CTNF 19/202,181 CTNF 79950 DETAILED ACTION This Office Action is in response to Application filed on 08 May 2025. Claims 1-20 are pending. The claims have been considered and examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 non-statutory subject matter, an abstract idea. The claims fall within at least one of the four categories of patent eligible subject matter. However, the claimed invention is directed to mental processes without significantly more. The following is an analysis of the claims regarding subject matter eligibility in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG): Subject Matter Eligibility Analysis Step 1: Do the Claims Specify a Statutory Category? Claims 1-7 are directed to a method/process, claims 8-14 are directed to a system, and claims 15-20 are directed to a non-transitory computer program product comprising a computer-readable storage medium, therefore satisfying Step 1 of the analysis. Step 2 Analysis for Claims 1-10 Step 2A – Prong 1: Is a Judicial Exception Recited? Independent claim 1 , recites the limitations “ identifying a root cause of a detected system fault based on a fused causal graph that represents a relationship of factors and correlation of multi-modality data by” (Mental Process), “ determining long-term temporal dependencies and causal relation from system entities and key performance indicators (KPI) “ (Mental Process), “ analyzing a correlation of factors from multi-modality data to assess contributions of the factors to causing a detected system fault;” (Mental Process), “ learning ,…, a relationship of the factors and correlation of multi-modality data” (Mental Process). The limitations cover concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). The limitations cite processes that, under their broadest reasonable interpretation, covers performance of the limitations in the human mind and organizing human activities but for the recitation of generic computer components (i.e., use of a processor or a generic computer). That is, nothing in the claim elements preclude the steps from practically being performed in the mind or managing personal behavior. The limitations involve identifying, determining, analyzing, data and making judgements about the data. Such an observation, evaluation, and/or opinion of data can be performed by a human and recites a mental process. Such a managing of personal behavior is a method of organizing human activity. If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind or organizing human activity but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping or “Certain methods of organizing human activity” of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance . Accordingly, the claim recites an abstract idea. Claims 2-7 cite further details pertaining to “identifying…”, “determining…”, “analyzing…”, and “learning…” specificed in claim 1; and additionally “aggregating…” (Mental Process), “mimicking…” (Mental Process), “encoding…”, (Mental Process), “reweighing…” (Mental Process), “maximizing mutual information…”, “recovering….” (Mental Process). Each of the limitations in these dependent claims describes processes that, under their broadest reasonable interpretation, contain mental processes directed to performing the abstract idea identified in claim 1. In claims 2-7, The limitations cover concepts performed in the human mind, or on paper with pencil (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, claims 2-7 each recite an abstract idea. Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application? Claim 1 , indicates the method is computer-implemented and a cloud computing system. Even if the described methods are implemented on a computer, there is no indication that the combination of elements in the claim solves any particular technological problem other than merely taking advantage of the inherent advantages of using existing computer technology in its ordinary, off-the-shelf capacity to apply the identified judicial exceptions. Simply implementing the abstract idea(s) on a general-purpose processor or other generic computer component is not a practical application of the abstract idea(s). The computer system cited in the claim is described at a high level of generality such that it represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). This limitation can also be viewed as nothing more than an attempt to generally link the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)). Claim 1 , further recites the limitation “using dilated convolutional neural networks” and “with the DCNN”. Using the words “apply it”, or an equivalent (i.e., “using”), are mere instructions to implement an abstract idea or other exception on a computer, thus amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. (See MPEP 2106.05(f)). These limitations describe insignificant extra-solution activity pertaining to generically applying a resolution to an identified problem, respectively, without providing any details regarding a specific problem being solved or specific remedial actions being taken. As such, these limitations do not integrate the abstract idea(s) into a practical application. Claim 1 , additionally recites the use a machine learning environment, “dilated convolutional neural network” and “contrastive representation learning” without any specification of details pertaining to how the associated machine learning environment is trained and/or how the actual machine learning is performed. Such details would include description of specific algorithms used in training the machine learning model. As currently written, the limitations in the claims describe merely certain data inputted to the machine learning environment and received. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s). Claim 1 recites KPI of “of a cloud computing system”. These limitations describe insignificant extra-solution activity pertaining to selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). Claims 2-7 recite additional use a machine learning environment, “using a graph neural network (GNN)”, “with contrastive learning regularization” and “employing multi-layer perceptrons (MLP)” without any specification of details pertaining to how the associated machine learning environment is trained and/or how the actual machine learning is performed. Such details would include description of specific algorithms used in training the machine learning model. As currently written, the limitations in the claims describe merely certain data inputted to the machine learning environment and received. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s). Claims 2-7 describe further details regarding the to “identifying…”, “determining…”, “analyzing…”, and “learning…”. These claims contain no additional elements which would integrate the abstract idea(s) into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the identified abstract idea(s). Step 2B: Do the Claims Provide an Inventive Concept? When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception. In the instant case, as detailed in the analysis for Step 2A-Prong 2, claim 1 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. The computer system recited in the claim describe a generic computer processor and/or computer components at a high level and do not represent “significantly more” than the judicial exception. The limitation pertaining to performing … one or more actions describe insignificant extra-solution activity and are written at a high level in a generic manner without providing any details regarding a specific problem being solved or specific remedial actions being taken. Therefore, these limitations recite no additional elements that would amount to significantly more than the abstract ideas defined in the claim. Claim 1 recite the limitation “performing system maintenance autonomously that corrects the detected system fault caused by the root cause” describe insignificant extra-solution activity and are written at a high level in a generic manner without providing any details regarding a specific problem being solved or specific remedial actions being taken. Therefore, these limitations recite no additional elements that would amount to significantly more than the abstract ideas defined in the claim. Claims 1-7 , recite limitations regarding the use of machine learning environment, DCNN, GNN etc. As discussed above in the Step 2A - Prong 2 analysis regarding integration of the abstract idea into a practical application, the limitations, as currently written, describe merely certain data inputted to the machine learning environment and received. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general-purpose processor or other generic computer component, or utilizing generic artificial intelligence technology to apply the identified judicial exception, does not describe an inventive concept. Conclusion In light of the above, the limitations in claims 1-7 recite and are directed to abstract ideas and recite no additional elements that would amount to significantly more than the identified abstract idea(s). Claims 1-7 are therefore not patent eligible. Step 2 Analysis for Claims 8-14 Claims 8-14 , contain limitations for a system which are similar to the limitations for the methods specified in claims 1-7, respectively. As such, the analysis under Step 2A – Prong 1, Step 2A – Prong 2, and Step 2B for claims 8-14 is similar to that presented above for claims 1-7. Step 2B: Do the Claims Provide an Inventive Concept? When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception. Claims 8-14 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. Claims 8-14 recites the additional elements of a “a system… comprising: a memory device; and one or more processor devices operatively coupled with memory device to”. The processors and memory cited in the claim describe generic computer components at a high level and do not represent “significantly more” than the identified judicial exception. The configuring of the processors recites intended use of the claimed limitations and does not represent “significantly more” than the identified judicial exception. Conclusion In light of the above, the limitations in claims 8-14 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract ideas(s). Claims 8-14 are therefore not patent eligible. Step 2 Analysis for Claims 15-20 Claims 15-20 , contains limitations for a non-transitory computer-readable medium which are similar to the limitations for the methods specified in claims 1-6, respectively. As such, the analysis under Step 2A – Prong 1, Step 2A – Prong 2, and Step 2B for claims 15-20 are similar to that presented above for claims 1-6. Step 2B: Do the Claims Provide an Inventive Concept? When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception. Claim 15-20 contain additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. Claims 15-20 recite the additional elements of a “A non-transitory computer program product comprising a computer- readable storage medium including program code for online multi-modality root cause analysis, where in the program code when executed on a computer causes the computer to:”. The computer-readable medium cited in the claim describe generic computer components at a high level and do not represent “significantly more” than the identified judicial exception. The executing of instructions on the processors recites intended use of the claimed limitations and does not represent “significantly more” than the identified judicial exception. Conclusion In light of the above, the limitations in claims 15-20 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract ideas(s). Claims 15-20 is therefore not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-2, 8-9, and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gusat et al., U.S. Patent App. Pub. 2023/0325269, hereinafter referred to as “Gusat”, in view of Gusat et al., U.S. Patent App. Pub. 2023/0259794, hereinafter referred to as “’794” and in view of She et al., U.S. Patent App. Pub. 2023/0088676, hereinafter referred to as “She” and in view of Chen et al., U.S. Pub. 2022/0382614, hereinafter referred to as “Chen” . Referring to claim 1 , Gusat1 discloses a method of root cause analysis of computerized system that is networked, monitoring various data collected (See Gusat, paragraphs 0004, 0031, and 0043). - A computer-implemented method for online multi-modality root cause analysis, comprising: Gusat discloses detecting an anomaly and using causal graphs to troubleshoot the detected anomaly, having causal relationships and correlation of the data (See Gusat, paragraphs 0004, 0014, 0018, 0078). - identifying a root cause of a detected system fault based on a fused causal graph that represents a relationship of factors and correlation of multi-modality data by: Gusat discloses extracting dependencies, discovering causal relationships, and key performance indicators (See Gusat, paragraphs 0014, 0018, 0069). - determining long-term temporal dependencies and causal relation from system entities and key performance indicators (KPI) of a cloud computing system Gusat discloses causal factors that lead to the occurrence of the anomaly (See Gusat, paragraph 0045). - analyzing a correlation of factors from multi-modality data to assess contributions of the factors to causing a detected system fault; Gusat discloses learning causal relationships of the KPIs (See Gusat, paragraph 0018). - learning a relationship of the factors and correlation of multi-modality data; and Gusat does not disclose “using dilated convolutional neural networks (DCNN);” “with the DCNN”, “contrastive representation learning”. Gusat also does not disclose “performing system maintenance autonomously that corrects the detected system fault caused by the root cause”. However, Gusat does disclose neural networks (See Gusat, paragraph 0074) and Gusat discloses resolving incidents (See Gusat, paragraph 0089). ‘794 discloses root cause analysis using KPIs with a neural network (See ‘794, paragraphs 0003-0004). ‘794 discloses the use of a dilated convolutional neural network (See ‘794 paragraph 0028). It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the root cause analysis with KPIs of Gusat with using a dilated convolutional neural network of ‘794. This would have been obvious because the DCNN allows for higher frequency and longer periods of sampling KPIs (See ‘794, paragraph 0028). She discloses the use of neural networks and contrastive representation learning (See She, paragraph 0007). It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the root cause analysis using neural networks of Gusat with the contrastive representation learning of She. This would have been obvious to do because contrastive representation learning allows for training of a neural network in an unsupervised manner (See She, paragraph 0007). Chen discloses identifying and addressing the root cause of a failure in a distributed computing system (See Chen, paragraph 0032). Chen discloses automatically resolving failures (See Chen, paragraph 0024). It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the root cause analysis of Gusat with automatically resolving the failure of Chen. This would have been obvious to do because allows automatic corrective action, thus providing faster return to service (See Chen, paragraph 0024). Referring to claim 2 , Gusat, ‘794, She, and Chen disclose all the limitations (see rejection of claim 1) including She disclose using graph neural networks and aggregating features from a set of neighbor nodes (See Chen, paragraph 0003). - The computer-implemented method of claim 1, wherein determining the long-term temporal dependencies further comprises aggregating information from neighboring system entities using a graph neural network (GNN). Claims 8 and 9 are rejected for similar reasons as claims 1 and 2, see above rejection. Additionally, Gusat discloses method can be performed on a computer with memory and processor (See Gusat, paragraph 0032, 0105, 0109). Claims 15 and 16 are rejected for similar reasons as claims 1 and 2, see above rejection. Additionally, Gusat discloses a computer readable storage medium with instructions for use by an instruction execution device (See Gusat, paragraph 0114-0116) . Allowable Subject Matter Claim 3-7, 10-14, and 17-20 are objected to as being dependent upon a rejected base claim, but would be allowable, over the prior art, if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and if the above 35 USC 101 abstract idea rejection is also overcome. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. U.S. Patent App. Pub. 2025/0147754 to Kholodkov et al. - Mulit-modal artificial intelligence root cause analysis U.S. Patent App. Pub. 2023/0353447 to Yaderna et al. - Root cause analysis1 Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH D MANOSKEY whose telephone number is (571)272-3648. The examiner can normally be reached M-F 7:30am to 3:30pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 571-272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOSEPH D MANOSKEY/Primary Examiner, Art Unit 2113 June 12, 2026 Application/Control Number: 19/202,181 Page 2 Art Unit: 2113 Application/Control Number: 19/202,181 Page 3 Art Unit: 2113 Application/Control Number: 19/202,181 Page 4 Art Unit: 2113 Application/Control Number: 19/202,181 Page 5 Art Unit: 2113 Application/Control Number: 19/202,181 Page 6 Art Unit: 2113 Application/Control Number: 19/202,181 Page 7 Art Unit: 2113 Application/Control Number: 19/202,181 Page 8 Art Unit: 2113 Application/Control Number: 19/202,181 Page 9 Art Unit: 2113 Application/Control Number: 19/202,181 Page 10 Art Unit: 2113 Application/Control Number: 19/202,181 Page 11 Art Unit: 2113 Application/Control Number: 19/202,181 Page 12 Art Unit: 2113 Application/Control Number: 19/202,181 Page 13 Art Unit: 2113 Application/Control Number: 19/202,181 Page 14 Art Unit: 2113 Application/Control Number: 19/202,181 Page 15 Art Unit: 2113 Application/Control Number: 19/202,181 Page 16 Art Unit: 2113
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