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 .
The communication filed on May 18, 2026 has been considered.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, and 4 of U.S. Patent No. 11,733,427 (Thielke et al.). Although the claims at issue are not identical, they are not patentably distinct from each other because U.S. Patent No. 11,733,427 anticipates instant claim 1.
1. A method for creating a weather forecasting module for forecasting a weather indicator (claim 1; claim 1, line 17), the method comprising:
receiving unstructured weather data, the unstructured weather data comprising one or more images including weather-related features (claim 1, lines 3-5), the images including at least one overlay with color enhancements (claim 4);
processing the unstructured weather data to generate values for at least one weather variable, the processing comprising segmenting each of the one or more images into a plurality of segments (claim 3) and extracting hue information from the overlays of each of the plurality of segments (claim 1, lines 8-9), wherein the generation of the values for the at least one weather variable is based on the extracted hue information (claim 1, lines 6-9);
receiving structured weather data (claim 1, line 10);
combining the structured weather data with the generated weather variable values to create a combined weather feature set (claim 1, lines 11-13);
selecting one or more algorithms based on one or more characteristics of the combined weather feature set (claim 1, lines 14-16); and
training the one or more algorithms to forecast the weather indicator at least in part using the combined weather feature set (claim 1, lines 17-19); wherein the weather forecasting module comprises the trained one or more algorithms (claim 1, line 17).
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.
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 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kapadia (Weather Forecasting using Satellite Image Processing and Artificial Neural Networks, 2016) in view of Ricci (DE 112013003595), Onishi et al. (Deep Convolutional Neural Network for Cloud Coverage estimation from Snapshot Camera Images, 2017) and Yang et al. (CN 108537807).
With respect to claim 13, Kapadia discloses a method for forecasting a weather indicator [see abstract], the method comprising:
receiving weather-related satellite image data (satellite image data of cloud coverage); (“This work proposes a simple approach for weather prediction that relies on satellite images and weather data as inputs”) [see abstract] & [pg. 1070; Sec III]
receiving structured weather data (initial weather dataset) [see abstract] & [pg. 1072; Sec. VI]; (“The initial weather dataset consists of four weather parameters: Mean Temperature, Mean Humidity, Mean Wind Speed and Precipitation”), including temperature (page 1072, paragraph 3), barometric pressure, wind speed (page 1072, paragraph 3), wind direction, solar irradiance, dew point, humidity (page 1072, paragraph 3), and precipitable water (page 1072, paragraph 3);
and
processing the output representation of the satellite image data and the structured weather data using a second neural network to generate a forecast for the weather indicator[pg. 1072; Sec VI; C] (using NARX neural network model with all input columns (temperature, humidity, wind speed, cloud cover).
Kapadia fails to disclose structured weather data including barometric pressure, wind direction, solar irradiance, dew point.
Ricci discloses structured weather data, including temperature, barometric pressure, wind speed, wind direction, solar irradiance, dew point, humidity, and precipitable water (page 14, paragraph 1).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Kapadia with structured weather data as disclosed by Ricci for the purpose of determining climate control parameters and settings (page 14, paragraph 1).
Kapadia fails to disclose preprocessing the weather-related satellite image data to enhance one or more weather- related features of the weather-related satellite image data, wherein the preprocessing comprises the use of edge-detection or sharpening filters.
Yang et al. discloses preprocessing satellite image data to enhance one or more features of the satellite image data, wherein the preprocessing comprises the use of edge-detection (Abstract, lines 1-4).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Kapadia with use of edge-detection as disclosed by Yang et al. for the purpose of preprocessing satellite image data to enhance one or more features of the satellite image data.
The feature of use of sharpening filters is an alternative feature since it recited in the alternative form.
Kapadia fails to necessarily disclose processing the weather-related satellite image data using a first neural network to generate an output representation of the satellite image data.
Kapadia does teach about processing weather-related satellite image data in order to determine cloud coverage (characterizing output representation of the satellite image data).
Onishi discloses a deep convolution neural network (CNN) approach for the accurate estimation of the cloud coverage (CC) from images [see abstract] & [pg. 238; sec 4] and teaches a two-step approach for the classification of pixels into sky, cloud and non-sky segments and that includes analyzing them in the hue, saturate, and value (brightness) (HSV) space, which is a common cylindrical-coordinate representation of pixels in the RGB color space [pg. 235; sec 2.1-2.3].
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the invention to modify the teachings of Kapadia with Onishi to further include processing the weather-related image data using a first neural network to generate an output representation of the image weather data motivated by a desire apply a known technique to a known device(method product) ready for improvement to yield predictable results (KSR) in order to modify or substitute the technique of Kapadia for determining cloud coverage from images with an improved technique using convolutional neural networks as taught by Onishi.
With respect to claim 15, Kapadia discloses wherein the first neural network comprises one or more of a convolutional neural network, a recurrent neural network, a stacked neural network, and a deep neural network [pg. 1071; Sec IV; A]. (characterized in non-linear autoregressive Neural Network model).
Claim 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapadia (Weather Forecasting using Satellite Image Processing and Artificial Neural Networks, 2016) in view of Ricci (DE 112013003595), Onishi et al. (Deep Convolutional Neural Network for Cloud Coverage estimation from Snapshot Camera Images, 2017) as applied to claim 13 above, and further in view of Zaytar et al. (Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks, 2016).
With respect to claim 16, claim 16 recites the same limitations as claim 3; therefore, claim 16 is rejected for the same reasons as stated above with respect to claim 3.
Claims 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapadia (Weather Forecasting using Satellite Image Processing and Artificial Neural Networks, 2016) in view of Ricci (DE 112013003595), Onishi et al. (Deep Convolutional Neural Network for Cloud Coverage estimation from Snapshot Camera Images, 2017) as applied to claim 13 above, and further in view of Peacock et al. (US 2017/0131435).
With respect to claim 17, claim 17 recites the same limitations as claim 8; therefore, claim 17 is rejected for the same reasons as stated above with respect to claim 8.
With respect to claim 19, Kapadia, Ricci, and Onishi fails to disclose wherein the forecast weather indicator is wind speed.
Peacock discloses a system and method for predicting localized weather for a location of interest using a plurality of different sources of weather variables associated with the location of interest [Par. 0006], and further teaches about forecasting a number of different potentially pertinent weather variables (for example, temperature, wind speed, cloud cover, pressure, humidity, dew-point, ozone, visibility, precipitation-intensity, precipitation-probability), and using feature selection to decide which weather variable is to be forecasted and which weather observations are to be used in forecasting of that variable [Par. 0035] that is then provided as input to a machine learning algorithm to build a predictive forecast model for the selected weather indicator [Par. 0036].
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the invention to modify the teachings of Kapadia with Peacock to implement forecasting for additional weather variables such as wind speed motivated by a desire to applying a known technique to a known device (method product) ready for improvement to yield predictable results (KSR) that incorporates forecasting for different kinds of weather variables based on what is pertinent.
With respect to claim 20, Kapadia, Ricci, and Onishi fails to disclose wherein the forecast weather indicator is solar irradiance or cloud cover percentage.
Peacock discloses a system and method for predicting localized weather for a location of interest using a plurality of different sources of weather variables associated with the location of interest [Par. 0006], and further teaches about forecasting a number of different potentially pertinent weather variables (for example, temperature, wind speed, cloud cover, pressure, humidity, dew-point, ozone, visibility, precipitation-intensity, precipitation-probability), and using feature selection to decide which weather variable is to be forecasted and which weather observations are to be used in forecasting of that variable [Par. 0035] that is then provided as input to a machine learning algorithm to build a predictive forecast model for the selected weather indicator [Par. 0036].
It would have been obvious to one of ordinary skill in the art at the time of the effective filing of the invention to modify the teachings of Kapadia with Peacock to implement forecasting for additional weather variables such as solar irradiance or cloud cover percentage motivated by a desire to applying a known technique to a known device(method product) ready for improvement to yield predictable results (KSR) that incorporates forecasting for different kinds of weather variables based on what is pertinent.
Allowable Subject Matter
Claim 1 would be allowable if a terminal disclaimer is received to overcome the nonstatutory double patenting rejection set forth in this office action.
Reasons For Allowance
The following is an examiner’s statement of reasons for allowance:
The combination as claimed wherein a method for creating a weather forecast correction module for a weather indicator, the method comprising: extracting hue information from the overlays of each of the plurality of segments, wherein the generation of the values for the at least one weather variable is based on the extracted hue information (claim 1) or training the one or more algorithms to forecast a correction value for the weather indicator using as input training data at least the local weather forecast data for the weather indicator, and as target training data at least the difference between the local weather data and the local weather forecast data (claim 10) is not disclosed, suggested, or made obvious by the prior art of record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Response to Arguments
Applicant’s arguments filed on May 18, 2026 have been fully considered.
Applicant’s arguments with respect to the rejections under 35 USC 103 of claims 1-4, 8, and 9 have fully considered and are persuasive. The rejections under 35 USC 103 of claims 1-4, 8, and 9 have been withdrawn.
With respect to rejections under 35 USC 103 of claims 13, 15-17, 19, and 20, Applicants argue “Applicant incorporates by reference its arguments made in prior responses that the prior art does not disclose or suggest the combination of Applicant's claim elements and that one or more references are non-analogous art and thus should be excluded from any obviousness analysis.”
In response, examiner refers Applicants to the prior office action, filed on May 9, 2025, Response to Arguments section.
Applicants further argue in the prior office action, "Ricci is non-analogous art and therefore should be excluded from the obviousness analysis. In Jn re Klein, the court held that none of five cited prior art references could be considered "analogous art." Under the Federal Circuit's exclusionary test, as non-analogous art the references were then entirely excluded from consideration in the nonobviousness analysis. Claim 13 explicitly recites a method for forecasting a weather indicator. Ricci is directed to vehicle climate control. Thus, Ricci is clearly in a different field of endeavor from the present invention. Moreover, not only is Ricci in a different field of endeavor, it is also not reasonably pertinent to the problem of forecasting a weather indicator."
Examiner's position is that, as acknowledged by the Applicants, "[a] reference
is reasonably pertinent if, even though it may be in a different field from that of the inventor's endeavor, it is one which, because of the matter with which it deals, logically would have commended itself to an inventor's attention in considering his problem." Clay, 966 F.2d at 659. "If a reference disclosure has the same purpose as the claimed invention, the reference relates to the same problem, and that fact supports use of that reference in an obviousness rejection." Id." Ricci is pertinent because it discloses structured (predicted) weather data, including temperature, barometric pressure, wind speed, wind direction, solar irradiance, dew point, humidity, and precipitable water (page 14, paragraph 1), as claimed.
Applicants further argue "[a]n inventor considering the problem of "forecasting a weather indicator," as in claim 13, would not have been motivated to look to Ricci since it deals with such a disparate problem, namely, vehicle climate control. Vehicle climate control does not involve forecasts. As such, Applicant respectfully requests withdrawal of the rejections under 35 USC § 103 and allowance of the claims."
Examiner's position is that an inventor considering the problem of "forecasting a
weather indicator," as in claim 13, would have been motivated to look to Ricci since Ricci teaches predicted weather data (page 14, paragraph 1, line 1), including temperature, barometric pressure, wind speed, wind direction, solar irradiance, dew point, humidity, and precipitable water (page 14, paragraph 1).
With regard to the nonstatutory double patenting rejections, Applicants argue “Applicant recognizes the Examiner’s position but does not admit to the correctness thereof. However, Applicant is prepared to file any necessary terminal disclaimer after otherwise allowable subject matter is identified”.
Examiner’s position is that the nonstatutory double patenting rejection is maintained as discussed above until a terminal disclaimer is filed.
Applicant’s remaining arguments have been considered but are traversed in view of the grounds of rejection and discussion above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hamann et al. (US 2018/0038994) discloses a method for creating a weather forecast correction module for a weather indicator (Fig. 9). Hamman et al. does not disclose training the one or more algorithms to forecast a correction value for the weather indicator using as input training data at least the local weather forecast data for the weather indicator, and as target training data at least the difference between the local weather data and the local weather forecast data (claim 10).
Kapadia (Weather Forecasting using Satellite Image Processing and Artificial Neural Networks, 2016) discloses a method for creating a weather forecasting module for forecasting a weather indicator (characterized as trained neural network model) [pg. 1070; sec II].
Reitan (US 2013/0249948) discloses images including at least one overlay with color enhancements (paragraph 0530, lines 4-6) for determining the weather (Person A then says "weather", paragraph 0530, line 3).
Guha et al. (US 2015/0186904) discloses techniques for managing and forecasting power from renewable energy sources [see abstract] that includes utilizing weather data and cloud coverage obtained from images of a camera system [Par. 0023].
However, none of the references above disclose extracting hue information from the overlays of each of the plurality of segments, wherein the generation of the values for the at least one weather variable is based on the extracted hue information (claim 1).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Nghiem whose telephone number is (571) 272-2277. The examiner can normally be reached on M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/MICHAEL P NGHIEM/ Primary Examiner, Art Unit 2857
July 20, 2026
Prosecution Insights