DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is in reply to communication filed on 03/10/2026.
Claims 1, 10 and 19 has been amended.
Claims 5 and 14 has been canceled.
Claims 1-4, 6-13 and 15-20 are currently pending and have been examined.
Response to Arguments
In response to Applicant Arguments /Remarks made in an amendment filled on 03/10/2026:
Regarding 35 USC § 101 rejection:
Applicant argument submitted under the title “Rejections under 35 U.S.C. §101” in pages 12-16, that:
“Claims 1-20 stand rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter, for the reasons provided at pages 6-13 of the Office Action.
In response, while not conceding the propriety of the rejection, as amended herein,
independent claim 1 recites the following features that a) are not directed to an abstract idea, b) are integrated into a practical application, and c) recite significantly more than an abstract idea: "the determining of the geospatial information is performed by using a machine learning model that is deemed to be sufficiently trained when at least one from among a least squares error rate, a true positive rate, a true negative rate, a false positive rate, and a false negative rate is within a predetermined range" [emphasis added]; and "the machine learning model being generated to be further trained on additional data" [emphasis added]; and "the machine learning model that has previously been deemed to be sufficiently trained is further trained by using the at least one uncertainty metric such that an updated version of the at least one uncertainty metric is obtained, and feeding the updated version of the at least one uncertainty metric back into the machine learning model for a feedback loop that further tunes the machine learning model" [emphasis added]. Further, as amended herein, each of independent claims 10 and 19 recites similar respective features.
The amended independent claims are integrated into a practical application.
At page 11 of the Office Action, the Examiner asserts that the claims are not integrated into a practical application under Step 2A, Prong 2 of its § 101 analysis because, in part, "the limitations merely amount to adding the words 'apply it' (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea."
However, Applicant respectfully submits that this objection does not apply to amended claims 1, 10, and 19. These claims have been amended to recite that "the machine learning model that has previously been deemed to be sufficiently trained is further trained by using the at least one uncertainty metric such that an updated version of the at least one uncertainty metric is obtained, and feeding the updated version of the at least one uncertainty metric back into the machine learning model for a feedback loop that further tunes the machine learning model" [emphasis added].
In this aspect, Applicant respectfully submits that these features require the use of a
machine learning model for which the training thereof is updatable based on additional data, and that although the machine learning model has previously been deemed to be sufficiently trained, the machine learning model is further trained by using an uncertainty metric by feeding an updated version of that uncertainty metric back into the machine learning model for a feedback loop that further tunes the model …
In this aspect, Applicant respectfully submits that in a Decision on Request for Rehearing in the case of Ex parte Desjardins et al., Appeal 2024-000567, dated September 26, 2025, the Director of the USPTO provides the following assertions: "Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. . . . Here, however, we are persuaded that the claims reflect such an improvement." Then, based on certain claim language, the Director continues that "We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation." Based on these circumstances, the Director concludes that "although independent claim 1 may recite an abstract idea, it is not directed to an abstract idea" and that the claim "when considered as a whole, integrates an abstract idea into a practical application" …
In a telephone interview conducted on March 5, 2026, the Examiner indicated that the recitation of a feature that corresponds to retraining the machine learning model may not be sufficient to overcome the rejection under 35 U.S.C. § 101 because in her view, such a feature refers to the customary way that machine learning models operate, and such, the retraining may not constitute a change or a technological improvement to the machine learning model. In response, Applicant respectfully disagrees, for the following reasons: First, conventionally, a machine learning model is trained by using a set of data, and then once the model is deemed as having been sufficiently trained, it may be used successfully without requiring retraining or updating, and many everyday users use such models without a need to retrain or update them. Second, the notion of obtaining a result of using a sufficiently trained model and then feeding the obtained result back in order to further tune the model objectively represents an improvement to the model, because in the absence of such feedback, the model was deemed as being sufficiently trained, whereas with the added aspect of the feedback and the corresponding update to the training, the performance of the model would necessarily be improved.
Accordingly, Applicant respectfully requests that the § 101 rejection be withdrawn”.
Applicant's arguments have been fully considered but they are not persuasive.
1) In response, the examiner respectfully disagrees and emphasizes none of the claims recitation of receiving data, processing the data using a trained machine learning model, and generating a result based on the processing. Such limitations describe mathematical concepts and mental processes, including evaluation and analysis of information, which fall within the abstract idea groupings identified in the MPEP 2106.04(a)(2)(I) & (III). Further the claims considered to recite a certain method of organizing human activity directed to managing personal behavior or relationships or interactions between people by following rules or instructions to determine geospatial information. See MPEP 2106.04(a)(2)(II)(C).
Although Applicant asserts that Desjardins discloses training the machine learning model, the claims do not recite any specific technological improvement in how the model is trained or how the computer operates. Rather, the claims teaches the “a machine learning model that is deemed to be sufficiently trained”, which is merely invoke generic machine learning functionality at a high level of abstraction to perform the recited analysis. Merely applying a generic trained model to data does not integrate the judicial exception into a practical application.
Further, the recitation of “training” or “sufficiently trained” the model constitutes result-oriented functional language because the claims do not specify:
• a particular training architecture,
• a specialized training technique,
• a defined loss function,
• a specific feature extraction process,
• a non-conventional dataset structure, or
• an improvement to computer functionality itself.
Instead, the claims broadly preempt the concept of using machine learning to analyze the recited data regardless of implementation details. Courts have consistently held that merely automating abstract analysis using generic computer technology or generic AI techniques is insufficient to confer eligibility.
Applicant also has not demonstrated that the claims improve the functioning of a computer or another technology. The alleged benefit relates only to improved analysis of information, which does not constitute a technological improvement under Step 2A, Prong Two.
Accordingly, the claims remain directed to an abstract idea without significantly more, and the rejection under 35 U.S.C. § 101 is maintained.
2) Applicant further argues that the claims are eligible because the machine learning model is continuously trained using updated data in a feedback loop. This argument is likewise unpersuasive.
At its core, “training” a machine learning model merely involves supplying additional or updated data to mathematical algorithms so that model parameters are adjusted according to statistical relationships identified within the data. Feeding updated data into a model to refine outputs or improve predictions is itself part of the abstract mathematical process and does not, without more, integrate the judicial exception into a practical application.
The claimed feedback loop merely recites iterative data collection, analysis, and model updating. Such operations constitute abstract data manipulation and mathematical optimization performed on generic computing components. The claims do not recite a specific improvement to computer functionality, a particular hardware implementation, or a specialized training mechanism that changes how the computer itself operates.
Rather, the alleged “feedback loop” simply uses newly received information to update the underlying mathematical model and generate revised outputs. This amounts to no more than refining an abstract idea using additional data. Courts and USPTO guidance have consistently explained that repeating or iteratively performing an abstract calculation does not render the claims patent eligible.
Moreover, the claims recite the desired result of improving or updating the model without specifying a concrete technological solution for achieving that result. The claims therefore remain directed to the abstract idea itself rather than a practical technological application of that idea. A feedback loop that merely feeds newly obtained data back into the same abstract analytical model does not transform the nature of the claim into a technological improvement; it merely repeats the abstract analysis with updated inputs.
Accordingly, the recited machine learning training and feedback operations amount to insignificant extra-solution activity and generic post-solution updating that do not provide significantly more than the abstract idea.
The additional elements merely use generic computer components as tools to perform iterative mathematical calculations and data analysis, which is insufficient to confer eligibility under Step 2A, Prong Two or Step 2B.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
Claims 1-4 and 6-9 recite a method, which is directed to a process.
Claims 10-13 and 15-18 recite a device, which is directed to a machine.
Claims 19-20 recite a non-transitory computer readable storage medium, which is directed to a manufacture.
Therefore, each claim falls within one of the four statutory categories.
Step 2A, Prong 1 (Is a judicial exception recited?):
The independent claims 1, 10 and 19 recite the abstract idea of process geospatial information to facilitate enrichment of transaction records, see [0002].
1) The claims recite a mathematical concepts grouping. The claims recite mathematical concepts of abstract ideas as the claims describe concepts of Mathematical Calculations. The claims teach receiving information, processing the information, linking data to each other, computing weighted score, calculating data centroid, and determining further results, then displaying the results.
The federal court found that “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018). Therefore, the examiner finds the claim to be directed to a mathematical relationship as the claim recite a process of relationship between variables or numbers. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, see Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. See calculating the difference between local and average data values, In re Abele, 684 F.2d 902, 903, 214 USPQ 682, 683-84 (CCPA 1982). See MPEP 2106.04(a)(2) (I)(C).
Offending clauses include:
“determining of the geospatial information is performed by using a machine learning model deemed to be sufficiently trained”, “the machine learning model that has previously been deemed to be sufficiently trained is further trained by using the at least one uncertainty metric such that an updated version of the at least one uncertainty metric is obtained, and feeding the updated version of the at least one uncertainty metric back into the machine learning model for a feedback loop that further tunes the machine learning model”
2) These claims recite a certain method of organizing human activity. The claims recite to a certain method of organizing human activity as the above abstract idea limitations are directed to managing personal behavior or relationships or interactions between people. The examiner finds the claims to simply recites activity of a person following rules or instructions to determine geospatial information. The Examiner additionally finds the claims to be similar to an example the courts have identified as being a certain method of organizing human activity:
i) considering historical usage information while inputting data, BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018).
Offending clauses include:
“determining of the geospatial information is performed by using a machine learning model deemed to be sufficiently trained”, “the machine learning model that has previously been deemed to be sufficiently trained is further trained by using the at least one uncertainty metric such that an updated version of the at least one uncertainty metric is obtained, and feeding the updated version of the at least one uncertainty metric back into the machine learning model for a feedback loop that further tunes the machine learning model”
3) The claims recite a mental process. Before computers one could mentally or a human using paper and pen to process geospatial information to facilitate enrichment of transaction records. The claims are merely directed to retrieving and identifying transaction data (i.e., inputting data), linking , computing calculating, determining, computing data using the inputted data (i.e., analyzing data), and generating graphical element of the results to be displayed (i.e., displaying results). The Examiner find the recited claims to be similar to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), which the courts have also found to recite a mental process.
Offending clauses include:
“determining of the geospatial information is performed by using a machine learning model deemed to be sufficiently trained”, “the machine learning model that has previously been deemed to be sufficiently trained is further trained by using the at least one uncertainty metric such that an updated version of the at least one uncertainty metric is obtained, and feeding the updated version of the at least one uncertainty metric back into the machine learning model for a feedback loop that further tunes the machine learning model”
Step 2A, Prong 2 (Is the exception integrated into a practical application?):
This judicial exception is not integrated into a practical application because the claims satisfy the following criteria, which indicate that the claims do not integrate the abstract idea into practical application:
The claimed additional limitations are:
Claim 1: processor, application programming interface, by the at least one processor via a graphical user interface, wherein the determining of the geospatial information is performed by using a machine learning model deemed to be sufficiently trained when at least one from among a least squares error rate, a true positive rate, a true negative rate, a false positive rate, and a false negative rate is within a predetermined range, the machine learning model being generated to be further trained on additional data,
Claim 10: computing device, processor; a memory; and a communication interface coupled to each of the processor and the memory, application programming interface, a graphical user interface, wherein the determining of the geospatial information is performed by using a machine learning model deemed to be sufficiently trained when at least one from among a least squares error rate, a true positive rate, a true negative rate, a false positive rate, and a false negative rate is within a predetermined range, the machine learning model being generated to be further trained on additional data, ,
Claim 19: a non-transitory computer readable storage medium storing instructions for providing geospatial information for at least one clustered merchant based on proximate transactional data, the storage medium comprising executable code which, when executed by a processor, application programming interface, a graphical user interface, wherein the determining of the geospatial information is performed by using a machine learning model deemed to be sufficiently trained when at least one from among a least squares error rate, a true positive rate, a true negative rate, a false positive rate, and a false negative rate is within a predetermined range, the machine learning model being generated to be further trained on additional data,
The additional limitations are directed to using a generic computer to process information and perform the abstract idea. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Further, utilizing a trained machine learning model is a mere recitation at a high level of generality and have not been shown to yield a technical improvement, but instead merely seek determining of the geospatial information which directly pertains to the abstract idea itself, which are results devoid of any technical improvement.
Claims 1, 10 and 19 further recite “and wherein the machine learning model that has previously been deemed to be sufficiently trained is further trained by using the at least one uncertainty metric such that an updated version of the at least one uncertainty metric is obtained, and feeding the updated version of the at least one uncertainty metric back into the machine learning model for a feedback loop that further tunes the machine learning model”, which considered merely constitutes insignificant extra-solution activity appended to the abstract idea.
The claims are directed to collecting data, analyzing information using a machine learning model, and generating a predictive or classificatory result, which constitute abstract mathematical concepts and mental processes. The additional limitations directed to:
• receiving updated data,
• feeding the updated data back into the model,
• retraining or updating model parameters, and
• generating revised outputs,
do not impose a meaningful limit on the judicial exception.
At its core, “training” a machine learning model involves adjusting mathematical parameters based on input data. Continuously supplying updated data to the model merely permits the same abstract mathematical analysis to be repeated with refreshed inputs. The recited feedback loop therefore refines or reiterates the abstract idea itself rather than applying the abstract idea in a meaningful technological manner.
Further, the claims do not recite:
• a particular technological improvement to computer operation,
• a specialized machine learning architecture,
• a non-conventional training mechanism,
• an improvement to processor efficiency, memory usage, or network operation,
• or a transformation of an article to a different state or thing.
Instead, the alleged improvement is limited to improving the quality or accuracy of the analytical result generated by the abstract model itself. Improving the underlying abstract analysis does not constitute integration into a practical application.
Moreover, the recited updating and retraining operations merely gather additional information and use that information within the abstract analytical process. Such data gathering and iterative recalculation have been identified as insignificant pre-solution or extra-solution activity that do not meaningfully limit a judicial exception.
Accordingly, the additional elements, individually and in combination, fail to integrate the abstract idea into a practical application under Step 2A, Prong Two.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?):
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
1) As for Step 2B analysis, knowing the consideration is overlapping with Step 2A, Prong 2. The Step 2B considerations have already been substantially addressed under Step 2A Prong 2, see Step 2A Prong 2 analysis above. As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
2) Under Step 2B, the additional elements, individually and as an ordered combination, do not amount to significantly more than the judicial exception because the claims merely recite generic computer implementation of iterative data analysis and mathematical optimization.
Specifically, the recited operations of:
• receiving updated data,
• feeding the updated data back into the machine learning model,
• retraining or updating model parameters, and
• generating revised outputs,
constitute routine and conventional data processing activities performed by generic computing systems. The claims do not recite any specialized hardware, unconventional training architecture, particular parameter-update technique, or technological improvement to computer functionality.
At its core, the recited “feedback loop” merely enables the abstract analytical process to be repeated using refreshed inputs. Feeding updated information into a machine learning model so the model may adjust statistical weights or parameters constitutes part of the underlying mathematical analysis itself. Reapplying an abstract calculation to newly obtained data does not transform the nature of the claims into patent-eligible subject matter.
Further, the additional limitations amount to insignificant extra-solution activity because they merely gather additional information and use that information to refine the abstract analysis. The claims do not recite a technical mechanism that improves the functioning of the computer, network, processor efficiency, memory utilization, or another technological process. Rather, the alleged improvement is limited to improving the quality of the abstract prediction or analytical result itself.
Applicant also does not identify any non-conventional arrangement of components or any ordered combination that departs from well-understood, routine, and conventional machine learning operations. Continuous retraining using updated data represents a basic and conventional aspect of machine learning systems and merely applies generic computing technology as a tool to perform the abstract idea. See Bilski v. Kappos, 561 U.S. 593, 611-12, 95 USPQ2d 1001, 1010 (2010) (well-known random analysis techniques to establish the inputs of an equation were token extra-solution activity)
Accordingly, when viewed individually and as an ordered combination, the additional elements fail to provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter under Step 2B.
In addition, the dependent claims recite:
Step 2A, Prong 1 (Is a judicial exception recited?):
Dependent claims 2-4, 6-9, 11-13, 15-18 and 20 recitations further narrowing the abstract idea recited in the independent claims 1, 10 and 19 and therefore directed towards the same abstract idea.
Step 2A, Prong 2 and Step 2B:
The dependent claims 2-4, 6-9, 11-13, 15-18 and 20 further narrow the abstract idea recited in the independent claims 1, 10 and 19 and are therefore directed towards the same abstract idea.
The dependent claims recite the following additional limitations:
Claims 5, 7: the at least one processor via a graphical user interface,
Claim 6: the at least one model including at least one from among a machine learning model, a statistical model, a mathematical model, a process model, and a data model,
Claims 11, 12, 13, 17, 18: computing device,
Claims 14, 16: computing device, graphical user interface,
Claim 15: computing device, processor, the at least one model including at least one from among a machine learning model, a statistical model, a mathematical model, a process model, and a data model,
Claim 20: on-transitory computer readable storage medium,
However, the examiner finds each of these additional elements to be directed to merely “apply it” or applying a generic technology to perform the recited abstract idea of process geospatial information to facilitate enrichment of transaction records, the recitation to the generic computer technology that is being used as a tool to execute the steps that define the abstract idea do not provide for integration at the 2nd prong and do not provide for significantly more at step 2B.
Therefore, the limitations on the invention of claims 1-4, 6-13 and 15-20, when viewed individually and in ordered combination are directed to in-eligible subject matter.
Distinguished Over Prior Art
The claims 1-4, 6-13 and 15-20, in present form, have overcome the prior art rejections and the examiner has been unable to find the claimed limitations in the prior art. Accordingly, the examiner recommends addressing the outstanding rejections above. The reason to withdraw the 35 USC 103 rejection of claims 1-4, 6-13 and 15-20 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
1. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
2. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AVIA SALMAN whose telephone number is (313)446-4901. The examiner can normally be reached Monday thru Friday; 9:00 AM to 5:00 PM EST.
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/AVIA SALMAN/Primary Patent Examiner, Art Unit 3627
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