DETAILED 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.
Claim(s) 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt, publication number: US 2022/0083571 in view of Rahnama Moghaddam (RD), publication number: US 202/0342326.
As per claim 1, Schmidt teaches a system for re-formulating a machine learning model, the system comprising:
a memory comprising: computer readable media; a first set of data for a first machine learning (ML) model; and a second set of data for a second ML model, the first set of data comprising a first set of loss values for a first set of training samples used to train the first ML model, the second set of data comprising a second set of loss values for the first set of training samples applied to the second ML model that was trained using a second set of training samples (multiple machine learning models with data loss associated with different subsets of training data, Fig. 3, [0046]);
identifying, from the first set of loss values for the first ML model, a first plurality of training sample pairs from the first set of training samples that have a difference in a loss value less than a threshold;
identifying, from the second set of data, a second plurality of training sample pairs that correspond to the first plurality of training sample pairs (allowable loss per input, training sample points corresponding to previously used inputs with known losses, [0042]);
determining, from the first set of loss values, a first average loss value distance between each pair from the first plurality of training sample pairs from the first set of training samples (average loss, [0025][0028][0050]);
determining, from the second set of loss values, a second average loss value distance between each pair from the second plurality of training sample pairs (average loss, [0025][0028][0050]);
Schmidt does not teach analyzing, as an exponential model, a dependency of a rate of separation between the first average loss value distance and the second average loss value distance versus a number of model runs;
based on the analyzing, determining whether the training of the second ML model using the second set of training samples is stable; and
causing the first ML model to be reformulated when the second ML model is not stable.
In an analogous art, RD teaches analyzing, as an exponential model, a dependency of a rate of separation between the first average loss value distance and the second average loss value distance versus a number of model runs;
based on the analyzing, determining whether the training of the second ML model using the second set of training samples is stable; and
causing the first ML model to be reformulated when the second ML model is not stable (Lyapunov based method for training DNN to perform with stability and robustness, [0018][0022][0056][0094]).
Therefore it would have been obvious to one of ordinary skill in the art prior to the effective filling date of the claimed invention to modify Schmidt’s loss comparison system to include a Lyapunov based training method as described in RD’s Neural Network training system for the advantages of protecting the system against adversarial attacks.
As per claim 2, the combination teaches wherein the first set of loss values are determined by the following:
receiving the first set of training samples; receiving ground truth data;
applying the first set of training samples as input into the first ML model; receiving, based on the applying, a set of predictions from the first ML model,
the set of predictions comprising a prediction corresponding to each training sample in the first set of training samples;
comparing each prediction in the set of predictions to a respective ground truth from the ground truth data; and
based on the comparing, determining a loss value for each training sample in the first set of training samples (Schmidt: Calculating loss, [0025][0036]).
As per claim 3, the combination teaches wherein the second set of loss values are determined by the following:
receiving the first set of training samples; receiving ground truth data;
applying the first set of training samples as input into the second ML model trained using the second set of training samples;
receiving, based on the applying, a set of predictions from the second ML
model, the set of predictions comprising a prediction corresponding to each training sample in the first set of training samples;
comparing each prediction in the set of predictions to a respective ground truth from the ground truth data; and
based on the comparing, determining a loss value for each training sample in the first set of training samples (Schmidt: Calculating loss, [0025][0036], using the same subset of data for different models, Fig. 3, [0023]).
As per claim 4, the combination teaches wherein the exponential model is d(k) - exp(k*lambda), where "k" is the number of ML models, "d(k)" is an average loss value distance for an ML model, and lambda is a parameter is extracted from the data (RD: Lyapunov based method for training DNN to perform with stability and robustness, [0018][0022][0056][0094]).
As per claim 5, the combination teaches wherein d(k) = Avg ILk(xi) -Lk(xj)I, wherein "L" is a loss value, "k" is the number of the ML model, "x" is the first set of training samples, and "i" and "j" are training sample pairs from the first set of training samples that have a difference in a loss value less than the threshold (RD: Lyapunov based method for training DNN to perform with stability and robustness, [0018][0022][0056][0094]).
As per claim 6, the combination teaches wherein when a value of lambda is less than or equal to zero, a current data generation process is stable and the training of the second ML model using the second set of training samples is stable; and
wherein the value oflambda is greater than zero, the current data generation process is divergent and the training of the second ML model using the second set of training samples is not stable (RD: Lyapunov based method for training DNN to perform with stability and robustness, [0018][0022][0056][0094]).
As per claim 7, the combination teaches wherein causing the first ML model to be reformulated comprises issuing an alert to a user (Schmidt: alert, [0068][0071]).
Claims 8 – 14 are rejected based on claims 1 – 7.
Claims 15 – 20 are rejected based on claims 1 – 6.
Conclusion
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/OLUGBENGA O IDOWU/Primary Examiner, Art Unit 2494