CTFR 18/149,348 CTFR 92068 DETAILED ACTION This office action is responsive to the response filed 2/26/2026. The application contains claims 1-20, all examined and rejected. Information Disclosure Statement The Information Disclosure Statement with references submitted 2/4/2026 has been considered and entered into the file. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. While independent claims 1, 13 and 18 are each directed to a statutory category, it recites a series of steps pertaining to analyze received data to identify features that are used to predict machine failure, which appears to be directed to an abstract idea (mental process, mathematical concept). Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below. When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG) STEP 1. Per Step 1, the claims are determined to include machine and process, and as in independent Claims 1, 13 and 18, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category. At step 2A, prong 1, determine if user resources should be transmitted between user’s accounts which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are: Claim 1 “generating groupings of training data” (Mental process, observation, evaluation and judgment) “determining, via a front-end algorithm, relationships between individual data included in the training data and the training data” (Mental process, observation, evaluation and judgment, Mathematical Concept) identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data” (Mental process, observation, evaluation and judgment) “determining that at least a portion of the user resource should be transmitted from the first user record to the second user record based on one or more predicted resource utilization improvements” (Mental process, observation, evaluation and judgment). Claim 13 “generating groupings of training data” (Mental process, observation, evaluation and judgment) “determining, via a front-end algorithm, relationships between individual data included in the training data and the training data” (Mental process, observation, evaluation and judgment, Mathematical Concept) identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data” (Mental process, observation, evaluation and judgment) “identifying an optimal threshold quantity of the first resource to be maintained in the first user record; and determining that a current quantity of the user resource is insufficient relative the optimal threshold quantity” Claim 1 recites additional elements as “A computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); “train one or more machine learning models of a neural network, the training including building layers of the neural network to process unstructured data associated with resource record utilization; deploy the neural network to process the unstructured data” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a neural network” limits the identified judicial exceptions “to process the unstructured data” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h); receive unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record; apply the neural network to the received unstructured user data (insignificant extra-solution activity, MPEP 2106.05(g)); provide, via a user interface of a user device, a recommended user action, the recommended user action including an indication of the one or more resource utilization improvements (insignificant extra-solution activity, MPEP 2106.05(g) and user interface of a user device is “Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C))). Claim 13 recites additional elements as “A computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); “train one or more machine learning models of a neural network, the training including building layers of the neural network to process unstructured data associated with resource record utilization; deploy the neural network to process the unstructured data” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a neural network” limits the identified judicial exceptions “to process the unstructured data” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h); receive unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record; apply the neural network to the received unstructured user data (insignificant extra-solution activity, MPEP 2106.05(g)); provide, via a user interface of a user device, a recommendation to the user, the recommendation including an indication that the current quantity of the user resource is insufficient to transmit any of the user resource from the first user record to the second user record (insignificant extra-solution activity, MPEP 2106.05(g) and user interface of a user device is “Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C))). This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract. STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s). When taken the steps individually, these steps are: A computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “train one or more machine learning models of a neural network, the training including building layers of the neural network to process unstructured data associated with resource record utilization; deploy the neural network to process the unstructured data” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a neural network” limits the identified judicial exceptions “to process the unstructured data” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h); receive unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record; apply the neural network to the received unstructured user data (WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); provide, via a user interface of a user device, a recommended user action, the recommended user action including an indication of the one or more resource utilization improvements (WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i) and user interface of a user device is merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)). The instant application includes in Claim 13 additional steps to those deemed to be abstract idea(s). When taken the steps individually, these steps are: A computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); “train one or more machine learning models of a neural network, the training including building layers of the neural network to process unstructured data associated with resource record utilization; deploy the neural network to process the unstructured data” merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a neural network” limits the identified judicial exceptions “to process the unstructured data” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h); receive unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record; apply the neural network to the received unstructured user data (WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); provide, via a user interface of a user device, a recommendation to the user, the recommendation including an indication that the current quantity of the user resource is insufficient to transmit any of the user resource from the first user record to the second user record (WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i) and user interface of a user device is merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)). In the instant case, Claims 1 and 13 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed. In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts. Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions. Independent claims 13 and 18 are the same analogy and rejected using similar analysis as claim 1. CONCLUSION It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish). The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. claims 2 disclose “recognize that the user is accessing an aggregation platform via the user device, and based thereon performing the providing the recommended user action” (Mental process), the claim does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “program instructions further receive a request to perform the recommended user action, the request being received via a selected input that is input by the user via the user device” (WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), the claim does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “program instructions further transmit, based on receiving the request, an amount of the user resource from the first user record to the second user record” (WURC, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), the claim does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “the unstructured data associated with resource record utilization includes financial data of a plurality of users and the resource record utilization includes utilizing financial resource of one or more financial accounts.” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claims 6 disclose “applying the neural network identifies an optimal threshold quantity of the first resource to be maintained in the first user record” merely indicates a field of use or technological environment in which the judicial exception is performed. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claims 7 disclose “the first user record comprises a total quantity of the first resource, and wherein at least the portion of the user resource that should be transmitted includes an amount of the total quantity of the first resource less the optimal threshold quantity of the first resource to be maintained in the first user record” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claims 8 disclose “the one or more resource utilization improvements include utilizing a percentage of untapped financial resources of the user resource to pay off a liability account having a comparatively high interest rate relative one or more other liability accounts, the second user record comprising the liability account” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claims 9 disclose “wherein the one or more resource utilization improvements include utilizing a percentage of untapped financial resources of the user resource to earn interest via an interest-bearing account, the second user record comprising the interest-bearing account” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claims 10 disclose “wherein the recommended user action includes a repeated action to be repeated periodically, the recommended user action including a selectable option to repeat the recommended user action” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claims 11 disclose “wherein the repeated action includes an automatic monthly financial transfer” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 12 disclose “wherein the recommended user action includes a one-time financial transfer” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 14 disclose “the optimal threshold quantity includes a quantity sufficient to satisfy a predicted expenditure of the user for a set time period” data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claim 15 disclose “wherein the applying the neural network to the received unstructured user data further includes determining that the second user record includes a transferrable resource quantity sufficient to satisfy the optimal threshold quantity of the first resource to be maintained in the first user record” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claim 16 disclose “based on the determining that the second user record includes the transferrable resource quantity, and wherein the recommendation provided to the user recommends transferring at least a portion of the transferrable resource quantity from the second user record to the first user record” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claim 17 disclose “recognize that the user is accessing an aggregation platform via the user device, and based thereon performing the providing the recommendation to the user” (mental process and generally linking the use of a judicial exception to a particular technological environment or field of use). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. With regard to Claim 19, Claim 19 is similar in scope to claim 2; therefore it rejected under similar rationale. With regard to Claim 20, Claim 20 is similar in scope to claim 3; therefore it rejected under similar rationale. The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 or claim 13 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed. For at least these reasons, the claimed inventions of each of dependent claims 2-12, 14-17, and 19-20,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-7, 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over GARNARA et al. [US 2023/0306432 A1, hereinafter D1] in view of Vos et al. [US 2022/0342115 A1, hereinafter D2] in view of Yeddu [US 20220214948 A1] . With regard to Claim 1, D1 disclose a computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory (¶¶67-68) to: train one or more machine learning models of a neural network (¶49, “machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm”) , the training to process unstructured data associated with resource record utilization (¶43, “machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data”, ¶¶11-14, ¶17, “transfer system may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users. For example, the entity may maintain credit card accounts for the plurality of users. The account device may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users. For example, the entity may maintain deposit accounts (e.g., checking accounts, savings accounts, or the like) for the plurality of users”) ; deploy the neural network to process the unstructured data (¶¶51-52, “machine learning system may apply the trained machine learning model 225 to a new observation”) ; receive unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record (¶18, “transfer system may obtain information indicating a first account of the user that is to be used for satisfying an outstanding balance of a second account”, ¶¶19-20, “ transfer system may obtain information relating to an amount available (e.g., an available balance) in the first account”, ¶¶21-22, “transfer system may determine the estimate of the amount available in the first account using a machine learning model. The machine learning model may be trained to identify the estimate of the amount available based on a current time of the month (e.g., a current date) and/or a last-known amount available in the first account, among other”, ¶¶23-24, “machine learning model may be trained to determine the threshold for the second account based on a feature set that includes a historical monthly outstanding balance of the second account, a historical rate of increase of the outstanding balance of the second account, a value of recurring exchanges …”) ; apply the neural network to the received unstructured user data, the applying including determining that at least a portion of the user resource should be transmitted from the first user record to the second user record (¶¶51-52, “machine learning system may apply the trained machine learning model 225 to the new observation to generate an output (e.g., a result)” (determine threshold) , ¶23, “transfer system may determine a threshold for the second account. The threshold may represent a balance amount for the second account that, if met or exceeded by the outstanding balance of the second account, is to trigger satisfaction of the outstanding balance of the second account (e.g., paying off of the outstanding balance of the second account) or at least a portion of the outstanding balance of the second account (e.g., paying down a credit card balance by satisfying a statement balance of the second account)” (transfer) ) based on one or more predicted resource utilization improvements (¶27, “transfer system may receive, from the user device, information indicating an objective of the user relating to the outstanding balance of the second account. For example, the objective may be to maintain a low balance, to improve a credit score, to lower payment amounts, to prevent overdraft, to minimize the frequency of payments, or the like”) ; and provide, via a user interface of a user device, a recommended user action, the recommended user action including an indication of the one or more resource utilization improvements (¶26, “threshold options may include information for presentation of one or more input elements, to be presented by the user device, that enables selection among the one or more threshold options. The transfer system may receive, from the user device, information indicating a selection of the one or more threshold options”, ¶32, “transfer system may transmit the one or more notifications based on determining the threshold for the second account. A notification transmitted by the transfer system may indicate the threshold for the second account determined by the transfer system … notification transmitted by the transfer system may include information for presentation of an input element, to be presented by the user device, that enables a request from the user device to increase or decrease the threshold … input element may enable transmission, to the transfer system, of a request to increase or decrease the threshold”, ¶38, “transmit, to the user device, a notification indicating that the transfer from the first account to satisfy the outstanding balance of the second account has been executed”) . D1 does not explicitly teach training including building layers of the neural network. D2 teach a computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory (Fig. 1, 4, 5, ¶2, “present invention disclose a method, a computer system, and a computer program product“) to: train one or more machine learning models of a neural network (¶2, “building a neural network in a reduced order space”, ¶34, “At 304, a neural network is built in a reduced order space“, ¶¶31-34, “PINN-based ROM 204 improves the parsimonious dynamical system model using sparse regression by providing an autoencoder that replaces the sparse regression with a neural network. The neural network may include a fully connected neural network (i.e., a dense neural network), a long-short term memory (LSTM) reduced neural network, an echo state neural network or a liquid time-constant network”) , the training including building layers of the neural network to process unstructured data associated with resource record utilization (¶2, “building a neural network in a reduced order space”, ¶44, “At 304, a neural network is built …“, ¶¶31-34, “include a neural network, an encoder, a decoder”, ¶22, “Autoencoders may be used as a type of neural network to ingest unsupervised data and to learn and encode the data”, ¶¶23-24) ; deploy the neural network to process the unstructured data (¶44, “neural network may be deployed in the latent space to predict latent dynamics”, ¶22, “neural network may interpret, label and classify raw data, such as unstructured data“, “Autoencoders may be used as a type of neural network to ingest unsupervised data and to learn and encode the data”, ¶¶23-24, “training data may be structured data or unstructured data”) ; apply the neural network to the received unstructured user data (¶22, “neural network may interpret, label and classify raw data, such as unstructured data “, ¶¶23-24, “training data may be structured data or unstructured data … Unstructured data may include data that is not organized and has an unconventional internal structure”, ¶44, “neural network may also be built to predict a timeseries in the latent space”, ¶37, “short-term predictions of expected weather over time may be deterministic”); a user interface of a user device (¶14, “machine learning represents translating and visualizing the outputs”,¶52) . D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of training and applying neural network to interpret, label and classify raw data, such as unstructured data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to allows neural network to learn complex, non-linear relationships by creating hierarchical feature representations, which increases its capacity to approximate intricate functions and solve real-world problems. This layered structure enables deep learning, where initial layers detect simple patterns, while deeper layers build upon those patterns to recognize increasingly complex features representations and relationships within the data. This is simply combining prior art elements according to known methods to yield predictable results; applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; and use of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). D1-D2 does not explicitly teach generating groupings of training data; determining, via a front-end algorithm, relationships between individual data included in the training data and the training data; identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data. Yeddu teach building layers of the neural network to process unstructured data (¶21, “ The method uses recurrent neural network (RNN) models such as Long Short-term Memory (LSTM) models“, ¶49, “ trains multiple ML models using the normalized KPI distribution data”) ; generating groupings of training data (¶21, “the method normalizes the historic data set using a statistical box technique to normalize the training data set”) ; determining, via a front-end algorithm, relationships between individual data included in the training data and the training data (¶21, “the method normalizes the historic data set using a statistical box technique to normalize the training data set and remove outlier/anomalies from the training data set. The statistical box normalizes the data along one or more data set dimensions defining a normalized statistical box which holds the entire training data set”) ; identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data (¶21, “classifying new log entry data sequences as either normal within the recognized boundaries of the sequence features of the normalized data set, or as abnormal having sequence features either outside the normal distribution of features, or far from the mean features for the normal distribution in terms of the standard deviation of the normal distribution”) ; and reducing, based on the individual data, dimensionality of the training data (¶13, “enable anomaly detection in activity log data with consideration of multiple dimensions of meta features of the log data when compared to historic training data sets“, ¶21, “normalize the training data set and remove outlier/anomalies from the training data set. The statistical box normalizes the data along one or more data set dimensions defining a normalized statistical box which holds the entire training data set. The resulting normalized data set yields a model capable of classifying new log entry data sequences as either normal within the recognized boundaries of the sequence features of the normalized data set, or as abnormal having sequence features either outside the normal distribution of features, or far from the mean features for the normal distribution in terms of the standard deviation of the normal distribution“, ¶38, “the method normalizes the KPI data across at least one data dimension forming a statistical box for the KPI data. The normalization of the data removes anomalous/outlier data from the training data set. Training the model(s) with the normalized data set results in models which recognise the normalized data set as the normal state for the system activity log data“); deploy the neural network to process the unstructured data (¶50, “After training the ML models, the method processes new time series log entry data”) . D1-D2 and Yeddu are analogous art to the claimed invention because they are from a similar field of endeavor of training and applying neural network to interpret, label and classify raw data, such as unstructured data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to improve neural network model training performance and reduce overfitting or incorrect training. This is simply combining prior art elements according to known methods to yield predictable results; applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; and use of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 2, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein the program instructions further recognize that the user is accessing an aggregation platform via the user device, and based thereon performing the providing the recommended user action (D1, ¶17, “account device may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users”, ¶18, “transfer system may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users”, ¶18, “transfer system may receive, from the user device, the information indicating the first account that is to be used for satisfying the outstanding balance of the second account. … The transfer system may receive the information indicating the first account from the user device via a user interface provided by the transfer system”, ¶14, “transfer system may transmit, to a user device of the user, a notification … notification enabling the user to increase an amount available in the payment account, a notification enabling the user to increase or decrease the threshold, and/or a notification indicating that the transfer to satisfy the outstanding balance of the account has been executed”, ¶16, “transfer system is able to provide the same functionality for multiple accounts (e.g., thousands, tens of thousands, hundreds of thousands, or millions of accounts) of an entity”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 3, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein the program instructions further receive a request to perform the recommended user action, the request being received via a selected input that is input by the user via the user device (D1, ¶30, “information indicating the adjusted threshold may include information for presentation of an input element, to be presented by the user device, that enables transmission of a message indicating acceptance of the adjusted threshold by the user”, ¶32, “information for presentation of an input element, to be presented by the user device, that enables a transfer to increase the amount available in the first account. For example, the input element may enable an execution of a transfer from a third account of the user to the first account”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 4, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 3, wherein the program instructions further transmit, based on receiving the request, an amount of the user resource from the first user record to the second user record (D1, ¶32, “information for presentation of an input element, to be presented by the user device, that enables a transfer to increase the amount available in the first account. For example, the input element may enable an execution of a transfer from a third account of the user to the first account”, ¶37, “To cause execution of a transfer from the first account, the transfer system may transmit, to the account device, a message requesting execution of a transfer from the first account to the second account”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 5, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein the unstructured data associated with resource record utilization includes financial data of a plurality of users and the resource record utilization includes utilizing financial resource of one or more financial accounts (D1, ¶11, “An entity (e.g., a company, an organization, or an institution, among other examples) may maintain accounts for a plurality of users. In some examples, a user may perform transactions in connection with an account maintained by the entity”, ¶17, “account device may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users”, ¶18, “transfer system may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users”, ¶43, “machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 6, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein applying the neural network identifies an optimal threshold quantity of the first resource to be maintained in the first user record (D1, ¶13, “machine learning model may determine the threshold further based on an objective of the user, such as maintaining a low balance, improving a credit score, lowering payment amounts, preventing overdraft , or the like”, ¶23, “threshold may represent a balance amount for the second account that, if met or exceeded by the outstanding balance of the second account, is to trigger satisfaction of the outstanding balance of the second account”, ¶24, “transfer system may determine the threshold using a machine learning model (e.g., the same machine learning used to determine the estimate or the prediction of the amount available in the first account or a different machine learning model). The machine learning model may be trained to determine the threshold for …, the amount available in the first account, a historical monthly amount available in the first account, the prediction of the future amount available in the first account, and/or a historical rate of increase of the amount available in the first account”, machine learning predict account balance to maintain specific balance in account ) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 7, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 6, wherein the first user record comprises a total quantity of the first resource, and wherein at least the portion of the user resource that should be transmitted includes an amount of the total quantity of the first resource less the optimal threshold quantity of the first resource to be maintained in the first user record (D1, ¶13, “machine learning model may determine the threshold further based on an objective of the user, such as maintaining a low balance , improving a credit score, lowering payment amounts, preventing overdraft , or the like”, ¶¶15-17) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 10, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein the recommended user action includes a repeated action to be repeated periodically, the recommended user action including a selectable option to repeat the recommended user action (D1, ¶26, “transfer system may receive, from the user device, information indicating a frequency (e.g., daily, weekly, semimonthly, or the like) preferred by the user for satisfying the outstanding balance of the second account … Based on the frequency, the transfer system may determine one or more threshold options for the second account … transfer system may determine the threshold options using the machine learning model, described herein … whereas if the indicated frequency is monthly the threshold options may include balance amounts of $2000 or $2500”, ¶32, ¶35) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 11, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 10, wherein the repeated action includes an automatic monthly financial transfer (¶26, “transfer system may receive, from the user device, information indicating a frequency (e.g., daily, weekly, semimonthly, or the like) preferred by the user for satisfying the outstanding balance of the second account … Based on the frequency, the transfer system may determine one or more threshold options for the second account … transfer system may determine the threshold options using the machine learning model, described herein … whereas if the indicated frequency is monthly the threshold options may include balance amounts of $2000 or $2500”, ¶32, ¶35) . The same motivation to combine for claim 10 equally applies for current claim. With regard to Claim 12, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein the recommended user action includes a one-time financial transfer (GARNARA, ¶35, “ transfer system may cause execution of a transfer (e.g., a payment) from the first account to satisfy the outstanding balance of the second account. For example, the transfer system may cause execution of the transfer based on monitoring the balance of the second account and determining that the outstanding balance of the second account satisfies the threshold“, ¶14, “transfer system may transmit, to a user device of the user, a notification indicating the threshold determined by the transfer system, a notification enabling the user to select the threshold from one or more threshold options, a notification enabling the user to increase an amount available in the payment account, a notification enabling the user to increase or decrease the threshold, and/or a notification indicating that the transfer to satisfy the outstanding balance of the account has been executed”) . The same motivation to combine for claim 1 equally applies for current claim. With regard to Claim 13, D1 teach a computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory (¶¶67-68) to: train one or more machine learning models of a neural network (¶49, “machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm” , the training of the neural network to process unstructured data associated with resource record utilization (¶43, “machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data”, ¶¶11-14, ¶17, “transfer system may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users. For example, the entity may maintain credit card accounts for the plurality of users. The account device may be associated with an entity (e.g., a financial institution) that maintains accounts for a plurality of users. For example, the entity may maintain deposit accounts (e.g., checking accounts, savings accounts, or the like) for the plurality of users”) ; deploy the neural network to process the unstructured data (¶¶51-52, “machine learning system may apply the trained machine learning model 225 to a new observation”) ; receive unstructured user data of a user that is associated with (i) a first user record comprising a user resource and (ii) a second user record (¶18, “transfer system may obtain information indicating a first account of the user that is to be used for satisfying an outstanding balance of a second account”, ¶¶19-20, “ transfer system may obtain information relating to an amount available (e.g., an available balance) in the first account”, ¶¶21-22, “transfer system may determine the estimate of the amount available in the first account using a machine learning model. The machine learning model may be trained to identify the estimate of the amount available based on a current time of the month (e.g., a current date) and/or a last-known amount available in the first account, among other”, ¶¶23-24, “ machine learning model may be trained to determine the threshold for the second account based on a feature set that includes a historical monthly outstanding balance of the second account, a historical rate of increase of the outstanding balance of the second account, a value of recurring exchanges …”) ; apply the neural network to the received unstructured user data (¶43, “machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data”, ¶49, “machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms”, ¶24, “machine learning model may be trained to determine the threshold for … the amount available in the first account, a historical monthly amount available in the first account, the prediction of the future amount available in the first account, and/or a historical rate of increase of the amount available in the first account, among other examples. The machine learning model may be trained to determine the threshold … based on the historical activity data 115 and/or the historical activity data 120”) , the applying including: identifying an optimal threshold quantity of the first resource to be maintained in the first user record (¶13, “machine learning model may determine the threshold further based on an objective of the user, such as maintaining a low balance , improving a credit score, lowering payment amounts, preventing overdraft , or the like”, ¶23, “transfer system may determine the threshold based on the amount available in the first account. For example, the threshold determined by the transfer system may be a first threshold (e.g., a lower threshold) if the amount available in the first account is a first amount (e.g., a lower amount), and the threshold determined by the transfer system may be a second threshold (e.g., a higher threshold) if the amount available in the first account is a second amount (e.g., a higher amount). Additionally, or alternatively, the transfer system may determine the threshold based on the prediction of the future amount available in the first account”, ¶¶26-27, “transfer system may receive, from the user device, information indicating an objective of the user … to maintain a low balance, to improve a credit score, to lower payment amounts, to prevent overdraft , to minimize the frequency of payments, or the like. Here, the transfer system may determine the threshold for the second account further based on the objective. For example, the transfer system may determine the threshold, based on the objective … if the objective is a first objective (e.g., preventing overdraft), then the transfer system may determine a first threshold for the second account”, ¶15, “reduces the likelihood of overdrafts or other payment failures”) ; and determining that a current quantity of the user resource is insufficient relative the optimal threshold quantity (¶32, “… a notification … may enable an execution of a transfer from a third account of the user to the first account (e.g., to ensure that the amount available in the first account is sufficient to satisfy the threshold for the second account) … notification transmitted by the transfer system may indicate that a predicted amount available in the first account … is insufficient for satisfying the outstanding balance of the second account”, ¶15, “reduces consumption of computing resources … used to attempt to execute a transfer (e.g., that may be declined), to decline the transfer … transfer was declined and/or that an overdraft occurred …”) ; and provide, via a user interface of a user device, a recommendation to the user, the recommendation including an indication that the current quantity of the user resource is insufficient to transmit any of the user resource from the first user record to the second user record (¶32, “… a notification … may enable an execution of a transfer from a third account of the user to the first account (e.g., to ensure that the amount available in the first account is sufficient to satisfy the threshold for the second account) … notification transmitted by the transfer system may indicate that a predicted amount available in the first account … is insufficient for satisfying the outstanding balance of the second account”, ¶14, “transfer system may transmit … a notification indicating the threshold … a notification enabling the user to select the threshold from one or more threshold options, a notification enabling the user to increase an amount available in the payment account … and/or a notification indicating that the transfer to satisfy the outstanding balance of the account has been executed”, ¶15, “to provide instructions or other resources to the user on how to increase an availability of funds or make transfers for satisfying the balance of the account”) . D1 does not explicitly teach training including building layers of the neural network. D2 teach a computing system for training and deploying a neural network, the system comprising: a memory; one or more processors in communication with the memory; and program instructions executable by the one or more processors via the memory (Fig. 1, 4, 5, ¶2, “present invention disclose a method, a computer system, and a computer program product“) to: train one or more machine learning models of a neural network (¶2, “building a neural network in a reduced order space”, ¶34, “At 304, a neural network is built in a reduced order space“, ¶¶31-34, “PINN-based ROM 204 improves the parsimonious dynamical system model using sparse regression by providing an autoencoder that replaces the sparse regression with a neural network. The neural network may include a fully connected neural network (i.e., a dense neural network), a long-short term memory (LSTM) reduced neural network, an echo state neural network or a liquid time-constant network”) , the training including building layers of the neural network to process unstructured data associated with resource record utilization (¶2, “building a neural network in a reduced order space”, ¶44, “At 304, a neural network is built …“, ¶¶31-34, “include a neural network, an encoder, a decoder”, ¶22, “Autoencoders may be used as a type of neural network to ingest unsupervised data and to learn and encode the data”, ¶¶23-24) ; deploy the neural network to process the unstructured data (¶44, “neural network may be deployed in the latent space to predict latent dynamics”, ¶22, “Autoencoders may be used as a type of neural network to ingest unsupervised data and to learn and encode the data”, ¶¶23-24, “training data may be structured data or unstructured data”) ; apply the neural network to the received unstructured data (¶¶23-24, “training data may be structured data or unstructured data”, ¶44, “neural network may also be built to predict a timeseries in the latent space”, ¶37, “short-term predictions of expected weather over time may be deterministic”); a user interface of a user device (¶14, “machine learning represents translating and visualizing the outputs”,¶52) . The same motivation to combine for claim 1 equally applies for current claim. Yeddu teach building layers of the neural network to process unstructured data (¶21, “ The method uses recurrent neural network (RNN) models such as Long Short-term Memory (LSTM) models“, ¶49, “ trains multiple ML models using the normalized KPI distribution data”) ; generating groupings of training data (¶21, “the method normalizes the historic data set using a statistical box technique to normalize the training data set”) ; determining, via a front-end algorithm, relationships between individual data included in the training data and the training data (¶21, “the method normalizes the historic data set using a statistical box technique to normalize the training data set and remove outlier/anomalies from the training data set. The statistical box normalizes the data along one or more data set dimensions defining a normalized statistical box which holds the entire training data set”) ; identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data (¶21, “classifying new log entry data sequences as either normal within the recognized boundaries of the sequence features of the normalized data set, or as abnormal having sequence features either outside the normal distribution of features, or far from the mean features for the normal distribution in terms of the standard deviation of the normal distribution”) ; and reducing, based on the individual data, dimensionality of the training data (¶13, “enable anomaly detection in activity log data with consideration of multiple dimensions of meta features of the log data when compared to historic training data sets“, ¶21, “normalize the training data set and remove outlier/anomalies from the training data set. The statistical box normalizes the data along one or more data set dimensions defining a normalized statistical box which holds the entire training data set. The resulting normalized data set yields a model capable of classifying new log entry data sequences as either normal within the recognized boundaries of the sequence features of the normalized data set, or as abnormal having sequence features either outside the normal distribution of features, or far from the mean features for the normal distribution in terms of the standard deviation of the normal distribution“, ¶38, “the method normalizes the KPI data across at least one data dimension forming a statistical box for the KPI data. The normalization of the data removes anomalous/outlier data from the training data set. Training the model(s) with the normalized data set results in models which recognise the normalized data set as the normal state for the system activity log data“); deploy the neural network to process the unstructured data (¶50, “After training the ML models, the method processes new time series log entry data”) . D1-D2 and Yeddu are analogous art to the claimed invention because they are from a similar field of endeavor of training and applying neural network to interpret, label and classify raw data, such as unstructured data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to improve neural network model training performance and reduce overfitting or incorrect training. This is simply combining prior art elements according to known methods to yield predictable results; applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; and use of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). With regard to Claim 14, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 13, wherein the optimal threshold quantity includes a quantity sufficient to satisfy a predicted expenditure of the user for a set time period (D1, ¶32, “determine a prediction of a future amount that will be available in the first account (e.g., at a future date). For example, the transfer system may predict the future amount using a machine learning model”, ¶26, “transfer system may receive, from the user device, information indicating a frequency (e.g., daily, weekly, semimonthly, or the like) preferred by the user for satisfying the outstanding balance of the second account … Based on the frequency, the transfer system may determine one or more threshold options for the second account … transfer system may determine the threshold options using the machine learning model”, ¶27, “transfer system may receive, from the user device, information indicating an objective of the user … to maintain a low balance, to improve a credit score, to lower payment amounts, to prevent overdraft , to minimize the frequency of payments, or the like. Here, the transfer system may determine the threshold for the second account further based on the objective. For example, the transfer system may determine the threshold, based on the objective … if the objective is a first objective (e.g., preventing overdraft), then the transfer system may determine a first threshold for the second account”, ¶15, “reduces the likelihood of overdrafts or other payment failures”) . The same motivation to combine for claim 13 equally applies for current claim. With regard to Claim 15, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 13, wherein the applying the neural network to the received unstructured user data further includes determining that the second user record includes a transferrable resource quantity sufficient to satisfy the optimal threshold quantity of the first resource to be maintained in the first user record (D1, ¶43, “machine learning system may identify a feature set (e.g., one or more features and/or feature values) by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data”, ¶49, ¶27, “transfer system may receive, from the user device, information indicating an objective of the user … to maintain a low balance, to improve a credit score, to lower payment amounts, to prevent overdraft , to minimize the frequency of payments, or the like. Here, the transfer system may determine the threshold for the second account further based on the objective. For example, the transfer system may determine the threshold, based on the objective … if the objective is a first objective (e.g., preventing overdraft), then the transfer system may determine a first threshold for the second account”, ¶14, “transfer system may transmit … a notification indicating the threshold … a notification enabling the user to select the threshold from one or more threshold options, a notification enabling the user to increase an amount available in the payment account … and/or a notification indicating that the transfer to satisfy the outstanding balance of the account has been executed”, ¶15, “reduces the likelihood of overdrafts or other payment failures”, ¶32, “information for presentation of an input element, to be presented by the user device, that enables a transfer to increase the amount available in the first account. For example, the input element may enable an execution of a transfer from a third account of the user to the first account (e.g., to ensure that the amount available in the first account is sufficient to satisfy the threshold”) . The same motivation to combine for claim 13 equally applies for current claim. With regard to Claim 16, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 15, wherein based on the determining that the second user record includes the transferrable resource quantity, and wherein the recommendation provided to the user recommends transferring at least a portion of the transferrable resource quantity from the second user record to the first user record (D1, ¶43, “machine learning system may identify a feature set … by extracting the feature set from structured data, by performing natural language processing to extract the feature set from unstructured data”, ¶49, ¶¶26-27, “transfer system may receive, from the user device, information indicating an objective of the user … to maintain a low balance, to improve a credit score, to lower payment amounts, to prevent overdraft , to minimize the frequency of payments, or the like. Here, the transfer system may determine the threshold for the second account further based on the objective. For example, the transfer system may determine the threshold, based on the objective … if the objective is a first objective (e.g., preventing overdraft), then the transfer system may determine a first threshold for the second account”, ¶14, “transfer system may transmit … a notification indicating the threshold … a notification enabling the user to select the threshold from one or more threshold options, a notification enabling the user to increase an amount available in the payment account … and/or a notification indicating that the transfer to satisfy the outstanding balance of the account has been executed”, ¶15, “reduces the likelihood of overdrafts or other payment failures”, ¶32, “information for presentation of an input element, to be presented by the user device, that enables a transfer to increase the amount available in the first account. For example, the input element may enable an execution of a transfer from a third account of the user to the first account (e.g., to ensure that the amount available in the first account is sufficient to satisfy the threshold”) . The same motivation to combine for claim 15 equally applies for current claim. With regard to Claim 17, Claim 17 is similar in scope to claim 2; therefore it rejected under similar rationale. With regard to Claim 18, Claim 18 is similar in scope to claim 1; therefore it rejected under similar rationale. With regard to Claim 19, Claim 19 is similar in scope to claim 2; therefore it rejected under similar rationale. With regard to Claim 20, Claim 20 is similar in scope to claim 3; therefore it rejected under similar rationale . 07-21-aia AIA Claim s 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over GARNARA et al. [US 2023/0306432 A1, hereinafter D1] in view of Vos et al. [US 2022/0342115 A1, hereinafter D2] in view of Yeddu [US 2022/0214948 A1] in view of Atkins [US 5,875,437] . With regard to Claim 8, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein the one or more resource utilization improvements include utilizing a percentage of untapped financial resources of the user resource (D1, ¶11, “make a payment to the credit card account, the user may execute a transfer to the credit card account from a payment account”, ¶13, “machine learning model may determine the threshold further based on an objective of the user, such as maintaining a low balance, improving a credit score, lowering payment amounts, preventing overdraft, or the like”) to pay off a liability account , the second user record comprising the liability account (D1, ¶11, ¶35, “ transfer system may cause execution of a transfer (e.g., a payment) from the first account to satisfy the outstanding balance of the second account …”) . The same motivation to combine for claim 1 equally applies for current claim. D1-D2-Yeddu does not explicitly disclose having a comparatively high interest rate relative one or more other liability accounts. Atkins teach one or more resource utilization improvements include utilizing a percentage of untapped financial resources of the user resource to pay off a liability account having a comparatively high interest rate relative one or more other liability accounts, the second user record comprising the liability account (Abstract, “prioritization function automatically suggests to the individual a portfolio of asset and liability accounts that may be credited and/or debited to provide the required funds for consumption and to form investments and borrowing to best realize her financial objectives over a defined time horizon”, “If the account is imbalanced, the client may reallocate the assets and liabilities within the client account and/or modify a set of constraints on the client account. If the client account is still not balanced after modification of the account, the system will deny authorization for certain requested transactions, and may initiate the liquidation of certain asset accounts and reduce the balances of one or more liability accounts“, Col. 39, lines 38-43, “PALAO is designed to earn the most revenue for the individual over a defined time period. Accounts are often prioritized on the basis of economic factors such as interest rates, dividends, forecast returns, or commissions or through a robust stochastic process that prioritizes over a wide range of scenarios”, Col. 16 lines 26-33, “model takes account of risk/return preferences, and personal and general economic and financial projections … choose to decrease a liability account other than the mortgage. Typically, the liability amount chosen will have a relatively high rate of interest, such as a credit card account balance”, Col. 51, lines 1-5, “outputs can be used to establish a system of expert sweeps or fund transfers that will either automatically or upon client approval sweeps funds from a HOME Account™ sub-account in order to implement the PALAO, PIBO and the individual's personal budget”, Fig. 3, Col. 35, lines 14-19, “The HOME Account™ liability and credit account file 60 contains information … including the type of liability 82, identifying information on the liability 84, liability access 86, date of origination of the liability 88, the liability balance 90 and the interest rate 92 on the liability”) . D1-D2-Yeddu and Atkins are analogous art to the claimed invention because they are from a similar field of endeavor of exchange, investment and borrowing that incorporates personal financial analysis, planning and management. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2-Yeddu resulting in resolutions as disclosed by Atkins with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-D2-Yeddu as described above to afforded consumers the ability to alter their consumption, investment or savings behavior to best suit their own or the economy's changing circumstances which Improve the resources utilization without confusing the client (Atkins, Col. 1, lines 61-67) . With regard to Claim 9, D1-D2-Yeddu teach the computing system for training and deploying a neural network of claim 1, wherein the one or more resource utilization improvements include utilizing a percentage of untapped financial resources of the user resource (D1, ¶11, “make a payment to the credit card account, the user may execute a transfer to the credit card account from a payment account”, ¶13, “machine learning model may determine the threshold further based on an objective of the user, such as maintaining a low balance, improving a credit score, lowering payment amounts, preventing overdraft, or the like”) . The same motivation to combine for claim 1 equally applies for current claim. D1-D2-Yeddu does not explicitly teach to earn interest via an interest-bearing account, the second user record comprising the interest-bearing account. Atkins teach one or more resource utilization improvements include utilizing a percentage of untapped financial resources of the user resource (Col. 50, lines 44-48, “transaction costs and applicable taxes are taken into consideration, and a surplus optimization model is utilized that explicitly analyzes the excess value of assets over liabilities under different economic scenarios”, Col. 51, lines 1-5, “outputs can be used to establish a system of expert sweeps or fund transfers that will either automatically or upon client approval sweeps funds from a HOME Account™ sub-account in order to implement the PALAO, PIBO and the individual's personal budget”) to earn interest via an interest-bearing account, the second user record comprising the interest-bearing account ( Col. 51, lines 1-5, “outputs can be used to establish a system of expert sweeps or fund transfers that will either automatically or upon client approval sweeps funds from a HOME Account™ sub-account in order to implement the PALAO, PIBO and the individual's personal budget”, “system of the present invention also offers a means of improved personal financial analysis, planning and management through a fully integrated and interactive means of asset and liability management, capital budgeting and portfolio optimization … such as increased savings for retirement, college education or the purchase of a home … various operations research techniques are used, such as stochastic programming, to assist with multiperiod optimization and scenario generation and to aid in the selection of credit and investment alternatives such as derivative financial instruments”, “account subsystem provides the individual the opportunity to make increased investments in designated asset accounts 16 instead of decreasing the principal of the mortgage. Typically, the designated asset accounts are accounts that are not subject to frequent withdrawal of funds. Thus, these asset accounts may accrue substantial interest and dividend revenue over the term of the mortgage loan and may appreciate in capital value” . D1-D2-Yeddu and Atkins are analogous art to the claimed invention because they are from a similar field of endeavor of exchange, investment and borrowing that incorporates personal financial analysis, planning and management. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2-Yeddu resulting in resolutions as disclosed by Atkins with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-D2-Yeddu as described above to afforded consumers the ability to alter their consumption, investment or savings behavior to best suit their own or the economy's changing circumstances which Improve the resources utilization without confusing the client (Atkins, Col. 1, lines 61-67) . Response to Arguments Applicant argue that similar to example 39 the presented claims does not include an abstract idea that could be categorized under mental process, human activity, or mathematical concept. Examiner respectfully disagrees, The provided claims include limitations directed to the abstract idea as in Claim 1 “generating groupings of training data” (Mental process, observation, evaluation and judgment), “determining, via a front-end algorithm, relationships between individual data included in the training data and the training data” (Mental process, observation, evaluation and judgment, Mathematical Concept), identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data” (Mental process, observation, evaluation and judgment), “determining that at least a portion of the user resource should be transmitted from the first user record to the second user record based on one or more predicted resource utilization improvements” (Mental process, observation, evaluation and judgment). Also, claim 13 “generating groupings of training data” (Mental process, observation, evaluation and judgment), “determining, via a front-end algorithm, relationships between individual data included in the training data and the training data” (Mental process, observation, evaluation and judgment, Mathematical Concept), identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data” (Mental process, observation, evaluation and judgment), “identifying an optimal threshold quantity of the first resource to be maintained in the first user record; and determining that a current quantity of the user resource is insufficient relative the optimal threshold quantity”. Applicant argue that human mind cannot group training data, determining, via a front-end algorithm, relationships between individual data included in the training data and the training data, identifying, based on the relationships, the individual data falls outside of a normal pattern of the training data; and reducing, based on the individual data, dimensionality of the training data. Examiner respectfully disagrees, human mind is capable to group data based on different criteria, use algorithms to identify relationships between data, and determine data that falls outside of normal pattern, and reduce dimensionality of the training data and using an algorithm include mental process and mathematical concepts under broadest reasonable interpretation. Applicant argue that the amended claims additional elements integrate the judicial exception into a practical application because they reflect improvement to technology by reducing dimensionality of training data. Examiner respectfully disagrees, reducing dimensionality of training data based on the detected outlier data or based on relationships between data is a mental process and the claims does not disclose additional elements that could be considered significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. Applicant argue that the current claims similar to claim 3 of example 47 disclose a solution to an existing problem of training neural network as identified in ¶93 of the specifications. Examiner respectfully disagrees, the argued limitation of reducing dimensionality of training data based on the detected outlier data or based on relationships between data is a mental process and the claims does not disclose additional elements that could be considered significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. Applicant argue that independent claims implement a judicial exception with a particular machine as the claims recite front-end algorithm. Examiner respectfully disagrees, using an algorithm include mental process and mathematical concepts under broadest reasonable interpretation Applicant argue that that this particular combination of steps that applies a specific training method to more accurate and less computationally demanding predictions. Because this combination of steps performs analytics in an unconventional way, claim 1 recites significantly more than judicial exception and is, therefore, patent eligible under Step 2B. Examiner respectfully disagrees, the argued limitation are part of the abstract idea and the claims does not disclose additional elements that could be considered significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant argue that Vos is directed to physics-informed regional climate modeling and Garnara concerns financial account threshold prediction, two fundamentally unrelated technological domain with no disclosed interoperability. Neither reference suggests modifying the climate-modeling framework of Vos to incorporate the financial- account machine-learning constructs of Garnara, nor vice versa. The Office Action identifies no teaching, suggestion, or principle in either reference that would motivate a skilled artisan to combine them in a manner that yields Applicant's claimed system. Absent a reasoned explanation grounded in the prior art, and not in hindsight, the proposed combination is improper under MPEP §§2143 and 2143.01, and therefore cannot support an obviousness rejection. Examiner respectfully disagrees, the Office Action clarified what would motivate a skilled artisan to combine D1 and D2 as both teachings are directed to the similar field of endeavor of training and applying neural network to interpret, label and classify raw data, such as unstructured data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to allows neural network to learn complex, non-linear relationships by creating hierarchical feature representations, which increases its capacity to approximate intricate functions and solve real-world problems. This layered structure enables deep learning, where initial layers detect simple patterns, while deeper layers build upon those patterns to recognize increasingly complex features representations and relationships within the data. This is simply combining prior art elements according to known methods to yield predictable results; applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; and use of known technique to improve similar devices (methods, or products) in the same way (MPEP 2143). As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action. Conclusion 07-96 The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. US Patent Application Publication No. 20150379488 filed by Ruff et al. that disclose An automated proactive electronic resource allocation processing system can allocate resources based at least in part on configuration information See at least Abstract. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) ( quoting In re Lemelson , 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT. 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, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148 Application/Control Number: 18/149,348 Page 2 Art Unit: 2148 Application/Control Number: 18/149,348 Page 3 Art Unit: 2148 Application/Control Number: 18/149,348 Page 4 Art Unit: 2148 Application/Control Number: 18/149,348 Page 5 Art Unit: 2148 Application/Control Number: 18/149,348 Page 6 Art Unit: 2148 Application/Control Number: 18/149,348 Page 7 Art Unit: 2148 Application/Control Number: 18/149,348 Page 8 Art Unit: 2148 Application/Control Number: 18/149,348 Page 9 Art Unit: 2148 Application/Control Number: 18/149,348 Page 10 Art Unit: 2148 Application/Control Number: 18/149,348 Page 11 Art Unit: 2148 Application/Control Number: 18/149,348 Page 12 Art Unit: 2148 Application/Control Number: 18/149,348 Page 13 Art Unit: 2148 Application/Control Number: 18/149,348 Page 14 Art Unit: 2148 Application/Control Number: 18/149,348 Page 15 Art Unit: 2148 Application/Control Number: 18/149,348 Page 16 Art Unit: 2148 Application/Control Number: 18/149,348 Page 17 Art Unit: 2148 Application/Control Number: 18/149,348 Page 18 Art Unit: 2148 Application/Control Number: 18/149,348 Page 19 Art Unit: 2148 Application/Control Number: 18/149,348 Page 20 Art Unit: 2148 Application/Control Number: 18/149,348 Page 21 Art Unit: 2148 Application/Control Number: 18/149,348 Page 22 Art Unit: 2148 Application/Control Number: 18/149,348 Page 23 Art Unit: 2148 Application/Control Number: 18/149,348 Page 24 Art Unit: 2148 Application/Control Number: 18/149,348 Page 25 Art Unit: 2148 Application/Control Number: 18/149,348 Page 26 Art Unit: 2148 Application/Control Number: 18/149,348 Page 27 Art Unit: 2148 Application/Control Number: 18/149,348 Page 28 Art Unit: 2148 Application/Control Number: 18/149,348 Page 29 Art Unit: 2148 Application/Control Number: 18/149,348 Page 30 Art Unit: 2148 Application/Control Number: 18/149,348 Page 31 Art Unit: 2148 Application/Control Number: 18/149,348 Page 32 Art Unit: 2148 Application/Control Number: 18/149,348 Page 33 Art Unit: 2148 Application/Control Number: 18/149,348 Page 34 Art Unit: 2148 Application/Control Number: 18/149,348 Page 35 Art Unit: 2148 Application/Control Number: 18/149,348 Page 36 Art Unit: 2148 Application/Control Number: 18/149,348 Page 37 Art Unit: 2148 Application/Control Number: 18/149,348 Page 38 Art Unit: 2148 Application/Control Number: 18/149,348 Page 39 Art Unit: 2148 Application/Control Number: 18/149,348 Page 40 Art Unit: 2148 Application/Control Number: 18/149,348 Page 41 Art Unit: 2148 Application/Control Number: 18/149,348 Page 42 Art Unit: 2148 Application/Control Number: 18/149,348 Page 43 Art Unit: 2148 Application/Control Number: 18/149,348 Page 44 Art Unit: 2148 Application/Control Number: 18/149,348 Page 45 Art Unit: 2148 Application/Control Number: 18/149,348 Page 46 Art Unit: 2148 Application/Control Number: 18/149,348 Page 47 Art Unit: 2148 Application/Control Number: 18/149,348 Page 48 Art Unit: 2148 Application/Control Number: 18/149,348 Page 49 Art Unit: 2148 Application/Control Number: 18/149,348 Page 50 Art Unit: 2148 Application/Control Number: 18/149,348 Page 51 Art Unit: 2148 Application/Control Number: 18/149,348 Page 53 Art Unit: 2148 Application/Control Number: 18/149,348 Page 54 Art Unit: 2148
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