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 action is in reply to the communications filed on 06/29/2026.
Claims 1, 7-8, 11, 17-18, 21, 27-28, and 31 have been amended and are hereby entered.
Claims 1-31 are currently pending and have been examined.
This action is made Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Claim Objections
Claims 1, 11, 21, and 31 are objected to because of the following informalities:
Claim 1: lines 14-15, Claim 11: lines 18-19, and Claim 21: lines 16-17 recite the limitation “a trained machine learning model.” Training a machine learning model is initially and previously recited in Claim 1: line 2, Claim 11: line 5, and Claim 21: line 4. Is the ‘trained machine learning model’ recited in Claim 1: lines 14-15, Claim 11: lines 18-19, and Claim 21: lines 16-17 different than ‘training a machine learning model’ initially and previously recited in Claim 1: line 2, Claim 11: line 5, and Claim 21: line 4? It appears there is a typographical mistake since the specification only points to a single instance of a trained machine learning model for this interpretation. For compact examination purposes, Examiner interpreted the instances recited in Claim 1: lines 14-15, Claim 11: lines 18-19, and Claim 21: lines 16-17 as “the trained machine learning model.” Appropriate correction is required.
Claim 1: line 19, Claim 11: line 24, and Claim 21: line 21 recite the limitation “the account becoming insolvent after an upgrade of the instrument.” ‘Instrument upgrades’ is initially and previously recited in Claim 1: line 3, Claim 11: lines 6-7, and Claim 21: line 5. Is the ‘upgrade of the instrument’ recited in Claim 1: line 19, Claim 11: line 24, and Claim 21: line 21 different than ‘instrument upgrades’ initially and previously recited in Claim 1: line 3, Claim 11: lines 6-7, and Claim 21: line 5? It appears there is a typographical mistake since the specification only points to a single instance of instrument upgrade(s) for this interpretation. For compact examination purposes, Examiner interpreted the instances recited in Claim 1: line 19, Claim 11: line 24, and Claim 21: line 21 as “the account becoming insolvent after the upgrade of the instrument.” Appropriate correction is required.
Claim 11: lines 18-19 recites the limitation “process the one or more batches and the one or more other batches through a trained machine learning model to cluster the account with similar and to generate a predictive simulation.” This limitation is awkwardly written because the limitation does not recite a noun (i.e., accounts) after the adjective ‘similar,’ as recited in the specification and other independent claims 1 and 21 (i.e., similar accounts). It appears that this is a typographical mistake. For compact examination purposes, Examiner interpreted the instance recited in Claim 11: lines 18-19 as “process the one or more batches and the one or more other batches through a trained machine learning model to cluster the account with similar accounts and to generate a predictive simulation.” Appropriate correction is required.
Claim 31: line 5 recites the limitation “the different layers retaining information.” ‘Information’ is initially and previously recited in Claim 31: line 4. Is the ‘information’ recited in Claim 31: line 5 different than ‘information’ initially and previously recited in Claim 31: line 4? It appears there is a typographical mistake since the specification only points to a single instance of information for this interpretation. For compact examination purposes, Examiner interpreted the instance recited in Claim 31: line 5 as “the different layers retaining the information.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-31 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. For instance, in In re Hayes Microcomputer Products, the written description requirement was satisfied because the specification disclosed the specific type of microcomputer used in the claimed invention as well as the necessary steps for implementing the claimed function. The disclosure was in sufficient detail such that one skilled in the art would know how to program the microprocessor to perform the necessary steps described in the specification. In re Hayes Microcomputer Prods., Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, ___ (Fed. Cir. 1992). In the present applicant, claims 1, 11, and 21 recite “pre-processing the one or more batches of usage data and one or more other batches of usage data associated with other accounts using normalization and reformatting according to a shared format;” where this limitation recites “pre-processing the one or more batches of usage data and one or more other batches of usage data associated with other accounts using normalization and reformatting according to a shared format” which are not disclosed within the application’s specification and to show possession of the invention at the time of filing. Furthermore, while one skilled in the art could have devised a way to accomplish this aspect of the invention, Applicant’s original disclosure lacks sufficient detail to explain how Applicant envisioned achieving the goal of “pre-processing the one or more batches of usage data …... using normalization and reformatting according to a shared format.” Simply stating or re-stating the claim limitation does not provide enough support to show possession. The specification does not recite “pre-processing the one or more batches of usage data” or “using normalization and reformatting according to a shared format;” and does not disclose sufficient detail explaining how the Applicant envisioned achieving the goal of pre-processing the one or more batches of usage data and one or more other batches of usage data associated with other accounts using normalization and reformatting according to a shared format. Since these important details about how the invention operates are not disclosed, it is not readily evident that Applicant has full possession of the invention at the time of filing (i.e., the original disclosure fails to provide adequate written description to support the claimed invention as a whole). Neither the specification nor the drawings disclose in detail the specific steps or algorithm needed to perform the operation. If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112a, for lack of written description must be made. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
Dependent claims 2-10, 12-20, and 22-31 are rejected by virtue of dependency on Independent Claims 1, 11, and 21, respectively.
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 1-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account without significantly more.
Claim 1 is directed to a method, which is one of the statutory categories of invention; and Claim 11 is directed to a system, which is one of the statutory categories of invention; and Claim 21 is directed to a non-transitory computer readable storage medium, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to a method, comprising: training a machine learning model to predict user account status, wherein training is based on training data that identifies user account statuses before and after instrument upgrades, wherein the trained machine learning model includes numeric weights that are set during training; extracting one or more batches of usage data from a usage data stream associated with an instrument, wherein the instrument controls access to an account associated with a user, and wherein the usage data stream identifies uses of the instrument and as the usage data stream continues to be received over time; pre-processing the one or more batches of usage data and one or more other batches of usage data associated with other accounts using normalization and reformatting according to a shared format; processing the one or more batches and the one or more other batches through a trained machine learning model to cluster the account with similar accounts and to generate a predictive simulation according to a prediction accuracy, wherein the predictive simulation identifies a predicted probability of the account becoming insolvent after an upgrade of the instrument, and wherein the trained machine learning model determines the predicted probability based on how often the similar accounts became insolvent in response to similar upgrades; dynamically updating the predictive simulation in real-time as new batches of usage data are received and processed using the normalization, the reformatting, and the trained machine learning model, wherein updating the predictive simulation updates the clustering and the predicted probability based on the new batches; upgrading the instrument based on the updated predicted probability crossing a threshold relative to the predicted probability; and updating the trained machine learning model based on whether an observed result of upgrading the instrument aligns with the updated predictive simulation, wherein updating modifies at least one of the numeric weights that was responsible for generating and updating the predictive simulation to improve prediction accuracy of further predictive simulations of further upgrades. These series of steps describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account (with the exception of the italicized and bolded terms above), which is mitigating risk of upgrading an ineligible account by predicting the probability of crossing a threshold; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing a transaction using an instrument after determining and recording the eligibility of an account’s upgrade, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Dependent claims 2-10 and 31 are directed to a method that recites series of steps that describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account. Furthermore, dependent claims 5 and 31 are directed to a method, which recite the steps: wherein upgrading the instrument includes modifying a record associated with the account in a database, and wherein the record is associated with the instrument; and wherein the numeric weights represent strength of connections between neurons of the trained machine learning model, and wherein information is shared among different layers of the neurons with the different layers retaining information as the information is processed. These series of steps describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account (with the exception of the italicized and bolded terms above), corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing a transaction using an instrument after determining and recording the eligibility of an account’s upgrade, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 2-10 and 31 recite an abstract idea. The additional elements of a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, database, neurons of the trained machine learning model, and different layers are no more than simply applying the abstract idea using generic computer elements. Specifically, the additional elements, a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, database, neurons of the trained machine learning model, and different layers are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, database, neurons of the trained machine learning model, and different layers is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, database, neurons of the trained machine learning model, and different layers, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Claim 11 is directed to a system comprising: a memory that stores instructions; and a processor coupled to the memory, wherein execution of the instructions by the processor causes the processor to: train a machine learning model to predict user account status, wherein training is based on training data that identifies user account statuses before and after instrument upgrades, wherein the trained machine learning model includes numeric weights that are set during training; extract one or more batches of usage data from a usage data stream associated with an instrument, wherein the instrument controls access to an account associated with a user, and wherein the usage data stream identifies uses of the instrument and as the usage data stream continues to be received over time; pre-process the one or more batches of usage data and one or more other batches of usage data associated with other accounts using normalization and reformatting according to a shared format; process the one or more batches and the one or more other batches through a trained machine learning model to cluster the account with similar [accounts] and to generate a predictive simulation according to a prediction accuracy, wherein the predictive simulation identifies a predicted probability of the account becoming insolvent after an upgrade of the instrument, and wherein the trained machine learning model determines the predicted probability based on how often the similar accounts became insolvent in response to similar upgrades; dynamically update the predictive simulation in real-time as new batches of usage data are received and processed using the normalization, the reformatting, and the trained machine learning model, wherein updating the predictive simulation updates the clustering and the predicted probability based on the new batches; upgrading the instrument based on the updated predicted probability crossing a threshold relative to the predicted probability; and update the trained machine learning model based on whether an observed result of upgrading the instrument aligns with the updated predictive simulation, wherein updating modifies at least one of the numeric weights that was responsible for generating and updating the predictive simulation to improve the prediction accuracy of further predictive simulations of further upgrades. These series of steps describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account (with the exception of the italicized and bolded terms above), which is mitigating risk of upgrading an ineligible account by predicting the probability of crossing a threshold; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing a transaction using an instrument after determining and recording the eligibility of an account’s upgrade, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 11 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 11 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 11 is not patent eligible.
Dependent claims 12-20 are directed to a system that performs steps that describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account. Furthermore, dependent claim 15 is directed to a system, which performs the steps: wherein upgrading the instrument includes modifying a record associated with the account in a database, and wherein the record is associated with the instrument. These series of steps describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account (with the exception of the italicized and bolded terms above), corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing a transaction using an instrument after determining and recording the eligibility of an account’s upgrade, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 12-20 recite an abstract idea. The additional elements of a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations and database are no more than simply applying the abstract idea using generic computer elements. Specifically, the additional elements of a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations and database are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations and database is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a memory, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations and database, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Claim 21 is directed to a non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method comprising: training a machine learning model to predict user account status, wherein training is based on training data that identifies user account statuses before and after instrument upgrades, wherein the trained machine learning model includes numeric weights that are set during training; extracting one or more batches of usage data from a usage data stream associated with an instrument, wherein the instrument controls access to an account associated with a user, and wherein the usage data stream identifies uses of the instrument and as the usage data stream continues to be received over time; pre-processing the one or more batches of usage data and one or more other batches of usage data associated with other accounts using normalization and reformatting according to a shared format; processing the one or more batches and the one or more other batches through a trained machine learning model to cluster the account with similar accounts and to generate a predictive simulation according to a prediction accuracy, wherein the predictive simulation identifies a predicted probability of the account becoming insolvent after an upgrade of the instrument, and wherein the trained machine learning model determines the predicted probability based on how often the similar accounts became insolvent in response to similar upgrades; dynamically updating the predictive simulation in real-time as new batches of usage data are received and processed using the normalization, the reformatting, and the trained machine learning model, wherein updating the predictive simulation updates the clustering and the predicted probability based on the new batches; upgrading the instrument based on the updated predicted probability crossing a threshold relative to the predicted probability; and updating the trained machine learning model based on whether an observed result of upgrading the instrument aligns with the updated predictive simulation, wherein updating modifies at least one of the numeric weights that was responsible for generating and updating the predictive simulation to improve the prediction accuracy of further predictive simulations of further upgrades. These series of steps describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account (with the exception of the italicized and bolded terms above), which is mitigating risk of upgrading an ineligible account by predicting the probability of crossing a threshold; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing a transaction using an instrument after determining and recording the eligibility of an account’s upgrade, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 21 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 21 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 21 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 21 is not patent eligible.
Dependent claims 22-30 are directed to a non-transitory computer readable storage medium that performs steps that describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account. Furthermore, dependent claim 25 is directed to a system, which performs the steps: wherein upgrading the instrument includes modifying a record associated with the account in a database, and wherein the record is associated with the instrument. These series of steps describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account (with the exception of the italicized and bolded terms above), corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing a transaction using an instrument after determining and recording the eligibility of an account’s upgrade, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 22-30 recite an abstract idea. The additional elements of a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, and database are no more than simply applying the abstract idea using generic computer elements. Specifically, the additional elements, a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, and database, are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, and database is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a program, processor, machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, further predictive simulations, and database, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 2-10, 12-20, and 22-31 have further defined the abstract idea that is present in their respective independent claims: Claims 1, 11, and 21, and thus correspond to Certain Methods of Organizing Human Activity; and hence are abstract in nature for the reason presented above. The dependent claims 2-10, 12-20, and 22-31 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, dependent claims 2-10, 12-20, and 22-31 are directed to an abstract idea without significantly more.
Thus, claims 1-31 are not patent-eligible.
Response to Arguments
With respect to the objection of claims 1, 11, and 21, the objection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 06/29/2026. However, new objections are given for claims 1, 11, 21, and 3, as presented above in paragraphs 3-7 of this office action. Appropriate corrections are required.
With respect to the 35 U.S.C. 112(a) rejection of dependent claim 31, the rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 06/29/2026. However, a new 35 U.S.C. 112(a) rejection is given for claims 1-31, as presented above in paragraphs 8-10 of this office action. Dependent claims 2-10, 12-20, and 22-31 are rejected by virtue of dependency on independent claims 1, 11, and 21.
Applicant's arguments filed on 06/29/2026 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of claims 1-31 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “the currently amended claims are patent-eligible for similar reasons as those provided in Appeals Review Panel decision Ex parte Desjardins (September 26, 2025), which was designated as precedential on the subject of 35 U.S.C. § 101 ….. Applicant submits that "training a machine learning model to predict user account status [...] based on training data" in Applicant's currently amended claims is analogous to the "machine learning model" being "trained on a first machine learning task using first training data" in Ex parte Desjardins, and that "updating the trained machine learning model based on whether an observed result of upgrading the instrument aligns with the updated predictive simulation, wherein updating modifies at least one of the numeric weights that was responsible for generating and updating the predictive simulation to improve prediction accuracy of further predictive simulations of further upgrades" in Applicant's currently amended claims is analogous to the "training the machine learning model" "on a second [...] machine learning task" using "second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model" in Ex parte Desjardins. …. Applicant submits that the currently amended claims thus recite "an improvement to how the machine learning model itself operates," and a technical solution to inaccurate predictive simulations, similarly to Ex parte Desjardins. Ex parte Desjardins, 9. Without addressing the propriety of the rejection further in order to expedite prosecution, claims __ have been amended as noted above. Claims 2-10 and 31 depend from Claim 1. Claims 12-20 depend from Claim 11. Claims 22-30 depend from Claim 21. Withdrawal of the rejection under 35 U.S. C. § 101 is therefore respectfully requested.”
Examiner respectfully disagrees.
Under Step 2A: Prong I, as previously discussed in the Non-Final dated 06/18/2025, Final dated 10/02/2025, and Non-Final dated 02/11/2026, Examiner respectfully notes that independent claims 1, 11, and 21, as amended, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account; without significantly more. The series of steps recited in claims 1, 11, and 21, as amended, describe the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account, which is mitigating risk of upgrading an ineligible account by predicting the probability of crossing a threshold; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing a transaction using an instrument after determining and recording the eligibility of an account’s upgrade, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations (Claim 1), do not necessarily restrict the claim from reciting an abstract idea.
Moreover, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea. Examiner respectfully notes that the recited features in the limitations, even as currently amended, are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea. For example, Claim 11, as amended, recites the following: “a system comprising: a memory that stores instructions; and a processor coupled to the memory, wherein execution of the instructions by the processor causes the processor to: train a machine learning model to predict user account status, wherein training is based on training data that identifies user account statuses before and after instrument upgrades, wherein the trained machine learning model includes numeric weights that are set during training; extract one or more batches of usage data from a usage data stream associated with an instrument, wherein the instrument controls access to an account associated with a user, and wherein the usage data stream identifies uses of the instrument and as the usage data stream continues to be received over time; pre-process the one or more batches of usage data and one or more other batches of usage data associated with other accounts using normalization and reformatting according to a shared format; process the one or more batches and the one or more other batches through a trained machine learning model to cluster the account with similar [accounts] and to generate a predictive simulation according to a prediction accuracy, wherein the predictive simulation identifies a predicted probability of the account becoming insolvent after an upgrade of the instrument, and wherein the trained machine learning model determines the predicted probability based on how often the similar accounts became insolvent in response to similar upgrades; dynamically update the predictive simulation in real-time as new batches of usage data are received and processed using the normalization, the reformatting, and the trained machine learning model, wherein updating the predictive simulation updates the clustering and the predicted probability based on the new batches; upgrading the instrument based on the updated predicted probability crossing a threshold relative to the predicted probability; and update the trained machine learning model based on whether an observed result of upgrading the instrument aligns with the updated predictive simulation, wherein updating modifies at least one of the numeric weights that was responsible for generating and updating the predictive simulation to improve the prediction accuracy of further predictive simulations of further upgrades.” Examiner respectfully notes that the recited features in the limitations of amended claims 1, 11, and 21 are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.
Under Step 2A: Prong II, as previously discussed in the Non-Final dated 06/18/2025, Final dated 10/02/2025, and Non-Final dated 02/11/2026, Examiner respectfully notes that there is no improved technology in simply providing, identifying, extracting, tracking, updating, clustering, generating, identifying, training, pre-processing, processing, receiving, clustering, updating, upgrading, reformatting, normalizing, predicting, batching, gathering, modifying, and outputting data (i.e., usage data, account status data, payment card data, records, tracking data, transactional data, account data, predicted data, and etc.). Unlike Ex parte Desjardins, the disclosed invention simply cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, the recited features in the limitations , as amended, do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. Unlike Ex parte Desjardins, as previously discussed, the recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution. The specification recites that the “the update 1555 to the ML model(s) 1525 can upgrade or improve the accuracy of further output(s) 1530 (e.g., further predictive simulation(s) 1535) generated after the update 1555 (using the ML model(s) 1525).” ( See, Specification Para. [0167] and [0206]). Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing of data (usage data, account status data, payment card data, records, tracking data, transactional data, account data, predicted data, and etc.), and no technical solution or improvement has been disclosed.
Moreover, there is no technology/technical improvement as a result of implementing the abstract idea. The recited limitations in the pending claims simply amount to the abstract idea of processing and tracking usage data to determine and record upgrade eligibility of an account. Unlike Ex parte Desjardins, there is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. As noted above, the specification recites that “the update 1555 to the ML model(s) 1525 can upgrade or improve the accuracy of further output(s) 1530 (e.g., further predictive simulation(s) 1535) generated after the update 1555 (using the ML model(s) 1525).” ( See, Specification Para. [0167] and [0206]). If there is an improvement, it is to the abstract idea and not to technology (machine learning models). The claim simply makes use of a computer (machine learning models) as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Furthermore, claims 1, 11, and 21, as amended, recites steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and output. See MPEP 2106.05. Additionally, the ‘automatically’ ( i.e., dynamically) features simply amounts to mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). Thus, the automation feature is not sufficient to show an improvement in computer-functionality or technology/technical improvements (see MPEP 2106.05(a)(1)). The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Additionally, these steps, as amended, are recited as being performed by a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations (Claim 1). The additional elements: a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations (Claim 1) are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). The claims 1, 11, and 21, as amended, recite a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations (Claim 1), which are simply used to perform an abstract idea, as discussed above in Step 2A, Prong 1, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “a machine learning model, trained machine learning model, usage data stream, predictive simulation, updated predictive simulation, and further predictive simulations” in the limitations of claim 1 merely indicates a field of use or technological environment in which the judicial exception is performed. The claims, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Hence, Claims 1, 11, and 21, as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-31.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are the following:
Lee (U.S. Patent Application Publication No. US- 2013/0073446-A1) “System and method for configuring a variable collateral revolving security”
Rice (U.S. Patent Application Publication No. US-2014/0201060-A1) “Computer program, system, and method for providing a consumer with immediate access to funds via a hybridized secured line of credit”
Petty (U.S. Patent Application Publication No. US-20140310150-A1) “Systems and methods for establishing or improving credit worthiness”
Anderson (U.S. Patent Application Publication No. US-2015/0052061-A1) “Methods and systems for facilitating e-commerce payments”
Dutta (U.S. Patent Application Publication No. US- 2019/0073669-A1) “System, Method, and Computer Program Product for Predicting Payment Transactions Using a Machine Learning Technique Based on Merchant Categories and Transaction Time Data”
Das (U.S. Patent Application Publication No. US-2022/0108121-A1) “Automatically updating a card-scan machine learning model based on predicting card characters”
Frohwein (U.S. Patent No. US-11,250,517-B1) “System to automatically categorize”
Gabriele (U.S. Patent Application Publication No. US-2021/0398100-A1) “Systems and methods for a user interface for making recommendations”
Pandey (U.S. Patent Application Publication No. US-2021/0374756-A1) “Methods and systems for generating rules for unseen fraud and credit risks using artificial intelligence”
Brosamer (U.S. Patent No. US-10,949,825-B1) “Adaptive merchant classification”
Song (U.S. Patent No. US-11,250,461-B2) “Deep learning systems and methods in artificial intelligence”
Yag (U.S. Patent Application Publication No. US-2022/0147908-A1) “Intelligent logistics web and app improvements”
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693
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