DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/23/2026 has been entered.
Response to Arguments
Applicant's arguments filed 03/23/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims are not directed to an abstract idea. The Examiner disagrees. The claims are directed to dynamic cabin mapping utilizing machine learning and the relevant raw data. This falls into the categories of certain methods of organizing human activity, namely, mathematical concepts; marketing or sales activities or behaviors; business relations but for the recitation of generic computer components.
Applicant further argues, “Even assuming, solely for the sake of argument, that some limitation of amended claim 1 could be viewed as involving a mathematical concept, amended claim 1 is not "directed to" any such concept because the claim as a whole integrates any alleged exception into a practical application… The claimed invention addresses a specific technological problem identified in the specification: different airlines use different reservation booking designators and fare ladders, and proprietary airline-specific mapping information is not available to third parties, making accurate and up-to-date airline agnostic cabin mapping difficult. See Spec., %% 1, 3. Amended claim 1 recites a particular technological solution to that technological problem. The Examiner disagrees. This is not a technical solution to a problem rooted in computer technology. In fact, the claim language Applicant references with respect to this argument is apart of the abstract idea.
Applicant likens the claims to Example 39, Example 40, Example 47 and Example 48. The Examiner disagrees and asserts that the claims of the instant application cannot be likened to all of these Examples, as they all share different elements and fact patterns. The Example most like Applicant’s claims is Example 39 but unlike this example, the mathematical concepts are recited in the claims. Therefore, the claims remain ineligible.
Applicant further argues, “Even if the Office were to conclude that amended claim 1 recites a judicial exception and is directed thereto under Step 2A, which Applicant does not concede, amended claim 1 still recites significantly more under Step 2B.
The claim does not merely say "apply machine learning" on a generic computer. Instead, the claim recites a specific ordered combination of operations: gathering identified raw ticketing data fields; distinguishing between direct and indirect airline participation; generating data variables or data points based on available data; generating fare type variables using association rules and fare basis code patterns; compiling an unlabeled data set; performing dimensionality reduction using specified techniques; creating first and second training sets; performing first stage and second stage training; evaluating first stage and second stage model accuracy; executing the second trained model only after satisfaction of a predetermined accuracy threshold; creating percentile-based references mapping airline identifiers and RBKD values to cluster values and corresponding cabin class clusters; storing resulting cabin class mapping in a database; and displaying the resulting mapping. The ordered combination of these limitations provides a specific technological solution that is far more than generic data gathering, generic processing, and generic output. See Spec., %% 12-19, 30-32, 96-97, 103-110. Independent claims 19 and 20 recite significantly more for at least parallel reasons.” The Examiner disagrees. The ordered combination would not apply as the claims do not require a fixed specific sequence of operations. Further, the claims recite capabilities rather than an ordered combination of steps. Accordingly. Applicant’s arguments are not persuasive and the rejections are maintained.
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 and 3-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter 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.
MPEP 2106 Step 2A-Prong 1
The claims recite:
gathering, raw ticketing data from one or more data sources for capturing reservation booking designator (RBKD) values for a plurality of airlines, the raw ticketing data including at least a carrier number, the RBKD values, a total ticketing amount, an average fare, and an average tax amount, wherein each of the plurality of airlines utilizes a different mechanism for designating a plurality of cabin classes;
determining, airline data participation for each of the plurality of airlines, wherein the airline data participation is direct or indirect, wherein, when the airline data participation of a respective airline is determined to be direct, the one or more data sources includes the respective airline, and when the airline data participation of the respective airline is determined to be indirect, one or more data variables or data points are generated based on available data;
executing a fare mapping algorithm for generating a fare type variable based on the raw ticketing data gathered, wherein the fare mapping algorithm utilizes association rules from the raw ticketing data and fare basis code patterns to parse fare basis codes into subsets and generate the fare type variable;
compiling the raw ticketing data gathered, the fare type variable, and the one or more generated data variables or data points into an for generating unlabeled data set;
performing, dimensionality reduction on the unlabeled data set for generating a set of input variables to input, wherein the dimensionality reduction is performed using correlation analysis and at least one of factor analysis or feature importance ratio technique;
creating a first training set comprising a mapping between the RBKD values and a plurality of cabin class clusters;
first training the model in a first stage using the first training set;
evaluating an accuracy of by identifying correctly identified cabin class mappings and incorrectly identified cabin class mappings;
creating a second training set comprising the first training set and a portion of the mapping between the RBKD values and the plurality of cabin class clusters that are incorrectly determined after the first stage of training;
second training the first trained in a second stage using the second training set; evaluating an accuracy of the second model;
in response to determining that the evaluated accuracy of the second model satisfies a predetermined accuracy threshold, executing, the second trained model to generate, from the set of input variables, a plurality of cabin class clusters for the plurality of airlines;
creating percentile-based references to assign class service names for each of the plurality of cabin class clusters, wherein the percentile-based references map airline identifiers and RBKD values to cluster values and corresponding cabin class clusters;
storing resulting cabin class mapping for each of the plurality of airlines
contemporaneously displaying, a graphical representation of cabin
class mapping for each of the plurality of airlines based on the percentile-based references.
The claims falls into the abstract idea groupings of (a) mathematical concepts-**mathematical relationships mathematical formulas or equations mathematical calculations** (b) Certain Methods Of Organizing Human Activity ** fundamental economic principles or practices (including hedging, insurance, mitigating risk) commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)**
The limitations under their broadest reasonable interpretation, covers performance of mathematical concepts; marketing or sales activities or behaviors; business relations but for the recitation of generic computer components. That is, other than recited, “non-transitory computer readable medium, memory, display, processor and machine learning model”, nothing in the claim element precludes the step from practically being certain methods of organizing human activity. Accordingly, the claims recite an abstract idea.
MPEP 2106 Step 2A-Prong 2
The recited limitations are not indicative of integration into a practical application. In particular, the claims only recite the following additional elements, non-transitory computer readable medium, memory, display, processor and machine learning model. These additional elements are recited at a high-level of generality such that in conjunction with the abstract limitations, they amount to no more than:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f);
- (non-transitory computer readable medium, memory, display, processor and machine learning model)
mere data gathering/post solution activity in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, -(display)
The claims do not include additional elements individually or in an ordered combination that are sufficient to amount to significantly more than the judicial exception. Integration into a practical application requires the additional element(s) to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. This is not the case in the instant application. Further, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than: mere instructions to apply the exception using a generic computer component; mere data gathering/post solution activity; generally linking the use of the judicial exception to a particular technological environment or field of use.
MPEP 2106 Step 2B
Eligibility requires that the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception. As discussed above, this is where the instant application falls short. The claims do not include additional elements individually or in an ordered combination that are sufficient to amount to significantly more than the judicial exception
Berkheimer-With respect to consideration of routine and conventional subject matter, the additional elements-("a display")-are described in published Specification, at a high level of generality and in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars of these elements to satisfy the statutory disclosure requirements.
The claims do not include additional elements that are sufficient to amount to
significantly more than the judicial exception for the same reasons as presented in Step
2A Prong 2.
Moreover, the additional elements recited are known and conventional computing elements (a display—see original specification 23, 42 and 65
and in a manner that indicates that the additional elements are sufficiently
well-known that the specification does not need to describe the particulars of such
additional elements to satisfy the statutory disclosure requirements).
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already
presented (that is, they further limit the organizing of human activities at step 2A —
Prong One without adding any new additional elements other than those already
analyzed above with respect to the independent claims at 2A — While the dependent claims describes machine learning models, these additional elements do not remedy the deficiencies.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment
and instructions to implement the abstract idea as the independent claims without
adding any new additional elements. Accordingly, they are not directed to significantly
more than the exception itself, and are not eligible subject matter under § 101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TONYA S JOSEPH whose telephone number is (571)270-1361. The examiner can normally be reached M-F 6:30-2:30, First Fridays Off.
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/TONYA JOSEPH/Primary Examiner, Art Unit 3628
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