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 .
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.
Claims 1, 2, 5-9, 12-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vaidya et al. (US PGPUB No. 2020/0380531) [hereinafter “Vaidya”] in view of Saleh et al. (US PGPUB No. 2021/0192524) [hereinafter “Saleh”].
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 stateful machine learning model from a memory ([0057], encoded states of the stateful machine learning 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 stateful machine learning 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]).
Vaidya does not explicitly teach an encoded state of a stateful machine learning model from a memory wherein the at least one encoded state is associated with at least one model state of the state machine learning model generated during processing of at least one prior transaction. Saleh teaches an encoded state of a stateful machine learning model from a memory (Abstract and [0055], encoded states of an RNN fraud model stored in a memory cache) wherein the at least one encoded state is associated with at least one model state of the state machine learning model generated during processing of at least one prior transaction ([0037] and claim 1, encoded states are derived from past transactions to help predict current and future risk scores).
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, an encoded state of a stateful machine learning model from a memory wherein the at least one encoded state is associated with at least one model state of the state machine learning model generated during processing of at least one prior transaction, to ensure that all data is fully changed for efficiency and security purposes.
As per claim 2, the combination of Vaidya and Saleh teaches the computer-implemented method of claim 1, wherein at least one encoded state with the at least one encoded updated state in the memory (Saleh; Abstract [0023], replacing current encoded states of an RNN fraud model are replaced with new encoded states of the model).
As per claim 5, the combination of Vaidya and Saleh 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 (Vaidya; [0015], card and terminal type); an account identifier (Vaidya; [0051], merchant data including account data); a payment device holder identifier (Vaidya; [0015], information related to the cardholder); or any combination thereof see id.
As per claim 6, the combination of Vaidya and Saleh teaches the computer-implemented method of claim 5, wherein the stateful machine learning model is a fraud detection model (Vaidya; [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 (Vaidya; [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 (Vaidya; [0051], merchant data including account data), the payment device holder identifier (Vaidya; [0015], information related to the cardholder), or any combination thereof see id.
As per claim 7, the combination of Vaidya and Saleh 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 (Vaidya; [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 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 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 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.
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.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Vaidya and Saleh in view of Chai et al. (WO-2019216938-A1) [hereinafter “Chai”].
As per claim 3, the combination of Vaidya and Saleh teaches the computer-implemented method of claim 1.
The combination of Vaidya and Saleh 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 and Saleh 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.
Claims 4, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Vaidya and Saleh in view of Wierstra et al. (US Patent No. 10,713,559) [hereinafter “Wierstra”] in further view of Chai.
As per claim 4, the combination of Vaidya and Saleh teaches the computer-implemented method of claim 1.
The combination of Vaidya and Saleh 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 and Saleh 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 stateful machine learning 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 stateful machine learning 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.
Response to Arguments
Applicant’s arguments with respect to the rejection of independent claims 1, 8 and 15 under 35 U.S.C. 103 using the prior art reference, Vaidya have been considered. In light of the new amendments, Examiner agrees and upon further consideration, the secondary prior art reference, Saleh, is determined to disclose the elements presented in the new amendments. Examiner suggests including in the independent claims, more specific details regarding the use of the encoded states in the detection of fraud and perhaps including some likely responses to such determinations.
To expedite prosecution, Examiner is open to conducting an after-final interview to discuss any other ideas to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zarkov et al. (US PGPUB No. 2020/0050988), Walters et al. (US Patent No. 11,080,707), Christensen et al. (US PGPUB No. 2020/0314101), Russell et al. ("On the memory properties of recurrent neural models," 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017, pp. 2596-2603, doi: 10.1109/IJCNN.2017.7966173) and Sawatzky et al. ("Visualizing RNN States with Predictive Semantic Encodings," arXiv:1908.00588, August 1, 2019) all disclose various aspects of detection fraud transactions using RNN encoding/decoding states.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached Max Flex.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carl Colin can be reached at 571-272-3862. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER C SHAW/Primary Examiner, Art Unit 2493 September 18, 2026
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