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.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 11/13/2025.
Status of Claims
Claims 1, 2, 7, 9, 10, 11, 12,17,19 and 20 have been amended by Applicant.
Claims 6 and 16 have been canceled by Applicant.
Claims 21 and 22 have been newly added by Applicant.
Claims 1-5,7-15 and 17-22 are currently pending and have been rejected as follows.
Response to Amendments / arguments
Applicant’s 11/13/2025 amendment necessitated new grounds of rejection in this action.
Response to Applicant’s rebuttal of the double patenting rejection
Remarks 11/13/2025 p.10 ¶3 argues the amended claims distinguish from those of ‘026 patent.
Examiner fully considered the Applicant’s argument but respectfully disagrees finding it unpersuasive. First, Examiner notes that the current independent Claims 1,11 were amended to include a few, albeit not all1, of the features of dependent Claim 9,19. Thus, Claims 1-5,7-15 and 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 1-5,7-15 and 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 1-5,7-15,17-22 of the current Application.
Response to Applicant’s rebuttal of 112(b) rejection
35 USC 112(b) rejection of dependent Claims 2,12, in the prior act is withdrawn in view of Applicant’s amendment as suggested by Examiner.
35 USC 112(b) rejection of dependent Claims 7,17 in the prior act is moot in view of Applicant amending said claims 7,17 to exclude recitation of “the plurality of prediction entities”
Response to Applicant’s subject matter eligibility (SME)
SME Argument #1: Remarks 11/13/2024 p.11 ¶3 cites Recentive Analytics, Inc., v. Fox Corp., et al. (No. 2023-2437 (Fed. Cir. (D. Del.) Apr. 18 2025)) to admit that application of generic
machine learning to new data environments are ineligible, yet Remarks 11/13/2024 p.11¶4-p.12 ¶1 cites Original Specification ¶ [0053]- ¶ [0055] to make a case that the current claims recite nonconventional machine learning techniques 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.
Examiner fully considered the Applicant’s SME argument #1 but respectfully disagrees finding it unpersuasive by first noting that what Applicant characterizes as technical domains are in fact transactional domains. For example, among Original Specification ¶ [0053]-¶ [0055] as mentioned by Applicant at Remarks 11/13/2024 p.12 ¶1, 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 according to Recentive Analytics supra remains patent ineligible.
This ineligibility is also confirmed by MPEP 2106.04(a) I ¶3 showing that narrow laws that have limited applications are still ineligible2 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, where it was found that the challenged patent in “SAP” proposed an analogous utilization of resampled statistical [solution] for analysis of financial data, which did not assume a normal probability distribution [as an exemplary problem]. One such method is a bootstrap method, which estimates 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 asserted, by Remarks 11/13/2024 p.11¶3, as reliable, 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” 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 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 he claims 1,11 as “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 11/13/2024 p.11¶3-p.12, is not improvement in technology. Step 2A prong one. Even when more granularly investigating the machine learning at Step 2A prong two and Step 2B, 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).
Accordingly, the claims are ineligible, and the SME Argument #1 is found unpersuasive.
SME Argument #2: Remarks 11/13/2024 p.13-p.14 ¶5 argues the specific implementation steps cannot be performed in a human mind; thus, the claims are not directed to judicial exception.
Examiner fully considered the SME argument #2 but respectful disagrees finding it unpersuasive. While, the Applicant attempts to argue that the human mind alone is not equipped to perform the claim limitation, the Applicant fails to recognize that performing a mental process in a computer environment and using a computer as tool to perform a mental process, both set forth the abstract computer-aided mental processes, as articulated by MPEP 2106.04(a)(2) III C # 2 and #3. Here, the Examiner asserts that the “machine learning model trained using gradient descent” and its associated algorithmic elements can be argued as such as such computer environment [MPEP 2106.04(a)(2) III C # 2] and/or computer tools [MPEP 2106.04(a)(2) III C #3], upon which the “predictions for the prediction entity” at the last limitation of independent Claims 1,11 are implemented, as examples of computer-aided evaluation and computer-aided judgment that falls within the abstract considerations of MPEP 2106.04(a)(2) III ¶2 and 2106.04(a)(2) III C.
Examiner also submits, in the arguendo, without conceding, just for the sake of argument, that even if the claims would not describe or set forth computer-aided mental processes, they would certainly recite, describe or set forth equally abstract mathematical relationships expressed in words, as tested per MPEP 2106.04(a)(2) I and evidenced by the preponderance recitations of “machine learning model”, “gradient descent”, “quantile regression distribution”, “outlier portions”, “first predictive component values” etc. 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”
Accordingly, the Examiner has provided a preponderance of legal evidence showing that the claims still recite, describe or at a minimum set forth the abstract exception of computer-aided mental processes (i.e. evaluation and judgement) and/or mathematical relationships.
Thus, SME argument #2 that the claims are not directed to the abstract idea is unpersuasive.
SME Argument #3: Remarks 11/13/2024 p.15 ¶2-p.16, cites Original Specification ¶ [0053] - ¶ [0055] to argue claim 1 includes several features that improve functioning of a computer.
Examiner fully considered the SME argument #3 but respectfully disagrees finding it unpersuasive by reincorporating herein all findings and rationales above. For example, the Examiner follows the Federal Circuit 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 alleged innovation in the non-abstract application realm. Specifically, the Examiner reincorporates the findings at Non-Final Act 08/13/2025 p.5-p.6 ¶2, where it was found that the challenged patent in “SAP” proposed an analogous utilization of resampled statistical methods for analysis of financial data, which did not assume a normal probability distribution. One such method is a bootstrap method, which estimates 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”. “Here, the focus of the claims is not any improved computer or network, but the improved mathematical analysis”.
Since implementation of a pool, plurality or sample space of data features, and the algorithmic properties of multiple models, hence boot-strap, jackknife, cross validation, and resampling [bolded emphasis added] in SAP’s modeling did not save the claims in SAP from ineligibility, the Examiner similarly reasons that here, the analogous predictive analysis, would at most use the analogous “machine learning model”, “quantile regression distribution” and “outlier portions” etc. 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” which would remain ineligible following the “SAP” test supra, as an alleged improved mathematical analysis for transactional (i.e. financial) domains for a customer entity. The Federal Circuit findings in “SAP” are corroborated by “Versata Dev Grp, Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015”, again undelaying the difference between an actual improvement to actual technology versus an improvement to an entrepreneurial goal or objective, which 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 MPEP 2106.04. In conclusion, a claim is not patent eligible merely because it applies an abstract idea in a narrow way. That is, an “improvement in the judicial exception itself is not an improvement in technology” per MPEP 2106.04(d)(1). Step 2A prong one. Even when more granularly investigating the machine learning elements at Step 2A prong 2 and Step 2B, Examiner finds that they represent mere computerizing of algorithms to implement the entrepreneurial, abstract, business method as identified above, which as tested per MPEP 2106.05(f)(2)(i), remains an example of invoking machines to apply the abstract exception, such as business method and its underlining algorithms without integrating the abstract exception into a practical application (Step 2A prong 2) or providing significantly more (Step 2B).
SME Argument 4 Remarks11/13/2024 p17 ¶1-¶2 argues the claims recite unconventional
combination of operations & data structures to provide non-routine results thus inventive concept
Examiner fully considered the SME argument #4 but respectfully disagrees finding it unpersuasive by reincorporating herein all the findings and rationales above. Examiner follows the guidelines of MPEP 2106.05 (d) II, ¶5-¶6, and carries over the findings of the 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 1-5,7-10,21 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 1-5,7-15 and 17-22 are not patent eligible.
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Response to Applicant’s rebuttal of the prior art rejections
Remarks 11/13/2025 p.17 ¶4 -¶5 states that current independent Claims 1,11 were amended to include features of allowable dependent Claim 9,19. Examiner notes that the current Claims 1,11 were amended to include a few, namely the “outlier portions”, but not all, of the features of the dependent claims, such as from Claim 9,19. Thus, the Examiner, submits that
Yao; Yonggang US 20180181541 A1 hereinafter Yao, still teaches or suggests:
- “determine one or more outlier portions for a particular quantile regression distribution associated with the one or more first predictive component values”; (Yao ¶ [0002] 4th sentence: One advantage of quantile regression, is that it estimates are more robust against outliers in response measurements. ¶ [0092] 1st-2nd sentences: prediction application 822 performs operations associated with predicting quantiles for response variable Y using quantile regression description 126 based on values for explanatory variable vector x stored in input dataset 824. Dependent on type of data stored in training dataset 124 and input dataset 824, prediction application 822 identify anomalies [outliers] as part of process control, for example, of a manufacturing process, for machine condition monitoring, electro-cardiogram device, etc.)
- “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”
(Yao ¶ [0068] last two sentences: The quantile level grid include [Symbol font/0x74] values, where a number of [Symbol font/0x74] values is q, ≥ 2. FQPR solves a QPR problem by recursively splitting training dataset 124 into smaller groups as described by operations 416 to 436. ¶ [0073] 1st sentence: In operation 416, quantile level [Symbol font/0x74]i* is selected in grid Gi that is closest to
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among all quantile levels in Gi , where [Symbol font/0x74]1U is maximum quantile level value according to claim 17. ¶ [0074] 1st sentence: In operation 418, a quantile regression solver is fit to the observation vectors indexed in Di of training dataset 124 plus the lower and upper counterweight vectors (yiL,xiL) and (yiU,xiU) using the indicated quantile regression solver. ¶ [0084] 1st sentence: Fig.5 depicts a table 500 that compares the computation time (in seconds) and displays an average difference between fitting QPR models using FQPR and using CQPR on the simulated training dataset 124. ¶ [0092] 2nd sentence: Dependent on type of data stored in training dataset 124 and input dataset 824, prediction application 822 identify anomalies [outliers] as part of process control. ¶ [0114] 1st sentence: master recursive regression application 1112 performs operations associated with defining quantile regression description 126 from data stored in training dataset 124 distributed across distributed control device 1002 and/or the distributed computing nodes 1004. ¶ [0121] 1st sentence: referring to Fig.13, operations associated with master recursive regression application 1112 are described. ¶ [0133] Similar to operation 416, in operation 1402, a quantile level [Symbol font/0x74]* is selected in the grid G that is closest to
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among am the quantile levels in [Symbol font/0x74]U. ¶ [0134] Similar to operation 418, in operation 1404, a quantile regression solver is fit to the observation vectors indexed in D of training dataset 124 plus the lower and the upper counterweight vectors (yL,xL) and (yU,xU) using the indicated quantile regression solver that also may be passed as a calling parameter);
Thus Yao, still teaches or suggests the partial features that were rolled up from dependent claims, and as such the Applicant’s rebuttal argument of the prior art is found unpersuasive.
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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 1-5,7-15 and 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 1-5,7-15 and 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 1-5,7-15,17-22 of the current Application.
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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.
Claims 1-5,7-15 and 17-22 are 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.
Claims 1,11 are independent and have been amended to each recite, among others:
generate, using a machine learning model trained using gradient descent, 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, using the machine learning model trained using gradient descent and based at least in part on a value-based predictive input associated with the prediction entity, one or more first predictive component values; [bolded emphasis added].
Claims 1,11 are rendered vague and indefinite because it is unclear if “gradient descent” as subsequently recited in said claims relate back to antecedently recited “gradient descent”.
Claims 1,11 are recommended to be amended, to each recite, as an example only:
generate, using a machine learning model trained using gradient descent, 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, using the machine learning model trained using the gradient descent and based at least in part on a value-based predictive input associated with the prediction entity, one or more first predictive component values;
Claims 2-5, 7-10, 21 are dependent and rejected based on rejected parent Claim 1. Claims 12-15, 17-20, 22 are dependent and rejected based on rejected parent Claim 11.
Claim 9 as amended, additionally recites, among others: “further cause the apparatus to determine one or more outlier portions for a particular quantile regression distribution” etc., rendering said claim vague and indefinite because it is unclear if expression “determine one or more outlier portions for a particular quantile regression distribution associated with the one or more first predictive component values” as subsequently recited in said dependent Claim 8 relates back to “determine one or more outlier portions for a particular quantile regression distribution” as previously and antecedently recited at parent independent Claim 1.
Claim 9 is recommended to be amended to recite, among others, and as an example only
wherein the apparatus determine the one or more outlier portions for [[a]] the particular quantile regression distribution associated with the one or more first predictive component values by: … etc. etc. etc.,
Claim 17 is dependent and recites, among others: “The computer-implemented method of claim 16”… etc. Yet, its parent Claim 16 has now been canceled by Applicant, thus rendering child dependent Claim 17 vague and indefinite because it is unclear upon which claim it depends.
Claim 22 is dependent and rejected upon rejected parent claim 17.
Examiner recommends Applicant amend claim 17 to depend from one of the pending claims.
Claim 21 is dependent and has been newly added to recite 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 § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 17,22 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 17 depends upon the now canceled parent Claim 16. Thus claim 17 fails to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 22 is dependent and rejected based on rejected parent dependent Claim 17.
Examiner recommends Applicant amend claim 17 to depend from one of the pending claims.
Clarification and correction are required.
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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 1-5,7-15 and 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 1-5,7-15 and 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 subsequentlly “determining”, “one or more first entity predictions for the prediction entity” as concluded by independent Claims 1,11, through what appears to be equally abstract3 mathematical relationships or calculations such as “generate, using a machine leaning model trained using gradient descent, 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, using the machine learning model trained using gradient descent and 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”, “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, based at least in part on the second subset of prediction engines, 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 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 the 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” (dependent Claims 9,19), “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 claims in SAP any less abstract and eligible, the Examiner similarly reasons that here, the analogous predictive analysis, as identified above, would also set forth the ineligible 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.
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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 further narrowed at dependent Claim 17. 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)4 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 1,11,17 the Examiner points to the legal finings in SAP supra as well as the MPEP 2106.05(f)(2) (i)5 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) iii6 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, 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.
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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 (d) 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