DETAILED ACTION
1. This communication is in response to the amendments filed on December 22, 2025 for Application No. 18/207,829 in which claims 1-3, 5-12, 14-15, 17-20, 22-27, and 29-35 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statement submitted on 12/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
4. The amendments filed on December 22, 2025 have been considered. Claims 1-3, 5-9, 12, 14, 17-20, 22-26, 29-31, and 34 have been amended. Thus, claims 1-3, 5-12, 14-15, 17-20, 22-27, and 29-35 are pending and presented for examination.
5. Applicant’s arguments filed December 22, 2025 with respect to the 35 U.S.C. 112(b) rejection have been fully considered and are persuasive. Thus, the 35 U.S.C. 112(b) rejection has been withdrawn.
6. Applicant's arguments filed December 22, 2025 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant’s Arguments on Pgs. 9-10 of Arguments/Remarks state:
“Applicant's claims meet these standards. For example, steps such as "generating a plurality of predictions . . . using a pretrained artificial neural network,", "retraining the artificial neural network using the additional training data," and "generating a new prediction using the retrained artificial neural network in response to a new prediction request" cannot practically be performed in the human mind at least because the human mind is not equipped to retrain a pretrained artificial neural network. Moreover, the claims do not set forth or describe any mathematical relationships or formulas.
However, even if the claims were still found to involve an abstract idea, the claims should still be found eligible because they are directed to a practical application of the abstract idea and recite significantly more than the abstract idea. The Office Action recites several claim limitations, but dismisses each individually as being "recited at a high level of generality." Office Action at 12-13. The Director in Desjardins specifically warned against this type of analysis by criticizing the Board for "evaluat[ing] claims at such a high level of generality" and "essentially equat[ing] any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation." Desjardins at 9. The Director emphasized that "Examiners and panels should not evaluate claims at such a high level of generality." Id.
In other words, the Director explained that detailed claim limitations cannot simply be dismissed with little explanation, but instead must be evaluated together as a whole and in light of the specification as to whether they integrate or add something beyond the alleged abstract idea. Id. at 7-10. In the Desjardins case, the Director found that the specification described, and the claims reflected, an improved training process that allowed the neural network to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks," as well as using less storage and reducing system complexity. Id. at 8-9.
The claims at issue recite similar features that correspond to similar benefits described in the specification. For example, Applicant's specification describes how to identify and remedy a case where the ANN needs retraining "to provide a solution to a novel problem," for example, due to "high variability of input data sets relative to the historical data sets used to train the training system." See US PG-Pub 2023/0316075 (Applicant's Spec.) at para. [4202]. These methods provide several other benefits as described in the specification, allowing the models to "add new functions" over time. Id. at [4299]. These features are reflected in the claims.
Therefore, Applicant's specification very closely parallels the eligible claims in
Desjardins by describing improved training techniques in the specification and reciting claim elements that reflect those improvements. For similar reasons, using the Director's analysis as a model, the claims should be found eligible under step 2A prong two.
Additionally, the same details analyzed above also amount to significantly more than any abstract idea, at least because they represent an unconventional combination of features. Therefore, the claims should also be found eligible under step 2B.
Accordingly, withdrawal of the eligibility rejections is respectfully requested.”
Examiner respectfully disagrees. The claims are not being evaluated at a high level of generality – the claims are being evaluated as currently drafted. At Step 2A Prong 1, the limitations “generating a plurality of predictions corresponding to a plurality of loans”, “[…] generates each of the plurality of predictions based on a respective input data set of a plurality of input data sets corresponding to the plurality of loans”, “detecting that retraining is needed by: detecting a performance deficiency of the pretrained artificial neural network for at least a subset of the plurality of predictions, wherein the performance deficiency is based on a variability between the plurality of input data sets and the set of training data used to train the artificial neural network”, “generating a new prediction […] in response to a new prediction request, wherein the prediction comprises a set of terms and/or conditions for a loan”, and “[…] modifying the loan based on the prediction” may all be practically performed by mental process. There are no technical limitations present in the currently drafted claim language which precludes these steps from being performed by mental process. For example, a user is clearly capable of generating a plurality of predictions corresponding to a plurality of loans and generating a new prediction comprising a set of terms and/or conditions for a loan by observing/analyzing the plurality of loans (and their associated terms and/or conditions) and accordingly using judgement/evaluation to cast a prediction based on said analysis of the plurality of loans – further examples of how each limitation may be performed by mental process are disclosed in the subsequent 35 U.S.C. 101 section below.
Regarding Applicant’s assertion that the human mind is not equipped to retrain a pretrained artificial neural network, Examiner agrees – the “retraining” limitation is not considered to be a mental process at Step 2A Prong 1 and instead amounts 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 - see MPEP 2106.05(f). Furthermore, at Step 2A Prong 2 and Step 2B, the Independent claims merely leverage the use of a pretrained artificial neural network to generate the plurality of predictions – this cannot provide an inventive concept. This pretrained ANN is merely an off the shelf, black box network, which is somehow already trained and configured to perform the specific operations/predictions of the claims, without significantly more. Again, merely utilizing a pretrained model cannot provide an inventive concept. Moreover, the “retraining” is described at a high-level of generality, such that the “retraining” is simply based on a generic “variability” (which is merely a variability between input data sets and the set of training data without significantly more) and additional training data which is generically “selected to reduce the performance deficiency” without significantly more. Again, this does not provide an inventive concept and the “retraining” amounts to merely adding the words “apply it” to the judicial exception without significantly more. Thus, even when considered as a whole, the claims still recite an abstract idea.
Thus, the 35 U.S.C. 101 rejection is maintained.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-3, 5-12, 14-15, 17-20, 22-27, and 29-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a system type claim. Therefore, Claims 1-3, 5-12, 14-15, 17, and 31-33 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
generating a plurality of predictions corresponding to a plurality of loans (mental process – other than reciting “using a pretrained artificial neural network”, generating a plurality of predictions corresponding to a plurality of loans may be performed manually by a user observing/analyzing the terms and/or conditions associated with a plurality of loans and accordingly using judgement/evaluation to cast a plurality of predictions regarding the plurality of loans, based on said analysis)
[…] generates each of the plurality of predictions based on a respective input data set of a plurality of input data sets corresponding to the plurality of loans (mental process – other than reciting “pretrained artificial neural network”, generating each of the plurality of predictions based on respective input data sets may be performed manually by a user observing/analyzing the plurality of input data sets corresponding to the plurality of loans (which may comprise terms and/or conditions) and accordingly using judgement/evaluation to generate each of a plurality of predictions based on said analysis of the respective input data sets)
detecting that retraining is needed by: detecting a performance deficiency of the pretrained artificial neural network for at least a subset of the plurality of predictions, wherein the performance deficiency is based on a variability between the plurality of input data sets and the set of training data used to train the artificial neural network (mental process – detecting that retraining is needed by detecting a performance deficiency may be performed manually by a user observing/analyzing the plurality of input data sets and the set of training data and accordingly using judgement/evaluation to determine a variability based on said analysis, which may indicate a performance deficiency hence detecting retraining is needed)
generating a new prediction […] in response to a new prediction request, wherein the prediction comprises a set of terms and/or conditions for a loan (mental process – generating a new prediction in response to a prediction request may be performed manually by a user observing/analyzing the prediction request and accordingly generating a new prediction comprising a set of terms and/or conditions for a loan (with the aid of pen and paper))
[…] modifying the loan based on the prediction (mental process – modifying the loan based on the prediction may be performed manually by a user observing/analyzing the prediction and accordingly using judgement/evaluation to modify the loan (with the aid of pen and paper) based on said analysis of the prediction)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a system for training and deploying an artificial neural network for managing loans […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model without significantly more)
the system comprising: one or more processors; and a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: […] (recited at a high-level of generality (i.e., as a generic system comprising generic computer components without significantly more) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
[…] using a pretrained artificial neural network, wherein the pretrained artificial neural network is associated with a set of training data that was used to train the artificial neural network […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
obtaining additional training data that is selected to reduce the performance deficiency (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
retraining the artificial neural network using the additional training data (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more)
[…] using the retrained artificial neural network […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model without significantly more)
autonomously modifying […] (recited at a high-level of generality (i.e., as a generic automation comprising generic computer components without significantly more) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a system for training and deploying an artificial neural network for managing loans […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model without significantly more. This cannot provide an inventive concept)
the system comprising: one or more processors; and a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
[…] using a pretrained artificial neural network, wherein the pretrained artificial neural network is associated with a set of training data that was used to train the artificial neural network […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
obtaining additional training data that is selected to reduce the performance deficiency (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
retraining the artificial neural network using the additional training data (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data without significantly more. This cannot provide an inventive concept)
[…] using the retrained artificial neural network […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a trained machine learning model without significantly more. This cannot provide an inventive concept)
autonomously modifying […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 1-3, 5-12, 14-15, 17, and 31-33. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2 & Step 2B:
wherein the new prediction is based at least in part on a prediction for a parameter of demand in a forward market for an asset (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the new prediction is based at least in part on a prediction for a parameter of demand in a forward market for an asset does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 3 depends on.
Step 2A Prong 2 & Step 2B:
wherein the new prediction is based at least in part on a prediction for a parameter of supply in a forward market for an asset (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the new prediction is based at least in part on a prediction for a parameter of supply in a forward market for an asset does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
wherein the new prediction is based at least in part on crowdsourced data (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the new prediction is based at least in part on crowdsourced data does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 6 depends on.
Step 2A Prong 2 & Step 2B:
wherein the new prediction is based at least in part on behavioral data collected from a set of IoT systems monitoring a set of entities in a set of environments (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the prediction is based on behavioral data collected from IoT systems does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2 & Step 2B:
wherein the artificial neural network comprises a recurrent neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the neural network comprises a recurrent neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 8:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 8 depends on.
Step 2A Prong 2 & Step 2B:
wherein the artificial neural network comprises a convolutional neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the neural network comprises a convolutional neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 9:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 9 depends on.
Step 2A Prong 2 & Step 2B:
wherein the artificial neural network comprises a combination of a recurrent neural network and a convolutional neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the neural network comprises a combination of a recurrent neural network and a convolutional neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 10:
Step 2A Prong 1:
See the rejection of Claim 6 above, which Claim 10 depends on.
Step 2A Prong 2 & Step 2B:
wherein the set of Internet of Things systems includes a set of smart home Internet of Things devices (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the set of IoT systems includes a set of smart home IoT devices does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 11:
Step 2A Prong 1:
See the rejection of Claim 6 above, which Claim 11 depends on.
Step 2A Prong 2 & Step 2B:
wherein the set of Internet of Things systems includes a set of workplace Internet of Things devices (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the set of IoT systems includes a set of workplace IoT devices does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 12:
Step 2A Prong 1:
See the rejection of Claim 6 above, which Claim 12 depends on.
Step 2A Prong 2 & Step 2B:
wherein the set of Internet of Things systems includes a set of Internet of Things devices to monitor a set of consumer goods stores (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the set of IoT systems includes a set of IoT devices to monitor a set of consumer goods stores does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 14:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 14 depends on.
determine a condition or value of items based on a status of at least one of a set of assets and a set of collateral for the loan (mental process – determining a condition or value of items based on the monitored status of the set of assets and collateral may be performed manually by a user evaluating the status of the assets and collateral to determine an according condition or value)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 15:
Step 2A Prong 1:
See the rejection of Claim 14 above, which Claim 15 depends on.
Step 2A Prong 2 & Step 2B:
wherein determining the condition is based on image data, sensor data, or location data (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the determining the condition is based on image, sensor, or location data does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 17:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 17 depends on.
Step 2A Prong 2 & Step 2B:
wherein the artificial neural network is one or more of a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LTSM) neural network, a self-organizing neural network, and hybrids and combinations of the foregoing (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying the types of neural networks which may be employed does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Independent Claim 18 recites substantially the same limitations as Claim 1, in the form of a method, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 18 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 19-20, 22-27, 29-30, and 34-35. The additional limitations of the dependent claims are addressed below.
Claim 19 recites substantially the same limitations as Claim 2 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 3 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 22 recites substantially the same limitations as Claim 5 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 23 recites substantially the same limitations as Claim 6 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 24 recites substantially the same limitations as Claim 7 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 25 recites substantially the same limitations as Claim 8 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 26 recites substantially the same limitations as Claim 9 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 27 recites substantially the same limitations as Claims 10, 11, and 12 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 29 recites substantially the same limitations as Claim 14 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 30 recites substantially the same limitations as Claim 17 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Regarding Claim 31:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 31 depends on.
undertake a loan-related action based on a status of at least one of a set of assets and a set of collateral for the loan (mental process – other than generically reciting “wherein the system is further configured to deploy a smart contract to automatically”, undertaking a loan-related action may be performed manually by a user undertaking a loan-related action based on analyzing/evaluating the status of the set of assets and the set of collateral)
Step 2A Prong 2 & Step 2B:
[…] wherein the system is further configured to deploy a smart contract to automatically […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 32:
Step 2A Prong 1:
See the rejection of Claim 31 above, which Claim 32 depends on.
Step 2A Prong 2 & Step 2B:
transmitting a signal to at least one of a smart lock and a smart container to lock the at least one of the set of assets and the set of collateral (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 33:
Step 2A Prong 1: See the rejection of Claim 31 above, which Claim 33 depends on.
initiating a foreclosure process on the at least one of the set of assets and the set of collateral (mental process – initiating a foreclosure process may be performed manually by a user initiating a foreclosure process on the set of assets and set of collateral)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Claim 34 recites substantially the same limitations as Claim 31 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 35 recites substantially the same limitations as Claim 32 in the form of a method including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Allowable Subject Matter
9. No prior art rejection is made for Claims 1-3, 5-12, 14-15, 17-20, 22-27, and 29-35. However, these claims are still rejected under 35 U.S.C. 101 – abstract idea.
10. Examiner has disclosed Sells et al. (US PG-PUB 20230043702) which is the closest prior art as compared to the instant application. In particular, Sells discloses a system of platforms including an orchestration platform, a blockchain transactional platform, a digital transactional platform, a merchant system, a user trust platform, and a distributed ledger for currency orchestration of transactions, utilizing a trained machine learning model for modifying a smart lending contract for a loan. However, Sells does not explicitly disclose “detecting that retraining is needed by: detecting a performance deficiency of the pretrained artificial neural network for at least a subset of the plurality of predictions, wherein the performance deficiency is based on a variability between the plurality of input data sets and the set of training data used to train the artificial neural network” and “generating a new prediction using the retrained artificial neural network in response to a new prediction request, wherein the prediction comprises a set of terms and/or conditions for a loan; and autonomously modifying the loan based on the prediction.”, in combination with the remaining limitations of the Independent claims.
Conclusion
11. 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.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on (571)270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123