DETAILED ACTION
This action is in response to the Applicant Response filed 15 May 2023 for application 18/317,803 filed 15 May 2023.
Claim(s) 1-20 is/are pending.
Claim(s) 1-20 is/are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites of the machine-learning architecture while failing to provide a proper antecedent basis for the term. It is suggested that the phrase be removed from the claim. Correction or clarification is required.
Claim 2 recites wherein identifying the plurality of dataset features includes … while failing to provide a proper antecedent basis for identifying the plurality of dataset features. Additionally, the claim recites comprising … but it is unclear to what this refers. Additionally, the claim recites a limited number, which is a relative term and is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Correction or clarification is required.
Examiner’s Note: For the purposes of examination, the claim will be interpreted as if it reads further comprising a step of identifying features which includes receiving one or more seed examples from real datasets which have a fixed number of data elements.
Claim 3 recites generates the one or more candidate labeled datasets using the plurality of dataset features. However, it is unclear whether “the plurality of dataset features refers to “the plurality of dataset features of the one or more seed examples” as recited in claim 3 or to “the plurality of dataset features of the one or more example datasets” as recited in claim 1. Correction or clarification is required.
Examiner’s Note: For the purposes of examination, the claim will be interpreted as the features of the seed examples are used for dataset generation.
Claim 11 recites of the machine-learning architecture while failing to provide a proper antecedent basis for the term. It is suggested that the phrase be removed from the claim. Correction or clarification is required.
Claim 12 recites wherein when identifying the plurality of dataset features … while failing to provide a proper antecedent basis for identifying the plurality of dataset features. Additionally, the claim recites comprising … but it is unclear to what this refers. Additionally, the claim recites a limited number, which is a relative term and is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Correction or clarification is required.
Examiner’s Note: For the purposes of examination, the claim will be interpreted as if it reads further comprising a step of identifying features which includes receiving one or more seed examples from real datasets which have a fixed number of data elements.
Claim 13 recites generates the one or more candidate labeled datasets using the plurality of dataset features. However, it is unclear whether “the plurality of dataset features refers to “the plurality of dataset features of the one or more seed examples” as recited in claim 3 or to “the plurality of dataset features of the one or more example datasets” as recited in claim 1. Correction or clarification is required.
Examiner’s Note: For the purposes of examination, the claim will be interpreted as the features of the seed examples are used for dataset generation.
Claims 2-10, 12-20 are rejected under 35 U.S.C. 112(b) due to their dependence, either directly or indirectly, on claim 1-3, 11-13.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets.
The limitation of generating ... one or more candidate labeled datasets based upon a plurality of dataset features of one or more example datasets associated with a domain, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-implemented, computer, database. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – generative model(s), machine-learning architecture. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites applying ... a generative model on the plurality of dataset features to train the generative model to identify a mislabeled dataset of the one or more candidate labeled datasets; applying ... the generative model of the machine-learning architecture to train the generative model to identify an unrelated dataset of the one or more candidate labeled datasets that is out of the domain which is simply generic training to perform the abstract idea of dataset generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites responsive to the computer determining that the generative model satisfies one or more training pass rates based upon a number of mislabeled datasets and a number of unrelated datasets, storing the generative model into a database, which is simply storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-implemented, computer, database amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
storing data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of storing and retrieving information in memory (MPEP 2016.05(d))
generative model(s), machine-learning architecture amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets.
The limitation of identifying the plurality of dataset features, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receiving, by the computer, one or more seed examples in the domain, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... the one or more example datasets having a limited number of datasets of a same data type which is simply additional information regarding the data, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets.
The limitation of identifying ... a plurality of dataset features of the one or more seed examples, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... generates the one or more candidate labeled datasets using the plurality of dataset features, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets.
The limitation of generating ... the one or more candidate labeled datasets by combining the limited number of datasets with a plurality of external datasets obtained from different sources, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets.
The limitation of ... generate a new labeled dataset having an accurate label, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – label discriminator. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein the generative model includes a label discriminator which is simply additional information regarding the generative model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites ... trains the label discriminator to identify the mislabeled dataset which is simply generic training to perform the abstract idea of dataset generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
label discriminator amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the generative model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets. The Step 2A Prong One Analysis for claim 5 is applicable here since claim 6 carries out the method of claim 5 but for the recitation of additional element(s) of wherein the label discriminator includes k-binary classification functions or a k-class classifier having k number of distinct labels.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the label discriminator includes k-binary classification functions or a k-class classifier having k number of distinct labels which is simply additional information regarding the label discriminator, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – k-binary classification functions, k-class classifier. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
k-binary classification functions, k-class classifier amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the label discriminator do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets.
The limitation of ... generate a new labeled dataset in the domain, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – domain discriminator. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein the generative model includes a domain discriminator which is simply additional information regarding the generative model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites ... trains the domain discriminator to identify the unrelated dataset out of the domain which is simply generic training to perform the abstract idea of dataset generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
domain discriminator amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the generative model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets. The Step 2A Prong One Analysis for claim 7 is applicable here since claim 8 carries out the method of claim 7 but for the recitation of additional element(s) of wherein the domain discriminator is a binary classifier based on a deep auto-encoder neural network.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the domain discriminator is a binary classifier based on a deep auto-encoder neural network which is simply additional information regarding the domain discriminator, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – binary classifier, deep auto-encoder neural network. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
binary classifier, deep auto-encoder neural network amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the domain discriminator do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 9 carries out the method of claim 1 but for the recitation of additional element(s) of receiving, by the computer, a request to generate one or more datasets in the domain; retrieving, by the computer from the database, the generative model trained for the domain indicated by the request; and executing, by the computer, the generative model to generate the one or more datasets in the domain.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receiving, by the computer, a request to generate one or more datasets in the domain; retrieving, by the computer from the database, the generative model trained for the domain indicated by the request, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites executing, by the computer, the generative model to generate the one or more datasets in the domain which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer-implemented method for training generative models to generate labeled datasets. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 10 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the computer trains the generative model using progressive generative adversarial networks learning algorithms.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the computer trains the generative model using progressive generative adversarial networks learning algorithms which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – progressive generative adversarial networks learning algorithms. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
progressive generative adversarial networks learning algorithms amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets.
The limitation of generate one or more candidate labeled datasets based upon a plurality of dataset features of one or more example datasets associated with a domain, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer system, database, computer. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – generative model(s), machine-learning architecture. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites apply a generative model on the plurality of dataset features to train the generative model to identify a mislabeled dataset of the one or more candidate labeled datasets; apply the generative model of the machine-learning architecture to train the generative model to identify an unrelated dataset of the one or more candidate labeled datasets that is out of the domain which is simply generic training to perform the abstract idea of dataset generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
The claim recites responsive to the computer determining that the generative model satisfies one or more training pass rates based upon a number of mislabeled datasets and a number of unrelated datasets, store the generative model into the database, which is simply storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer system, database, computer amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
storing data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of storing and retrieving information in memory (MPEP 2016.05(d))
generative model(s), machine-learning architecture amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets.
The limitation of identifying the plurality of dataset features, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receive one or more seed examples in the domain, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites ... the one or more example datasets having a limited number of datasets of a same data type which is simply additional information regarding the data, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets.
The limitation of identify a plurality of dataset features of the one or more seed examples, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of ... generates the one or more candidate labeled datasets using the plurality of dataset features, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets.
The limitation of generate the one or more candidate labeled datasets by combining the limited number of datasets with a plurality of external datasets obtained from different sources, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets.
The limitation of ... generate a new labeled dataset having an accurate label, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – label discriminator. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein the generative model includes a label discriminator which is simply additional information regarding the generative model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites ... trains the label discriminator to identify the mislabeled dataset which is simply generic training to perform the abstract idea of dataset generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
label discriminator amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the generative model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 16 carries out the system of claim 15 but for the recitation of additional element(s) of wherein the label discriminator includes k-binary classification functions or a k-class classifier having k number of distinct labels.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the label discriminator includes k-binary classification functions or a k-class classifier having k number of distinct labels which is simply additional information regarding the label discriminator, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – k-binary classification functions, k-class classifier. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
k-binary classification functions, k-class classifier amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the label discriminator do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets.
The limitation of ... generate a new labeled dataset in the domain, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – domain discriminator. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein the generative model includes a domain discriminator which is simply additional information regarding the generative model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites ... trains the domain discriminator to identify the unrelated dataset out of the domain which is simply generic training to perform the abstract idea of dataset generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
domain discriminator amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the generative model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets. The Step 2A Prong One Analysis for claim 17 is applicable here since claim 18 carries out the system of claim 17 but for the recitation of additional element(s) of wherein the domain discriminator is a binary classifier based on a deep auto-encoder neural network.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the domain discriminator is a binary classifier based on a deep auto-encoder neural network which is simply additional information regarding the domain discriminator, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – binary classifier, deep auto-encoder neural network. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
binary classifier, deep auto-encoder neural network amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the domain discriminator do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 19, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 19 carries out the system of claim 11 but for the recitation of additional element(s) of receive a request to generate one or more datasets in the domain; retrieve, from the database, the generative model trained for the domain indicated by the request; and execute the generative model to generate the one or more datasets in the domain.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites receive a request to generate one or more datasets in the domain; retrieve, from the database, the generative model trained for the domain indicated by the request, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites execute the generative model to generate the one or more datasets in the domain which is simply applying the model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
applying the model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a system with a computer, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) computer system for training and managing generative models that generate labeled datasets. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 20 carries out the system of claim 11 but for the recitation of additional element(s) of wherein the computer trains the generative model using progressive generative adversarial networks learning algorithms.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the computer trains the generative model using progressive generative adversarial networks learning algorithms which is simply additional information regarding the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites additional element(s) – progressive generative adversarial networks learning algorithms. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
progressive generative adversarial networks learning algorithms amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 7-8, 11-15, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kudo et al. (US 2022/0139062 A1 - Learning Apparatus, Learning Method, and Learning Program, Region-Of-Interest Extraction Apparatus, Region-Of-Interest Extraction Method, and Region-Of-Interest Extraction Program, and Learned Extraction Model, hereinafter referred to as "Kudo").
Regarding claim 1, Kudo teaches a computer-implemented (Kudo, [0101]-[0103] – teaches processor, memory and instructions to perform methods) method for training generative models to generate labeled datasets (Kudo, [0107]-[0109] - teaches training a model to generate labeled dataset for various domains; see also Kudo, [0007]), the method comprising:
generating, by a computer, one or more candidate labeled datasets based upon a plurality of dataset features of one or more example datasets associated with a domain (Kudo, [0097]-[0098] - teaches example data; Kudo, [0107] - teaches generating candidate labeled datasets for domain and label based on the feature map of the input image; Kudo, [0110] - teaches generating a feature map from a real image [example data set]; Kudo, [0107]-[0109] – teaches inputting the features to generate candidate labeled datasets as input for discriminators; see also Kudo, Fig. 3);
applying, by the computer, a generative model on the plurality of dataset features to train the generative model to identify a mislabeled dataset of the one or more candidate labeled datasets (Kudo, [0107]-[0109] - teaches a second discriminator of the generative model for region extraction [labels]; Kudo, [0130] - teaches the second discriminator identifying the image as correctly or incorrectly labeled; see Kudo, Fig. 3);
applying, by the computer, the generative model of the machine-learning architecture to train the generative model to identify an unrelated dataset of the one or more candidate labeled datasets that is out of the domain (Kudo, [0107]-[0108] - teaches a first discriminator of the generative model for domains; Kudo, [0117] - teaches the first discriminator identifying images as incorrect/virtual [unrelated]; see also Kudo, [0119]; Kudo, Fig. 3); and
responsive to the computer determining that the generative model satisfies one or more training pass rates based upon a number of mislabeled datasets and a number of unrelated datasets (Kudo, [0118]-[0124] - teaches loss of the first discriminator [domain]; Kudo, [0130]-[0134] - teaches loss of the extraction model [labels]; Kudo, [0145] - teaches repeating training steps until the losses have reached predetermined thresholds), storing the generative model into a database (Kudo, [0100] – teaches storing the learned model for future use on network storage).
Regarding claim 2, Kudo teaches all of the limitations of the method of claim 1 as noted above.
Kudo further teaches wherein identifying the plurality of dataset features includes receiving, by the computer, one or more seed examples in the domain (Kudo, [0097]-[0098] - teaches seed example data acquired from CT/MRI machines) and comprising the one or more example datasets having a limited number of datasets of a same data type (Kudo, [0097]-[0098] - teaches seed example data acquired from CT/MRI machines [Actual data is limited to stored data actually taken using the machines]).
Regarding claim 3, Kudo teaches all of the limitations of the method of claim 2 as noted above.
Kudo further teaches identifying, by the computer, a plurality of dataset features of the one or more seed examples, wherein the computer generates the one or more candidate labeled datasets using the plurality of dataset features (Kudo, [0097]-[0098] - teaches example data; Kudo, [0107] - teaches generating candidate labeled datasets for domain and label based on the feature map of the input image; Kudo, [0110] - teaches generating a feature map from a real image [example seed data set]; Kudo, [0107]-[0109] – teaches inputting the features to generate candidate labeled datasets as input for discriminators; see also Kudo, Fig. 3).
Regarding claim 4, Kudo teaches all of the limitations of the method of claim 2 as noted above.
Kudo further teaches generating, by the computer, the one or more candidate labeled datasets by combining the limited number of datasets with a plurality of external datasets obtained from different sources (Kudo, [0117]-[0119] - teaches combining the virtual images of the first decoder with real images as input to the first discriminator; Kudo, [0129]-[0133] - teaches combining the virtual images of the second decoder with ground-truth masks for input into the second discriminator).
Regarding claim 5, Kudo teaches all of the limitations of the method of claim 1 as noted above.
Kudo further teaches wherein the generative model includes a label discriminator, wherein the computer trains the label discriminator to identify the mislabeled dataset (Kudo, [0107]-[0109] - teaches a second discriminator of the generative model for region extraction [labels]; Kudo, [0130] - teaches the second discriminator identifying the image as correctly or incorrectly labeled; see Kudo, Fig. 3), and generate a new labeled dataset having an accurate label (Kudo, [0145] – teaches repeating learning steps [including generating new labeled data] until losses reach predetermined values).
Regarding claim 7, Kudo teaches all of the limitations of the method of claim 1 as noted above.
Kudo further teaches wherein the generative model includes a domain discriminator, wherein the computer trains the domain discriminator to identify the unrelated dataset out of the domain (Kudo, [0107]-[0108] - teaches a first discriminator of the generative model for domains; Kudo, [0117] - teaches the first discriminator identifying images as incorrect/virtual [unrelated]; see also Kudo, [0119]; Kudo, Fig. 3), and generate a new labeled dataset in the domain (Kudo, [0145] – teaches repeating learning steps [including generating new labeled data] until losses reach predetermined values).
Regarding claim 8, Kudo teaches all of the limitations of the method of claim 7 as noted above.
Kudo further teaches wherein the domain discriminator is a binary classifier (Kudo, [0107]-[0108] - teaches a first discriminator of the generative model for domains; Kudo, [0117] - teaches the first discriminator identifying images as correct/incorrect) based on a deep auto-encoder neural network (Kudo, [0110] - teaches multi-layer CNN encoder; Kudo, [0115] - teaches first decoder with plurality of deconvolutional layers).
Regarding claim 11, it is the computer system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1.Kudo further teaches a computer system for training and managing generative models that generate labeled datasets (Kudo, [0107]-[0109] - teaches training a model to generate labeled dataset for various domains; see also Kudo, [0007]), the system comprising:
a non-transitory storage of a database configured to store one or more generative models trained for corresponding one or more domains (Kudo, [0101]-[0103] – teaches processor, memory and instructions to perform methods); and
a computer in communication with the database (Kudo, [0101]-[0103] – teaches processor, memory and instructions to perform methods) and configured to: …
Regarding claim 12, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Kudo for the reasons set forth in the rejection of claim 2.
Regarding claim 13, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Kudo for the reasons set forth in the rejection of claim 3.
Regarding claim 14, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Kudo for the reasons set forth in the rejection of claim 4.
Regarding claim 15, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Kudo for the reasons set forth in the rejection of claim 5.
Regarding claim 17, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Kudo for the reasons set forth in the rejection of claim 7.
Regarding claim 18, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Kudo for the reasons set forth in the rejection of claim 8.
Claim(s) 6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kudo in view of Yang et al. (Task-Adversarial Co-Generative Nets, hereinafter referred to as “Yang”).
Regarding claim 6, Kudo teaches all of the limitations of the method of claim 5 as noted above. However, Kudo does not explicitly teach wherein the label discriminator includes k-binary classification functions or a k-class classifier having k number of distinct labels.
Yang teaches wherein the label discriminator includes k-binary classification functions or a k-class classifier having k number of distinct labels (Yang, section 3.1 – teaches a k-class classifier to determine class labels as part of the discriminator [which is k-binary classification discriminator]).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Kudo with the teachings of Yang in order to learn the task-invariant representations of features to bridge the domain shift among tasks and to generates the plausible examples for each task to tackle the data scarcity issue in the field of generative models to generate domain specific datasets (Yang, Abstract – “In this paper, we propose Task-Adversarial co-Generative Nets (TAGN) for learning from multiple tasks. It aims to address the two fundamental issues of multi-task learning, i.e., domain shift and limited labeled data, in a principled way. To this end, TAGN first learns the task-invariant representations of features to bridge the domain shift among tasks. Based on the task-invariant features, TAGN generates the plausible examples for each task to tackle the data scarcity issue. In TAGN, we leverage multiple game players to gradually improve the quality of the co-generation of features and examples by using an adversarial strategy. It simultaneously learns the marginal distribution of task-invariant features across different tasks and the joint distributions of examples with labels for each task. The theoretical study shows the desired results: at the equilibrium point of the multi-player game, the feature extractor exactly produces the task-invariant features for different tasks, while both the generator and the classifier perfectly replicate the joint distribution for each task. The experimental results on the benchmark data sets demonstrate the effectiveness of the proposed approach.”).
Regarding claim 16, the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Kudo in view of Yang for the reasons set forth in the rejection of claim 6.
Claim(s) 9-10, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kudo in view of Karras et al. (Progressive Growing of GANs for Improved Quality, Stability, and Variation, hereinafter referred to as “Karras”).
Regarding claim 9, Kudo teaches all of the limitations of the method of claim 1 as noted above. However, Kudo does not explicitly teach receiving, by the computer, a request to generate one or more datasets in the domain; retrieving, by the computer from the database, the generative model trained for the domain indicated by the request; and executing, by the computer, the generative model to generate the one or more datasets in the domain.
Karras teaches
receiving, by the computer, a request to generate one or more datasets in the domain (Karras, section 6 – teaches generating various domain specific datasets using the trained model [An experimental request was made to test the model]);
retrieving, by the computer from the database, the generative model trained for the domain indicated by the request (Karras, section 6 – teaches generating various domain specific datasets using the trained model [In order to perform tests, the trained model must be retrieved from memory]); and
executing, by the computer, the generative model to generate the one or more datasets in the domain (Karras, section 6 – teaches generating various domain specific datasets using the trained model).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Kudo with the teachings of Karras in order to improve quality, stability and variation in the field of dataset generation with GANs (Karras, Abstract – “We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CELEBA images at 10242. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8:80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CELEBA dataset.”).
Regarding claim 10, Kudo teaches all of the limitations of the method of claim 1 as noted above. However, Kudo does not explicitly teach wherein the computer trains the generative model using progressive generative adversarial networks learning algorithms.
Karras teaches wherein the computer trains the generative model using progressive generative adversarial networks learning algorithms (Karras, section 2 – teaches progressive growing the GAN during training).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Kudo with the teachings of Karras in order to improve quality, stability and variation in the field of dataset generation with GANs (Karras, Abstract – “We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CELEBA images at 10242. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8:80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CELEBA dataset.”).
Regarding claim 19, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Kudo in view of Karras for the reasons set forth in the rejection of claim 9.
Regarding claim 20, the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Kudo in view of Karras for the reasons set forth in the rejection of claim 10.
Conclusion
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125