; identifying hidden database structures and patterns; grouping or indexing records using EM probabilistic clusters; reducing record retrieval latency or network delay; assigning posterior responsibilities or soft fractional memberships; updating means, variances, or mixing coefficients; or guaranteeing monotonic log-likelihood convergence.
Accordingly, the newly added specification material cannot be relied upon either to provide written description support or to establish that the originally disclosed invention provides a technological improvement.
Applicant argues “Applicant respectfully asserts that an Expectation-Maximization (EM) algorithm may be a mathematical concept by itself, but Applicant strongly asserts that the use of an EM algorithm in the claimed invention and on the computer system of the claimed invention improves computer functioning to convert the alleged abstract ideas into patent eligible claims. As stated in paragraph 0078, "If the current class is obscured, a suitable estimation algorithm, for example an expectation maximization (EM) algorithm 235 (not shown in the figures), is applied in an iterative fashion to calculate an unobscured demand for the current class (step 230)." (remarks p. 9)
The Examiner agrees that the clamed EM operations recite a mathematical concept. Claim 1 recites, among other things; transforming booking information into probabilistic models; grouping information according to probabilistic clusters; calculating unobscured demand using an EM algorithm; alternating between expectation and maximization operations; determining a proportion of price oriented bookings; and multiplying that proportion by a monetary loss. These limitations recite mathematical relationships, calculations, probability modeling, and multiplication, which fall within the mathematical concepts grouping.
Claim 1 also uses the mathematical results to forecast airline seat demand, identify customer purchasing orientation, evaluate losses from closing fare classes, and generate booking instructions. These limitations concern airline revenue management, sales behavior, price optimization, and commercial booking decisions and therefore additionally recite commercial interactions or sales activities within the “certain methods of organizing human activity” grouping.
Applicant further asserts that EM compacts databases, streamlines memory and cloud storage, lowers search latency, and minimizes network delay. These assertions are not persuasive because te application as originally filed does not describe such an improvement. The disclosed EM algorithm estimates unobscured airline demand. It is not described as changing how the processor, memory, database, cloud storage system, network or indexing system technically operates.
The original disclosure computer and database discussion is generic, it states that forecasting systems may include processor, memories, databases, servers, and networks; databases may have relational, hierarchical graphical, or object oriented configurations; conventional database products may be used; records may be associated using known search, merge, sorting, key field or SQL techniques; and conventional database tuning may include placing indexes on separate file systems to reduce I/O bottlenecks.
The disclosure also states that changes to existing database and system tools are not necessarily required. Nothing connects these generic database passages to EM based compaction, probabilistic indexing, memory streamlining, or reduced network latency.
Under MPEP 2106.04(d)(1) and 2106.05(a), the specification must provide sufficient technical detail for the improvement to be apparent to a person of ordinary skill in the art. A bare assertion of improved performance is insufficient. Evidence or argument submitted during prosecution may explain what the original specification conveys, but it cannot supplement the specification with a newly disclosed technical improvement.
Applicant argues “Moreover, the EM algorithm provides practical applications of the alleged abstract ideas. As stated in paragraph 0026 "Various embodiments of principles of the present disclosure employ forecasting, statistical analysis and/or optimization techniques." (emphasis added) The claimed invention also provides improvements to "other technologies" by applying the EM algorithm to the generation of booking instructions, the optimization of 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. 9-10).
Applicant argument is not persuasive. The Examiner has considered all additional elements individually and in combination, without determining at Step 2A, prong Two, whether those elements are well-understood, routine, or conventional. Giving weight to every additional element does not mean that every recited use of a mathematical calculation constitutes integration into a practical application.
Paragraph 26 states generally that embodiments may employ forecasting, statistical analysis, and optimization techniques. That passage confirms that the disclosed system uses mathematical and statistical analysis to perform forecasting. It does not identify a technical problem in computer operation or explain a particular technical solution that improves a computer, database, network, or other technology.
Generating booking instructions based on calculated demand applies the mathematical analysis within the field of airline booking and revenue management. Claim 1 does not recite executing the booking instructions to control a particular machine or to produce a technical change in a physical system. The recitation of generating booking instructions therefore does not, by itself, integrate the mathematical calculations into a technological practical application.
Similarly, the claim’s “top-down format” represents booking information according to fare class relationships. As defined in the originally filed disclosure, a booking value for a fare class includes bookings for that fare class and higher fare classes. That is a particular organization of commercial booking data for purposes of demand forecasting. It is not disclosed as a new database architecture, memory structure, file system organization, or computer processing technique.
The newly recited database results, compacting the database, grouping and indexing similar booking information, retrieving records faster, and minimizing network delay are stated in functional and result oriented terms. The claim does not recite a particular database architecture, index structure, compression technique, query processing mechanism, memory arrangement, network protocol, or other technical implementation that accomplishes those results.
Thus, even after considering the claimed steps and data flows as an ordered combination, the claim uses mathematical analysis in the particular commercial environment of airline demand forecasting and booking management. Merely limiting an abstract idea to a particular field of use, or generally instructing that the abstract idea be applied using computer components, does not integrate the exception into a practical application.
Applicant argues “In particular, the EM algorithm improves computer functioning by optimizing how software processes complex, incomplete, or corrupted data. By converting (i.e., transforming) messy real-world information into structured probabilistic models, the EM algorithm allows computers to run advanced applications with less computational waste. As such, Applicant also asserts that the EM algorithm also provides a transformation.” (remarks p. 10).
Applicant’s argument is not persuasive. The originally filed disclosure does not describe the claimed EM algorithm as processing “corrupted data” reducing “computation waste” or enabling computers to run advanced applications more efficiently. The original disclosure describes using an EM algorithm to estimate unobscured demand where fare class booking data is obscured.
Furthermore, converting booking information into a probabilistic model is a mathematical transformation of information. The claimed booking information and probabilistic models are intangible data and mathematical representations. The claim does not transform a particular physical article into a different state or thing.
Accordingly, the asserted conversion of booking data into a structured probabilistic model does not establish a particular transformation under the eligibility analysis (see MPEP 2106.05(c)). Nor does the assertion establish an improvement to computer functionality because neither the originally filed specification nor the claim explains a technical mechanism by which the processor itself operates more efficiently.
Applicant argues “The EM algorithm also improves computer functioning by streamlining memory and streamlining cloud data storage. In particular, the EM algorithm compacts huge databases by identifying hidden structures and patterns within records allowing databases to group and index similar data tightly together. The EM algorithm also lowers search latency by grouping data based on EM probabilistic clusters. As such, the EM algorithm enables the computer to retrieve related records from the database faster, minimizing network delays.” (remarks p. 10).
Applicant’s argument is not persuasive. The originally filed disclosure does not reasonably convey that the disclosed EM algorithm streamlines memory; streamlines cloud data storage; compacts databases; identifies hidden structures or patterns for database compaction; creates database indexes based on EM clusters; groups records to reduce search latency; or minimizes network delay.
The originally filed specification contains generic computer implementation and database tuning disclosure, including conventional database tables, records, key fields, indexes, and databases tuning to reduce input/output bottlenecks. Those general disclosures are not connected to the EM algorithm and do not describe the EM algorithm as performing database compaction, database indexing, memory management, cloud storage management, query optimization, or network optimization.
The newly added claim language likewise recites desired results without reciting how the results are technically achieved. For example, the claim does not specify how “hidden structures and patterns” are represented, how the database is physically or logically compacted, what index is generated, how that index differs from conventional database index, what retrieval operation is modified, or how network traffic is reduced.
The assertion that an EM calculation produces probabilistic clusters does not itself establish that the claim improves database technology. Mathematically clustering may produce information that can be used in connection with a database, but the claim must reflect a disclosed technical improvement to the database itself. A bare assertion of improved storage, indexing, latency, or network performance, without sufficient technical detail explaining how the improvement is achieved, does not establish a technological improvement under step 2A, prong Two (please see MPE 2106.04(1) and MPEP 2106.05(a)).
Moreover, these alleged technical benefits were first introduced during prosecution. They cannot be relied upon as originally filed disclosure of a computer improvement when determining whether the specification describes the claimed invention as providing that improvement.
Applicant argues “Applicant asserts that the EM algorithm starts with a random guess and alternates between estimating the missing properties and optimizing the model parameters. The EM algorithm alternates between an expectation step and a maximization step until the parameters converge or stabilize. As stated in paragraph 0087, "Using an exemplary EM algorithm, for example an iterative algorithm applied until a convergent solution is reached" (emphasis added) In particular, during the expectation steps, the EM algorithm uses the current parameter estimates to evaluate the conditional expectation of the log-likelihood. The EM algorithm then calculates the posterior probabilities or responsibilities for the latent variables given the observed data. The EM algorithm assigns a soft fractional membership or probability weight to each data point for every hidden component. During the maximization steps, the EM algorithm computes a new set of parameters that maximizes the expected log-likelihood derived during the expectation step (the first half of the EM optimization cycle, where the algorithm estimates missing data based on its current parameters). The EM algorithm treats the soft assignments from the expectation step as fixed weights and updates values such as means, variances, or mixing coefficients. The updated configuration serves as the input for the subsequent expectation step. As such, the EM algorithm provides guaranteed convergence in that the total log-likelihood is mathematically proven to increase monotonically with every iteration cycle. The maximization portion of the EM algorithm uses the soft temporary probabilistic labels to create a complete dataset to upgrade the model's parameters.” (remarks p. 10-11).
Applicant’s argument is not persuasive. Claim 1 recites “alternating, by the processor using the expectation maximization algorithm, between an expectation step and a maximization step until the parameters converge”. Claim 1 does not recite: beginning with a random guess; evaluating the conditional expectation of a log likelihood; calculating posterior probabilities or responsibilities for latent variables; assigning soft fractional memberships or probability weights to hidden components; maximizing an expected log likelihood; treating soft assignments as fixed weights; updating means, variances, or mixing coefficients; guaranteeing that total log likelihood increases monotonically; or using temporary probabilistic labels to create a complete dataset.
These additional implementation details appear in Applicant’s remarks and proposed specification amendment, but they are not limitations of claim 1. Patent eligibility is evaluated based on the claim as written. Unclaimed implementation details cannot be imported from the specification or Applicant’s arguments to narrow the claim or establish that the claim integrates the judicial exception into a practical application.
Paragraph 87 states that an exemplary EM algorithm may be “an iterative algorithm applied until a convergent solution is reached” This disclosure is consistent with iterative application of an EM algorithm until convergence. It does not cause claim 1 to include the additional unclaimed operations discussed in Applicant’s remarks.
The only expressly claimed internal EM operation is alternating between an expectation step and a maximization step until parameters converge. That limitation further describes the operation of the mathematical algorithm, alternating between mathematical estimation and maximization operations until mathematical parameters converge remains part of the recited mathematical concept.
Applicant has not identified a claimed technological mechanism by which this alternating operation improves the functioning of a processor, database, memory, storage system, or network. The claim does not recite how the expectation and maximization steps produce the separately alleged database compaction, database indexing, faster record retrieval, or reduced network delay. Instead, those results are recited functionally, without a claimed technical implementation connecting them to the expectation and maximization operations.
Applicant’s discussion of posterior probabilities, latent variables, soft assignments, likelihood functions, and parameter updates therefore does not establish eligibility for two independent reasons. First, those details are not recited in claim 1. Second, even if the claim had recited those details, they describe mathematical calculations performed as part of an EM algorithm rather than a particular improvement to computer functionality of another technology.
Accoringdly, Applicant’s detailed explanation of how an EM algorithm may operate does not show that claim 1 integrates the recited mathematical concepts into a practical application under step 2A, prong two.
Specification
The amendment filed 06/18/2026 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
The added material not supported by the application as originally filed includes the following assertions:
That the EM algorithm improves computer functioning by:
Optimizing how software processes complex, incomplete, or corrupted data;
Transforming messy real world information into structured probabilistic models;
Permitting computers to run advanced applications with less computational waste; or
Providing a transformation for patent eligibility purposes.
That the EM algorithm improves memory, storage, database, search, or network operation by:
Streamlining memory or cloud data storage;
Compacting large databases;
Identifying hidden structure and patterns within database records for purposes of database compaction;
Allowing databases to group and index similar records tightly together;
Grouping booking information according to EM probabilistic clusters;
Lowering search latency;
Enabling faster retrieval of related records; or
Minimizing network delays.
That the particular EM algorithm disclosed in the application:
starts with a random guess;
evaluates a conditional expectation of log-likelihood;
calculates posterior probabilities or responsibilities for latent variables;
assigns soft fractional memberships or probability weights to data points for hidden components;
maximizes an expected log-likelihood;
treats soft assignments as fixed weights;
updates means, variances, or mixing coefficients;
guarantees a monotonic increase in total log-likelihood during every iteration cycle; or
uses temporary probabilistic labels to create a complete dataset for updating model parameters.
The application as originally filed discloses that, when a fare class is obscured, a suitable estimation algorithm, such as EM algorithm, may be applied iteratively to calculate unobscured demand for the current fare class. See originally filed paragraph 0078. The original disclosure further provides a particular calculation involving expected bookings, lower and upper bounds, mean, standard deviation, cumulative distribution values, and probability density function values.
Originally filed paragraph 87 further states that an exemplary EM algorithm may be an iterative algorithm applied until a convergent solution is reached. Thus, the original disclosure supports applying an EM algorithm iteratively to calculate unobscured airline demand until a convergent solution is obtained.
Those disclosure do not reasonably convey that Applicant possessed an EM implementation that improves processor operation, compacts databases, creates database indexes, streamlines memory or cloud storage, reduces computation waste, lowers search latency, or minimizes network delays.
Originally filed paragraph 26 states generally that embodiments may employ forecasting, statistical analysis, or optimization techniques. That general statement does not disclose the subsequently asserted computer performance functions or establish that the disclosed EM algorithm performs those functions.
The original disclosure also contains general descriptions of databases, data tables, database searches, key fields, indexes, and conventional database tuning. For example, the specification states that frequently used files such as indexes may be placed on separate file systems to reduce input/output bottlenecks. However, these general database disclosures are not attributes to EM algorithm and do not disclose using EM probabilistic clusters to compact the database, create index, group record, accelerate retrieval, or reduce network delays.
The originally filed disclosure states that changes to existing databases and system tools are not necessarily required. The originally disclosed benefits concern matters such as forecasting accuracy, revenue, cost, seat utilization, planning, and operation efficiency, not an improvement to the internal operation of a processor, memory, database, cloud storage system, or network.
Although the term “expectation maximization algorithm” and the disclosed iteration to a convergent solution may provide a basis for describing the general iterative nature of the EM algorithm, they do not provide support for the newly asserted particular implementation details above.
Also, the mere fact that certain operations may be known features of some EM implementations does not establish that the inventors possessed and disclosed those particular operations as part of the claimed unobscuring system on the application’s filing data. The required support must be found expressly, implicitly, or inherently in the application as originally filed.
Applicant’s attempt to add specification support during prosecution for newly added claim limitations cannot cure the absence of written description support in the application as originally filed. A specification amendment filed after the application’s filing data cannot retroactively establish possession of newly claimed subject matter. Thus, the specification amendment is not entered.
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.
Claim(s) 1-20 is/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.
Claim 1 recite “transforming, by the processor, the bookings table files into structured probabilistic models; compacting, by the processor, the database by identifying hidden structures and patterns within records allowing the database to group and index similar booking information tightly together; grouping, by the processor, the booking information in the database based on expectation maximization probabilistic clusters to retrieve related records from the database faster, minimizing network delays; 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 structured probabilistic models of the booking information from the bookings table file, in response to the current fare class being obscured”
The limitations above are new matter because the application as originally filed does not expressly, implicitly, or inherently describe these limitations or their claimed combination.
The original disclosure supports converting flight booking information into a “top down” representation in which the booking number associated with each fare class represents bookings in that fare class together with bookings in high er fare classes. The original disclosure also supports storing and processing booking information using conventional databases and data tables.
Regarding the EM algorithm, originally filed paragraph 78 explains that the algorithm is “applied in an iterative fashion to calculate an unobscured demand for the current class” the original disclosure identifies a particular demand estimation calculation using expected bookings, upper and lower demand bounds, cohort means, standard deviation, cumulative distribution function values, and probability density functions values.
Originally filed paragraph 87 further describes “an iterative algorithm applied until a convergent solution is reached.” Thus, the original disclosure reasonably supports iteratively using an EM type estimation algorithm to calculate an unobscured demand value for an obscured fare class until a convergent solution is reached.
However, the original disclosure does not describe transforming bookings table files into structured probabilistic models, using EM to compact a database, identifying hidden structures ad patterns to group or index database records, forming EM probabilistic clusters for database retrieval, retrieving records faster, or minimizing network delays.
The generic disclosure of databases, data tables, key fields, indexes, database tuning, and networks do not provide support because those features are not attributed to the EM algorithm or connected to the newly claimed database compaction, clustering, indexing, retrieval, and network functions.
Accordingly, claim 1 lacks adequate written description support. Claim 20 is rejected for similar reasons. Claims 2-19 are also rejected under 112a for failing to cure the deficiencies 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; transforming the bookings table files into structured probabilistic models; identifying hidden structures and patterns within records allowing the database to group and index similar booking information tightly together; determining, in an iterative fashion, the unobscured demand for a current fare class using an expectation maximization algorithm and the structured probabilistic models of the the booking information from the bookings table file, in response to the current fare class being obscured; alternating, using the expectation maximization algorithm, between an expectation step and a maximization step until the parameters converge; 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 concept 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, “the processor in communication with the unobscuring system”, “unobscuring system”, “compacting the database”. These limitations are recited at a high level of generality and amounts to apply it instructions. 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, 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; transforming the bookings table files into structured probabilistic models; identifying hidden structures and patterns within records allowing the database to group and index similar booking information tightly together; alternating, using the expectation maximization algorithm, between an expectation step and a maximization step until the parameters converge; identifying constrained fare classes; converting, using the structured probabilistic models, 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, 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 “ a processor in communication with an unobscuring system”, “compacting the database”. These limitations are recited at a high level of generality and amounts to apply it instructions. 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 Campbell (US 5918209), Fayyad (US 6633882), Cereghini (US 6615205), Hassine (US 20090234710) and Weatherford, “Revenue maximization with implementation variations of unconstraining methods in a semi restricted fare environment”, published by Journal of Revenue and Pricing Management in 2013, hereinafter “Weatherford”.
Campbell discloses airline revenue management using booking classes, demand forecasts, iterative optimization, and booking control instructions. Campbell does not disclose converting booking information into the claimed “top-down format”. As defined in the specification, each fare class value must equal the bookings in that class plus all higher fare classes. Campbell’s nested booking limits concern seats availability and do not disclose this cumulative booking data representation.
Weatherford discloses using expectation maximization to estimate unconstrained airline demand from booking data affected by capacity or booking control limits. They do not disclose the claimed top-down format or using EM to compact, group, or index database records for faster retrieval and reduced network delay.
Fayyad discloses database compression and rapid query processing using probabilistic cluster models, including EM clustering. Fayyad does not disclose airline fare classes, top-down booking data, class by class unobscuring, or generating booking instructions from unobscured demand.
Cereghini discloses EM clustering within a relational database, including alternating expectation and maximization steps until convergence. Cereghini does not disclose airline bookings, the claimed top-down format, or using EM to determine unobscured demand successively through the highest fare class.
Hassine discloses airline choice modeling, buy up and recapture probabilities, closed fare classes, and opportunity cost calculations. Hassine does not disclose identifying bookings modified by an unobscuring algorithm, determining the proportion associated with price-oriented customers, and multiplying that proportion by a closed class loss to generate looking instructions based on unobscured demand.
Even when combined, the references do not teach the claimed method. Most importantly, none discloses the specification defined top-down format. The combination also does not disclose using the same structured probabilistic models for EM based database compaction, clustering, and unobscured demand determination, followed by the claimed price-oriented loss calculation and generation of booking instructions. Constructing the claimed arrangement would require using Applicant’s disclosure as a roadmap to combine otherwise separate airline forecasting, database clustering, and customer choice teachings.
Accordingly, the prior art, individually or in combination, does not render independent claim 1/20 obvious.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
Prosecution Insights