DETAILED ACTION
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 3/22/2024. Claims 1-20 are pending in the case. Claims 1 and 11 are independent claims.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 3 and 13 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. The claims include formulas that are too blurry to be legible. It is unclear what the formulas recite, and thus how they define the scopes of the claims. They appear similar to formulas in specification paragraphs 0009, 0019, 0039, and 0150, which are equally blurry and not useful to establish written description support under 35 U.S.C. § 112(a). For the purposes of prior art and subject matter eligibility analyses Examiner assumes:
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Claim Rejections - 35 U.S.C. § 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “calculates operator selection probability variables for respective layers based on candidate operators included in the respective layers in a supernet learning framework in which multiple layers are sequentially connected” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “calculates result values of the multiple layers based on the operator selection probability variables” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “selects any one of the candidate operators included in the multiple layers based on the result values” is the abstract idea of 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).
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).
No, the limitation “one or more processors” is 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).
No, the limitation “memory for storing at least one program executed by the one or more processors, wherein the at least one program” is 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).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “one or more processors” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “memory for storing at least one program executed by the one or more processors, wherein the at least one program” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “wherein the candidate operators are respectively assigned operator selection variables in advance” is the abstract idea of 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).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 3:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “wherein the operator selection variables are generated to have an initial value of 0, which is a real number” is the abstract idea of 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).
Yes, the limitation “wherein the operator selection probability variable is calculated through a conditional expression below: [conditional expression]
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where m is a number of candidate operators in each layer, j is a number of each candidate operator, xi is the operator selection variable, and probj is the operator selection probability variable” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 4:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “wherein the at least one program [] calculates a result value of the subsequent layer using the transferred result value and the operator selection probability variable of the subsequent layer” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
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).
No, the limitation “wherein the at least one program transfers a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the at least one program transfers a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the at least one program transfers a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the at least one program transfers a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “wherein the at least one program calculates the result value of the subsequent layer by multiplying a sum of the operator section probability variable and a bias value by a value calculated by inputting the result value calculated in the any one of the multiple layers to a candidate operator selected in the subsequent layer” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 6:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “wherein the bias value is a fixed value that is preset so as not to be learned in a machine-learning process” is the abstract idea of 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).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 7:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “wherein the at least one program [] selects candidate operators included in a subnet having highest performance by comparing results of evaluation of performance of the subnets” is the abstract idea of 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).
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).
No, the limitation “wherein the at least one program generates subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the at least one program generates subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the at least one program generates subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the at least one program generates subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
The analysis of the parent claim is incorporated.
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).
No, the limitation “wherein the at least one program receives input data for machine learning” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “wherein the at least one program [] performs machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the at least one program [] performs machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the at least one program receives input data for machine learning” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “wherein the at least one program [] performs machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the at least one program [] performs machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
Yes, the limitation “wherein the at least one program determines neural network loss based on a result of comparison of result data of the machine learning with label data corresponding to the input data” is the abstract idea of 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).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 10:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
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).
The analysis of the parent claim is incorporated.
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).
No, the limitation “wherein the at least one program changes connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the at least one program changes connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the at least one program changes connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the at least one program changes connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “calculating operator selection probability variables for respective layers based on candidate operators included in the respective layers in a supernet learning framework in which multiple layers are sequentially connected” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “calculating result values of the multiple layers based on the operator selection probability variables” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “selecting any one of the candidate operators included in the multiple layers based on the result values” is the abstract idea of 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).
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).
No, the limitation “an apparatus for searching for a neural network architecture” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “an apparatus for searching for a neural network architecture” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “an apparatus for searching for a neural network architecture” is 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).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “an apparatus for searching for a neural network architecture” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “an apparatus for searching for a neural network architecture” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “an apparatus for searching for a neural network architecture” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 12:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “wherein the candidate operators are respectively assigned operator selection variables in advance” is the abstract idea of 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).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 13:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “wherein the operator selection variables are generated to have an initial value of 0, which is a real number” is the abstract idea of 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).
Yes, the limitation “wherein the operator selection probability variable is calculated through a conditional expression below: [conditional expression]
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where m is a number of candidate operators in each layer, j is a number of each candidate operator, xi is the operator selection variable, and probj is the operator selection probability variable” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 14:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “wherein calculating the result values comprises [] calculating a result value of the subsequent layer using the transferred result value and the operator selection probability variable of the subsequent layer” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
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).
No, the limitation “wherein calculating the result values comprises transferring a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein calculating the result values comprises transferring a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein calculating the result values comprises transferring a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein calculating the result values comprises transferring a result value calculated in any one of the multiple layers to a subsequent layer” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “wherein calculating the result values comprises calculating the result value of the subsequent layer by multiplying a sum of the operator section probability variable and a bias value by a value calculated by inputting the result value calculated in the any one of the multiple layers to a candidate operator selected in the subsequent layer” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 16:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “wherein the bias value is a fixed value that is preset so as not to be learned in a machine-learning process” is the abstract idea of 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).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 17:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “wherein selecting the any one of the candidate operators comprises [] selecting candidate operators included in a subnet having highest performance by comparing results of evaluation of performance of the subnets” is the abstract idea of 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).
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).
No, the limitation “wherein selecting the any one of the candidate operators comprises generating subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein selecting the any one of the candidate operators comprises generating subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein selecting the any one of the candidate operators comprises generating subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein selecting the any one of the candidate operators comprises generating subnets in which multiple candidate operators are selected for the respective layers” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 18:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
The analysis of the parent claim is incorporated.
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).
No, the limitation “before calculating the operator selection probability variables, receiving input data for machine learning” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “after selecting the any one of the multiple candidate operators, performing machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “after selecting the any one of the multiple candidate operators, performing machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “before calculating the operator selection probability variables, receiving input data for machine learning” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “after selecting the any one of the multiple candidate operators, performing machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “after selecting the any one of the multiple candidate operators, performing machine learning using the supernet learning framework in which the candidate operators are selected” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 19:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
Yes, the limitation “after performing the machine learning, determining neural network loss based on a result of comparison of result data of the machine learning with label data corresponding to the input data” is the abstract idea of 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).
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).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 20:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
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).
The analysis of the parent claim is incorporated.
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).
No, the limitation “after determining the neural network loss, changing connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “after determining the neural network loss, changing connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “after determining the neural network loss, changing connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “after determining the neural network loss, changing connection weights and operator selection variables for the respective layers so as to minimize the neural network loss” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
Claim Rejections - 35 U.S.C. § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-5, 7-15, and 17-20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Wu et al. (US 11748615 B1, hereinafter Wu).
As to independent claim 1, Wu discloses an apparatus for searching for a neural network architecture, comprising:
one or more processors (figure 1 part 12); and
memory (figure 1 part 14) for storing at least one program executed by the one or more processors, wherein the at least one program
calculates operator selection probability variables for respective layers (“During the inference of the super net, at each layer a subset (e.g., only one) candidate block of the layer is sampled and executed with the sampling probability of:
P
ϴ
i
b
l
=
b
l
,
i
=
s
o
f
t
m
a
x
θ
1
,
i
;
θ
l
=
e
x
p
(
θ
l
,
i
)
∑
e
x
p
(
θ
l
,
i
)
.
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 45-56) based on candidate operators included in the respective layers in a supernet learning framework (“constructing a stochastic super net defining a layer-wise search space having a number of candidate layers, each of the candidate layers specifying one or more operators for a neural network architecture,” column 2 lines 5-9) in which multiple layers are sequentially connected (figure 3 parts 25, 37a-37n; figure 4),
calculates result values of the multiple layers based on the operator selection probability variables (“the output of layer-l can be expressed as:
x
l
+
1
=
∑
i
m
l
,
i
⋅
b
l
,
i
(
x
l
)
where mu is a random variable in {0,1} and is evaluated to 1 if block
b
l
,
i
is sampled. The sampling probability is determined by equation (4),” column 9 lines 56-66), and
selects any one of the candidate operators included in the multiple layers based on the result values (“the output of layer-l can be expressed as:
x
l
+
1
=
∑
i
m
l
,
i
⋅
b
l
,
i
(
x
l
)
where mu is a random variable in {0,1} and is evaluated to 1 if block
b
l
,
i
is sampled. The sampling probability is determined by equation (4),” column 9 lines 56-66).
As to dependent claim 2, Wu further discloses an apparatus wherein the candidate operators are respectively assigned operator selection variables in advance (“
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 55-56).
As to dependent claim 3, Wu further discloses an apparatus wherein the operator selection variables are generated to have an initial value of 0, which is a real number (“softmax,” column 9 line 50), and the operator selection probability variable is calculated through a conditional expression below: [conditional expression]
p
r
o
b
j
=
e
x
p
(
x
j
)
∑
i
=
0
m
-
1
e
x
p
(
x
i
)
where m is a number of candidate operators in each layer, j is a number of each candidate operator, xi is the operator selection variable, and probj is the operator selection probability variable (“During the inference of the super net, at each layer a subset (e.g., only one) candidate block of the layer is sampled and executed with the sampling probability of:
P
ϴ
i
b
l
=
b
l
,
i
=
s
o
f
t
m
a
x
θ
1
,
i
;
θ
l
=
e
x
p
(
θ
l
,
i
)
∑
e
x
p
(
θ
l
,
i
)
.
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 45-56).
As to dependent claim 4, Wu further discloses an apparatus wherein the at least one program transfers a result value calculated in any one of the multiple layers to a subsequent layer and calculates a result value of the subsequent layer using the transferred result value and the operator selection probability variable of the subsequent layer (figure 3 parts 25, 37a-37n; figure 4).
As to dependent claim 5, Wu further discloses an apparatus wherein the at least one program calculates the result value of the subsequent layer by multiplying a sum of the operator section probability variable and a bias value by a value calculated by inputting the result value calculated in the any one of the multiple layers to a candidate operator selected in the subsequent layer (“neural network architecture,” abstract line 7).
As to dependent claim 7, Wu further discloses an apparatus wherein the at least one program generates subnets in which multiple candidate operators are selected for the respective layers (“During the inference of the super net, at each layer a subset (e.g., only one) candidate block of the layer is sampled and executed with the sampling probability of:
P
ϴ
i
b
l
=
b
l
,
i
=
s
o
f
t
m
a
x
θ
1
,
i
;
θ
l
=
e
x
p
(
θ
l
,
i
)
∑
e
x
p
(
θ
l
,
i
)
.
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 45-56) and selects candidate operators included in a subnet having highest performance by comparing results of evaluation of performance of the subnets (“This process has the technical benefit of selecting operators having better accuracy and lower latency and suppressing the selection of the opposite ones,” column 11 lines 5-7).
As to dependent claim 8, Wu further discloses an apparatus wherein the at least one program receives input data for machine learning (“100 classes are randomly chosen from the original 1000 classes to train stochastic super net 34,” column 11 lines 33-35) and performs machine learning using the supernet learning framework in which the candidate operators are selected (“Stochastic super net 34 is trained for 90 epochs. In each epoch, the operator weights ωa are first trained, and then the architecture probability parameter θ. ωa is trained on 80% of ImageNet training set using SGD with momentum. The architecture distribution parameter θ is trained on the rest 20% of ImageNet training set,” column 11 lines 35-40).
As to dependent claim 9, Wu further discloses an apparatus wherein the at least one program determines neural network loss based on a result of comparison of result data of the machine learning with label data corresponding to the input data (“the loss used to train stochastic super net 34 consists of both the cross-entropy loss that leads to better accuracy and the latency loss that penalizes the network's latency on a target device,” column 6 lines 34-37).
As to dependent claim 10, Wu further discloses an apparatus wherein the at least one program changes connection weights and operator selection variables for the respective layers so as to minimize the neural network loss (“Given an architecture space 𝒜, , we seek to find an optimal architecture a∈ 𝒜 such that after training its weights ωa, it can achieve the minimal loss 𝓛(a, wa). In some examples, we focus on three factors of the problem: a) the search space 𝒜, b) the loss function 𝓛(a, wa) that considers actual or expected latency, and c) an efficient search algorithm,” column 7 lines 11-16).
As to independent claim 11, Wu discloses a method for searching for a neural network architecture, performed by an apparatus for searching for a neural network architecture, comprising:
calculating operator selection probability variables for respective layers (“During the inference of the super net, at each layer a subset (e.g., only one) candidate block of the layer is sampled and executed with the sampling probability of:
P
ϴ
i
b
l
=
b
l
,
i
=
s
o
f
t
m
a
x
θ
1
,
i
;
θ
l
=
e
x
p
(
θ
l
,
i
)
∑
e
x
p
(
θ
l
,
i
)
.
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 45-56) based on candidate operators included in the respective layers in a supernet learning framework (“constructing a stochastic super net defining a layer-wise search space having a number of candidate layers, each of the candidate layers specifying one or more operators for a neural network architecture,” column 2 lines 5-9) in which multiple layers are sequentially connected (figure 3 parts 25, 37a-37n; figure 4);
calculating result values of the multiple layers based on the operator selection probability variables (“the output of layer-l can be expressed as:
x
l
+
1
=
∑
i
m
l
,
i
⋅
b
l
,
i
(
x
l
)
where mu is a random variable in {0,1} and is evaluated to 1 if block
b
l
,
i
is sampled. The sampling probability is determined by equation (4),” column 9 lines 56-66); and
selecting any one of the candidate operators included in the multiple layers based on the result values (“the output of layer-l can be expressed as:
x
l
+
1
=
∑
i
m
l
,
i
⋅
b
l
,
i
(
x
l
)
where mu is a random variable in {0,1} and is evaluated to 1 if block
b
l
,
i
is sampled. The sampling probability is determined by equation (4),” column 9 lines 56-66).
As to dependent claim 12, Wu further discloses a method wherein the candidate operators are respectively assigned operator selection variables in advance (“
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 55-56).
As to dependent claim 13, Wu further discloses a method wherein the operator selection variables are generated to have an initial value of 0, which is a real number (“softmax,” column 9 line 50), and the operator selection probability variable is calculated through a conditional expression below: [conditional expression]
p
r
o
b
j
=
e
x
p
(
x
j
)
∑
i
=
0
m
-
1
e
x
p
(
x
i
)
where m is a number of candidate operators in each layer, j is a number of each candidate operator, xi is the operator selection variable, and probj is the operator selection probability variable (“During the inference of the super net, at each layer a subset (e.g., only one) candidate block of the layer is sampled and executed with the sampling probability of:
P
ϴ
i
b
l
=
b
l
,
i
=
s
o
f
t
m
a
x
θ
1
,
i
;
θ
l
=
e
x
p
(
θ
l
,
i
)
∑
e
x
p
(
θ
l
,
i
)
.
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 45-56).
As to dependent claim 14, Wu further discloses a method wherein calculating the result values comprises transferring a result value calculated in any one of the multiple layers to a subsequent layer and calculating a result value of the subsequent layer using the transferred result value and the operator selection probability variable of the subsequent layer (figure 3 parts 25, 37a-37n; figure 4).
As to dependent claim 15, Wu further discloses a method wherein calculating the result values comprises calculating the result value of the subsequent layer by multiplying a sum of the operator section probability variable and a bias value by a value calculated by inputting the result value calculated in the any one of the multiple layers to a candidate operator selected in the subsequent layer (“neural network architecture,” abstract line 7).
As to dependent claim 17, Wu further discloses a method wherein selecting the any one of the candidate operators comprises generating subnets in which multiple candidate operators are selected for the respective layers (“During the inference of the super net, at each layer a subset (e.g., only one) candidate block of the layer is sampled and executed with the sampling probability of:
P
ϴ
i
b
l
=
b
l
,
i
=
s
o
f
t
m
a
x
θ
1
,
i
;
θ
l
=
e
x
p
(
θ
l
,
i
)
∑
e
x
p
(
θ
l
,
i
)
.
θ
l
contains parameters that determine the sampling probability of each block at layer-l,” column 9 lines 45-56) and selecting candidate operators included in a subnet having highest performance by comparing results of evaluation of performance of the subnets (“This process has the technical benefit of selecting operators having better accuracy and lower latency and suppressing the selection of the opposite ones,” column 11 lines 5-7).
As to dependent claim 18, Wu further discloses a method comprising:
before calculating the operator selection probability variables, receiving input data for machine learning (“100 classes are randomly chosen from the original 1000 classes to train stochastic super net 34,” column 11 lines 33-35); and
after selecting the any one of the multiple candidate operators, performing machine learning using the supernet learning framework in which the candidate operators are selected (“Stochastic super net 34 is trained for 90 epochs. In each epoch, the operator weights ωa are first trained, and then the architecture probability parameter θ. ωa is trained on 80% of ImageNet training set using SGD with momentum. The architecture distribution parameter θ is trained on the rest 20% of ImageNet training set,” column 11 lines 35-40).
As to dependent claim 19, Wu further discloses a method comprising: after performing the machine learning, determining neural network loss based on a result of comparison of result data of the machine learning with label data corresponding to the input data (“the loss used to train stochastic super net 34 consists of both the cross-entropy loss that leads to better accuracy and the latency loss that penalizes the network's latency on a target device,” column 6 lines 34-37).
As to dependent claim 20, Wu further discloses a method comprising: after determining the neural network loss, changing connection weights and operator selection variables for the respective layers so as to minimize the neural network loss (“Given an architecture space 𝒜, , we seek to find an optimal architecture a∈ 𝒜 such that after training its weights ωa, it can achieve the minimal loss 𝓛(a, wa). In some examples, we focus on three factors of the problem: a) the search space 𝒜, b) the loss function 𝓛(a, wa) that considers actual or expected latency, and c) an efficient search algorithm,” column 7 lines 11-16).
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 6 and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Wu in view of Castelaz et al. (US 5,003,490 A, hereinafter Castelaz).
As to dependent claim 6, the rejection of claim 5 is incorporated.
Wu does not appear to expressly teach an apparatus wherein the bias value is a fixed value that is preset so as not to be learned in a machine-learning process.
Castelaz teaches an apparatus wherein the bias value is a fixed value that is preset so as not to be learned in a machine-learning process (“fixed bias,” column 10 line 49).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network of Wu to comprise the fixed bias of Castelaz. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely simplifying the training process. Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 16, the rejection of claim 15 is incorporated.
Wu does not appear to expressly teach a method wherein the bias value is a fixed value that is preset so as not to be learned in a machine-learning process.
Castelaz teaches a method wherein the bias value is a fixed value that is preset so as not to be learned in a machine-learning process (“fixed bias,” column 10 line 49).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network of Wu to comprise the fixed bias of Castelaz. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely simplifying the training process. Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 2005/0049983 A1 disclosing an adaptive neural network with operator selection probability
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e-mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Michelle Bechtold can be reached at 571 431 0762. The fax phone number for the organization where this application or proceeding is assigned is 571 273 8300.
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
Prosecution Insights