DETAILED ACTION
This action is responsive to the amendment filed on 04/14/2026. Claims 1-20 are pending in the case. Claims 1, 8, and 15 are independent claims. Claims 1, 8, and 15 are amended.
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/18/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The terms with good anomalous behavior and bad anomalous behavior in claim 1, 8 and 15 is a relative term which renders the claim indefinite. The terms good and bad are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The anomalous behavior evaluation has been rendered indefinite by the use of the terms good and bad as there is no defined understanding of good and bad behavior. As dependent claims inherent deficiencies from parent claims the dependent claims 2-6, 9-13 and 16-20 are also rejected.
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 1-20 are rejected under 35 U.S.C. 101 because the invention is directed towards abstract idea(s) without significantly more.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites …generate content-irrelevant latent code comprising a first compressed version of the sampled runtime input data 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 creating a list of information based on a set of information. See 2106.04.(a)(2).III.C.
The claim recites …generate domain-irrelevant latent code comprising a second compressed version of the sampled runtime input data 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 creating a list of information based on a set of information. See 2106.04.(a)(2).III.C.
The claim recites … generate reconstructed sampled runtime input data; wherein the reconstructed sampled runtime input data comprises a reconstruction of the sampled runtime input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code 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 using multiple lists of information to create a set of information, wherein those lists of information were creating from the same source. See 2106.04.(a)(2).III.C.
The claim recites generating a reconstruction loss based at least in part on the reconstructed sampled runtime input data which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a person creating a value that based on set of information. See 2106.04.(a)(2).III.C.
The claim recites using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompasses using judgement to correlate a value to a set and performing an evaluation to form an opinion on whether anomalous information that do not fit a pattern in a set. See 2106.04.(a)(2).III.C.
The claim recites ..determine whether anomalous data candidates…associated with good anomalous behavior or bad anomalous behavior which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompasses using judgement to select whether an outcome is good or back. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
receiving…runtime input data generated by a sensor network that senses runtime states of the SUA(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
at a data collection 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)))
wherein the SUA comprises an operational component and a dynamic environment in which the operational component performs runtime tasks, wherein the dynamic environment comprises an unknown domain (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)))
wherein the runtime input data comprises content data and domain data(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)))
wherein the content data is sourced from the operational component while the operational component performs the runtime tasks(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
wherein the domain data is sourced from the dynamic environment in which the operational component performs the runtime tasks(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
wherein the data collection system comprises a neural network trained to perform an anomalous data detection task on the runtime input data(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)))
wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component(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)))
using the neural network of the data collection system to perform iterations of the anomalous data detection task(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
receiving sampled runtime input data comprising a sample of the runtime input data(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
Using a first encoder stage of the neural network 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)))
Using a second encoder stage of the neural network 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)))
Using a decoder stage of the neural network 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)))
operational component performs the runtime tasks(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
in the continuous or streaming stream of the runtime input data(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)))
using additional downstream analysis 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)))
generated by the iterations of the anomalous data detection tasks are(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) (e) (f) and (j) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) (c) (d) (g) (h) and (o) 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)).
Additional elements (i) (k) (l) (m) (n) (p) and (q) 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) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n) (o) (p) and (q) in the claim 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 generating the content-irrelevant latent code comprises identifying similarities and differences among the content-irrelevant latent code 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 creating a list of information based on a set of information where that list is based on perceived similarities and differences of the set. See 2106.04.(a)(2).III.C.
The claim recites generating the domain-irrelevant latent code comprises identifying similarities and differences among the domain-irrelevant latent code 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 creating a list of information based on a set of information where that list is based on perceived similarities and differences of the set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The additional elements recited in Claim 2 do not integrate the abstract idea into a
practical application. Specifically the claim lists the additional elements:
first encoder stage (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
second encoder stage(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 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 1 is incorporated and further claim recites further additional elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites generate content-irrelevant and domain- irrelevant (CIDI) latent code from the sampled runtime input data 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 creating a list of information based on a set of information. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The additional elements recited in Claim 3 do not integrate the abstract idea into a
practical application. Specifically the claim lists the additional elements:
the first encoder stage of the neural network 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 3 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 4:
The rejection of claim 3 is incorporated and further claim recites further additional elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the reconstruction of the sampled runtime input data is also based at least in part on the CIDI latent code 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 creating a list of information based on a set of information. 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:
Claim 4 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 to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5:
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 disentangle the content-irrelevant code from the sampled runtime input data 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 deciding and separating elements in a list. See 2106.04.(a)(2).III.C.
The claim recites disentangle the domain-irrelevant code from the sampled runtime input data 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 deciding and separating elements in a list. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The additional elements recited in Claim 5 do not integrate the abstract idea into a
practical application. Specifically the claim lists the additional elements:
wherein the neural network has been trained 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 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 5 is incorporated and further claim recites further additional elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites disentangle the content-irrelevant code from the sampled runtime input data 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 deciding and separating elements in a list. See 2106.04.(a)(2).III.C.
The claim recites disentangle the domain-irrelevant code from the sampled runtime input data 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 deciding and separating elements in a list. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The additional elements recited in Claim 6 do not integrate the abstract idea into a
practical application. Specifically the claim lists the additional elements:
adversarial content discriminator has been used to train the neural network (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)))
adversarial domain discriminator has been used to train the neural network (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 (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 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) and (b) 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 2 is incorporated and further claim recites further additional elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites identify similarities and differences among the content-irrelevant latent code 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 creating a list of information based on a set of information where that list is based on perceived similarities and differences of the set. See 2106.04.(a)(2).III.C.
The claim recites identify similarities and differences among the domain-irrelevant latent code 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 creating a list of information based on a set of information where that list is based on perceived similarities and differences of the set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The additional elements recited in Claim 7 do not integrate the abstract idea into a
practical application. Specifically the claim lists the additional elements:
the first encoder stage of the neural network has been trained (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)))
the second encoder stage of the neural network has been trained (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 (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 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) and (b) 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:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites …generate content-irrelevant latent code comprising a first compressed version of the sampled runtime input data 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 creating a list of information based on a set of information. See 2106.04.(a)(2).III.C.
The claim recites …generate domain-irrelevant latent code comprising a second compressed version of the sampled runtime input data 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 creating a list of information based on a set of information. See 2106.04.(a)(2).III.C.
The claim recites … generate reconstructed sampled runtime input data; wherein the reconstructed sampled runtime input data comprises a reconstruction of the sampled runtime input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code 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 using multiple lists of information to create a set of information, wherein those lists of information were creating from the same source. See 2106.04.(a)(2).III.C.
The claim recites generating a reconstruction loss based at least in part on the reconstructed sampled runtime input data which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a person creating a value that based on set of information. See 2106.04.(a)(2).III.C.
The claim recites using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompasses using judgement to correlate a value to a set and performing an evaluation to form an opinion on whether anomalous information that do not fit a pattern in a set. See 2106.04.(a)(2).III.C.
The claim recites ..determine whether anomalous data candidates…associated with good anomalous behavior or bad anomalous behavior which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompasses using judgement to select whether an outcome is good or back. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
receiving…runtime input data generated by a sensor network that senses runtime states of the SUA(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
at a data collection 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)))
wherein the SUA comprises an operational component and a dynamic environment in which the operational component performs runtime tasks, wherein the dynamic environment comprises an unknown domain(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)))
wherein the runtime input data comprises content data and domain data(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)))
wherein the content data is sourced from the operational component while the operational component performs the runtime tasks(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
wherein the domain data is sourced from the dynamic environment in which the operational component performs the runtime tasks(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
wherein the data collection system comprises a neural network trained to perform an anomalous data detection task on the runtime input data(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)))
wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component(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)))
using the neural network of the data collection system to perform iterations of the anomalous data detection task(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
receiving sampled runtime input data comprising a sample of the runtime input data(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
Using a first encoder stage of the neural network 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)))
Using a second encoder stage of the neural network 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)))
Using a decoder stage of the neural network 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)))
a memory and a 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)))
operational component performs the runtime tasks(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
in the continuous or streaming stream of the runtime input data(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)))
using additional downstream analysis 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)))
generated by the iterations of the anomalous data detection tasks are(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) (e) (f) and (j) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) (c) (d) (g) (h) (p) and (r) 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)).
Additional elements (i) (k) (l) (m) (n) (o) and (q) 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) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n) (o) (p) (q) and (r) in the claim 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 Claims 9-14:
Claims 9-14 are rejected under that same 101 claim analysis as claims 2-7 due to the substantially similar limitations and elements in claims 2-7 respectively.
Regarding claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites …generate content-irrelevant latent code comprising a first compressed version of the sampled runtime input data 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 creating a list of information based on a set of information. See 2106.04.(a)(2).III.C.
The claim recites …generate domain-irrelevant latent code comprising a second compressed version of the sampled runtime input data 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 creating a list of information based on a set of information. See 2106.04.(a)(2).III.C.
The claim recites … generate reconstructed sampled runtime input data; wherein the reconstructed sampled runtime input data comprises a reconstruction of the sampled runtime input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code 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 using multiple lists of information to create a set of information, wherein those lists of information were creating from the same source. See 2106.04.(a)(2).III.C.
The claim recites generating a reconstruction loss based at least in part on the reconstructed sampled runtime input data which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a person creating a value that based on set of information. See 2106.04.(a)(2).III.C.
The claim recites using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items… which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompasses using judgement to correlate a value to a set and performing an evaluation to form an opinion on whether anomalous information that do not fit a pattern in a set. See 2106.04.(a)(2).III.C.
The claim recites ..determine whether anomalous data candidates…associated with good anomalous behavior or bad anomalous behavior which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompasses using judgement to select whether an outcome is good or back. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
receiving…runtime input data generated by a sensor network that senses runtime states of the SUA(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
at a data collection 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)))
wherein the SUA comprises an operational component and a dynamic environment in which the operational component performs runtime tasks, wherein the dynamic environment comprises an unknown domain (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)))
wherein the runtime input data comprises content data and domain data(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)))
wherein the content data is sourced from the operational component while the operational component performs the runtime tasks(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
wherein the domain data is sourced from the dynamic environment in which the operational component performs the runtime tasks(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
wherein the data collection system comprises a neural network trained to perform an anomalous data detection task on the runtime input data(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)))
wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component(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)))
using the neural network of the data collection system to perform iterations of the anomalous data detection task(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
receiving sampled runtime input data comprising a sample of the runtime input data(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)))
Using a first encoder stage of the neural network 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)))
Using a second encoder stage of the neural network 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)))
Using a decoder stage of the neural network 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)))
a computer program product … a computer readable storage medium(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
operational component performs the runtime tasks(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
in the continuous or streaming stream of the runtime input data(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)))
using additional downstream analysis 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)))
generated by the iterations of the anomalous data detection tasks are(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) (e) (f) and (j) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) (c) (d) (g) (h) (p) and (r) 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)).
Additional elements (i) (k) (l) (m) (n) (o) and (q) 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) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) (n) (o)(p) (q) and (r) in the claim 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 Claims 16-20:
Claims 16-18 are rejected under that same 101 claim analysis as claims 2-4 due to the substantially similar limitations and elements in claims 2-4 respectively.
Claim 19 are rejected under that same 101 claim analysis as claims 5-6 due to the substantially similar limitations and elements in claims 5-6.
Claims 20 are rejected under that same 101 claim analysis as claim 7 due to the substantially similar limitations and elements in claim 7 respectively.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al.( “Dual Encoder-Decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning”, henceforth known as Hu) in view of Liu et al.(US20200265219A1, “DISENTANGLED REPRESENTATION LEARNING GENERATIVE ADVERSARIAL NETWORK FOR POSE-INVARIANT FACE RECOGNITION”, henceforth known as Liu) and in further view of Hyunseok et al.( US20210058424A1, “ANOMALY DETECTION FOR MICROSERVICES”, henceforth known as Hyunseok) and Prasenjeet et al.(US20220103418A1, “ANOMALY DETECTION AND FILTERING BASED ON SYSTEM LOGS”, henceforth known as Prasenjeet)
Regarding claim 1,
Hu discloses receiving, at a data collection system(Hu, Page 4, Figure 1d, where the computer is considered data collection system as it is a system that collects and distributes data and DED-GAN is a computer based system that has images input into it), runtime input data(Hu, Page 6, Col 2, Paragraph 1, “Our models were trained separately on the Multi-PIE…and CASIA…dataset” where the images and the image dataset are considered runtime input data as they are contextual data being input into the GAN at runtime) generated by a sensor network that senses runtime states of the SUA(Hu, Page 6, Col 2, Paragraph 1, “Our models were trained separately on the Multi-PIE…and CASIA…dataset” where the image datasets have images that are created by sensors that are considered a sensor network and the images sense features of the image(pose, color, saturation, identity, image/video steam, lighting etc.…) are considered runtime states)
Hu discloses wherein the runtime input data comprises content data and domain data(Hu, Page 4, Col. 2, Paragraph 2 “Given a face image x with label y = {ya, yd, yc}, where ya, yd and yc represent the labels for real/fake, identity and pose” where pose is considered content data and identity is considered domain data)
Hu discloses wherein the data collection system comprises a neural network trained to perform an anomalous data detection task on the runtime input data(Hu, Page 6, Col. 1, Paragraph 4, “The code layer of the autoencoder is followed by Da, Dc and Dd where Da(x) is for real-fake classification, Dc(x) is for pose regression and Dd is for identity prediction” where the process of the GAN classification and prediction of images as real/fake is considered to be detecting anomalous data of input data)
Hu discloses and using the neural network of the data collection system to perform iterations of the anomalous data detection task, wherein each of the iterations of the anomalous data detection task (Hu, Page 6, Col. 2, Algorithm 1, iteration t, where iteration t shows that the GAN operates in iteration and algorithm 1 prediction of images as real/fake is considered to be iterations of the anomalous data detection task)
Hu discloses receiving sampled runtime input data comprising a sample of the runtime input data(Hu, Page 4, Col 2, Paragraph 2, “…Given a face image x…” where the given face image x is considered an input data being received)
Hu discloses using a first encoder stage of a the neural network to generate content-irrelevant latent code comprising a first compressed version of the sampled runtime input data and using a second encoder stage of the neural network to generate domain-irrelevant latent code comprising a second compressed version of the sampled runtime input data(Hu, Page 4, Col 2, Paragraph 1, “The generator generates unlabelled realistic samples from the latent variable model to improve the discriminative ability of the discriminator” where the latent variable model contains pose and facial identity which are considered content-irrelevant latent code and domain-irrelevant latent code from input data (Hu, Page 5, Col 1, Paragraph 3,“The encoder-decoder structured generator is used for face rotation and untangling the identity from pose variation”) and the output is considered a compressed version of the input. Further, the encoder for the generator/discriminator is considered to be both the first and second encoder stage of the claims as it performs the function of both he first and second encoder)
Hu discloses using a decoder stage of the neural network to generate reconstructed sampled runtime input data; (Hu, Page 5, Col. 1, Paragraph 3, “The representation is one part of the input to the decoder to synthesise various faces of the same subject with different attributes, i.e., by virtually rotating the facial pose code” where Hu’s use of a decoder to create images with the same subject and different attributes is considered using a decoder stage of the neural network to generate reconstructed input data which corresponds to reconstructed sampled runtime input data)
Hu discloses wherein the reconstructed sampled runtime input data comprises a reconstruction of the sampled runtime input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code(Hu, Page 4, Figure 1d shows the latent code from the generator’s encoder is sent to the generator’s decoder)
Hu discloses generating a reconstruction loss based at least in part on the reconstructed sampled runtime input data(Algorithm 1, where LDpixel includes the reconstruction error of synthetic images(See also Hu, Page 5, Col. 1, Paragraph 4, “The discriminator aims to classify the face image x as real or fake, to maximise the gap between the reconstruction error of real image and that of the synthetic image, and to estimate its identity and pose” where maximizing the gap between the reconstruction error for real images compared to synthetic images corresponds to a reconstruction loss based on reconstructed input data and Hu, Equation 7, Equation 13 and Equation 18, that shows the reconstruction loss by comparing the generated image to the real image in terms of pixel values))
Hu discloses and using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate…(Algorithm 1, Step 3 and 6(See also, Hu, Page 5, Equation 9 and 13, where a loss function being significantly higher than expected would indicate an outlier and would be deemed fake/anomalous and Hu, Page 4, Figure 1d, where the real/fake is determining outlier/anomaly))
Hu does not explicitly disclose:
receiving, at a data collection system, runtime input data generated by a sensor network that senses runtime states of the SUA… wherein the SUA comprises an operational component, and a dynamic environment in which the operational component performs runtime task…wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component
wherein the runtime input data comprises content data and domain data, wherein the content data is sourced from the operational component while the operational component performs the runtime tasks and wherein the domain data is sourced from the environment in which the operational component performs the runtime tasks
using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data
using additional downstream analysis to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior.
Liu discloses receiving, at a data collection system, runtime input data generated by a sensor network that senses runtime states of the SUA(Liu, Figure 1 and [0037],” As shown in FIG. 1, the system 100 may communicate with one or more image capture device(s). In general, the system 100 may be any device, apparatus or system configured for carrying out instructions for, and may operate as part of, or in collaboration with, various computers, systems, devices, machines, mainframes, networks or servers.” where the computer is considered data collection system as it is a system that collects and distributes data and the videos captured by the image capture devices are considered runtime input data as it creates data by sensors that sense runtime states (pose, color, saturation, identity, image/video steam, lighting etc.…) that is input into the GAN)…wherein the SUA comprises an operational component, and a dynamic environment in which the operational component performs runtime task(Liu, Figure 1 and [0037],” As shown in FIG. 1, the system 100 may communicate with one or more image capture device(s). In general, the system 100 may be any device, apparatus or system configured for carrying out instructions for, and may operate as part of, or in collaboration with, various computers, systems, devices, machines, mainframes, networks or servers” where the image capture devices are considered an operational component that performs a runtime task of capturing videos of a dynamic environment), wherein the dynamic environment comprises an unknown domain(Liu, [0061] “With a single-image DR GAN, an identity representation f(x) can be extracted from a single image x, and different faces of the same person, in any pose, can be generated. In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions” where identifying representation f(x) of an extracted image is considered performing a runtime task in a unknown dynamic environment )…wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component(Liu, [0061], “In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions.” where the input is a video captured by a camera/a video feed is considered a streaming time-series of sensor measurements during operation of the operational component as the camera/video corresponds to an operational component)
Liu discloses wherein the runtime input data comprises content data and domain data(Liu, [0047], “Then, at process block 204, a trained DR-GAN may be applied to generate an identity representation of the subject, or object. This step may include extracting the identity representation, in the form of features or feature vectors, by inputting received one or more images into one or more encoders of the DR-GAN. In some aspects, a pose of the subject or object in the received image(s) may be determined at process block 204.” where the pose in the input data is considered content data and identity in the input data is considered domain data(See also Liu, [0032], “While G in the present framework serves as a face rotator, D may be trained to…predict face identity and pose at substantially the same time”)), wherein the content data is sourced from the operational component while the operational component performs the runtime tasks and wherein the domain data is sourced from the environment in which the operational component performs the runtime tasks(Liu, [0046], “As shown, the process 200 may begin at process block 202 with providing images depicting at least one subject to be identified. The imaging may include single or multiple images acquired, for example, using various monitoring devices or cameras” where the monitoring devices/cameras are considered operational components that perform that runtime task of capturing videos that used as a source to input into the GAN)
References Hu and Liu are analogous art because they are from the same field of endeavor of using machine learning (GANs) for image recognition.
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 Hu and Liu before him or her, to modify the offline image input of Hu to include the steaming video feeding inputs of Liu for multiple views of the same subject and realistic input modality for law enforcement/biometric applications Liu states “…In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions.” (Liu, [0061]) and “Nevertheless, the ability to generate realistic frontal faces and accurately recognize subjects would be beneficial in many biometric applications, including identifying suspects or witnesses in law enforcement.”(Liu, [0005])
Hu-Liu does not explicitly disclose:
using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data
using additional downstream analysis to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior.
Hyunseok discloses using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data(Hyunseok, [0057]-[0058], “During training, ML system 204 may be trained for each microservice or microservice type. ML system 204 learns to capture representative features of “normal” time series data into fixed-length feature vectors, which it may use to reconstruct the original training time-series data. During testing, if ML system 204 is fed with abnormal time series data not seen during training, it may yield a relatively high reconstruction loss from which the abnormality of the time series data is detected…When the reconstruction loss is greater than a reconstruction loss threshold, an anomaly or outlier is detected in the structured dataset” where the reconstruction loss exceeding a threshold detecting an anomaly/outlier corresponds to determining whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data as the reconstruction loss being higher when abnormal or unseen data is processed corresponds to determining anomalous data candidate that does not conform to an expected pattern )
References Hu-Liu and Hyunseok are analogous art because they are from the same field of endeavor of using machine learning for automatically learning a representation of complex observed data to make a reliable classification.
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 Hu-Liu and Hyunseok before him or her, to modify the detection of an anomalous data candidate using reconstruction loss of Hu-Liu to include the pattern recognition of Hyunseok for efficient and automated way of detecting different types of performance and security anomalies as Hyunseok states “The anomaly detector provides an efficient and automated way of detecting different types of performance and security anomalies so that microservice architectures can be deployed in a more effective and secure manner”(Hyunseok, [0005])
Hu-Liu-Hyunseok does not explicitly disclose:
using additional downstream analysis to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior.
Prasenjeet discloses using additional downstream analysis(Prasenjeet, [0025], “Accordingly, the system 200 post-processes the anomalous logs 210 to identify those anomalous logs 210 that are noteworthy before including those logs 210 in an anomaly report” where the post-processing of anomalous logs corresponds to an addition downstream analysis) to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior(Prasenjeet, [0046], “In various embodiments, the sentiment polarity threshold is set at zero, so all negative value sentiment log entries are marked as noteworthy and all neutral or positive value sentiment log entries are marked as not noteworthy. In other embodiments, the sentiment polarity threshold can be set at other values to allow some neutral and low-positive sentiment log entries to be included as noteworthy or set to mark some low-negative sentiment log entries as not noteworthy” where rating each anomaly by a sentiment polarity rating and categorizing each of the anomalies as positive, neutral or negative corresponds to determining whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior)
References Hu-Liu-Hyunseok and Prasenjeet are analogous art because they are from the same field of endeavor of using machine learning for automatically learning a representation of complex observed data to make a reliable classification.
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 Hu-Liu-Hyunseok and Prasenjeet before him or her, to modify the model of Hu-Liu-Hyunseok to include the anomaly post processing of Prasenjeet for efficient and automated way of processing anomalies to reduce false positives and determine noteworthiness of anomalies as Prasenjeet states “Further post processing and filtering is used to determine whether the contents of the anomalous log entry are noteworthy so that noteworthy anomalies are elevated to the attention of a network operator and non-noteworthy anomalies are ignored. Accordingly, the present disclosure provides for improvements in the efficiency and accuracy of reporting network anomalies, and reduces the incidence of false positive or extraneous anomaly reports, among other benefits.”(Prasenjeet, [0014])
Regarding claim 2, Hu-Liu-Hyunseok-Prasenjeet teaches the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated).
Hu discloses a first encoder stage generating the content-irrelevant latent code comprises identifying similarities and differences among the content-irrelevant latent code and the second encoder stage generating the domain-irrelevant latent code comprises identifying similarities and differences among the domain-irrelevant latent code (Hu, Page 4, Col. 1, Paragraph 1, “The encoder-decoder structured generator is used for face rotation and untangling the identity from pose variation” where the generator untangling the identity from the pose is considered identifying similarities and differences among content-irrelevant latent code and domain-irrelevant latent code)
Regarding claim 3, Hu-Liu-Hyunseok-Prasenjeet teaches the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated).
Hu discloses wherein the anomalous data detection task further comprises using the first encoder stage of the neural network to generate content-irrelevant and domain-irrelevant (CIDI) latent code from the sampled runtime input data. (Hu, Page 4, Col. 1, Paragraph 1, “The encoder-decoder structured generator is used for face rotation and untangling the identity from pose variation” where the generator untangling the identity from the pose is considered identifying similarities and differences among content-irrelevant latent code and domain-irrelevant latent code)
Regarding claim 4, Hu-Liu-Hyunseok-Prasenjeet teaches the computer-implemented method of claim 3 (and thus the rejection of claim 3 is incorporated).
Hu discloses wherein the reconstruction of the sampled runtime input data is also based at least in part on the CIDI latent code (Figure 1d, the latent code from the generator’s encoder is considered to be contain pose and identity information which is considered content-irrelevant and domain-irrelevant latent code)
Regarding claim 5, Hu-Liu-Hyunseok-Prasenjeet teaches the computer-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated).
Hu discloses wherein the neural network has been trained to: disentangle the content-irrelevant code from the sampled runtime input data; and disentangle the domain-irrelevant code from the sampled runtime input data(Hu, Page 2, Col 2, Paragraph 2, “This paper addresses the problem of learning a generative model for disentangled facial representation extraction. By combining the advanced techniques of GAN-based representation learning methods, we propose to learn disentangled pose-robust features by modeling the complex non-linear transform between face images with different poses” where untangling the identity from the pose is considered disentangle the content-irrelevant code from the input data and disentangle the domain-irrelevant code from the input data)
Regarding claim 6, Hu-Liu-Hyunseok-Prasenjeet teaches the computer-implemented method of claim 5 (and thus the rejection of claim 5 is incorporated)
Hu discloses an adversarial content discriminator has been used to train the neural network to disentangle the content-irrelevant code from the sampled runtime input data; and an adversarial domain discriminator has been used to train the neural network to disentangle the domain-irrelevant code from the sampled runtime input data. (Hu, Page 4, Col. 2, Paragraph 1, “… a discriminative model, D, is trained to distinguish the samples synthesised by G and real ones from the training data” where the discriminator being trained to synthesize and distinguish images is considered to being trained to disentangle pose and identity which are considered content-irrelevant latent/domain-irrelevant latent code (Hu, Page 4, Col. 1. Paragraph 1, “The encoder-decoder structured discriminator is used for facial reconstruction, pose estimation, identity classification and real/fake adversarial learning”))
Regarding claim 7, Hu-Liu-Hyunseok-Prasenjeet teaches the computer-implemented method of claim 2 (and thus the rejection of claim 2 is incorporated).
Hu discloses the first encoder stage of the neural network has been trained to identify similarities and differences among the content-irrelevant latent code and the second encoder stage of the neural network has been trained to identify similarities and differences among the domain-irrelevant latent code. (Hu, Page 4, Col. 2, Paragraph 1, “a generative model, G, is trained to synthesise images resembling the real data … from the training data” where the generator being trained to synthesize and distinguish images of samples is considered to being trained to disentangle pose and identity which are considered content-irrelevant latent/domain-irrelevant latent code (Hu, Page 4, Col. 2, Paragraph 1, “The encoder-decoder structured generator is used for face rotation and untangling the identity from pose variation”))
Regarding claim 8, Hu-Liu-Hyunseok-Prasenjeet discloses a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor system comprising a data collection system(Hu, Page 6, Col. 2, Algorithm 1, where algorithm 1 is considered a computer implemented method that detects real/fake images (anomalous data) and is associated with a system under analysis as it is a system that is doing an analysis on images)
Hu discloses receiving, at a data collection system(Hu, Page 4, Figure 1d, where the computer is considered data collection system as it is a system that collects and distributes data and DED-GAN is a computer based system that has images input into it), runtime input data(Hu, Page 6, Col 2, Paragraph 1, “Our models were trained separately on the Multi-PIE…and CASIA…dataset” where the images and the image dataset are considered runtime input data as they are contextual data being input into the GAN at runtime) generated by a sensor network that senses runtime states of the SUA(Hu, Page 6, Col 2, Paragraph 1, “Our models were trained separately on the Multi-PIE…and CASIA…dataset” where the image datasets have images that are created by sensors that are considered a sensor network and the images sense features of the image(pose, color, saturation, identity, image/video steam, lighting etc.…) are considered runtime states)
Hu discloses wherein the runtime input data comprises content data and domain data(Hu, Page 4, Col. 2, Paragraph 2 “Given a face image x with label y = {ya, yd, yc}, where ya, yd and yc represent the labels for real/fake, identity and pose” where pose is considered content data and identity is considered domain data)
Hu discloses wherein the data collection system comprises a neural network trained to perform an anomalous data detection task on the runtime input data(Hu, Page 6, Col. 1, Paragraph 4, “The code layer of the autoencoder is followed by Da, Dc and Dd where Da(x) is for real-fake classification, Dc(x) is for pose regression and Dd is for identity prediction” where the process of the GAN classification and prediction of images as real/fake is considered to be detecting anomalous data of input data)
Hu discloses and using the neural network of the data collection system to perform iterations of the anomalous data detection task(Hu, Page 6, Col. 2, Algorithm 1, iteration t, where iteration t shows that the GAN operates in iteration)
Hu discloses receiving sampled runtime input data comprising a sample of the runtime input data(Hu, Page 4, Col 2, Paragraph 2, “…Given a face image x…” where the given face image x is considered an input data being received)
Hu discloses using a first encoder stage of a the neural network to generate content-irrelevant latent code comprising a first compressed version of the sampled runtime input data and using a second encoder stage of the neural network to generate domain-irrelevant latent code comprising a second compressed version of the sampled runtime input data(Hu, Page 4, Col 2, Paragraph 1, “The generator generates unlabelled realistic samples from the latent variable model to improve the discriminative ability of the discriminator” where the latent variable model contains pose and facial identity which are considered content-irrelevant latent code and domain-irrelevant latent code from input data (Hu, Page 5, Col 1, Paragraph 3,“The encoder-decoder structured generator is used for face rotation and untangling the identity from pose variation”) and the output is considered a compressed version of the input. Further, the encoder for the generator/discriminator is considered to be both the first and second encoder stage of the claims as it performs the function of both he first and second encoder)
Hu discloses using a decoder stage of the neural network to generate reconstructed sampled runtime input data; (Hu, Page 5, Col. 1, Paragraph 3, “The representation is one part of the input to the decoder to synthesise various faces of the same subject with different attributes, i.e., by virtually rotating the facial pose code” where Hu’s use of a decoder to create images with the same subject and different attributes is considered using a decoder stage of the neural network to generate reconstructed input data which corresponds to reconstructed sampled runtime input data)
Hu discloses wherein the reconstructed sampled runtime input data comprises a reconstruction of the sampled runtime input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code(Hu, Page 4, Figure 1d shows the latent code from the generator’s encoder is sent to the generator’s decoder)
Hu discloses generating a reconstruction loss based at least in part on the reconstructed sampled runtime input data(Algorithm 1, where LDpixel includes the reconstruction error of synthetic images(See also Hu, Page 5, Col. 1, Paragraph 4, “The discriminator aims to classify the face image x as real or fake, to maximise the gap between the reconstruction error of real image and that of the synthetic image, and to estimate its identity and pose” where maximizing the gap between the reconstruction error for real images compared to synthetic images corresponds to a reconstruction loss based on reconstructed input data and Hu, Equation 7, Equation 13 and Equation 18, that shows the reconstruction loss by comparing the generated image to the real image in terms of pixel values))
Hu discloses and using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate(Algorithm 1, Step 3 and 6(See also, Hu, Page 5, Equation 9 and 13, where a loss function being significantly higher than expected would indicate an outlier and would be deemed fake/anomalous and Hu, Page 4, Figure 1d, where the real/fake is determining outlier/anomaly))
While Hu does not explicitly disclose:
receiving, at a data collection system, runtime input data generated by a sensor network that senses runtime states of the SUA… wherein the SUA comprises an operational component, and a dynamic environment in which the operational component performs runtime task…wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component
wherein the runtime input data comprises content data and domain data, wherein the content data is sourced from the operational component while the operational component performs the runtime tasks and wherein the domain data is sourced from the environment in which the operational component performs the runtime tasks
Liu discloses receiving, at a data collection system, runtime input data generated by a sensor network that senses runtime states of the SUA(Liu, Figure 1 and [0037],” As shown in FIG. 1, the system 100 may communicate with one or more image capture device(s). In general, the system 100 may be any device, apparatus or system configured for carrying out instructions for, and may operate as part of, or in collaboration with, various computers, systems, devices, machines, mainframes, networks or servers.” where the computer is considered data collection system as it is a system that collects and distributes data and the videos captured by the image capture devices are considered runtime input data as it creates data by sensors that sense runtime states (pose, color, saturation, identity, image/video steam, lighting etc.…) that is input into the GAN)… wherein the SUA comprises an operational component, and a dynamic environment in which the operational component performs runtime task(Liu, Figure 1 and [0037],” As shown in FIG. 1, the system 100 may communicate with one or more image capture device(s). In general, the system 100 may be any device, apparatus or system configured for carrying out instructions for, and may operate as part of, or in collaboration with, various computers, systems, devices, machines, mainframes, networks or servers” where the image capture devices are considered an operational component that performs a runtime task of capturing videos of a dynamic environment), wherein the dynamic environment comprises an unknown domain(Liu, [0061] “With a single-image DR GAN, an identity representation f(x) can be extracted from a single image x, and different faces of the same person, in any pose, can be generated. In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions” where identifying representation f(x) of an extracted image is considered performing a runtime task in a unknown dynamic environment )…wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component(Liu, [0061], “In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions.” where the input is a video captured by a camera/a video feed is considered a streaming time-series of sensor measurements during operation of the operational component as the camera/video corresponds to an operational component)
Liu discloses wherein the runtime input data comprises content data and domain data(Liu, [0047], “Then, at process block 204, a trained DR-GAN may be applied to generate an identity representation of the subject, or object. This step may include extracting the identity representation, in the form of features or feature vectors, by inputting received one or more images into one or more encoders of the DR-GAN. In some aspects, a pose of the subject or object in the received image(s) may be determined at process block 204.” where the pose in the input data is considered content data and identity in the input data is considered domain data(See also Liu, [0032], “While G in the present framework serves as a face rotator, D may be trained to…predict face identity and pose at substantially the same time”)), wherein the content data is sourced from the operational component while the operational component performs the runtime tasks and wherein the domain data is sourced from the environment in which the operational component performs the runtime tasks(Liu, [0046], “As shown, the process 200 may begin at process block 202 with providing images depicting at least one subject to be identified. The imaging may include single or multiple images acquired, for example, using various monitoring devices or cameras” where the monitoring devices/cameras are considered operational components that perform that runtime task of capturing videos that used as a source to input into the GAN)
References Hu and Liu are analogous art because they are from the same field of endeavor of using machine learning (GANs) for image recognition.
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 Hu and Liu before him or her, to modify the offline image input of Hu to include the steaming video feeding inputs of Liu for multiple views of the same subject and realistic input modality for law enforcement/biometric applications Liu states “…In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions.” (Liu, [0061]) and “Nevertheless, the ability to generate realistic frontal faces and accurately recognize subjects would be beneficial in many biometric applications, including identifying suspects or witnesses in law enforcement.”(Liu, [0005])
Hu-Liu does not explicitly disclose:
using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data
using additional downstream analysis to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior.
Hyunseok discloses using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data(Hyunseok, [0057]-[0058], “During training, ML system 204 may be trained for each microservice or microservice type. ML system 204 learns to capture representative features of “normal” time series data into fixed-length feature vectors, which it may use to reconstruct the original training time-series data. During testing, if ML system 204 is fed with abnormal time series data not seen during training, it may yield a relatively high reconstruction loss from which the abnormality of the time series data is detected…When the reconstruction loss is greater than a reconstruction loss threshold, an anomaly or outlier is detected in the structured dataset” where the reconstruction loss exceeding a threshold detecting an anomaly/outlier corresponds to determining whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data as the reconstruction loss being higher when abnormal or unseen data is processed corresponds to determining anomalous data candidate that does not conform to an expected pattern )
References Hu-Liu and Hyunseok are analogous art because they are from the same field of endeavor of using machine learning for automatically learning a representation of complex observed data to make a reliable classification.
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 Hu-Liu and Hyunseok before him or her, to modify the detection of an anomalous data candidate using reconstruction loss of Hu-Liu to include the pattern recognition of Hyunseok for efficient and automated way of detecting different types of performance and security anomalies as Hyunseok states “The anomaly detector provides an efficient and automated way of detecting different types of performance and security anomalies so that microservice architectures can be deployed in a more effective and secure manner”(Hyunseok, [0005])
Hu-Liu-Hyunseok does not explicitly disclose:
using additional downstream analysis to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior.
Prasenjeet discloses using additional downstream analysis(Prasenjeet, [0025], “Accordingly, the system 200 post-processes the anomalous logs 210 to identify those anomalous logs 210 that are noteworthy before including those logs 210 in an anomaly report” where the post-processing of anomalous logs corresponds to an addition downstream analysis) to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior(Prasenjeet, [0046], “In various embodiments, the sentiment polarity threshold is set at zero, so all negative value sentiment log entries are marked as noteworthy and all neutral or positive value sentiment log entries are marked as not noteworthy. In other embodiments, the sentiment polarity threshold can be set at other values to allow some neutral and low-positive sentiment log entries to be included as noteworthy or set to mark some low-negative sentiment log entries as not noteworthy” where rating each anomaly by a sentiment polarity rating and categorizing each of the anomalies as positive, neutral or negative corresponds to determining whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior)
References Hu-Liu-Hyunseok and Prasenjeet are analogous art because they are from the same field of endeavor of using machine learning for automatically learning a representation of complex observed data to make a reliable classification.
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 Hu-Liu-Hyunseok and Prasenjeet before him or her, to modify the model of Hu-Liu-Hyunseok to include the anomaly post processing of Prasenjeet for efficient and automated way of processing anomalies to reduce false positives and determine noteworthiness of anomalies as Prasenjeet states “Further post processing and filtering is used to determine whether the contents of the anomalous log entry are noteworthy so that noteworthy anomalies are elevated to the attention of a network operator and non-noteworthy anomalies are ignored. Accordingly, the present disclosure provides for improvements in the efficiency and accuracy of reporting network anomalies, and reduces the incidence of false positive or extraneous anomaly reports, among other benefits.”(Prasenjeet, [0014])
Regarding claims 9-14, The rejection of claim 8 is incorporated in claims 9-14; further, claims 9-14 are rejected under the same rationale as set forth in the rejection of claims 2-7 respectively due to the substantially similar limitations.
Regarding claim 15, Hu discloses a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor system comprising a data collection system (Hu, Page 6, Col. 2, Algorithm 1, where algorithm 1 is considered a computer implemented method that detects real/fake images (anomalous data) and is associated with a system under analysis as it is a system that is doing an analysis on images)
Hu discloses receiving, at a data collection system(Hu, Page 4, Figure 1d, where the computer is considered data collection system as it is a system that collects and distributes data and DED-GAN is a computer based system that has images input into it), runtime input data(Hu, Page 6, Col 2, Paragraph 1, “Our models were trained separately on the Multi-PIE…and CASIA…dataset” where the images and the image dataset are considered runtime input data as they are contextual data being input into the GAN at runtime) generated by a sensor network that senses runtime states of the SUA(Hu, Page 6, Col 2, Paragraph 1, “Our models were trained separately on the Multi-PIE…and CASIA…dataset” where the image datasets have images that are created by sensors that are considered a sensor network and the images sense features of the image(pose, color, saturation, identity, image/video steam, lighting etc.…) are considered runtime states)
Hu discloses wherein the runtime input data comprises content data and domain data(Hu, Page 4, Col. 2, Paragraph 2 “Given a face image x with label y = {ya, yd, yc}, where ya, yd and yc represent the labels for real/fake, identity and pose” where pose is considered content data and identity is considered domain data)
Hu discloses wherein the data collection system comprises a neural network trained to perform an anomalous data detection task on the runtime input data(Hu, Page 6, Col. 1, Paragraph 4, “The code layer of the autoencoder is followed by Da, Dc and Dd where Da(x) is for real-fake classification, Dc(x) is for pose regression and Dd is for identity prediction” where the process of the GAN classification and prediction of images as real/fake is considered to be detecting anomalous data of input data)
Hu discloses and using the neural network of the data collection system to perform iterations of the anomalous data detection task(Hu, Page 6, Col. 2, Algorithm 1, iteration t, where iteration t shows that the GAN operates in iteration)
Hu discloses receiving sampled runtime input data comprising a sample of the runtime input data(Hu, Page 4, Col 2, Paragraph 2, “…Given a face image x…” where the given face image x is considered an input data being received)
Hu discloses using a first encoder stage of a the neural network to generate content-irrelevant latent code comprising a first compressed version of the sampled runtime input data and using a second encoder stage of the neural network to generate domain-irrelevant latent code comprising a second compressed version of the sampled runtime input data(Hu, Page 4, Col 2, Paragraph 1, “The generator generates unlabelled realistic samples from the latent variable model to improve the discriminative ability of the discriminator” where the latent variable model contains pose and facial identity which are considered content-irrelevant latent code and domain-irrelevant latent code from input data (Hu, Page 5, Col 1, Paragraph 3,“The encoder-decoder structured generator is used for face rotation and untangling the identity from pose variation”) and the output is considered a compressed version of the input. Further, the encoder for the generator/discriminator is considered to be both the first and second encoder stage of the claims as it performs the function of both he first and second encoder)
Hu discloses using a decoder stage of the neural network to generate reconstructed sampled runtime input data; (Hu, Page 5, Col. 1, Paragraph 3, “The representation is one part of the input to the decoder to synthesise various faces of the same subject with different attributes, i.e., by virtually rotating the facial pose code” where Hu’s use of a decoder to create images with the same subject and different attributes is considered using a decoder stage of the neural network to generate reconstructed input data which corresponds to reconstructed sampled runtime input data)
Hu discloses wherein the reconstructed sampled runtime input data comprises a reconstruction of the sampled runtime input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code(Hu, Page 4, Figure 1d shows the latent code from the generator’s encoder is sent to the generator’s decoder)
Hu discloses generating a reconstruction loss based at least in part on the reconstructed sampled runtime input data(Algorithm 1, where LDpixel includes the reconstruction error of synthetic images(See also Hu, Page 5, Col. 1, Paragraph 4, “The discriminator aims to classify the face image x as real or fake, to maximise the gap between the reconstruction error of real image and that of the synthetic image, and to estimate its identity and pose” where maximizing the gap between the reconstruction error for real images compared to synthetic images corresponds to a reconstruction loss based on reconstructed input data and Hu, Equation 7, Equation 13 and Equation 18, that shows the reconstruction loss by comparing the generated image to the real image in terms of pixel values))
Hu discloses and using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate(Algorithm 1, Step 3 and 6(See also, Hu, Page 5, Equation 9 and 13, where a loss function being significantly higher than expected would indicate an outlier and would be deemed fake/anomalous and Hu, Page 4, Figure 1d, where the real/fake is determining outlier/anomaly))
While Hu does not explicitly disclose:
receiving, at a data collection system, runtime input data generated by a sensor network that senses runtime states of the SUA… wherein the SUA comprises an operational component, and a dynamic environment in which the operational component performs runtime task…wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component
wherein the runtime input data comprises content data and domain data, wherein the content data is sourced from the operational component while the operational component performs the runtime tasks and wherein the domain data is sourced from the environment in which the operational component performs the runtime tasks
Liu discloses receiving, at a data collection system, runtime input data generated by a sensor network that senses runtime states of the SUA(Liu, Figure 1 and [0037],” As shown in FIG. 1, the system 100 may communicate with one or more image capture device(s). In general, the system 100 may be any device, apparatus or system configured for carrying out instructions for, and may operate as part of, or in collaboration with, various computers, systems, devices, machines, mainframes, networks or servers.” where the computer is considered data collection system as it is a system that collects and distributes data and the videos captured by the image capture devices are considered runtime input data as it creates data by sensors that sense runtime states (pose, color, saturation, identity, image/video steam, lighting etc.…) that is input into the GAN)… wherein the SUA comprises an operational component, and a dynamic environment in which the operational component performs runtime task(Liu, Figure 1 and [0037],” As shown in FIG. 1, the system 100 may communicate with one or more image capture device(s). In general, the system 100 may be any device, apparatus or system configured for carrying out instructions for, and may operate as part of, or in collaboration with, various computers, systems, devices, machines, mainframes, networks or servers” where the image capture devices are considered an operational component that performs a runtime task of capturing videos of a dynamic environment), wherein the dynamic environment comprises an unknown domain(Liu, [0061] “With a single-image DR GAN, an identity representation f(x) can be extracted from a single image x, and different faces of the same person, in any pose, can be generated. In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions” where identifying representation f(x) of an extracted image is considered performing a runtime task in a unknown dynamic environment )…wherein the runtime input data comprises streaming time-series sensor measurements captured as a continuous or streaming stream of the runtime input data during operation of the operational component(Liu, [0061], “In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions.” where the input is a video captured by a camera/a video feed is considered a streaming time-series of sensor measurements during operation of the operational component as the camera/video corresponds to an operational component)
Liu discloses wherein the runtime input data comprises content data and domain data(Liu, [0047], “Then, at process block 204, a trained DR-GAN may be applied to generate an identity representation of the subject, or object. This step may include extracting the identity representation, in the form of features or feature vectors, by inputting received one or more images into one or more encoders of the DR-GAN. In some aspects, a pose of the subject or object in the received image(s) may be determined at process block 204.” where the pose in the input data is considered content data and identity in the input data is considered domain data(See also Liu, [0032], “While G in the present framework serves as a face rotator, D may be trained to…predict face identity and pose at substantially the same time”)), wherein the content data is sourced from the operational component while the operational component performs the runtime tasks and wherein the domain data is sourced from the environment in which the operational component performs the runtime tasks(Liu, [0046], “As shown, the process 200 may begin at process block 202 with providing images depicting at least one subject to be identified. The imaging may include single or multiple images acquired, for example, using various monitoring devices or cameras” where the monitoring devices/cameras are considered operational components that perform that runtime task of capturing videos that used as a source to input into the GAN)
References Hu and Liu are analogous art because they are from the same field of endeavor of using machine learning (GANs) for image recognition.
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 Hu and Liu before him or her, to modify the offline image input of Hu to include the steaming video feeding inputs of Liu for multiple views of the same subject and realistic input modality for law enforcement/biometric applications Liu states “…In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions.” (Liu, [0061]) and “Nevertheless, the ability to generate realistic frontal faces and accurately recognize subjects would be beneficial in many biometric applications, including identifying suspects or witnesses in law enforcement.”(Liu, [0005])
Hu-Liu does not explicitly disclose:
using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data
using additional downstream analysis to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior.
Hyunseok discloses using the reconstruction loss to determine whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data(Hyunseok, [0057]-[0058], “During training, ML system 204 may be trained for each microservice or microservice type. ML system 204 learns to capture representative features of “normal” time series data into fixed-length feature vectors, which it may use to reconstruct the original training time-series data. During testing, if ML system 204 is fed with abnormal time series data not seen during training, it may yield a relatively high reconstruction loss from which the abnormality of the time series data is detected…When the reconstruction loss is greater than a reconstruction loss threshold, an anomaly or outlier is detected in the structured dataset” where the reconstruction loss exceeding a threshold detecting an anomaly/outlier corresponds to determining whether the sampled runtime input data comprises an anomalous data candidate that does not conform to an expected pattern or to other items in the continuous or streaming stream of the runtime input data as the reconstruction loss being higher when abnormal or unseen data is processed corresponds to determining anomalous data candidate that does not conform to an expected pattern )
References Hu-Liu and Hyunseok are analogous art because they are from the same field of endeavor of using machine learning for automatically learning a representation of complex observed data to make a reliable classification.
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 Hu-Liu and Hyunseok before him or her, to modify the detection of an anomalous data candidate using reconstruction loss of Hu-Liu to include the pattern recognition of Hyunseok for efficient and automated way of detecting different types of performance and security anomalies as Hyunseok states “The anomaly detector provides an efficient and automated way of detecting different types of performance and security anomalies so that microservice architectures can be deployed in a more effective and secure manner”(Hyunseok, [0005])
Hu-Liu-Hyunseok does not explicitly disclose:
using additional downstream analysis to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior.
Prasenjeet discloses using additional downstream analysis(Prasenjeet, [0025], “Accordingly, the system 200 post-processes the anomalous logs 210 to identify those anomalous logs 210 that are noteworthy before including those logs 210 in an anomaly report” where the post-processing of anomalous logs corresponds to an addition downstream analysis) to determine whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior(Prasenjeet, [0046], “In various embodiments, the sentiment polarity threshold is set at zero, so all negative value sentiment log entries are marked as noteworthy and all neutral or positive value sentiment log entries are marked as not noteworthy. In other embodiments, the sentiment polarity threshold can be set at other values to allow some neutral and low-positive sentiment log entries to be included as noteworthy or set to mark some low-negative sentiment log entries as not noteworthy” where rating each anomaly by a sentiment polarity rating and categorizing each of the anomalies as positive, neutral or negative corresponds to determining whether anomalous data candidates generated by the iterations of the anomalous data detection tasks are associated with good anomalous behavior or bad anomalous behavior)
References Hu-Liu-Hyunseok and Prasenjeet are analogous art because they are from the same field of endeavor of using machine learning for automatically learning a representation of complex observed data to make a reliable classification.
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 Hu-Liu-Hyunseok and Prasenjeet before him or her, to modify the model of Hu-Liu-Hyunseok to include the anomaly post processing of Prasenjeet for efficient and automated way of processing anomalies to reduce false positives and determine noteworthiness of anomalies as Prasenjeet states “Further post processing and filtering is used to determine whether the contents of the anomalous log entry are noteworthy so that noteworthy anomalies are elevated to the attention of a network operator and non-noteworthy anomalies are ignored. Accordingly, the present disclosure provides for improvements in the efficiency and accuracy of reporting network anomalies, and reduces the incidence of false positive or extraneous anomaly reports, among other benefits.”(Prasenjeet, [0014])
Regarding claims 16-18, The rejection of claim 15 is incorporated in claims 16-18; further, claims 16-18 are rejected under the same rationale as set forth in the rejection of claims 2-4 respectively due to the substantially similar limitations.
Regarding claim 19, the rejection of claim 15 is incorporated in claim 19; further, claim 19 are rejected under the same rationale as set forth in the rejection of claims 5-6 due to the substantially similar limitations.
Regarding claim 20, the rejection of claim 15 is incorporated in claim 20; further, claim 20 are rejected under the same rationale as set forth in the rejection of claim 7 due to the substantially similar limitations.
Response to Arguments
Applicant's arguments filed 04/14/2026 have been fully considered but they are not persuasive. A breakdown of 101 and 103 arguments can be found below:
101:
Applicant appears to argue on pages 10-11 that limitations that have been categorized as mental are not mental as the limitations cannot be practically be performed in the human mind. Applicant appears to assert that the limitation of receiving information comprising a streaming time series sensor measurements during operation as one such limitation and analyzing data having the claimed level of complexity with the use of the neural network to perform iterative anomalous data detection tasks on said complex data. Applicant further asserts a human mind cannot receive and process a continuous stream of time-series sensor measurements during operation.
Examiner respectfully disagrees as none of the receiving limitation is categorized as mental or abstract idea and instead is categorized as mainly insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) with specifying a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))).
Further, as each step in processing is categorized as a mental step and the decoder/encoder are used as mere tools to perform, i.e. merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)), the overall focus of the claims are directed towards abstract ideas without significantly more.
Applicant appears to argue on pages 12 that the implied complexity of the streaming features cannot reasonably be construed broadly enough to encompass list-making.
Examiner respectfully disagrees as the broadest reasonable interpretation of the claims encompass using judgment and evaluation to create a list of information as the technological aspects(decoder/encoder) are used at a high level to perform abstract ideas, , i.e. merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)), as the claims do not tie the process into the encoders or decoders.
Applicant appears to argue on pages 12 that the claims as a whole recite a concrete machine-implemented anomaly-detection process operating on a particular type of runtime input through a particular neural-network architecture.
Examiner respectfully disagrees as the claims does not meet the requirements to be considered a particular machine(an analysis can be found below) for patent eligibility. It is noted that while the application of a judicial exception by or with a particular machine is an important clue, it is not a stand-alone test for eligibility.
Particular Machine, MPEP 2106.05(b)(I): THE PARTICULARITY OR GENERALITY OF THE ELEMENTS OF THE MACHINE OR APPARATUS: The limitation is cited at a high level with no/minimal details or identifying features as no specificity of the sensor network, encoder, decoder or neural network given. It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine . As no specific details or identifying features were given of the sensor network, encoder, decoder or neural network that can be considered specifically identifiable the limitations does not meet MPEP 2106.5(b)(I) threshold.
Particular Machine, 2106.05(b)(II): WHETHER THE MACHINE OR APPARATUS IMPLEMENTS THE STEPS OF THE METHOD: As the focus of the claims are not focused on using the sensor network to obtain data it is not used as an integral part to achieve performance or the encoder/decoder/neural network detailing the steps to performing the abstract idea and does not meet MPEP 2106.5(b)(II) threshold.
Particular Machine, 2106.05(b)(III): WHETHER ITS INVOLVEMENT IS EXTRA-SOLUTION ACTIVITY OR A FIELD-OF-USE: As cited, the entire receiving limitation merely describe the Extra-Solution activity or field-of-use. The encoder, decoder and neural are recited without steps to perform the mental abstract ideas.
Applicant appears to argue on pages 12-13 that the streamlining-runtime-data language defines the type of data the claimed anomaly-detection process much operate on and constrains the scope of subsequence processing steps.
Examiner respectfully disagrees as limiting an invention to a particular data source or a particular type of data corresponds with a field of use (see MPEP 2106.05(h)) as claims appear silent on how claim alters the process steps. Should the claim be made eligible it should recite "how" the type of data constrains the particular functional steps of the claim.
Applicant appears to argue on pages 13-15 that claims do not recite a mathematical concept.
The 101 rejection is made because the claims recite mental evaluations, the Examiner did not map any of the limitations of additional elements in the rejection to mathematical and only stated in a response to arguments that “the performing encoding to disentangle content and domain factors, reconstruct inputs and output and compute then apply reconstruction-loss decisions are all mental or mathematical concepts”. Examiner will take the arguments into consideration if further amendments are categorized as mathematical.
Applicant appears to argue on pages 15-16 that the claims as a whole recite a practical application as the claims require receiving runtime sensor data in streaming time- series form during operation of an SUA in a dynamic environment including an unknown domain, iteratively generating different compressed latent-code representations through different encoder stages, reconstructing sampled runtime input data through a decoder stage, generating reconstruction loss, and using reconstruction loss to determine anomalous data candidates.
Examiner respectfully disagrees as in the current form the claims do not recite the steps for the generic computing components(encoder/decoder/neural network) to perform the abstract ideas or tie the specific limitations with architecture and merely recites generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)).
Additionally, the runtime sensor data in streaming time-series merely is limiting an invention to a particular data source or a particular type of data corresponds with a field of use (see MPEP 2106.05(h)) as claims appear silent on how claim alters the process steps. Should the claim be made eligible it should recite "how" the type of data constrains the particular functional steps of the claim and
103:
Applicant appears to argue on pages 18-19 that it that a system under analysis and runtime states of such a system are not taught. Additionally, Applicant appears to argue that that training or operating is not the same as receiving runtime sensor measurements from a system under analysis and asserts that images datasets cannot be runtime input data as applicant asserts that runtime input data must be generated runtime data of a system under analysis.
Examiner respectfully disagrees as Applicant arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Specifically, the combination of Hu and Liu that discloses the system under analysis and runtime states. Through the combination of Hu and Liu a video capturing device(an operational component) video dynamic environment and performs an analysis on the captured dynamic environment meets the broadest reasonable interpretation of claims and is supported in the specification found below that support video/images and sound being the sensors-captured information from the system under analysis:
[0042], “Real-world data in its native form (e.g., images, sound, text, or time series data) is converted to a numerical form (e.g., a vector having magnitude and direction) that can be understood and manipulated by a computer. The neural network is "trained" by performing multiple iterations of learning-based analysis on the real-world data vectors until patterns (or relationships) contained in the real-world data vectors are uncovered and learned”
[0056] – [0057] “During runtime, the inputs 502 are runtime input data received from a DCS 522 operable to gather runtime input data from a system-under-analysis (SUA) 520. The SUA 520 includes task-based domain characteristics 524 and/or task-based content characteristic… In embodiments of the invention where the SUA 520 is the vehicle 120 (shown in FIG. 4), the characteristics of the runtime acoustic sound (e.g., pitch, tone, loudness, etc.) are included among the task- based content characteristics 526; and the context characteristics of the runtime acoustic sound (vehicle make/model, weather, road conditions, driving habits of the vehicle operator, driving through tunnels, etc.) "
[0070] “…Examples of classification tasks include identifying objects in images (e.g., stop signs, pedestrians, lane markers, etc.), recognizing gestures in video, detecting voices, detecting voices in audio, identifying particular speakers, transcribing speech into text, and the like...”
Further, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant appears to be interpreting a narrower claim as the current claims does not preclude the operational component being an image-capturing device that uses sensors to capture images of a dynamic environment. Further, the system under analysis in the claims is described as comprising an operational component and a dynamic environment and Liu’s image capture devices mapped to an operational component and taking video of a dynamic environment meets the claimed requirements of a system under analysis. Even further, the combination of refences maps runtime states that are generated by a sensor network “pose, color, saturation, identity, image/video steam, lighting, etc..” captured from images/video feed as cameras rely on image sensors. Support can be found below:
Liu, Figure 1 and [0037],” As shown in FIG. 1, the system 100 may communicate with one or more image capture device(s). In general, the system 100 may be any device, apparatus or system configured for carrying out instructions for, and may operate as part of, or in collaboration with, various computers, systems, devices, machines, mainframes, networks or servers.” where the computer is considered data collection system as it is a system that collects and distributes data and the videos captured by the image capture devices are considered runtime input data as it creates data by sensors that sense runtime states (pose, color, saturation, identity, image/video steam, lighting etc.…) that is input into the GAN and where the image capture devices are considered an operational component that performs a runtime task of capturing videos of a dynamic environment
Liu, [0061], “In practice, a number of images may often be available, for instance, from video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions.” where the input is a video captured by a camera/a video feed is considered a streaming time-series of sensor measurements during operation of the operational component as the camera/video corresponds to an operational component” where the input is a video captured by a camera/a video feed is considered a streaming time-series of sensor measurements during operation of the operational component as the camera/video corresponds to an operational component
Hu, Page 6, Col. 2, Algorithm 1, where algorithm 1 is considered a computer implemented method that detects real/fake images (anomalous data) and is associated with a system under analysis as it is a system that is doing an analysis on images
Applicant appears to argue on page 19-20 that detecting fake/real images with reconstruction loss is not using reconstruction loss to determine an anomalous data candidate without substantive argument.
Examiner respectfully disagrees as detecting fake images through using reconstruction error corresponds to detecting an anomalous data candidate using reconstruction error with the broadest reasonable interpretation of the claims as detecting fake images. Anomalous in the specification is defined as something that falls outside of normal or an expected pattern(cited specification found below) and a loss function being significantly higher is a detection that something is different compared to the applied/input samples:
[0057] In accordance with aspects of the invention, a novel zero-shot training methodology (shown in FIGS. 6 and 7) is used to train the CIDI neural network 450A, using the training dataset 510, to generate output 506 that is accurately classified as either normal 506A (i.e., non-anomalous) or anomalous 506B.
[0032] In this detailed description, anomalous samples include sampled data that does not conform to an expected pattern or to other items in the relevant sampled dataset.
Applicant appears to argue on page 20 that 2 separate and distinct encoders are needed as the claims state a first encoder stage generates content-irrelevant latent code and a second encoder state generates domain-irrelevant latent code and Hu’s mapping to a single encoder performing both is improper. Applicant appears to assert that it is improper for the first and second encoder stages to be mapped to the single encoder
Examiner respectfully disagrees, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant appears to be interpreting a narrower claim as the current claims do not require 2 distinct encoders as there is no limitation highlighted by Applicant that would preclude the BRI of one encoder containing both encoder stages.
Applicant appears to argue on pages 20-21 that the combination of Hu and Lui do not disclose the invention as the claims “require runtime input data generated by a sensor network that senses runtime states of the system under analysis” and asserts video feeds are not a streamlining time series sensor measurements and that video cameras do not have an operation component that is performing runtime tasks that generate content data while a distinct dynamic environment generates domain data. Additionally, Applicant asserts the rejection does not identify content and domain sourcing limitations as content data is be sourced from the operational component during runtime tasks and domain data is sourced from the dynamic environment in which the operational component performs runtime tasks.
Examiner respectfully disagrees as, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant appears to be interpreting a narrower claim as the current claims does not preclude the operational component being an image-capturing device that uses sensors to capture images of a dynamic environment. Further, the system under analysis in the claims is described as comprising an operational component and a dynamic environment and Liu’s image capture devices mapped to an operational component and taking video of a dynamic environment meets the claimed requirements of a system under analysis. Even further, the combination of refences maps runtime states that are generated by a sensor network “pose, color, saturation, identity, image/video steam, lighting, etc..” captured from images/video feed as cameras rely on image sensors.
Additionally, as the combination of Hu and Liu image-capturing device(an operational component) captures video/images of a dynamic environment, performs an analysis on the captured dynamic environment to discern identity and pose sourced from the dynamic environment that is sourced from the image-capturing device as the image-capturing device captures the dynamic environment meets the broadest reasonable interpretation of claims.
Applicant appears to argue on page 21-22 that Liu does not support a dynamic environment comprising an unknown domain as Applicant asserts identifying representation f(x) of an extracted image/video with video feeds provided by different cameras capturing a person with different poses, expressions, and under different lighting conditions is an unsupported substitution of a dynamic environment comprising an unknown domain and camera inputs of subjects appeared does not correspond to an unknown domain. Additionally, applicant appears to assert Liu’s cited passages do not support a capture device as an operational component, treating a captured video environment as a claimed dynamic environment, treats pose and identity as the claimed content and domain data and treats a video feed as a time series sensor measurements. Specifically, Applicant appears to assert that Liu does not teach an operation component of the system under analysis is performing runtime tasks that generate content data while a distinct dynamic environment generates domain data.
Examiner respectfully disagrees as the images are in a dynamic environment that comprises an unknown domain as Applicant’s specification defines unknown to refer to “situations where the training data of the content and domain in which the neural network will attempt classify runtime data is not available”([0032]) and Liu describes real world use with “uncontrolled” image sets([0004-0010] “This indicates that facial pose variation among images (e.g., two pictures of the same person) is indeed a significant, long-felt challenge and obstacle to usability of facial recognition software on real world or “uncontrolled” image sets…Nevertheless, the ability to generate realistic frontal faces and accurately recognize subjects would be beneficial in many biometric applications, including identifying suspects or witnesses in law enforcement… In one aspect of the present disclosure, a method for identifying a subject using imaging is provided. The method includes receiving an image depicting a subject to be identified”) that discloses the system does not and has not trained on the inputs. Additionally, the system separating training and testing by subjects and testing the system on subjects that the system was not trained on corresponds to an unknown domain as the training of the system did not involve subjects in the test set ([0087], “images from 337 subjects with neutral expression were used…The first 200 subjects are used for training and the rest 137 for testing.”)
Additionally, Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant appears to be interpreting a narrower claim as the current claims does not preclude the operational component being an image-capturing device that uses sensors to capture images of a dynamic environment. Further, the system under analysis in the claims is described as comprising an operational component and a dynamic environment and Liu’s image capture devices mapped to an operational component and taking video of a dynamic environment meets the claimed requirements of a system under analysis. Even further, the combination of refences maps runtime states that are generated by a sensor network “pose, color, saturation, identity, image/video steam, lighting, etc..” captured from images/video feed as cameras rely on image sensors and the combination of Hu and Liu that discloses the system under analysis and runtime states. Through the combination of Hu and Liu a video capturing device(an operational component) video dynamic environment and performs an analysis on the captured dynamic environment meets the broadest reasonable interpretation of claims and is supported in the specification found below that support video/images and sound being the sensors-captured information from the system under analysis.
Applicant appears to argue on page 22 that claimed use of reconstruction loss to determine whether sampled runtime input data comprises an anomalous data candidate is improper as Applicant asserts that the use of detecting fake/real facial images is not the same as the claimed detection of anomalous data candidates.
Examiner respectfully disagrees as detecting fake images through using reconstruction error corresponds to detecting an anomalous data candidate using reconstruction error with the broadest reasonable interpretation of the claims as detecting fake images. Anomalous in the specification is defined as something that falls outside of normal or an expected pattern(cited specification found below) and a loss function being significantly higher is a detection that something is different compared to the applied/input samples:
[0057] In accordance with aspects of the invention, a novel zero-shot training methodology (shown in FIGS. 6 and 7) is used to train the CIDI neural network 450A, using the training dataset 510, to generate output 506 that is accurately classified as either normal 506A (i.e., non-anomalous) or anomalous 506B.
[0032] In this detailed description, anomalous samples include sampled data that does not conform to an expected pattern or to other items in the relevant sampled dataset.
Applicant appears to argue on page 22-23 that 2 separate and distinct encoders are needed as the claims stat a first encoder stage generates content-irrelevant latent code and a second encoder state generates domain-irrelevant latent code and Hu’s mapping to a single encoder performing both is improper. Applicant appears to assert that it is improper for the first and second encoder stages to be mapped to the single encoder
Examiner respectfully disagrees, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant appears to be interpreting a narrower claim as the current claims do not require 2 distinct encoders as there is no limitation highlighted by Applicant that would preclude the BRI of one encoder containing both encoder stages.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.J.J./Examiner, Art Unit 2122
/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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