DETAILED ACTION
This action is regarding amendment filed 07/28/2025 application number 17/676,944. Claim 10 has been canceled, claims 1, 11 and 12 have been amended and claim 21 has been added. Claims 1-9 and 11-21 have been examined and are pending.
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 11-21 rejected under 35 U.S.C. 101 because they claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites sampling combinations of groups of sensors and operational modes which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user extracting a set from a population of options. See
2106.04.(a)(2).III.C.
The claim recites determining a best combination model based on performance measured during training which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a person deciding to use a model. See 2106.04.(a)(2).III.C.
The claim recites fine-tuning the best combination model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing parameters. See 2106.04.(a)(2).III.C.
The claim recites detecting an anomaly which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user determining if something is correct or violates a pattern. See 2106.04.(a)(2).III.C.
The claim recites performing a corrective action responsive to the anomaly that is selected from the group which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing an action to perform. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
computer-implemented method for training a neural network (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer"(see MPEP 2106.05(f)))
training a plurality of models(Training recited at a high level as merely a step and is Insignificant Extra-Solution Activity (see MPEP §2106.05(g)))
for respective groups of sensors in a cyber-physical system(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
training a combination model for each of the sampled combinations(Training recited at a high level as merely a step and is Insignificant Extra-Solution Activity (see MPEP §2106.05(g)))
using the fine-tuned best combination model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
consisting of changing a security setting for an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface's status or settings.(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (b) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation recites a well understood and conventional practice of training a neural network as quoted from Text, Speech, and Dialogue (Page 37, Paragraph 2,“After pre-training, the network has to be trained further using some conventional training method like backpropagation”)
Additional elements (c) and (f) do not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h));
The additional element(s) (a) (b) (c) (e) and (f) in claim 1 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 2:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites model merging of the plurality of models which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a person combining data into 1 dataset. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
training the combination model (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in claim 2 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 3:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein model merging of the plurality of models includes concatenating models of the plurality of models using a fully connected layer which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites model merging of the plurality of models includes initializing weights of a merged model with weight values of the plurality of models which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing weights . See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5:
The rejection of claim 2 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein each of the plurality of models includes a long- short term memory autoencoder mode(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in claim 5 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 6:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
training the combination model includes model decomposition of the plurality of models(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in claim 6 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 7:
The rejection of claim 6 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
decomposition of the plurality of models includes combining outputs of models of the plurality of model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in claim 7 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 8:
The rejection of claim 6 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the plurality of models are represented as long- short term memory auto-encoders connected with a projection layer in a source model.(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in claim 8 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 9:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the operational modes each correspond to a different operational mode of the cyber-physical system(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in claim 9 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites sampling combinations of groups of sensors and operational modes which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user extracting a set from a population of options. See
2106.04.(a)(2).III.C.
The claim recites determining a best combination model based on performance measured during training which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a person deciding to use a model. See 2106.04.(a)(2).III.C.
The claim recites fine-tuning the best combination model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing parameters. See 2106.04.(a)(2).III.C.
The claim recites detect an anomaly which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user determining if something is correct or violates a pattern. See 2106.04.(a)(2).III.C.
The claim recites performing a corrective action responsive to the anomaly that is selected from the group which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing an action to perform. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
computer-implemented method for training a neural network (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer"(see MPEP 2106.05(f)))
training a plurality of models(Training recited at a high level as merely a step and is Insignificant Extra-Solution Activity (see MPEP §2106.05(g)))
for respective groups of sensors in a cyber-physical system(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
training a combination model for each of the sampled combinations(Training recited at a high level as merely a step and is Insignificant Extra-Solution Activity (see MPEP §2106.05(g)))
using the fine-tuned best combination model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
consisting of changing a security setting for an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface's status or settings.(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
each operational mode corresponding to a different operational mode of the cyber-physical system (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
training a combination model for each of the sampled combinations using one of model merging and model decomposition(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (e) and (h) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (b) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation recites a well understood and conventional practice of training a neural network as quoted from Text, Speech, and Dialogue (Page 37, Paragraph 2,“After pre-training, the network has to be trained further using some conventional training method like backpropagation”)
Additional elements (c) (f) and (g) do not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h));
The additional element(s) (a) (b) (c) (d) (e) (f) (g) and (h) in claim 11 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites sample combinations of groups of sensors and operational modes which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user extracting a set from a population of options. See 2106.04.(a)(2).III.C.
The claim recites determine a best combination model based on performance measured during training which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a person deciding to use a model. See 2106.04.(a)(2).III.C.
The claim recites fine-tune the best combination model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing parameters. See 2106.04.(a)(2).III.C.
The claim recites detect an anomaly which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user determining if something is correct or violates a pattern. See 2106.04.(a)(2).III.C.
The claim recites performing a corrective action responsive to the anomaly that is selected from the group which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user choosing an action to perform. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
system for training a neural network(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
hardware processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
a memory that includes a computer program, which, when executed by the hardware processor, causes the hardware processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
train a plurality of models (Training recited at a high level as merely a step and is Insignificant Extra-Solution Activity (see MPEP §2106.05(g)))
for respective groups of sensors in a cyber-physical system; (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
train a combination model for each of the sampled combinations; (Training recited at a high level as merely a step and is Insignificant Extra-Solution Activity (see MPEP §2106.05(g)))
using the fine-tuned best combination model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
consisting of changing a security setting for an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface's status or settings.(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (b) (c) and (g) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (d) and (f) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation recites a well understood and conventional practice of training a neural network as quoted from Text, Speech, and Dialogue (Page 37, Paragraph 2,“After pre-training, the network has to be trained further using some conventional training method like backpropagation”)
Additional elements (e) and (h) do not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h));
The additional element(s) (a) (b) (c) (d) (e) (f) (g) and (h) in claim 12 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 13:
The rejection of claim 12 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites model merging of the plurality of models which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a person combining data into 1 dataset. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
computer program further causes the hardware processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
training the combination model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in claim 13 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 14:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites concatenate models of the plurality of models using a fully connected layer which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the computer program further causes the hardware processor to(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in claim 14 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 15:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
computer program further causes the hardware processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
wherein the computer program further causes the hardware processor to initialize weights of a merged model with weight values of the plurality of models(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in claim 15 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 16:
The rejection of claim 13 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein each of the plurality of models includes a long-short term memory autoencoder model(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h));
The additional element(s) (a) in claim 16 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 17:
The rejection of claim 12 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
computer program further causes the hardware processor(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
train the combination model using model decomposition of the plurality of models(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) and (b) in claim 17 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 18:
The rejection of claim 17 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
decomposition of the plurality of models includes combining outputs of models of the plurality of models(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in claim 18 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 19:
The rejection of claim 17 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the plurality of models are represented as long-short term memory auto-encoders connected with a projection layer in a source model(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h));
The additional element(s) (a) in claim 19 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 20:
The rejection of claim 12 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the operational modes each correspond to a different operational mode of the cyber-physical system(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h));
The additional element(s) (a) in claim 20 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 21:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the cyber-physical system is a system that combines a physical system with an electronic and/or software system and wherein the sensors monitor information about states of the cyber-physical system (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h));
The additional element(s) (a) in claim 21 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. 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) 1-9 and 11-21 is /are rejected under 35 U.S.C. 103 as being unpatentable over Niculescu-Mizil et al.(US20190197236, henceforth known as Niculescu) in view of Tung Kieu et al.(“ Outlier Detection for Time Series with Recurrent Autoencoder Ensembles”, henceforth known as Kieu) and further in view of Tian Cong et al.(“Anomaly Detection and Mode Identification in Multimode Processes Using the Field Kalman Filter”, henceforth known as Cong)
Regarding claim 1:
Niculescu discloses a computer-implemented method for training a neural network Niculescu, FIG. 3 where the FIG. 3 showing a processer and block diagram for the anomalous detection that is trained is considered a computer implemented method for training a neural network.
Niculescu discloses detecting an anomaly using the fine-tuned best combination model([0015] “… If the actual behavior of the monitored system 102 deviates from the predicted behavior by more than a threshold amount, the anomaly detection system 106 identifies the behavior as being anomalous.”)
Niculescu discloses performing a corrective action responsive to the anomaly that is selected from the group consisting of changing a security setting for an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface's status or settings([0016] “Once anomalous behavior has been detected, the anomaly detection system 106 communicates with a system control unit 108 to alter one or more parameters of the monitored system 102 to correct the anomalous behavior. Exemplary corrective actions include changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component (for example, an operating speed), halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, changing a network interface's status or settings, etc. The anomaly detection system 106 thereby automatically corrects or mitigates the anomalous behavior.”)
Niculescu discloses training … models for respective groups of sensors in a cyber-physical system(Niculescu, [0021] “The autoencoder model is then trained… using a loss function that reflects the difference between the input sensor measurements and the reconstructed sensor measurements”)
Niculescu does not explicitly discloses sampling combinations of groups of sensors and operational modes, training…for each of the sampled combinations, training a plurality of models training a combination model, determining a best combination model based on performance measured during training or fine-tuning the best combination model
Cong discloses sampling combinations of groups of sensors and operational modes(Cong, Page 9 , Col. 1, Paragraph 4, “After the data labeling step, the training data set contains samples 1, 2, . . . , 500 from Modes 1, 2, and 3, and the validation data set contains samples 501, 502, . . . , 1000 from Modes 1, 2, and 3” where the samples 1-500 and modes 1-3 are considered a group of sensors (as the data is received from monitoring systems) and operational modes(See also Page 1, Col 2, Paragraph 2: “The residuals generated from a bank of Kalman filters … have been used across a wide range of applications, such as differentiating various sensors and actuators in aircraft engines”) and training … for each of the sampled combinations(Cong, Abstract, “a method is proposed for off-line training an FKF monitoring model and online monitoring. The off-line part comprises training an FKF model based on multivariate autoregressive state-space (MARSS) models fitted to historical process data.” )
References Niculescu and Cong are analogous art because they are from the same field of endeavor of using machine learning for outlier detection.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Niculescu and Cong before him or her, to modify the sampling of a sensor of Niculescu to include the sampling of groups of sensors and operational modes of Cong. The motivation for the combination would be “The fundamental goal of anomaly detection and mode identification is to differentiate various known operating modes and to identify anomalies in a timely way to reduce or avoid downtime incidents”(Cong, Page 1, Col 1., Paragraph 1)
Niculescu does not explicitly discloses training a plurality of models training a combination model, determining a best combination model based on performance measured during training or fine-tuning the best combination model
Kieu discloses training a plurality of models…(Kieu, Page 2727 col. 2 Paragraph 3, “The ensemble contains N S-RNN autoencoders”)
Kieu discloses training a combination model… (Kieu, Abstract, “The two solutions are ensemble frameworks, specifically an independent framework and a shared framework, both of which combine multiple S-RNN based autoencoders to enable outlier detection” where combining multiple S-RNN based encoders is considered to be a combination model that is trained)
Kieu discloses determining a best combination model based on performance measured during training and fine-tuning the best combination model(Kieu, Page 2729, col. 2 Paragraph 4, “For all deep learning based methods, we use Adadelta…as the optimizer, and we set their learning rates to 10-3” where the adjustment of learning rates and use of an optimizer is considered fine-tuning a model and determining a best model based on performance measured during training as an optimizer adjusts the networks internal parameters to minimize errors and improves performance during training.
References Niculescu and Kieu are analogous art because they are from the same field of endeavor of using machine learning for outlier detection.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Niculescu and Kieu before him or her, to modify the training of models of detecting anomalies of Niculescu to include the training and fine tuning of multiple models to determine a bester combination of Kieu as Kieu states “To perform outlier detection in sequential data such as time series, autoencoders based on recurrent neural networks are proposed while reusing the idea that large reconstruction errors indicate outliers”(Kieu, Page 2726, col. 1 Paragraph 8)
Regarding claim 2:
The rejection of claim 1 with is incorporated and further:
Kieu discloses wherein training the combination model includes model merging of the plurality of models(Kieu, Page 2728, Figure 5, where the shared framework combining the output of multiple models of LSTMs is considered merging of model)
Regarding claim 3:
The rejection of claim 2 with is incorporated and further:
Kieu discloses wherein model merging of the plurality of models includes concatenating models of the plurality of models using a fully connected layer(Kieu, Page 2728, Figure 5, and Col. 2, Paragraph 1“The shared framework, shown in Figure 5, uses a shared layer, denoted as hC(E), to concatenate the linear combination (using linear weight matrices W(Ei)) of all the last hidden states of all the encoders” where the hC(E) is considered a fully connected layer.)
Regarding claim 4:
The rejection of claim 2 with is incorporated and further:
Kieu discloses wherein model merging of the plurality of models includes initializing weights of a merged model with weight values of the plurality of models(Kieu, Page 2728, Col. 2, Paragraph 1, “The shared framework, shown in Figure 5, uses a shared layer, denoted as hC(E), to concatenate the linear combination (using linear weight matrices W(Ei) )) of all the last hidden states of all the encoders” where the use of weight matrices at the last hidden state of the encoders are considered using weight values of the plurality of models to initialize weights of a merged model)
Regarding claim 5:
The rejection of claim 2 with is incorporated and further:
Kieu discloses wherein each of the plurality of models includes a long-short term memory autoencoder model(Kieu, Page 2729, Col. 1, Paragraph 4, “For the proposed ensemble frameworks, we use an LSTM unit … set the number of hidden LSTM units to 8;”)
Regarding claim 6:
The rejection of claim 1 with is incorporated and further:
Kieu discloses wherein training the combination model includes model decomposition of the plurality of models(Kieu, Page 2728, Figure 5, where the shared framework combining the output is considered including model decomposition of plurality of models during training and where decomposition is understood to be explained as combining outputs via specification, [0027], “In model decomposition, a neural network model has components for the sensor groups and a component to combine outputs from the sensor group components”)
Regarding claim 7:
The rejection of claim 6 with is incorporated and further:
Kieu discloses wherein decomposition of the plurality of models includes combining outputs of models of the plurality of models(Kieu, Page 2728, Figure 5, where the shared framework combining the output is considered combining the outputs of multiple models)
Regarding claim 8:
The rejection of claim 6 with is incorporated and further:
Kieu discloses wherein the plurality of models are represented as long-short term memory auto-encoders connected with a projection layer in a source model. Kieu, Page 2728, Figure 5 and Col. 1, Paragraph 4, “The shared framework, shown in Figure 5, uses a shared layer, denoted as hC(E), to concatenate the linear combination (using linear weight matrices W(Ei)) of all the last hidden states of all the encoders” where the operation of linear weights on each encoder output to concatenation in hc(E ) is considered a projection layer.
Regarding claim 9:
The rejection of claim 1 with is incorporated and further:
Cong discloses wherein the operational modes each correspond to a different operational mode of the cyber-physical system(Cong, Page 9, Col. 1, Paragraph 2 “The data used in this article are from three normal operating mode data sets and one fault. Specific set points for normal operating modes are summarized in Table II” where each mode has different monitored data associated with an operation mode(See also Cong, Page 9, Table II)
Regarding claim 11:
Niculescu discloses detect an anomaly using the fine-tuned best combination model([0015] “… If the actual behavior of the monitored system 102 deviates from the predicted behavior by more than a threshold amount, the anomaly detection system 106 identifies the behavior as being anomalous.”)
Niculescu discloses perform a corrective action responsive to the anomaly that is selected from the group consisting of changing a security setting for an application or hardware component, halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, and changing a network interface's status or settings([0016] “Once anomalous behavior has been detected, the anomaly detection system 106 communicates with a system control unit 108 to alter one or more parameters of the monitored system 102 to correct the anomalous behavior. Exemplary corrective actions include changing a security setting for an application or hardware component, changing an operational parameter of an application or hardware component (for example, an operating speed), halting and/or restarting an application, halting and/or rebooting a hardware component, changing an environmental condition, changing a network interface's status o