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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
The applicant’s submitted drawings appear to be acceptable for examination purposes. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the drawings.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statements, dated 29 March 2024, 24 February 2025, 12 March 2025, 8 July 2025, 14 July 2025, 7 October 2025, 22 December 2025, 17 February 2026, and 27 August 2026, are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 forms, initialed and dated by the examiner, are attached to the instant office action.
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 1, 3-12, 14-17, 19, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 6-12 of copending Application No. 18/622,046 in view of Peng (US 2025/0272960).
This is a provisional nonstatutory double patenting rejection.
As per claim 1, the claim is compared with claim 1 of Application No. 18/622,046 —where any differences between them have been highlighted (in bold)—as follows:
Instant Application
Application No. 18/622,046
A method for managing an inference model, the method comprising
A method for managing an inference model, the method comprising
obtaining input data from one or more data sources to generate a prediction using the inference model
obtaining input data from one or more data sources to generate a prediction using the inference model
obtaining an anomaly condition associated with the input data, the anomaly condition being identified using an anomaly classifier
obtaining an anomaly condition associated with the input data using the measure of anomalousness
ingesting the anomaly condition into an attention mechanism of the inference model to obtain an updated inference model
ingesting the anomaly condition into an attention mechanism of the inference model to obtain an updated inference model
obtaining the prediction using the updated inference model and the input data
obtaining a prediction using the updated inference model and the input data
providing a computer-implemented service based, at least in part, on the prediction
providing a computer-implemented service based, at least in part, on the prediction
As illustrated above, claim 1 of Application ‘046 claims all of the limitations set forth in the instant application, except for using an anomaly classifier.
Peng teaches a method for managing an inference model, the method comprising:
obtaining an anomaly condition associated with the input data, the anomaly condition being identified using an anomaly classifier [the anomaly detector can include an anomaly classifier to identify abnormal conditions/anomalies (paras. 0011-13, 0119, 0148-150, 0154, etc.)];
Application ‘046 and Peng are analogous art, as they are within the same field of endeavor, namely utilizing anomaly detection models to provide anomaly data for further inference/models to make predictions.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize an anomaly classifier for anomaly detection, to provide anomaly data to downstream models/tasks, as taught by Peng, in the anomaly condition measurement/determination provided to the attention mechanism of the inference model in the system claimed by Application ‘046.
Peng provides motivation as [utilizing an anomaly classifier/class data allows models to avoid false positives and account for subtle anomalies, while generalizing well to new types of anomalies (paras. 0060-63, etc.)].
As per claim 3, Application ‘046/Peng teaches wherein the anomaly classifier is trained to detect an anomaly in the input data and/or classify the anomaly by the anomaly condition [the anomaly detector can include an anomaly classifier to identify abnormal conditions/anomalies (Peng: paras. 0011-13, 0119, 0148-150, 0154, etc.), where the system trains the anomaly detector model(s) (Peng: paras. 0058-63, etc.) and uses it to fine-tune downstream model(s) (Peng: para. 0069, etc.); so the anomaly classifier is trained to detect an anomaly in the input data/images and classify the anomaly by the anomaly condition].
As per claim 4, see claim 6 of Application ‘046.
As per claim 5, see claim 7 of Application ‘046.
As per claim 6, see claim 7 of Application ‘046, wherein modifying weights of the neural network modifies the importance of different features of the input data on predictions by the neural network.
As per claim 7, see claim 8 of Application ‘046.
As per claim 8, see claim 9 of Application ‘046.
As per claim 9, see claim 10 of Application ‘046.
As per claim 10, see claim 11 of Application ‘046.
As per claim 11, see claim 12 of Application ‘046.
As per claim 12, see the rejection of claim 1 above, wherein Application ‘046/Peng also teaches a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations [of the method] [the system, including the anomaly classifier, may be implemented via instructions stored in one or more memories and executed by one or more connected processors (Peng: paras. 0037, 0175-176; fig. 12; etc.)].
As per claim 14, see the rejection of claim 3 above.
As per claim 15, see the rejection of claim 4 above.
As per claim 16, see the rejection of claim 5 above.
As per claim 17, see the rejection of claim 1 above, wherein Application ‘046/Peng also teaches a data processing system comprising:
a processor; and
a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations [of the method] [the system, including the anomaly classifier, may be implemented via instructions stored in one or more memories and executed by one or more connected processors (Peng: paras. 0037, 0175-176; fig. 12; etc.)].
As per claim 19, see the rejection of claim 3 above.
As per claim 20, see the rejection of claim 4 above.
Claims 2, 13, and 18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/622,046 in view of Peng (US 2025/0272960), and further in view of Liu (US 2025/0168181).
This is a provisional nonstatutory double patenting rejection.
As per claim 2, Application ‘046/Peng teaches the method of claim 1, as described above.
While Application ‘046/Peng teaches utilizing the anomaly condition for a number of downstream model tasks, including supply chain improvements (see, e.g., Peng: para. 0174), it has not been relied upon for teaching wherein the anomaly condition comprises one selected from a group of anomaly conditions consisting of: war; inflation; pestilence; famine; poverty; overabundance of material comfort; material scarcity; and supply chain failure.
Liu teaches wherein the anomaly condition comprises one selected from a group of anomaly conditions consisting of:
war;
inflation;
pestilence;
famine;
poverty;
overabundance of material comfort;
material scarcity; and
supply chain failure [the system provides anomaly detection and provides alerts/service in response (abstract, etc.), where the anomalies can include equipment failures, process deviations, and/or supply chain disruptions (para. 0032, etc.); which includes at least material scarcity and supply chain failure(s)].
Application ‘046/Peng and Liu are analogous art, as they are within the same field of endeavor, namely providing anomaly detection that can be integrated into additional services/models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include equipment failures, process deviations, and/or supply chain disruption anomalies in the anomaly detection, as taught by Liu, in the anomaly detection provided for supply chain improvement models in the system taught by Application ‘046/Peng.
Liu provides motivation as [providing various anomaly detection services including anomalies that can disrupt supply chains or security of services allows the anomaly detection to provide security, support operational efficiency, enable cost reduction, support quality control, support customer experience, support regulatory compliance, enable predictive maintenance, support supply chain management, etc. (paras. 0030-38, etc.)].
As per claim 13, see the rejection of claim 2 above.
As per claim 18, see the rejection of claim 2 above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 13, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The terms “overabundance of material comfort” and “material scarcity” in claim 2 are relative terms which renders the claim indefinite. The terms “overabundance of material comfort” and “material scarcity” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
As per claim 13, see the rejection of claim 2 above.
As per claim 18, see the rejection of claim 2 above.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1-11 recite a method, claims 12-16 recite a manufacture, and claims 17-20 recite a device. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process elements:
to generate a prediction – a data scientist generates a prediction based upon input data.
obtaining an anomaly condition associated with the input data, the anomaly condition being identified – the data scientist identifies any anomaly condition(s) associated with the input data.
obtaining the prediction – the data scientist generates a prediction based upon input data and any anomaly conditions.
If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method for managing an inference model, the method comprising: – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
obtaining input data from one or more data sources – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage. See MPEP § 2106.05(g).
using the inference model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
using an anomaly classifier – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
ingesting the anomaly condition into an attention mechanism of the inference model to obtain an updated inference model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
using the updated inference model and the input data – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
providing a computer-implemented service based, at least in part, on the prediction – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method for managing an inference model, the method comprising: – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
obtaining input data from one or more data sources – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage. The courts have found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
using the inference model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
using an anomaly classifier – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
ingesting the anomaly condition into an attention mechanism of the inference model to obtain an updated inference model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
using the updated inference model and the input data – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
providing a computer-implemented service based, at least in part, on the prediction – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the anomaly condition comprises one selected from a group of anomaly conditions consisting of:
war;
inflation;
pestilence;
famine;
poverty;
overabundance of material comfort;
material scarcity; and
supply chain failure – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the anomaly condition comprises one selected from a group of anomaly conditions consisting of:
war;
inflation;
pestilence;
famine;
poverty;
overabundance of material comfort;
material scarcity; and
supply chain failure – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
The claim recites the following additional mental process elements:
detect an anomaly in the input data and/or classify the anomaly by the anomaly condition – the data scientist detects anomalies in the input data and/or classifies the anomalies by the anomaly condition.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the anomaly classifier is trained to – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the anomaly classifier is trained to – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 4:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the inference model is a neural network, the neural network being trained using a transformer architecture – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the inference model is a neural network, the neural network being trained using a transformer architecture – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 5:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein ingesting the anomaly condition comprises:
modifying weights of the neural network based on the anomaly condition to obtain the updated inference model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein ingesting the anomaly condition comprises:
modifying weights of the neural network based on the anomaly condition to obtain the updated inference model – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 6:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the weights modify importance of different features of the input data on predictions generated by the updated inference model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the weights modify importance of different features of the input data on predictions generated by the updated inference model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 7:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein modifying the weights contextualizes the prediction with respect to the anomaly condition – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein modifying the weights contextualizes the prediction with respect to the anomaly condition – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the attention mechanism impacts operation of an input layer of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the attention mechanism impacts operation of an input layer of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the attention mechanism impacts operation of at least one hidden layer of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the attention mechanism impacts operation of at least one hidden layer of the neural network – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 10:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the prediction comprises information usable to manage a condition impacting a business at a future point in time – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the prediction comprises information usable to manage a condition impacting a business at a future point in time – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 11:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the condition impacting the business at the future point in time is a change in availability of supply of a product from a supplier – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the condition impacting the business at the future point in time is a change in availability of supply of a product from a supplier – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 12:
See the rejection of claim 1 above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A non-transitory machine-readable medium having instructions stored therein – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
which when executed by a processor, cause the processor to perform operations [of the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A non-transitory machine-readable medium having instructions stored therein – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
which when executed by a processor, cause the processor to perform operations [of the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 13, see the rejection of claim 2 above.
As per claim 14, see the rejection of claim 3 above.
As per claim 15, see the rejection of claim 4 above.
As per claim 16, see the rejection of claim 5 above.
As per claim 17:
See the rejection of claim 1 above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A data processing system comprising:
a processor; and
a memory coupled to the processor to store instructions – this amounts to mere instructions to apply the exception using generic computer components, recited at a high level of generality. See MPEP § 2106.05(f).
which when executed by the processor, cause the processor to perform operations [of the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A data processing system comprising:
a processor; and
a memory coupled to the processor to store instructions – this amounts to mere instructions to apply the exception using generic computer components, recited at a high level of generality. See MPEP § 2106.05(f).
which when executed by the processor, cause the processor to perform operations [of the method] – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 18, see the rejection of claim 2 above.
As per claim 19, see the rejection of claim 3 above.
As per claim 20, see the rejection of claim 4 above.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
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.
Claim(s) 1, 3-10, 12, 14-17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Datta (US 2024/0249395) in view of Peng (US 2025/0272960) or, alternatively, over Peng in view of Datta, both as described below.
As per claim 1, Datta teaches a method for managing an inference model, the method comprising:
obtaining input data from one or more data sources to generate a prediction using the inference model [the system takes multi-contrast images and uses a second deep network (inference) model to generate a predicted image with improved quality (abstract; para. 0072; etc.)];
obtaining an anomaly condition associated with the input data, the anomaly condition being identified [the system uses a first deep learning network to generate an anomaly mask (anomaly condition) from the input images (abstract, etc.)];
ingesting the anomaly condition into an attention mechanism of the inference model to obtain an updated inference model [the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for training (updating) the second deep learning network (the inference model) (abstract; paras. 0003, 0008, 0027-29; etc.)];
obtaining the prediction using the updated inference model and the input data [the trained model may be deployed for making inferences by predicting the anomaly mask (by the first model) and synthesizing the enhanced image (by the inference/second model) (para. 0072, etc.)];
providing a computer-implemented service based, at least in part, on the prediction [the trained model may be deployed for making inferences by predicting the anomaly mask (by the first model) and synthesizing the enhanced image (by the inference/second model) (para. 0072, etc.) and a user interface may be provided so that users may interact with the predicted results or the training process (para. 0073, etc.); which is a computer-implemented service that is provided based on the prediction result].
While Datta teaches obtaining anomaly conditions associated with an input from an anomaly detection model (see above), it has not been relied upon for teaching using an anomaly classifier.
Peng teaches a method for managing an inference model, the method comprising:
obtaining input data from one or more data sources to generate a prediction using the inference model [the system utilizes a model for detecting an anomaly in an image(s) (abstract; etc.) and utilizing the anomaly detection model to fine-tune additional models for downstream tasks (paras. 0069, 0079, 0166-169, etc.), which downstream model tasks can include root cause analysis, automated decision making, classification, alert and warning generation, process optimization, quality control feedback loop, predictive maintenance, supply chain adjustments, continuous improvement, feedback to system design, regulatory compliance, and/or customer communication (para. 0174, etc.); where the downstream model(s) is the inference model];
obtaining an anomaly condition associated with the input data, the anomaly condition being identified using an anomaly classifier [the anomaly detector can include an anomaly classifier to identify abnormal conditions/anomalies (paras. 0011-13, 0119, 0148-150, 0154, etc.)];
obtain an updated inference model [the system trains the anomaly detector model(s) (paras. 0058-63, etc.) and uses it to fine-tune downstream model(s) (para. 0069, etc.)];
obtaining the prediction using the updated inference model [the system may output results of the anomaly detection (para. 0180, etc.) and utilizing the anomaly detection model to fine-tune additional models for downstream tasks (paras. 0069, 0079, 0166-169, etc.); where the downstream model(s) is the inference model providing predictions (see para. 0174 for examples)]; and
providing a computer-implemented service based, at least in part, on the prediction [the system may output results of the anomaly detection (para. 0180, etc.) and utilizing the anomaly detection model to fine-tune additional models for downstream tasks (paras. 0069, 0079, 0166-169, etc.); where the downstream tasks are the computer-implemented service (see para. 0174 for examples)].
Datta and Peng are analogous art, as they are within the same field of endeavor, namely utilizing anomaly detection models to provide anomaly data for further inference/models to make predictions.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize an anomaly classifier for anomaly detection, to provide anomaly data to downstream models/tasks, as taught by Peng, in the anomaly detector providing an anomaly-aware attention mechanism to the second/downstream inference model in the system taught by Datta.
Peng provides motivation as [utilizing an anomaly classifier/class data allows models to avoid false positives and account for subtle anomalies, while generalizing well to new types of anomalies (paras. 0060-63, etc.)].
Alternatively/additionally, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the anomaly detector for providing an anomaly-aware attention mechanism to train/fine-tune additional (downstream) models for making inferences, as taught by Datta, in the anomaly detector providing anomaly data for downstream models/tasks in the system taught by Peng.
Datta provides motivation as [the anomaly-aware attention mechanism allows the additional model(s) to identify slices/regions of anomalies that can facilitate further analysis (para. 0003, etc.) improve the quality of the outputs of the additional model(s) (para. 0011, etc.) and improve the model’s overall performance compared to other models without (para. 0027, etc.)].
As per claim 3, Datta/Peng teaches wherein the anomaly classifier is trained to detect an anomaly in the input data and/or classify the anomaly by the anomaly condition [the anomaly detector can include an anomaly classifier to identify abnormal conditions/anomalies (Peng: paras. 0011-13, 0119, 0148-150, 0154, etc.), where the system trains the anomaly detector model(s) (Peng: paras. 0058-63, etc.) and uses it to fine-tune downstream model(s) (Peng: para. 0069, etc.); and the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for training (updating) the second deep learning network (the inference model) (Datta: abstract; paras. 0003, 0008, 0027-29; etc.); so the anomaly classifier is trained to detect an anomaly in the input data/images and classify the anomaly by the anomaly condition].
As per claim 4, Datta/Peng teaches wherein the inference model is a neural network, the neural network being trained using a transformer architecture [the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for training (updating) the second deep learning network (the inference model) (Datta: abstract; paras. 0003, 0008, 0027-29; etc.) which can be a neural network (Datta: para. 0047, etc.); and where the reconstruction model can be a transformer trained to reconstruct features (Peng: paras. 0115-117, 0130, 0142, etc.); so that the neural network inference model is trained using a transformer architecture].
As per claim 5, Datta/Peng teaches wherein ingesting the anomaly condition comprises:
modifying weights of the neural network based on the anomaly condition to obtain the updated inference model [the system trains the anomaly detector model(s) (Peng: paras. 0058-63, etc.) and uses it to fine-tune downstream model(s) (Peng: para. 0069, etc.); and the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for training (updating) the second deep learning network (the inference model) (Datta: abstract; paras. 0003, 0008, 0027-29; etc.); which can include updating the weights of the inference model (Datta: paras. 0047-54, etc.)].
As per claim 6, Datta/Peng teaches wherein the weights modify importance of different features of the input data on predictions generated by the updated inference model [the system trains the anomaly detector model(s) (Peng: paras. 0058-63, etc.) and uses it to fine-tune downstream model(s) (Peng: para. 0069, etc.); and the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for training (updating) the second deep learning network (the inference model) (Datta: abstract; paras. 0003, 0008, 0027-29; etc.); which can include updating the weights of the inference model (Datta: paras. 0047-54, etc.); where weights modified during training of a neural network modify the importance of different features of the input data on prediction outputs ].
As per claim 7, Datta/Peng teaches wherein modifying the weights contextualizes the prediction with respect to the anomaly condition [the system trains the anomaly detector model(s) (Peng: paras. 0058-63, etc.) and uses it to fine-tune downstream model(s) (Peng: para. 0069, etc.); and the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for training (updating) the second deep learning network (the inference model) (Datta: abstract; paras. 0003, 0008, 0027-29; etc.); where using the anomaly condition as an attention mechanism during training (modifying the weights) of the model contextualizes the prediction with respect to the anomaly condition (i.e., it changes the prediction in relation to the weights, based upon the anomaly condition)].
As per claim 8, Datta/Peng teaches wherein the attention mechanism impacts operation of an input layer of the neural network [the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for the second deep learning network (the inference model) (Datta: abstract; paras. 0003, 0008, 0027-29; etc.), including utilizing the attention on the input layer of the model (Datta: paras. 0046, 0053, etc.)].
As per claim 9, Datta/Peng teaches wherein the attention mechanism impacts operation of at least one hidden layer of the neural network [the attention mask created by the first deep learning network may be used in an anomaly-aware attention mechanism for the second deep learning network (the inference model) (Datta: abstract; paras. 0003, 0008, 0027-29; etc.), including utilizing the attention on the input layer of the model (Datta: paras. 0046, 0053, etc.); where operating on the input layer also impacts operation of at least one hidden layer of a neural network (the neural network includes a number of hidden layers; see, e.g., Datta: para. 0047, etc.)].
As per claim 10, Datta/Peng teaches wherein the prediction comprises information usable to manage a condition impacting a business at a future point in time [downstream tasks may include, but are not limited to, root cause analysis, automated decision making, classification, alert and warning generation, process optimization, quality control feedback loop, predictive maintenance, supply chain adjustments, continuous improvement, feedback to system design, regulatory compliance, and customer communication (Peng: para. 0174, etc.); which are managing conditions impacting a business in the future (e.g., performing predictive maintenance to avoid future failures, adjusting supply chains, providing quality control feedback, etc.)].
As per claim 12, see the rejection of claim 1 above, wherein Datta/Peng also teaches a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations [of the method] [the system may be implemented via instructions stored in one or more memories and executed by one or more connected processors (Datta: paras. 0005, 0074-76; Peng: paras. 0037, 0175-176; fig. 12; etc.)].
As per claim 14, see the rejection of claim 3 above.
As per claim 15, see the rejection of claim 4 above.
As per claim 16, see the rejection of claim 5 above.
As per claim 17, see the rejection of claim 1 above, wherein Datta/Peng also teaches a data processing system comprising:
a processor; and
a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations [of the method] [the system may be implemented via instructions stored in one or more memories and executed by one or more connected processors (Datta: paras. 0005, 0074-76; Peng: paras. 0037, 0175-176; fig. 12; etc.)].
As per claim 19, see the rejection of claim 3 above.
As per claim 20, see the rejection of claim 4 above.
Claim(s) 2, 11, 13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Datta and Peng as applied to claims 1, 10, 12, and 17 above, and further in view of Liu (US 2025/0168181).
As per claim 2, Datta/Peng teaches the method of claim 1, as described above.
While Datta/Peng teaches utilizing the anomaly condition for a number of downstream model tasks, including supply chain improvements (see above), it has not been relied upon for teaching wherein the anomaly condition comprises one selected from a group of anomaly conditions consisting of: war; inflation; pestilence; famine; poverty; overabundance of material comfort; material scarcity; and supply chain failure.
Liu teaches wherein the anomaly condition comprises one selected from a group of anomaly conditions consisting of:
war;
inflation;
pestilence;
famine;
poverty;
overabundance of material comfort;
material scarcity; and
supply chain failure [the system provides anomaly detection and provides alerts/service in response (abstract, etc.), where the anomalies can include equipment failures, process deviations, and/or supply chain disruptions (para. 0032, etc.); which includes at least material scarcity and supply chain failure(s)].
Datta/Peng and Liu are analogous art, as they are within the same field of endeavor, namely providing anomaly detection that can be integrated into additional services/models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include equipment failures, process deviations, and/or supply chain disruption anomalies in the anomaly detection, as taught by Liu, in the anomaly detection provided for supply chain improvement models in the system taught by Datta/Peng.
Liu provides motivation as [providing various anomaly detection services including anomalies that can disrupt supply chains or security of services allows the anomaly detection to provide security, support operational efficiency, enable cost reduction, support quality control, support customer experience, support regulatory compliance, enable predictive maintenance, support supply chain management, etc. (paras. 0030-38, etc.)].
As per claim 11, Datta/Peng teaches the method of claim 10, as described above.
While Datta/Peng teaches utilizing the anomaly condition for a number of downstream model tasks, including supply chain improvements (see above), it has not been relied upon for teaching wherein the condition impacting the business at the future point in time is a change in availability of supply of a product from a supplier.
Liu teaches wherein the condition impacting the business at the future point in time is a change in availability of supply of a product from a supplier [the system provides anomaly detection and provides alerts/service in response (abstract, etc.), where the anomalies can include equipment failures, process deviations, and/or supply chain disruptions (para. 0032, etc.); which includes at least changing availability of supplies of products from suppliers].
Datta/Peng and Liu are analogous art, as they are within the same field of endeavor, namely providing anomaly detection that can be integrated into additional services/models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include equipment failures, process deviations, and/or supply chain disruption anomalies in the anomaly detection, as taught by Liu, in the anomaly detection provided for supply chain improvement models in the system taught by Datta/Peng.
Liu provides motivation as [providing various anomaly detection services including anomalies that can disrupt supply chains or security of services allows the anomaly detection to provide security, support operational efficiency, enable cost reduction, support quality control, support customer experience, support regulatory compliance, enable predictive maintenance, support supply chain management, etc. (paras. 0030-38, etc.)].
As per claim 13, see the rejection of claim 2 above.
As per claim 18, see the rejection of claim 2 above.
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-20 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu et al. (Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, June 2022, pgs. 1-20 – cited in an IDS) – discloses an inference model utilizing an anomaly attention layer/block for unsupervised anomaly detection.
Han (US 2025/0147948) – discloses a system/method for anomaly detection in log data, including a self-attention mechanism assigning anomaly scores.
Kong (US 2023/0306489) – discloses attribute weights of an attention mechanism used for determining element contribution for anomaly detection.
Makhija (US 2025/0272652) – discloses a system/method for predicting supply chain scenarios, which can include anomaly analysis/detection.
Montoya (US 2023/0014241) – discloses a system/method for supply chain prediction including anomaly detection using neural networks.
Makler (US 2025/0307676) – related application publication.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE GIROUX whose telephone number is (571)272-9769. The examiner can normally be reached M-F 10am-6pm.
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, Omar Fernandez Rivas can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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