DETAILED ACTION
Status of Claims
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Accordingly, Applicant's filed response has been entered.
This is a Non-Final office action in response to communication received on June 05, 2026. Claims 1-20 are pending and examined herein.
Claim Rejections - 35 USC § 101
2. 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. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-7 are a system; claims 8-14 are a method; and claims 15-20 are a non-transitory computer readable medium. Thus, each claim 1-20, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-20 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of evaluating market data for a financial instrument to detect suspicious activity and provide trade surveillance and compliance which invokes certain methods of organizing human activity, mental processes, and mathematical concepts.
The phrase "Certain methods of organizing human activity" applies to 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). Further, see MPEP 2106.04(a)(2) II. A-C. Similarly here financial data or commercial interaction is being evaluated to maintain legal or contractual obligations of financial services provider with the government and/or consumer to maintain their license with SEC and/or FINRA, and/or maintain good business relations with consumers by providing fraud detection services, for instance see as-filed spec. paras. [0025]-[0027].
The phrase "Mental processes" applies to concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Further, see MPEP 2106.04(a)(2) III. A-C. Similarly here an analyst can selectively initiate a more thorough investigation by evaluating a fraud alert using/activating a trained machine learning model that correlates most closely with the detected unusual pattern(s) to ascertain whether fraudulent activity is indeed occurring or not.
The phrase "Mathematical concepts" applies to mathematical relationships, mathematical formulas or equations, mathematical calculations, note claim recitation of “model” (per independent claims) and recitation of ANN (per claims 7, 14, 20) as claimed, and see at least as-filed spec. para. [0062] and [0064]-[0066]. Further, see MPEP 2106.04(a)(2) I. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-20 at least computer system comprising at least one processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the at least one processor, to perform operations, storing in a memory, a trained shape detection machine learning model, automatically, receiving input data and transmitting alert (per claims 1 and 8); a non-transitory computer-readable medium having stored thereon computer-readable instructions executable by at least one processor to perform operations, (additionally per claim 15); machine learning model (per claims 1, 8, and 15); interface (claims 4, 6, 11, 13, 18-19). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are generic computer components. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions such as enabling, disabling, and applying machine learning analysis to input data (see MPEP 2106.05(f)). The additional elements are generic as they are described at a high level of generality, see at least as-filed Figs. 1, 4, and their associated disclosure. Provided data and outputting alert on an interface is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes data to ascertain whether suspicious activity using machine learning as a tool which is described at a high level of generality. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group). For example, a mere data gathering such as a step of obtaining information about credit card transactions so that the information can be analyzed in order to detect whether the transactions were fraudulent, see MPEP 2106.05(g). The abstract idea is intended to be merely carried out in a technical environment such as receiving input data via a network, analyzing said data via a generic processor using a trained machine learning to detect suspicious activity in financial data and transmitting alert via network to a user of the system, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of evaluating market data for a financial instrument to detect suspicious activity and provide trade surveillance and compliance (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of evaluating market data for a financial instrument to detect suspicious activity and provide trade surveillance and compliance (prong one) - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however, fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here financial instrument (see claim 2) data is provided and analyzed];
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)."; and (iii) collecting and analyzing information to detect misuse and notifying a user when misuse is detected (FairWarning) [similarly here as a post solution suspicious activity alert is provided].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
1. The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of machine learning as follows:
i) Chandramouli, Patent: US 8,442,683 note para. [0005]-[0007] and [0029]-[0033]; (ii) Lee, Pub. No.: US 2002/0107926 note para. [0020]; (iii) Kwok, Pub. No.: US 2002/0150295 note para. [0015]; (iv) Teller, Pub. No.: US 2004/0133081 [0236]-[0238]; (v) Agrawal and Srikant Patent No.: US 6546389 note "As recognized herein, the primary task of data mining is the development of models about aggregated data. Accordingly, the present invention understands that it is possible to develop accurate models without access to precise information in individual data records."; (vi) Deshpande et al., Pub. No.: US 2015/0134413 [0046] Using the target and input features, in step F3 of FIG. 1, a plurality of forecasting models are built for a product or a product category, a location, and a time window. A plurality of forecasting models can be built using existing machine learning based methods and/or time-series forecasting methods, and using the standard training-testing-validation methods. In an exemplary embodiment, only the highest quality models with high quality (high accuracy, precision, recall, etc.) are retained.; [0078] The processing system forecasting engine 202 can also include a forecasting model building engine 224 and a forecast calculation engine 226. In the model building stage, target and input features based on a customer or a customer segment's past data are used to train, test, and validate different types of forecasting models using machine learning and/or time series forecasting based approaches. Individual models are retained depending on the performance. The output of plurality of these retained models can then be fused into a single model 228. The fusion can be based on a rule-based approach or by assigning weights to individual model and combining those using ranking or combination techniques." (vii) Wei et al., Pub. No.: US 2015/0235260 [0080] Then, analysis module 532 may determine one or more predefined model(s) 546 based on event data 538 and the one or more targeting criteria. For example, analysis module 532 may use training and testing subsets of this information to generate one or more machine-learning models. The one or more predefined model(s) 546 may allow estimates of the number of future events to be determined for terms 544 in the one or more targeting criteria 542.; (viii) Beatty, Pub. No.: US 2012/0166267 see [0177] note "the prediction of conversion rate is performed by a machine-learning system that is trained using historical purchase data available to the ad system. The training set contains instances of purchase/no purchase decisions and many data points about the (user, context, offer). For example, the training examples might contain the following data points about the offer that was made to a user: price of offer, % discount of offer, popularity of merchant, time of day, gender of user, income of user, interests of user, websites visited by user, categories of websites visited by user, search queries by user, category of business, number of friends that had purchased the offer, "closeness" of friends that had purchased the offer, physical distance between the user's home and the business, physical distance between the user's workplace and the business, the "cluster id" of the user (generated by a clustering algorithm that placed, and users into clusters based on similar attributes of preferences)."
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Reason(s) for Withdrawal of Prior Art Based Rejection
3. Claims 1-20 were previously as being unpatentable and obvious over combination of Adjaoute (Pub. No.: US2020/0074472), in view of Smith et al. (Pub. No.: US 2019/0379589) referred to hereinafter as Smith.
Upon conducting an updated search, the Examiner considers the following as the closest prior art references in view of an updated search conducted based on the claim amendments filed 06/05/2026:
*Being noted initially
- Pub. No.: US2025/0390879 see Abstract note “A device, system and method for machine-generated automatic fraud detection using a large language model to generate a human-readable summary to detect anomalies in a user's transaction history behavior. A prompt may be input into a large language model comprising a set of features of the user's current and past transactions and instructions to generate a summary explaining deviation in the user's behavior between the current and past transactions. The summary may be analyzed to detect if the deviation in the user's behavior is anomalous. When the analysis detects deviant behavior patterns between the user's current and past transactions, fraud may be suspected to automatically trigger a preventative anti-fraud action, e.g., to pre-emptive cancel, delay execution or escalate interrogation, of the current transaction.”; [0010] FIG. 1 is a flowchart of a method for automated fraud detection using a large language model, in accordance with some embodiments of the invention; [0011] FIG. 2 schematically illustrates an example system for automated fraud detection using a large language model, in accordance with some embodiments of the invention; [0012] FIGS. 3-4 are flowcharts of methods for automated fraud detection using a large language model, in accordance with some embodiments of the invention; [0022] Embodiments of the invention bridge the human-machine divide in fraud detection by executing a machine-driven phase using a large language model to generate transaction summaries with human-readable fraud analysis descriptions, rational and/or insights (conventionally absent from a black box ML that outputs only a fraud conclusion) to prompt a human fraud analyst to improve speed and accuracy in the human-driven phase. The human fraud analyst, and/or an automated (e.g., ML or rule-based) model, may compare the LLM-generated summary of a user's behavior with other users' behavior, for example, to detect anomalies or atypical behavior of the user. Other users' behavior may be transactions executed by the other users that are related to the user, e.g., such as those using the same or connected device(s), transacting with the same recipient(s), bank(s), account(s) and/or entit(ies), etc.).
*Previously noted
- Pub. No.: US2024/0144662, see [0003] Such training using feedback data may improve the accuracy of an ML classifier, but is usually not sufficient to provide new learnings if the profile of the input data significantly changes. However, in real-time or near real-time analytics, insights or decisions may need to be obtained instantly based on an incoming data stream rather than static data stored in repositories. This is because, in such business scenarios, insights or decisions are based on incremental information and are perishable in the sense that latency between the incoming data and the decisions that are drawn based on this data may significantly reduce usefulness of the decisions. It is not surprising, therefore, that real-time analytics has gained considerable attention in recent years. Some applications of real-time analytics using streaming data include financial fraud detection, purchase recommendation, weather forecasting, network management, operations research, security surveillance, and algorithmic trade using stock market data.
[0007] Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support adaptive machine learning classification that mitigates effects of catastrophic forgetting while also reducing overall processing and memory resource requirements. In order to mitigate catastrophic forgetting with fewer processing and memory resource requirements than conventional continuous learning systems, one or more aspects described herein adaptively determine assignment of streaming data between a pretrained machine learning (ML) classifier that does not use continuous learning, and therefore is less complex and requires less resource use and power consumption, and an ML classifier that is trained to perform continuous learning (e.g., a continuous learning ML classifier). The assignment of the streaming data as input may be dynamically switched from one type of ML classifier to the other type of ML classifier based on characteristics of the streaming data, such as data drift (e.g., concept drift). In this manner, the more resource-intensive continuous learning ML classifier may only be used to compensate for increases in data drift that sufficiently degrade performance of the less complex ML classifier, thereby reducing processing and memory requirements as compared to conventional continuous learning-based systems while also mitigating the effects of catastrophic forgetting as compared to conventional ML systems that do not employ continuous learning.
[0068]; [0069] "The method 400 includes receiving an unlabeled data feed (e.g., one or more data stream), at 402. The unlabeled data feed may be any type of streaming or high volume data provided for the performance of classification services. As a non-limiting example, the unlabeled data feed may represent multiple transactions by customers of a credit card company, and the classification (e.g., prediction) services may be configured to predict whether the transactions are approved or fraudulent. The method 400 includes activating a lower complexity ML classifier and providing the data feed as input to the lower complexity ML classifier, at 404. For example, the unlabeled data feed may be handled for classification in portions (e.g., windows), and a window of the unlabeled data stream may be provided as input to the lower complexity ML classifier to cause the lower complexity ML classifier to output classification output (e.g., predictions) for the window of the unlabeled data feed. The lower complexity ML classifier is less complex, and therefore is associated with less stringent resource requirements and consumes less power, than continuous learning ML classifiers that may also be used during performance of the method 400. For example, the lower complexity ML classifier may include or correspond to the first ML classifier 126 of FIG. 1 or the pretrained ML classifier 204 of FIG. 2."; [0070]-[0074].
- US20190392351A1 see Abstract "evaluating and deploying machine learning models for anomaly detection of a monitored system and related systems. Candidate machine learning algorithms are configured for anomaly detection of the monitored system. For each combination of candidate machine learning algorithm with type of anomalous activity, training and cross-validation sets are drawn from a benchmarking dataset. Using each of the training and cross-validation sets, a machine-learning model is trained and validated using the cross-validation set with average precision as a performance metric. A mean average precision value is then computed across these average precision performance metrics. A ranking value is computed for each candidate machine learning algorithm, and a machine learning algorithm is selected from the candidate machine learning algorithms based upon the computed ranking values. The selected machine learning model is deployed to a monitoring system that executes the deployed machine learning model to detect anomalies of the monitored system."
- IN202341058336A Machine learning-based fraud detection system for financial transactions
ABSTRACT The present invention leverages the power of advanced machine learning algorithms and data analytics techniques to detect, prevent, and mitigate potential fraudulent behavior across diverse financial platforms. The system's core components encompass data collection from multiple sources, preprocessing, and feature engineering to ensure data quality. It employs state-of-the- art anomaly detection models trained through supervised and unsupervised learning methods to identify anomalous patterns and deviations from normal transactional behavior. Real-time monitoring and alerting mechanisms promptly flag suspicious activities, while an adaptive learning system continuously updates the models to stay effective against evolving fraud patterns. The user-friendly interface provides transparent insights, aiding authorized personnel in making informed decisions and complying with regulatory requirements. The method's scalability, privacy measures, and regular performance evaluations underscore its suitability for integration into existing financial infrastructures, offering an efficient and reliable fraud detection solution.
- KR 102443269 B1 "The artificial neural network may be modified through the process of adjusting the weights for at least one node included in the artificial neural network while comparing.
- US 10,552,735 see Abstract “techniques are described for using machine-learning artificial intelligence to improve how trading data can be processed to detect improper trading behaviors such as trade spoofing. In an example embodiment, semi-supervised machine learning is applied to positively labeled and unlabeled training data to develop a classification model that distinguishes between trading behavior likely to qualify as trade spoofing and trading behavior not likely to qualify as trade spoofing. Also, clustering techniques can be employed to segment larger sets of training data and trading data into bursts of trading activities that are to be assessed for potential trade spoofing status”
The live commerce server 1000 may further include a Fraud Detection System (FDS) in addition to the artificial intelligence algorithm to detect abnormal financial transactions. When an abnormal financial transaction is detected in the specific live commerce platform server, the live commerce server 1000 may stop collecting sales information from the specific live commerce platform server. The abnormal financial transaction detection system can be used for the number of views, the number of comments, and various viewer reaction information. Through the abnormal financial transaction detection system, abnormal data such as hoarding, ranking manipulation, connection failure, macro reaction, etc. can be detected from sales information. Sales information in which abnormal data is detected may be excluded from input to an artificial intelligence algorithm."
However, one or more prior art references of record fail to teach the claims as amended on 10/27/2025 as previously noted, i.e. enabling plurality of disabled machine learning models to save resources when a particular suspicious trading activity has been detected based on a trained shape detection machine learning model.
Response to Applicant’s Remarks
4. As per 101, the Applicant argues “The Applicant submits that the amended independent claims 1, 8, and 15 are significantly more than an abstract idea. The Applicant respectfully submits that the claimed subject matter recites a concept of pattern detection in data graphs and selective dynamic activation of analytical models stored in a disabled configuration, which is not practically performable using human cognitive processes and is not similar to any abstract idea previously identified by the courts.” The Applicant is reminded the claims must be given their broadest reasonable interpretation in light of the as-filed specification and the evaluation of 101 is carried out using 2019 Patent Eligibility Guidance (hereinafter PEG) which is incorporated in the MPEP. Under prong one, the evaluation is based on abstract recitation, not additional elements. The additional elements are first considered under prong two when the claim is evaluated as a whole (contrary to the Applicant’s assertion under “Applicant submits that the examination did not adequately assess the claims in their entirety”) and the additional element(s) is/are exclusively considered singularly and in-combination under step 2B analysis to assess whether the additional element(s) is/are significantly more. With that said, prong one analysis has been updated to ensure model as recited in the claim is properly evaluated, note the rejection as updated. Thus, based on abstract recitation it is squarely clear that the claims invoke certain methods of organizing human activity, mental processes, and mathematical concepts. The Examiner has also considered machine learning as additional element under prong two. Accordingly, the Applicant’s argument in view of additional elements such as “machine learning architecture”, “storing, in memory”, and “significantly more” are misplaced – as the evaluation under prong one is based on abstract recitation. The Examiner also finds no correlation between instant application, and Example 39 and Enfish as the unique facts of instant application, and Example 39 and Enfish are indeed different. The Applicant also argues “as a whole, integrate any alleged abstract idea into a practical application and therefore are patent-eligible” under prong one which erroneous as that is prong two inquiry where the entirety of claim recitation, i.e. abstract recitation and additional element as recited in the claims are considered as a whole. Therefore the Applicant’s arguments against prong one and allegation that claim as whole is not evaluated are unpersuasive.
Next, the Applicant argues against “Step 2A-PRONG 2 ANALYSIS” “Instead, the amended claims 1, 8, and 15 require algorithmic determination of relationships between computed data points in a data graph, generation of a shape detection metric based on shapes formed by connecting the data points corresponding to known suspicious activity patterns, mapping of the shape detection metric to specific analytical models stored in a disabled configuration, automatic activation of only the mapped analytical models while maintaining all remaining analytical models in their disabled configuration, computation of a confirmation value, transmission of an alert, and modification of data associated with the input data. Thus, the claimed subject matter is tied to concrete trade surveillance computer system behavior and applies the recited shape detection machine learning architecture to dynamically alter the operational state of the system by selectively activating specific analytical models in real time based on detected patterns.” And, the Examiner is aware that well-understood, routine, or conventional (hereinafter WURC) or significantly more is not to be evaluated under prong two as it is step 2B inquiry. The Applicant on the other hand has argued significantly more under both prong one and prong two (for instance note on pg. 27 “As such, the Applicant's amended claims recite significantly more.”). The Examiner respectfully disagrees as neither computer is improved nor machine learning as both are utilized as tools to evaluate input data. There is a clear distinction between improving a computer or a technical field and merely using it to evaluate data to selectively use additional machine learning models to further investigate. Thus, just as a financial or government analyst would further investigate when a suspicious activity is detected, here, there is one or more different models other than the one or more models that initially detected suspicious activity, is/are utilized to confirm the suspicious activity. model or algorithm such as ANN and/or shape detection were already evaluated under prong as mathematical concepts and the courts have already noted (i) SAP v. Investpic: Page 2, line 22 through Page 3, line 13 “Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting.”; and as it pertains to machine learning, note (2) “Page 12, lines 1-4: The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.
Page 2, lines 15-18: We affirm because the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.
Page 10, lines 16-19: claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible.
Page 13, lines 1-26: claims that do not delineate steps through which machine learning technology achieves an improvement are not patent eligible.
Page 14, lines 13-25: an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.
Page 14, line 26 through Page 15, line 13: disclosure of an "already available [technology] with [its] already available basic functions, to use as [a] tool[] in executing the claimed process" is still an abstract idea.
Page 15, line 14 through Page 16, line 3: the use of existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could be previously achieved does not render a claim eligible.”
Therefore, further investigating to confirm remains an abstract idea which is a tiered approach in utilizing model(s), as such, when “viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of evaluating market data for a financial instrument to detect suspicious activity and provide trade surveillance and compliance (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.” Therefore, the Examiner respectfully finds the Applicant’s arguments unpersuasive.
Next the Applicant presents arguments against “III. Claim Rejections - 35 U.S.C. § 103” which noted generally relevant prior art. The Applicant’s thoroughness in arguing against prior art references that are newly discovered and not relied upon is appreciated. The rejection remains withdrawn.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution, at least note the following:
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIPEN M PATEL whose telephone number is (571)272-6519. The examiner can normally be reached Monday-Friday, 08:30-17:00 EST.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
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