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 .
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 2025-11-17 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 102 rejections were found to be persuasive however the amendments have necessitated a change in the references applied and in a new ground of rejection as a 35 U.S.C 103 rejection. The amendments regarding the 35 U.S.C 112 rejections have overcome the rejection.
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.
Accordingly, the claims should not be treated as reciting an abstract idea and are patent eligible.”.
Examiner’s response:
Examiner respectfully disagrees. Regarding the argument that the claims do not per se recite mathematical concepts, methods of organizing human activity or mental processes, the Examiner finds that the claims recite at least “calculating, via the telemetry aggregation system, node edge weights based upon how similar certain data center asserts are to other data center assets” which is found to be a mathematical calculation (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 a system unlike a generic computer. '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, the improvement can be provided by the additional element(s) in combination with the recited judicial exception.' “If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)”. MPEP 2106.05(b).“It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine”. MPEP 2016.05(b)(I). A person having ordinary skill in the art would find a ‘telemetry aggregation system’ to be considered a conventional computer function within the field of machine learning and artificial intelligence. The claim as a whole is still directed to an abstract idea mental process.
35 U.S.C 102/103
Applicant’s response:
Applicant asserts “Applicant respectfully traverses the rejections. When discussing the element of calculating a data center asset health score using the neural network graph, the examiner cites to Carcano. Specifically, the examiner cites to a portion of Carcano which generally discloses calculating a health status rank and an infection risk of a site (see e.g., Carcano, Col. 11, lines 20-25). However, it is respectfully submitted that the data center asset health score as disclosed and claimed is patentably distinct from the health status rank and the infection risk of a site 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, a data center asset health score using the neural network graph, 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, as required by claims 1, 7 and 13. This deficiency of Carcano is not cured by Habel.
Accordingly, claims 1, 7, and 13 are allowable over Carcano and Habel. Claims 2-6 depend from claim 1 and are allowable for at least this reason. Claims 8-12 depend from claim 7 and are allowable for at least this reason. Claims 14-20 depend from claim 13 and are allowable for at least this reason.”.
Examiner’s response:
Examiner respectfully disagrees. While the amended claims, do overcome the 35 U.S.C 102 rejection, as the Examiner agrees with the applicant that Carcano does not explicitly teach all the amended limitations of the independent claims 1, 7 and 13, the Examiner finds that these amended limitations are taught by Habel. Habel teaches establishing a secure communication channel between the 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 via the use of a fiber channel (see at least para [0109]). Habel further teaches a data center asset health score representing predetermined metrics of corresponding data center assets via health messages related to status and performance (see at least para [0061]).
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 — 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)).
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.
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 — 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 7, 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 7 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 7 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 7 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.
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 — 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 18 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 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 18 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 18 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 referred to as Carcano). in view of Habel et al. (US20240126636 hereinafter referred to as Habel).
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”)
Regarding claim 2:
Carcano in view of Habel 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 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 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 claim 5:
Carcano in view of Habel 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 claim 6 (currently amended):
Carcano in view of Habel teaches the method according to claim 5.
Carcano further teaches the associated weight of a data center asset issue is based on a uniqueness of the data center asset issue (see col 4 lines 27-33: “The set of forecasting values also comprises an aging frequency value for each of the identified assets in the identified sites, wherein the aging frequency value for the next iteration is calculated, by the computerized data processing unit, for each of the assets by applying a predetermined decay factor to the actual asset infection factor in the actual iteration.”.)
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”).
Regarding claim 10:
Carcano in view of Habel teaches the system of claim 7.
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 claim 16:
Claim 16 recites analogous limitations to claim 10 and therefore is rejected on the same grounds as claim 10.
Regarding claim 11:
Carcano in view of Habel teaches the system of claim 7.
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 claim 17:
Claim 17 recites analogous limitations to claim 11 and therefore is rejected on the same grounds as claim 11.
Regarding claim 12:
Carcano in view of Habel teaches the system of claim 11.
Carcano further teaches the associated weight of a data center asset issue is based on a uniqueness of the data center asset issue (see col 4 lines 27-33: “The set of forecasting values also comprises an aging frequency value for each of the identified assets in the identified sites, wherein the aging frequency value for the next iteration is calculated, by the computerized data processing unit, for each of the assets by applying a predetermined decay factor to the actual asset infection factor in the actual iteration.”.)
Regarding claim 18 (currently amended):
Claim 18 recites analogous limitations to claim 12 and therefore is rejected on the same grounds as claim 12.
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”).
Regarding claim 19:
Carcano in view of Habel 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 and Habel 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 teaches the non-transitory, computer-readable medium of claim 13.
Habel further 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 and Habel 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANDREW BRACERO/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126