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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application is a continuation application of U.S. application 16386913 filed on 04/17/2019. See MPEP §201.07.
In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
DETAILED ACTION
The following NON-FINAL Office action is in response to Applicant’s request for continued examination filed on 05/19/2026.
Status of Claims
Claims 1,7,11,17 have been amended with the 05/19/2026 amendment.
Claims 9 and 19 have been canceled with the 05/19/2026 amendment.
Claims 1-5, 7-8, 10-15, 17-22 are currently pending and have been rejected as follows.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/19/2026 has been entered.
Priority
Examiner noted Applicants claiming Priority from Application 16386913 filed 04/17/2019, which, at its turn, claims priority from Provisional US Application 62660100 filled 04/19/2018.
Response to Applicant rebuttal argument on the Nonstatutory Double Patenting rejection
Remarks 05/19/2026 p.9 ¶3 argues that the amended claims distinguish over U.S. Patent No. 11810026 B2 and as such Nonstatutory Double Patenting rejection should be withdrawn.
Examiner fully considered the argument but respectfully disagrees noting that the newly amended limitations from the now canceled dependent claims 9,19 into the now amended independent Claims 1,11 of current Application under examination, correspond to the limitations of dependent Claim 2 of U.S. Patent No. 11810026 B2. As such, Claims 1-5,7-8,10-15,17-22 remain rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1,2,19,20 of patent US11810026 B2 because although the claims at issue are not identical, they are not patentably distinct from each other because Claims 1,2,19,20 of patent US 11810026 B2 recite substantially similar limitations as Claims 1-5, 7-8, 10-15, 17-22 of the current Application, with the major difference being that the limitations of Claim 1,2,19,20 of US 11810026 B2 appear to be spread throughout Claims 1-5, 7-8, 10-15, 17-22 of the current Application.
Response to Applicant rebuttal argument on the 112(b) rejection
Remarks 05/19/2026 p.10 ¶1 argues that Applicant has amended claims 1 and 11, and asserts that the rejections under 35 U.S.C. 112(b) are therefore rendered moot.
Examiner fully considered the 112(b) argument and clarifies the following:
- 112(b) rejection in the prior act at independent Claims 1,11 with respect to recitations of “gradient descent” is withdrawn in view of Applicant’s amendment.
- 112(b) rejection of dependent claim 9 is moot in view of Applicant canceling said claim
- 112(b) rejection of claim 17 is withdrawn in view of Applicant’s amending said claim 17 to correctly dependent from method claim 12.
- 112(b) rejection of claim 21 in prior act is maintained pending correction from Applicant.
Response to Applicant rebuttal argument on the 112(d) rejection
Remarks 05/19/2026 p.10 ¶2 argues that 35 USC 112(d) rejection is rendered moot in view of Applicant amending claim 17. Examiner considered the argument which is persuasive.
- 112(d) rejection in the prior act is now withdrawn.
Response to Applicant rebuttal argument on the 101 rejection
Step 2A prong one: Remarks 05/19/2026 p.13 ¶2 argues that the claim is not directed to a judicial exception according to the Step 2A Prong One analysis. However, for the sake of brevity, Applicant reserves comment regarding the reasons.
Step 2A prong one argument has been fully considered but is unpersuasive.
Examiner reincorporates all findings and rationales at Final Act 12/19/2025 p.13 last ¶ -p.16 ¶1 which found the claims to recited escribe or set forth the abstract idea of predictive data analysis using value-based predictive inputs as summarized by the title of the invention and reflected in the body of current claims 1-5,7-8,10-15, 17-22. Thus, the argument is unpersuasive.
Step 2A prong two: Remarks 05/19/2026 p.13 ¶3-p.15 ¶3 again cites Specification ¶ [0053]-¶ [0055] to allegedly show improvement in a technical field by providing "innovative techniques for generating entity-level predictive inputs ... [and] creating effective feature data structures that can be used with many predictive data analysis algorithms” to "address reliability problems associated with many existing predictive data analysis problems resulting from the inability of many conventional predictive data analysis models to integrate value-based prediction input information/data and improve existing technologies for predictive data analysis in many technical domains”. Previously, at the same Remarks 05/19/2026 p.10 ¶4-p.12 ¶2, the Applicant similarly argued the amended claims recite improvements in machine-learning technology, namely non-conventional predictive data analysis technique that address reliability problems associated with many existing predictive data analysis problems resulting from inability of many conventional predictive data analysis models to integrate value-based prediction input information/data and improve existing technologies for predictive data analysis in many technical domains, citing Specification ¶ [0053]-[0055] and Ex Parte Desjardins, to argue such improvement in computer functionality integrates any abstract idea into a practical application.
Step 2A prong two argument has been fully considered but is unpersuasive.
First, as an issue of claim construction and claim interpretation, it is noted that many of the features argued by the Applicant above, do not appear recited in the Specification, let alone in the claims. For example, at no point do the claims recite a severity score indicating a complexity
associated with one or more cancer treatments for the patient, as alleged by Applicant at Remarks 05/19/2026 p.12 by citation to Original Specification ¶ [0053] - ¶ [0055]. This finding is important since the “101 inquiry must focus on language of Asserted Claims themselves” as in “Synopsys, Inc. v Mentor Graphics Corp, U.S. Court of Appeals Federal Circuit, No 2015-1599, October 17 2016 2016 BL 344522 839 F3d 1138” citing “Accenture Global Servs., GmbH
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v
PNG
media_image1.png
1
1
media_image1.png
Greyscale
. Guidewire Software, Inc. 728
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F.3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1336, 1345 108 USPQ2d 1173 Fed Cir. 2013: admonishing that the important inquiry for a 101 analysis is to look to the claim”, citing “Content Extraction & Transmission LLC
PNG
media_image1.png
1
1
media_image1.png
Greyscale
v.
PNG
media_image1.png
1
1
media_image1.png
Greyscale
Wells Fargo Bank Nat’l Ass’n 776
PNG
media_image1.png
1
1
media_image1.png
Greyscale
F3d
PNG
media_image1.png
1
1
media_image1.png
Greyscale
1343, 1346 113 USPQ2d 1354 (Fed. Cir. 2014): We focus here on whether the claims of the asserted patents fall within the excluded category of abstract ideas”, cert. denied, 136 S Ct 119, 193 L. Ed. 2d 208 2015). This is consistent with MPEP 2103 I.C stating that “claims define the property rights provided by patent, thus require careful scrutiny. The goal of claim analysis is to identify boundaries of protection sought by applicant and to understand how claims relate to and define what applicant indicated is the invention. USPTO personnel must first determine the scope of a claim by thoroughly analyzing the language of claim before determining if claim complies with each statutory requirement for patentability”. Simply said “[T]he name of the game is the claim”.
Second, with respect to Original Specification ¶ [0053]-¶ [0055] as mentioned by Applicant at Remarks 05/19/2026 p.11-p.12 ¶1, p.14-p.15 ¶2, the Examiner notes Original Specification
¶ [0054] 2nd sentence: …”by generating entity-level prediction input information/data based at least in part on aggregation of underlying data, various embodiments of the present invention create more representative features for many transactional records, which in turn enable more accurate and reliable predictive data analysis in many transactional domains”.
Such “transactional domains” are further exemplified as “financial domains” at Original Specification ¶ [0049] 1st sentence with respect to an entity such as a customer entity, as read in light of at least Original Specification ¶¶ [0061]-[0062],[0067],[0076]-[0078], [0084]-[0086] etc.
This is reflected at independent Claims 1,11 by recitation of the generat[ed] “entity-level prediction data for a prediction entity based at least in part on raw transactional data”… to ultimately “determine, based at least in part on the first subset of prediction engines, one or more first entity predictions for the prediction entity”. It is then clear that, the current claims do recite application of machine learning to transactional data environments associated with business entities (i.e. customers) which remains patent ineligible. Moreover, in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025), and cited by PTAB Appeal 2025-003304, it was found that: “The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted based on real time changes do not represent a technological improvement” at least because they are “incident to the very nature of machine learning”. It the follows that here, “determining a minimal ratio of a plurality of quantile regression values that exceed a minimal prediction threshold”, “determining an outlier parameter for the particular quantile regression distribution”, “and” “determining the one or more outlier portions based at least in part on the minimal ratio and the outlier parameter”; as recited at independent Claims 1,11 would analogously be incident to the very nature of machine learning.
This ineligibility is also confirmed by MPEP 2106.04(a) I ¶3 showing that narrow laws that have limited applications are still ineligible1 and, further corroborated by MPEP 2106.04(a)(2) I. C (i) which cites SAP Am, Inc v InvestPic to state that performing a computerized algorithm such as resampled statistical analysis to generate a resampled distribution still recites, describes or sets forth the abstract exception. Examiner follows the Federal Circuit’s rationale in SAP Am Inc v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ.2d 1638 (Fed. Cir. 2018) as cited by MPEP 2106.04 (a)(2) I. C (i), and submits that here, as in SAP supra, given the transactional environments of business entities (i.e. customers), “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly of the alleged innovation to be innovation in a non-abstract realm. Specifically, the Examiner reincorporates the findings of Non-Final Act 08/13/2025 p.5-p.6 ¶2 and Final-Act 12/19/2025 p.2-p.8, p.13 last ¶ - p.18 ¶4, where it was found that the challenged patent in “SAP” similarly proposed an analogous utilization of resampled statistical [solution] to solve analysis of financial data, which did not assume a normal probability distribution [as an exemplary problem]. One such method disclosed in SAP was a bootstrap method, which estimated distribution of data in a pool (a sample space) by repeated sampling of the data in the pool. A sample space in a boot-strap method can be defined by selecting a specific investment or a particular period of time. Data samples are drawn from the sample space with replacement: samples are drawn from the sample space and then returned to the pool before next sample is drawn. Yet, the Federal Circuit noted: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715 . Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”…“the focus of the claims is not any improved computer or network, but the improved mathematical analysis”.
Since the solution in SAP of implementing a pool or sample space of data features, and the algorithmic properties of multiple models, such as boot-strap, jackknife, cross validation, and resampling in the algorithmic modeling did not save the claims in SAP from patent ineligibility, the Examiner similarly reasons that here, the analogous predictive analysis as asserted, by Remarks 05/19/2026 p.13 ¶3-p.15 ¶3, would at most represent the use of analogous “machine learning model”, “quantile regression distribution” and “outlier portions” as algorithms to “determine, based at least in part on the first subset of prediction engines, one or more first entity predictions for the prediction entity”, including the amended features of “determining a minimal ratio of a plurality of quantile regression values that exceed a minimal prediction threshold”, “determining an outlier parameter for the particular quantile regression distribution”, “and” “determining the one or more outlier portions based at least in part on the minimal ratio and the outlier parameter”; as rolled onto independent Claims 1,11, which should remain patent ineligible following the legal test in SAP supra, because they would represent an alleged improved mathematical analysis for transactional (i.e. financial) domains for a customer related entity. The Federal Circuit findings in “SAP” were further corroborated by “Versata Dev Grp, Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015” which again underlined the difference between an actual improvement to actual technology versus an abstract and ineligible improvement to an entrepreneurial goal or objective. Such improvement to an abstract, entrepreneurial goal or objective is set forth here as predictive customer lifetime, when read in light of Original Specification mid-¶ [0035], ¶ [0051], ¶ [0055], ¶ [0067], ¶ [0078], ¶ [0085], ¶ [0088] etc. and reflected in the claims 1,11 recitation of the generat[ed] “entity-level prediction data for a prediction entity based at least in part on raw transactional data”… to ultimately “determine, based at least in part on the first subset of prediction engines, one or more first entity predictions for the prediction entity”.
Yet, according to MPEP 2106.04 I ¶ 3, a claim is not patent eligible merely because it applies an abstract idea in a narrow way. Also as articulated by MPEP 2106.04(d)(1) ¶1, an argument of improvement in the judicial exception itself, as attempted by Applicant at Remarks 05/19/2026 p.10 last ¶ - p.12 ¶1, p.13 ¶3, is not improvement in technology. Step 2A prong one. Even when more granularly investigating the machine learning at Step 2A prong two and later at Step 2B below, the Examiner finds that they represent mere computerized algorithms to implement an entrepreneurial, abstract, business method, which, as tested per MPEP 2106.05(f)(2)(i), remains an example of invoking machines to apply the abstract exception, such as a business method and underlining algorithms without integrating the abstract exception into a practical application (Step 2A prong 2) or providing significantly more (Step 2B).
With respect to Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision), as raised by Applicant at Remarks 05/19/2026 p.10 last ¶-p.11 ¶1, the Examiner finds that in Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, the enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Here, however, at no point do the claims recite anything remotely similar to a technological mechanism pertinent to the reduction of storage capacity as in Desjardins. Rather, here, all or nearly all, of the features, argued by Remarks 05/19/2026 p.13 ¶3-p.15 ¶3, with respect to Step 2A prong two, remain mathematical, algorithmic for fundamentally economic or entrepreneurial processes, and thus integral to the abstract exception itself as identified and mapped at above with respect to Step 2A prong one. For example, the limitations of: “determining a minimal ratio of a plurality of quantile regression values that exceed a minimal prediction threshold, determining an outlier parameter for the particular quantile regression distribution, and determining the one or more outlier portions based at least in part on the minimal ratio and the outlier parameter” together with “selecting, from among a plurality of prediction engines and based at least in part on the one or more first predictive component values and the one or more outlier portions, a first subset of prediction engines with highest quantile regression values of a plurality of quantile regression values as a first most predicted value corresponding to the prediction entity”; would be not meaningfully different than organizing of information and manipulating it through mathematical correlations found by MPEP 2106.04(a)(2) I A as abstract mathematical relationships expressed in words, such as the repetitive algorithm found ineligible in Flook, or generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form found abstract in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014).
Also, when tested per MPEP 2106.05(f)(2)(i), the executions of such algorithms on a computer, would also not integrate the abstract exception into a practical application because, according to MPEP 2106.05(f), such computer components would represent mere invocation of machinery to execute the processes identified above.
Thus the claims are ineligible, and the Step 2A prong two argument is found unpersuasive.
Step 2B: Remarks 05/19/2026 p.15 ¶4 - p.16 ¶1 the claims recite an unconventional
combination of operations and data structures that provides non-routine results-and thus the
claims provide an inventive concept.
Examiner fully considered the step 2B argument but respectfully disagrees.
Examiner follows the guidelines of MPEP 2106.05 (d) II, ¶5-¶6, and carries over the findings of MPEP 2106.05 (f) and/or (h) tests above to submit that, even when tested as additional computer-based elements, the use of “machine learning model” and associated “quantile regression distribution” and “outlier portions” would also not provide significantly more, without the need to rely on the well-understood, routine and conventional test of MPEP 2106.05(d). Yet, assuming arguendo that further evidence would be required to demonstrate conventionality of the additional, computer-based elements above, the Examiner would also point as evidence to the high level of generality of the additional elements as read in light of Original Disclosure, such as:
* Original Spec. ¶ [0035] last sentence, reciting at high level of generality: “a person of ordinary skill in the art will recognize that the disclosed techniques can be utilized to generate predicted business intelligence predictions and/or perform prediction- based actions for any transactional network, such as a commercial transactional network, a medical transactional network, a scholastic transactional network, a social media transactional network, and/or the like”.
* Original Spec. ¶ [0091] - ¶ [0092] reciting at high level of generality: “Many modifications and other embodiments will come to mind to one skilled in the art to which this disclosure pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosure is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. For example, the foregoing description provides various examples of utilizing systems and methods for monitoring cognitive capability of a user. However, it should be understood that various embodiments of the systems and methods discussed herein may be utilized for providing reminders of any activity, such as exercising, eating healthy snacks, performing a particular task, calling another individual, and/or the like”.
* Original Spec. ¶ [0022], ¶ [0029], ¶ [0030] exemplifying at high level of generality
various conventional memories
* Original Spec. ¶ [0039] last sentence, exemplifying at high level of generality processors as “CPLDs, microprocessors, multi-core processors, coprocessing entities, ASIPs, microcontrollers, and/or controllers”
* Original Spec. ¶ [0090] reciting portals associated with commercially available Seacoast's RPS technology package. If necessary conventionality of banking portal is further shown by
* Original Spec. ¶ [0046] reciting at high level of generality external computing entity 102 may be embodied as an artificial intelligence (AI) computing entity, such as an Amazon Echo, Amazon Echo Dot, Amazon Show, Google Home, and/or the like, including training the machine learning model using a training algorithm such as gradient descent, gradient descent with backpropagation, and/or gradient descent with backpropagation over time.
In conclusion, the argued claims although directed to statutory categories (“apparatus” or machine at Claims 1-5,7-8,10,21 and “method” or process at Claims 11-15,17-19,20,22) they still recite, describe or at least set forth the abstract exception (Step 2A prong one), with their additional, computer based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Therefore, the Claims 1-5, 7-8, 10-15, 17-22 are not patent eligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Response to Applicant rebuttal argument on the 103 rejection
Remarks 05/19/2026 p. 16 ¶3 argues Applicant amend independent claims 1 and 11 to recite features of claims 9 and 19 found allowable at Final act 12/19/2025 p.26 and as such the 103 rejection should be withdrawn.
Examiner fully considered the argument which is found persuasive. Examiner resubmits closest prior art remains Yao; Yonggang US 20180181541 A1 and Eban, US 20190266513 A1.
The current, updated prior art search also revealed Radulovic et al, US 20060161402 A1
Radulovic ¶ [0057] 4th - 6th sentences: Since the history will inevitably contain some true abnormalities, a technique for trimming outliers should be implemented. If not, some severe abnormal events will occur eventually and will otherwise be included in the data history. This in turn could mean that for a very long time any other irregularities (less severe than the mentioned bad event) would not be classified as unusual.
Radulovic ¶ [0058] According to one exemplary technique, a predetermined length of initial data (e.g., 1000 values) is used to compute the median, “smooth” and “spiky” sequences, XSm(i), XSp(i), i=1,1000 and then all the appropriate windows Sm(ki), Sp(ki). Next, for each of these sequences the appropriate quantile function can be computed. For example, the following resolution mapping may be used in generating the quantile function:
For 1%→97% at 1% resolution
For 97%→100% at 0.033% resolution
For 0%→1% at 0.33% resolution. Consequently at
Radulovic ¶ 0062] complete history for each quantile function is captured with only 200 numbers
3 numbers for 0% to 1%,
97 numbers for 1% to 97%, and
100 numbers for 97% to 100%.
The increased resolution of 0.033% is used because an important portion of the analysis and thresholding may be expected to relate to high P-values (0.99% and more).
The increased resolution of 0.33% for small values may be used in applications of the invention where too fast errors are of interest. For example, when measuring network download times, unusually fast-dowffload times may be indicative of erroneous operation and therefore may be of interest. Of course, the above resolution map is merely exemplary and a different mapping function can be tailored to the particular needs of the analysis and/or system resources).
Radulovic ¶ [0063], outlier data can be trimed so that the histogram is smooth. The histogram of collected data in Fig.2 indicates probability distribution with a well defined tail. This suggests that there is an underlying acceptable probability distribution that is corrupted with a long string of outliers stretching out several orders of magnitude. With this in mind, the quantile function may be cut at certain level (i.e. at 98%) then extended from that cut off point with probability density p(t) = c / tq where q depends on window size
Radulovic ¶ [0069] 1st - 3rd sentences: According to one preferred embodiment, outliers are trimmed periodically from the quantile functions. Trimming prevents the predetermined cut-off point from being unstable over time. For example, as more and more data is added, the point M98 can converge to zero, for it would effectively become 98% of 98% of 98%, etc.
Radulovic ¶ [0064]-¶ [0067] For example, starting with the 98th percentile (in this example Q(133), where Q is the quantile function), the histogram is smoothly extended with formula. For I = 1,…67, we let
PNG
media_image2.png
69
342
media_image2.png
Greyscale
where k stands for the windows order (i.e. first, second…). With this modification the quantile functions may be extended in a smooth and conservative fashion. For example, the smallest window size (i.e. k=1) yields an extension that does not have a variance. In other words, from 98% (M98) onwards, a heavy tailed distribution (no variance) is applied. If data supports tails heavier than x−3, such data will be treated as outlier data and discarded in this example. If the real data is not heavily tailed, the described approach will shield against detection of false positives, as it will be harder to declare an observation unusual. ¶ 0069] According to one preferred embodiment of the present invention, outliers are trimmed periodically from the quantile functions. Trimming prevents the predetermined cut-off point from being unstable over time. For example, as more and more data is added, the point M98 can converge to zero, for it would effectively become 98% of 98% of 98%, etc. The collected data may be split into blocks (e.g., of length 1000) and trimming can be performed on these blocks. In other words, after a fixed period of time (e.g., 1000 observations), the quantile functions for these new 1000 data points will be computed and then polynomially extended at the predetermined (e.g., 98%) level using the above formula. Only then will the new data be added to the historical quantile functions. Again, this may be performed for all windows sizes and for all breakdowns (e.g., both smooth and spiky). Two quantile functions may be added by combining two sets of data (each produced by the one of the quantile functions), and then recomputing the quantile function. ¶ [0080] Finally, the method may be immunized to gradual deterioration of data. The periodic-dynamic update takes care of all the cases seen in real data. To be conservative, a buffer of predetermined length (e.g., 1000 data points) may be kept between the history and the last update. The buffer may be part, for example, of memory 290. For example, an initial period of 1000 points may be used to build the history and the next 1000 data points are kept unchecked in a buffer. The analysis then starts at the 2001st point. Once the 3001st point is reached, points 1001-2000 are used to construct and trim the quantile function (e.g., smoothly on the 98% level) and added dynamically to the “Old” quantile function. The buffer's 1000 points is then replaced with observations 2001-3000. Only then is the 3001st point compared with the Old histogram. Thus, in this example, there is always at least a 1000-point gap between the observed point and the history it is being compared with. Of course the buffer size used may be varied and, if desired, tailored to the application. In most applications, it is desirable that the buffer is large enough to hold a statistically significant sample of the collected data
However, neither Yao, nor Eban, now Radulovic teaches either alone or in combination with adequate rationales the recitation of: determine an outlier portion for a particular quantile regression distribution associated with a particular predictive component value of the plurality of predictive component values, by:
1. determining a minimal ratio of the plurality of quantile regression values that exceed a minimal prediction threshold;
2. determining an outlier parameter for the particular quantile regression distribution; and
3. determining the one or more outlier portions based at least in part on the minimal ratio and the outlier parameter as recited, with the other limitations, at independent Claims 1,11.
Claims 2-5,7-8,10,21 overcome the prior art by dependency to parent Claim 1.
Claims 12-15,17-20,22 overcome the prior art by dependency to parent Claim 11.
To be clear, the Examiner resubmits that novelty (35 USC 102) and non-obviousness (35 USC 103) still pertain to features that are mostly abstract that do not render the claims patent eligible (35 USC 101). Simply said the novel (35 USC 102) and non-obviousness (35 USC 103) rationale above do not necessarily render the claims patent eligible (35 USC 101). See for example MPEP 2106.04 I ¶5, 3rd sentence citing Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 "the novelty of the mathematical algorithm is not a determining factor at all”.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Objection
Claim 19 is objected for the following informality: while it is listed as canceled it still maintains the claim language. Claim 19 is recommended to be listed as canceled without the text similar to sister dependent Clam 9. Clarification and/or correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1-5,7-8,10-15 ,17-22 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1,2,19,20 of patent US11810026 B2 because although the claims at issue are not identical, they are not patentably distinct from each other because Claims 1,2,19,20 of patent US 11810026 B2 recite substantially similar limitations as Claims 1-5, 7-8, 10-15, 17-22 of the current Application, with the major difference being that the limitations of Claim 1,2,19,20 of US 11810026 B2 appear to be spread throughout Claims 1-5, 7-8, 10-15, 17-22 of the current Application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), ¶2, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 21 is dependent and has been previously added and still recites among others:
- “obtaining a quantile regression distribution for the predictive component value, wherein the quantile regression distribution indicates a distribution of a corresponding predictive component that is associated with the predictive component value across the one or more prediction entities via a plurality of quantile regression values”, [bolded emphasis added].
Claim 21 is rendered vague and indefinite because there is insufficient antecedent basis for “the” “prediction entities” as covered by expression “the one or more prediction entities” in said dependent Claim 21, as well as its parent dependent Claim 7, as newly amended, and ultimately, in parent independent claim 1, when following the claim hierarchy along the claim tree.
Claim 21 is recommended to be amended to recite, as an example only:
- obtaining a quantile regression distribution for the predictive component value, wherein the quantile regression distribution indicates a distribution of a corresponding predictive component that is associated with the predictive component value across one or more prediction entities via a plurality of quantile regression values,
Clarifications and/or corrections are required.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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 1-5, 7-8, 10-15, 17-22 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, here abstract idea) without significantly more. The claim(s) recite(s) describe or set forth the abstract predictive data analysis using value-based predictive inputs as summarized by the title of the invention and reflected in the body of the current claims 1-5, 7-8, 10-15, 17-22. This predictive analysis falls within the abstract grouping of computer-aided mental processes as tested per MPEP 2106.04(a)(2) III C #2,#3. Examiner follows USPTO’s 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence as well as MPEP 2106.04(a)(2) III C to submit that per # 2. Performing a mental process in a computer environment and per # 3. Using a computer as tool to perform a mental process, both set forth the abstract exception. Such a computerized tool or environment is reflected here by recitation of “machine leaning model trained using gradient descent”, “a first subset of prediction engines” (independent Claims 1,11) and “second subset of prediction engine” (dependent Claims 2,12) and “subset of prediction engines” (dependent Claims 8,18) select[ed] for subsequently “determining”, “one or more first entity predictions for the prediction entity” as concluded by independent Claims 1,11, through what appears to be equally abstract2 mathematical relationships or calculations such as “generate”, “entity-level prediction data for a prediction entity based at least in part on raw transactional data and one or more entity-level aggregation rules”; “determine” “based at least in part on a value-based predictive input associated with the prediction entity, one or more first predictive component values”; “determine one or more outlier portions for a particular quantile regression distribution associated with the one or more first predictive component values by determining a minimal ratio of a plurality of quantile regression values that exceed a minimal prediction threshold, determining an outlier parameter for the particular quantile regression distribution, and determining the one or more outlier portions based at least in part on the minimal ratio and the outlier parameter”; “select, from among a plurality of prediction engines and based at least in part on the one or more first predictive component values and the one or more outlier portions, a first subset of prediction engines with highest quantile regression values of a plurality of quantile regression values as a first most predicted value corresponding to the prediction entity”; (independent Claims 1,11), and similarly “determine” “one or more second entity predictions for the prediction entity” (dependent Claims 2,12), “wherein the one or more second entity predictions represent increased granularity as compared to the one or more first entity predictions” (dependent Claims 3,13), “generating aggregated entity-level data for the prediction entity based at least in part on aggregating the entity-level prediction data”; “generating, based at least in part on the aggregated entity-level data, the value-based predictive input”; “generating, one or more scaled quantile regression values based on a quantile regression value, a predictive component value, and a quantile regression ratio for the quantile regression value”; (dependent Claims 7, 17), “reducing the complexity of the entity-level prediction data comprises selecting a subset of prediction engines with highest scaled regression values” (dependent Claims 8,18), “determine, based at least in part on the value-based predictive input for the prediction entity, an entity closure prediction of the one or more action-based predictive outputs” (dependent Claims 10,20), and “for each predictive component value of the one or more first predictive component values: obtaining a quantile regression distribution for the predictive component value, wherein the quantile regression distribution indicates a distribution of a corresponding predictive component that is associated with the predictive component value across the one or more prediction entities via a plurality of quantile regression values, determining a non-minimum ratio for the quantile regression distribution as a ratio of a non-minimum portion of the quantile regression distribution that falls below or equals a minimum threshold value, determining non-outlier ratio for the quantile regression distribution based on a deviation between a full ratio and a product of the non-minimum ratio and an outlier parameter, and determining a non-outlier portion of the quantile regression distribution as a subset of the quantile regression distribution that comprises each segment of the quantile regression distribution whose respective quantile regression value fall below or equals the non-outlier ratio” (dependent Claims 21,22).
These are not meaningfully different than the abstract performing of a resampled statistical analysis to generate a resampled distribution, as in SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161,1163-65, 127 USPQ2d 1597,1598-1600 (Fed Cir 2018) cited by MPEP 2106.04(a)(2) I C i. Here, as in SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ.2d 1638 (Fed. Cir. 2018), “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” [namely predictive analysis] with no plausibly alleged innovation in non-abstract application realm. Specifically, the Examiner finds that the challenged patent in “SAP” proposed utilization of resampled statistical methods for analysis of financial data, which did not assume a normal probability distribution. One such method was found as a bootstrap method, which estimated distribution of data in a pool (sample space) by repeated sampling of the data in the pool, defined by selecting a specific investment or a particular period of time. Data samples are drawn from the sample space with replacement: samples are drawn from the sample space and then returned to the pool before next sample is drawn. Yet the Federal Circuit ruled: “Dependent method claims 2-7 and 10 add limitations… [that] require the resampling method to be a bootstrap method." SAP, 260 F. Supp. 3d at 715. Likewise, "[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method." Id. at 716. Because bootstrap, jack-knife, and cross-validation methods are all "particular methods of resampling," those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same). Dependent method claims 12-21 are no different”.
Since implementation of sample space, and algorithmic properties of boot-strap, jackknife, cross validation, and resampling in SAP’s modeling did not render the SAP claims less abstract and eligible, the Examiner similarly reasons that here, the analogous predictive analysis, identified and detailed above, would also set forth the abstract exception. This finding is corroborated by MPEP 2106.04(a)(2) III. A., 5th bullet point, which cites “Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54,119 USPQ2d 1739,1741-42 (Fed Cir 2016), to state that combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, still falls within the abstract exception. It then follows that here the select[ion] or collection, and subsequent determinat[ions] or analysis of mathematical information, which take the form of algorithms and mathematical relationships and calculations, to finally come up with certain prediction report[s] (dependent Claims 4,5,14,15) as examples of results of such collection and analysis would similarly recite, describe or set forth the abstract exception. Step 2A prong one.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
This judicial exception is not integrated into a practical application because per Step 2A prong two, because the individual or combination of the additional, computer-based elements is/are found to merely apply the already recited abstract idea. Here, the additional computer-based elements are represented by the memory instruct[ed] one or more processors of independent Claims 1,11 as well as the “user device” of dependent Claims 4,5,14,15, and possibly the computerized functionality of the “machine learning algorithm” of independent Claims 1,11.
As per the “user device” used for presentat[ion] report of dependent Claims 4,5,14,15, the Examiner points to MPEP 2106.05(f)(2)(v)3 stating that requiring use of a computer component to tailor information and provide it to the user on a generic computer represents mere invocation of computer or machinery as a tool to apply the abstract idea or an existing process and thus does not integrate the abstract exception into a practical application. As per the memory instruct[ed] one or more processors as well the “machine learning algorithm” of Claims 1,11 the Examiner points to the legal finings in SAP supra as well as the MPEP 2106.05(f)(2) (i)4 test which states that applying a mathematical algorithm on a computer, represents mere invocation of computers or machinery as a tool, which again does not integrate the abstract idea into a practical application. Similarly, MPEP 2106.05(f)(2) iii5 finds that a process for monitoring audit log data that is executed on a general-purpose computer also represents a mere invocation of computers or machinery as a tool, which again does not integrate the abstract idea into a practical application. Additionally and/or alternatively, such abstract exception as identified above, could also be viewed as narrowed to a field of use or technological environment represented by computerization and machine learning, and selection of prediction engines in a manner not meaningfully different than narrowing the combination of collection of collecting information, analyzing it, and displaying certain results of the collection and analysis to a technological environment, as in Electric Power Group, LLC v Alstom S.A., 830 F.3d 1350,1354, 119 USPQ2d 1739,1742 (Fed. Cir. 2016) as cited by MPEP 2106.05(h) vi.
Here, no matter which of the MPEP 2106.05(f) and/or MPEP 2106.05(h) tests is/are being used, the result is the same, namely; that the additional computer-based elements, do not integrate the abstract idea into a practical application. Step 2A prong two.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, the Examiner follows the guidelines of MPEP 2106.05 II, ¶5-¶6 , and carries over the findings of MPEP 2106.05 (f) and/or (h) tests above to submit that the purported automation or computerization above, even if construed as additional computer-based elements would also not provide significantly more, without the need to rely on the well-understood, routine and conventional test of MPEP 2106.05(d). Yet, assuming arguendo that further evidence would be required to demonstrate conventionality of the additional, computer-based elements above, the Examiner would also point as evidence to the high level of generality of the additional elements as read in light of Original Disclosure, such as:
* Original Spec. ¶ [0035] last sentence, reciting at high level of generality: “a person of ordinary skill in the art will recognize that the disclosed techniques can be utilized to generate predicted business intelligence predictions and/or perform prediction- based actions for any transactional network, such as a commercial transactional network, a medical transactional network, a scholastic transactional network, a social media transactional network, and/or the like”.
* Original Spec. ¶ [0091] - ¶ [0092] reciting at high level of generality: “Many modifications and other embodiments will come to mind to one skilled in the art to which this disclosure pertains having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosure is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. For example, the foregoing description provides various examples of utilizing systems and methods for monitoring cognitive capability of a user. However, it should be understood that various embodiments of the systems and methods discussed herein may be utilized for providing reminders of any activity, such as exercising, eating healthy snacks, performing a particular task, calling another individual, and/or the like”.
* Original Spec. ¶ [0022], ¶ [0029], ¶ [0030] exemplifying at high level of generality
various conventional memories
* Original Spec. ¶ [0039] last sentence, exemplifying at high level of generality processors as “CPLDs, microprocessors, multi-core processors, coprocessing entities, ASIPs, microcontrollers, and/or controllers”
* Original Spec. ¶ [0090] reciting portals associated with commercially available Seacoast's RPS technology package. If necessary conventionality of banking portal is further shown by
* Original Spec. ¶ [0046] reciting at high level of generality external computing entity 102 may be embodied as an artificial intelligence (AI) computing entity, such as an Amazon Echo, Amazon Echo Dot, Amazon Show, Google Home, and/or the like, including training the machine learning model using a training algorithm such as gradient descent, gradient descent with backpropagation, and/or gradient descent with backpropagation over time.
In conclusion, Claims 1-5, 7-8, 10-15 and 17-22 although directed to statutory categories (“apparatus” or machine at claims 1-5,7-8,10,21 and “method” or process at claims 11-15,17-20,22) they still recite, describe or at least set forth the abstract exception (Step 2A prong one), with their additional, computer based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B). Therefore, the Claims 1-5, 7-8, 10-15, and 17-22 are not patent eligible.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Conclusion
Following art is made of record and considered pertinent to Applicant’s disclosure:
* GB 2547993 A teaching Real Time Autonomous Archetype Outlier Analytics
* Quantile regression, wikipedia, archives org, Feb 20, 2017
* US 20170206466 A1 relevant to the newly amended threshold and outlier limitations of independent Claims 1,11 since US 20170206466 A1 recites at ¶ [0042] To reduce the occurrences of the low probability assigned to an archetype and improve the resolution with a concentration of higher probability for each archetype, in one implementation, a threshold Pt may be chosen such that only the PANs with maximum probability greater than the threshold Pt are assigned to the corresponding archetypes. ¶ [0047] 4th sentence: The distributions may vary from time t1 to t2. And the two sets of vertical lines show the locations of the quantiles 95% (dashed) and 99% (solid). Additional details at ¶ [0048] - ¶ [0056]. ¶ [0059] the leftover subset (dashed vertical line, subset 21) takes over 27% of PANs for the threshold of Pt=0.8 in this example, i.e., about 27% of PANs have maximum probability in all the archetypes less than Pt=0.8. For example, if the score is in the score interval [0, 0.8*C], C is a user-set maximum score constant, the transaction may be labeled as normal transaction and if in the score in the interval (0.8*C, C], the transaction may be labeled as a fraudulent [or outlier] transaction.
* US 7756676 B1 teaching Detecting Data Change Based On Adjusted Data Values
* US 20140344023 A1 teaching Methodology And Process To Price Benchmark Bundled Telecommunications Products And Services
* US 20130036036 A1 Multiple funding account payment instrument analytics teaching the identification of ourtliers and non-outliers in quantile functions at Fig.2 and associated text
* US 20180365298 A1 teaching methods and systems to reduce time series data and detect outliers
* US 20090024427 A1 Analyzing Time Series Data That Exhibits Seasonal Effects
* US 20160142256 A1 Automatically recommending point of presence centers
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/OCTAVIAN ROTARU/
Primary Examiner, Art Unit 3624 A
September 21st, 2026
1 Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971
2 MPEP 2106.04(a) last ¶ which states that: “…examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible, if a claim limitation(s) is determined to fall within multiple groupings…”.
3 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
4 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014);
Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972);
Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
5 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
Prosecution Insights