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 .
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 a judicial exception (i.e., an abstract idea) without “significantly more.” Claims 1-20 are directed to training a model, receiving input data, detecting events, determining nodes of the events and initiating remediation actions, which is considered an abstract idea. Further, the claim(s) as a whole, when examined on a limitation-by-limitation basis and in ordered combination do not include an inventive concept.
Step 1 – Statutory Categories
As indicated in the preamble of the claims, the examiner finds the claims are directed to a process, machine, or article of manufacture.
Step 2A – Prong One - Abstract Idea Analysis
Exemplary claim 1 (and similarly claims 8 and 15) recites the following abstract concepts, in italics below, which are found to include an “abstract idea”:
A method, comprising:
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;
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;
detecting one or more discrete events in the additional input data, wherein the one or more discrete events are indicative of anomalous behavior in the supply chain network;
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; and
initiating one or more remediation actions based on the anomalous behavior detected from the one or more source nodes, wherein initiating the one or more remediation actions comprises generating a modification to 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 claim features in italics above as drafted, under its broadest reasonable interpretation, are mathematical concepts, mental processes and/or certain methods of organizing human activity performed by generic computer components. That is, other than reciting “training, by one or more processors, a machine learning model,” nothing in the claim element precludes the step from practically being a mathematical concept, performed in the mind or a method of organized human activity. For example, but for the “training, by one or more processors, a machine learning model” language, “training… a … model based on historical enterprise resource planning (ERP) input data associated with a time series dataset and a corresponding training algorithm” in the context of this claim encompasses a mathematical concept. If the claim limitations, under its broadest reasonable interpretation, covers mathematical relationship, mathematical formula or mathematical calculation but for the recitation of generic computer components, then it falls within the “mathematical concept” grouping of abstract ideas. Further, “training…a … model based on historical enterprise resource planning (ERP) input data associated with a time series dataset …, wherein the historical ERP input data corresponds to one or more entities and one or more clients in a supply chain network… detecting one or more discrete events in the additional input data, wherein the one or more discrete events are indicative of anomalous behavior in the supply chain network; 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 ” in the context of this claim encompass mental processes. If the claim limitations, under its broadest reasonable interpretation, covers steps which could be performed in the human mind including an observation, evaluation, judgement of opinion but for the recitation of generic computer components, then it falls within the “mental process” grouping of abstract ideas. Even further, “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… initiating one or more remediation actions based on the anomalous behavior detected from the one or more source nodes, wherein initiating the one or more remediation actions comprises generating a modification to 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” in the context of this claim encompass certain methods of organizing human activity. If the claim limitations, under its broadest reasonable interpretation, covers fundamental economic practice, commercial or legal interaction or managing personal behavior or relationships or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two - Abstract Idea Analysis
This judicial exception is not integrated into a practical application. In particular, the claim only recites two additional elements – “training, by one or more processors, a machine learning model,”. The “one or more processors” and “machine learning model” are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f), i.e. the training, detecting determining and initiating steps) and data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g), i.e. the receiving step). 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 abstract idea. The claim is directed to an abstract idea.
Step 2B - Significantly More Analysis
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more processors” and “a machine learning model” amount to no more than mere instructions to apply the exception using a generic computer component and insignificant extra-solution activity. Mere instructions to apply the exception using a generic computer component and insignificant extra-solution activity cannot provide an inventive concept. Further, the background does not provide any indication that the “one or more processors” and “machine learning model” are anything other than a generic, off-the-shelf computer components. For these reasons, there is no inventive concept. The claim is not patent eligible.
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, 5-9, 12-15 and 18-20 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. 2022/0188757 A1 to Morris (“Morris”).
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 in 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 wherein initiating the one or more remediation actions comprises generating a modification to 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 comprises generating a modification to 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: ¶¶ 0006 and 0033)
Reddy 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 comprises generating a modification to 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 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 Morris includes:
wherein the corresponding training algorithm comprises a change point detection algorithm (Reddy: ¶¶ 0107-0111 and Morris: ¶¶ 0050 and 0058).
The motivation for combining the teachings of Reddy and Morris 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 Morris 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 and Morris: ¶¶ 0060 and 0064).
The motivation for combining the teachings of Reddy and Morris are discussed in the rejection of claims 1, 8 and 15, and are incorporated herein.
As per claims 6, 13 and 19, the claimed subject matter that is met by Reddy and 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: ¶ 0055).
The motivation for combining the teachings of Reddy and Morris are discussed in the rejection of claims 1, 8 and 15, and are incorporated herein.
As per claims 7, 14 and 20, the claimed subject matter that is met by Reddy 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 and Morris 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 Morris 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 Morris 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 Morris 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 Morris 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, Morris 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, Morris and Cella are discussed in the rejection of claims 3, 10 and 16, and are incorporated herein.
Conclusion
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