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 .
This action is in response to amendments and remarks filed on 04/13/2026. In the current amendments, the specification is amended, claims 1, 7, 11, and 14 are amended, claim 2-3 and 12-13 are cancelled, and claim 17 is newly presented. Claims 1, 4-11, and 14-17 are pending and have been examined.
In response to amendments and remarks filed on 04/13/2026, the specification objection, the claim objections, the 35 U.S.C. 112(b), and the 35 U.S.C. 103 prior art rejections made in the previous office action are withdrawn.
Claim Rejections - 35 USC § 101
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, 4-11, and 14-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“computing a total variation loss for use in backpropagation during training of a neural network which individually classifies data points”
“predicting, …, a respective label for each data point in a set of input data points”
“determining a variation indicator that indicates a variance between: (i) smoothness of the predicted labels among neighboring data points and (ii) smoothness of the ground truth labels among the same neighboring data points”
“wherein determining the smoothness of the predicted labels among neighboring data points comprises determining differences in the predicted labels between the neighboring data points”
“determining the smoothness of the ground truth labels among neighboring data points comprises determining differences in the ground truth labels between the neighboring data points”
“determining the variation indicator comprises determining a norm of a difference between the smoothness of the predicted labels among neighboring data points and the smoothness of the ground truth labels among the same neighboring data points”
“computing a total variation loss based on the variation indicator”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass computing a total variation loss for use in backpropagation during neural network training (corresponds to mathematical calculations of computing a loss and backpropagation); predicting a respective label for each data point in a set of input data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of each data point in an input set of data points); determining a variation indicator that indicates a variance between smoothness of predicted labels among neighboring data points and smoothness of ground truth labels among same neighboring data points (corresponds to mathematical calculations of determining a variance); determining differences in the predicted labels between the neighboring data points to determine the smoothness of the predicted labels among neighboring data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine differences between predicted labels for neighboring data points); determining differences in the ground truth labels between the neighboring data points to determine the smoothness of the ground truth labels among neighboring data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine differences between ground truth labels for neighboring data points); determining a norm of a difference between the smoothness of the predicted labels among neighboring data points and the smoothness of the ground truth labels among the same neighboring data points to determine the variation indicator (corresponds to mathematical calculations of determining a norm of a difference); and computing a total variation loss based on the variation indicator (corresponds to mathematical calculations for computing a total variation loss).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“training of a neural network”
“using the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstracts ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the data points are image pixels, and neighboring data points are defined a by a defined pixel distance”
As drafted, are part of the abstract ideas of claim 1 of predicting a respective label for each data point and determining a variation indicator that indicates a variance. The limitations of claim 4 further limit the limitations of claim 1 by further defining what the data points comprise and what the neighboring data points are defined by. The above limitations in the context of this claim encompass predicting a respective label for each data point in a set of input data points, the data points being image pixels (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of each data point image pixel in an input set of data points); determining a variation indicator that indicates a variance between smoothness of predicted labels among neighboring data points and smoothness of ground truth labels among same neighboring data points, the neighboring data points being defined by a defined pixel distance (corresponds to mathematical calculations of determining a variance).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the data points are point cloud data points of a point cloud and neighboring data points are defined by a nearest neighbor identification algorithm”
As drafted, are part of the abstract ideas of claim 1 of predicting a respective label for each data point and determining a variation indicator that indicates a variance. The limitations of claim 5 further limit the limitations of claim 1 by further defining what the data points comprise and what the neighboring data points are defined by. The above limitations in the context of this claim encompass predicting a respective label for each data point in a set of input data points, the data points being point cloud data points of a point cloud (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of point cloud data point of a point cloud in an input set of data points); determining a variation indicator that indicates a variance between smoothness of predicted labels among neighboring data points and smoothness of ground truth labels among same neighboring data points, the neighboring data points being defined by a nearest neighbor identification algorithm (corresponds to mathematical calculations of determining a variance).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the total variation loss is incorporated into a total loss function for the neural network to generate a total loss for the neural network”
“determining update values for plurality of parameters of the neural network as part of gradient decent training of the neural network”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass generating a total loss by incorporating the total variation loss into a total loss function for the neural network (corresponds to mathematical calculations for computing a total variation loss and a total loss function); and performing gradient descent training to determined update values for a plurality of parameters of the neural network (corresponds to mathematical calculations for performing gradient descent to determine update values).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“predicting, …, a respective label for each data point in a set of input data points”
“for each data point, determining: (i) a predicted label difference value between the predicted label for the data point and a predicted label for at least one neighbor data point of the data point”
“for each data point, determining: … (ii) a ground truth label difference value between a ground truth label for the data point and a ground truth label for the least one neighbor data point of the data point”
“for each data point, determining a norm of a difference between the predicted label difference value and the ground truth label difference value”
“computing a total variation loss for the set of input data points based on a sum of the norms”
“performing backpropagation to update a set of parameters of the neural network based at least on the total variation loss”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass predicting a respective label for each data point in a set of input data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of each data point in an input set of data points); for each data point, determining a predicted label difference value between the predicted label for the data point and a predicted label for a neighbor data point (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a predicted label difference value between predicted labels for a data point and a neighboring data point); for each data point, determining a ground truth label difference value between the ground truth label for the data point and a ground truth label for a neighbor data point (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine a ground truth label difference value between ground truth labels for a data point and a neighboring data point); for each data point, determining a norm of a difference between the predicted label difference value and the ground truth label difference value (corresponds to mathematical calculations of determining a norm of a difference); computing a total variation loss based on a sum of the norms (corresponds to mathematical calculations for computing a total variation loss); and performing backpropagation to update parameters of the neural network based on the total variation loss (corresponds to mathematical calculations for performing backpropagation for parameter updates).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“training a neural network”
“using the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstracts ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining the predicted label difference values comprises: for all the data points (i,j) and values Δi and Δj, where (i,j) is a data point index and Δi,Δj are respective step values in the data point index, computing an absolute value of y{(i+Δi)(j)} - y{I,j}, where y{i,j} is the predicted label for data point (i,j) for inclusion in a corresponding location of a tensor variable Y{(Δi),(j)}, and computing the absolute value of y{(i),(j+Δj)} - y{i,j} for inclusion in a corresponding location of a tensor variable Y{(Δi),(j)}”
“determining the ground truth label difference values comprises: for all the data points (i,j) and values Δi and Δj, computing the absolute value of
y
^
{(i+Δi),(j)} -
y
^
{i,j}, where
y
^
{i,j} is the ground truth label for data point i,j, for inclusion in a corresponding location of a tensor variable
Y
^
{(i),(Δj)}, and computing the absolute value of
y
^
{(i),(j+Δj)} -
y
^
{i,j} for inclusion in a corresponding location of a tensor variable
Y
^
{(i),(Δj)}”
“determining the norm of the difference indicators comprises: computing a first p, q norm of Y{(Δi),(j)} and
Y
^
{(Δi),(j)} for all pairs of (Δi),(j) and computing a p, q norm of Y{(i),(Δj)} and
Y
^
{(i),(Δj)} for all pairs of (i),(Δj)”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass determining the predicted label difference values by calculating absolute values using the given equations for the data points (corresponds to mathematical calculations and mathematical formulas or equations); determining the ground truth label difference values by calculating absolute values using the given equations for the data points (corresponds to mathematical calculations and mathematical formulas or equations); and determining the norm of the difference indications by computing p,q norms (corresponds to mathematical calculations of determining norms).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 7 of a generic neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“wherein the set of input data points comprises an image”
As drafted, is part of the abstract idea of claim 7 of predicting a respective label for each data point in a set of input data points. The limitation of claim 9 further limits the limitation of claim 7 by further defining what the set of input data points comprises. The above limitation in the context of this claim encompasses predicting a respective label for each data point in a set of input data points comprising an image (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of each data point in an input set of data points comprising an image).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 7 of a generic neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“wherein the set of input data points comprises data points of a point cloud”
As drafted, is part of the abstract idea of claim 7 of predicting a respective label for each data point in a set of input data points. The limitation of claim 10 further limits the limitation of claim 7 by further defining what the set of input data points comprises. The above limitation in the context of this claim encompasses predicting a respective label for each data point in a set of input data points comprising data points of a point cloud (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of each data point in an input set of data points comprising data points of a point cloud).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 7 of a generic neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“compute a total variation loss for use in backpropagation during training of a neural network which individually classifies data points”
“predicting, …, a respective label for each data point in a set of input data points”
“determining a variation indicator that indicates a variance between: (i) smoothness of the predicted labels among neighboring data points and (ii) smoothness of the ground truth labels among the same neighboring data points”
“wherein determining the smoothness of the predicted labels among neighboring data points comprises determining differences in the predicted labels between the neighboring data points”
“determining the smoothness of the ground truth labels among neighboring data points comprises determining differences in the ground truth labels between the neighboring data points”
“determining the variation indicator comprises determining a norm of a difference between the smoothness of the predicted labels among neighboring data points and the smoothness of the ground truth labels among the same neighboring data points”
“computing the total variation loss based on the variation indicator”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass computing a total variation loss for use in backpropagation during neural network training (corresponds to mathematical calculations of computing a loss and backpropagation); predicting a respective label for each data point in a set of input data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of each data point in an input set of data points); determining a variation indicator that indicates a variance between smoothness of predicted labels among neighboring data points and smoothness of ground truth labels among same neighboring data points (corresponds to mathematical calculations of determining a variance); determining differences in the predicted labels between the neighboring data points to determine the smoothness of the predicted labels among neighboring data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine differences between predicted labels for neighboring data points); determining differences in the ground truth labels between the neighboring data points to determine the smoothness of the ground truth labels among neighboring data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine differences between ground truth labels for neighboring data points); determining a norm of a difference between the smoothness of the predicted labels among neighboring data points and the smoothness of the ground truth labels among the same neighboring data points to determine the variation indicator (corresponds to mathematical calculations of determining a norm of a difference); and computing a total variation loss based on the variation indicator (corresponds to mathematical calculations for computing a total variation loss).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The limitations:
“one or more processors”
“non-volatile memory coupled to the one or more processors, the memory storing instructions that when executed by the one or more processors”
“training of a neural network”
“using the neural network”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Therefore, the additional elements do not integrate the abstracts ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic processors, memory, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the data points are image pixels, and neighboring data points are defined by a defined pixel distance”
As drafted, are part of the abstract ideas of claim 11 of predicting a respective label for each data point and determining a variation indicator that indicates a variance. The limitations of claim 14 further limit the limitations of claim 11 by further defining what the data points comprise and what the neighboring data points are defined by. The above limitations in the context of this claim encompass predicting a respective label for each data point in a set of input data points, the data points being image pixels (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of each data point image pixel in an input set of data points); determining a variation indicator that indicates a variance between smoothness of predicted labels among neighboring data points and smoothness of ground truth labels among same neighboring data points, the neighboring data points being defined by a defined pixel distance (corresponds to mathematical calculations of determining a variance).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 11 of generic processors, memory, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic processors, memory, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the data points are point cloud data points of a point cloud and neighboring data points are defined by a nearest neighbor identification algorithm”
As drafted, are part of the abstract ideas of claim 11 of predicting a respective label for each data point and determining a variation indicator that indicates a variance. The limitations of claim 15 further limit the limitations of claim 11 by further defining what the data points comprise and what the neighboring data points are defined by. The above limitations in the context of this claim encompass predicting a respective label for each data point in a set of input data points, the data points being point cloud data points of a point cloud (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can predict a label of point cloud data point of a point cloud in an input set of data points); determining a variation indicator that indicates a variance between smoothness of predicted labels among neighboring data points and smoothness of ground truth labels among same neighboring data points, the neighboring data points being defined by a nearest neighbor identification algorithm (corresponds to mathematical calculations of determining a variance).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 11 of generic processors, memory, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic processors, memory, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a computer system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the total variation loss is incorporated into a total loss function for the neural network to generate a total loss for the neural network”
“determining update values for plurality of parameters of the neural network as part of gradient decent training of the neural network”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass generating a total loss by incorporating the total variation loss into a total loss function for the neural network (corresponds to mathematical calculations for computing a total variation loss and a total loss function); and performing gradient descent training to determined update values for a plurality of parameters of the neural network (corresponds to mathematical calculations for performing gradient descent to determine update values).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 11 of generic processors, memory, neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic processors, memory, neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the total variation loss is:
PNG
media_image1.png
182
608
media_image1.png
Greyscale
yi,j is a ground truth label for a data point at location (i,j),
y
^
I,j is a predicted label, | • | is an absolute value function and ||•||p,q is the p, q norm”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)). The above limitations in the context of this claim encompass using the given equation to calculate the total variation loss (corresponds to mathematical calculations and mathematical formulas or equations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)). The recitation of additional elements in claim 1 of a generic neural network, and generic training of the neural network, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Response to Arguments
Applicant’s arguments, filed 04/13/2026, with respect to the specification objections have been fully considered and are persuasive. Therefore, the specification objections have been withdrawn.
Applicant’s arguments, filed 04/13/2026, with respect to the claim objections have been fully considered and are persuasive. Therefore, the claim objections have been withdrawn.
Applicant’s arguments, filed 04/13/2026, with respect to the rejections of claim 16 under 35 U.S.C. 112(b) have been fully considered and are persuasive. Therefore, the 35 U.S.C. 112(b) rejection has been withdrawn.
Applicant’s arguments, filed 04/13/2026, with respect to the claim rejections under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the 35 U.S.C. 103 prior art rejections have been withdrawn.
Applicant's arguments, filed 04/13/2026, with respect to the 35 U.S.C. 101 abstract idea rejections to the claims have been fully considered but they are not persuasive. Applicant asserts “Former claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims have been amended for clarity and in the interest of advancing prosecution. In particular, present claim 1 has been amended to clarify the subject matter. The Applicant submits that the claims, as amended, are directed to patentable subject matter for at least the following reasons. …
Without conceding anything with respect to the Office Action's analysis under Step 2A, Prong One of the subject matter eligibility analysis framework, the Applicant submits that each of the independent claims, when looked at as a whole, integrates the subject matter into a practical application and therefore should be found to be patent eligible at least under Step 2A, Prong Two. …
Page 5 of the Office Action asserts that with respect to former claim 1, ''Step 2A Prong 2: ... Regarding the ''neural network'', no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper... The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (mental process) on a generic computer... The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea''. Page 6 of the Office Action asserts that analysis of independent claim 1 under Step 2A Prong 2 and Step 2B is applied to claim 2 and claim 3 as well.
The Applicant disagrees with the assertion with respect to former claims 2 and 3 and submits that the generalized comments that apply the analysis of independent claim 1 to former claims 2 and 3 is insufficient for the conclusion of 35 USC § 101 rejection because former claims 2 and 3 recite additional features that integrate the subject matter into a practical application.
Notwithstanding the above, in an effort to advance the application toward allowance, present claim 1 has been amended to incorporate features of former claims 2 and 3, and the Applicant will point out why the subject matter of present claim 1 is patent eligible at least under Step 2A, Prong Two.
Present claim 1 recites details of a variation indicator that is utilized to compute a total variation loss function in a neural network. In particular, present claim 1 recites, inter alia, features:
''... wherein determining the smoothness of the predicted labels among neighboring data points comprises determining differences in the predicted labels between the neighboring data points,
determining the smoothness of the ground truth labels among neighboring data points comprises determining differences in the ground truth labels between the neighboring data points; and
determining the variation indicator comprises determining a norm of a difference between the smoothness of the predicted labels among the neighboring data points and the smoothness of the ground truth labels among the neighboring data points''.
These features provide details of how to determine a variation indicator of a total variation loss function that is utilized to train the neural network.
Therefore, details of how to compute a variation indicator among neighboring data points and how to compute a total variation loss are recited and are considered as additional elements that integrate the abstract idea into a practical application at least because the additional elements provide a particular solution (e.g., details of how to compute a variation indicator and how to compute a total variation loss function based on the variation indicator in the neural network such that information (e.g., predicted labels and/or ground truth labels) associated with neighboring data points is computed/considered) to a technical problem and improve an existing technology for training a machine learning (ML) for performing semantic segmentation. …
To improve the described existing technology in conventional solutions of training the ML based semantic segmentation models, the claimed subject matter of present claim 1 provides a method of computing a variation indicator and computing a total variation loss (incorporates a summation of errors related both to a target data point and neighboring data points of the target data point) based on the variation indicator. In particular, the total variation loss is computed based on the variation indicator, and the variation indicator is computed by: (1) determining the smoothness of the predicted labels among neighboring data points comprises determining differences in the predicted labels between the neighboring data points; (2) determining the smoothness of the ground truth labels among neighboring data points comprises determining differences in the ground truth labels between the neighboring data points; (3) determining the variation indicator comprises determining a norm of a difference between the smoothness of the predicted labels among neighboring data points and the smoothness of the ground truth labels among the same neighboring data points.
Thus, the claimed total variation loss that is determined by the variation indicator may enable information (e.g., predicted labels and/or ground truth labels) of neighboring data points to be considered when training the neural network, which may help to improve efficiency in training a neural network constructed and arranged for semantic segmentation and to further improve the accuracy of the neural network.
As discussed above, the additional elements of present claim 1 provide the details of the variation indicator and details to how to calculate the total variation loss function that incorporates a summation of errors related both to the target data point as well as its neighboring data points, which enables information of neighboring data points of a target data point to be considered when training a neural network utilized for semantic segmentation.
Therefore, independent claim 1 clearly recites details of how to compute the variation indicator and how to compute the total variation loss based on the variation indicator, which provide a particular solution to address the technical problems of an existing solution for training a neural network (e.g., utilized for semantic segmentation) as set forth in the specification and therefore improves the existing technology. …
Indeed, as discussed above, present claim 1 clearly recites how a particular solution to a problem is accomplished, namely the features of''...wherein determining the smoothness of the predicted labels among neighboring data points comprises determining differences in the predicted labels between the neighboring data points; determining the smoothness of the ground truth labels among neighboring data points comprises determining differences in the ground truth labels between the neighboring data points; and determining the variation indicator comprises determining a no11n of a difference between the smoothness of the predicted labels among neighboring data points and the smoothness of the ground truth labels among the same neighboring data points; and computing a total variation loss based on the variation indicator''.
Furthermore, in consideration of whether a claim is directed to an improvement in the functioning of a computer or ''any other technology or technical field," the Applicant draws the Examiner's attention to the Appeals Review Panel (ARP) decision of Ex Parle Desjardins et al., Appeal 2024-000567, issued September 26, 2025. The claims in that case relate to a method of training a machine learning model, which had been rejected by the Patent Trial and Appeal Board (PTAB) under 35 USC 101. The ARP vacated the 35 USC 101 rejection of the PTAB, finding that the claims are directed to patent-eligible subject matter. More specifically, on page 7 the ARP agreed that ''the claimed subject matter provides technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training''. Indeed, with respect to independent claim 1, the ARP indicated that the claim, when evaluated as a whole, ''constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation''. …
In view of the ARP decision of Ex Parle Desjardins et al., the present claim 1 is clearly directed to an improved training method that applies a total variation loss function incorporating a summation of errors related both to a target data point and its neighboring data points, which reflects various technical advantages (e.g., improved efficiency in training a neural network that is to be utilized for semantic segmentation and improved accuracy for the trained neural network due to the consideration of information of neighboring data points) and improvements in the field of ML models applied in semantic segmentation. Therefore, the claimed subject matter of present claim 1 is integrated into a practical application, and present claim 1 should be found to be patent eligible at least under Step 2A, Prong Two.
For the completeness, notwithstanding the foregoing, in the purely hypothetical case where Step 2A would have revealed that the claims are directed to a judicial exception, then Step 28 would be required. The following will establish that Step 28 of the subject matter eligibility analysis framework would find that the claims include additional elements that are sufficient to amount to significantly more than the judicial exception, from which it follows that the claim qualifies as eligible subject matter under 35 USC § 101.
The Applicant submits that present claim 1 provides an improvement to the functioning of a computer or other technology or technical field and also includes elements that, when considered in combination, add unconventional steps that confine the claim to a particular useful application, and amount to more than applying the exception using a generic computer.
Present claim 1 provides an improved method of computing a total variation loss that incorporates a summation of errors related both to a target data point and to neighboring data points of the target data point. This provides improvements (e.g., improved efficiency to train the neural network to perform semantic segmentation and/or improved accuracy for the neural network to perform semantic segmentation) in the field of machine learning. The steps to achieve the improvements are unconventional steps that have been not found in the cited art, as discussed below with respect to the rejections under 35 U.S.C. 103.
Therefore, claim 1 recites significantly more than any alleged judicial exception and is also patent eligible under Step 2B.
In view of the foregoing, the Applicant submits that present claim 1 is directed to patent eligible subject matter.
Independent claims 7 and 11 recite features similar to claim 1 and thus are directed to patent eligible subject matter. Dependent claims 4-6, 8-10, and 14-17 are directed to patent eligible subject matter for at least the same reasons as the independent claims, at least by virtue of their claim dependency.
In view of the foregoing, the Applicant submits that the present claims are all directed to patent eligible subject matter, and the rejection under 35 U.S.C. 101 should be withdrawn” (Remarks Pages 8-15)
Examiner’s Response:
The examiner respectfully disagrees. Applicant has made general assertions that claim 1 recites claim elements that are not directed to an abstract idea and that even if the claim elements are directed to an abstract idea, the judicial exceptions are integrated into a practical application because the claims recite elements that cannot reasonable be characterized as covering mental processes or reflect an improvement to a technology or technical field. Regarding the “wherein determining the smoothness of the predicted labels among neighboring data points comprises determining differences in the predicted labels between the neighboring data points”, “determining the smoothness of the ground truth labels among neighboring data points comprises determining differences in the ground truth labels between the neighboring data points”, and “determining the variation indicator comprises determining a norm of a difference between the smoothness of the predicted labels among the neighboring data points and the smoothness of the ground truth labels among the neighboring data points” limitations of claim 1, these limitations, under their broadest reasonable interpretations, are considered abstract ideas that encompass determining differences in the predicted labels between the neighboring data points to determine the smoothness of the predicted labels among neighboring data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine differences between predicted labels for neighboring data points); determining differences in the ground truth labels between the neighboring data points to determine the smoothness of the ground truth labels among neighboring data points (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine differences between ground truth labels for neighboring data points); determining a norm of a difference between the smoothness of the predicted labels among neighboring data points and the smoothness of the ground truth labels among the same neighboring data points to determine the variation indicator (corresponds to mathematical calculations of determining a norm of a difference). Furthermore, since these limitations are directed to a judicial exception, they cannot provide any alleged solution or improvement. See MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.”
Additionally, claim 1 recites computing a total variation loss for use in backpropagation during neural network training (corresponds to mathematical calculations of computing a loss and backpropagation); predicting a respective label for each data point in a set of input data points (corresponds to evaluation and judgement with the assistance of pen and paper); determining a variation indicator that indicates a variance between smoothness of predicted labels among neighboring data points and smoothness of ground truth labels among same neighboring data points (corresponds to mathematical calculations of determining a variance); and computing a total variation loss based on the variation indicator (corresponds to mathematical calculations for computing a total variation loss). Since these limitations are directed to a judicial exception, they cannot provide any alleged solution or improvement. See MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.”
Thus, it is the additional elements that are analyzed to determine whether the judicial exception is integrated into a practical application, not the judicial exception itself. The additional elements of claim 1 of “training of a neural network” and “using the neural network”, as drafted, under their broadest reasonable interpretations, are additional elements that are high level recitations of applying a generic computer and generic computer components to implement the abstract ideas such that it amounts to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f): “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. … Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Accordingly, the additional elements do not integrate the abstract ideas into a practical application.
Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic neural network, and generic training of the neural network for applying the abstract ideas). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Furthermore, regarding Appellant’s assertions regarding Ex Parte Desjardins, Examiner respectfully disagrees. The claims are distinguishable from Ex Parte Desjardins because Desjardins includes an improvement to machine learning related to addressing catastrophic forgetting while the current claims only use well-known backpropagation training of neural network models at a high level and a generic neural network to perform the abstract ideas related to semantic segmentation and calculating total variation loss. In other words, Desjardins provided a specific training strategy that allows the model to preserve performance on earlier tasks even as it learns new ones, while the current claims merely employ a generic neural network and well-known backpropagation training of a generic neural network model at a high level for performing the abstract ideas of semantic segmentation and calculating total variation loss. In addition, the current claims are similar to those discussed in Recentive Analytics, Inc. v. Fox Corp., et al. because the claims employ a high level backpropagation neural network training process and the Recentive court has instructed that “using a machine learning technique … necessarily includes an iterative training step. Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Furthermore, Recentive states “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
Therefore, considered as a whole, claim 1 is directed to mental processes and mathematical concepts performed by using a generic computer programmed with a generically recited class of computer algorithm and do not recite additional element(s) that can provide any alleged solution, improvement, or inventive concept. As such, the judicial exception is not integrated into a practical application, nor do the claims contain significantly more than the judicial exception.
In other words, the limitations of “wherein determining the smoothness of the predicted labels among neighboring data points comprises determining differences in the predicted labels between the neighboring data points”, “determining the smoothness of the ground truth labels among neighboring data points comprises determining differences in the ground truth labels between the neighboring data points”, and “determining the variation indicator comprises determining a norm of a difference between the smoothness of the predicted labels among the neighboring data points and the smoothness of the ground truth labels among the neighboring data points” are abstract ideas that are directed to a judicial exception, so they cannot provide any alleged solution or improvement. Furthermore, the additional elements recited in claim 1 are directed to mere instructions to apply an abstract idea using generic computer components. Therefore, claim 1 does not recite additional element(s) that can provide any alleged solution, improvement, or inventive concept. As such, the judicial exception is not integrated into a practical application, nor do the claims contain significantly more than the judicial exception.
Applicant relies on the arguments above regarding independent claims 7 and 11 and dependent claims 4-6, 8-10, and 14-17, therefore the response above is applicable to those claims.
Conclusion
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 BRIAN J HALES whose telephone number is (571)272-0878. The examiner can normally be reached M-F 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN J HALES/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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