DETAILED ACTION
This action is in response to the Applicant Response filed 28 April 2026 for application 18/317,803 filed 15 May 2023.
Claim(s) 1-5, 7, 9, 11-15, 17, 19 is/are currently amended.
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 .
Response to Arguments
Applicant's arguments regarding the 35 U.S.C. 112(b) rejection(s) of claim(s) 1-20 have been fully considered and, in light of the amendments to the claims, are persuasive. The 35 U.S.C. 112(b) rejection(s) of claim(s) 1-20 has/have been withdrawn. However, in light of the amendments to the claims, new 35 U.S.C. 112(b) rejections have arisen, as noted below.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 101 rejection of the claims below.
Applicant’s arguments regarding the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims are based on the newly amended subject matter. All arguments are addressed in the 35 U.S.C. 102 and/or 35 U.S.C. 103 rejections of the claims below.
Claim Objections
Claim(s) 5-8, 11-20 is/are objected to because of the following informalities:
Claim 5, line 3, the comma should be removed after “dataset”
Claim 7, line 3, the comma should be removed after “domain”
Claim 11, line 9, training should read “train”
Claim 11, line 12, training should read “train”
Claim 15, line 3, the comma should be removed after “dataset”
Claim 17, line 3, the comma should be removed after “domain”
Claims 6, 8, 12-20 are objected to due to their dependence, either directly or indirectly on claims 5, 7, 11, 15, 17
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites training … a domain discriminator … on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset. However, the specification does not provide support for excluding rejected mislabeled datasets during training. While the specification discloses that the label discriminator and domain discriminator identify and exclude the candidate examples that are mislabeled and out of the domain ([0054]), it does not disclose any mention of excluding mislabeled datasets during training of the domain discriminator. Therefore, there is no support in the original description for the inclusion of the amendment to the claims and the claims fail to comply with the written description requirement. Correction or clarification is required.
Claim 11 recites training the generative model of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset. However, the specification does not provide support for excluding rejected mislabeled datasets during training. While the specification discloses that the label discriminator and domain discriminator identify and exclude the candidate examples that are mislabeled and out of the domain ([0054]), it does not disclose any mention of excluding mislabeled datasets during training of the domain discriminator. Therefore, there is no support in the original description for the inclusion of the amendment to the claims and the claims fail to comply with the written description requirement. Correction or clarification is required.
Claims 2-10, 12-20 are rejected under 35 U.S.C. 112(a) due to their dependence, either directly or indirectly, on claim(s) 1, 11.
Claim Rejections - 35 USC § 112(b)
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.
Claim(s) 3-4, 9-10, 13-14 is/are 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 3 recites wherein the computer generates the one or more candidate labeled datasets using the plurality of dataset features of the one or more seed examples while failing to provide a proper antecedent basis. Claim 1, from which claim 3 indirectly depends, recites that the one or more candidate labeled datasets are received by a computer. It is unclear how the datasets can be received and generated or, if the computer does both, how the computer performs both tasks. Clarification or correction is required.
Examiner’s Note: For the purposes of examination, it will be interpreted as if the computer can perform both actions such that the computer can generate the one or more candidate labeled datasets and send/receive the datasets at another component of the computer.
Claim 4 recites wherein the one or more candidate labeled datasets are generated by combining the limited number of datasets with a plurality of external datasets obtained from different sources while failing to provide a proper antecedent basis. Claim 1, from which claim 4 indirectly depends, recites that the one or more candidate labeled datasets are received by a computer. It is unclear how the datasets can be received and generated or, if the computer does both, how the computer performs both tasks. Clarification or correction is required.
Examiner’s Note: For the purposes of examination, it will be interpreted as if the computer can perform both actions such that the computer can generate the one or more candidate labeled datasets and send/receive the datasets at another component of the computer. Examiner further notes that the claim can potentially be amended to depend from claim three if the above 35 U.S.C. 112(b) issue is corrected for claim 3.
Claim 9 recites the generative model while failing to provide a proper antecedent basis for the term. It is suggested that the first instance of the term be amended to recite “a generative model.” Clarification or correction is required.
Claim 10 recites the generative model while failing to provide a proper antecedent basis for the term. It is suggested that the term be amended to recite “a generative model.” Clarification or correction is required.
Claim 13 recites wherein the computer generates the one or more candidate labeled datasets using the plurality of dataset features of the one or more seed examples while failing to provide a proper antecedent basis. Claim 11, from which claim 13 indirectly depends, recites that the one or more candidate labeled datasets are received by a computer. It is unclear how the datasets can be received and generated or, if the computer does both, how the computer performs both tasks. Clarification or correction is required.
Examiner’s Note: For the purposes of examination, it will be interpreted as if the computer can perform both actions such that the computer can generate the one or more candidate labeled datasets and send/receive the datasets at another component of the computer.
Claim 14 recites wherein the one or more candidate labeled datasets are generated by combining the limited number of datasets with a plurality of external datasets obtained from different sources while failing to provide a proper antecedent basis. Claim 11, from which claim 14 indirectly depends, recites that the one or more candidate labeled datasets are received by a computer. It is unclear how the datasets can be received and generated or, if the computer does both, how the computer performs both tasks. Clarification or correction is required.
Examiner’s Note: For the purposes of examination, it will be interpreted as if the computer can perform both actions such that the computer can generate the one or more candidate labeled datasets and send/receive the datasets at another component of the computer. Examiner further notes that the claim can potentially be amended to depend from claim three if the above 35 U.S.C. 112(b) issue is corrected for claim 3.
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) 3-8, 13-18 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 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 ... generates the one or more candidate labeled datasets using the 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.
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 models, label discriminator, machine-learning architecture, 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 receiving, by a computer, one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the label discriminator satisfies a first training pass rate based upon any rejected number of mislabeled datasets and that the domain discriminator satisfies a second training pass rate based upon any rejected unrelated datasets, storing the trained label discriminator and the trained domain discriminator of the machine-learning architecture into a database; storing, by the computer, one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained label discriminator and the trained domain discriminator; receiving, by the computer, one or more seed examples in the domain and comprising the one or more example datasets having a limited number of datasets of a same data type with parts of the one or more seed examples being correctly labeled; receiving, by the computer, a plurality of dataset features of the one or more seed examples, which is simply receiving and storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites training, by the computer, a label discriminator of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; in response to training the label discriminator, training, by the computer, a domain discriminator of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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)).
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))
receiving and 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 receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
generative models, label discriminator, machine-learning architecture, 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))
The additional element(s) do(es) not 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 wherein the one or more candidate labeled datasets are generated 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 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 models, label discriminator, machine-learning architecture, 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 receiving, by a computer, one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the label discriminator satisfies a first training pass rate based upon any rejected number of mislabeled datasets and that the domain discriminator satisfies a second training pass rate based upon any rejected unrelated datasets, storing the trained label discriminator and the trained domain discriminator of the machine-learning architecture into a database; storing, by the computer, one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained label discriminator and the trained domain discriminator; receiving, by the computer, one or more seed examples in the domain and comprising the one or more example datasets having a limited number of datasets of a same data type with parts of the one or more seed examples being correctly labeled, which is simply receiving and storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites training, by the computer, a label discriminator of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; in response to training the label discriminator, training, by the computer, a domain discriminator of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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)).
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))
receiving and 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 receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
generative models, label discriminator, machine-learning architecture, 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))
The additional element(s) do(es) not 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 ... identify a mislabeled dataset, 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 ... 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) – 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 models, label discriminator, machine-learning architecture, domain discriminator, generative model. 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 receiving, by a computer, one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the label discriminator satisfies a first training pass rate based upon any rejected number of mislabeled datasets and that the domain discriminator satisfies a second training pass rate based upon any rejected unrelated datasets, storing the trained label discriminator and the trained domain discriminator of the machine-learning architecture into a database; storing, by the computer, one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained label discriminator and the trained domain discriminator, which is simply receiving and storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites training, by the computer, a label discriminator of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; in response to training the label discriminator, training, by the computer, a domain discriminator of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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)).
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))
receiving and 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 receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
generative models, label discriminator, machine-learning architecture, domain discriminator, generative model 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 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 ... identify an unrelated dataset out of 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.
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) – 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 models, label discriminator, machine-learning architecture, domain discriminator, generative model. 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 receiving, by a computer, one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the label discriminator satisfies a first training pass rate based upon any rejected number of mislabeled datasets and that the domain discriminator satisfies a second training pass rate based upon any rejected unrelated datasets, storing the trained label discriminator and the trained domain discriminator of the machine-learning architecture into a database; storing, by the computer, one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained label discriminator and the trained domain discriminator, which is simply receiving and storing data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
The claim recites training, by the computer, a label discriminator of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; in response to training the label discriminator, training, by the computer, a domain discriminator of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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)).
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))
receiving and 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 receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
generative models, label discriminator, machine-learning architecture, domain discriminator, generative model 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 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 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 ... generates the one or more candidate labeled datasets using the 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.
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 models, machine-learning architecture, trained generative model. 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 training a generative model of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; training the generative model of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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 a non-transitory storage of a database configured to store one or more generative models trained for corresponding one or more domains; receive one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the generative model satisfies one or more training pass rates based upon any rejected mislabeled datasets and any rejected unrelated datasets, store the trained generative model into the database; store one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained generative model; receive one or more seed examples in the domain and comprising the one or more example datasets having a limited number of datasets of a same data type with parts of the one or more seed examples being correctly labeled; receive a plurality of dataset features of the one or more seed examples, which is simply storing and receiving 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 and receiving 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))
generative models, machine-learning architecture, trained generative model 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 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 wherein the one or more candidate labeled datasets are generated 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 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 models, machine-learning architecture, trained generative model. 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 training a generative model of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; training the generative model of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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 a non-transitory storage of a database configured to store one or more generative models trained for corresponding one or more domains; receive one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the generative model satisfies one or more training pass rates based upon any rejected mislabeled datasets and any rejected unrelated datasets, store the trained generative model into the database; store one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained generative model; receive one or more seed examples in the domain and comprising the one or more example datasets having a limited number of datasets of a same data type with parts of the one or more seed examples being correctly labeled, which is simply storing and receiving 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 and receiving 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))
generative models, machine-learning architecture, trained generative model 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 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 ... identify a mislabeled dataset, 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 ... 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) – 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 models, machine-learning architecture, trained generative model, 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 training a generative model of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; training the generative model of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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 a non-transitory storage of a database configured to store one or more generative models trained for corresponding one or more domains; receive one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the generative model satisfies one or more training pass rates based upon any rejected mislabeled datasets and any rejected unrelated datasets, store the trained generative model into the database; store one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained generative model, which is simply storing and receiving 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 and receiving 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))
generative models, machine-learning architecture, trained generative model, 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))
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 ... identify an unrelated dataset out of 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.
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) – 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 models, machine-learning architecture, trained generative model, 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 training a generative model of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any mislabeled dataset of the one or more candidate labeled datasets; training the generative model of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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 a non-transitory storage of a database configured to store one or more generative models trained for corresponding one or more domains; receive one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from one or more example datasets associated with a domain; responsive to the computer determining that the generative model satisfies one or more training pass rates based upon any rejected mislabeled datasets and any rejected unrelated datasets, store the trained generative model into the database; store one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained generative model, which is simply storing and receiving 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 and receiving 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))
generative models, machine-learning architecture, trained generative model, 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))
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.
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") in view of Park et al. (US 2020/0193269 A1 – Recognizer, Object Recognition Method, Learning Apparatus, and Learning Method for Domain Adaptation, hereinafter referred to as “Park”).
Regarding claim 1 (Currently Amended), 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:
receiving, by a computer, one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from 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; Kudo, [0107]-[0109] – teaches inputting the features to generate candidate labeled datasets as input for discriminators; see also Kudo, Fig. 3);
training, by the computer, a label discriminator of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any 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);
in response to training the label discriminator, training, by the computer, a domain discriminator of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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);
responsive to the computer determining that the label discriminator satisfies a first training pass rate based upon any rejected number of mislabeled datasets and that the domain discriminator satisfies a second training pass rate based upon any rejected 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 trained label discriminator and the trained domain discriminator of the machine-learning architecture into a database (Kudo, [0100] – teaches storing the learned model for future use on network storage).
However, Kudo does not explicitly teach storing, by the computer, one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained label discriminator and the trained domain discriminator.
Park teaches storing, by the computer, one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained label discriminator and the trained domain discriminator (Park, [0040] - teaches generating data for assisting various domains; Park, [0050] - teaches generating fake samples that are indistinguishable from real samples while identifying and excluding fake samples).
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 Park in order to make training in a new domain more efficient in the field of dataset generation (Park, [0004] – “ If a labelled data set for retraining a DNN in a new domain needs to be secured every time it applies to the new domain, for example, a target domain, to achieve the performance, it may cause a high cost issue. In addition, it is difficult to collect a sufficient labelled data set for retraining in the new domain.”).
Regarding claim 2 (Currently Amended), Kudo in view of Park teaches all of the limitations of the method of claim 1 as noted above. Kudo further teaches 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 with parts of the one or more seed examples being correctly labeled (Kudo, [0097]-[0098] - teaches seed example data acquired from CT/MRI machines [Actual data is limited to stored data actually taken using the machines]; see also Kudo, [0112]).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 1 above.
Regarding claim 3 (Currently Amended), Kudo in view of Park teaches all of the limitations of the method of claim 2 as noted above. Kudo further teaches receiving, 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 of the one or more seed examples (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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 2 above.
Regarding claim 4 (Currently Amended), Kudo in view of Park teaches all of the limitations of the method of claim 2 as noted above. Kudo further teaches wherein the one or more candidate labeled datasets are generated 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 2 above.
Regarding claim 5 (Currently Amended), Kudo in view of Park teaches all of the limitations of the method of claim 1 as noted above. Kudo further teaches wherein a generative model includes the label discriminator, wherein the computer trains the label discriminator to identify a 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 1 above.
Regarding claim 7 (Currently Amended), Kudo in view of Park teaches all of the limitations of the method of claim 1 as noted above. Kudo further teaches wherein a generative model includes the domain discriminator, wherein the computer trains the domain discriminator to identify an 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 1 above.
Regarding claim 8 (Original), Kudo in view of Park 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 7 above.
Regarding claim 11 (Currently Amended), Kudo 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 (udo, [0101]-[0103] – teaches processor, memory and instructions to perform methods) and configured to:
receive one or more candidate labeled datasets based upon a plurality of dataset features including labeled objects and content extracted from 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);
training a generative model of a machine-learning architecture on the labeled objects of the plurality of dataset features to identify and reject any 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);
training the generative model of the machine-learning architecture on the content of the plurality of dataset features of the one or more candidate labeled datasets excluding each rejected mislabeled dataset to identify and reject any 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);
responsive to the computer determining that the generative model satisfies one or more training pass rates based upon any rejected mislabeled datasets and any rejected 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), store the trained generative model into the database (Kudo, [0100] – teaches storing the learned model for future use on network storage).
However, Kudo does not explicitly teach store one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained generative model.
Park teaches store one or more datasets for the domain based on the one or more candidate labeled datasets that were not rejected by the trained generative model (Park, [0040] - teaches generating data for assisting various domains; Park, [0050] - teaches generating fake samples that are indistinguishable from real samples while identifying and excluding fake samples).
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 Park in order to make training in a new domain more efficient in the field of dataset generation (Park, [0004] – “ If a labelled data set for retraining a DNN in a new domain needs to be secured every time it applies to the new domain, for example, a target domain, to achieve the performance, it may cause a high cost issue. In addition, it is difficult to collect a sufficient labelled data set for retraining in the new domain.”).
Regarding claim 12 (Currently Amended), Kudo in view of Park teaches all of the limitations of the system of claim 11 as noted above. Kudo further teaches receive 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 with parts of the one or more seed examples being correctly labeled (Kudo, [0097]-[0098] - teaches seed example data acquired from CT/MRI machines [Actual data is limited to stored data actually taken using the machines]; see also Kudo, [0112]).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 11 above.
Regarding claim 13 (Currently Amended), Kudo in view of Park teaches all of the limitations of the system of claim 12 as noted above. Kudo further teaches receive 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 of the one or more seed examples (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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 12 above.
Regarding claim 14 (Currently Amended), Kudo in view of Park teaches all of the limitations of the system of claim 12 as noted above. Kudo further teaches wherein the one or more candidate labeled datasets are generated 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 12 above.
Regarding claim 15 (Currently Amended), Kudo in view of Park teaches all of the limitations of the system of claim 11 as noted above. Kudo further teaches wherein the generative model includes a label discriminator, wherein the computer trains the label discriminator to identify a 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 11 above.
Regarding claim 17 (Currently Amended), Kudo in view of Park teaches all of the limitations of the system of claim 11 as noted above. Kudo further teaches wherein the generative model includes a domain discriminator, wherein the computer trains the domain discriminator to identify an 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 11 above.
Regarding claim 18 (Original), Kudo in view of Park teaches all of the limitations of the system of claim 17 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).
It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Kudo and Park for the same reasons as disclosed in claim 17 above.
Claim(s) 6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kudo in view of Park and further in view of Yang et al. (Task-Adversarial Co-Generative Nets, hereinafter referred to as “Yang”).
Regarding claim 6, Kudo in view of Park teaches all of the limitations of the method of claim 5 as noted above. However, Kudo in view of Park 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 in view of Park 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 (Original), Kudo in view of Park teaches all of the limitations of the system of claim 15 as noted above. However, Kudo in view of Park 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 in view of Park 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.”).
Claim(s) 9-10, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kudo in view of Park and further 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 in view of Park teaches all of the limitations of the method of claim 1 as noted above. However, Kudo in view of Park 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 the 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 in view of Park 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 in view of Park teaches all of the limitations of the method of claim 1 as noted above. However, Kudo in view of Park 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 in view of Park 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 (Currently Amended), Kudo in view of Park teaches all of the limitations of the system of claim 11 as noted above. However, Kudo in view of Park does not explicitly teach 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.
Karras teaches
receive 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]);
retrieve, 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
execute 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 in view of Park 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 20 (Original), Kudo in view of Park teaches all of the limitations of the system of claim 11 as noted above. However, Kudo in view of Park 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 in view of Park 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.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
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
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