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.
Claim(s) 1-2, 12-13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Patent 11989643 B2-Branco et al (Hereinafter referred to as “Branco”).
Regarding claim 1, Branco discloses a method for computing a prediction using a machine learning model (column 1, lines 15-30) comprising:
receiving a current data sample of a sequence of data samples (column 3, lines 50-65, sequence of transactions);
retrieving, from a data store, a state value representing a learned embedding of previous samples of the sequence of data samples (column 5, lines 5-15, wherein retrieving a state; 27-40, wherein the state is stored in a vector. The examiner notes that a learned embedding is merely a vector; column 10, lines 23-35, discloses another data store; column 4, lines 5-10, wherein GRU model is configured to store one or more states; lines 29-35, wherein storage 240 is configured to store states. Storage includes multiple databases);
computing, by a recurrent neural network, based on the current data sample and the state value (column 5, lines 35-45, wherein recurrent neural network uses current data sample and stat values to train or compute):
an output value representing an inference regarding the current data sample (column 5, lines55-65, wherein an output result is associated with fraud threat, which is interpreted as an inference); and
an updated state value representing a learned embedding of the current data sample and the previous samples of the sequence of data samples (Column 5, lines 35-45, wherein determining a new second state and recursive update of the state; lines 50-60, wherein updating the state after an event; column 6, lines 1-30, wherein updated states are learnable parameters, which are learnable vectors);
storing the updated state value in the data store (column 4, lines 5-10, wherein GRU model is configured to store one or more states; lines 29-35, wherein storage 240 is configured to store states. Storage includes multiple databases); and
outputting the output value regarding the current data sample (column 4, lines 15-20, outputting values).
Regarding claim 2, Branco discloses the method of claim 1, wherein the recurrent neural network is trained to compute state values representing learned embeddings of sequences of data samples and output values based on training sequences of training data samples and corresponding labels (Column 6, lines 1-40, wherein learned bias vectors of state values computed by recurrent neural networks; column 4, lines 50-55, wherein the vectors have corresponding labels)), wherein the state value comprises a vector of numerical values in a multi-dimensional latent space (column 4, lines 50-65).
Regarding claim 12, analyses are analogous to those presented for claim 1 and are applicable for claim 12.
Regarding claim 13, analyses are analogous to those presented for claim 2 and are applicable for claim 13.
Regarding claim 20, Branco discloses the non-transitory computer-readable medium of claim 12, wherein the sequence machine learning model comprises a recurrent neural network (column 5, lines 35-45, wherein recurrent neural network).
Claim(s) 3-7, 14-18 rejected under 35 U.S.C. 103 as being unpatentable over Patent 11989643 B2-Branco et al (Hereinafter referred to as “Branco”), in view of US 20210312528 A1-Benkreira et al (Hereinafter referred to as “Ben”).
Regarding claim 3, Branco disclose the method of claim 1, wherein the data samples comprise a plurality of dimensions (according to instant applicant’s specification publication [0037], dimensions refers to a credit card number, merchant identifier or the like. To be consistent with instant applicant’s specification, Branco discloses a plurality of dimensions in column 13, lines 40-65, wherein credit card number, debit card number, etc) , and wherein the data samples of the sequence of data samples and the current data sample have a same value in a first dimension (lines 15-45, wherein analyzing transaction data and identify anomalies).
Branco fails to disclose wherein the data samples of the sequence of data samples and the current data sample have a same value in a first dimension
However, in the same field of endeavor, Ben discloses wherein the data samples comprise a plurality of dimensions (Fig 2-3) and wherein the data samples of the sequence of data samples and the current data sample have a same value in a first dimension (According to instant applicant’s specification publication in [0037], discloses along the same dimension (e.g., analyze spending habits to generate targeted advertising to the holder of the credit card number). Therefore, to be consistent with Applicant’s specification, Ben discloses samples of the sequence of data samples and the current data sample have a same value in a first dimension in [0039], wherein various factors can be used in generating the machine learning procedure including, but not limited to, the consumer's past spending habits, other consumer spending habits, where the other consumers may have similar traits to the consumer (e.g., biographical information, income, etc.). If the repurchase falls within predicted spend determined by the machine learning procedure, then the repurchase can be initiated. If it does not fall within the predicted spend, then the repurchase can be denied. Other fraud measures can include requiring a multifactor authentication for the repurchase (e.g., sending the consumer a text message or push notification to confirm the repurchase).
Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Branco to disclose wherein the data samples of the sequence of data samples and the current data sample have a same value in a first dimension as taught by Ben, to provide an additional layer of security, as certain items will not be made available for repurchase, which would prevent someone with unauthorized access to the transaction history from repurchasing an expensive item. As discussed below, the exemplary system, method and computer-accessible medium can utilize machine learning (E.g., using neural networks) to identify items for repurchase ([0043]).
Regarding claim 4, Branco discloses the method of claim 3, further comprising: determining a second sequence of second data samples having a same value as the current data sample in a second dimension of the plurality of dimensions (column 6, lines 25-35); retrieving, from the data store, a second state value representing a learned embedding of previous second data samples of the second sequence of second data samples (column 6, lines 30-40, wherein receiving second state); computing, by a second recurrent neural network, based on the current data sample and the second state value (fig. 4, shows multiple GRU, which are recurrent neural networks): a second output value representing a second inference regarding the current data sample (Column 6, lines 40-45 wherein predicted result associated with fraud threat of second state); and a second updated state value representing a learned embedding of the current data sample and the previous second data samples of the second sequence of second data samples(column 6, lines 35-40, second state updated); storing the second updated state value in the data store (wherein GRU model is configured to store one or more states; lines 29-35, wherein storage 240 is configured to store states. Storage includes multiple databases); and outputting the second output value regarding the current data sample (column 4, lines 15-20, outputting values).
Regarding claim 5, Ben discloses the method of claim 4, wherein the data samples correspond to transactions, wherein the dimensions comprise: a merchant identifier (Ben, fig. 1); and a credit card number ([0030]), entering in their identifying information and a credit card number to pay for the item(s, and wherein the inference corresponds to a likelihood that the current data sample is a fraudulent transaction in the sequence of data samples having a same merchant identifier ([0040]).
Regarding claim 6, Ben discloses the method of claim 5, wherein the second inference corresponds to a likelihood that the current data sample is a second fraudulent transaction in the second sequence of second data samples having a same credit card number ([0046], fraudulent).
Regarding claim 7, Ben discloses the method of claim 3, wherein the data samples correspond to product selections, wherein the dimensions comprise: a consumer identifier (Fig. 1-7); and a product identifier (Fig 1-7), and wherein the inference corresponds to a likelihood that a consumer will select a product corresponding to the current data sample ([0020-0025]).
Regarding claim 14, analyses are analogous to those presented for claim 3 and are applicable for claim 14.
Regarding claim 15, analyses are analogous to those presented for claim 4 and are applicable for claim 15.
Regarding claim 16, analyses are analogous to those presented for claim 5 and are applicable for claim 16.
Regarding claim 17, analyses are analogous to those presented for claim 6 and are applicable for claim 17.
Regarding claim 18, analyses are analogous to those presented for claim 7 and are applicable for claim 18.
Allowable Subject Matter
Claims 8-11 are allowed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LERON BECK whose telephone number is (571)270-1175. The examiner can normally be reached M-F 8 am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Czekaj can be reached at (571) 272-7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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LERON . BECK
Examiner
Art Unit 2487
/LERON BECK/Primary Examiner, Art Unit 2487
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