DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see Response to Non-Final Office Action (“Response”), filed 13 July 2026, with respect to the rejection under 35 USC 101 have been fully considered and are persuasive. Therefore, the rejection under 35 USC 101 has been withdrawn. Applicant’s arguments in the Response with respect to the rejection under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of United States Patent Application Publication No. 2019/0272207 A1 to Pandey et al. (“Pandey”).
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 1-2, 5, 8-9, 12, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No. 2023/0206348 A1 to Reddy et al. (“Reddy”) in view of United States Patent Application Publication No. 2019/0272207 A1 to Pandey et al. (“Pandey”).
As per claims 1, 8 and 15, the claimed subject matter that is met by Reddy includes:
a method, comprising (Reddy: ¶ 0003):
training, by one or more processors, a machine learning model based on historical enterprise resource planning (ERP) input data associated with a time series dataset and a corresponding training algorithm, wherein the historical ERP input data corresponds to one or more entities and one or more clients in a supply chain network (Reddy: ¶¶ 0035, 0047 and 0054-0055);
receiving additional input data from the one or more entities and/or from the one or more clients, wherein the additional input data represents real-time data from the supply chain network (Reddy: ¶¶ 0017, 0039 and 0047);
detecting one or more discrete events by applying the machine learning model to the additional input data, wherein the one or more discrete events are indicative of anomalous behavior in the supply chain network (Reddy: ¶¶ 0038-0039 and 0052);
determining one or more source nodes of the one or more discrete events indicative of anomalous behavior in the supply chain network, wherein the one or more source nodes comprise at least one entity or at least one client (Reddy: ¶ 0085); and
initiating one or more remediation actions based on the anomalous behavior detected from the one or more source nodes (Reddy: ¶¶ 0017 and 0049-0050).
Reddy fails to specifically teach 1.) the one or more source nodes comprise at least one computing device in the supply chain network corresponding to at least one entity or at least one client and 2.) wherein initiating the one or more remediation actions comprises at least one of disconnecting, routing around, rerouting from, dropping, or blocking the one or more source nodes indicative of the anomalous behavior. The Examiner provides Pandey to teach and disclose this claimed feature.
The claimed subject matter that is met by Pandey includes:
the one or more source nodes comprise at least one computing device in the supply chain network corresponding to at least one entity or at least one client (Pandey: ¶¶ 0112, 0132, 0114 and 0163-0164);
wherein initiating the one or more remediation actions comprises at least one of disconnecting, routing around, rerouting from, dropping, or blocking the one or more source nodes indicative of the anomalous behavior (Pandey: ¶¶ 0114, 0153 and 0163-0164);
Reddy teaches a system and method for detecting anomalous data. Pandey teaches a comparable system and method for detecting anomalous data that was improved in the same way as the claimed invention. Pandey offers the embodiment of the one or more source nodes comprise at least one computing device in the supply chain network corresponding to at least one entity or at least one client and wherein initiating the one or more remediation actions comprises at least one of disconnecting, routing around, rerouting from, dropping, or blocking the one or more source nodes indicative of the anomalous behavior. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the specific remediation actions as disclosed by Pandey to the remediation actions as taught by Reddy for the predicted result of improved systems and methods for detecting anomalous data. No additional findings are seen to be necessary.
As per claims 2 and 9, the claimed subject matter that is met by Reddy and Pandey includes:
wherein the corresponding training algorithm comprises a change point detection algorithm (Reddy: ¶¶ 0107-0111).
The motivation for combining the teachings of Reddy and Pandey are discussed in the rejection of claims 1 and 8, and are incorporated herein.
As per claims 5, 12 and 18, the claimed subject matter that is met by Reddy and Pandey includes:
wherein prior to training the machine learning model, the method further comprises generating one or more features based on the historical ERP input data, wherein the one or more features comprise measurable characteristics corresponding to identified patterns in the historical ERP input data (Reddy: ¶¶ 0085 and 0107-0111).
The motivation for combining the teachings of Reddy and Pandey are discussed in the rejection of claims 1, 8 and 15, and are incorporated herein.
Claims 3, 4, 10, 11, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Pandey as applied in claims 1, 8 and 15, and further in view of United States Patent Application Publication No. 2023/0252047 A1 to Cella et al. (“Cella”).
As per claims 3, 10 and 16, Reddy and Pandey fail to specifically teach wherein the change point detection algorithm comprises Bayesian change point criteria for detecting one or more discrete events in the additional input data, and wherein the Bayesian change point criteria comprise a predetermined posterior probability threshold and/or a Bayes factor. The Examiner provides Cella to teach and disclose this claimed feature.
The claimed subject matter that is met by Cella includes:
wherein the change point detection algorithm comprises Bayesian change point criteria for detecting one or more discrete events in the additional input data, and wherein the Bayesian change point criteria comprise a predetermined posterior probability threshold and/or a Bayes factor (Cella: ¶¶ 1158 and 1180).
Reddy and Pandey teach systems and methods for analyzing data. Cella teaches a comparable system and method for analyzing data that was improved in the same way as the claimed invention. Cella offers the embodiment of wherein the change point detection algorithm comprises Bayesian change point criteria for detecting one or more discrete events in the additional input data, and wherein the Bayesian change point criteria comprise a predetermined posterior probability threshold and/or a Bayes factor. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the Bayesian change point criteria as disclosed by Cella to the algorithm as taught by Reddy and Pandey for the predicted result of improved systems and methods for analyzing data. No additional findings are seen to be necessary.
As per claims 4, 11 and 17, the claimed subject matter that is met by Reddy, Pandey and Cella includes:
wherein the change point detection algorithm comprises kernel-based change point criteria for detecting one or more discrete events in the additional input data, and wherein the kernel-based change point criteria comprise kernel density estimates and shifts (Cella: ¶¶ 1158 and 1180).
The motivation for combining the teachings of Reddy, Pandey and Cella are discussed in the rejection of claims 3, 10 and 16, and are incorporated herein.
Claims 6-7, 13-14 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Reddy in view of Pandey as applied in claims 1, 8 and 15, and further in view of United States Patent Application Publication No. 2022/0188757 A1 to Morris (“Morris”).
As per claims 6, 13 and 19, Reddy and Pandey fail to specifically teach wherein initiating the one or more remediation actions further comprises overriding the one or more inventory management configurations at the one or more entities and/or the one or more clients to adjust for the anomalous behavior detected from the one or more source nodes. The Examiner provides Morris to teach and disclose this claimed feature.
The claimed subject matter that is met by Morris includes:
wherein initiating the one or more remediation actions further comprises overriding the one or more inventory management configurations at the one or more entities and/or the one or more clients to adjust for the anomalous behavior detected from the one or more source nodes (Morris: ¶ 0058);
Reddy and Pandey teaches a system and method for detecting anomalous data. Morris teaches a comparable system and method for detecting anomalous data that was improved in the same way as the claimed invention. Morris offers the embodiment of wherein initiating the one or more remediation actions further comprises overriding the one or more inventory management configurations at the one or more entities and/or the one or more clients to adjust for the anomalous behavior detected from the one or more source nodes. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the specific remediation actions as disclosed by Morris to the remediation actions as taught by Reddy and Pandey for the predicted result of improved systems and methods for detecting anomalous data. No additional findings are seen to be necessary.
As per claims 7, 14 and 20, the claimed subject matter that is met by Reddy, Pandey and Morris includes:
wherein the historical ERP input data and the additional input data comprise multivariate data received from the one or more entities and the one or more clients in the supply chain network (Morris: ¶ 0058).
The motivation for combining the teachings of Reddy, Pandey and Morris are discussed in the rejection of claims 6, 13 and 19, and are incorporated herein.
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 Hunter Wilder whose telephone number is (571)270-7948. The examiner can normally be reached Monday-Friday 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached at (571)272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A. Hunter Wilder/Primary Examiner, Art Unit 3627
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