DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 4/10/2023. Acknowledgment is made with respect to a claim of priority to Issued Patent US 11,625,627 filed on 6/30/2020 and Provisional Application 63/013,162 filed on 4/21/2020.
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 13 and 16 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.
Claim 13 recites the limitation “the forecast source is a weather station” (emphasis added). There is insufficient antecedent basis for the claimed “the forecast source”. For examination purposes, the limitation will be interpreted to mean “[[the]] a forecast source is a weather station”. Appropriate correction is required.
Claim 16 recites the limitations beginning with “the short scale neural network …” and “the long scale neural network” (emphasis added). There is insufficient antecedent basis for the claimed “the short scale neural network …” and “the long scale neural network”. For examination purposes, the limitations will be interpreted to mean “[[the]] a short scale neural network …” and “[[the]] a long scale neural network …”. Appropriate correction is required.
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).
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Claim 1 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the training phase, the training of the AI model the GAN, the binary classifier, the discriminator, the generator, and the multi-level attention mechanism as claimed.
Claim 2 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 and 13 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 and 13 of U.S. Patent No. 11,625,627 disclose the binary cross entropy as claimed.
Claim 3 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the multi-level attention mechanism as claimed.
Claim 4 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the preprocessing module as claimed.
Claim 5 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the forecast offset computation module as claimed.
Claim 6 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the signal decomposition as claimed.
Claim 7 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the short and long scale neural networks as claimed.
Claim 8 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the multi-level attention mechanism as claimed.
Claim 9 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the decoder as claimed.
Claim 10 is non-provisionally rejected on the ground of nonstatutory double patenting as being anticipated by claims 1 and 12 of U.S. Patent No. 11,625,627. That is, claims 1 and 12 of U.S. Patent No. 11,625,627 disclose the run-time processes as claimed.
This is a non-provisional double patenting rejection because the claims of the parent case (Issued Patent No. 11,625,627) of the present application have in fact been patented.
Allowable Subject Matter
Claims 11, 12, 14, 15, and 17-20 are allowed.
Conclusion
The claims have been searched but no prior art was uncovered.
The closest prior art of record Saxena et al., “D-GAN: Deep Generative Adversarial Nets for Spatio Temporal Prediction”, discloses a novel deep generative adversarial network based model (named, D-GAN) for more accurate ST prediction by implicitly learning ST feature representations in an unsupervised manner, but fails to explicitly disclose train an artificial intelligence model based on the training data to output a predicted forecast offset between a current value of a remotely sourced forecast and a future locally sourced measurement for the parameter … the generator generates a second batch of training data including forecast offsets and associated predicted forecast offsets based thereon, and a multi-level attention mechanism of the predictive program of the generator is updated while the generator is pitted against the discriminator, all taught in the context of the remaining claim limitations and when considered as a whole, as claimed.
Further Khodayar et al., “Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting”, discloses a graph deep learning model to learn the powerful spatio-temporal features from the wind speed and wind direction data in neighboring wind farms, but fails to explicitly disclose train an artificial intelligence model based on the training data to output a predicted forecast offset between a current value of a remotely sourced forecast and a future locally sourced measurement for the parameter … the generator generates a second batch of training data including forecast offsets and associated predicted forecast offsets based thereon, and a multi-level attention mechanism of the predictive program of the generator is updated while the generator is pitted against the discriminator, all taught in the context of the remaining claim limitations and when considered as a whole, as claimed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Saxena et al., “D-GAN: Deep Generative Adversarial Nets for Spatio Temporal Prediction”, Jul. 19, 2019, arXiv:1907.08556v1, pp. 1-8.
Khodayar et al., “Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting”, Jun. 4, 2018, IEEE TRANSACTIONS ONSUSTAINABLEENERGY,VOL.10, NO.2, pp. 670-681.
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127