20DETAILED 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 November 10, 2025 has been considered.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 10, 2025 has been entered.
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.
Claim(s) 1-2 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 Reitan (US 2013/0249948) and Guha et al. (US 2015/0186904).
With respect to claim 1, Kapadia discloses a method for creating a weather forecasting module for forecasting a weather indicator (characterized as trained neural network model) [pg. 1070; sec II], the method comprising:
receiving unstructured weather data, the unstructured weather data comprising one or more images including weather-related features (satellite image data); (“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] (characterized by satellite images used for extracting cloud coverage),
processing the unstructured weather data to generate values for at least one weather variable (characterized by cloud cover percentage) [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”)
combining the structured weather data with the generated weather variable values to create a combined weather feature set [pg. 1072; Sec VI]; (“After Image Processing and Segmentation is complete, the cloud cover information is extracted and is combined with the weather data table to create a consolidated database which is then used for predicting different parameters”)
selecting one or more algorithms based on one or more characteristics of the combined weather feature set [pg. 1071; Sec. IV]; and (characterized in using NAR model and/or NARX model depending on which target variable is evaluated) [pg. 1072; sec VI; B & C] (“As the other parameters tend to exhibit a cyclic yearly pattern it is suitable to apply NAR model to predict the future value of data. Therefore, each column is initially independently trained”) & (“NARX model is trained with x[(t)] as all the input columns”)
training the one or more algorithms to forecast the weather indicator at least in part using the combined weather feature set [pg. 1072; Sec VI; B & C];
wherein the weather forecasting module comprises the trained one or more algorithms [pg. 1072; sec VI; C]. (“The final prediction is to obtain precipitation values. These values can be easily obtained by testing the above trained NARX neural network using the input values obtained from training the individual columns through the NAR neural network as explained in the previous section”).
Kapadia does not disclose the images including at least one overlay with color enhancements.
Reitan 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).
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 images including at least one overlay with color enhancements as disclosed by Reitan for the purpose of determining the weather.
Kapadia further fails to disclose 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 and 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. However, Kapadia discloses evaluating each of the one or more images and extracting color information related to the weather data (characterized by extracting brightness value and using pixel values of image matrix (i.e., number of nonzero pixels) [pg. 1070; sec III].
Guha 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]. Guha further teaches about processing the image data that includes segmenting each of the one or more images into a plurality of segments and extracting hue information from the overlays of each of the plurality of segments (extracting information from the color of clouds, paragraph 0024, lines 1-2; cloud covers, paragraph 0003, line 6, of dark cloud, white cloud, paragraph 0024, lines 3-5, represent overlays) [Par .0024]. (with the observed colors (e.g., values for red, blue and green pixels) of the camera can be used to calibrate the system) & (From the cloud movement observed by the sky camera system, the speed of wind and direction can be estimated based on cloud tracking.)
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 Guha to further include wherein the processing of the historical unstructured data comprises segmenting each of the one or more images into a plurality of segments and extracting hue information from each of the plurality of segments motivated by a desire to applying a known technique to a known device(method product) ready for improvement to yield predictable results (KSR) that can track cloud movements to estimate wind speed and direction for forecasting power.
With respect to claim 2, Kapadia discloses wherein the one or more algorithms comprises one or more of regression models, Markov chains, time series models, state space models, Bayesian models, boosted decision trees, neural networks, convolutional neural networks, and recurrent neural networks. [pg. 1071; Sec IV] (characterized by using artificial neural networks, uses autoregressive models)
Claim(s) 3 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 Reitan (US 2013/0249948) and Guha et al. (US 2015/0186904) as applied to claim 2 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 3, Kapadia, Reitan, and Guha fail to disclose wherein the one or more algorithms comprises one or more long short-term networks; however, Kapadia is directed towards using neural network algorithms [see abstract].
Zaytar teaches about weather forecasting with Long Short-term Memory Recurrent Neural Networks, and further teaches how the use of long short-term memory networks improve over other neural networks by the ability to process and predict time series sequences without forgetting unimportant information [pg. 7; Sec 1; ln 17-26], and how use of such can forecast general weather variables including by combining numerical models and image recognition ones (in satellite images) [pg. 11; sec 5; ln 1-10].
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 in view of Reita, and Guha with Zaytar to implement one or more algorithms using one or more long short-term networks motivated by a desire to use of a known technique to improve similar devices (methods products) in the same way (KSR).
Claim(s) 4 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 Reitan (US 2013/0249948) and Guha et al. (US 2015/0186904) as applied to claim 1 above, and further in view of Brusch et al. (Synergetic Use of Radar and Optical Satellite Images to Support Severe Storm Prediction for Offshore Wind Farming, 2008).
With respect to claim 4, Kapadia, Reitan, and Guha fails to explicitly disclose wherein the module is configured to forecast weather for an offshore region, and wherein the unstructured weather data comprises data regarding the offshore region.
Brusch teaches about weather prediction in offshore regions based on satellite Images including evaluation of cloud parameters [see abstract].
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, Reitan, and Guha with Brusch to implement that the module is configured to forecast weather for an offshore region, and wherein the unstructured weather data comprises data regarding the offshore region motivated by a desire that known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art (KSR).
Claim(s) 8-9 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 Reitan (US 2013/0249948) and Guha et al. (US 2015/0186904) as applied to claim 1 above, and further in view of Peacock et al. (US 2017/0131435).
With respect to claim 8, Kapadia, Reitan, and Guha fails to explicitly disclose wherein the forecast for the weather indicator comprises a prediction for the weather indicator between approximately 3 hours and approximately 3 days in the future.
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 generating a weather prediction model for short term weather forecasting that concerns predicting the values of meteorological variables typically between 1 and 48 hours in advance) [Par. 0001 & 0003].
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, Reitan, and Guha with Peacock to further implement a forecasting technique wherein the forecast for the weather indicator comprises a prediction for the weather indicator between approximately 3 hours and approximately 3 days in the future motivated by a desire to provide accurate forecasting of wind speed, cloud cover, temperature, and other such variables for a variety of applications that need have short term prediction concerns (see Peacock [Par. 0003-0004]) .
With respect to claim 9, Kapadia, Reitan, and Guha fails to disclose wherein the weather indicator is selected from the set comprising wind speed, wind direction, solar irradiance, and 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, Reitan, and Guha with Peacock to implement forecasting for additional weather variables such as wind speed, wind direction, solar irradiance, and 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.
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
Claims 10-12 are allowed.
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:
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 November 10, 2025 have been fully considered and are not persuasive.
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.
With regard to the rejections under 35 USC 103, Applicants argue “neither reference discloses that the satellite images include “at least one overlay with color enhancements, as recited by claim 1.””
Examiner’s position is that Kapadia in view of Reitan discloses “at least one overlay with color enhancements”, as discussed above. In particular, Reitan discloses images including at least one overlay with color enhancements (Person A looks around and sees color-coded imaging with satellite cloud image overlays with sighted clouds through lenses, paragraph 0530, lines 4-6) for determining the weather (Person A then says “weather”, paragraph 0530, line 3).
Applicants further argue neither reference discloses ““extracting hue information from the overlays” or that the extracted overlay hue information is used in the generation of the values for the at least one weather variable.”
Examiner’s position, as discussed above, is that Kapadia discloses evaluating each of the one or more images and extracting color information related to the weather data (characterized by extracting brightness value and using pixel values of image matrix (i.e., number of nonzero pixels) [pg. 1070; sec III].
Guha discloses extracting hue information from the overlays of each of the plurality of segments (extracting information from the color of clouds, paragraph 0024, lines 1-2; cloud covers, paragraph 0003, line 6, of dark cloud (low), white cloud (high), paragraph 0024, lines 3-5, represent overlays) [Par .0024]. (with the observed colors (e.g., values for red, blue and green pixels) of the camera can be used to calibrate the system) & (From the cloud movement observed by the sky camera system, the speed of wind and direction can be estimated based on cloud tracking.)
Applicants further argue “[w]ith respect to independent claim 13, 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.”
In response, examiner refers Applicants to examiner’s positions in prior office actions, filed on May 9, 2025 and August 28, 2024.
Applicant’s remaining arguments have been considered but are traversed in view of the discussions and rejections above.
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.
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/MICHAEL P NGHIEM/ Primary Examiner, Art Unit 2857
November 14, 2025