Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/06/2026 has been entered.
DETAILED ACTION
Claims 1-20 are presented for examination in this application (17/974,588) filed 2022-10-27. The Examiner cites particular sections in the references as applied to the claims below for the convenience of the applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant(s) fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Response to Arguments
Applicant’s arguments and remarks filed 05/06/2026 have been fully considered. The arguments and remarks regarding the 35 U.S.C 101 rejections were not found to be persuasive. The arguments and remarks regarding the 35 U.S.C 103 rejections were not found to be persuasive.
35 U.S.C 101
Applicant’s response:
Applicant asserts “It is respectfully submitted that the claims do not recite matter that falls within one of the enumerated groupings of abstract ideas set forth in the Revised Patent Subject Matter Eligibility Guidance effective January 7, 2019. Specifically, the claims do not per se recite mathematical concepts, methods of organizing human activity or mental processes.
Additionally, the claims are directed to a practical application. More specifically, the claims are generally directed to the practical application of calculating a data center asset health score using the neural network graph where the neural network graph is generated using data center asset health information from a plurality of respective data center assets.
Additionally, it is respectfully submitted that the claims address technical improvements by improving how a system operates and how the system moves data. Specifically, at a minimum, the claimed elements of establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system, receiving data center asset health information from respective data center assets from the plurality of data center assets to the telemetry aggregation system and generating, via the telemetry aggregation system, a neural network graph using the data center asset health information from the plurality of respective data center assets, the neural network graph comprising a plurality of nodes improve how a system operates and how the system moves data.”.
Examiner’s response:
Examiner respectfully disagrees. Regarding the argument that claims do not recite matter that falls within one of the enumerated groupings of abstract ideas, the Examiner finds that the claims recite at least “calculating node edge weights based upon how similar certain data center assets are to other data center assets, data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph” and “calculating a data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics” which are found to be mathematical calculations (see MPEP 2106.04(a)(2) I. C.).
Regarding the argument that the claims are directed to a practical application, the claims do not recite enough details of the telemetry aggregation system that would render the aggregation system as being anything other than a tool to perform the mathematical judicial exceptions. 'It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II.
In regard to prong 2B, even though the claims are examined with the broadest reasonable interpretation in light of the specification, the current claims do not recite additional elements that make the claims eligible. A consideration when determining whether a claim integrates the judicial exception into a practical application is whether the additional elements amount to adding insignificant extra-solution activities to the judicial exception, of which the courts have identified as not integrating a judicial exception into a practical application. The claims recite “establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console”, “receiving data center asset health information from respective data center assets from the plurality of data center assets to the telemetry aggregation system”, and “generating, via the telemetry aggregation system, a neural network graph using the data center asset health information from the plurality of respective data center assets, the neural network graph comprising a plurality of nodes”. Furthermore, under Step 2B, a consideration for determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. The aforementioned limitations as recited in independent claims 1, 7, and 13 recite well-understood, routine, conventional functions claimed as insignificant extra-solution activities that amount to merely receiving or transmitting data over a network. Thus, for at least the reasons described, the examiner respectfully finds the claims ineligible.
35 U.S.C 103
Applicant’s response:
Applicant asserts “when discussing the element of calculating, via the telemetry aggregation system, node edge weights based upon how similar certain data center assets are to other data center assets, the examiner cites to Carcano. Specifically, the examiner cites to a portion of Carcano which discloses evaluating an actual heath status of assets according to a predefined set of asset health status values (see e.g., Carcano, Col. 7, lines 31-49). However, it is respectfully submitted that the calculating node edge weights based upon how similar certain data center assets are to other data center assets as disclosed and claimed is patentably distinct from the evaluating an actual health status of assets disclosed by Carcano. Specifically, it is respectfully submitted that nowhere within Carcano taken alone or in combination is there any disclosure or suggestion of calculating, via the telemetry aggregation system, node edge weights based upon how similar certain data center assets are to other data center assets, data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph, as required by claims 1, 7 and 13. This deficiency of Carcano is not cured by Habel.”
Examiner’s response:
Arguments regarding the amended limitations are considered but are moot in view of the new grounds of rejection.
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 as being unpatentable because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
Regarding claim 1:
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
calculating node edge weights based upon how similar certain data center assets are to other data center assets, data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph — this limitation is directed to the abstract idea of a mathematical calculation (see MPEP 2106.04(a)(2) I. C.).
calculating a data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics — this limitation is directed to the abstract idea of a mathematical calculation (see MPEP 2106.04(a)(2) I. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
receiving data center asset health information from respective data center assets from the plurality of data center assets to the telemetry aggregation system — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
generating, via the telemetry aggregation system, a neural network graph using the data center asset health information from the plurality of respective data center assets, the neural network graph comprising a plurality of nodes — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
via the telemetry aggregation system — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
The operational health score of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics — this limitation amounts to merely indicating a field of use, specifically that of multi-task learning, or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “receiving data center asset health information from respective data center assets from the plurality of data center asset to the telemetry aggregation system” limitation was found to be insignificant extra-solution activity in claim 1. This limitation is directed at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.)
Regarding claim 2:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
clustering a set of data center assets — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
each node of the plurality of nodes of the neural network graph represents a cluster of data center assets — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the nodes of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 3:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
data center assets having at least one of similar attributes and similar operational characteristics are clustered in the cluster of data center assets — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 4:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the neural network graph uses a set of data center issue information and an anticipated data center asset health score as initial training data for the neural network graph — the process of classifying and organizing data amounts to a mere pre-solution data gathering activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “the neural network graph uses a set of data center issue information and an anticipated data center asset health score as initial training data for the neural network graph” limitation was found to be insignificant extra-solution activity in claim 6. This limitation is directed at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 5:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
each data center asset issue has an associated weight — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the weights of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 6 (currently amended):
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites an additional abstract idea:
the uniqueness of the data center asset issue being determined based upon whether respective data center asset attributes overlap with data center asset attributes of another data center asset — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the associated weight of a data center asset issue is based on a uniqueness of the data center asset issue — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the weights of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 7 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a system.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
calculating node edge weights based upon how similar certain data center assets are to other data center assets, data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph — this limitation is directed to the abstract idea of a mathematical calculation (see MPEP 2106.04(a)(2) I. C.).
calculating a data center asset health score, the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics — this limitation is directed to the abstract idea of a mathematical calculation (see MPEP 2106.04(a)(2) I. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
receiving data center asset health information from respective data center assets from the plurality of data center assets to the telemetry aggregation system — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
generating, via the telemetry aggregation system, a neural network graph using the data center asset health information from the plurality of respective data center assets, the neural network graph comprising a plurality of nodes — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
via the telemetry aggregation system — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “receiving data center asset health information from respective data center assets from the plurality of data center asset to the telemetry aggregation system” limitation was found to be insignificant extra-solution activity in claim 7. This limitation is directed at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 8:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent of claim 1, which recited an abstract idea. The claim recites additional abstract ideas:
clustering a set of data center assets — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
each node of the plurality of nodes of the neural network graph represents a cluster of data center assets — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the nodes of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 9:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
data center assets having at least one of similar attributes and similar operational characteristics are clustered in the cluster of data center assets — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 10:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the neural network graph uses a set of data center issue information and an anticipated data center asset health score as initial training data for the neural network graph — the process of classifying and organizing data amounts to a mere pre-solution data gathering activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “the neural network graph uses a set of data center issue information and an anticipated data center asset health score as initial training data for the neural network graph” limitation was found to be insignificant extra-solution activity in claim 10. This limitation is directed at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 11:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
each data center asset issue has an associated weight — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the weights of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 12 (currently amended):
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites an additional abstract idea:
the uniqueness of the data center asset issue being determined based upon whether respective data center asset attributes overlap with data center asset attributes of another data center asset — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the associated weight of a data center asset issue is based on a uniqueness of the data center asset issue — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the weights of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 13 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a non-transitory, computer-readable medium (manufacture).
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
calculating node edge weights based upon how similar certain data center assets are to other data center assets, data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph — this limitation is directed to the abstract idea of a mathematical calculation (see MPEP 2106.04(a)(2) I. C.).
calculating a data center asset health score, the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics — this limitation is directed to the abstract idea of a mathematical calculation (see MPEP 2106.04(a)(2) I. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
receiving data center asset health information from respective data center assets from the plurality of data center assets to the telemetry aggregation system — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
generating, via the telemetry aggregation system, a neural network graph using the data center asset health information from the plurality of respective data center assets, the neural network graph comprising a plurality of nodes — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
via the telemetry aggregation system— this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “receiving data center asset health information from respective data center assets from the plurality of data center asset to the telemetry aggregation system” limitation was found to be insignificant extra-solution activity in claim 13. This limitation is directed at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 14:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent of claim 13, which recited an abstract idea. The claim recites additional abstract ideas:
clustering a set of data center assets — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
each node of the plurality of nodes of the neural network graph represents a cluster of data center assets — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the nodes of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 15:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 13 which recited an abstract idea. The claim recites additional abstract ideas:
data center assets having at least one of similar attributes and similar operational characteristics are clustered in the cluster of data center assets — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 16:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 13 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the neural network graph uses a set of data center issue information and an anticipated data center asset health score as initial training data for the neural network graph — the process of classifying and organizing data amounts to a mere pre-solution data gathering activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “the neural network graph uses a set of data center issue information and an anticipated data center asset health score as initial training data for the neural network graph” limitation was found to be insignificant extra-solution activity in claim 16. This limitation is directed at a high level of generality and amounts to transmitting data over a network, which is a well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 17:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 13 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
each data center asset issue has an associated weight — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the weights of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 18 (currently amended):
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 13 which recited an abstract idea. The claim recites an additional abstract idea:
the uniqueness of the data center asset issue being determined based upon whether respective data center asset attributes overlap with data center asset attributes of another data center asset — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the associated weight of a data center asset issue is based on a uniqueness of the data center asset issue — this limitation is directed to the field of use (see MPEP 2106.05(h) VI.) as it merely specifies the weights of the neural network to the field of data center assets.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components to perform the abstract idea amounts to no more than field of use to apply the exception. Generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Thus, the claim is not patent eligible.
Regarding claim 19:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 13 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the computer executable instructions are deployable to a client system from a server system at a remote location — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 20:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 13 which recited an abstract idea.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
the computer executable instructions are provided by a service provider to a user on an on-demand basis — this limitation amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C 103 as being unpatentable over Carcano et al. (US11586921B2 hereinafter, Carcano) in view of Habel et al. (US20240126636 hereinafter, Habel) in further view of Lanciano et al. (“Using Self-Organizing Maps for the Behavioral Analysis of Virtualized Network Functions”, hereinafter Lanciano).
Regarding claim 1 (currently amended):
Carcano teaches a computer-implementable method for performing a data center asset management and monitoring operation, comprising:
…
receiving data center asset health information from respective data center assets from the plurality of data center assets (see col 6 lines 4-18: “The method for forecasting health status of a distributed network by artificial neural network comprising according to the present invention comprises three main phases, in particular a phase of identifying the objects in the distributed network, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified assets, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified sites and, finally, a phase of forecasting, in a subsequent iteration and by the artificial neural network, the subsequent health status of each of the identified sites. The method is preferably carried out by making use of one or more computerized data processing unit and, in particular, the artificial neural network is operated by one or more of said computerized data processing unit.”.);
generating a neural network graph using the data center asset health information from the plurality of respective data center assets (see col 6 lines 4-18: “The method for forecasting health status of a distributed network by artificial neural network comprising according to the present invention comprises three main phases, in particular a phase of identifying the objects in the distributed network, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified assets, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified sites and, finally, a phase of forecasting, in a subsequent iteration and by the artificial neural network, the subsequent health status of each of the identified sites. The method is preferably carried out by making use of one or more computerized data processing unit and, in particular, the artificial neural network is operated by one or more of said computerized data processing unit.”.);
the neural network graph comprising a plurality of nodes (see col 8 lines 31-37: “In an embodiment, the artificial neural network of the present invention is of the feed-forward type trained with backpropagation. A feed-forward neural network is an artificial neural network wherein connections between the nodes do not form a cycle, wherein the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes.”.);
calculating node edge weights based upon how similar certain data center assets are to other data center assets (see col 7 lines 31-49: “In the extent to evaluate the aforementioned values, the method according to the present invention comprises the phase of evaluating, in an actual iteration, the actual health status of each of the identified assets. In particular, such a phase comprises a step of evaluating, by the computerized data processing unit, the actual asset health status rank of each of the identified assets according to a predefined set of asset health status values ranging from the worst asset health status to the best asset health status. A further step of evaluating, by the computerized data processing unit, the actual asset infection risk of each of the identified assets according to a predefined set of asset infection risk values ranging from the maximum asset infection risk to no asset infection risk is carried out. Finally, a step of calculating, by the artificial neural network operated by the computerized data processing unit, the actual asset infection factor of each of the identified assets as probability that an infection of the asset can spread to other assets according to the identified links is carried out”. Also see col 5 lines 57-63: “The term “infection” means, in the present invention, the occurrence of some malware inside a network, and particularly affecting one (or more) assets, usually due to some form of vulnerability. Another property of an infection is the infection factor (I-Factor), expressed in term of probability P that the infection can spread to another asset given that it is also affected by the same vulnerability.”); and
calculating a data center asset health score using the neural network graph (see col 11 lines 20-25: “The approach of the present invention allows to calculate the health status rank and infection risk of a site (based on the same computations for the corresponding assets), and the computation of these two values over time allows to track and predict the cyber security posture of complex, geographically distributed and interconnected networks.”.)
Carcano does not explicitly teach establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system, a telemetry aggregation system, or wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics.
Habel, however, analogously teaches teach establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123).” Also see [0109]: “(see para [0109]: “The virtual storage system 410 a (which may be analogous to a node of data management storage solution 130, one of nodes 202, and/or one of nodes 336 a-n) may present storage over a network to clients 405 (which may be analogous to clients 205 and 305) using various protocols (e.g., small computer system interface (SCSI), Internet small computer system interface (ISCSI), fibre channel (FC), common Internet file system (CIFS), network file system (NFS), hypertext transfer protocol (HTTP), web-based distributed authoring and versioning (WebDAV), or a custom protocol.”) [(Examiner’s note: i.e., emphasis added. Fibre channels use light pulses to transmit data which makes them resistant to common cybersecurity risks)]”),
a telemetry aggregation system (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123).”), and
wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123). The data may be automatically reported from the data management storage solutions. This can happen on a fixed schedule, periodically, upon detection of certain events, upon request, or the like. In some cases, the data reported may vary depending on the timing (e.g., general check-in or health messages to full report of all available system information)”. Also see para [0092]: “The telemetry mechanism may proactively monitor the health of a particular data storage system or cluster with which it is associated and automatically send information regarding configuration, status, performance, and/or system updates relating to the particular data storage system or cluster to the vendor. ”)
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 Carcano and Habel before him or her, to modify the method of claim 1 to include attributes of establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system, a telemetry aggregation system, or wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics in order to proactively detect and avoid potential issues (see para [0031]: “At present, some storage equipment and/or data management storage solution vendors monitor customer clusters using automated support (“ASUP”). ASUP is often used to proactively monitor the health of the storage system and automatically send messages to the vendor, internal support teams, or support partners. These messages can include telemetry data, configuration details, system status, performance metrics, system events, as well as other data that may be useful to proactively detect and avoid potential issues”).
Carcano does not explicitly teach data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph.
Lanciano, however, analogously teaches teach data center assets with a higher similarity creating a higher node edge weight data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph (see pg. 6 section 2: “The principal application of unsupervised techniques is clustering that consists in the formation of groups (the clusters) of data samples that are similar, where similarity is defined according to the employed distance function. A SOM is a particular kind of neural network that leverages on the competitive learning approach for cluster formation [17]. In this context, when a new sample is presented to the SOM during the training, the Best Matching Unit (BMU) – the closest neuron to the data sample according to the employed distance function – is selected and BMU and its neighbors are rewarded by updating their weights so to make them more similar to the selected sample.”. Also see pg. 12 section 4: “In this paper, we propose the use of SOMs in order to perform a behavioral analysis of the VMs that implement VNFs within an NFV data center infrastructure.”).
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 Carcano, Habel, Lanciano before him or her, to modify the method of claim 1 to include attributes where data center assets with a higher similarity creating a higher node edge weight data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph in order to perform behavioral analysis of virtual machines within a data center infrastructure (see pg. 12 section 4: “In this paper, we propose the use of SOMs in order to perform a behavioral analysis of the VMs that implement VNFs within an NFV data center infrastructure.”).
Regarding claim 2:
Carcano in view of Habel in further view teaches the method of claim 2.
Carcano does not explicitly teach clustering a set of data center assets; and wherein, each node of the plurality of nodes of the neural network graph represents a cluster of data center assets.
Habel, however, teaches analogously clustering a set of data center assets; and wherein, each node of the plurality of nodes of the neural network graph represents a cluster of data center assets (see [0054]: “As described herein a “risk” may identify an issue within a cluster of nodes and/or individual nodes of a distributed computing system (e.g., data management storage solution). ”)
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 Carcano and Habel before him or her, to modify the method of claim 2 to include attributes of clustering a set of data center assets; and wherein, each node of the plurality of nodes of the neural network graph represents a cluster of data center assets in order to represent a distributed data management storage system (see Habel at [0002]: “representing a distributed data management storage system and facilitates automated remediation of issues by identifying corresponding appropriate courses of action.”).
Regarding claims 8 and 14:
Claims 8 and 14 recite analogous limitations to claim 2 and therefore are rejected on the same grounds as claim 2.
Regarding claim 3:
Carcano in view of Habel in further view of Lanciano teaches the method of claim 2.
Habel further teaches data center assets having at least one of similar attributes and similar operational characteristics are clustered in the cluster of data center assets (see [0052]: “As used herein “AutoSupport” or “ASUP” generally refers to a telemetry mechanism that proactively monitors the health of a cluster of nodes (e.g., implemented in physical or virtual form) and/or individual nodes of a distributed computing system. A non-limiting example of a distributed computing system is a distributed data management storage solution (or a distributed storage system), for example, in the form of a cluster of nodes.”. Also see [0054]: As described herein a “risk” may identify an issue within a cluster of nodes and/or individual nodes of a distributed computing system (e.g., data management storage solution)”.).
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 Carcano and Habel before him or her, to modify the method of claim 3 to include attributes of data center assets having at least one of similar attributes and similar operational characteristics are clustered in the cluster of data center assets in order to represent a distributed data management system (see Habel at [0002]: “representing a distributed data management storage system and facilitates automated remediation of issues by identifying corresponding appropriate courses of action.”).
Regarding claims 9 and 15:
Claims 9 and 15 recite analogous limitations to claim 3 and therefore are rejected on the same grounds as claim 3.
Regarding claim 4:
Carcano in view of Habel in further view Lanciano teaches the method according to claim 1.
Carcano further teaches the neural network graph uses a set of data center issue information and an anticipated data center asset health score as initial training data for the neural network graph (see col 12 lines 29-48: The method is trained in this way. At any given time, for asseta, we have that Xa has the most recent “m” entries, to allow the method to evolve over time and not be biased to past behavior. In the first iterations (actual), patterns are recorded observing the behavior. The method adds to the available patterns Xa the pairs (x, y) computing the features for “x” considering the previous health status rank and taking “y” as the current health status rank. When at least “z” iterations have been done (learning phase), with “z” being a parameter being set during the learning phase, the method starts to predict the behavior. For each asseta it trains itself to estimate fAsset_a splitting taking a random ⅔ of Xa and using the remainder ⅓ to validate its performance using some form of metric like overall accuracy, not described in detail. If the overall prediction accuracy is above a predetermined number, i.e. 0.9—that means the prediction error has been less than 10% on the test set—the predicted value of the health status rank for the asset is fAsset_a(X)=y.”.).
Regarding claims 10 and 16:
Claims 10 and 16 recite analogous limitations to claim 4 and therefore are rejected on the same grounds as claim 4.
Regarding claim 5:
Carcano in view of Habel in further view of Lanciano teaches the method according to claim 1.
Carcano further teaches wherein each data center asset issue has an associated weight (see col 10 lines 31-40: “Taking into account some events to be evaluated, an event could be defined by a “connection”, which occurs whenever an asset communicates with another asset, with a given protocol and application. When this event occurs, a link is either created or updated accordingly.
A further event is could be defined by an “attack”, which may occur at given time on a target asset by an attacker asset, or an external attacker, causing a new infection to be created.”. Also see col 4 lines 13-18: “The output values are compared with the real value to compute the value of some predefined error-function. The error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by some small amount.”.)
Regarding claims 11 and 17:
Claims 11 and 17 recite analogous limitations to claim 5 and therefore are rejected on the same grounds as claim 5.
Regarding claim 6 (currently amended):
Carcano in view of Habel in further view of Lanciano teaches the method according to claim 5.
Carcano does not explicitly teach wherein the associated weight of a data center issue is based on a uniqueness of the data center asset issue, the uniqueness of the data center asset issue being determined based upon whether respective data center asset attributes overlap with data center asset attributes of another data center asset.
Lanciano, however, analogously teaches wherein the associated weight of a data center issue is based on a uniqueness of the data center asset issue, the uniqueness of the data center asset issue being determined based upon whether respective data center asset attributes overlap with data center asset attributes of another data center asset (see pg. 16 section 4.3: “However, from the viewpoint of data center operators, a set of close-by neurons with relatively similar weight vectors needs to be considered as a single behavioral cluster/group. For this reason, after the SOM processing stage, we added a step consisting of a top-down clustering strategy, based on recursively separating weight-vector’s sets whose diameter is higher than a given threshold. The principal aim of this technique is to offer the possibility of collapsing similar SOM neurons, according to the distances among their representative vectors, in order to decrease the possibility to raise an alarm when it is not needed (e.g., consider very frequent movements of a VM between two similar neurons over time) and to facilitate the human operators in interpreting the results and spotting anomalous behaviors.”)
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 Carcano, Habel, and Lanciano before him or her, to modify the method of claim 1 to include attributes of wherein the associated weight of a data center issue is based on a uniqueness of the data center asset issue, the uniqueness of the data center asset issue being determined based upon whether respective data center asset attributes overlap with data center asset attributes of another data center asset in order to provide a detailed view of behaviors of virtual machines [assets] that raise alerts (see pg. 20 section 4.4: “The aim of a dashboard-like alerting system is to provide a detailed view of the behaviors which raise the alert, providing also further information on the geometrical distance between the actual and the expected behavior in terms of weight of the SOM or also a count of the frequency of VM/Days which are clustered into rare groups”).
Regarding claims 12 and 18:
Claims 12 and 18 recite analogous limitations to claim 6 and therefore are rejected on the same grounds as claim 6.
Regarding claim 7 (currently amended):
Carcano teaches receiving data center asset health information from respective data center assets from a plurality of data center assets (see col 6 lines 4-18: “The method for forecasting health status of a distributed network by artificial neural network comprising according to the present invention comprises three main phases, in particular a phase of identifying the objects in the distributed network, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified assets, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified sites and, finally, a phase of forecasting, in a subsequent iteration and by the artificial neural network, the subsequent health status of each of the identified sites. The method is preferably carried out by making use of one or more computerized data processing unit and, in particular, the artificial neural network is operated by one or more of said computerized data processing unit.”.);
generating a neural network graph using the data center asset health information from the plurality of respective data center assets (see col 6 lines 4-18: “The method for forecasting health status of a distributed network by artificial neural network comprising according to the present invention comprises three main phases, in particular a phase of identifying the objects in the distributed network, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified assets, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified sites and, finally, a phase of forecasting, in a subsequent iteration and by the artificial neural network, the subsequent health status of each of the identified sites. The method is preferably carried out by making use of one or more computerized data processing unit and, in particular, the artificial neural network is operated by one or more of said computerized data processing unit.”.);
the neural network graph comprising a plurality of nodes (see col 8 lines 31-37: “In an embodiment, the artificial neural network of the present invention is of the feed-forward type trained with backpropagation. A feed-forward neural network is an artificial neural network wherein connections between the nodes do not form a cycle, wherein the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes.”.);
calculating node edge weights based upon how similar certain data center assets are to other data center assets (see col 7 lines 31-49: “In the extent to evaluate the aforementioned values, the method according to the present invention comprises the phase of evaluating, in an actual iteration, the actual health status of each of the identified assets. In particular, such a phase comprises a step of evaluating, by the computerized data processing unit, the actual asset health status rank of each of the identified assets according to a predefined set of asset health status values ranging from the worst asset health status to the best asset health status. A further step of evaluating, by the computerized data processing unit, the actual asset infection risk of each of the identified assets according to a predefined set of asset infection risk values ranging from the maximum asset infection risk to no asset infection risk is carried out. Finally, a step of calculating, by the artificial neural network operated by the computerized data processing unit, the actual asset infection factor of each of the identified assets as probability that an infection of the asset can spread to other assets according to the identified links is carried out”. Also see col 5 lines 57-63: “The term “infection” means, in the present invention, the occurrence of some malware inside a network, and particularly affecting one (or more) assets, usually due to some form of vulnerability. Another property of an infection is the infection factor (I-Factor), expressed in term of probability P that the infection can spread to another asset given that it is also affected by the same vulnerability.”); and
calculating a data center asset health score using the neural network graph (see col 11 lines 20-25: “The approach of the present invention allows to calculate the health status rank and infection risk of a site (based on the same computations for the corresponding assets), and the computation of these two values over time allows to track and predict the cyber security posture of complex, geographically distributed and interconnected networks.”.)
Carcano does not explicitly teach a processor; a data bus coupled to the processor; a data center asset client module; and, a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor, establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system, a telemetry aggregation system, or wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics.
Habel, however, analogously teaches teach a processor; a data bus coupled to the processor (see [0112]: “The various layers described herein, and the processing described below may be implemented in the form of executable instructions stored on a machine readable medium and executed by one or more processing resources (e.g., one or more of a microcontroller, a microprocessor, central processing unit core(s), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), and the like) and/or in the form of other types of electronic circuitry. For example, the processing may be performed by one or more virtual or physical computer systems of various forms (e.g., servers, blades, network storage systems or appliances, and storage arrays, such as the computer system described with reference to FIG. 19 below.”)
a data center asset client module (see [0055]: “In some embodiments, in order to facilitate auto-healing, remediations may be comprised of Python code. In other cases, remediations may be provided in the form of detailed directions (e.g., similar to the type of guidance and/or direction that might be received via level 1 (L1) or level 2 (L2) technical support) to allow an administrative user to perform remediations manually. Non-limiting examples of remediation actions include configuration recommendations for a data management storage solution or node thereof, command recommendations to be issued to a data management storage solution or node thereof, for example, via a command-line interface (CLI) or graphical user interface (GUI).”);
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor (see [0112]: “The various layers described herein, and the processing described below may be implemented in the form of executable instructions stored on a machine readable medium and executed by one or more processing resources (e.g., one or more of a microcontroller, a microprocessor, central processing unit core(s), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), and the like) and/or in the form of other types of electronic circuitry. For example, the processing may be performed by one or more virtual or physical computer systems of various forms (e.g., servers, blades, network storage systems or appliances, and storage arrays, such as the computer system described with reference to FIG. 19 below.”).
establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123).” Also see [0109]: “(see para [0109]: “The virtual storage system 410 a (which may be analogous to a node of data management storage solution 130, one of nodes 202, and/or one of nodes 336 a-n) may present storage over a network to clients 405 (which may be analogous to clients 205 and 305) using various protocols (e.g., small computer system interface (SCSI), Internet small computer system interface (ISCSI), fibre channel (FC), common Internet file system (CIFS), network file system (NFS), hypertext transfer protocol (HTTP), web-based distributed authoring and versioning (WebDAV), or a custom protocol.”) [(Examiner’s note: i.e., emphasis added. Fibre channels use light pulses to transmit data which makes them resistant to common cybersecurity risks)]”),
a telemetry aggregation system (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123).”), and
wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123). The data may be automatically reported from the data management storage solutions. This can happen on a fixed schedule, periodically, upon detection of certain events, upon request, or the like. In some cases, the data reported may vary depending on the timing (e.g., general check-in or health messages to full report of all available system information)”. Also see para [0092]: “The telemetry mechanism may proactively monitor the health of a particular data storage system or cluster with which it is associated and automatically send information regarding configuration, status, performance, and/or system updates relating to the particular data storage system or cluster to the vendor. ”).
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 Carcano and Habel before him or her, to modify the system of claim 7 to include attributes of establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system, a telemetry aggregation system, or wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics in order to proactively detect and avoid potential issues (see para [0031]: “At present, some storage equipment and/or data management storage solution vendors monitor customer clusters using automated support (“ASUP”). ASUP is often used to proactively monitor the health of the storage system and automatically send messages to the vendor, internal support teams, or support partners. These messages can include telemetry data, configuration details, system status, performance metrics, system events, as well as other data that may be useful to proactively detect and avoid potential issues”).
Carcano does not explicitly teach data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph.
Lanciano, however, analogously teaches teach data center assets with a higher similarity creating a higher node edge weight data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph (see pg. 6 section 2: “The principal application of unsupervised techniques is clustering that consists in the formation of groups (the clusters) of data samples that are similar, where similarity is defined according to the employed distance function. A SOM is a particular kind of neural network that leverages on the competitive learning approach for cluster formation [17]. In this context, when a new sample is presented to the SOM during the training, the Best Matching Unit (BMU) – the closest neuron to the data sample according to the employed distance function – is selected and BMU and its neighbors are rewarded by updating their weights so to make them more similar to the selected sample.”. Also see pg. 12 section 4: “In this paper, we propose the use of SOMs in order to perform a behavioral analysis of the VMs that implement VNFs within an NFV data center infrastructure.”).
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 Carcano, Habel, and Lanciano before him or her, to modify the system of claim 7 to include attributes where data center assets with a higher similarity creating a higher node edge weight data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph in order to perform behavioral analysis of virtual machines within a data center infrastructure (see pg. 12 section 4: “In this paper, we propose the use of SOMs in order to perform a behavioral analysis of the VMs that implement VNFs within an NFV data center infrastructure.”).
Regarding claim 13 (currently amended):
Carcano teaches receiving data center asset health information from respective data center assets from the plurality of data center assets (see col 6 lines 4-18: “The method for forecasting health status of a distributed network by artificial neural network comprising according to the present invention comprises three main phases, in particular a phase of identifying the objects in the distributed network, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified assets, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified sites and, finally, a phase of forecasting, in a subsequent iteration and by the artificial neural network, the subsequent health status of each of the identified sites. The method is preferably carried out by making use of one or more computerized data processing unit and, in particular, the artificial neural network is operated by one or more of said computerized data processing unit.”.);
generating a neural network graph using the data center asset health information from the plurality of respective data center assets (see col 6 lines 4-18: “The method for forecasting health status of a distributed network by artificial neural network comprising according to the present invention comprises three main phases, in particular a phase of identifying the objects in the distributed network, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified assets, a subsequent phase of evaluating, in an actual iteration, the actual health status of each of the identified sites and, finally, a phase of forecasting, in a subsequent iteration and by the artificial neural network, the subsequent health status of each of the identified sites. The method is preferably carried out by making use of one or more computerized data processing unit and, in particular, the artificial neural network is operated by one or more of said computerized data processing unit.”.);
the neural network graph comprising a plurality of nodes (see col 8 lines 31-37: “In an embodiment, the artificial neural network of the present invention is of the feed-forward type trained with backpropagation. A feed-forward neural network is an artificial neural network wherein connections between the nodes do not form a cycle, wherein the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes.”.);
calculating node edge weights based upon how similar certain data center assets are to other data center assets (see col 7 lines 31-49: “In the extent to evaluate the aforementioned values, the method according to the present invention comprises the phase of evaluating, in an actual iteration, the actual health status of each of the identified assets. In particular, such a phase comprises a step of evaluating, by the computerized data processing unit, the actual asset health status rank of each of the identified assets according to a predefined set of asset health status values ranging from the worst asset health status to the best asset health status. A further step of evaluating, by the computerized data processing unit, the actual asset infection risk of each of the identified assets according to a predefined set of asset infection risk values ranging from the maximum asset infection risk to no asset infection risk is carried out. Finally, a step of calculating, by the artificial neural network operated by the computerized data processing unit, the actual asset infection factor of each of the identified assets as probability that an infection of the asset can spread to other assets according to the identified links is carried out”. Also see col 5 lines 57-63: “The term “infection” means, in the present invention, the occurrence of some malware inside a network, and particularly affecting one (or more) assets, usually due to some form of vulnerability. Another property of an infection is the infection factor (I-Factor), expressed in term of probability P that the infection can spread to another asset given that it is also affected by the same vulnerability.”); and
calculating a data center asset health score using the neural network graph (see col 11 lines 20-25: “The approach of the present invention allows to calculate the health status rank and infection risk of a site (based on the same computations for the corresponding assets), and the computation of these two values over time allows to track and predict the cyber security posture of complex, geographically distributed and interconnected networks.”.)
Carcano does not explicitly teach a non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions, establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system, a telemetry aggregation system, or wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics.
Habel, however, analogously teaches a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor (see [0112]: “The various layers described herein, and the processing described below may be implemented in the form of executable instructions stored on a machine readable medium and executed by one or more processing resources (e.g., one or more of a microcontroller, a microprocessor, central processing unit core(s), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), and the like) and/or in the form of other types of electronic circuitry. For example, the processing may be performed by one or more virtual or physical computer systems of various forms (e.g., servers, blades, network storage systems or appliances, and storage arrays, such as the computer system described with reference to FIG. 19 below.”).
establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123).” Also see [0109]: “(see para [0109]: “The virtual storage system 410 a (which may be analogous to a node of data management storage solution 130, one of nodes 202, and/or one of nodes 336 a-n) may present storage over a network to clients 405 (which may be analogous to clients 205 and 305) using various protocols (e.g., small computer system interface (SCSI), Internet small computer system interface (ISCSI), fibre channel (FC), common Internet file system (CIFS), network file system (NFS), hypertext transfer protocol (HTTP), web-based distributed authoring and versioning (WebDAV), or a custom protocol.”) [(Examiner’s note: i.e., emphasis added. Fibre channels use light pulses to transmit data which makes them resistant to common cybersecurity risks)]”),
a telemetry aggregation system (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123).”), and
wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics (see para [0061]: “As illustrated in FIG. 1B, the AIOps platform may collect and aggregate data (e.g., telemetry data) generated by data management storage solutions (or components thereof) in use by thousands of deployed assets of a given vendor (block 123). The data may be automatically reported from the data management storage solutions. This can happen on a fixed schedule, periodically, upon detection of certain events, upon request, or the like. In some cases, the data reported may vary depending on the timing (e.g., general check-in or health messages to full report of all available system information)”. Also see para [0092]: “The telemetry mechanism may proactively monitor the health of a particular data storage system or cluster with which it is associated and automatically send information regarding configuration, status, performance, and/or system updates relating to the particular data storage system or cluster to the vendor. ”)
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 Carcano and Habel before him or her, to modify the non-transitory, computer-readable storage medium of claim 13 to include attributes of establishing a secure communication channel between a plurality of data center assets and a data center monitoring and management console, the data center monitoring and management console including a telemetry aggregation system, a telemetry aggregation system, or wherein the data center asset health score representing an operational health of a corresponding data center asset, the operational health of the corresponding data center asset referring to performance of the corresponding data center asset relative to predetermined metrics in order to proactively detect and avoid potential issues (see para [0031]: “At present, some storage equipment and/or data management storage solution vendors monitor customer clusters using automated support (“ASUP”). ASUP is often used to proactively monitor the health of the storage system and automatically send messages to the vendor, internal support teams, or support partners. These messages can include telemetry data, configuration details, system status, performance metrics, system events, as well as other data that may be useful to proactively detect and avoid potential issues”).
Carcano does not explicitly teach data center assets with a higher similarity creating a higher node edge weight and data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph.
Lanciano, however, analogously teaches teach data center assets with a higher similarity creating a higher node edge weight data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph (see pg. 6 section 2: “The principal application of unsupervised techniques is clustering that consists in the formation of groups (the clusters) of data samples that are similar, where similarity is defined according to the employed distance function. A SOM is a particular kind of neural network that leverages on the competitive learning approach for cluster formation [17]. In this context, when a new sample is presented to the SOM during the training, the Best Matching Unit (BMU) – the closest neuron to the data sample according to the employed distance function – is selected and BMU and its neighbors are rewarded by updating their weights so to make them more similar to the selected sample.”. Also see pg. 12 section 4: “In this paper, we propose the use of SOMs in order to perform a behavioral analysis of the VMs that implement VNFs within an NFV data center infrastructure.”).
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 Carcano, Habel, and Lanciano before him or her, to modify the non-transitory, computer-readable storage medium of claim 13 to include attributes where data center assets with a higher similarity creating a higher node edge weight data center assets with no similarity causing a cluster of nodes to be in a same layer of the neural network graph in order to perform behavioral analysis of virtual machines within a data center infrastructure (see pg. 12 section 4: “In this paper, we propose the use of SOMs in order to perform a behavioral analysis of the VMs that implement VNFs within an NFV data center infrastructure.”).
Regarding claim 19:
Carcano in view of Habel in further view of Lanciano teaches the non-transitory, computer-readable medium of claim 13.
Habel further teaches the computer executable instructions are deployable to a client system from a server system at a remote location (see [0109]: “A representative client of clients 405 may comprise an application, such as a database application, executing on a computer that “connects” to the virtual storage system 410 over a computer network, such as a point-to-point link, a shared local area network (LAN), a wide area network (WAN), or a virtual private network (VPN) implemented over a public network, such as the Internet.”. Also see [0115]: “With the system manager, the administrator may be able to perform many common tasks, such as”. Also see [0125]: “Configure service processors to remotely log in, manage, monitor, and administer the node, regardless of the state of the node.”.)
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 Carcano, Habel, and Lanciano before him or her, to modify the non-transitory computer-readable medium of claim 19 to include attributes of computer executable instructions being able to be deployable to a client system from a server system at a remote location in order to allow a system manager to perform many common tasks remotely (see Habel at [0115]: “With the system manager, the administrator may be able to perform many common tasks, such as: monitor and manage HA configurations in a cluster. Configure service processors to remotely log in, manage, monitor, and administer the node, regardless of the state of the node.”).
Regarding claim 20:
Carcano in view of Habel in further view of Lanciano teaches the non-transitory, computer-readable medium of claim 13.
Carcano does not explicitly teach computer executable instructions are provided by a service provider to a user on an on-demand basis.
Habel, however, analogously teaches the computer executable instructions are provided by a service provider to a user on an on-demand basis (see [0115]: “With the system manager, the administrator may be able to perform many common tasks, such as”. Also see [0125]: “Configure service processors to remotely log in, manage, monitor, and administer the node, regardless of the state of the node.”.)
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 Carcano, Habel, and Lanciano before him or her, to modify the non-transitory computer-readable medium of claim 20 to include attributes of computer executable instructions are provided by a service provider to a user on an on-demand basis in order to allow a system manager to perform many common tasks remotely (see Habel at [0115]: “With the system manager, the administrator may be able to perform many common tasks, such as: monitor and manage HA configurations in a cluster. Configure service processors to remotely log in, manage, monitor, and administer the node, regardless of the state of the node.”).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
US20230259117A1 — discloses a system that predicts predict future asset health status
US12032702B2 — discloses a system that predicts predict future asset health status
US20220321434A1 — discloses telemetry system that collects metrics and health
Conclusion
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/ANDREW BRACERO/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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