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 .
Claim Rejections - 35 USC § 103
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 1 and 2 are rejected under 35 U.S.C. 103 as being unpatentable Petty (US Pub.20180373993A1), hereinafter Petty in view of Wang (US Pub.20200313763A1), hereinafter Wang.
Regarding Claim 1, Petty disclose a street flood warning method comprising:
operating a system along a target route and receive rainfall-related data (para [0004], [0024]-[0025], [0044], where FIG. 3, in one embodiment measured streamflow data 330 from streamgages 300 may be provided to a flood forecasting system 320. A flood forecasting system may be any system that monitors, predicts, or otherwise transmits information related to flooding conditions in a region. In the embodiment of FIG. 3, flood forecasting system 320 may use streamflow data from streamgages 300 to forecast flooding along one or more rivers or streams associated with streamgages 300…a flood forecasting system could include both automated and manual components);
predict a rain intensity (para [007], where (3) creating a predictive model using the measured streamflow data and the clustering information; and (4) using the predictive model to generate synthetic streamflow data for the target streamflow source if an interruption in measured streamflow data, para [0027], where predictive models for generating predicted (i.e., synthetic) streamflow data, and pass the synthetic streamflow data 334) using a trained linear regression model (para [0033], [0036], [0037], [0050], where machine learning methods may include, but are not limited to:… linear regression);
predict a flood level using a random forest model (claims 7, 11, 13, where creating a predictive model using the measured streamflow data and the clustering information; and using the predictive model to generate synthetic streamflow data for the target streamflow source if an interruption in measured streamflow data from the target streamflow source has been determined… the predictive model is selected from a group consisting of a random forest model, a multi-classification model, a boosted decision tree model); and
outputting an alert when the predicted flood level is above a threshold level (para [0039]-[0041], claims 13-15 and Fig. 5-6. Where Predictive model 500 outputs synthetic streamflow data for the malfunctioning streamgage so that data for all streamgages can be passed along to a flood forecasting system. Though not shown in FIG. 5, data from the functioning streamgages can be passed directly to a flood forecasting system or another system, as depicted in FIG. 3. In some embodiments, a predictive model could receive measured streamgage data as input and then output that same measured streamgage data along with synthetic data for one or more malfunctioning streamgages. generating synthetic (predicted) streamflow data and using the data to generate information or messages related to a flooding (e.g., flood warnings)).
Petty does not disclose operating a distributed fiber optic sensing (DFOS) system and receive vibration data from aerial cable.
Wang disclose operating a distributed fiber optic sensing (DFOS) system and receive vibration data from aerial cable (para [0050],[0051], [0057], [0073]-[0074], where sections of aerial cable may be determined based on measured temperature swings as well. As may be appreciated, locations of the manholes/handholes and aerial cable are clearly indicated in the plots which can be advantageously employed for physical optical fiber (cable) position calibration; and Claims 1-3, where distributed fiber optic sensing (DFOS) signals and optical telecommunications signals simultaneously coexist on the length of optical fiber…the DFOS signals are ones selected from the group consisting of distributed vibration sensing and distributed temperature sensing, distributed strain sensing, and distributed acoustic sensing; the DFOS signals comprise backscattered light resulting from interrogator pulses injected into an end of the optical fiber).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide (DFOS) system and receive vibration data from aerial cable, as taught by Wang into Petty in order to serve as distributed fiber sensing platforms that simultaneously convey live, high-speed telecommunications signals representing the telecommunications data and environmental conditions of the optical fiber.
Regarding Claim 2, Petty and Wang disclose the method of claim 1, further comprising Petty disclose training the linear regression model using training data of rain intensity and duration (para [0033], [0036], [0050], where predicting the streamflow using the linear regression model).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable Petty in view of Wang as applied above and further in view of Zhang (CN109272484A), hereinafter Zhang.
Regarding Claim 3, Petty and Wang disclose the method of claim 2 but do not disclose comprising extracting features from the training data according to four classes including no rain, light rain, moderate rain, and heavy rain.
Zhang disclose extracting features from the training data according to four classes including no rain, light rain, moderate rain, and heavy rain (Abstract, where the training sample image according to rainfall is divided into rainless, light rain, moderate rain and heavy rain and labelling of S2).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide the training sample according to rainfall is divided into rainless, light rain, moderate rain and heavy rain, as taught by Zhang into synthetic streamflow data extraction of Petty and further into Wang in order to provide more accurately a prediction of street flood status.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable Petty in view of Wang and Zhang, as applied to the claim 3 and further in view of Michitsugu (JP2018146559A), hereinafter Michitsugu.
Regarding Claim 4, Petty and Wang and Zhang disclose the method of claim 3, further Petty disclose wherein the random forest model predicted flood levels (claims 7, 11, 13, where creating a predictive model using the measured streamflow data and the clustering information; and using the predictive model to generate synthetic streamflow data for the target streamflow source if an interruption in measured streamflow data from the target streamflow source has been determined… the predictive model is selected from a group consisting of a random forest model, a multi-classification model, a boosted decision tree model).
Petty and Wang do not disclose flood levels include level 1, stand by;
level 2, preparation; and level 3, evacuation levels.
Michitsugu discloses flood levels include level 1, stand by;
level 2, preparation; and
level 3, evacuation levels (Page 4, lines 30-32, where the water level observation system can set flood warning level, evacuation judgment level, flood warning level, flood warning level, flood protection standby level, etc. as warning conditions).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide flood levels include level 1, stand by;
level 2, preparation and level 3, evacuation levels, as taught by Michitsugu in combination of Petty and Wang and Zhang in order to avoid the damaging the infrastructure in the area and help to maintain natural ecosystems.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable Petty in view of Wang, Zhang and Michitsugu, as applied to the claim 4 and further in view of Lee(KR102308526B1), hereinafter Lee.
Regarding Claim 5, Petty and Wang and Zhang and Michitsugu disclose the method of claim 4, but do not disclose further comprising splitting a dataset into dependent and independent variables (Fig. 4, # 402, 404) in which historical flood level are independent variables(Fig. 4, para [0032], where a historical baseline of streamflow data; para 0033, where create a historical profile (a yearly event)) and flood level is a dependent variable (para [003], where monitor and test surface bodies of water within watershed basins, with their primary function generally being the hydrologic measurements of water level surface elevation (also referred to as ‘gage height’ or stream ‘stage’)).
Petty and Wang do not disclose variable in which rain intensity, rain duration, are independent variables.
Lee disclose rain intensity, rain duration, are independent variables (Page 4, lines 10-14, where measuring the amount or intensity of rain, usually measured by electrical resistance that changes when raindrops fall and shows the time rainfall intensity based on the value measured for 1 minute) and flood level is a dependent variable (Page 4, lines 4-9, where Fig. 1, the flood prediction device 20 includes a rainfall measurement unit 24 and a water level measurement unit 25 for measuring the amount of rainfall and the river level in a specific area; topographic information, physical distance, and water level and rainfall information in a specific area).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide rain intensity, rain duration, as taught by Lee in combination of Petty and Wang and Zhang and Michitsugu in order to provide more accurately prediction of street flood status.
Regarding Claim 6, Petty and Wang and Zhang and Michitsugu and Lee disclose the method of claim 5, further Petty disclose further comprising using dependent and independent variable datasets to train the random forest model (para 0036 and claims 7, 11 and 13, random forest models are an ensemble of decision trees and output the mean predication pf the ensemble to limit overfitting, and provide a streamflow prediction for a flood prediction).
Regarding Claim 7, Petty and Wang disclose the method of claim 6 further Petty disclose comprising outputting the alert using a real-time flood map along the target route (Claims 13, 15 and 20, where outputting a flood warning message based on a real-time flood map).
Conclusion
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/KALERIA KNOX/
Examiner, Art Unit 2857
/MICHAEL J DALBO/Primary Examiner, Art Unit 2857