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 .
Election/Restrictions
Examiner acknowledges Applicant’s elections of Group II claims 8-13 for examination.
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.
Claim 8-13 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. Regarding claim 8, it is not clear what the preamble and body of the claim are and therefore the claim is indefinite because it is not clear what the method comprises. Claim 13 recites “the super-resolution”. There is insufficient antecedent basis for this limitation in the claim. Claim 13 recites “any of the techniques described in co-pending patent application Ser. No. 17/529,670, “Climate Scenario Analysis And Risk Exposure Assessments At High Resolution”. However it is not clear what these techniques are and is therefore indefinite. Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 8-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Albert (US 2020/0082041 A1).
With respect to Claim 8 Albert teaches A method of updating and/or correcting biases in model simulations based on a generative machine learning system that learns the properties of climate projections from historic observations. (Para[0017]-[0019] The one or more computing devices 110 operate to obtain the observational data and to obtain numeric simulation data. The numeric simulation data includes, for example, at least one of weather simulators, climate simulators, See Claim 2 wherein the observational data additionally includes historical data retrieved or collected from one or more databases. And See Para[0067] FIG. 4 shows components of a spatial-temporal model, according to an embodiment. At least some embodiments utilize generative machine learning models such as Generative Adversarial Networks (GANs), With respect to Claim 9 Albert teaches The method of according to claim 8, further comprising updating and/or correcting biases in model simulations based on a generative adversarial machine learning system (See Para[0067]) With respect to Claim 10 Albert teaches The method according to claim 9, further comprising using one or more of climate observations, topography, land cover, and/or other environmental sensor data as inputs to a discriminator module in the generative adversarial machine learning system. (See Abstract) With respect to Claim 11 Albert teaches The method according to claim 10 that uses historic weather observations as inputs to the discriminator module in the generative adversarial machine learning system With respect to Claim 12 Albert teaches The method according to claim 11 wherein the inputs to the modeling system or the outputs are super resolved for high spatial resolution of projections from the generative adversarial machine learning system (See Claim 2) With respect to Claim 13 Albert teaches The method according to any of claim 12, wherein the super-resolution is performed according to any of the techniques described in co-pending patent application Ser. No. 17/529,670, “Climate Scenario Analysis And Risk Exposure Assessments At High Resolution”. (See Para[0026])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Price (US 2023/0143145 A1) teaches the use of a conditional Generative Adversarial Network (GAN) to simultaneously correct and downscale (super-resolve) global ensemble weather or climate forecasts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSHIHISA ISHIZUKA whose telephone number is (571)270-7050. The examiner can normally be reached M-F 11:00-7:00.
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YOSHIHISA . ISHIZUKA
Examiner
Art Unit 2857
/YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2857
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