DETAILED ACTION Claims 1-20 are pending in this action. Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on 9/11/2023 and 9/5/2024are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim s 1, 5-8, 12-15 and 18-20 are rejected under 35 U.S.C. 102 (a)(1) and 102(a)(2) as being anticipated by Vaidya et al. (US PGPUB No. 2020/0380531) [hereinafter “Vaidya”] . As per claim 1, Vaidya teaches a computer-implemented method, comprising: receiving, with at least one processor, at least one transaction authorization request for at least one transaction ([0054], receiving transaction data including authorization data – this inherently signals that a transaction authorization request was received ) ; in response to receiving the at least one transaction authorization request, loading, with the at least one processor, at least one encoded state of a recurrent neural network (RNN) model from a memory ( [0057], encoded states of the RNN model containing entity transaction data are formed using the RNN encoder ) ; decoding, with the at least one processor, the at least one encoded state by passing each encoded state of the at least one encoded state through a decoder network to provide at least one decoded state ( [0057], the encoded states are decoded using the RNN decoder ) ; generating, with the at least one processor, at least one updated state and an output for the at least one transaction by inputting at least a portion of the at least one transaction authorization request and the at least one decoded state into the RNN model ( [0072], updating model parameters using single transaction data which includes the transaction authorization data see [0054]) with ([0056]-[0057], these model parameters are inputted into an encoded RNN which are decoded back to the original input state so that it can be worked with, i.e. updated ) ; encoding, with the at least one processor, the at least one updated state by passing each updated state of the at least one updated state through an encoder network to provide at least one encoded updated state ( [0077], different transactions are added to the state of the encoder and compressed ) ; and storing, with the at least one processor, the at least one encoded updated state in the memory ( [0077], encoder network is stored for later access and comparison see also [0062] ) . As per claim 5, Vaidya teaches the computer-implemented method of claim 1, wherein loading the at least one encoded state from memory comprises identifying the at least one encoded state associated with at least one of the following, based on the at least one transaction: a payment device identifier ([0015], card and terminal type) ; an account identifier ([0051], merchant data including account data) ; a payment device holder identifier ([0015], information related to the cardholder) ; or any combination thereof see id . As per claim 6, Vaidya teaches the computer-implemented method of claim 5, wherein the RNN model is a fraud detection model ([0058], RNN model used to detect fraud) , and wherein the output generated for the at least one transaction is a likelihood of fraud for the at least one transaction based on a transaction history ([0051], transaction history of merchants also used to determine fraud see also [0058]) associated with at least one of the payment device identifier, the account identifier ([0051], merchant data including account data) , the payment device holder identifier ([0015], information related to the cardholder) , or any combination thereof see id . As per claim 7, Vaidya teaches the computer-implemented method of claim 6, further comprising regenerating, with the at least one processor, the at least one updated state in response to, and in real-time with, receiving each transaction authorization request of a plurality of ongoing transaction authorization requests ([0076], autoencoder-decoder of RNN model used to update in real-time transaction data including authorization data see [0015]) . As per claim 8, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale. As per claim 12, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale. As per claim 13, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale. As per claim 14, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale. As per claim 15, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale. As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale. As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale. As per claim 20, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale. Claim Rejections - 35 USC § 103 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claim s 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Vaidya in view of Saleh et al. (US PGPUB No. 2021/0192524) [hereinafter “Saleh”] . As per claim 2, Vaidya teaches the computer-implemented method of claim 1. Vaidya does not explicitly teach at least one encoded state with the at least one encoded updated state in the memory. Saleh teaches at least one encoded state with the at least one encoded updated state in the memory (Abstract [0023], replacing current encoded states of an RNN fraud model are replaced with new encoded states of the model). At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Vaidya with the teachings of Saleh, at least one encoded state with the at least one encoded updated state in the memory , to ensure that all data is fully changed for efficiency and security purposes. As per claim 9, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale. As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale. Claim s 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Vaidya in view of Chai et al. (WO-2019216938 -A1 ) [hereinafter “Chai”] . As per claim 3, Vaidya teaches the comp uter-implemented method of claim 1 . Vaidya does not explicitly teach wherein a size of the at least one encoded state is equal to or smaller than a quarter of a size of the at least one decoded state. Chai teaches wherein a size of the at least one encoded state is equal to or smaller than a quarter of a size of the at least one decoded state ([0104] -[ 0106], using various compression tools like pruning and quantization techniques to reduce the size of the model up to 2X or 4X, i.e. quarter). At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Vaidya with the teachings of Chai, wherein a size of the at least one encoded state is equal to or smaller than a quarter of a size of the at least one decoded state , to provide more efficient storage and transferring of RNN states across the system. As per claim 10, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale. Claim s 4, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Vaidya in view of Wierstra et al. (US Patent No. 10,713,559) [hereinafter “ Wierstra ”] . As per claim 4, Vaidya teaches the computer-implemented method of claim 1 . Vaidya does not explicitly teach wherein the at least one encoded state comprises a cell state and a hidden state . Wierstra teaches wherein the at least one encoded state comprises a cell state and a hidden state (Col. 3 lines 18-32, encoded representation of previous state includes a cell state and a hidden state see also Abstract ) . At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Vaidya with the teachings of Wierstra , wherein the at least one encoded state comprises a cell state and a hidden state , to allow for a more accurate prediction of an observed final state based on previous states . The combination of Vaidya and Wierstra does note explicitly teach wherein the RNN model is a long short-term memory model . Chai teaches wherein the RNN model is a long short-term memory model ( [0137] and [0155], LSTM RNN models ). At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Vaidya and Wierstra with the teachings of Chai , wherein the RNN model is a long short-term memory model , to capture long-term dependencies in the previous states. As per claim 11, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale. As per claim 17, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Horn et al. (US Patent No. 9,941,900), Branco et al. (US PGPUB No. 2021/0248448), Wong et al. (WO-2022008131-A1), Delestre et al. ("Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow," 2025 International Conference on Emerging Technologies and Computing (IC_ETC), Brest, France, 2025, pp. 1-7, doi : 10.1109/IC_ETC65981.2025.11141339), V. R et al. ("Fraud Detection in Telecommunications Exploiting NADAM Optimizer with Long Short-Term Memory Algorithm," 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Tirupur, India, 2025, pp. 299-306, doi : 10.1109/ICIMIA67127.2025.11200597) and Jagwani et al. ("Real-Time Fraud Detection and Securing Customer Transactions in E-Commerce Using all disclose various aspects of detection fraud transactions using RNN encoding/decoding states. 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