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 final office action is in response to the amendment filed 13 November 2025.
Claims 1-2, 6-7, and 9-15 are pending. Claims 3-5 and 8 are cancelled. Claims 12-15 are newly added. Claims 1, 10, and 11 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.
Claims 1-2, 6-7, and 9-15 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 USC 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; MPEP 2106.03). If the claim falls within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed toward a judicial exception (Step 2A; MPEP 2106.04). This step is broken into two prongs.
The first prong (Step 2A, Prong 1) determines 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 at Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2; MPEP 2106.04). The second prong (Step 2A, Prong 2) determines whether the claims integrate the judicial exception into a practical application. If the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determine whether the claim is a patent-eligible exception (Step 2B; MPEP 2106.05).
If an abstract idea is present int the claim, in order to recite statutory subject matter, 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 amounts to significantly more than the abstract idea itself (see: 2019 PEG).
Step 1:
According to Step 1 of the two Step analysis, claims 1-2 and 6-7 are directed toward a system (machine). Claims 10 and 12-15 are directed toward a method (process). Claim 11 is directed toward a system (machine). Therefore, each of these claims falls within one of the four statutory categories.
(NOTE: With respect to Step 2A, Prong 1; Step 2A, Prong 2; and Step 2B; each of the claims will be addressed individually below.)
Claim 1:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 1, the claims recite:
detect a specific area from a plurality of unlabeled images and generate unlabeled area images including the specific area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of unlabeled images to detect a specific area of the image and to observed these specific areas as “unlabeled area images”)
configure self-learning data, as training data to be used to train a neural network, by using the generated area images, including performing at least one operation of shuffling or augmenting the unlabeled area images based on a type of the specific area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the data to augment the observed data by adding a mental label. This causes the data to be configured for ingestion as training data for a neural network)
selecting the neural network, from among a plurality of candidate neural networks to be trained (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement to select a neural network to be trained)
selecting a first learning method in which an output of the neural network follows an output of a target network or a second learning method in which the neural network estimates an augmentation method of augmenting self-learning data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to select a first learning method or a second learning method based upon an observation of the desired output)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
one or more processors configured to:
This element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim further recites the elements:
training the neural network by performing a self-learning process using the configured self-learning data
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
one or more processors configured to:
This element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim further recites the elements:
training the neural network by performing a self-learning process using the configured self-learning data
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2:
With respect to dependent claim 2, the claim depends upon independent claim 1. The analysis of independent claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 2, the claims recite:
wherein the specific area comprises a face area, an object area, a semantic area, and an entire area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of unlabeled images to detect a specific area of the image)
Claim 6:
With respect to dependent claim 6, the claim depends upon independent claim 1. The analysis of independent claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure unlabeled self-learning data by shuffling the area images when the first learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure unlabeled self-learning data by shuffling the area images when the first learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 7:
With respect to dependent claim 7, the claim depends upon independent claim 1. The analysis of independent claim 1 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure self-learning data by augmenting the area images and labeling with an augmentation method when the second learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure self-learning data by augmenting the area images and labeling with an augmentation method when the second learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 9:
With respect to dependent claim 9, the claim depends upon independent claim 1. The analysis of independent claim 1 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 2, the claims recite:
wherein a number of labeled images used for generating labeled area images is less than a number of unlabeled images (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation that the number of labeled images is less than the number of unlabeled images)
Claim 10:
Step 2A, Prong 1:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 10, the claims recite:
detect a specific area from a plurality of unlabeled images and generate unlabeled area images including the specific area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of unlabeled images to detect a specific area of the image and to observed these specific areas as “unlabeled area images”)
configure self-learning data, as training data to be used to train a neural network, by using the generated area images, including performing at least one operation of shuffling or augmenting the unlabeled area images based on a type of the specific area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the data to augment the observed data by adding a mental label. This causes the data to be configured for ingestion as training data for a neural network)
selecting the neural network, from among a plurality of candidate neural networks to be trained (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement to select a neural network to be trained)
selecting a first learning method in which an output of the neural network follows an output of a target network or a second learning method in which the neural network estimates an augmentation method of augmenting self-learning data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to select a first learning method or a second learning method based upon an observation of the desired output)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claim recites the elements:
training the neural network by performing a self-learning process using the configured self-learning data
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
one or more processors configured to:
This element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim further recites the elements:
training the neural network by performing a self-learning process using the configured self-learning data
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 11:
Following the determination that the claims fall within one of the statutory categories (Step 1), it must be determined if the claims recite a judicial exception (Step 2A, Prong 1). In this instance, the claims are determined to recite a judicial exception (abstract idea; mental process).
With respect to claim 11, the claims recite:
detect a specific area from a plurality of unlabeled images and generate unlabeled area images including the specific area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of unlabeled images to detect a specific area of the image and to observed these specific areas as “unlabeled area images”)
configure self-learning data, as training data to be used to train a neural network, by using the generated area images, including performing at least one operation of shuffling or augmenting the unlabeled area images based on a type of the specific area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation of the data to augment the observed data by adding a mental label. This causes the data to be configured for ingestion as training data for a neural network)
selecting the neural network, from among a plurality of candidate neural networks to be trained (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses a judgement to select a neural network to be trained)
selecting a first learning method in which an output of the neural network follows an output of a target network or a second learning method in which the neural network estimates an augmentation method of augmenting self-learning data (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an evaluation to select a first learning method or a second learning method based upon an observation of the desired output)
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
a database in which unlabeled images are stored
one or more processors configured to:
These elements, which is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim further recites the elements:
training the neural network by performing a self-learning process using the configured self-learning data
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
a database in which unlabeled images are stored
one or more processors configured to:
This element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim further recites the elements:
training the neural network by performing a self-learning process using the configured self-learning data
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 12:
With respect to dependent claim 12, the claim depends upon independent claim 10. The analysis of independent claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 12, the claims recite:
wherein the specific area comprises a face area, an object area, a semantic area, and an entire area (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation of unlabeled images to detect a specific area of the image)
Claim 13:
With respect to dependent claim 13, the claim depends upon independent claim 10. The analysis of dependent claim 10 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure unlabeled self-learning data by shuffling the area images when the first learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure unlabeled self-learning data by shuffling the area images when the first learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 14:
With respect to dependent claim 14, the claim depends upon independent claim 10. The analysis of independent claim 10 is incorporated herein by reference.
Step 2A, Prong 2:
Accordingly, after determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (MPEP 2106.04(d)).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure self-learning data by augmenting the area images and labeling with an augmentation method when the second learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B:
Based on the determination in Step 2A of the analysis that the claims are directed toward a judicial exception, in must be determined if any claims contain any element or combination of elements sufficient to ensure that the claims amount to significantly more than the judicial exception (Step 2B).
The claims disclose the following additional elements:
one or more processors
The element, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The claim discloses the following additional elements:
configure self-learning data by augmenting the area images and labeling with an augmentation method when the second learning method is selected
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
In this instance, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 15:
With respect to dependent claim 15, the claim depends upon independent claim 10. The analysis of independent claim 10 is incorporated herein by reference.
Step 2A, Prong 1:
With respect to claim 15, the claims recite:
wherein a number of labeled images used for generating labeled area images is less than a number of unlabeled images (mental process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses an observation that the number of labeled images is less than the number of unlabeled images)
Claim Rejections - 35 USC § 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, 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 CFR 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 1, 6-7, 9-11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Joze et al. (US 2022/0358332, filed 7 March 2021) and further in view of Lee (KR 10-2019-0125781, published 07 November 2019) and further in view of Huang et al. (US 11164384, filed 24 July 2019, hereafter Huang).
As per independent claim 1, Joze discloses a network learning system comprising:
one or more processors (paragraph 0020: Here, a computing device includes hardware resources including a processor) configured to:
detect a specific area from a plurality of unlabeled images, and generated unlabeled area images including the specific area (paragraphs 0024 and 0029: Here, a second data set comprising a plurality of unlabeled source images are received. These unlabeled source images are processed to generate tiles (paragraph 0003) of unlabeled areas (bounding boxes) based upon detecting various features such as people, animals, bodies, faces, eyes, and/or hands. This includes detecting target features in the plurality of generated tiles and generating feature anchors)
configure a self-learning data, as training data to be used to training a neural network, by using the generated area images, including performing at least one operation of shuffling or augmenting the unlabeled area images based on a type of the specific area (Figure 8, items 830 and 840; paragraphs 0037 and 0043-0044: Here, self-learning data set, third data set, is created by combining the first data set of labeled data and the second data set. This second dataset is augmented by resizing bounding boxes corresponding to those generated area images (bounding boxes) with a confidence level above a threshold. This third data set is used to train a neural network)
training the neural network by performing a self-learning process using the configured self-learning data (paragraphs 0049-0050: Here, the neural network is trained using the third data set. This training may be repeated to improve accuracy of the neural network model)
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed (paragraphs 0049-0050: Here, the neural network is trained using the third data set. This training may be repeated to improve accuracy of the neural network model)
Joze fails to specifically disclose:
selecting the neural network, from among a plurality of candidate networks, to be trained
selecting a first learning method in which an output of the neural network follows an output of a target network, or a second learning method in which the neural network estimates an augmentation method of augmented self-learning data
However, Lee, which is analogous to the claimed invention because it is directed toward training a neural network, discloses:
selecting the neural network to be trained (page 9, paragraphs 1-3: Here, the convolutional neural network to be trained is identified and provided the learning image data)
selecting a first learning method in which an output of the neural network follows an output of a target network (page 12, paragraph 4: Here, a learning image is selected according to selection criteria for use in training the neural network), or a second learning method in which the neural network estimates an augmentation method of augmented self-learning data (page 14, paragraph 2- page 17, paragraph 3: Here, the image is processed through multiple convolutional layers to extract and identify objects within the image for training the neural network. The neural network is then evaluated, and parameters modified, in order to improve performance of the neural network)
It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lee with Joze, with a reasonable expectation of success, as it would have allowed for acquiring and processing images for ingestion to train a self-learning neural network (Lee: Abstract).
Additionally, Huang, which is analogous to the claimed invention because it is directed toward training neural networks, discloses selecting the neural network from among a plurality of neural networks, to be trained (claim 1: Here a neural network is selected, from among a plurality of neural networks, based upon the object being classified in an object category. The neural network selected is the neural network corresponding to the object type of the classified object). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Huang with Joze-Lee, with a reasonable expectation of success, as it would have allowed for selecting a neural network corresponding to the specific identified object type (Huang: claim 1).
As per dependent claim 6, Joze, Lee, and Huang disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Joze fails to specifically disclose wherein the one or more processors are configured to configure unlabeled self-learning data by shuffling the area images when the first learning method is selected.
However, Lee discloses wherein the configuration module is configured to configure unlabeled self-learning data by shuffling the area images when the first learning method is selected (page 13, paragraph 3- page 17, paragraph 3: Here, input images are modified by using filtering/pooling layers to extract object data. This includes selecting filters to determine the size of the object, characteristic information of the object, and current layer stage of the object. Examples include the color of the object and the shape information of the object. Extracting different portions of the image constitutes shuffling the area images). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Lee with Joze, with a reasonable expectation of success, as it would have allowed for pre-processing data to extract portions of the image improve processing by the neural network (Lee: Abstract).
As per dependent claim 7, Joze, Lee, and Huang disclose the limitations similar to those in claim 5, and the same rejection is incorporated herein. Joze discloses wherein the configuration module is configured to configure self-learning data by augmenting the area images and labeling with an augmentation method when the second learning method is selected (Figure 8, items 830 and 840; paragraphs 0037 and 0043-0044: Here, self-learning data set, third data set, is created by combining the first data set of labeled data and the second data set. This second dataset is augmented by resizing bounding boxes corresponding to those generated area images (bounding boxes) with a confidence level above a threshold. This third data set is used to train a neural network).
As per dependent claim 9, Joze, Lee, and Huang disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Joze fails to specifically disclose wherein a number of labeled images used for generating labeled area images is less than a number of unlabeled images.
However, the examiner takes official notice that it was notoriously well-known in the art at the time of the applicant’s effective filing date that training a neural network on a set of images is performed on a set of images having fewer images than the set of images to be evaluated by the neural network. It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined the well-known with Joze-Lee-Huang, with a reasonable expectation of success, as it would have allowed for training a neural network on a limited set of training images in order to process a larger set of images.
With respect to independent claim 10, the claim discloses the limitations substantially similar to those in claim 1. Claim 10 is similarly rejected.
With respect to independent claim 11, the claim discloses the limitations substantially similar to those in claim 1. Claim 11 is similarly rejected.
With respect to dependent claim 13, the claim discloses the limitations substantially similar to those in claim 6. Claim 13 is similarly rejected.
With respect to dependent claim 14, the claim discloses the limitations substantially similar to those in claim 7. Claim 14 is similarly rejected.
With respect to dependent claim 15, the claim discloses the limitations substantially similar to those in claim 9. Claim 15 is similarly rejected.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Joze, Lee, and Huang and further in view of Choi et al. (KR 10-2020-0075069, published 26 June 2020, hereafter Choi).
As per dependent claims 2, Joze, Lee, and Huang disclose the limitations similar to those in claim 1, and the same rejection is incorporated herein. Joze fails to specifically disclose wherein the specific area comprises a face area, an object area, a semantic area, and an entire area.
However, Choi, which is analogous to the claimed invention because it is directed toward extracting a frame from video data, discloses wherein the specific area comprises a face area, an object area, a semantic area, and an entire area (Figure 1; page 21: Here, an image including a baseball player having a face, is analyzed to identify an object area, represented by the baseball and bat, wherein an event occurs, the baseball and bat make contact (semantic area), with the image (entire area)). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Choi with Lee, with a reasonable expectation of success, as it would have allowed for identifying and extracting a representative image (Choi: Abstract) for use in training a neural network (Lee: claim 1).
With respect to dependent claim 12, the claim recites the limitations substantially similar to those in claim 2. Claim 12 is similarly rejected.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Joze, Lee, and Huang.
The examiner notes that factual assertion set forth in the Office Action dated 14 August 2025 (see: page 38) has not been traversed. According to MPEP 2144.03 (C) the official notice statement is taken to be admitted prior art because the appellant failed to traverse the examiner’s assertion.
Applicant's arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
The applicant arguments with respect to the claims appear to be directed toward the amendments filed 13 November 2025. The examiner has provided a detailed analysis above with respect to how the amended claim limitations are addressed under Step 1; Step 2A, Prong 1; Step 2A, Prong 2; and Step 2B.
The examiner notes that the applicant throughout the remarks states that limitations such as “training the neural network by performing a self-learning process using the configured self-learning data” and “performing additional training of the neural network by using labeled specific area images after the self-learning process is completed” are limitations that cannot practically be performed in the human mind (see: pages 6-9).
The examiner agrees with this assessment. However, the examiner does not allege that “training the neural network by performing a self-learning process using the configured self-learning data” and “performing additional training of the neural network by using labeled specific area images after the self-learning process is completed” are performed in the human mind. Instead, these limitations are considered under Step 2A, Prong 2 and Step 2B. As noted by the examiner above:
The claim recites the elements:
training the neural network by performing a self-learning process using the configured self-learning data
performing additional training of the neural network by using labeled specific area images after the self-learning process is completed
This training elements are recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
For this reason, this argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Laszlo (US 2022/0051079): Discloses selecting a neural network architecture for performing a task based upon a candidate graph (paragraph 0025)
Krishnan et al. (US 2022/0036260): Discloses selecting, via an inference module, among a plurality of neural network mapping algorithms, each corresponding to one or more data classifications (Abstract)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
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/KYLE R STORK/Primary Examiner, Art Unit 2128