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 responsive to the application filed on 02/23/2022. Claim(s) 1 and 11-14 have been amended. Claims 1-14 are pending in this case. Claims 1 and 11-14 are independent claims.
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.
Claim(s) 1-14 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If itis determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claim(s) 1-10 are drawn to a computer-implemented method, claim 11 is drawn to a neural network, claims 12-13 are drawn to a system and claims 14 are drawn to a non-transitory machine-readable medium, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1).
Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1 and 11-14 are non- verbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
As to claim 1:
Claim 1 recites: A computer-implemented method for determining an output signal based on an input signal using a neural network, the method comprising:
determining, by the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 1 is directed to an abstract idea, specifically, a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)).
Independent claim 1 recites: A computer-implemented method for determining an output signal based on an input signal using a neural network, the method comprising:
determining, by the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
The limitation above is broadly and reasonably interpreted as a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)). The “scaling and shifting” layer input is a fundamental mathematical concept, specifically involving multiplying every value in a dataset (scaling) and adding a constant value to every data point (shifting).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites: A computer-implemented method for determining an output signal based on an input signal using a neural network, the method comprising:, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
determining, by the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an
additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites: A computer-implemented method for determining an output signal based on an input signal using a neural network, the method comprising:
as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Furthermore the additional element is well‐understood, routine, and conventional.
determining, by the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these 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 when considered as an ordered combination and as a whole.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
As to claim 11:
Claim 11 recites: A neural network configured to determine an output signal based on an input signal using a neural network, neural network configured to: determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 11 is directed to an abstract idea, specifically, a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)).
Independent claim 11 recites: A neural network configured to determine an output signal based on an input signal using a neural network, neural network configured to: determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
The limitation above is broadly and reasonably interpreted as a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)). The “scaling and shifting” layer input is a fundamental mathematical concept, specifically involving multiplying every value in a dataset (scaling) and adding a constant value to every data point (shifting).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 11 recites: A neural network configured to determine an output signal based on an input signal using a neural network, neural network configured to: as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an
additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 11 recites: A neural network configured to determine an output signal based on an input signal using a neural network, neural network configured to: as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Furthermore the additional element is well‐understood, routine, and conventional.
determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an additional
element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these 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 when considered as an ordered combination and as a whole.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
As to claim 12:
Claim 12 recites: A training system configured to train a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, the neural network configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, the training system being configured to:
provide a training input signal, a desired output signal characterizing a desired classification and/or regression value, and a plurality of auxiliary inputs, each of the auxiliary inputs characterizing meta information of the training input signal;
determine a training output signal for the training input signal using the neural network; and
adapt at least one parameter of the neural network according to a loss value characterizing a deviation of the training output signal with respect to the desired output signal, wherein the input signal is obtained by a sensor
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 12 is directed to an abstract idea, specifically, a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)).
Independent claim 12 recites in part: determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer
The limitation above is broadly and reasonably interpreted as a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)). The “scaling and shifting” layer input is a fundamental mathematical concept, specifically involving multiplying every value in a dataset (scaling) and adding a constant value to every data point (shifting).
adapt at least one parameter of the neural network according to a loss value characterizing a deviation of the training output signal with respect to the desired output signal
The limitation above is broadly and reasonably interpreted as a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)).
Claim 12 in part also is directed to an abstract idea, specifically, a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
characterizing a desired classification and/or regression value, and a plurality of auxiliary inputs, each of the auxiliary inputs characterizing meta information of the training input signal
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can give a training signal that they want to learn from, a desired output that shows what they want to achieve, and several extra details that describe the training signal.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 12 recites: A training system configured to train a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
the neural network configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, the training system being configured to, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 12 recites: A training system configured to train a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Furthermore the additional element is well‐understood, routine, and conventional.
the neural network configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, the training system being configured to, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
determine a training output signal for the training input signal using the neural network as drafted, amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Accordingly, these 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 when considered as an ordered combination and as a whole.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
As to claim 13:
Claim 13 recites: A control system configured to control an actuator based on an output of a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, and neural network being configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 13 is directed to an abstract idea, specifically, a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)).
Independent claim 13 recites: A control system configured to control an actuator based on an output of a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, and neural network being configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
The limitation above is broadly and reasonably interpreted as a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)). The “scaling and shifting” layer input is a fundamental mathematical concept, specifically involving multiplying every value in a dataset (scaling) and adding a constant value to every data point (shifting).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 13 recites: A control system configured to control an actuator based on an output of a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
and neural network being configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an additional element
that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 13 recites: A control system configured to control an actuator based on an output of a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Furthermore the additional element is well‐understood, routine, and conventional.
and neural network being configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an additional element
that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these 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 when considered as an ordered combination and as a whole.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
As to claim 14:
Claim 14 recites: A non-transitory machine-readable storage medium on which is stored a computer program for determining an output signal based on an input signal using a neural network, the computer program, when executed by a processor, causing the processor to perform the following: determining, using the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 14 is directed to an abstract idea, specifically, a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)).
Independent claim 14 recites: A non-transitory machine-readable storage medium on which is stored a computer program for determining an output signal based on an input signal using a neural network, the computer program, when executed by a processor, causing the processor to perform the following: determining, using the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor
The limitation above is broadly and reasonably interpreted as a mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)). The “scaling and shifting” layer input is a fundamental mathematical concept, specifically involving multiplying every value in a dataset (scaling) and adding a constant value to every data point (shifting).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 14 recites: A non-transitory machine-readable storage medium on which is stored a computer program for determining an output signal based on an input signal using a neural network, the computer program, when executed by a processor, causing the processor to perform the following: as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
determining, using the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an additional element
that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 14 recites: A non-transitory machine-readable storage medium on which is stored a computer program for determining an output signal based on an input signal using a neural network, the computer program, when executed by a processor, causing the processor to perform the following: as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Furthermore the additional element is well‐understood, routine, and conventional.
determining, using the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, as drafted, amount to insignificant “extra-solution activity” using post-solution activity. See MPEP §§ 2106.05(g).
wherein the input signal is obtained by a sensor, as drafted, amount to an additional element
that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Accordingly, these 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 when considered as an ordered combination and as a whole.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-10 which are dependent on claim 1, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claim 2 is dependent on claim 1, and is part of the abstract idea as shown above. Additional limitation recited in dependent claim 2 does not integrate the judicial exception into a practical application.
Claim 3 is dependent on claim 2, and is part of the abstract idea as shown above. Additional limitation recited in dependent claim 3 does not integrate the judicial exception into a practical application and no additional element recognized as well-understood, routine, and conventional. See MPEP §§ 2106.04(d), 2106.05(g).
Claim 4 is dependent on claim 2, and is part of the abstract idea as shown above. Additional limitation recited in dependent claim 4 does not integrate the judicial exception into a practical application and no additional element recognized as well-understood, routine, and conventional. See MPEP §§ 2106.04(d), 2106.05(g).
Claim 5 is dependent on claim 1, is part of the abstract idea as shown above. Additional limitation recited in dependent claim 5 does not integrate the judicial exception into a practical application and no additional element recognized as well-understood, routine, and conventional. See MPEP §§ 2106.04(d), 2106.05(h).
Claim 6 is dependent on claim 5, is part of the abstract idea as shown above. Additional limitation recited in dependent claim 6 does not integrate the judicial exception into a practical application and no additional element recognized as well-understood, routine, and conventional, an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Claim 7 is dependent on claim 1, is part of the abstract idea as shown above. Additional limitation recited in dependent claim 7 does not integrate the judicial exception into a practical application and no additional element recognized as well-understood, routine, and conventional and include an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Claim 8 is dependent on claim 1, is part of the abstract idea as shown above. Additional limitation recited in dependent claim 8 does not integrate the judicial exception into a practical application and no additional element recognized as well-understood, routine, and conventional an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Claim 9 is dependent on claim 5, is part of the abstract idea as shown above. Additional limitation recited in dependent claim 9 does not integrate the judicial exception into a practical application.
Claim 10 is dependent on claim 7, is part of the abstract idea as shown above. Additional limitation recited in dependent claim 2 does not integrate the judicial exception into a practical application.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-14 are rejected under 35 U.S.C 102(a)(2) as being unpatentable over Zhang et al. (US Patent No.12,182,685 B2), hereinafter referred to as Zhang.
With respect to claim 1, Zhang disclose:
A computer-implemented method for determining an output signal based on an input signal using a neural network, the method comprising: determining, by the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor (In Fig. 4 and Col. 15, lines 11-33, Zhang discloses the SWBN (neural network)determining a plurality of normalization statistics for the batch from the first layer outputs. In Col. 16, lines 50-64, Zhang discloses the SWBN rescaling a first set of parameters and shifting a second set of parameters provided by the first layer outputs 407. In Col. 19, lines 38-45, Zhang discloses that the output of the SWBN is then computed by scaling and shifting the whitened standardized input (see TABLE-US-00003 Algorithm 3: Forward Propagation of SWBN Layers at Prediction Phase). In Col. 24, lines 51-62, Zhang further discloses that the input layer 320 receives pixel data from one or more image sensors.)
Regarding claim 2, Zhang disclose the elements of claim 1. In addition, Zhang disclose:
The method according to claim 1, wherein the layer output is determined based on separating the layer input into a definable amount of subsets, wherein, for each subset of the subsets: an auxiliary input is provided to the first layer (In Col. 13, lines 22-38, Zhang discloses that the operations of the convolutional neural network (first neural network) are divided between the neural network layer A 304 and the neural network layer B 318 from the output from the SWBN layer.)
the subset is scaled based on a first value determined based on the auxiliary input and shifted based on a second value determined based on the auxiliary input (In Col. 16, lines 50-64, Zhang discloses the SWBN rescaling a first set of parameters and shifting a second set of parameters provided by the first layer outputs 407. In Col. 19, lines 38-45, Zhang discloses that the output of the SWBN is then computed by scaling and shifting the whitened standardized input (see TABLE-US-00003 Algorithm 3: Forward Propagation of SWBN Layers at Prediction Phase).)
Regarding claim 3, Zhang disclose the elements of claim 2. In addition, Zhang disclose:
The method according to claim 2, wherein the layer input is in form of a tensor and the separating of the layer input is achieved by splitting the tensor along a dimension of the tensor (In Col. 11, lines 50-61, Zhang discloses that the input to the SWBN layer 314 is a tensor and splits the convolutional layer: a 4D tensor. The dimensions are: d, h, w, n)
Regarding claim 4, Zhang disclose the elements of claim 2. In addition, Zhang disclose:
The method according to claim 2, wherein the first value is determined by a first sub-network of the neural network and/or the second value is determined by a sub-network of the neural network (In Col. 13, lines 22-27, Zhang discloses generating outputs by modifying inputs to the layer in accordance with current values of a set of task parameters for the first neural network layer.)
Regarding claim 5, Zhang disclose the elements of claim 2. In addition, Zhang disclose:
The method according to claim 1, wherein the input signal includes at least one image and/or at least one audio datum, and the output signal characterizes a classification and/or a regression value and/or a probability of the input signal with respect to a training dataset (In Col. 8, lines 13-16, Zhang discloses the neural network system 200 can be configured to obtain any kind of digital data input and to generate any kind of score or classification output based on the input. In Col. 24, lines 51-53, Zhang discloses that the input layer 1320 is coupled (e.g., configured) to receive various inputs 1302 (e.g., image data).)
Regarding claim 6, Zhang disclose the elements of claim 5. In addition, Zhang disclose:
The method according to claim 5, wherein an auxiliary input characterizes meta- information corresponding to the input signal (In Col. 19, lines 45-55 (Algorithm 3: Forward Propagation of SWBN Layers in Prediction Phase), Zhang discloses an additional input features vector to the layer x, referring to features about features )
Regarding claim 7, Zhang disclose the elements of claim 1. In addition, Zhang disclose:
The method according to claim 1, wherein the output signal characterizes an image (In Col. 8, lines 13-23, Zhang discloses the digital image data input and to generate any kind of score or classification output based on the input image.)
Regarding claim 8, Zhang disclose the elements of claim 1. In addition, Zhang disclose:
The method according to claim 7, wherein an auxiliary input characterizes attributes of the image characterized by the output signal (In Col. 8, lines 13-23, Zhang discloses the digital image data input and generates any kind of score or classification output based on the input image.)
Regarding claim 9, Zhang disclose the elements of claim 5. In addition, Zhang disclose:
The method according to claim 5, wherein the method further comprises training the neural network, wherein the training includes the following steps: providing a training input signal, a desired output signal characterizing a desired classification and/or regression value, and a plurality of auxiliary inputs, each of the auxiliary inputs characterizing meta information of the training input signal (In Fig. 4 and Col. 8, lines 13-23, Zhang uses the neural network system that processes digital data inputs, such as images, and produces outputs that indicate classifications or scores related to those inputs.)
determining a training output signal for the training input signal using the neural network (Col. 15, lines 11-33, Zhang discloses that the SWBN (neural network) determines a plurality of normalization statistics for the batch from the first layer outputs.)
adapting at least one parameter of the neural network according to a loss value characterizing a deviation of the training output signal with respect to the desired output signal (In Col. 22, lines 10-22, Zhang discloses the loss curves with 1 standard deviation error bars for a batch size of 32 are shown in FIG. 7A, For a batch size of 128 is shown in FIG. 7B and for batch size of 512 is shown in FIG. 7C.)
Regarding claim 10, Zhang disclose the elements of claim 7. In addition, Zhang disclose:
The method according to claim 7, wherein the method further comprises training the neural network, wherein the training includes the following steps: determining, from a training dataset, a first training input signal (In Fig. 4 and Col. 1-4, Zhang discloses determining a plurality of normalization statistics for the batch from the first layer outputs.)
determining a plurality of auxiliary inputs based on the training input signal and by using a second machine learning model (In Col. 19, lines 1-5, Zhang discloses that the SWBN method 400 involves providing the SWBN layer output as an input to the second neural network layer.)
determining at least one randomly drawn value (In Col. 21, lines 33-42, Zhang disclose choosing 5000 images fed into the trained model at random.)
determining a second training input signal by using the neural network and based on the at least one randomly drawn value and the determined plurality of auxiliary inputs (In Col. 14, lines 35-39, Zhang discloses generating a respective SWBN layer output for each of the training data from the transformed whitened data for each training data, and providing the SWBN layer output as an input to the second neural network layer.)
determining a first loss value characterizing a classification of the first training input signal, a second loss value characterizing a classification of the second training input signal, and a third loss value characterizing a difference between an output of the second machine learning model for the second training image and the plurality of auxiliary inputs(In Col. 4, lines 52-60, Zhang discloses generating the transformed data as a new SWBN layer output by applying the first set of task parameters for rescaling the whitened data and applying the second set of task parameters for shifting the whitened data to the normalized components of the new first layer; and provide the new SWBN layer output as a new layer input to the second neural network layer. In some implementations, the stochastic whitening on the normalized components of the new first layer is performed without using a covariance matrix.)
adapting at least one parameter of the neural network according to a gradient of the first loss value, a gradient of the second loss value, and a gradient of the third loss value, each of the gradients being with respect to the parameter (In Col. 19, lines 5-15, Zhang disclose the gradients are adjusted to update the two sets of parameters for rescaling and shifting the whitened data in the SWBN layer, and have no effect on other parameters in the neural network system. This is in contrast to other implementations that use backpropagation to calculate gradient of the task loss with respect to all the parameters. In some implementations, in the backpropagation phase, there are other model parameters that are updated by the gradients computed from the task loss.)
With respect to claim 11, Zhang disclose:
A neural network configured to determine an output signal based on an input signal using a neural network, neural network configured to: determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor (In Fig. 4 and Col. 15, lines 11-33, Zhang discloses the SWBN (neural network)determining a plurality of normalization statistics for the batch from the first layer outputs. In Col. 16, lines 50-64, Zhang discloses the SWBN rescaling a first set of parameters and shifting a second set of parameters provided by the first layer outputs 407. In Col. 19, lines 38-45, Zhang discloses that the output of the SWBN is then computed by scaling and shifting the whitened standardized input (see TABLE-US-00003 Algorithm 3: Forward Propagation of SWBN Layers at Prediction Phase). In Col. 24, lines 51-62, Zhang further discloses that the input layer 320 receives pixel data from one or more image sensors.)
With respect to claim 12, Zhang disclose:
A training system configured to train a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, the neural network configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, the training system being configured to: provide a training input signal, a desired output signal characterizing a desired classification and/or regression value, and a plurality of auxiliary inputs, each of the auxiliary inputs characterizing meta information of the training input signal (In Fig. 4 and Col. 8, lines 13-23, Zhang uses the neural network system that processes digital data inputs, such as images, and produces outputs that indicate classifications or scores related to those inputs. In Fig. 4 and Col. 15, lines 11-33, Zhang discloses the SWBN (neural network)determining a plurality of normalization statistics for the batch from the first layer outputs. In Col. 16, lines 50-64, Zhang discloses the SWBN rescaling a first set of parameters and shifting a second set of parameters provided by the first layer outputs 407. In Col. 19, lines 38-45, Zhang discloses that the output of the SWBN is then computed by scaling and shifting the whitened standardized input (see TABLE-US-00003 Algorithm 3: Forward Propagation of SWBN Layers at Prediction Phase).))
determine a training output signal for the training input signal using the neural network (Col. 15, lines 11-33, Zhang discloses that the SWBN (neural network) determines a plurality of normalization statistics for the batch from the first layer outputs)
adapt at least one parameter of the neural network according to a loss value characterizing a deviation of the training output signal with respect to the desired output signal, wherein the input signal is obtained by a sensor (In Col. 22, lines 10-22, Zhang discloses the loss curves with 1 standard deviation error bars for a batch size of 32 are shown in FIG. 7A, For a batch size of 128 is shown in FIG. 7B and for batch size of 512 is shown in FIG. 7C. In Col. 24, lines 51-62, Zhang further discloses that the input layer 320 receives pixel data from one or more image sensors.)
With respect to claim 13, Zhang disclose:
A control system configured to control an actuator based on an output of a neural network, the neural network configured to determine an output signal based on an input signal using a neural network, and neural network being configured to determine the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor (In Fig. 4 and Col. 15, lines 11-33, Zhang discloses the SWBN (neural network)determining a plurality of normalization statistics for the batch from the first layer outputs. In Col. 16, lines 50-64, Zhang discloses the SWBN rescaling a first set of parameters and shifting a second set of parameters provided by the first layer outputs 407. In Col. 19, lines 38-45, Zhang discloses that the output of the SWBN is then computed by scaling and shifting the whitened standardized input (see TABLE-US-00003 Algorithm 3: Forward Propagation of SWBN Layers at Prediction Phase). In Col. 24, lines 51-62, Zhang further discloses that the input layer 320 receives pixel data from one or more image sensors.)
With respect to claim 14, Zhang disclose:
A non-transitory machine-readable storage medium on which is stored a computer program for determining an output signal based on an input signal using a neural network, the computer program, when executed by a processor, causing the processor to perform the following: determining, using the neural network, the output signal based on a layer output determined by a first layer of the neural network, the layer output being determined based on scaling a layer input of the first layer and shifting the scaled layer input, wherein the scaling and shifting is based on a plurality of auxiliary inputs provided to the first layer, wherein the input signal is obtained by a sensor (In Fig. 4 and Col. 15, lines 11-33, Zhang discloses the SWBN (neural network)determining a plurality of normalization statistics for the batch from the first layer outputs. In Col. 16, lines 50-64, Zhang discloses the SWBN rescaling a first set of parameters and shifting a second set of parameters provided by the first layer outputs 407. In Col. 19, lines 38-45, Zhang discloses that the output of the SWBN is then computed by scaling and shifting the whitened standardized input (see TABLE-US-00003 Algorithm 3: Forward Propagation of SWBN Layers at Prediction Phase). In Col. 24, lines 51-62, Zhang further discloses that the input layer 320 receives pixel data from one or more image sensors.)
Response to Arguments
The applicant's arguments filed 02/23/2022 have been fully considered, but in part are not persuasive
Pertaining to Rejection under 101
The examiner respectfully remains convinced independent claims 1 and 11-14 still do not overcome rejection under 35 U.S.C. 101. Independent claims 1 and 11-14 are interpreted as mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2)). Under step 2A, Prong 1, claim 1 recites “scaling and shifting layer input”, wherein scaling involves multiplying every value in a dataset by a constant factor, changing its range or spread and shifting: involves adding a constant value to every data point, which moves the entire dataset along the number line without changing its relative distribution. Therefore, it is an abstract idea.
Also, under step 2A Prong Two, claim 1 recites an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “Neural Network” is used nor the specification makes it clear how these actions are performed. See MPEP § 2106.05(f) and § 2106.04(d).
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
Conclusion
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 EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
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EVEL HONORE
Examiner
Art Unit 2142
/HAIMEI JIANG/ Primary Examiner, Art Unit 2142
Prosecution Insights