DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 04/30/2026 is in compliance with the provisions of 37 CFR 1.97, 1.98, and MPEP § 609. It has been placed in the application file, and the information referred to therein has been considered as to the merits.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 6, 13, 14, 17, and 18 of the instant application are provisionally rejected on the ground of obviousness-type nonstatutory double patenting as being unpatentable over Claims 1, 3, and 14 of copending Application No. 18/977,623 in view of Kiss et al. (WIPO Publication No. WO 2025/190589 A1), hereinafter “Kiss.”
This is a provisional nonstatutory double patenting rejection.
The elements of the aforementioned claims are outlined below in the provided table. Normal-font claim elements are anticipated, and underlined claim elements are not.
18/977,730 (Instant Application)
18/977,623 (Copending Application)
(Claim) 1
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2
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3
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4
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5
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1. A system, comprising:
one or more processors to:
detect a failure in a job performed using a plurality of resources;
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify a subset of the resources potentially associated with the failure;
(see Claim 6 below)
provide identifying information for the subset as input to a trained language model, the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the job;
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
6. The system of claim 1, wherein the one or more processors are further to:
perform anomaly detection with respect to a collection of time series data, obtained from the two or more sources, to identify a second subset of the resources potentially associated with the failure; and
provide identifying information for the second subset as additional input to the trained language model.
(Claim) 1. A system, comprising:
one or more processors to:
detect a failure in a job performed using a plurality of resources;
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify a first subset of the resources potentially associated with the failure;
perform anomaly detection with respect to a collection of time series data, obtained from two or more sources, to identify a second subset of the resources potentially associated with the failure;
provide identifying information for the first subset and the second subset, along with supporting evidence, as input to a trained language model;
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
7
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8
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9
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10
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11
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12
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11. A system, comprising:
one or more processors to:
detect a failure in a job performed using a plurality of resources;
(see Claim 13 below)
perform anomaly detection with respect to a collection of time series data, obtained from two or more sources, to identify a subset of the resources potentially associated with the failure;
provide identifying information for the subset as input to a trained language model, the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the job;
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
13. The system of claim 11, wherein the one or more processors are further to:
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify a second subset of the resources potentially associated with the failure; and
provide identifying information for the second subset as additional input to the trained language model.
1. A system, comprising:
one or more processors to:
detect a failure in a job performed using a plurality of resources;
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify a first subset of the resources potentially associated with the failure;
perform anomaly detection with respect to a collection of time series data, obtained from two or more sources, to identify a second subset of the resources potentially associated with the failure;
provide identifying information for the first subset and the second subset, along with supporting evidence, as input to a trained language model;
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
14
3
15
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16
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17. At least one processor, comprising:
one or more logical units to:
detect a failure in a job performed using a plurality of resources;
provide, as input to a trained language model, identifying information for a subset of the resources determined to be potentially associated with the failure, along with supporting evidence for the subset,
the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the job;
receive, as output of the trained language model, indication of one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the output, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
10. At least one processor, comprising:
one or more logical units to:
detect a failure in a job performed using a plurality of resources;
provide, as input to a trained language model, identifying information for a subset of the resources determined to be potentially associated with the failure,
(see Claim 14 below)
the identifying information comprising, for each resource in the subset, a resource identifier and operational evidence generated during execution of the job;
receive, as output of the trained language model, indication of one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the indication, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
14. The at least one processor of claim 10, wherein the one or more logical units are further to:
provide supporting evidence as additional input to the trained language model, the supporting evidence including at least one of content of a message, a timestamp, an importance score, or a counter value.
18
14
19
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20
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Application No. 18/977,623 does not explicitly teach, per the underlined claim language:
provide identifying information for the first subset and the second subset, along with supporting evidence, as input to a trained language model;…
the identifying information comprising, for each resource in the subset, a resource identifier and operational evidence generated during execution of the job.
However, Kiss teaches:
provide identifying information for the first subset and the second subset, along with supporting evidence (Fig. 3 and ¶ 0061; regarding, e.g., network telemetry data from devices.), as input to a trained language model;…
the identifying information comprising, for each resource in the subset, a resource identifier (Fig. 3 ¶ 0061; and ¶ 0069; regarding, e.g., information about network devices and/or network topology.) and operational evidence generated during execution of the job (Fig. 3 and ¶ 0061; regarding, e.g., network telemetry data from devices.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Application No. 18/977,623 with the use of various data sources such as telemetry data, device information, and/or topology information for inputs into a trained LLM as taught by Kiss because use of a multi-model LLM, with varied modality sources, to detect an anomaly condenses complexity and dimensions of inputs, enabling a faster time to resolve anomalies and process and use diverse information (Kiss: ¶ 0069-0070).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11, and 17 recite, in part:
Claims 1 and 11: “…initiate, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.”
Claim 17: “…initiate, based at least on the output, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.”
In page 14 of the Remarks paperwork, filed 04/30/2026, Applicant points to the instant specification: ¶ 0029 as support for this amendment. However, in ¶ 0029, the following is recited: “…An authorized entity can then review the report and take appropriate action, as may be based in part on the recommendations. This may include, for example, working with the resource manager 112 to remove or restart resources, among other such options. In some embodiments, at least some amount of automatic remediation may be taken, such as to prevent additional jobs from being allocated to a suspected node until such time as the state of the node may be investigated.”
The only actions mentioned, automatic or not, are: removing or restarting resources and/or preventing additional jobs from being allocated. The Examiner respectfully asserts that these actions are not equivalent to automatically modifying one or more operational parameters associated with at least one of the one or more resources. For example, one skilled in the art understands that removing or restarting resources can occur using the same resource configuration(s) without modifying one or more operational parameters.
A further search of the remainder of the instant disclosure does not provide the support for at least this claim amendment. As such, this claim amendment constitutes new matter.
Claims 2-10, 12-16, and 18-20 depend upon Claims 1, 11, and 17, respectfully, and are additionally rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to (an) abstract idea(s) without significantly more.
Claim 1 recites:
detect a failure in a job performed using a plurality of resources;
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify a subset of the resources potentially associated with the failure;
provide identifying information for the subset as input to a trained language model,
the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the job;
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘detect’ limitation in # 1 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. For example, “detecting” in the context of this claim encompasses a person performing an observation associated with data.
The ‘perform…analysis…to identify’ limitation in # 2 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “performing / identifying” in the context of this claim encompasses the person performing an evaluation and an observation associated with data.
The ‘initiate’ limitation in # 6 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “initiating” in the context of this claim encompasses the person performing a judgment, e.g., thinking about modified values, associated with data.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘provide’ limitation in # 3 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
In # 4 above, the claimed identifying information is merely further described in the context of a field of use. See MPEP 2106.05(h).
The ‘receive’ limitation in # 5 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Additionally, the claim recites the following additional element:
one or more processors.
This additional element is recited at a high level of generality (i.e. as a generic computer component) such that it amounts to no more than a component comprising mere instructions to apply an exception. Accordingly, the additional element does not integrate the abstract idea(s) into a practical application because it does not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional element amounts to no more than a component comprising mere instructions to apply an exception. Mere instructions to apply an exception using one or more generic computer components cannot provide an inventive concept.
Additionally, with regards to # 3 and 5 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claim 2 merely further describes the claimed statistical analysis of Claim 1 in the context of a field of use. See MPEP 2106.05(h).
Claim 3 recites:
wherein the importance of a respective text entry is compared against an average importance across the plurality of resources, identified as a set of nodes associated with respective importance values, to identify anomalous text messages associated with specific resources.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘is compared’ limitation in # 7 above, as claimed and under BRI, is a mathematical concept defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “comparing” in the context of this claim encompasses the mathematical concepts and/or equation outlined in at least the instant specification: ¶ 0037.
Claim 4 recites:
wherein a first subset of log entry data is used to generate a reference importance matrix and
a remaining subset of the log entry data is used to generate an attribution importance matrix to be compared against the reference importance matrix.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘to generate’ limitations in # 8 and 9 above, as claimed and under BRI, are mathematical concepts defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “generating” in the context of this claim encompasses the mathematical concepts and/or equation outlined in at least the instant specification: ¶ 0035-0038.
Claim 5 merely further describes the claimed two or more sources of Claim 1 in the context of a field of use. See MPEP 2106.05(h).
Claim 6 recites:
perform anomaly detection with respect to a collection of time series data, obtained from the two or more sources, to identify a second subset of the resources potentially associated with the failure; and
provide identifying information for the second subset as additional input to the trained language model.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘perform…detection…to identify’ limitation in # 10 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “performing / identifying” in the context of this claim encompasses the person performing an evaluation and an observation associated with data.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘provide’ limitation in # 11 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
With regards to # 11 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claim 7 recites:
provide supporting evidence as additional input to the trained language model, the supporting evidence including at least one of content of a message, a timestamp, an importance score, or a counter value.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s). The abstract idea(s) of Claim 1 are the same as the abstract idea(s) of Claim 7.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘provide’ limitation in # 12 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
With regards to # 12 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claim 8 merely further describes the claimed supporting evidence of Claim 7 in the context of a field of use. See MPEP 2106.05(h).
Claim 9 merely further describes the claimed attribution report of Claim 1 in the context of a field of use. See MPEP 2106.05(h).
Claim 10 merely further describes the claimed system of Claim 1 in the context of a field of use. See MPEP 2106.05(h).
Claim 11 recites:
detect a failure in a job performed using a plurality of resources;
perform anomaly detection with respect to a collection of time series data, obtained from two or more sources, to identify a subset of the resources potentially associated with the failure;
provide identifying information for the subset as input to a trained language model,
the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the job;
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘detect’ limitation in # 13 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. For example, “detecting” in the context of this claim encompasses a person performing an observation associated with data.
The ‘perform…detection…to identify’ limitation in # 14 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “performing / identifying” in the context of this claim encompasses the person performing an evaluation and an observation associated with data.
The ‘initiate’ limitation in # 18 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “initiating” in the context of this claim encompasses the person performing a judgment, e.g., thinking about modified values, associated with data.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘provide’ limitation in # 15 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
In # 16 above, the claimed identifying information is merely further described in the context of a field of use. See MPEP 2106.05(h).
The ‘receive’ limitation in # 17 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Additionally, the claim recites the following additional element:
one or more processors.
This additional element is recited at a high level of generality (i.e. as a generic computer component) such that it amounts to no more than a component comprising mere instructions to apply an exception. Accordingly, the additional element does not integrate the abstract idea(s) into a practical application because it does not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional element amounts to no more than a component comprising mere instructions to apply an exception. Mere instructions to apply an exception using one or more generic computer components cannot provide an inventive concept.
Additionally, with regards to # 15 and 17 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claim 12 merely further describes the claimed time series data of Claim 11 in the context of a field of use. See MPEP 2106.05(h).
Claim 13 recites:
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify a second subset of the resources potentially associated with the failure; and
provide identifying information for the second subset as additional input to the trained language model.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘perform…analysis…to identify’ limitation in # 19 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “performing / identifying” in the context of this claim encompasses the person performing an evaluation and an observation associated with data.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘provide’ limitation in # 20 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
With regards to # 20 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claim 14 merely further describes the claimed statistical analysis of Claim 13 in the context of a field of use. See MPEP 2106.05(h).
Claim 15 recites:
wherein the importance of a respective text entry is compared against an average importance across the plurality of resources, identified as a set of nodes associated with respective importance values, to identify anomalous text messages associated with specific resources.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘is compared’ limitation in # 21 above, as claimed and under BRI, is a mathematical concept defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “comparing” in the context of this claim encompasses the mathematical concepts and/or equation outlined in at least the instant specification: ¶ 0037.
Claim 16 recites:
provide supporting evidence as additional input to the trained language model, the supporting evidence including at least one of content of a message, a timestamp, an importance score, or a counter value.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s). The abstract idea(s) of Claim 16 are the same as the abstract idea(s) of Claim 11.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘provide’ limitation in # 22 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
With regards to # 22 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claim 17 recites:
detect a failure in a job performed using a plurality of resources;
provide, as input to a trained language model, identifying information for a subset of the resources determined to be potentially associated with the failure, along with supporting evidence for the subset,
the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the job;
receive, as output of the trained language model, indication of one or more resources inferred to be at least partially responsible for the failure, along with an explanation for selection of the one or more resources; and
initiate, based at least on the output, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘detect’ limitation in # 23 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. For example, “detecting” in the context of this claim encompasses a person performing an observation associated with data.
The ‘initiate’ limitation in # 27 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “initiating” in the context of this claim encompasses the person performing a judgment, e.g., thinking about modified values, associated with data.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The ‘provide’ limitation in # 24 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “providing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
In # 25 above, the claimed identifying information is merely further described in the context of a field of use. See MPEP 2106.05(h).
The ‘receive’ limitation in # 26 above, as claimed and under BRI, is an additional element that is insignificant extra-solution activity. For example, “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
Additionally, the claim recites the following additional elements:
at least one processor, and
one or more logical units.
These additional elements are recited at a high level of generality (i.e. as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components comprising mere instructions to apply the exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Additionally, with regards to # 24 and 26 above, per MPEP 2106.05(d)(Il), the courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claim 18 merely further describes the claimed supporting evidence of Claim 17 in the context of a field of use. See MPEP 2106.05(h).
Claim 19 recites:
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify one or more of the resources potentially associated with the failure.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘perform…analysis…to identify’ limitation in # 28 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “performing / identifying” in the context of this claim encompasses the person performing an evaluation and an observation associated with data.
Claim 20 recites:
perform anomaly detection with respect to a collection of time series data, obtained from two or more sources, to identify one or more of the resources potentially associated with the failure.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes: a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The ‘perform…detection…to identify’ limitation in # 29 above, as claimed and under BRI, is a mental process that covers performance of the limitation in the mind. For example, “performing / identifying” in the context of this claim encompasses the person performing an evaluation and an observation associated with data.
For at least the reasoning provided above, Claims 1-20 are patent ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-13, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kiss, and further in view of Watson (U.S. Patent Application Publication No. US 2025/0231862 A1), hereinafter “Watson.”
With regards to Claim 1, Kiss teaches:
a system, comprising:
one or more processors (Fig. 6 and ¶ 0077.) to:
detect a failure in a “system” performed using a plurality of resources (¶ 0050 and ¶ 0064.);
perform a statistical analysis of a collection of text entries (Fig. 5; ¶ 0072-0073; regarding, e.g., a Word2Vec transformation process [a statistical analysis]; Fig. 2; ¶ 0055; Fig. 3; and ¶ 0061.), obtained from two or more sources (Fig. 5; ¶ 0072-0073; Fig. 2; ¶ 0055; Fig. 3; and ¶ 0061.), to identify a subset of the resources potentially associated with the failure (Fig. 3; ¶ 0061; regarding, e.g., vector embeddings showing, e.g., a 1 indicating anomaly [identify a subset]; and ¶ 0064.);
provide identifying information for the subset as input to a trained language model (Fig. 4 and ¶ 0068-0069.), the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the “system;” (Fig. 3; ¶ 0061; regarding, e.g., network telemetry data from devices; Fig. 4; and ¶ 0068-0069.);
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure (Fig. 5 and ¶ 0074.), along with an explanation for selection of the one or more resources (Fig. 5 and ¶ 0074.); and
initiate, based at least on the attribution report, one or more corrective actions (Fig. 5 and ¶ 0074.).
Kiss does not explicitly teach:
a job;
one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
However, Watson teaches:
a job (¶ 0067-0069; regarding, e.g., one or more certain functions of a deployed application in a software package that have stopped working due to a software bug.);
one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources (¶ 0067-0069; regarding, e.g., a change of parameters of the software package.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Kiss with the remediation of a buggy software package associated with parameter changes as taught by Watson because the system automatically implements remediation processes on an environment to effectively prevent potential issues from interfering with the entity's processes and workflows (Watson: ¶ 0069).
With regards to Claim 5, Kiss in view of Watson teaches the system of Claim 1 as referenced above. Kiss in view of Watson further teaches:
wherein the two or more sources include one or more of networking sources (Kiss: Fig. 3 and Kiss: ¶ 0061.), compute sources, or storage sources associated with the plurality of resources.
With regards to Claim 6, Kiss in view of Watson teaches the system of Claim 1 as referenced above. Kiss in view of Watson further teaches:
wherein the one or more processors are further to:
perform anomaly detection with respect to a collection of time series data, obtained from the two or more sources (Kiss: Fig. 3 and Kiss: ¶ 0061; regarding, e.g., network telemetry from devices.), to identify a second subset of the resources potentially associated with the failure (Kiss: Fig. 5; Kiss: ¶ 0072-0073; Kiss: Fig. 2; Kiss: ¶ 0055; Kiss: Fig. 3; and Kiss: ¶ 0061.); and
provide identifying information for the second subset as additional input to the trained language model (Kiss: Fig. 4 and Kiss: ¶ 0068-0069.).
With regards to Claim 7, Kiss in view of Watson teaches the system of Claim 1 as referenced above. Kiss in view of Watson further teaches:
wherein the one or more processors are further to:
provide supporting evidence as additional input to the trained language model, the supporting evidence including at least one of content of a message (Kiss: Fig. 3 and Kiss: ¶ 0061.), a timestamp, an importance score, or a counter value.
With regards to Claim 8, Kiss in view of Watson teaches the system of Claim 7 as referenced above. Kiss in view of Watson further teaches:
wherein the supporting evidence includes at least one of timestamp, message (Kiss: Fig. 3 and Kiss: ¶ 0061.), importance score, or counter value data.
With regards to Claim 9, Kiss in view of Watson teaches the system of Claim 1 as referenced above. Kiss in view of Watson further teaches:
wherein the attribution report further includes one or more suggested actions to be performed in response to the failure, based in part on the one or more resources determined to be at least partially responsible for the failure (Kiss: Fig. 5 and Kiss: ¶ 0074.).
With regards to Claim 10, Kiss in view of Watson teaches the system of Claim 1 as referenced above. Kiss in view of Watson further teaches:
wherein the system is at least one of:
a system for performing simulation operations;
a system for performing simulation operations to test or validate autonomous machine applications;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for rendering graphical output;
a system for performing deep learning operations;
a system for performing generative AI operations using a large language model (LLM) (Kiss: ¶ 0050.);
a system implemented using an edge device;
a system for generating or presenting virtual reality (VR) content;
a system for generating or presenting augmented reality (AR) content;
a system for generating or presenting mixed reality (MR) content;
a system incorporating one or more Virtual Machines (VMs);
a system implemented at least partially in a data center;
a system for performing hardware testing using simulation;
a system for performing generative operations using a language model (LM);
a system for synthetic data generation;
a collaborative content creation platform for 3D assets; or
a system implemented at least partially using cloud computing resources.
With regards to Claim 11, Kiss teaches:
a system, comprising:
one or more processors (Fig. 6 and ¶ 0077.) to:
detect a failure in a “system” performed using a plurality of resources (¶ 0050 and ¶ 0064.);
perform anomaly detection with respect to a collection of time series data (Kiss: Fig. 3 and Kiss: ¶ 0061; regarding, e.g., network telemetry from devices.), obtained from two or more sources (Fig. 5; ¶ 0072-0073; Fig. 2; ¶ 0055; Fig. 3; and ¶ 0061.), to identify a subset of the resources potentially associated with the failure (Fig. 3; ¶ 0061; regarding, e.g., vector embeddings showing, e.g., a 1 indicating anomaly [identify a subset]; and ¶ 0064.);
provide identifying information for the subset as input to a trained language model, the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the “system;” (Fig. 3; ¶ 0061; regarding, e.g., network telemetry data from devices; Fig. 4; and ¶ 0068-0069.);
receive, as output of the trained language model, an attribution report indicating one or more resources inferred to be at least partially responsible for the failure (Fig. 5 and ¶ 0074.), along with an explanation for selection of the one or more resources (Fig. 5 and ¶ 0074.); and
initiate, based at least on the attribution report, one or more corrective actions (Fig. 5 and ¶ 0074.).
Kiss does not explicitly teach:
a job;
one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
However, Watson teaches:
a job (¶ 0067-0069; regarding, e.g., one or more certain functions of a deployed application in a software package that have stopped working due to a software bug.);
one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources (¶ 0067-0069; regarding, e.g., a change of parameters of the software package.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Kiss with the remediation of a buggy software package associated with parameter changes as taught by Watson because the system automatically implements remediation processes on an environment to effectively prevent potential issues from interfering with the entity's processes and workflows (Watson: ¶ 0069).
With regards to Claim 12, Kiss in view of Watson teaches the system of Claim 11 as referenced above. Kiss in view of Watson further teaches:
wherein the time series data includes telemetry data for the plurality of resources (Kiss: Fig. 3 and Kiss: ¶ 0061.) and counter values associated with the telemetry data (Kiss: Fig. 3 and Kiss: ¶ 0061; regarding, e.g., numeric parts of the telemetry data that are part of resulting vector embeddings.).
With regards to Claim 13, Kiss in view of Watson teaches the system of Claim 11 as referenced above. Kiss in view of Watson further teaches:
wherein the one or more processors are further to:
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify a second subset of the resources potentially associated with the failure (Kiss: Fig. 5; Kiss: ¶ 0072-0073; regarding, e.g., a Word2Vec transformation process [a statistical analysis]; Kiss: Fig. 2; Kiss: ¶ 0055; Kiss: Fig. 3; and Kiss: ¶ 0061.); and
provide identifying information for the second subset as additional input to the trained language model (Kiss: Fig. 4 and Kiss: ¶ 0068-0069.).
With regards to Claim 16, Kiss in view of Watson teaches the system of Claim 11 as referenced above. Kiss in view of Watson further teaches:
wherein the one or more processors are further to:
provide supporting evidence as additional input to the trained language model, the supporting evidence including at least one of content of a message (Kiss: Fig. 3 and Kiss: ¶ 0061.), a timestamp, an importance score, or a counter value.
With regards to Claim 17, Kiss teaches:
at least one processor (Fig. 6 and ¶ 0077.), comprising:
one or more logical units (Fig. 6 and ¶ 0077.) to:
detect a failure in a “system” performed using a plurality of resources (¶ 0050 and ¶ 0064.);
provide, as input to a trained language model, identifying information for a subset of the resources determined to be potentially associated with the failure (Fig. 3; ¶ 0061; regarding, e.g., vector embeddings showing, e.g., a 1 indicating anomaly [a subset]; ¶ 0064; Fig. 4 and ¶ 0068-0069.), along with supporting evidence for the subset (Fig. 3 and ¶ 0061.), the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the “system;” (Fig. 3; ¶ 0061; regarding, e.g., network telemetry data from devices; Fig. 4; and ¶ 0068-0069.);
receive, as output of the trained language model, indication of one or more resources inferred to be at least partially responsible for the failure (Fig. 5 and ¶ 0074.), along with an explanation for selection of the one or more resources (Fig. 5 and ¶ 0074.); and
initiate, based at least on the output, one or more corrective actions (Fig. 5 and ¶ 0074.).
Kiss does not explicitly teach:
a job;
one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources.
However, Watson teaches:
a job (¶ 0067-0069; regarding, e.g., one or more certain functions of a deployed application in a software package that have stopped working due to a software bug.);
one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the one or more resources (¶ 0067-0069; regarding, e.g., a change of parameters of the software package.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Kiss with the remediation of a buggy software package associated with parameter changes as taught by Watson because the system automatically implements remediation processes on an environment to effectively prevent potential issues from interfering with the entity's processes and workflows (Watson: ¶ 0069).
With regards to Claim 18, Kiss in view of Watson teaches the at least one processor of Claim 17 as referenced above. Kiss in view of Watson further teaches:
wherein the supporting evidence includes at least one of content of a message (Kiss: Fig. 3 and Kiss: ¶ 0061.), a timestamp, an importance score, or a counter value related to the job.
With regards to Claim 19, Kiss in view of Watson teaches the at least one processor of Claim 17 as referenced above. Kiss in view of Watson further teaches:
wherein the one or more logical units are further to:
perform a statistical analysis of a collection of text entries, obtained from two or more sources, to identify one or more of the resources potentially associated with the failure (Kiss: Fig. 5; Kiss: ¶ 0072-0073; regarding, e.g., a Word2Vec transformation process [a statistical analysis]; Kiss: Fig. 2; Kiss: ¶ 0055; Kiss: Fig. 3; and Kiss: ¶ 0061.).
With regards to Claim 20, Kiss in view of Watson teaches the at least one processor of Claim 17 as referenced above. Kiss in view of Watson further teaches:
wherein the one or more logical units are further to:
perform anomaly detection with respect to a collection of time series data, obtained from two or more sources, to identify one or more of the resources potentially associated with the failure (Kiss: Fig. 3 and Kiss: ¶ 0061; regarding, e.g., network telemetry from devices [a collection of time series data]; Kiss: Fig. 5; Kiss: ¶ 0072-0073; Kiss: Fig. 2; and Kiss: ¶ 0055.).
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kiss, further in view of Watson, and further in view of Sun et al. (U.S. Patent No. US 11,016,838 B2), hereinafter “Sun.”
With regards to Claim 2, Kiss in view of Watson teaches the system of Claim 1 as referenced above. Kiss in view of Watson does not explicitly teach:
wherein the statistical analysis includes generation of one or more importance matrices using term frequency-inverse document frequency (TF-IDF) values of the text entries in accordance with the system of Claim 1.
However, Sun teaches:
wherein the statistical analysis includes generation of one or more importance matrices using term frequency-inverse document frequency (TF-IDF) values of the text entries (col. 5, lines 36-41 and col. 6, lines 29-42.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Kiss in view of Watson with the use of a TF-IDF matrix as taught by Sun because a simple substitution of one known element (a Word2Vec transformation process – Kiss: Fig. 5 and Kiss: ¶ 0072-0073) for another (the use of a TF-IDF matrix) can be performed to obtain predictable results (providing known language corpus update means for providing accurate results for new errors – Sun: col. 12, lines 25-30).
With regards to Claim 14, Kiss in view of Watson teaches the system of Claim 13 as referenced above. Kiss in view of Watson does not explicitly teach:
wherein the statistical analysis includes generation of one or more importance matrices using term frequency-inverse document frequency (TF-IDF) values of the text entries in accordance with the system of Claim 13.
However, Sun teaches:
wherein the statistical analysis includes generation of one or more importance matrices using term frequency-inverse document frequency (TF-IDF) values of the text entries (col. 5, lines 36-41 and col. 6, lines 29-42.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Kiss in view of Watson with the use of a TF-IDF matrix as taught by Sun because a simple substitution of one known element (a Word2Vec transformation process – Kiss: Fig. 5 and Kiss: ¶ 0072-0073) for another (the use of a TF-IDF matrix) can be performed to obtain predictable results (providing known language corpus update means for providing accurate results for new errors – Sun: col. 12, lines 25-30).
Allowable Subject Matter
Claims 3, 4, and 15 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 04/30/2026 have been fully considered, but they are not persuasive.
With regards to the 35 U.S.C. 101 rejections of Claims 1-20, the Remarks argue that:
Applicant submits that amended Claim 1 cannot be categorized as a mental process at least because the claims cannot reasonably be interpreted as steps that can be practically performed in the human mind. Applicant submits that amended Claim 1 is analogous to claims that were determined as not reciting a mental process under the October 2019 Guidance, since the claimed operations are computer-implemented and require specialized data processing that extends far beyond human mental capability. By way of example, embodiments are analogous to claims directed to complex data processing pipelines that involve transforming heterogeneous system-generated data into structured representations and performing model-based inference to achieve a technical result, such as identifying a root-cause resource in a distributed computing system. See, Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1148 (Fed. Cir. 2016).
Moreover, features recited in the amended Claim 1 cannot be practically performed in the human mind, much like the claims in Research Corp. Tech. v. Microsoft Corp., which were directed toward the manipulation of data structures in order to render an image. See, Research Corp. Tech. v. Microsoft Corp., 627 F.3d 859, 868 (Fed. Cir. 2010).
There is no reasonable way that a human, either mentally or with pencil and paper, could practically perform the claimed steps of analyzing multi-source distributed system data, generating structured evidence tied to resources, performing model-based attribution, and modifying operational parameters of computing resources. Each of these steps involves large-scale system data processing and machine learning inference requiring specialized computing systems.
Accordingly, the practical application of claim 1 is directed toward improving operation of distributed computing systems through resource-specific failure attribution and corrective control, rather than merely analyzing information in the abstract. The improvements associated with the practical application are directly provided by the claims by, at least, (i) identifying subsets of resources using multi-modal analysis of execution-time data, (ii) providing structured, resource-specific identifying information and operational evidence to a trained language model for attribution, and (iii) initiating corrective actions by modifying operational parameters of identified resources based on the attribution result.
The October 2019 Update notes that: "In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. … Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement." See, October 2019 Update at 12. As set forth in the specification, conventional approaches suffer from limited coverage, lack of scalability, and inability to integrate multi-source, multi-modal data for accurate failure attribution. The claims provide a solution by reciting a specific sequence of operations including detecting a failure, performing statistical analysis of multi-source system-generated data over heterogeneous data sources to identify candidate resource subsets, generating identifying information and supporting evidence, and providing that information to a trained language model to produce an attribution report that identifies responsible resources and supports corrective actions.
Further, amended claim 1 recites initiating, based at least on the attribution report, one or more corrective actions by automatically modifying one or more operational parameters associated with at least one of the resources. The specification explains that attribution results may be used to control system operation, including removing or restarting resources or restricting allocation of jobs to suspected nodes, which directly impacts how the distributed system allocates and utilizes computing resources. This integration of attribution with system-level control provides a concrete technological improvement by enabling targeted remediation and preventing repeated job failures, thereby improving system reliability, resource utilization, and overall operational efficiency.
However, the Examiner respectfully disagrees.
With regards to A above, it is unclear to the Examiner what eligible patent(s) are being referred to in the provided Synopsis court case. In Synopsis, the 35 U.S.C. 101 invalidity appears to have been affirmed.
Regardless of this particular decision, the Examiner asserts that at least Claims 1, 11, and 17 of the instant application, even if computer-implemented, require no specialized data processing that extends far beyond human mental capability. See MPEP 2106.04(a)(2)(III)(C): “…examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.” Applicant’s claims are performed using generic computer components, not specialized data processing, as asserted above in this Office action.
With regards to B above, the claims in Research Corp. Tech. were determined to have no abstract ideas and, as such, fell out of Step 2A, Prong I. As such, the claims in Research Corp. Tech. are completely non-analogous to the claims of the instant application because at least Claims 1, 11, and 17 contain abstract ideas as asserted above in this Office action. Therefore, the comparison of Research Corp. Tech. to the instant application is moot.
With regards to C above, the Examiner respectfully submits that “large-scale system data processing” is not mentioned anywhere within the claim language. A person could, e.g., detect a failure in data printed on paper such as a graph, perform a simple statistical analysis of the data such as identifying words associated with outliers on the graph, and think of modifications for parameters to provide a solution to a problem. A computer isn’t even in use for these mental processes; how, then, can they require large-scale system data processing?
With regards to D above, similar to C, the Examiner has asserted why the mental processes are merely abstract. The other additional element, (ii), is mere data gathering. Information is gathered to be used as an input for a model; there is no improvement to a computer or a computing technology with regards to gathering data as described in at least MPEP 2106.05(g).
With regards to E above, the Examiner asserts that the claim has been evaluated and does not reflect any improvement disclosed in the specification. The claimed additional elements are merely insignificant extra-solution activities and/or generic computer components / instructions that apply judicial exceptions as recited above in this Office action.
With regards to F above, the Examiner asserts that the claimed “initiate” step can be performed mentally. For example, a person can think or perceive of parameter modifications as a type of initiation of a corrective action. The claim does not comprise any meaningful change to a computer component based on these perceived modifications.
With regards to the 35 U.S.C. 103 rejections involving the Kiss reference, the Remarks argue that:
Applicant submits that the cited references, individually or in combination, do not teach or suggest at least the feature of "provide identifying information for the subset as input to a trained language model, the identifying information comprising, for individual resources in the subset, operational evidence generated during execution of the job." Applicant respectfully submits that Kiss does not teach or suggest at least this limitation. Kiss is directed to multi-modal anomaly detection in a communications network using a large language model that receives vector embeddings derived from various input modalities. In particular, Kiss describes transforming respective modalities into vector embeddings and inputting those embeddings into a trained or fine-tuned large language model to determine whether an anomaly has occurred and, in some embodiments, to provide a human-readable explanation or suggested action. However, Kiss does not disclose first identifying a subset of resources associated with execution of a particular job failure and then constructing, for individual resources in that subset, identifying information that includes operational evidence generated during execution of the job (Emphasis added). Rather, Kiss processes aggregated multi-modal inputs for anomaly detection at a system level. The embeddings in Kiss are modality-based representations, not resource-specific evidence packages corresponding to individual candidate resources used for execution of a particular job. Thus, Kiss does not teach or suggest the claimed per-resource input structure.
However, the Examiner respectfully disagrees.
With regards to G above, in response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “resource-specific evidence packages”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s remaining arguments with respect to Claims 1, 11, and 17 have been considered but are moot because the new ground of rejection relies on the Watson reference as necessitated by the scope-changing amendments.
With regards to Claims 2, 5-10, 12-14, 16, and 18-20, the Remarks do not discuss any further reasons as to why Kiss and/or Watson do not teach the limitations of these claims; therefore, Claims 2, 5-10, 12-14, 16, and 18-20 remain rejected using at least the reasoning provided above in this Office action.
Additionally, no further arguments associated with 35 U.S.C. 101 are made with regards to Claims 2-10, 12-16, and 18-20; therefore, Claims 2-10, 12-16, and 18-20 remain rejected using at least the reasoning provided above in this Office action.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH KUDIRKA whose telephone number is (571)270-7126. The examiner can normally be reached M-F 7:30am - 5pm ET.
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/JOSEPH R KUDIRKA/ Primary Patent Examiner, Art Unit 2114
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