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 .
Response to Amendment
The amendment filed February 13, 2025 has been entered. Claims 1, 4-5, 7-10, and 12-13 remain pending in the application.
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, 4-5, 7-10, and 12-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method of organizing human activity because the claim recites a method that includes receiving one or more user inputs, wherein the one or more user inputs comprise one of flight information, passenger information, or a combination thereof; fetching a price range and contextual information corresponding to the one or more user inputs, wherein the contextual information is dynamically derived from the one or more user inputs, wherein the contextual information comprises origin, destination, flight date, booking date, number of passengers, adult count, child count, flight number, time of flight, days to departure, booking date, category of booking, date context, and destination context; calculating the optimized price of the airline ticket within the price range by using the contextual information using a machine learning (ML) system, wherein the optimized price is calculated by: extracting a plurality of features from the contextual information; identifying one or more relevant features from the plurality of features based on combination of a statistical technique with aggregating analysis from a set of machine learning regression models, wherein the one or more relevant features are identified by: performing feature selection from the plurality of features based on historical data indicative of successful bookings; evaluating relationships between various attributes and volume of bookings for feature selection by utilizing the statistical technique corresponding to one of Pearson's correlation or regression analysis; identifying the one or more relevant features based on a combination of the statistical technique with aggregating analysis from the set of machine learning regression models selected from one of XGBoost, Random Forest, CatBoost regressors, or a combination thereof; utilizing a classification model (205), comprising an XGBoost classifier trained at a flight route level based on labelled one or more demand clusters, to obtain a probability score for each demand cluster from the one or more demand clusters, based on the one or more extracted features from the contextual information, wherein the one or more demand clusters are segmented and labelled using a density-based clustering algorithm, wherein the classification model is trained using training data comprising flight data, ticket sales data, customer data, competitor data, and load factor data to capture route-specific demand patterns; identifying a demand cluster, from the one or more demand clusters, corresponding to the contextual information and based on the probability score for each demand cluster; calculating a demand score, wherein the demand score is calculated by summation of element wise multiplication of probabilities and scaled cluster densities of each clusters; calculating the optimized price of the airline ticket utilizing the price range and the demand score; and presenting the calculated optimized price to the user for consideration wherein the system (100) utilizes machine learning (ML) explainability technique to present insights on an AEO (Airline Experience Quotient) demand scoring in a user-friendly jargon-free explanation of the ML system's calculation of the demand-based optimized airline ticket pricing. This is a method of managing commercial interactions between people (e.g., the user and the seller). The mere nominal recitation of a processor, a memory communicatively coupled with the processor, wherein the memory stores processor-executable instructions, a user interface (UI) (101) does not take the claim out of the method of organizing human activity grouping. Furthermore, these limitations, as drafted, are processes that, under its broadest reasonable interpretation, covers mathematical concepts. Thus, the claim falls within the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” groupings of abstract ideas.
This judicial exception is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concepts of receiving, fetching, calculating, and presenting in a computer environment. The claimed processor, memory, and user interface are merely invoked as tools to perform the claimed method, whether viewed individually or in combination. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. 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 with respect to Step 2A, the claim as a whole merely describe how to generally “apply” the concepts of receiving, fetching, calculating and presenting in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Dependent claims 4-5, 7-10, and 12 are directed to substantially the same abstract idea as claim 1 and are rejected for substantially the same reasons. Claims 4-5 further narrow the abstract idea of claim 1 by e.g., further defining the price range and demand score. Claim 7 further narrows the abstract idea of claim 1 by e.g., further defining the machine learning system. Claim 8 further narrows the abstract idea of claim 1 by e.g., further defining that the system (100) ensures revenue increasing of airline. Claim 9 further narrows the abstract idea of claim 1 by e.g., further defining fetching a price range corresponding to an available fare class. Claim 10 further narrows the abstract idea of claim 1 by e.g., further defining enabling users to search for a flight ticket in response to the one or more inputs. Claim 12 further narrows the abstract idea of claim 1 by e.g., further defining that the system (100) supports MLOps based automated cluster configuration, model training and efficient model deployment. These limitations are all directed to a method of managing commercial interactions between people (e.g., the user and seller). Thus, claims 4-5, 7-10, and 12 are directed to substantially the same abstract idea as claim 1 and do not add any additional elements to evaluate at Steps 2A prong two or 2B. Therefore, claims 4-5, 7-10, and 12 describe neither a practical application of nor significantly more than the abstract idea.
Claim 13 recites a method of organizing human activity because the claim recites a method that includes receiving (401) one or more user inputs through, wherein the one or more user inputs comprises one of flight information, passenger information, or a combination thereof; fetching (402) a price range and contextual information corresponding to the one or more user inputs, wherein the contextual information is dynamically derived from the one or more user inputs, wherein the contextual information comprises origin, destination, flight date, booking date, number of passengers, adult count, child count, flight number, time of flight, days to departure, booking date, category of booking, date context, and destination context; calculating the optimized price of the airline ticket within the price range by using the contextual information, wherein the optimized price is calculated by: extracting (403) a plurality of features from the contextual information; identifying one or more relevant features from the plurality of features based on a combination of a statistical technique with aggregating analysis from a set of machine learning regression models, wherein the one or more relevant features are identified by: performing feature selection from the plurality of features based on historical data indicative of successful bookings; evaluating relationships between various attributes and volume of bookings for feature selection by utilizing the statistical technique corresponding to one of Pearson's correlation or regression analysis; identifying the one or more relevant features based on a combination of the statistical technique with aggregating analysis from the set of machine learning regression models selected from one of XGBoost, Random Forest, CatBoost regressors, or a combination thereof; utilizing (405) a classification model (205), comprising an XGBoost classifier trained at a flight route level based on labelled one or more demand clusters, to obtain a probability score for each demand cluster from the one or more demand clusters, based on the one or more extracted features from the contextual information, wherein the one or more demand clusters are segmented and labelled using a density-based clustering algorithm, wherein the classification model is trained using training data comprising flight data, ticket sales data, customer data, competitor data, and load factor data to capture route-specific demand patterns; identifying (404) a demand cluster, from one or more demand clusters, corresponding to the contextual information and based on the probability score for each demand cluster, calculating (406) a demand score, wherein the demand score is calculated by summation of element wise multiplication of probabilities and scaled cluster densities of each clusters, calculating (407) optimized price of the airline ticket utilizing the price range and the demand score, and presenting (408) the calculated optimized price to the user for consideration, wherein the system (100) utilizes machine learning (ML) explainability technique to present insights on an AEO (Airline Experience Quotient) demand scoring in a user-friendly jargon-free explanation of the ML system's calculation of the demand-based optimized airline ticket pricing. This is a method of managing commercial interactions between people (e.g., the user and the seller). The mere nominal recitation of a user interface (UI) (101), a processor (201), and a machine learning (ML) system does not take the claim out of the method of organizing human activity grouping. Furthermore, these limitations, as drafted, are processes that, under its broadest reasonable interpretation, covers mathematical concepts. Thus, the claim falls within the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” groupings of abstract ideas.
This judicial exception is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concepts of receiving, fetching, calculating, identifying, calculating, calculating, and presenting in a computer environment. The claimed user interface (UI) (101), processor (201), and machine learning (ML) system are merely invoked as tool to perform the claimed method, whether viewed individually or in combination. Simply implementing sthe abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. 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 with respect to Step 2A, the claim as a whole merely describe how to generally “apply” the concepts of receiving, fetching, calculating, identifying, calculating, calculating, and presenting in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Novel & Non-Obvious Subject Matter
Claims 1, 4-5, 7-10, and 12-13 would be allowable if rewritten to overcome the 35 U.S.C. 101 rejections.
The following is a statement of reasons for the indication of allowable subject matter:
Independent claims 1 and 13 would be allowable for disclosing a method of determining a price for an airline ticket where demand clusters are generated and a probability score is calculated for each cluster. A demand score is calculated by summation of element wise multiplication of probabilities and scaled cluster densities of each cluster; and, an optimized price of the airline ticket is calculated utilizing demand score.
C. S. Kumar and R. Ponnala, "Leveraging Machine Learning Techniques to Estimate Airline Ticket Pricing," 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), Faridabad, India, 2023, pp. 269-274 teaches a method for leveraging machine learning techniques to estimate airline ticket pricing. In order to maximize revenue, airlines frequently adjust their ticket pricing, which may result in higher prices during periods of high demand. To determine the airfare for a given route, data was collected over a specific period of time, which included various parameters such as flight schedules, airlines, and other relevant factors. Machine learning models were then employed to extract useful features from this data. Understanding the drivers of airfare price fluctuations is crucial in developing a mechanism that assists consumers and revenue management systems in making informed decisions about ticket purchases, considering the influence of various distinct factors on the cost of a plane ticket. However, nothing within Kumar teaches calculating a probability score for demand clusters, where a demand score is calculated by summation of element wise multiplication of probabilities and scaled cluster densities of each cluster.
Coulthurst US 20230186411 teaches the process of generating a demand model by forming a plurality of clusters, wherein each cluster comprises a corresponding weight and cluster probabilities. Zhang CN 110175865 A teaches a real-time pricing method based on ubiquitous sensing technology of electric automobile charging. The method includes clustering an algorithm depending on the local density and distance of elements of a cluster sample. However, nothing within the prior art teaches calculating a demand score by summation of element wise multiplication of probabilities and scaled cluster densities of each cluster.
Response to Arguments
Applicant’s arguments with respect to the 35 U.S.C. 101 rejections have been fully considered but they are not persuasive. Applicant argues that “[t]he claims do not recite methods of organizing human activity” because:
when the claimed features are considered as a whole, the focus of the claimed limitations is not the presentation of a price, but the technical computation of an optimized price through a machine-learning-based demand modeling pipeline … The step of presenting the optimized price via the UI is merely the final output of this computational pipeline and does not define the character of the invention
(pp. 12-13). The Examiner disagrees. Even if Applicant is correct, the focus of the claim (as asserted by Applicant) is “the technical computation of an optimized price,” which is a process that is a part of a method of organizing human activity (commercial interactions, advertising, marketing or sales activities or behaviors).
Applicant argues that “the claimed limitations replaces traditional rule-based fare determination mechanisms with a probabilistic demand-state estimation framework using machine learning” (p. 14 (emphasis added)). This supports the Examiner’s position that any alleged improvement solves a business problem – i.e., in Applicant’s own words, “fare determination” – and not a technical problem.
Applicant argues that “[t]he claims do not recite mental processes” (pp. 18-19, 22, 16, 32). In response, the Examiner notes that the claims are rejected as falling within the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” groupings of abstract ideas.
Applicant argues that “[t]he claims do not recite mathematical concept” (p. 19). More specifically, Applicant argues that “[t]hese mathematical operations are therefore embedded within a structured probabilistic demand-state reconstruction pipeline executed by the processor and ML system. They are not abstract formulas untethered to application, but part of a defined technical framework that models route-specific demand patterns and performs adaptive price optimization” (p. 20). The Examiner disagrees and notes that Applicant’s argument supports the Examiner’s position.
Applicant argues that “by performing relevant feature selection from the plurality of features based on relationships between various attributes and volume of bookings determined using statistical technique corresponding to one of Pearson's correlation or regression analysis, there is a significant improvement in system” (p. 20). Such alleged improvements are at best improvements to the commercial process (of optimizing airline ticket pricing) implemented via the generically recited devices and not an improvement to any of those devices or any technology associated with those devices.
Applicant argues that:
Similar to Diamond v. Diehr, where mathematical equations were integrated into a specific industrial process, the computations recited in claimed limitations are integrated into a real-world technical optimization process, namely, the determination of demand intensity and optimized airline ticket pricing using density-based clustering, route-level supervised XGBoost classification, and probability-weighted scaled cluster-density aggregation. The mathematical elements serve as components of a concrete machine-learning architecture that improves the operation of airline revenue-management systems
(p. 21 (emphasis added)). Applicant’s argument supports the Examiner’s position that the alleged improvements are at best improvements to the commercial process.
Applicant argues that:
As shown by the comparative results: (described in detail below in the section on practical application) the claimed limitations provides significant technological advancements.
• With four demand the ML-based fare computation completed in parameters, 510 time units, compared to 1970 time units for a traditional database approach.
• With real-world conditions requiring at least 15 parameters (e.g., origin, destination, days to departure, class of booking, number of passengers, onward/return type, etc.), extrapolated results show that the traditional approach would take 10 times longer than the ML-based approach.
• The ML system consumed approximately one-tenth the memory footprint of the
database approach (0.89 MB vs. 9.01 MB for four parameters).
• CPU usage scaled more efficiently, with traditional methods overtaking ML usage by 150% with just five parameters.
These results confirm that the claimed limitations are not a mere mathematical formula, but a solution specific, computer-implemented that improves how airline ticket pricing is computed … the mathematical operations are part of a practical application that improves computer functionality and do not preempt a mathematical concept itself, but are integrated into a novel technological process that achieves a technical solution … the
claimed machine-learning framework constrains and integrates the mathematical operations into a specific technological implementation, thereby satisfying § 101 … the recited probability scoring of demand clusters and demand score computation using element-wise multiplication of probabilities and scaled cluster densities are integrated into a concrete machine-learning framework that improves accuracy, scalability, and
robustness of demand modeling for airline ticket pricing. The mathematics are therefore applied within a defined technical machine learning architecture and not claimed in isolation … With just 4 demand parameters, the time taken to compute the fares using ML approach is approximately 25 % of what traditional database approach takes … Mathematically extrapolating the data from the experiments to 15 parameters, it can be seen that traditional database approach would take approximately 10 times more time than ML approach to compute the same number of price points … While computing ticket fares based on demand, in a real-world scenario, as per this study, the ML models will outperform the traditional database approaches significantly, in terms of the time it taken for the computation of fares … By comparing the memory footprint for traditional database approach vs the Machine Learning approach, it can be clearly seen that the database approach consumes nearly 10 times more memory as compared to the Machine Learning approach while computing fares with 4 parameters.
(pp. 21-24, 29-31). The Examiner disagrees. See FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (accelerating a process of analyzing data when the increased speed comes solely from the capabilities of a general-purpose computer not sufficient to show an improvement in computer-functionality).
Applicant argues that “Amended Claim 1 recites such a non-generic ordered combination: contextual feature extraction [Wingdings font/0xE0] density-based clustering [Wingdings font/0xE0] route-level supervised
classification [Wingdings font/0xE0] probability-weighted aggregation [Wingdings font/0xE0] optimized price computation. This architecture improves computational demand estimation and adaptive pricing beyond conventional static fare systems” (pp. 24-25). The Examiner disagrees. Even assuming arguendo that the abstract limitations are novel/non-obvious, “a claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016).
Applicant argues that:
the Office Action acknowledges that none of the cited references, Kumar, Coulthurst, or Zhang, teach calculating a demand score by summation of element-wise multiplication of probabilities and scaled cluster densities, nor calculating an optimized price using such a demand score. This acknowledgment confirms that the specific aggregation structure recited in pending Claim 1 is
not conventional … Amended Claim 1 recites such a non-generic ordered combination: contextual feature extraction [Wingdings font/0xE0] density-based [Wingdings font/0xE0] clustering [Wingdings font/0xE0] route-level supervised XGBoost probabilistic classification [Wingdings font/0xE0] probability-weighted scaled cluster-density aggregation [Wingdings font/0xE0] demand score computation [Wingdings font/0xE0] optimized price generation. This structured aggregation framework is neither routine nor generic and is not disclosed in Kumar, Coulthurst, or Zhang
(pp. 26-27). Again, even assuming arguendo that the abstract limitations are not conventional, “a claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016).
Applicant argues that:
the claimed invention clearly solves the technical problem of overpriced fares
resulting from static pricing methods by dynamically selecting relevant features from historical data using the collective approach that combines statistical techniques with ML regression models. As a result, the system identifies and leverages relevant features crucial for successful bookings
(p. 27 (emphasis added)). The Examiner disagrees. Contrary to the position taken by Applicant, “pricing” is a business problem, not a “technical problem.”
Applicant argues that the claims include additional elements that are indicative of a practical because they are similar to Example 37 of the Revised Guidance (p. 33). The Examiner disagrees because the present claims are distinguishable from the claims in Example 37 of the Revised Guidance. The claims in Example 37 of the Revised Guidance include additional elements that recite a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems. To the contrary, the claims of the present application do not include automatically display icons based on usage. Instead, the claims merely recite presenting a single price to a user via a UI.
Applicant argues that “the Office Action acknowledges that none of the cited references, Kumar, Coulthurst, or Zhang, teach the specific demand-score computation using probability-weighted scaled cluster densities. This confirms that the aggregation mechanism recited in pending Claim 1 is not conventional” (p. 35). Again, even assuming arguendo that the abstract limitations are not conventional, “a claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016).
Applicant argues that:
the Office fails to form a prima facie case of subject matter ineligibility because it does not provide any evidence that any limitations are "well-understood, routine and conventional activities previously known in the art." In the recent Memorandum from April 19, 2018, titled "Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.) (hereinafter "Berkheimer Memo"), the USPTO indicated that "[i]n a Step 2B analysis, an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing
(p. 36). The Examiner disagrees and notes that the non-final Office action does not assert that the claims include additional elements that are well-understood, routine or conventional, nor is the Examiner required to in order for the 35 U.S.C. 101 rejection to be proper.
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 extension fee 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 date of this final action.
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/D.N.M./Examiner, Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628