DETAILED ACTION
This action is in response to the Applicant Response filed 18 February 2026 for application 17/967,650 filed 17 October 2022.
Claim(s) 1, 6, 11, 15 is/are currently amended.
Claim(s) 4-5, 9-10, 14, 17-20 is/are cancelled.
Claim(s) 1-3, 6-8, 11-13, 15-16, 21-22 is/are pending.
Claim(s) 1-3, 6-8, 11-13, 15-16, 21-22 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 18 February 2026 has been entered.
Examiner’s Note
Claim 13 recites similar language to that included in claim 11. Further, claim 13 recites claim language that was canceled in analogous claims based on the amendments to the independent claims. The lack of cancelation of claim 13, in light of the cancelation of analogous claims, appears to be an oversight, and Examiner is bringing this to applicant’s attention for the purposes of consistency.
Response to Arguments
Applicant's arguments regarding the 35 U.S.C. 101 rejection of claims 1-3, 6-8, 11-13, 15-16, 21-22 have been fully considered but are not persuasive. During the interview Examiner noted that rolling up claims 4-5 into the dependent claim would not overcome the 101 rejection. While the parties discussed potential approaches to overcome the 101 rejection, no language was agreed upon that would overcome the 101. The remainder of applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on newly amended subject matter which is addressed in the 35 U.S.C. 101 rejection of the claims below or discuss limitations and/or features not recited in the claims. Therefore the 35 U.S.C. 101 rejection of claims 1-3, 6-8, 11-13, 15-16, 21-22 is maintained.
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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-3, 6-8, 11-13, 15-16, 21-22 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) transfer model training method.
The limitation of obtaining a plurality of data segments of each dimension of a plurality of dimensions of data in to-be-processed data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data, and wherein the to-be-processed data comprises unlabeled data from a target domain and labeled data from a source 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 obtaining a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension, 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 obtaining a second structure feature between data segments of different dimensions in the plurality of data segments, wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions, 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) – transfer 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 transfer model based on the plurality of data segments, to obtain a trained transfer model; training the transfer model based on the first structure feature; training the transfer model based on the second structure feature which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
transfer 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 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) transfer model training method. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the to-be-processed data includes to-be-processed time series data, wherein the plurality of data segments comprise a plurality of start time points, and the plurality of start time points are determined based on a start moment and an end moment of the to-be-processed time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) transfer model training method. The Step 2A Prong One Analysis for claim 2 is applicable here since claim 3 carries out the method of claim 2 but for the recitation of additional element(s) of wherein when the plurality of start time points are determined based on the start moment and the end moment of to-be-processed time series data: the plurality of start time points comprise all moments from the start moment to the end moment; and an end time point of a data segment of the plurality of data segments is the end moment of the to-be-processed time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does 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) fault detection method.
The limitation of obtaining a plurality of data segments of each dimension of a plurality of dimensions of data in fault detection data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data, the fault detection data comprises unlabeled fault detection data from a target domain and labeled fault detection data from a source domain, and the labeled fault detection data comprises a fault type label and corresponding fault detection data, 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 obtaining a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension, 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 obtaining a second structure feature between data segments of different dimensions in the plurality of data segments, wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions, 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) – fault detection 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 fault detection model based on the plurality of data segments, to obtain a trained fault detection model; training the fault detection model based on the first structure feature; training the fault detection model based on the second structure feature which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
fault detection 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 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) fault detection method. The Step 2A Prong One Analysis for claim 6 is applicable here since claim 7 carries out the method of claim 6 but for the recitation of additional element(s) of wherein the fault detection data includes fault detection time series data, wherein the plurality of data segments comprise a plurality of start time points, and wherein the plurality of start time points are determined based on a start moment and an end moment of the fault detection time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does 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) fault detection method. 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 that the plurality of start time points are determined based on the start moment and the end moment of the fault detection time series data comprises: the plurality of start time points comprise all moments from the start moment to the end moment; and an end time point of a data segment of the plurality of data segments is the end moment of the fault detection time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) transfer model training apparatus.
The limitation of obtain a plurality of data segments of each dimension of a plurality of dimensions of data in to-be-processed data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data, and wherein the to-be-processed data comprises unlabeled data from a target domain and labeled data from a source 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 obtain a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension, 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 obtain a second structure feature between data segments of different dimensions in the plurality of data segments, wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions, 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) – apparatus, memory, executable instructions, processor. 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) – transfer 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 train a transfer model based on the plurality of data segments, to obtain a trained transfer model; train the transfer model based on the first structure feature; train the transfer model based on the second structure feature which is simply generic training to perform the abstract idea of model 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:
apparatus, memory, executable instructions, processor 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))
transfer 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 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) transfer model training apparatus. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 12 carries out the apparatus of claim 11 but for the recitation of additional element(s) of wherein the to-be-processed data includes to-be-processed time series data, wherein the plurality of data segments comprise a plurality of start time points, and the plurality of start time points are determined based on a start moment and an end moment of the to-be-processed time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does 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 an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) transfer model training apparatus.
The limitation of obtain a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on a dependency between the data segments of the same dimension, 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 train the transfer model based on the first structure feature which is simply generic training to perform the abstract idea of model generation and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
generic training to perform the abstract idea amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f))
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 an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) fault detection apparatus.
The limitation of obtain a plurality of data segments of each dimension of a plurality of dimensions of data in fault detection data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data, the fault detection data comprises unlabeled fault detection data from a target domain and labeled fault detection data from a source domain, and the labeled fault detection data comprises a fault type label and corresponding fault detection data, 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 obtain a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension, 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 obtain a second structure feature between data segments of different dimensions in the plurality of data segments, wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions, 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) – apparatus, memory, executable instructions, processor. 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) – fault detection 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 train a fault detection model based on the plurality of data segments, to obtain a trained fault detection model; train the fault detection model based on the first structure feature; train the fault detection model based on the second structure feature which is simply generic training to perform the abstract idea of model 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:
apparatus, memory, executable instructions, processor 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))
fault detection 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 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 an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) fault detection apparatus. The Step 2A Prong One Analysis for claim 15 is applicable here since claim 16 carries out the apparatus of claim 15 but for the recitation of additional element(s) of wherein the fault detection data includes fault detection time series data, wherein the plurality of data segments comprise a plurality of start time points, and wherein the plurality of start time points are determined based on a start moment and an end moment of the fault detection time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 21, 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 21 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) transfer model training apparatus. The Step 2A Prong One Analysis for claim 12 is applicable here since claim 21 carries out the apparatus of claim 12 but for the recitation of additional element(s) of wherein when the plurality of start time points are determined based on the start moment and the end moment of to-be-processed time series data: the plurality of start time points comprise all moments from the start moment to the end moment; and an end time point of a data segment of the plurality of data segments is the end moment of the to-be-processed time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 22, 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 22 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) fault detection apparatus. The Step 2A Prong One Analysis for claim 16 is applicable here since claim 22 carries out the apparatus of claim 16 but for the recitation of additional element(s) of wherein when the plurality of start time points are determined based on the start moment and the end moment of the fault detection time series data comprises: the plurality of start time points comprise all moments from the start moment to the end moment; and an end time point of a data segment of the plurality of data segments is the end moment of the fault detection time series data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does 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) 1-3, 6-8, 11-13, 15-16, 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over da Costa et al., (Remaining Useful Lifetime Prediction via Deep Domain Adaptation, hereinafter referred to as “Costa”) in view of Wang et al. (Transferrable Attention for Domain Adaptation, hereinafter referred to as “Wang”) and further in view of Geertzen et al. (A Multidimensional to Utterance Segmentation and Dialogue Act Classification, hereinafter referred to as “Geertzen”).
Regarding claim 1 (Currently Amended), Costa teaches a transfer model training method (Costa, section 1 – teaches unsupervised domain adaptation), comprising:
obtaining a plurality of data segments of each dimension of a plurality of dimensions of data in to-be-processed data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same (Costa, sections 3.1-3.2 – teaches obtaining different time section lengths for each dimension) …, and wherein the to-be-processed data comprises unlabeled data from a target domain and labeled data from a source domain (Costa, section 3.1 – teaches labeled source domain data and unlabeled target domain data, wherein the data has a plurality of features [dimensions]); and
training a transfer model based on the plurality of data segments, to obtain a trained transfer model (Costa, section 5 – teaches training the model based on the data segments).
While Costa teaches different data segments for the dimensions, Costa does not explicitly teach wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data. Further, Costa does not explicitly teach wherein the training further comprises at least one of: obtaining a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension ;and training the transfer model based on the first structure feature; or obtaining a second structure feature between data segments of different dimensions in the plurality of data segments, wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions; and training the transfer model based on the second structure feature.
Wang teaches wherein the training further comprises at least one of:
obtaining a first structure feature between data segments of a same dimension in the plurality of data segments (Wang, p. 5347, Transferable Local Attention – teaches determining dependency between the same regions of the source and target domains [regions is interpreted to include data segments]), wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension (Wang, p. 5348, Transferable Local Attention – teaches transferability [impact weight] for each region to quantify the correlation of segments for the region); and
training the transfer model based on the first structure feature (Wang, p. 5349 – teaches training the model based on the local attention [first structure] and global attention [second structure] using backpropagation); or
obtaining a second structure feature between data segments of different dimensions in the plurality of data segments (Wang, p. 5348, Transferable Global Attention – teaches determining dependency between different regions of the data [regions is interpreted to include data segments]), wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions (Wang, p. 5348, Transferable Global Attention – teaches transferability [impact weight] for all regions to quantify the correlation of segments for the combination of different regions); and
training the transfer model based on the second structure feature (Wang, p. 5349 – teaches training the model based on the local attention [first structure] and global attention [second structure] using backpropagation).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Costa with the teachings of Wang in order to improve domain adaptation by analyzing the transferability of regions of a datum as well as the data as a whole in the field of domain adaptation (Wang, Conclusion – “This paper presented Transferable Attention for Domain Adaptation (TADA), a novel multi-adversarial domain adaptation approach with both global and local attention mechanism. Unlike previous adversarial domain adaptation methods that only match the feature extracted from the entire images across domains, the proposed approach further exploits the complex multimodal structures by considering the transferability of different regions or images. Our approach studies two types of complementary transferable attention: local attention generated by multiple region-level domain discriminators to highlight transferable regions, and global attention generated by single image-level domain discriminator to highlight transferable images. Comprehensive experiments show that the proposed approach outperforms state of the art results on standard domain adaptation datasets.”).
While Costa in view of Wang teaches different data segments for the dimensions, Costa in view of Wang does not explicitly teach wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data.
Geertzen teaches obtaining a plurality of data segments of each dimension of a plurality of dimensions of data in to-be-processed data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data (Geertzen, section 2 – teaches segmenting each dimension separately; Geertzen, section 3- teaches segmenting each dimension separately with different segment sizes) …
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Costain view of Wang with the teachings of Geertzen in order to improve classification using separate segmentation for each dimension of data in the field of multi-dimensional model training (Geertzen, Abstract – “In this paper we present a multidimensional approach to utterance segmentation and automatic dialogue act classification. We show that the use of multiple dimensions in distinguishing and annotating units not only supports a more accurate analysis of human communication, but can also help to solve some notorious problems concerning the segmentation of dialogue into functional units. We introduce the use of per-dimension segmentation for dialogue act taxonomies that feature multi-functionality and show that better classification results are obtained when using a separate segmentation for each dimension than when using one segmentation that fits all dimensions. Three machine learning techniques are applied and compared on the task of automatic classification of multiple communicative functions of utterances. The results are encouraging and indicate that communicative functions in important dimensions are easy machine-learnable.”).
Regarding claim 2 (Previously Presented), Costa in view of Wang and further in view of Geertzen teaches all of the limitations of the method of claim 1 as noted above. Costa further teaches wherein the to-be-processed data includes to-be-processed time series data (Costa, sections 3.1-3.2 – teaches time series data), wherein the plurality of data segments comprise a plurality of start time points (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences), and the plurality of start time points are determined based on a start moment and an end moment of the to-be-processed time series data (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences).
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 Costa, Wang and Geertzen for the same reasons as disclosed in claim 1 above.
Regarding claim 3 (Previously Presented), Costa in view of Wang and further in view of Geertzen teaches all of the limitations of the method of claim 2 as noted above. Costa further teaches wherein when the plurality of start time points are determined based on the start moment and the end moment of to-be-processed time series data:
the plurality of start time points comprise all moments from the start moment to the end moment (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences); and
an end time point of a data segment of the plurality of data segments is the end moment of the to-be-processed time series data (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences).
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 Costa, Wang and Geertzen for the same reasons as disclosed in claim 2 above.
Regarding claim 6 (Currently Amended), Costa teaches a fault detection method (Costa, section 1 – teaches unsupervised domain adaptation for fault detection), comprising:
obtaining a plurality of data segments of each dimension of a plurality of dimensions of data in fault detection data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same (Costa, sections 3.1-3.2 – teaches obtaining different time section lengths for each dimension) …, the fault detection data comprises unlabeled fault detection data from a target domain and labeled fault detection data from a source domain (Costa, section 3.1 – teaches labeled source domain data and unlabeled target domain data for determining end of life [fault detection]), and the labeled fault detection data comprises a fault type label and corresponding fault detection data (Costa, section 3.1 – teaches data examples with fault detection data and a remaining useful lifetime value label); and
training a fault detection model based on the plurality of data segments, to obtain a trained fault detection model (Costa, section 5 – teaches training the fault detection model based on the data segments).
While Costa teaches different data segments for the dimensions, Costa does not explicitly teach wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data. Further, Costa does not explicitly teach wherein the training further comprises at least one of: obtaining a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension; and training the fault detection model based on the first structure feature; or obtaining a second structure feature between data segments of different dimensions in the plurality of data segments, wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions; and training the fault detection model based on the second structure feature.
Wang teaches wherein the training further comprises at least one of:
obtaining a first structure feature between data segments of a same dimension in the plurality of data segments (Wang, p. 5347, Transferable Local Attention – teaches determining dependency between the same regions of the source and target domains [regions is interpreted to include data segments]), wherein the first structure feature is determined based on an impact weight quantifying a correlation between the data segments of the same dimension (Wang, p. 5348, Transferable Local Attention – teaches transferability [impact weight] for each region to quantify the correlation of segments for the region); and
training the fault detection model based on the first structure feature (Wang, p. 5349 – teaches training the model based on the local attention [first structure] and global attention [second structure] using backpropagation); or
obtaining a second structure feature between data segments of different dimensions in the plurality of data segments (Wang, p. 5348, Transferable Global Attention – teaches determining dependency between different regions of the data [regions is interpreted to include data segments]), wherein the second structure feature is determined based on an impact weight quantifying a correlation between the data segments of the different dimensions (Wang, p. 5348, Transferable Global Attention – teaches transferability [impact weight] for all regions to quantify the correlation of segments for the combination of different regions); and
training the fault detection model based on the second structure feature (Wang, p. 5349 – teaches training the model based on the local attention [first structure] and global attention [second structure] using backpropagation).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Costa with the teachings of Wang in order to improve domain adaptation by analyzing the transferability of regions of a datum as well as the data as a whole in the field of domain adaptation (Wang, Conclusion – “This paper presented Transferable Attention for Domain Adaptation (TADA), a novel multi-adversarial domain adaptation approach with both global and local attention mechanism. Unlike previous adversarial domain adaptation methods that only match the feature extracted from the entire images across domains, the proposed approach further exploits the complex multimodal structures by considering the transferability of different regions or images. Our approach studies two types of complementary transferable attention: local attention generated by multiple region-level domain discriminators to highlight transferable regions, and global attention generated by single image-level domain discriminator to highlight transferable images. Comprehensive experiments show that the proposed approach outperforms state of the art results on standard domain adaptation datasets.”).
While Costa in view of Wang teaches different data segments for the dimensions, Costa in view of Wang does not explicitly teach wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data.
Geertzen teaches obtaining a plurality of data segments of each dimension of a plurality of dimensions of data in fault detection data, wherein the plurality of data segments for each one of the plurality of dimensions of data are not the same as the plurality of data segments for any other one of the plurality of dimensions of data (Geertzen, section 2 – teaches segmenting each dimension separately; Geertzen, section 3- teaches segmenting each dimension separately with different segment sizes) ...
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Costain view of Wang with the teachings of Geertzen in order to improve classification using separate segmentation for each dimension of data in the field of multi-dimensional model training (Geertzen, Abstract – “In this paper we present a multidimensional approach to utterance segmentation and automatic dialogue act classification. We show that the use of multiple dimensions in distinguishing and annotating units not only supports a more accurate analysis of human communication, but can also help to solve some notorious problems concerning the segmentation of dialogue into functional units. We introduce the use of per-dimension segmentation for dialogue act taxonomies that feature multi-functionality and show that better classification results are obtained when using a separate segmentation for each dimension than when using one segmentation that fits all dimensions. Three machine learning techniques are applied and compared on the task of automatic classification of multiple communicative functions of utterances. The results are encouraging and indicate that communicative functions in important dimensions are easy machine-learnable.”).
Regarding claim 7 (Previously Presented), Costa in view of Wang and further in view of Geertzen teaches all of the limitations of the method of claim 6 as noted above. Cost further teaches wherein the fault detection data includes fault detection time series data (Costa, sections 3.1-3.2 – teaches remaining useful lifetime [fault detection] time series data), wherein the plurality of data segments comprise a plurality of start time points (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences), and wherein the plurality of start time points are determined based on a start moment and an end moment of the fault detection time series data (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences).
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 Costa, Wang and Geertzen for the same reasons as disclosed in claim 6 above.
Regarding claim 8 (Previously Presented), Costa in view of Wang and further in view of Geertzen teaches all of the limitations of the method of claim 7 as noted above. Costa further teaches wherein that the plurality of start time points are determined based on the start moment and the end moment of the fault detection time series data comprises:
the plurality of start time points comprise all moments from the start moment to the end moment (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences); and
an end time point of a data segment of the plurality of data segments is the end moment of the fault detection time series data (Costa, section 3.2 – teaches a plurality of sequence lengths of the time series data and windowing the sequences).
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 Costa, Wang and Geertzen for the same reasons as disclosed in claim 7 above.
Regarding claim 11 (Currently Amended), it is the apparatus embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Costa further teaches a transfer model training apparatus (Costa, section 1 – teaches unsupervised domain adaptation), comprising:
a memory, configured to store executable instructions (Costa, section 6 - teaches computing system with processor and memory); and
a processor, configured to call and execute the executable instructions in the memory, to perform operations of (Costa, section 6 - teaches computing system with processor and memory) …
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 Costa, Wang and Geertzen for the same reasons as disclosed in claim 1 above.
Regarding claim 12 (Previously Presented), the rejection of claim 11 is incorporated herein. Further, the limitations in this claim are taught by Costa in view of Wang and further in view of Geertzen for the reasons set forth in the rejection of claim 2.
Regarding claim 13 (Previously Presented), Costa in view of Wang and further in view of Geertzen teaches all of the limitations of the apparatus of claim 11 as noted above. Wang further teaches wherein, to train the transfer model based on the plurality of data segments, the processor is further to:
obtain a first structure feature between data segments of a same dimension in the plurality of data segments, wherein the first structure feature is determined based on a dependency between the data segments of the same dimension (Wang, p. 5347, Transferable Local Attention – teaches determining dependency between the same regions of the source and target domains [regions is interpreted to include data segments]); and
train the transfer model based on the first structure feature (Wang, p. 5349 – teaches training the model based on the local attention [first structure] and global attention [second structure] using backpropagation).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Costa, Wang and Geertzen in order to train the transfer model to improve domain adaptation by analyzing the transferability of regions of a datum as well as the data as a whole (Wang, Conclusion).
Regarding claim 15 (Currently Amended), it is the apparatus embodiment of claim 6 with similar limitations to claim 6 and is rejected using the same reasoning found in claim 6. Costa further teaches a fault detection apparatus, comprising:
a memory, configured to store executable instructions (Costa, section 6 - teaches computing system with processor and memory); and
a processor, configured to call and execute the executable instructions in the memory, to perform operations of (Costa, section 6 - teaches computing system with processor and memory) …
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 Costa, Wang and Geertzen for the same reasons as disclosed in claim 6 above.
Regarding claim 16 (Previously Presented), the rejection of claim 15 is incorporated herein. Further, the limitations in this claim are taught by Costa in view of Wang and further in view of Geertzen for the reasons set forth in the rejection of claim 7.
Regarding claim 21 (Previously Presented), the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Costa in view of Wang and further in view of Geertzen for the reasons set forth in the rejection of claim 3.
Regarding claim 22 (Previously Presented), the rejection of claim 16 is incorporated herein. Further, the limitations in this claim are taught by Costa in view of Wang and further in view of Geertzen for the reasons set forth in the rejection of claim 8.
Conclusion
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
