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 .
Examiner's Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 09/19th/2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
101 Rejection
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter
Step 1 Analysis for all claims:
Claims 1-9 are directed to a method, which is directed to a process, one of the statutory categories. Claims 10-17 are directed to a computing device, which is directed to a machine, one of the statutory categories. Claims 18-20 are directed to a non-transitory media, which is directed to a product, one of the statutory categories.
Regarding Claim 1:
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1 Analysis:
Claim 1 recites in part process steps which, under the broadest reasonable interpretation, are a series of mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process or a mathematical concept but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. The claim recites in part:
determine an accuracy for each of the first set of labels and an accuracy for each of the second set of labels under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator determining the percentage of correct labels out of the entire dataset). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
select, for each of the plurality of data records, a label from either the first set of labels or the second set of labels based on a determination of which has a higher degree of accuracy for a data record under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator selecting a label for each datapoint in a dataset either from the labels generated from a first or a second discriminator based on how accurate the label is). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2 Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
generating, by a generator of a generative adversarial network (GAN) model, a plurality of data records as training data to train one or more discriminators which amounts to mere data outputting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
sending, to a first discriminator, the plurality of data records, wherein the first discriminator comprises a trained discriminator which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
sending, to a second discriminator, the plurality of data records, wherein the second discriminator is an untrained discriminator which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
receiving, from the first discriminator, a first set of labels corresponding to the plurality of data records which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g).
receiving, from the second discriminator, a second set of labels corresponding to the plurality of data records which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g).
providing, as input to the generator of the GAN model, the plurality of data records and the selected labels for each of the plurality of data records which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B Analysis:
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
generating, by a generator of a generative adversarial network (GAN) model, a plurality of data records as training data to train one or more discriminators which amounts to mere data outputting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
sending, to a first discriminator, the plurality of data records, wherein the first discriminator comprises a trained discriminator which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
sending, to a second discriminator, the plurality of data records, wherein the second discriminator is an untrained discriminator which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
receiving, from the first discriminator, a first set of labels corresponding to the plurality of data records which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g). The courts have found limitations directed to gathering information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “gathering statistics”, and "storing and retrieving information in memory").
receiving, from the second discriminator, a second set of labels corresponding to the plurality of data records which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g). The courts have found limitations directed to gathering information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “gathering statistics”, and "storing and retrieving information in memory").
providing, as input to the generator of the GAN model, the plurality of data records and the selected labels for each of the plurality of data records which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception.
The additional limitations of the dependent claims contain no additional elements that provide a practical application or amount to significantly more than the abstract idea and are addressed briefly below
Dependent claim 2 recites:
Step 2A Prong 1 Analysis: The claim is directed to the same abstract idea identified
above.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein the accuracy for each of the first set of labels and the accuracy for each of the second set of labels is determined using a loss function This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the accuracy for each of the first set of labels and the accuracy for each of the second set of labels is determined using a loss function This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception.
Dependent claim 3 recites:
Step 2A Prong 1 Analysis: The claim is directed to the same abstract idea identified
above.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
wherein the training data comprises the plurality of data records and a first set of real data records This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
providing second training data to the first discriminator prior to sending the plurality of data records to the first discriminator and the second discriminator which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
wherein the second training data comprises a second set of real data records, a second plurality of data records and a label for each record of the plurality of second data records This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the training data comprises the plurality of data records and a first set of real data records This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
providing second training data to the first discriminator prior to sending the plurality of data records to the first discriminator and the second discriminator which amounts to mere data transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
wherein the second training data comprises a second set of real data records, a second plurality of data records and a label for each record of the plurality of second data records This is a field of use limitation which amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception.
Dependent claim 4 recites:
Step 2A Prong 1 Analysis: The claim is directed to the same abstract idea identified
above.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
training the second discriminator until a prediction accuracy of the second discriminator surpasses a predication accuracy of the first discriminator is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the accuracy for each of the first set of labels and the accuracy for each of the second set of labels is determined using a loss function is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception.
Dependent claim 5 recites:
Step 2A Prong 1 Analysis: The claim is directed to the same abstract idea identified
above.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
after training the second discriminator, generating a new GAN model comprising the generator and the second discriminator is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
after training the second discriminator, generating a new GAN model comprising the generator and the second discriminator is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception.
Dependent claim 6:
Step 2A Prong 1: The claim recites process steps that are a mental process:
selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is above a threshold value, selecting the label from the first set of labels. Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator comparing values to a threshold and selecting labels based on the outcome of said comparison). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 7:
Step 2A Prong 1: The claim recites process steps that are a mental process:
selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is below a threshold value, selecting the label from the second set of labels. Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator comparing values to a threshold and selecting labels based on the outcome of said comparison). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The claim does not include additional elements that would integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Dependent claim 8:
Step 2A Prong 1: The claim recites process steps that are a mental process:
selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: using a machine learning model to determine a selection of the label from the first set of labels and the second set of labels. Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator comparing values to a threshold and selecting labels based on the outcome of said comparison). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. In particular, the additional element of:
using a machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
using a machine learning model is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception.
Dependent claim 9:
Step 2A Prong 1: The claim recites process steps that are a mental process:
based on a determination that a confidence level associated with the fourth set of label is above a threshold value, selecting, for the each of the plurality of data records, the label from the fourth set of labels. Under the broadest reasonable interpretation, this limitation is a process step that covers a mental process including observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper (such as an operator comparing values to a threshold and selecting labels based on the outcome of said comparison). If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
Step 2A Prong 2 Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of:
receiving, as output from the generator of the GAN model, improved training data which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g).
sending, to the first discriminator, the improved training data which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
sending, to the second discriminator, the improved training data which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
receiving, from the first discriminator, a third set of labels corresponding to the improved training data which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g).
receiving, from the second discriminator, a fourth set of labels corresponding to the improved training data which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g).
providing, as the input to the generator of the GAN model, the selected label to generate new improved training data which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B Analysis:
Claim 9 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
receiving, as output from the generator of the GAN model, improved training data which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g). The courts have found limitations directed to gathering information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “gathering statistics”, and "storing and retrieving information in memory").
sending, to the first discriminator, the improved training data which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
sending, to the second discriminator, the improved training data which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
receiving, from the first discriminator, a third set of labels corresponding to the improved training data which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g). The courts have found limitations directed to gathering information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “gathering statistics”, and "storing and retrieving information in memory").
receiving, from the second discriminator, a fourth set of labels corresponding to the improved training data which amounts to insignificant extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g). The courts have found limitations directed to gathering information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “gathering statistics”, and "storing and retrieving information in memory").
providing, as the input to the generator of the GAN model, the selected label to generate new improved training data which amounts to mere data outputting and transmitting and amounts to insignificant extra-solution activity and does not integrate the claim into a practical application. See MPEP 2106.05(g). Lastly, The courts have found limitations directed to outputting/transmitting data, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “Presenting offers", and “Determining an estimated outcome and setting a price”).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception.
Regarding Claim 10:
Claim 10 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites similar steps to claim 1 (see above for analysis), with the additional element of A computing device.
Step 2A Prong 2, Step 2B:
The additional element of A computing device is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Implementing an abstract idea on generic computer components does not integrate the abstract idea into a practical application, nor does it add significantly more to the exception. Thus, the claim is not patent eligible.
Dependent claim 11 recites:
Claim 11 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing device with similar steps to claim 2, and thus is not patent eligible for the same reasons (see above).
Dependent claim 12 recites:
Claim 12 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing device with similar steps to claim 3, and thus is not patent eligible for the same reasons (see above).
Dependent claim 13 recites:
Claim 13 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing device with similar steps to claim 4, and thus is not patent eligible for the same reasons (see above).
Dependent claim 14 recites:
Claim 14 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing device with similar steps to claim 5, and thus is not patent eligible for the same reasons (see above).
Dependent claim 15 recites:
Claim 15 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing device with similar steps to claim 6, and thus is not patent eligible for the same reasons (see above).
Dependent claim 16 recites:
Claim 16 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing device with similar steps to claim 7, and thus is not patent eligible for the same reasons (see above).
Dependent claim 17 recites:
Claim 17 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A computing device with similar steps to claim 8, and thus is not patent eligible for the same reasons (see above).
Regarding Claim 18:
Claim 18 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites similar steps to claim 1 (see above for analysis), with the additional element of A non-transitory media.
Step 2A Prong 2, Step 2B:
The additional element of A non-transitory media is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Implementing an abstract idea on generic computer components does not integrate the abstract idea into a practical application, nor does it add significantly more to the exception. Thus, the claim is not patent eligible.
Dependent claim 19 recites:
Claim 19 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A non-transitory media with similar steps to claim 2, and thus is not patent eligible for the same reasons (see above).
Dependent claim 20 recites:
Claim 20 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites A non-transitory media with similar steps to claims 4 and 5, and thus is not patent eligible for the same reasons (see above).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all
obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 6-11, 13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over LI (US20220084204A1), in view of SON (US20190188882A1), in view of Nguyen (Ensemble Selection based on Classifier Prediction Confidence – 2020), further in view of Kearney (US20220180447A)1.
Regarding claim 1 LI teaches generating, by a generator of a generative adversarial network (GAN) model, a plurality of data records as training data to train one or more discriminators ([0067] In one embodiment, the training dataset 202 is generated using a generative adversarial network (GAN) that generates synthetic images and an associated trained neural network that generates labels for synthetic images generated by the GAN).
sending, to a second discriminator, the plurality of data records, wherein the second discriminator is an untrained discriminator ([0108] At operation 710, an untrained first discriminator network of GAN receives as an input synthetic image that is generated by generator network of GAN).
receiving, from the second discriminator, a second set of labels corresponding to the plurality of data records ([0108] At operation 715, first discriminator determines a first score for synthetic image that is generated by generator network).
determining an accuracy for each of the first set of labels and an accuracy for each of the second set of labels ([0078] a first discriminator network of a GAN's two discriminator networks takes as an input a synthetic image that was generated by generator network of GAN, and outputs a first score for synthetic image. First score represents a probability that synthetic image is a real image. A second discriminator network of GAN's two discriminator networks takes as a first input a synthetic image and as a second input one or more generated labels and/or other outputs associated with synthetic image, and outputs a second score for synthetic image and associated generated labels).
However, LI is not relied upon to explicitly teach sending, to a first discriminator, the plurality of data records, wherein the first discriminator comprises a trained discriminator. LI is also not relied upon to explicitly teach receiving, from the first discriminator, a first set of labels corresponding to the plurality of data records. LI is also not relied upon to explicitly teach selecting, for each of the plurality of data records, a label from either the first set of labels or the second set of labels based on a determination of which has a higher degree of accuracy for a data record. LI is also not relied upon to explicitly teach providing, as input to the generator of the GAN model, the plurality of data records and the selected labels for each of the plurality of data records.
On the other hand, SON teaches sending, to a first discriminator, the plurality of data records, wherein the first discriminator comprises a trained discriminator ([0109] In another example, the training apparatus trains the image processing apparatus 1101 based on the discriminator network 1102 that is trained in advance. The discriminator network 1102 is trained in advance to determine that the training result image 1104 is a fake image and determine that the image 1105 is a real image. The examiner notes that SON teaches using images and a trained discriminator to classify said images as real or fake. The examiner further notes that LI and SON are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate sending, to a first discriminator, the plurality of data records, wherein the first discriminator comprises a trained discriminator as taught by SON [0109] to determine the training result image 1104 as a real image, not a fake image [0109]).
Furthermore, SON teaches receiving, from the first discriminator, a first set of labels corresponding to the plurality of data records ([0109] The training apparatus trains the image processing apparatus 1101 so that the pre-trained discriminator network 1102 determines the training result image 1104 as a real image, not a fake image. The examiner notes that SON teaches a discriminator that labels classified images as real or fake. The examiner further notes that LI and SON are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate receiving, from the first discriminator, a first set of labels corresponding to the plurality of data records as taught by SON [0109] to determine the training result image 1104 as a real image, not a fake image [0109]).
Furthermore, Nguyen teaches selecting, for each of the plurality of data records, a label from either the first set of labels or the second set of labels based on a determination of which has a higher degree of accuracy for a data record ([Page 3, Section 3.1] Assume that we have a committee of K experts {Kk} each of whom gives an answer to a problem. Classically, the answers from all experts are received and combined to obtain the final decision. However, some of the answers do not have high enough confidence and should be excluded from the final committee decision. Here we assume that each answer has its own confidence and that we prefer highly confident answers to those with low confidence before making the final decision. Moreover, we also assume that each of the experts has its own level of domain expertise (credibility) as they come with different background. Our approach takes account of each expert’s credibility threshold and selects an expert’s answer for aggregation if and only if its confidence is higher than the credibility threshold. The examiner notes that Nguyen teaches selecting highly confident answers (label) for any given question from multiple classifiers. The examiner further notes that LI and Nguyen are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate selecting, for each of the plurality of data records, a label from either the first set of labels or the second set of labels based on a determination of which has a higher degree of accuracy for a data record as taught by Nguyen [Page 3, Section 3.1] to exclude answers that do not have high enough confidence [Page 3, Section 3.1]).
Furthermore, Kearney teaches providing, as input to the generator of the GAN model, the plurality of data records and the selected labels for each of the plurality of data records ([0339] The generator 2302 takes as inputs an image 2306, a tooth label 2308 (e.g., pixel mask showing pixels representing a tooth), and a restoration label 2310 (e.g., pixel mask showing pixels representing a restoration on the tooth). The examiner notes that LI and Kearney are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate providing, as input to the generator of the GAN model, the plurality of data records and the selected labels for each of the plurality of data records as taught by Kearney [0339] to obtain a synthetic caries label 2312, e.g. a pixel mask showing one or more caries corresponding to the dental image, tooth of interest, and corresponding restoration represented by the label 2310, 2308, 2306 [0339]).
Regarding claim 2, LI teaches The computer-implemented method of claim 1. However, LI is not relied upon to explicitly teach wherein the accuracy for each of the first set of labels and the accuracy for each of the second set of labels is determined using a loss function. On the other hand, Nguyen teaches wherein the accuracy for each of the first set of labels and the accuracy for each of the second set of labels is determined using a loss function ([Page 4, Section 3.3] The credibility thresholds of the base classifiers are obtained by minimizing the empirical loss function L0 −1 in Eq (9). The examiner notes that LI and Nguyen are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate wherein the accuracy for each of the first set of labels and the accuracy for each of the second set of labels is determined using a loss function as taught by Nguyen [Page 4, Section 3.3] to quantify the classifier’s level of expertise for a problem [Page 13, Section 6]).
Regarding claim 4 LI teaches training the second discriminator until a prediction accuracy of the second discriminator surpasses a predication accuracy of the first discriminator ([0068] In at least one embodiment, training framework 204 trains untrained neural network 206 until untrained neural network 206 achieves a desired accuracy.
Regarding claim 6, LI teaches The computer-implemented method of claim 1. However, LI is not relied upon to explicitly teach Wherein selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is above a threshold value, selecting the label from the first set of labels. On the other hand, Nguyen teaches Wherein selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is above a threshold value, selecting the label from the first set of labels ([Page 3, Section 3.1] Assume that we have a committee of K experts {Kk} each of whom gives an answer to a problem. Classically, the answers from all experts are received and combined to obtain the final decision. However, some of the answers do not have high enough confidence and should be excluded from the final committee decision. Here we assume that each answer has its own confidence and that we prefer highly confident answers to those with low confidence before making the final decision. Moreover, we also assume that each of the experts has its own level of domain expertise (credibility) as they come with different background. Our approach takes account of each expert’s credibility threshold and selects an expert’s answer for aggregation if and only if its confidence is higher than the credibility threshold. The examiner notes that Nguyen teaches selecting answers labeled as having high confidence (labels) from multiple classifiers (multiple sets of labels). The confidence level is obtained by minimizing an empirical loss function [Page 3, Section 3.3]. The examiner further notes that LI and Nguyen are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate Wherein selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is above a threshold value, selecting the label from the first set of labels as taught by Nguyen [Page 3, Section 3.1] to quantify the classifier’s level of expertise for a problem [Page 13, Section 6]).
Regarding claim 7, LI teaches The computer-implemented method of claim 1. However, LI is not relied upon to explicitly teach wherein selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is below a threshold value, selecting the label from the second set of labels. On the other hand, Nguyen teaches wherein selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is below a threshold value, selecting the label from the second set of labels ([Page 3, Section 3.1] Assume that we have a committee of K experts {Kk} each of whom gives an answer to a problem. Classically, the answers from all experts are received and combined to obtain the final decision. However, some of the answers do not have high enough confidence and should be excluded from the final committee decision. Here we assume that each answer has its own confidence and that we prefer highly confident answers to those with low confidence before making the final decision. Moreover, we also assume that each of the experts has its own level of domain expertise (credibility) as they come with different background. Our approach takes account of each expert’s credibility threshold and selects an expert’s answer for aggregation if and only if its confidence is higher than the credibility threshold. The examiner notes that Nguyen teaches selecting answers labeled as having high confidence (labels) from multiple classifiers (multiple sets of labels). The confidence level is obtained by minimizing an empirical loss function [Page 3, Section 3.3]. The examiner further notes that LI and Nguyen are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate wherein selecting, for the each of the plurality of data records, the label from either the first set of labels or the second set of labels comprises: based on a determination that a loss function associated with the second set of labels is below a threshold value, selecting the label from the second set of labels as taught by Nguyen [Page 3, Section 3.1] to quantify the classifier’s level of expertise for a problem [Page 13, Section 6]).
Regarding claim 8, LI teaches The computer-implemented method of claim 1. However, LI is not relied upon to explicitly teach using a machine learning model to determine a selection of the label from the first set of labels and the second set of labels. On the other hand, Nguyen teaches using a machine learning model to determine a selection of the label from the first set of labels and the second set of labels ([Page 3, Section 3.1] Assume that we have a committee of K experts {Kk} each of whom gives an answer to a problem. Classically, the answers from all experts are received and combined to obtain the final decision. However, some of the answers do not have high enough confidence and should be excluded from the final committee decision. Here we assume that each answer has its own confidence and that we prefer highly confident answers to those with low confidence before making the final decision. Moreover, we also assume that each of the experts has its own level of domain expertise (credibility) as they come with different background. Our approach takes account of each expert’s credibility threshold and selects an expert’s answer for aggregation if and only if its confidence is higher than the credibility threshold. The examiner notes that Nguyen teaches using classifiers to select labels from multiple sets of labels. The examiner further notes that LI and Nguyen are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate using a machine learning model to determine a selection of the label from the first set of labels and the second set of labels as taught by Nguyen [Page 3, Section 3.1] to quantify the classifier’s level of expertise for a problem [Page 13, Section 6]).
Regarding claim 9 LI teaches receiving, as output from the generator of the GAN model, improved training data ([0067] In one embodiment, the training dataset 202 is generated using a generative adversarial network (GAN) that generates synthetic images and an associated trained neural network that generates labels for synthetic images generated by the GAN). The examiner notes that the generator of a GAN iteratively generates improved synthetic data for use by the GAN).
sending, to the second discriminator, the improved training data ([0110] At operation 725, an untrained second discriminator network of GAN receives two inputs; synthetic image that is generated by generator network of GAN and corresponding labels of synthetic image).
receiving, from the second discriminator, a fourth set of labels corresponding to the improved training data ([0108] At operation 715, first discriminator determines a first score for synthetic image that is generated by generator network. The examiner notes that the generator of the GAN generates multiple updated datasets as the iterative process of adversarial machine learning is executed, a new set for every iteration).
However, LI is not relied upon to explicitly teach sending, to the first discriminator, the improved training data. LI is also not relied upon to explicitly teach receiving, from the first discriminator, a third set of labels corresponding to the improved training data. LI is also not relied upon to explicitly teach based on a determination that a confidence level associated with the fourth set of labels is above a threshold value, selecting, for the each of the plurality of data records, the label from the fourth set of labels. LI is also not relied upon to explicitly teach providing, as the input to the generator of the GAN model, the selected label to generate new improved training data.
On the other hand, SON teaches sending, to the first discriminator, the improved training data ([0109] In another example, the training apparatus trains the image processing apparatus 1101 based on the discriminator network 1102 that is trained in advance. The discriminator network 1102 is trained in advance to determine that the training result image 1104 is a fake image and determine that the image 1105 is a real image. The examiner notes that SON teaches using images and a trained discriminator to classify said images as real or fake. The examiner further notes that LI and SON are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate sending, to the first discriminator, the improved training data as taught by SON [0109] to determine the training result image 1104 as a real image, not a fake image [0109]).
Furthermore, SON teaches receiving, from the first discriminator, a third set of labels corresponding to the improved training data ([0109] The training apparatus trains the image processing apparatus 1101 so that the pre-trained discriminator network 1102 determines the training result image 1104 as a real image, not a fake image. The examiner notes that SON teaches a discriminator that labels classified images as real or fake. The examiner further notes that LI and SON are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate receiving, from the first discriminator, a third set of labels corresponding to the improved training data as taught by SON [0109] to determine the training result image 1104 as a real image, not a fake image [0109]).
Furthermore, Nguyen teaches based on a determination that a confidence level associated with the fourth set of labels is above a threshold value, selecting, for the each of the plurality of data records, the label from the fourth set of labels ([Page 3, Section 3.1] Assume that we have a committee of K experts {Kk} each of whom gives an answer to a problem. Classically, the answers from all experts are received and combined to obtain the final decision. However, some of the answers do not have high enough confidence and should be excluded from the final committee decision. Here we assume that each answer has its own confidence and that we prefer highly confident answers to those with low confidence before making the final decision. Moreover, we also assume that each of the experts has its own level of domain expertise (credibility) as they come with different background. Our approach takes account of each expert’s credibility threshold and selects an expert’s answer for aggregation if and only if its confidence is higher than the credibility threshold. The examiner notes that Nguyen teaches selecting highly confident answers (label) for any given question from multiple classifiers. The examiner further notes that LI and Nguyen are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate based on a determination that a confidence level associated with the fourth set of labels is above a threshold value, selecting, for the each of the plurality of data records, the label from the fourth set of labels as taught by Nguyen [Page 3, Section 3.1] to exclude answers that do not have high enough confidence [Page 3, Section 3.1]).
Furthermore, Kearney teaches providing, as the input to the generator of the GAN model, the selected label to generate new improved training data ([0339] The generator 2302 takes as inputs an image 2306, a tooth label 2308 (e.g., pixel mask showing pixels representing a tooth), and a restoration label 2310 (e.g., pixel mask showing pixels representing a restoration on the tooth). The examiner notes that LI and Kearney are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate providing, as the input to the generator of the GAN model, the selected label to generate new improved training data as taught by Kearney [0339] to obtain a synthetic caries label 2312, e.g. a pixel mask showing one or more caries corresponding to the dental image, tooth of interest, and corresponding restoration represented by the label 2310, 2308, 2306 [0339]).
Claim 10 is rejected based upon the same rationale as the rejection of claim 1 since it’s the computing device claim corresponding to the method claim.
Claim 11 is rejected based upon the same rationale as the rejection of claim 2 since it’s the computing device claim corresponding to the method claim.
Claim 13 is rejected based upon the same rationale as the rejection of claim 4 since it’s the computing device claim corresponding to the method claim.
Claim 15 is rejected based upon the same rationale as the rejection of claim 6 since it’s the computing device claim corresponding to the method claim.
Claim 16 is rejected based upon the same rationale as the rejection of claim 7 since it’s the computing device claim corresponding to the method claim.
Claim 17 is rejected based upon the same rationale as the rejection of claim 8 since it’s the computing device claim corresponding to the method claim.
Claim 18 is rejected based upon the same rationale as the rejection of claim 1 since it’s the computing device claim corresponding to the method claim.
Claim 19 is rejected based upon the same rationale as the rejection of claim 2 since it’s the computing device claim corresponding to the method claim.
Claim 20 is rejected based upon the same rationale as the rejection of claims 4, and 5 since it’s the computing device claim corresponding to these method claims.
Claims 3, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over LI (US20220084204A1), in view of SON (US20190188882A1), in view of Nguyen (Ensemble Selection based on Classifier Prediction Confidence – 2020), in view of Kearney (US20220180447A)1, in view of Ali (US20240046012A1), further in view of KUO (US20230081306A1).
Regarding claim 3, LI teaches The computer-implemented method of claim 1. However, LI is not relied upon to explicitly teach the training data comprises the plurality of data records and a first set of real data records. LI is also not relied upon to explicitly teach providing second training data to the first discriminator prior to sending the plurality of data records to the first discriminator and the second discriminator, wherein the second training data comprises a second set of real data records, a second plurality of data records and a label for each record of the plurality of second data records. On the other hand, Ali teaches the training data comprises the plurality of data records and a first set of real data records ([0050] the training system 200 uses the training data 425 and 430 as real data 115 in training the synthetic data generator 205 as shown in FIG. 2. The examiner notes that LI and Ali are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate the training data comprises the plurality of data records and a first set of real data records as taught by Ali [0050] so that the generator can configure itself and adjusts its output to improve the generated fake data output to appear more like the real data [0034]).
Furthermore, KUO teaches providing second training data to the first discriminator prior to sending the plurality of data records to the first discriminator and the second discriminator, wherein the second training data comprises a second set of real data records, a second plurality of data records and a label for each record of the plurality of second data records ([0044] An RNN-T based SLU model can be created in two steps: by constructing an ASR model and then adapting it to an SLU model through transfer learning. In the first step, the model is pre-trained on large amounts of general purpose ASR data to allow the model to effectively learn how to transcribe speech into text. Given that the targets in the pre-training step are only graphemic/phonetic tokens, prior to the model being adapted using SLU data, semantic labels are added as additional output targets. The examiner notes that KUO teaches pre-training an ASR model on ASR data different than the SLU data that will be used to adapt the ASR model. KUO also teaches that the training data will also include labels as additional output targets. The examiner also notes that LI and KUO are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate providing second training data to the first discriminator prior to sending the plurality of data records to the first discriminator and the second discriminator, wherein the second training data comprises a second set of real data records, a second plurality of data records and a label for each record of the plurality of second data records as taught by KUO [0044] to allow the model to effectively learn how to transcribe speech into text [0044]).
Claim 12 is rejected based upon the same rationale as the rejection of claim 3 since it’s the computing device claim corresponding to the method claim.
Claims 5, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over LI (US20220084204A1), in view of SON (US20190188882A1), in view of Nguyen (Ensemble Selection based on Classifier Prediction Confidence – 2020), in view of Kearney (US20220180447A)1, in view of MUKHOPADHYAY (US20230367995A1).
Regarding claim 5, LI teaches The computer-implemented method of claim 1. However, LI is not relied upon to explicitly teach after training the second discriminator, generating a new GAN model comprising the generator and the second discriminator. On the other hand, MUKHOPADHYAY teaches after training the second discriminator, generating a new GAN model comprising the generator and the second discriminator ([0125] To this end, the system can train a GAN on only the natural images of the CIFAR10 dataset. The system can then transfer both the generator and discriminator weights to a new GAN. The examiner notes that LI and MUKHOPADHYAY are both directed towards machine learning and are seen as reasonably pertinent analogous art. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified LI’s model learning method to incorporate after training the second discriminator, generating a new GAN model comprising the generator and the second discriminator as taught by MUKHOPADHYAY [0125] in order to test the impact features have to the entire adversarial process [0125].
Claim 14 is rejected based upon the same rationale as the rejection of claim 5 since it’s the computing device claim corresponding to the method claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
LERER - US 2019/0156149 Al
“LERER teaches a discrimination system based on unsupervised machining learning to predict a plausibility of objects' behaviors between a starting and ending time point”
Cruz (Dynamic classifier selection Recent advances and perspectives)
“Cruz teaches an updated taxonomy based on the main characteristics found in a dynamic selection system”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAMCY ALGHAZZY whose telephone number is (571)272-8824. The examiner can normally be reached on M-F 7:30am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, OMAR FERNANDEZ RIVAS can be reached on (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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