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 11/24/2025.
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/24/2025 has been entered.
Response to Arguments
In response to Applicant Arguments /Remarks made in an amendment filled on 11/24/2025:
Regarding 35 USC § 101 rejection:
Applicant argument submitted under the title “Rejections under 35 U.S.C. §101” in pages 11-15, 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 7-14 of the Office Action … Pages 11-12 of the Office Action assert 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 determining of the geospatial information is performed by using a machine learning model that is trained such that 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" [emphasis added]; and that "the machine learning model is further trained by using the at least one uncertainty metric" [emphasis added].
In this aspect, Applicant respectfully submits that these features require the use of a machine learning model that is trained such that at least one metric falls within a particular range, and that the training of the machine learning model is updatable based on additional data, and that the machine learning model is further trained by using an uncertainty metric.
Applicant respectfully submits that these features integrate the claims into a practical application, because by virtue of the use of a machine learning model for which training is updatable based on both additional data and an uncertainty metric, the claims recite improvements in training the machine learning model itself … 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".
Similarly as in the above-cited Decision, Applicant respectfully submits that in the instant application, the features recited in each of independent claims 1, 10, and 19 as amended herein require the use of a machine learning model for which training is updatable based on both additional data and an uncertainty metric, and as such, the claims recite improvements in training the machine learning model itself. Therefore, Applicant respectfully submits that as amended herein, when considered as a whole, each of independent claims 1, 10, and 19 integrates an abstract idea into a practical application … However, even assuming arguendo that these assertions were reasonable with respect to the previous version of independent claims 1, 10, and 19 Applicant respectfully submits that as amended herein, each of claims 1, 10, and 19 does effect a technological improvement. In particular, the recitations of "the determining of the geospatial information is performed by using a machine learning model that is trained such that 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" and that "the machine learning model is further trained by using the at least one uncertainty metric" require the use of a machine learning model that is trained such that at least one metric falls within a particular range, and that the training of the machine learning model is updatable based on additional data, and that the machine learning model is further trained by using an uncertainty metric.
Applicant respectfully submits that these features integrate the claims into a practical application, because by virtue of the use of a machine learning model for which training is updatable based on both additional data and an uncertainty metric, the claims recite improvements in training the machine learning model itself. In this aspect, Applicant notes that the Examiner's assertions in the Advisory Action do not address the claim amendments at all.
Applicant further notes that the Examiner has indicated that "The proposed amendments . ..will not be entered because they raise new issues that would require further consideration and/or search" and that "The examiner suggests filing an RCE to have the new issues fully considered." As such, Applicant respectfully submits that an RCE is being filed concurrently herewith, in order to ensure full consideration of the claims as amended herein. Accordingly, Applicant respectfully requests that the § 101 rejection be withdrawn”.
Applicant's arguments have been fully considered but they are not persuasive.
In response, the examiner respectfully disagrees and emphasizes none of the retrieving, identifying, linking, computing, calculating, determining, computing, generating, displaying steps, whether taken individually or collectively, have not been shown to affect any form of technical change or improvement whatsoever, and are abstract idea. Applicant's claims have not been shown to modify, reconfigure, manipulate, or transform the computer, computer software, or any technical elements in any discernible manner, much less yield an improvement thereto. There is simply no showing of implementing any of the claim steps, individually or in combination, amounts to a technological improvement, nor the alleged “the claims recite improvements in training the machine learning model itself” suggested by Applicant. The Examiner first notes that process geospatial information to facilitate enrichment of transaction records is not reasonably understood as a technology, but instead involves mathematical concepts, organizing of human activity and mental process.
Further, utilizing a trained machine learning model is recited at a high level of generality and have not been shown to yield a technical improvement, but instead merely seek determining geospatial information which directly pertains to the abstract idea itself, which are results devoid of technical improvement comparable to the claimed method of training a machine learning model of the example recited by the applicant. Accordingly, Applicant’s reliance on the eligibility rationale of the case of Ex parte Desjardins et al., Appeal 2024-000567 is not persuasive
Furthermore, the recited “processor”, “application programming interface”, and displaying results (i.e., information that relates to a clustered merchant, corresponding geospatial information, and a corresponding uncertainty metric) “by the at least one processor via a graphical user interface”, this 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.
Moreover, automating the claims steps with “processor”, “memory” and/or “storage” and/or “interface” of a generic computer is similar to simply adding the words “apply it,” which is not enough to transform an abstract idea into eligible subject matter. See, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976. Applicant's Specification acknowledges that nothing more than general purpose computers is needed to implement the invention. Thus, any improvement achieved by automating the claim steps (i.e., using generic computing devices/software) is not a technical improvement, but instead would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim.
Even assuming, for the sake of argument, that the claims amount to an improvement over prior art techniques for process geospatial information to facilitate enrichment of transaction records, such an improvement would be considered, at most, an improvement confined within the abstract idea itself, which is not enough to confer eligibility on the claim. For the reasons above, Applicant’s argument is not persuasive.
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]. This idea is described by the steps of:
Retrieving transaction data for a geographical location that corresponds to the at least one clustered merchant based on a predetermined parameter;
identifying, from the transaction data, the proximate transactional data that correspond to the at least one clustered merchant;
linking at least one transaction in the proximate transactional data to each of the at least one clustered merchant;
computing a weighted score for each of the at least one transaction based on at least one characteristic;
calculating a transaction centroid for each of the at least one clustered merchant by using the corresponding weighted score and a result of the linking; and
determining the geospatial information for each of the at least one clustered merchant based on a distance to the corresponding transaction centroid,
wherein the method further comprises:
computing at least one uncertainty metric for the geospatial information that corresponds to each of the at least one clustered merchant, the at least one uncertainty metric corresponding to a relative uncertainty value of the determined geospatial information;
generating at least one graphical element, the at least one graphical element including information that relates to the at least one clustered merchant, the corresponding geospatial information, and the corresponding at least one uncertainty metric; and
displaying the at least one graphical element
1) These steps fall into the 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).
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).
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.
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 that is trained such that 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, and wherein the machine learning model is further trained by using the at least one uncertainty metric,
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 that is trained such that 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, and wherein the machine learning model is further trained by using the at least one uncertainty metric,
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 that is trained such that 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, and wherein the machine learning model is further trained by using the at least one uncertainty metric,
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.
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.
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).
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
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/AVIA SALMAN/Primary Patent Examiner, Art Unit 3627
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