CTNF 18/963,139 CTNF 83742 DETAILED ACTION 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. This office correspondence is in response to the application filed on November 27, 2024. Claims 1-20 are pending. Information Disclosure Statement 06-52 AIA The information disclosure statement (IDS) submitted on 02/10/2025 and 06/08/2026 was filed after the mailing date of the in instant application on 11/27/2024 . The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 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 they are directed to a judicial exception without significantly more. Step 1 (Statutory Categories) The four categories of statutory subject matter are: (1) a process, (2) a machine, (3) a manufacture and (4) a composition of matter. MPEP § 2106.03. These claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. (2014). Independent claims 1, 11, and 16, recite a series of steps and, therefore, is a process that are directed to the abstract idea because they cover the concepts of a mental process (process in the human mind) including grouping of certain methods of observing, organizing human activity. Hence, the steps in the independent claims fall within the mental process grouping of abstract idea. Claims 1-20 are directed to an apparatus, system, or a method, and the underlying invention is merely to detect anomalies in a network, and is therefore an abstract idea (Analysis: Step 2A-Prong 1). The claimed invention is not directed to patent eligible subject matter. Based upon consideration of all of the relevant factors with respect to the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The underlying invention is merely anomalies are detected in an actual operating network, and is therefore an abstract idea . The claim recites the limitation of obtaining a plurality of subsets comprising a plurality of nodes of a graph representing a network. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are merely instructions to implement the abstract idea and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry (e.g. obtaining, performing, and training related data). There is nothing in the claim element precludes the step from practically being performed in the mind. For example, obtaining a plurality of subsets comprising a plurality of nodes of a graph representing a network, the claim encompasses simply obtaining information of resources in his/her mind. The mere nominal recitation of a generic performance and does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process. The claim recites additional elements of performing at least one iteration until a criteria is satisfied and followed by the transmitting information step . The claims do not recite any limitations that improve the functioning of a computer or to any other technology or technical field. The receiving step is recited at a high level of generality (i.e., as a general means of gathering computing resources to use in the obtaining step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation is no more than mere instructions to apply the exception using a generic computer. Subject Matter Eligibility Examples: Abstract Ideas 2019-01-07 13 The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (2A – Prong 2). Therefore, claim fails to provide an inventive concept (2B). As discussed with respect to Step 2A Prong 2, the additional element in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B. Here, the receiving step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the example does not provide any indication other than a generic, off the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐ understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the obtaining, performing, and training steps are well-understood, routine, conventional activity is supported under Berkheimer Option 2. For these reasons, there is no inventive concept in the claim, and thus it is ineligible. Claims 2-10, 12-15, and 17-20 recites further collection of properties of the computing resources. The information collected do not add any significant more to the Judicial Exception as they do not add any improvement to the computer system or a technology field. Hence, the claims do not add significant more. In light of the explanation and evidence provided above, the Examiner asserts that the claimed invention is directed in view of those case laws are directed towards the abstract idea. Lacking significantly more for the remainder of the claim, the invention is nothing more than an abstract idea without significantly more. 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vuda et al. (US Publication 2024/0291718) hereafter Vuda, in view of Bertiger et al. (US Publication 2021/0194907) hereafter Bertiger . As per claim 1, Vuda discloses a processor comprising: one or more circuits to at least: divide a plurality of nodes of a graph representing a network into a plurality of subsets comprising a first subset (paragraphs 68, 103); use a portion of the plurality of subsets not including the first subset to train a prediction model to predict connections between pairs of nodes in the first subset (paragraphs 68-69, 106); use the trained model to generate a prediction of the connections between the pairs of nodes in the first subset; and use the prediction to identify a set of anomalies in the network (paragraphs 68, 106-108). Although, Vuda discloses analytics for network topology subsets, he fails to expressly disclose generate a prediction of the connections between the pairs of nodes in the first subset. However, in the same field of endeavor, Bertiger discloses the claimed limitation of generate a prediction of the connections between the pairs of nodes and to identify a set of anomalies in the network (paragraphs 12-13, 21-22, 45). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bertigers’ teaching with Vuda. One would be motivated to identify network prediction to identify anomalies and remove those analysis of network topology information, thereby improving system performance. As per claim 2, Vuda discloses the processor wherein the set of anomalies comprise at least one missing connection between one of the pairs of nodes in the first subset (paragraphs 38, 51). As per claim 3, Vuda discloses the processor wherein the set of anomalies comprise at least one unnecessary connection between one of the pairs of nodes in the first subset (paragraphs 51, 60-61, 90, 92-93). As per claim 4, Vuda discloses the processor wherein features of a portion of the plurality of nodes included in the portion of the plurality of subsets not including the first subset are utilized to train the prediction model (paragraphs 51, 93-96). As per claim 5, Vuda discloses the processor wherein the features of the portion of the plurality of nodes comprise node connections included in the portion of the plurality of subsets not including the first subset (paragraphs 69, 86, 92). As per claim 6, Vuda discloses the processor wherein the features of the portion of the plurality of nodes comprise a name of each node in the portion of the plurality of nodes included in the portion of the plurality of subsets not including the first subset (paragraphs 37, 60, 94). As per claim 7, Vuda discloses the processor wherein the features of the portion of the plurality of nodes comprise semantic information related to the portion of the plurality of nodes included in the portion of the plurality of subsets not including the first subset (paragraphs 65, 105-106). As per claim 8, Vuda discloses the processor wherein one or more circuits are to: divide the plurality of nodes into a plurality of new subsets comprising a second subset, train a new model using a portion of the plurality of new subsets not including the second subset, use the trained new model to generate a new prediction of connections between pairs of nodes in the second subset, and use the new prediction to identify a new set of anomalies in the network (paragraphs 67-72, 79). As per claim 9, Vuda discloses the processor wherein one or more circuits are to: repeat at least until a set of criteria is satisfied: updating the graph to correct at least one of the set of anomalies, dividing the plurality of nodes into a plurality of new subsets comprising a second subset, training a new model using a portion of the plurality of new subsets not including the second subset, using the trained new model to generate a new prediction of connections between pairs of nodes in the second subset, and using the new prediction to reidentify the set of anomalies in the network (paragraphs 79-83, 86-87). Although, Vuda discloses analytics for network topology subsets, he fails to expressly disclose dividing the plurality of nodes of the updated graph into a plurality of new subsets comprising a second subset. However, in the same field of endeavor, Bertiger discloses the claimed limitation of dividing the plurality of nodes of the updated graph into a plurality of new subsets comprising a second subset (paragraphs 12-13, 21-22, 45). The same motivation that was utilized in the combination of claim 1 applies equally as well to claim 9. As per claim 10, Vuda discloses the processor wherein one or more circuits are to at least: repeat, at least until a set of criteria is satisfied: causing the network to be modified based at least in part on the set of anomalies (paragraphs 99, 111), obtaining an updated graph representing the modified network, dividing a plurality of nodes of the updated graph into a plurality of new subsets comprising a second subset, training a new model using a portion of the plurality of new subsets not including the second subset, using the trained new model to generate a new prediction of connections between pairs of nodes in the second subset, and using the new prediction to reidentify the set of anomalies in the modified network (paragraphs 79-83, 86-87). Although, Vuda discloses analytics for network topology subsets, he fails to expressly disclose obtaining an updated graph representing the modified network. However, in the same field of endeavor, Bertiger discloses the claimed limitation of obtaining an updated graph representing the modified network (paragraphs 12-13, 21-22, 45). The same motivation that was utilized in the combination of claim 1 applies equally as well to claim 10. Claim 11 is an Independent claim with similar limitation but different in preamble and hence are rejected based on the rejection provided in claim 1. As per claim 12, Vuda discloses the data center wherein one or more circuits are to: divide the plurality of nodes into a plurality of new subsets comprising a second subset, train a new model using a portion of the plurality of new subsets not including the second subset, use the trained new model to generate a new prediction of connections between pairs of nodes in the second subset, and use the new prediction to identify a new set of anomalies in the second subset (paragraphs 67-72, 79). As per claim 13, Vuda discloses the data center wherein one or more circuits are to: divide a plurality of nodes into a plurality of new subsets comprising a second subset that does not include any nodes in the first subset; train a new model using a portion of the plurality of new subsets not including the second subset, use the trained new model to generate a new prediction of connections between pairs of nodes in the second subset, use the new prediction to identify a new set of anomalies in the second subset (paragraphs 67-72, 92); and repeat, at least until a trained new model has generated a new prediction of connections between all pairs of nodes in the data center network (paragraphs 79-83, 86-87). As per claim 14, Vuda discloses the data center wherein features of the plurality of nodes included in the portion of the plurality of subsets not including the first subset are utilized to train the prediction model (paragraphs 70-72, 79-83). As per claim 15, Vuda discloses the data center wherein the features of the plurality of nodes comprises at least one of a set of features comprising node connections within the portion of the plurality of subsets not including the first subset, a name of a portion of the plurality of nodes included in the portion of the plurality of subsets not including the first subset, and semantic information related to the portion of the plurality of nodes included in the portion of the plurality of subsets not including the first subset (paragraphs 79-83, 86-87, 94). As per claim 16, Vuda discloses a method comprising: obtaining a plurality of subsets, each subset in the plurality of subsets comprising a portion of a plurality of nodes representing a network (paragraphs 68, 103); and performing at least one iteration until a set of criteria is satisfied, each iteration comprising: training a model using a portion of the plurality of subsets not including a particular subset, using the trained model to generate predictions of connections between pairs of nodes in the particular subset (paragraphs 68-69, 106), using the predictions to identify a set of anomalies in the network, obtaining an updated topology based at least in part on the set of anomalies, and obtaining the plurality of subsets based at least in part on the updated topology (paragraphs 68, 106-108). Although, Vuda discloses analytics for network topology subsets, he fails to expressly disclose a plurality of nodes of a graph representing a network, obtaining an updated graph based at least in part on the set of anomalies and obtaining the plurality of subsets based at least in part on the updated graph. However, in the same field of endeavor, Bertiger discloses the claimed limitation of a plurality of nodes of a graph representing a network, obtaining an updated graph based at least in part on the set of anomalies and obtaining the plurality of subsets based at least in part on the updated graph (paragraphs 12-13, 21-22, 45). The same motivation that was utilized in the combination of claim 1 applies equally as well to claim 16. As per claim 17, Vuda discloses the method wherein within each iteration, the set of anomalies comprise at least one of a missing connection between one of the pairs of nodes in the particular subset or an unnecessary connection between one of the pairs of nodes in the particular subset (paragraphs 51, 60-61, 90, 92-93). As per claim 18, Vuda discloses the method wherein performing one or more of the at least one iteration further comprises correcting at least one of the set of anomalies in the network, and the updated graph is obtained based at least in part on the network after the at least one anomaly has been corrected (paragraphs 67-72, 79). As per claim 19, Vuda discloses the method wherein features of the plurality of nodes represented by the graph of the plurality of subsets not including the particular subset are utilized to train the model (paragraphs 79-83, 86-87). As per claim 20, Vuda discloses the method wherein the features of the plurality of nodes comprises at least one of a set of features comprising node connections represented by the graph of the plurality of subsets not including the particular subset, a name of the nodes represented by the graph of the plurality of subsets not including the particular subset, and semantic information related to the nodes represented by the graph of the plurality of subsets not including the particular subset (paragraphs 37, 60, 79, 94) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mysore et al. (US Publication 2026/0121916) discloses systems and methods for improving the efficiency and accuracy of network operation validation anomaly detection in conglomerate-application-based ecosystems are disclosed. The disclosed anomaly evaluation platform can provide a first network operation to a first software application for generation of a second network operation, as in a flow-based processing system. The platform can generate a communication map that characterizes the architecture and/or performance of the system. In response to providing the communication map and the network operations to a validation model, the anomaly evaluation platform can determine a validation status and execute a corrective action to cure detected anomalies preventing validation of the network operation. As such, the anomaly evaluation platform enables dynamic monitoring, evaluation, and mitigation of detected anomalies in real-time and in a performance-dependent manner. Miriyala et al. (US Publication 2026/0095471) discloses analyzing anomalies in a network. In an example, a method comprises obtaining, by a system, a graph query and a network graph for a network, wherein the network graph includes one or more nodes having one or more properties that indicate a plurality of anomalies for the network; executing, by the system, the graph query on the network graph for the network to determine a matching subgraph of the network graph, wherein the graph query matches on the one or more nodes and the one or more properties; and based on the determination of the matching subgraph, outputting an indication of an association of the plurality of anomalies. Sternby et al. (US Publication 2021/0160266) discloses method and an apparatus for classifying anomalies of one or more feature-associated anomalies in network data traffic between devices in a first part of a network and devices in a second part of the network. The method comprises retrieving at least one network data traffic sample and determining one or more feature-associated anomaly scores for the retrieved at least one network data traffic sample. The method further comprises determining feature importance of each feature of a feature-associated anomaly score and classifying one or more anomalies based on the determined one or more feature-associated anomaly scores and the determined feature importance. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARZANA B HUQ whose telephone number is (571)270-3223. The examiner can normally be reached Monday - Friday: 8:30-5:30 ET. 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, Emmanuel L Moise can be reached at 571-272-3865. 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. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /FARZANA B HUQ/Primary Examiner, Art Unit 2455 Application/Control Number: 18/963,139 Page 2 Art Unit: 2455 Application/Control Number: 18/963,139 Page 3 Art Unit: 2455 Application/Control Number: 18/963,139 Page 4 Art Unit: 2455 Application/Control Number: 18/963,139 Page 5 Art Unit: 2455 Application/Control Number: 18/963,139 Page 6 Art Unit: 2455 Application/Control Number: 18/963,139 Page 7 Art Unit: 2455 Application/Control Number: 18/963,139 Page 8 Art Unit: 2455 Application/Control Number: 18/963,139 Page 9 Art Unit: 2455 Application/Control Number: 18/963,139 Page 10 Art Unit: 2455 Application/Control Number: 18/963,139 Page 11 Art Unit: 2455 Application/Control Number: 18/963,139 Page 12 Art Unit: 2455 Application/Control Number: 18/963,139 Page 13 Art Unit: 2455
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