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 .
This Final Office Action is responsive to Applicant's amendment filed on 30 September 2025. Applicant’s amendment on 30 September 2025 amended Claims 1, and 28. Claims 31 and 32 were previously rejected. Currently Claims 1-30 are pending and have been examined. The Examiner notes that the 101 rejection has been maintained.
Examiner’s Note
The Examiner finds the Applicant’s arguments persuasive with respect to the prior art rejections and the prior art rejections have been withdrawn, therefore claims 1-30 would be viewed as allowable if rewritten or amended to overcome the 101 rejection(s).
Response to Arguments
Applicant's arguments filed 30 September 2025 have been fully considered but they are not persuasive.
The Applicant argues on pages 14-16 that “Claim 1 recites a practical application, namely a computer-implemented method of reducing data storage requirements, of providing travel-related price estimates, and of inferring which fare classes are available for a journey from a starting location to a destination location on a particular date, the method including training classifiers comprising structured data using historical price quotes from an incomplete historical travel-related price dataset embodied on a non-transitory storage medium, and repeating steps (i) to (ii) and (iv) to (xi), including training the classifiers periodically using the historical price quotes from the incomplete historical travel-related price dataset embodied on the non-transitory storage medium, to infer the fare class availability for the journey from the starting location to the destination location on the particular date periodically, and to store the inferred fare class availability periodically, therefore amended Claim 1 is not "directed to" a judicial exception, and thus is patent eligible”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that Applicant's argument stating that amended Claim 1 recites a practical application through an improvement to computer functionality is not persuasive for the following reasons: The Alleged Improvement is a Business Solution, Not a Technological Improvement. While applicant asserts that the claim improves computer functionality by "reducing data storage requirements," this characterization misapprehends the nature of the improvement required under MPEP 2106.05(a). The claim reduces storage requirements by storing an incomplete historical dataset rather than a complete one. This is fundamentally a business decision to collect and store less data, not a technological improvement to how computers store data. As explained in the specification ([0089]), the incomplete cache exists because "it is not practical" to build a complete cache due to GDS query costs—this is an economic constraint, not a technological problem with computer storage itself.
A genuine improvement to computer functionality involves enhancing how a computer operates or improving computer technology or another technology. See Enfish, LLC v. Microsoft Corp., (claims directed to specific data structure that improved computer performance were eligible). Here, the claims do not improve data storage technology; they merely store less data as a consequence of using statistical prediction to avoid expensive queries. The computer's storage capability remains unchanged - it is simply used to store a smaller dataset. This is analogous to reducing server load by serving fewer users rather than improving server technology.
The Claimed Periodic Training Process Does Not Transform the Abstract Idea into a Practical Application: Applicant emphasizes the periodic training of classifiers using the incomplete dataset. However, this iterative refinement remains an implementation of the underlying abstract idea—using statistical analysis and machine learning (Naive Bayes classification) to predict prices from historical data. The claim recites this process at a high level of generality without specifying how the training process improves computer functionality beyond applying conventional machine learning techniques to the travel pricing domain.
The USPTO's recent AI subject matter eligibility examples are instructive. In Example 49 Claim 1, which was found ineligible, the claim recited using a neural network to generate predictions for medical treatment, but only required high-level steps like "using a DNN to determine embedding vectors" without reflecting the technical improvements described in the specification. The analysis noted that "the claim, however, only requires determining the embedding vectors and therefore does not reflect the improvement discussed in the disclosure." 2024 AI SME Update Examples, Example 49 at 29.
Similarly, amended Claim 1 recites training Naive Bayes classifiers and periodically repeating this training, but provides no technical details about how this training improves computer operations. The claim does not specify particular data structures, algorithmic refinements, or processing techniques that enhance computational efficiency or accuracy beyond generic application of known statistical methods. Like Example 49 Claim 1, the claim recites the abstract idea at too high a level to reflect a technological improvement.
Novelty Over the Prior Art is Not Determinative of Patent Eligibility: Applicant argues that the claimed combination "was not provided for by the prior art." This argument conflates the separate requirements of 35 U.S.C. 101, 102, and 103. As the Supreme Court stated in Diamond v. Diehr, "[t]he 'novelty' of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the 101 categories of possibly patentable subject matter." A claim directed to a judicial exception does not become eligible merely because it applies that exception in a novel way or to a novel field. See also Ariosa Diagnostics, Inc. v. Sequenom, Inc., ("The claims at issue in this appeal are directed to a patent ineligible concept, even though that subject matter was new and useful. The addition of novel or unconventional steps to the diagnostic method cannot render the claimed method patent eligible.").
The Claim Uses Generic Computer Components to Implement Abstract Ideas: Examining the claim as an ordered combination, the additional elements beyond the abstract idea consist of: (1) generic computer servers, processors, and non-transitory storage media; (2) routine functions of receiving, storing, and sending data; (3) communicating with a Distribution System; and (4) outputting results to a computing device. These elements are described at a high level of generality and amount to generic computer implementation of the abstract idea. See MPEP 2106.05(f). The claim essentially instructs practitioners to apply the abstract idea (statistical price prediction and inference) using conventional computer technology, which is insufficient to confer eligibility. See Alice Corp. v. CLS Bank Int'l, (merely requiring generic computer implementation fails to transform a patent-ineligible abstract idea into a patent-eligible invention).
Comparison to Eligible AI Claims: The USPTO's recent guidance provides examples of AI-related claims that were found eligible by demonstrating specific technical improvements. In Example 47 Claim 1 (anomaly detection using ANN), eligibility was established because the claim recited specific hardware components (ASIC with neurons comprising registers and microprocessors) that implement the artificial neural network, rather than merely invoking the concept of using an ANN. In Example 48 Claim 1 (speech separation), eligibility was found where the claim reflected technical improvements to speech processing by reciting specific details of how a DNN trained on source separation aids in cluster assignments and enables separation of speech from different sources.
In contrast, amended Claim 1 recites using Naive Bayes classifiers, grouping quotes, deriving statistics, and training classifiers, but provides no comparable technical specificity. The claim does not explain how the classifiers are structured to improve computational efficiency, how the incomplete dataset is specifically leveraged to enhance processing, or how the periodic training refines the system beyond routine iterative application of known statistical methods. The claim reads on implementing the described method using off-the-shelf machine learning libraries and conventional database systems.
For the foregoing reasons, amended Claim 1 and remaining additional independent claims that do not integrate the recited abstract ideas into a practical application. The claim is directed to abstract ideas of a mental processes, and commercial price estimation methods, implemented using generic computer technology. Accordingly, the rejection is maintained.
The Applicant argues on pages 16 that “Claim 1 includes steps (iv) and (xii), including training classifiers comprising structured data using historical price quotes from an incomplete historical travel-related price dataset embodied on a non-transitory storage medium, and repeating steps (i) to (ii) and (iv) to (xi), including training the classifiers periodically using the historical price quotes from the incomplete historical travel-related price dataset embodied on the non-transitory storage medium, to infer the fare class availability for the journey from the starting location to the destination location on the particular date periodically, and to store the inferred fare class availability periodically, and that such steps cannot practically be performed in the human mind”.
The Examiner respectfully disagrees.
In response to the arguments the Examiner notes that the Applicant's argument stating that the claim does not recite a mental process because certain steps "cannot practically be performed in the human mind" misapprehends the mental process analysis under MPEP 2106.04(a)(2). The test is not whether every step is practically performable by a human, but whether the claim recites concepts that are mental processes or other abstract ideas. Moreover, automating mental processes using generic computer components does not transform an abstract idea into patent-eligible subject matter.
The Claim Recites Mental Processes and Mathematical Concepts Regardless of Practical Performability: The claim recites numerous limitations that constitute mental processes and mathematical concepts, including: analyzing patterns in historical price datasets to predict future prices (step iv); grouping price quotes by category (step iv(b)); deriving statistics for each group (step iv(c)); calculating estimates using statistics (step v); comparing calculated estimates with Distribution System prices (step ix); and inferring fare class availability from the comparison (step ix). These are observations, evaluations, comparisons, and analytical processes that constitute mental processes under MPEP 2106.04(a)(2)(III), even if performed using large datasets that make manual performance impractical.
As explained in MPEP 2106.04(a)(2)(III), "Courts have found concepts that can be performed in the human mind, or by a human using a pen and paper, to be abstract ideas." The Federal Circuit has consistently held that claims reciting mental processes implemented on generic computers remain abstract. In CyberSource Corp. v. Retail Decisions, Inc, the court held claims ineligible where "the recited method can be performed in the human mind, or by a human using pen and paper," even though the method involved processing large amounts of data that would be impractical for humans to process manually. The court noted that the additional computer limitations were "insignificant post-solution activity" that did not add meaningful limitations to the abstract idea.
Training Classifiers Using Large Datasets Remains an Abstract Mathematical/Statistical Process: Applicant emphasizes steps (iv) and (xii), which recite training Naive Bayes classifiers using historical price data and periodically repeating this training. While the volume of data may make manual performance impractical, the fundamental nature of this process remains a mathematical and statistical analysis—deriving probability distributions from historical data to predict future outcomes. This is quintessentially a mathematical concept as described in MPEP 2106.04(a)(2)(I), which identifies "mathematical relationships, mathematical formulas or equations, and mathematical calculations" as abstract ideas.
The Supreme Court has recognized that using computers to perform calculations that would be burdensome for humans does not avoid the abstract idea exception. In Parker v. Flook, the Court held that a process for updating alarm limits through calculation was abstract even though the calculations were performed by a computer and "may not be obvious to an observer." The Court explained that the novelty of the mathematical algorithm and its practical utility did not transform it into patentable subject matter when implemented using conventional computer technology.
The Claim Implements Abstract Ideas Using Generic Computer Components: The claim recites training classifiers on "a non-transitory storage medium" and storing inferred availability - these are generic computer components and conventional data storage functions. As stated in MPEP 2106.05(f), "courts have held that mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, do not amount to significantly more than the abstract idea itself." The claim essentially automates the mental processes and mathematical calculations using generic computer technology, which is insufficient for eligibility under Alice Corp. v. CLS Bank Int'l, (holding that "simply appending conventional steps, specified at a high level of generality" to an abstract idea does not supply an inventive concept).
Periodic Repetition Does Not Confer Eligibility: Applicant's reliance on the periodic repetition of steps (step xii) does not overcome the abstract idea rejection. Repeating an abstract process, even periodically to refine results, remains an application of the abstract idea. The claim does not specify how the periodic training improves computer functionality or provides any technical innovation beyond iteratively applying known statistical methods to update predictions. This is analogous to periodically recalculating mathematical formulas with new data—a process that remains abstract regardless of repetition frequency. See SAP America, Inc. v. InvestPic, LLC, (holding that "the claims' invocation of a generic computer to perform generic computer functions does not alone save them from ineligibility" even when applied repeatedly to financial data).
Practical Performability is Not the Determinative Test: The determinative question under Step 2A Prong One is whether the claim recites a judicial exception—specifically, whether it recites abstract ideas such as mental processes, mathematical concepts, or certain methods of organizing human activity. MPEP 2106.04(a). The fact that a mental process or mathematical calculation would be impractical for a human to perform manually due to the volume or complexity of data does not mean the claim fails to recite a mental process or mathematical concept. See Electric Power Group, LLC v. Alstom S.A., (claims directed to collecting, analyzing, and displaying data were abstract even though they involved real-time monitoring of complex power grid systems that would be impractical to perform manually).
The claim recites mental processes (analyzing patterns, grouping data, comparing results, inferring availability) and mathematical concepts (statistical analysis, Naive Bayes classification, deriving probabilities) implemented using generic computer components. The impracticality of manually performing these processes with large datasets does not alter their fundamental abstract nature. Accordingly, the claim recites abstract ideas under Step 2A Prong One, and the rejection is maintained for the reasons previously stated.
The Applicant argues on pages 16-18 that “In amended Claim 1, the claim includes more than mere instructions to perform the method on a generic component or machinery, because there is provided a computer- implemented method of reducing data storage requirements, of providing travel-related price estimates, and of inferring which fare classes are available for a journey from a starting location to a destination location on a particular date, the method including training classifiers comprising structured data using historical price quotes from an incomplete historical travel-related price dataset embodied on a non-transitory storage medium, and repeating steps (i) to (ii) and (iv) to (xi), including training the classifiers periodically using the historical price quotes from the incomplete historical travel-related price dataset embodied on the non-transitory storage medium, to infer the fare class availability for the journey from the starting location to the destination location on the particular date periodically, and to store the inferred fare class availability periodically. The improvement in technology is provided because in amended Claim 1 there is provided a computer-implemented method of reducing data storage requirements, of providing travel-related price estimates, and of inferring which fare classes are available for a journey from a starting location to a destination location on a particular date, the method including training classifiers comprising structured data using historical price quotes from an incomplete historical travel-related price dataset embodied on a non-transitory storage medium, and repeating steps (i) to (ii) and (iv) to (xi), including training the classifiers periodically using the historical price quotes from the incomplete historical travel-related price dataset embodied on the non-transitory storage medium, to infer the fare class availability for the journey from the starting location to the destination location on the particular date periodically, and to store the inferred fare class availability periodically. Training classifiers comprising structured data using historical price quotes from an incomplete historical travel-related price dataset embodied on a non-transitory storage medium, and repeating steps (i) to (ii) and (iv) to (xi), including training the classifiers periodically using the historical price quotes from the incomplete historical travel- related price dataset embodied on the non-transitory storage medium, to infer the fare class availability for the journey from the starting location to the destination location on the particular date periodically, and to store the inferred fare class availability periodically, in a computer- implemented method, was not provided for by the prior art. Accordingly, Claim 1 is not "directed to" a judicial exception, and thus is patent eligible”. Therefore, amended Claim 1 is patent eligible.”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that Applicant's argument that amended Claim 1 provides an improvement to technology is not persuasive because the claim does not improve the functioning of a computer or any other technology, but rather applies conventional computer technology and known statistical methods to solve a business problem in the travel industry. The repeated assertion that the claim was "not provided for by the prior art" conflates patent eligibility under 35 U.S.C. 101 with novelty and non-obviousness under 35 U.S.C. 102 and 103, which are separate and distinct requirements.
I. The Claim Does Not Improve Computer Technology or Any Other Technology: To qualify as an improvement to technology under MPEP 2106.05(a), a claim must improve the functioning of a computer itself or improve another technology or technical field. The specification and claim language must demonstrate how the claimed invention provides a technological improvement beyond merely using computers to perform abstract ideas more efficiently or applying abstract ideas in a particular field.
In Enfish, LLC v. Microsoft Corp., the court found claims patent-eligible because they were "directed to a specific improvement to the way computers operate"—namely, a self-referential logical table that improved computer performance by providing faster searching and more effective data storage compared to conventional database structures. Critically, the improvement was to the computer's functionality itself: how data was organized and accessed in memory. The court emphasized that the claims were "not simply directed to any form of storing tabular data" but to "a specific type of data structure designed to improve the way a computer stores and retrieves data in memory.".
Similarly, in McRO, Inc. v. Bandai Namco Games Am. Inc., the court found patent eligibility where claims recited a specific technique for automated lip-synchronization in computer animation that improved upon manual techniques and previous automation methods. The improvement was to the technological process of 3D animation itself—providing more realistic and efficient automated character animation.
The Alleged "Reduction in Data Storage Requirements" Is Not a Technological Improvement: Applicant characterizes the invention as providing "a computer-implemented method of reducing data storage requirements." However, examination of the claim reveals that this "reduction" is achieved by collecting and storing an incomplete historical dataset rather than a complete one. This is explicitly stated in steps (i)-(ii): "storing the received observable, live bookable prices... to provide a stored incomplete historical travel-related price dataset which does not include complete fare class information... wherein the stored incomplete historical travel-related price dataset uses a smaller data storage capacity than a complete historical travel-related price dataset."
The claim does not improve how computers store data or enhance data storage technology. Rather, it simply stores less data - specifically, an incomplete dataset lacking complete fare class information. As explained in the specification at paragraph [0089], the motivation for maintaining an incomplete cache is economic: "The massive increase in the number of queries needed to form a 'complete picture' of a scheduled model route combined with the costs of GDSs means that it is not practical (i.e. it is disadvantageous) to build such a cache." The incomplete dataset exists because of the prohibitive cost of querying Global Distribution Systems, not because of any limitation in computer storage technology.
Reducing data storage requirements by choosing to store less data is fundamentally different from improving data storage technology. This is analogous to reducing server load by serving fewer users rather than improving server processing efficiency—it may be a practical business decision, but it does not constitute a technological improvement to the server itself. The computer's storage capabilities remain unchanged; it is merely used to store fewer records. This does not satisfy the requirement for an improvement to computer functionality under MPEP 2106.05(a).
Training Classifiers on Incomplete Datasets Does Not Improve Technology: Applicant emphasizes "training classifiers comprising structured data using historical price quotes from an incomplete historical travel-related price dataset" and repeating this training periodically. While the claim specifies that classifiers are trained on incomplete data, this specification relates to the type of input data used, not to an improvement in machine learning technology or computer processing. The claim recites conventional Naive Bayes classification (step (iv) and dependent claim 5), which is a well-known statistical method.
The claim does not describe how the classifiers are structured or implemented in a manner that improves computational efficiency, accuracy beyond conventional machine learning, or any aspect of computer technology. It merely applies known machine learning techniques to travel price data. As the Federal Circuit explained in BSG Tech LLC v. BuySeasons, Inc., "Inventing a new way of using an abstract idea does not escape 101." The court held that even if the claimed database structure was novel, it merely applied conventional database technology to a new problem (online retailing) without improving how databases function.
The Claim Provides Mere Instructions to Apply Abstract Ideas Using Generic Components: Examining the claim elements individually and as an ordered combination, the claim recites: generic computer servers, processors, and non-transitory storage media (steps (i)-(ii), (iv)); routine computer functions of receiving, storing, grouping, and calculating data (steps (i)-(v)); applying Naive Bayes classification to predict prices (steps (iv)-(v)); comparing results (step (ix)); and outputting and storing inferred data (steps (x)-(xi)). These elements are described at a high level of generality and amount to instructions to implement abstract ideas—statistical price prediction, data analysis, and inference—using conventional computer technology.
As explained in MPEP 2106.05(f), "Courts have held that mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, do not amount to significantly more than the abstract idea itself." The claim here instructs practitioners to use generic computers and known machine learning algorithms to predict travel prices and infer fare availability—tasks that are fundamentally analytical and predictive in nature. The computers function as tools to execute these abstract processes more quickly than could be done manually, but the claim does not improve the computers' functioning or any other technology.
Periodic Repetition Does Not Transform Abstract Ideas Into Technological Improvements: Applicant's emphasis on "repeating steps... including training the classifiers periodically" (step (xii)) does not establish a technological improvement. Periodically retraining machine learning models with updated data is a conventional practice in data science to maintain prediction accuracy as underlying data changes. The claim does not specify any inventive technique for this periodic retraining or explain how it improves computer operations beyond the routine updating of statistical models. Automating the periodic application of abstract ideas using conventional computer technology does not provide an inventive concept. See Intellectual Ventures I LLC v. Capital One Bank (USA), (holding that "the concept of collecting information, analyzing it, and displaying certain results of the collection and analysis" is abstract, and automating these steps with computers does not render claims patent-eligible).
Novelty Is Not Determinative of Patent Eligibility: Applicant repeatedly asserts that the claimed combination "was not provided for by the prior art." However, novelty and non-obviousness under 102 and 103 are separate inquiries from patent eligibility under 101. A claim may recite a novel application of an abstract idea and still be ineligible. As the Supreme Court stated in Mayo Collaborative Services v. Prometheus Laboratories, Inc., "to transform an unpatentable law of nature into a patentable application of such a law, one must do more than simply state the law of nature while adding the words 'apply it.'" The Court emphasized that even inventive applications of abstract ideas can be ineligible if they lack additional elements that integrate the exception into a practical application or provide an inventive concept.
The Federal Circuit has consistently applied this principle. In Ariosa Diagnostics, Inc. v. Sequenom, Inc., the court held claims ineligible even though they involved a "groundbreaking" discovery that was "valuable" and involved "inventive" steps, because the claims were directed to a natural phenomenon without additional elements sufficient to ensure the claims amount to significantly more. Similarly, in SAP America, Inc. v. InvestPic, LLC, the court held that claims to a method of using statistical analysis to evaluate investment risk were abstract even though they may have been "new or non-obvious."
Comparison to Recent USPTO AI Examples Demonstrates Lack of Technological Improvement: The USPTO's 2024 Subject Matter Eligibility Update provides instructive examples of AI-related claims. Example 47 involved an application-specific integrated circuit (ASIC) implementing an artificial neural network with specific hardware components (neurons comprising registers and microprocessors connected via synaptic circuits with stored weights). The claim was found eligible at Step 2A Prong One because it recited specific physical hardware, not merely abstract algorithmic concepts.
Example 48 analyzed claims to speech separation methods using deep neural networks. Claim 1 of Example 48 was found eligible because "the claim reflects the improvement discussed in the disclosure by reciting details of how the DNN aids in the cluster assignments to correspond to the sources identified in the mixed speech signal, which are then synthesized into separate speech waveforms in the time domain and converted into a mixed speech signal, excluding audio from the undesired source." The eligibility analysis emphasized that the claim reflected specific technical improvements to speech separation technology, not merely the application of DNNs to speech processing.
In contrast, Example 49 Claim 1 involved using a DNN to predict medical treatment outcomes but was found ineligible because "the claim, however, only requires determining the embedding vectors and therefore does not reflect the improvement discussed in the disclosure. The recited generic DNN merely adds a generic computer component to perform the method and therefore fails to provide an improvement to the technology or technical field."
Amended Claim 1 here is analogous to Example 49 Claim 1. While the specification may describe benefits of the invention, the claim recites using Naive Bayes classifiers and generic computer components at a high level of generality without reflecting specific technical improvements to machine learning technology, data storage technology, or any other technology. The claim instructs practitioners to apply known statistical methods to travel price data using conventional computer systems, which does not constitute an improvement to technology under MPEP 2106.05(a).
Amended Claim 1 does not provide an improvement to the functioning of a computer or any other technology. The claim applies conventional machine learning algorithms (Naive Bayes classification) using generic computer components to perform abstract analytical tasks (price prediction and fare availability inference) in the travel industry. The alleged reduction in data storage requirements results from a business decision to maintain an incomplete dataset rather than from any technological advancement in data storage. The claim provides instructions to implement abstract ideas using generic technology, which is insufficient for patent eligibility under the framework established by Alice and its progeny. Accordingly, the rejection is 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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-30 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-30 is/are directed to the abstract idea of providing a set of price estimates for multiple forms of transportation. 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 than the judicial exception itself. Claim(s) (1-30) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 §101 Examination Guidelines), claim(s) (1-27) is/are directed to a method, and claim(s) (28-30) is/ are directed to a server, and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) 1-30 recite(s) mental process, while it could fall under the category of organizing human activity, for the purpose of this rejection as the information is collected, analyzed and the specific data is provide for analysis purposes the Examiner will focus on a mental process. Specifically, the independent claims a mental process as drafted, the claim recites the limitation of providing a set of price estimates for multiple forms of transportation which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a computer server nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the computer server language, the claim encompasses the user manually determining price estimates. The mere nominal recitation of a generic a computer server does not take the claim limitation out of the mental processes grouping. This limitation is a mental process. While the Guidance provides that claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations (GPS position calculation, network monitoring, data encryption for communication, rendering images). However with regard to the instant application the Examiner has reviewed the disclosure and determined that the recited claim limitations are described as a concept that can be performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Note to the Applicant per the 2019 October Guidance: The 2019 PEG sets forth a test that distills the relevant case law to aid in examination, and does not attempt to articulate each and every decision. As further explained in the 2019 PEG, the Office has shifted its approach from the case-comparison approach in determining whether a claim recites an abstract idea and instead uses enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent. By grouping the abstract ideas, the 2019 PEG shifts examiners’ focus from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. In sum, the 2019 PEG synthesizes the holdings of various court decisions to facilitate examination.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer additionally, explain that data receiving, storing, receiving, obtaining, storing, providing, outputting , and storing steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a computer server is used to perform the determining, grouping, deriving, identifying, and configuring steps. The computer server in both steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (providing a price estimates). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): receiving, storing, receiving obtaining, grouping, storing, providing, outputting, and storing performs the determining, grouping, deriving, identifying, and configuring steps. The receiving, obtaining, grouping, storing, providing, outputting, and storing steps are recited at a high level of generality (i.e., as a general means of managing data for use in the determining, grouping, deriving, identifying, and configuring step), and amounts to mere data manipulation, which is a form of insignificant extra-solution activity. The computer server that performs the determining, grouping, deriving, identifying, and configuring steps are also recited at a high level of generality, and merely automates the determining, grouping, deriving, identifying, and configuring steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the computer server).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, which in this case it is not clear that the specification sets forth an improvement in technology, the claim must not reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
Note to the Applicant from the MPEP 2106.05(a): Generally, Examiners are not expected to make a qualitative judgment on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 C.F.R. § 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification. For example, in response to a rejection under 35 U.S.C. § 101, an applicant could submit a declaration under § 1.132 providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion.
For further clarification the Examiner points out that the claim(s) 1-30 recite(s) receiving, determining, obtaining, grouping, deriving, storing, identifying, configuring, providing outputting, and storing which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for receiving, storing, receiving, determining, obtaining, grouping, deriving, storing, identifying, configuring, providing outputting, and storing which is the abstract idea steps of valuing an idea (providing a set of price estimates for multiple forms of transportation) in the manner of “apply it”.
Thus, the claims recite an abstract idea directed to a mental process (i.e. to provide a set of price estimates for multiple forms of transportation). Using a computer to receiving, storing, receiving, determining, obtaining, grouping, deriving, storing, identifying, configuring, providing outputting, and storing the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The providing a set of price estimates for multiple forms of transportation would clearly be to a mental activity that a company would go through in order to determine price estimates. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine price estimates for travel purposes:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 3, 5, 6, 8, 9, 11, 23, 27, and 29 recite limitations which further limit the claimed analysis of data.
Claims 24 and 26 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally, with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Stefanescu which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 2, 4, 7, 10, 12-22, 25, 28, and 30 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on to provide a set of price estimates for multiple forms of transportation. This is not a technical or technological problem but is rather in the realm of travel management and therefore an abstract idea.
Step 2B
The claim(s) 1-30 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
With respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the claims that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication could include a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WILLIAMS et al. (U.S. Patent Publication 2021/0295361 A1) discloses a method and server for providing a set of price estimates, such as air fare price estimates.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625