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 § 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 1-7 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 1 recites “… obtaining initial peak occurrence time by using N continuous first-order difference values in a first-order difference sequence of the runoff time sequence data, wherein N is a positive integer …”. The claim recites “initial peak occurrence time” in singular which mathematically creates ambiguity. If this step only yields a singular “time” (one peak), then the subsequent step of screening out determined peak occurrence time (singular or plural) from the initial peak occurrence time is confusing and doesn’t reflect the mathematical operation performed by the algorithm. The applicant should amend the claim to pluralize this term to reflect that a set of candidate peak times is obtained. For the purpose of examination, we consider the phrase “obtaining initial peak occurrence times”.
Claims 2, 5, and 7 replicate the incorrect use of the singular “time” as explained above.
Claim 1 recites “… obtaining initial start and end time by using M continuous first-order difference values in the first-order difference sequence …” and it subsequently recites “… screening out start and end time corresponding to the peak occurrence time from the initial start and end time …”. The claim repeatedly utilizes the singular noun “time” in the compound phrase “initial start and end time” and “start and end time corresponding to the peak occurrence time”. A single temporal coordinate cannot represent both upward rising boundary (start) and the downward receding boundary (end) of a physical event. The applicant is required to amend the claim to replace the singular “time” with the plural “times”.
Claims 4, 5, and 7 also use the singular word “time” as explained above.
Claim 1 further recites “… if the two difference multiples are both greater than or equal to a difference multiple threshold, the start discharge of the previous flood is less than or equal to the end discharge, the start discharge of the next flood is greater than or equal to the end discharge …”. The claim refers to “the end discharge”. However, it is not clear whether the start discharge of the previous flood is compared to its own end discharge or it is compared to the end discharge of the next flood or it is compared to some shared valley boundary discharge. Because of this reason the exact mathematical boundary conditions required to trigger a multi-peak flood merge cannot be determined with reasonable certainty. The applicant is required to amend the claim to identify whether the end discharge belongs to the previous flood or the next flood. For the purpose of the examination, we consider the phrase “the end discharge of the previous flood”.
Claim 1 recites “determining that the two floods are multi-peak floods”. This term is internally inconsistent and hydrologically contradictory. The claim’s subsequent steps merge these contiguous runoff sequences to establish a single combined event. For the purpose of the examination we consider the single form, “determining that the two floods are a single multi-peak flood”.
Claim 7 recites the same limitation as claim 1 as stated above and it is rejected for the same reason.
Claims 2, 4-6 are rejected because they inherit the indefiniteness identified in claim 1 because of their dependency from claim 1.
Claim 3 recites “… the first first-order difference value and an absolute value of a first-order difference value next to the first first-order difference value …”. The term “next to” is directionally ambiguous in the context of a discrete time-series difference sequence, as any given index has both a preceding neighbor and a succeeding neighbor. The claim fails to identify which neighbor is evaluated. For the purpose of the examination, we consider “next to” to be the succeeding index step.
Claim 3 is dependent from claim 2 and inherits the same indefiniteness issues identified with respect to claim 2.
Claim 6 introduces the term “the preset flood peak threshold” with a definite article “the”. However, claim 1 has no mention of any “flood peak threshold” for peak screening. There is insufficient antecedent basis for this limitation in the claim. For the purpose of the examination, we consider “a preset flood peak threshold”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) abstract idea as discussed below. This judicial exception is not integrated into a practical application because of the reasons discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the reasons discussed below.
Step 1 - Statutory Category: Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, claims 1-6 are directed to a process (method) and claim 7 to a machine (device/system). Accordingly, claims 1-7 fall within at least one of the four statutory categories of invention (process, machine, manufacture, or composition of matter) under 35 U.S.C. 101.
Claim 1 is reproduced below with the abstract idea underlined.
A flood event identification method, comprising:
obtaining runoff time sequence data;
obtaining initial peak occurrence time by using N continuous first-order difference values in a first-order difference sequence of the runoff time sequence data, wherein N is a positive integer;
obtaining initial start and end time by using M continuous first-order difference values in the first-order difference sequence, wherein M is a positive integer; and
screening out determined peak occurrence time from the initial peak occurrence time, and screening out start and end time corresponding to the peak occurrence time from the initial start and end time;
wherein after screening out the determined peak occurrence time from the initial peak occurrence time, and screening out the start and end time corresponding to the peak occurrence time from the initial start and end time, the method further includes:
determining a flood peak discharge corresponding to the peak occurrence time;
respectively obtaining difference multiples of flood peak discharges and fluctuation point discharges of two continuous floods, wherein each of the difference multiples is a multiple obtained by dividing a first difference by a second difference; for a previous flood, the first difference is a difference between the flood peak discharge and a start discharge, and the second difference is a difference between the flood peak discharge and an end discharge; and for a next flood, the first difference is a difference between the flood peak discharge and an end discharge, and the second difference is a difference between the flood peak discharge and a start discharge; and if the two difference multiples are both greater than or equal to a difference multiple threshold, the start discharge of the previous flood is less than or equal to the end discharge, the start discharge of the next flood is greater than or equal to the end discharge, and a difference between start time of the next flood and end time of the previous flood is less than or equal to an average duration of flood events that is determined based on characteristics of a river basin, determining that the two floods are multi-peak floods.
Under Step 2A, Prong 1, Claim 1’s underlined limitation recites performing mathematical operations on a discrete numerical data set. The steps of calculating differences, comparing those values to numerical thresholds, and grouping data points can be performed through mental steps or with the aid of pen and paper. Accordingly claim 1 recites a judicial exception under the categories of mathematical concepts and mental processes.
Step 2A, Prong 2: examiner needs to determine if the claim(s) recite additional elements that integrate the exception into a practical application of the exception. The additional elements in the claim 1 have been left in normal font. Claim 1 does not integrate the judicial exception into a practical application because of the following reasons:
The step of obtaining runoff time sequence data is insignificant pre-solution data gathering which is considered insignificant extra-solution activity. The claim does not recite any specialized physical sensors nor does it require any structural modification to standard flow-monitoring equipment.
Claim 7 is the apparatus counterpart to claim 1. Claim 7 merely takes the method steps of claim 1 and recites them as computer modules configured to execute the exact same mathematical steps. Claim 7 is directed to the same judicial exception as claim 1 and it does not integrate the judicial exception into practical application for the same reasons with respect to claim 1.
Dependent claims 2-6 merely extend the abstract idea of claim 1 by appending narrower mathematical equations. These claims merely detail the mathematical sub-steps used to analyze, filter, and smooth raw numbers. More specifically, claims 2 and 4 recite specific mathematical sign conditions for identifying inflection points. Claim 3 recites adding a nominal mathematical threshold check to the absolute differences. Claim 5 details the proximity rules for coordinate grouping. Claim 6 details a dual- track calculation to overwrite smoothed coordinate with original values. Accordingly claims 2-6 recite a judicial exception under the categories of mathematical concepts and mental processes.
There is no additional element, beyond what was identified in claim 1, in claim 2-6 to be analyzed under Step 2A, Prong 2. Accordingly, claims 2-6 do not integrate the judicial exception into practical application.
Step 2B: Claims 1-7: the additional elements, considered individually and in combination, do not amount to significantly more than the abstract idea for the same reasons set forth with respect to Step 2A, Prong 2.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 2, 4, 5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Li Kuang, et.al (CN112561214) hereinafter Li, see the attached English translation, and further in view of Yiwen Mei, et.al (A hydrograph separation method based on information from rainfall and runoff records, Journal of Hydrology 523 (2015) 636-649) hereinafter Mei.
Regarding claim 1, Li teaches a method and system for automatically identifying individual flood events, ¶ [1] (A flood event identification method). Li further teaches that its method comprises extracting a discharge sequence, denoted as Q() , having N1 elements,(step 51, ¶ [10]) (obtaining runoff time sequence data). Li teaches performing the peak sign check on the first-order backward difference y’() of array y(), (step 21 and 23 ¶ [6]) and it further discloses that the calculations are performed by utilizing step 1-4 (substituting the Q() as the input array y()), ¶ [11] (using N continuous first-order difference values in a first-order difference sequence of the runoff time sequence data, wherein N is a positive integer). Li teaches traversing the first order difference sequence to locate all points of negative slope transition, and recording their index coordinates to an initial array ypIndex’(), ¶ [16], which represents the raw candidate peaks before distance-based merging or further hydrological screening is executed. Because Li operates on equal time-interval hydrological sequences, any array index (e.g., coordinate j or i) represents an elapsed time duration from the start of calculation (obtaining initial peak occurrence times).
Li further teaches searching backward and forward from the peak coordinate on a smoothed sequence ya(t) using second-order backward differences (ya’’) to find the start valley (ybIndex) and end valley (yeIndex) where the curvature flattens out ((ya’’(j)>0) and (ya’’(j+1)=0)), (step 43 and 44, ¶ [19]). (obtaining initial start and end time by using second-order difference values in the first-order difference sequence)
Li utilizes a second order difference to find the start and end valleys. While a second-order difference is mathematically the first-order difference of the first-order difference sequence and in order to calculate the second-order backward difference, Li presumably must first calculate, store, and utilize the first-order difference sequence, but it does not explicitly teach that.
Li does not teach obtaining initial start and end time by using M continuous first-order difference values in the first-order difference sequence, wherein M is a positive integer.
Mei discloses obtaining the initial start time by identifying rising points (start) on the hydrograph using equation (4) (
Q
t
-
1
≥
Q
t
<
Q
(
t
+
1
)
), page 640. The left side of the equation can be written as (
Q
t
-
Q
(
t
-
1
)
≤
0
) which shows the preceding first order differences in the window must be less than or equal to zero. The right side (rising limb) of equation (4) can be written as (
Q
t
+
1
-
Q
t
>
0
) which shows that the subsequent first-order differences in the window must be greater than zero (obtaining initial start). For a 2-point window (M=2, even positive number), this is formula is analogous to formulation of valley detection sign check (by using M continuous first-order difference values).
Mei utilizes the characteristic point method to automatically identify the end (recessing/turning points) of distinct runoff events, Fig. 2. It determines these points by calculating the recession coefficient k and its change rate k* over a sliding regression window (LRW) set as 19 continuous hours, (LR between dQ/dt and Q), (step(1), page 640) (obtaining end time by using M continuous first-order difference values).
It would have been obvious to a person having ordinary skill in the art before effective filling date of the claimed invention to substitute Li’s localized second-order difference calculations with Mei’s rising point equations and 19 hour first-order sliding window regression to determine initial start and end times. Because while taking a first order difference amplifies high-frequency noise, taking a second order difference (as taught by Li) amplifies the noise exponentially. Relying on a highly localized second-derivative zero-crossing triggers false-positive event starts and end. Performing a linear regression of the first derivative over 19-hour continuous window, (as taught by Mei) acts as a powerful, integrated low-pass filter. By leveraging the statistical degrees of freedom of 19 data points, the algorithm smooths out high-frequency fluctuations, ensuring that boundaries are only marked when there is a sustained trend transition rather than a localized spike. Moreover, utilizing Mei’s rising point equation is more beneficial because the valley floor (local minimum) is a more hydrologically accurate and physically meaningful start boundary for separating direct runoff from baseflow.
Li teaches filtering raw peaks using a height threshold, y(i)>= ypthres to record candidate peak positions to the array ypIndex’(), (step23, ¶ [5]). Li then screens those candidates using a distance threshold (yd<= ydthres), ¶ [7-8] (screening out determined peak occurrence time from the initial peak occurrence time)
Li further teaches using the determined peak position array (ypIndex(iii)) as the anchor index to search backward and forward to isolate and pair the matching start and end times for each individual peak (start position mapping, step 43 ¶ [9]) and (end position mapping, step 44 ¶ [9]) (screening out determined peak occurrence time from the initial peak occurrence time, and screening out start and end time corresponding to the peak occurrence time from the initial start and end time)
Li teaches first identifying/screening peak indices, then locating their corresponding start/end boundary indices and then transitioning to calculating/extracting the physical peak discharges and flood process limits (executing Steps 2 and 3 and Steps 43 & 44 and then transition from Step 4 to Step 5 and Step 52), (¶ [5-10]) (wherein after screening out the determined peak occurrence time from the initial peak occurrence time, and screening out the start and end time corresponding to the peak occurrence time from the initial start and end time)
Li teaches that the method further includes calculating the flood peak Qp() (determining a flood peak discharge), the flood peak position QpIndex() (corresponding to the peak occurrence time), the flow process start position QbIndex(), and the flow process end position QeIndex(), ( ¶ [22]) (It should be noted that in discrete signal processing of equal-time-interval hydrological sequences, any array index acts as a direct proxy for chronological time).
Li discloses a method and system to identify multi-peak flood events, ¶ [25] (determining that the two floods are multi-peak floods). Li utilizes temporal distance threshold (ydthres) based on index coordinates to decide whether to merge or discard closely occurring peaks, (step 31, ¶ [7 & 8]). However, Li does not perform mathematical evaluation of the intermediate flow drop (the valley depth) relative to the peak height to identify the multi-peak floods.
Li does not teach respectively obtaining difference multiples of flood peak discharges and fluctuation point discharges of two continuous floods, wherein each of the difference multiples is a multiple obtained by dividing a first difference by a second difference; for a previous flood, the first difference is a difference between the flood peak discharge and a start discharge, and the second difference is a difference between the flood peak discharge and an end discharge; and for a next flood, the first difference is a difference between the flood peak discharge and an end discharge, and the second difference is a difference between the flood peak discharge and a start discharge; and if the two difference multiples are both greater than or equal to a difference multiple threshold, the start discharge of the previous flood is less than or equal to the end discharge, the start discharge of the next flood is greater than or equal to the end discharge, and a difference between start time of the next flood and end time of the previous flood is less than or equal to an average duration of flood events that is determined based on characteristics of a river basin , determining that the two floods are a single flood.
Mei describes its physical arrangement as adjacent, contiguous peaks that occur in close succession due to multiple rainfall pulses. When a subsequent storm hits before the previous wave has fully receded, these contiguous peaks constitute a candidate multi-peak flow event, (page 644, the paragraph above section 4) (two continuous flood). Mei teaches utilizing peak flow discharge Q(tp) (where tp is the peak flow hour)( flood peak discharges), and Q(tb) Q(te) (the beginning and ending flow rate of the flow event), (formula 25, page 644)( fluctuation point discharges).
Mei further teaches that the physical relationship of incomplete recession is evaluated by calculating direct boundary to peak ratios,
R
a
t
i
o
b
=
Q
(
t
b
)
Q
(
t
p
)
and
R
a
t
i
o
e
=
Q
(
e
)
Q
(
t
p
)
(difference multiples). Mei teaches formula (25),
max
Q
t
b
Q
t
p
,
Q
e
Q
t
p
>
B
F
I
e
, which
B
F
I
e
is a localized baseflow index, (page 644). (This formula evaluates whether the intermediate valley between adjacent peaks is too shallow (incomplete recession) to consider them separate events, if the valley floor remains highly elevated relative to the peak the algorithm classifies the waves as a single event.)
Mei teaches that when individual peak flow points share the same rising or recessing points or both, these peak flow points are grouped together as a multi-peak event, (section 3.2.1, step 6). It further teaches when a subsequent rainfall pulse (Group C event) hits the watershed after the first peak but before the first wave can fully recede, it acts as a flow-peak-triggering rainfall, (page 644, above section 4). Because no turning point (recess point) is registered on the first recession limb, the streamflow never recedes to baseflow. The shared intermediate valley floor remains highly elevated relative to the initial rising start point and final recessing end point, (figure 8 also shows that the intermediate shared valley is significantly elevated) (the start discharge of the previous flood is less than or equal to the end discharge, the start discharge of the next flood is greater than or equal to the end discharge).
Mei teaches that the maximum time length storm-runoff event is a basin-scale dependent parameter governed by the physical properties of the watershed using formula (6) in which A is the basin area (section 3.1.2 (i)). Mei further teaches formula (18) that uses this basin-specific duration to group adjacent, contiguous runoff peaks into a single multi-peak event if their chronological peak to peak event in their chronological peak to peak interval is less than or equal to the basin’s duration (page 642, section 3.2.1, step (4))( a difference between start time of the next flood and end time of the previous flood is less than or equal to an average duration of flood events that is determined based on characteristics of a river basin)
It would have been obvious to a person having ordinary skill in the art before effective filling date of the claimed invention to replace the event merging logic taught by Li with the ratio-based methodology of Mei for the following reasons.
Li relies on static chronological timers, specifically a flat distance threshold (Qpdist, set as 72 hours) and flat flow-threshold (Qvthres) to decide whether to merge or separate contiguous waves. This static rule fails to account for watershed state. Mei teaching of ratio-based event combination check, that evaluates valley to peak ratio against a localized baseflow index, ensures that merging decisions are governed by actual mass balance and hydraulic routing physics rather than arbitrary chronological timers.
Moreover, using difference multiple as recited in claim 1, is rather a predictable mathematical re-arrangement of boundary to peak ratios taught by Mei. If one divides recited difference multiple ratio in claim 1 by the peak flow and then substituted the equivalent physical variable taught by Mei, one gets
1
-
Q
t
b
Q
t
p
1
-
Q
t
e
Q
t
p
or
1
-
Q
t
b
Q
t
p
1
-
Q
t
v
a
l
l
e
y
Q
t
p
.
For flood # two the ratio would be similar as
Q
t
e
1
&
Q
t
b
2
are both the shared intermediate valley. A PHOSITA would recognize that reordering these steps-moving Mei’s dimensionless ratio calculation to serve as an upfront merging gate rather than a post-merging exclusion filter- share similar underlying math. In both configurations, the math serves the similar physical and functional purpose: verifying if the intermediate valley is shallow enough to represent a single, physically continuous multi-peak process rather than two independent events.
Furthermore, combining Li’s discrete difference pipeline with Mei’s physical trend and basin-scale parameters bridges the gap between pure computer mathematics and river physics. It ensures that the computer-extracted events represent true, hydrologically cohesive storm-runoff processes, which are required to calibrate the models mentioned in both references.
Claim 4 is rejected for the same reasons as set forth with respect to the rejection of claim 1, since the method of Li in view of Mei, discussed above, of obtaining initial start and end time by using M continuous first-order difference values in the first-order difference sequence meets the additional limitations of claim 4.
Claim 7, recites an apparatus with different modules that are configured to perform the steps of claim 1. Li in view of Mei describes an automatic flood identification system containing a data acquisition module, a peak determination module, and a flood calculation module (Li, ¶ [14 & 40]). Claim 7 is rejected for the same reasons disclosed with respect to rejection of claim 1.
Regarding claim 2, Li in view of Mei teaches the method according to claim 1, as set forth with respect to rejection of claim 1.
Li in view of Mei further teaches Li teaches performing the peak sign check on the first-order backward difference y’() of array y(), (Li, step 21 and 23 ¶ [6]) and the combination further discloses that the calculations are performed by utilizing step 1-4 (substituting the Q() as the input array y()), (Li, ¶ [11]). Li in view of Mei teaches traversing the first order difference sequence to locate all points of negative slop transition, and recording their index coordinates to an initial array ypIndex’(), (Li,¶ [16]), which represents the raw candidate peaks before distance-based merging or further hydrological screening is executed. Because Li in view of Mei operates on equal time -interval hydrological sequences, any array index (e.g., coordinate j or i) represents an elapsed time duration from the start of calculation (obtaining initial peak occurrence time by using N continuous first-order difference values in a first-order difference sequence of the runoff time sequence data).
Li in view of Mei further teaches that the method comprises a discrete time-series representing runoff observation over a continuous period y() (corresponding to first runoff data in the runoff time sequence data), and the sequence of rate of change values (local slopes) generated by subtracting adjacent discharge points y’()(first-order difference value) (Li, step 21 and 23 ¶ [6]). Li in view of Mei teaches a localized window of contiguous slope measurements on either side of a candidate transition coordinate y’(i) and y’(i+1), (Li, step 21 and 23 ¶ [6])(if a first first-order difference value… and N-1 first-order difference values continuous with [it], (where N=2)). Li in view of Mei further teaches peak-sign check condition: (y’(i)>0 and y’(i+1)<0) (meet a first condition) and finds a peak and records the peak position to an array ypIndex’(determining that time corresponding to the first runoff data is initial peak occurrence time),(Li, step 21 and 23 ¶ [6]).
By defining a positive slope immediately followed by a negative slope, Li in view of Mei is evaluating a continuous 2-point (N=2) first-order difference window to verify a local hydrograph maximum.( wherein the first condition includes the first first-order difference value and N/2-1 continuous first-order difference values before the first first-order difference value being all greater than or equal to zero, and N/2 continuous first-order difference values after the first first-order difference value being all less than or equal to zero, N being an even number greater than zero.)
The probability of two consecutive hourly gauge readings being mathematically identical to the last decimal place, which is required to yield a first-order difference of exactly zero, is practically zero. So mathematical domain of (> 0 or < 0) is analogous to (>=0 or =<0).
Regarding claim 5, Li in view of Mei teaches the method according to claim 1, including the screening out determined peak occurrence time from the initial peak occurrence time, and screening out start and end time corresponding to the peak occurrence time from the initial start and end time as set forth with respect to rejection of claim 1.
Li further teaches the details of the screening process by specifying screening candidate peak flow points against a predefined flood peak threshold (ypthres),
y
(
i
)
≥
y
p
t
h
r
e
s
, to filter out minor, hydrologically insignificant peaks, (Li, step 23 ¶ [16]) (screening out the initial peak occurrence time at which a corresponding runoff volume is greater than or equal to a preset flood peak threshold as the determined peak occurrence time)
Li segments events sequentially by calculating inflection points directly around the wave but it does not use a proximity-pairing rule. Li does not teach screening out the initial start and end time earlier than and closest to the peak occurrence time as start time; and screening out the initial start and end time later than and closest to the peak occurrence time as end time.
Mei teaches that for each peak flow, use its closest rising/recessing hours as the event beginning/end, (page 642, section 3.2, step 6) (screening out the initial start and end time earlier than and closest to the peak occurrence time as start time; and screening out the initial start and end time later than and closest to the peak occurrence time as end time.)
It would have been obvious to a person having ordinary skill in the art before effective filling date of the claimed invention to integrate Mei’s proximity-pairing rules into automated streamflow segmentation framework of Li. Because sequential boundary tracing, as taught by Li, is highly sensitive to noise as it relies on second derivatives. Mei’s method provides mathematically simpler, and more stable way to segment noisy hydrological datasets without introducing temporal phase lags.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Mei as applied to claim 2 above, and further in view of Wang Fan et.al (CN110929956A) hereinafter Wang, see attached English Translation.
Regarding claim 3, Li in view of Mei teaches the method according to claim 2, as set forth with respect to rejection of claim 2.
Li in view of Mei doesn’t teach the method of claim 2 wherein the first condition further comprises an absolute value of the first first-order difference value and an absolute value of a first-order difference value next to the first first-order difference value being both greater than a preset threshold.
Wang teaches setting an absolute slope threshold (Thslp) on the first-order difference sequence to filter out micro-fluctuations and flat segments, ¶ [62-63]). Wang teaches the relative test formula:
M
i
n
i
-
M
i
n
1
<
T
h
s
l
p
.
m
a
x
(
d
1
,
…
,
d
i
)
Where d is the first order difference sequence of the runoff/flow time series (an absolute value of the first first-order difference value and an absolute value of a first-order difference value next to the first first-order difference value being both greater than a preset threshold).
It would have been obvious to a person having ordinary skill in the art before effective filling date of the claimed invention to combine the symmetric peak-finding sign check, as taught by Li in view of Mei, with the first order difference threshold constraints taught by wang because the modification creates a highly stable, derivative based slop-sensitivity noise gate. This ensures that a peak is only registered when the rising limb leading into the crest and the falling limb leading out of the crest are both sufficiently steep to represent a true, physically significant flood wave, while successfully ignoring flat plateaus and fluctuations.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Mei as applied to claim 1 above, and further in view of Tom O’Haver (Pragmatic introduction to signal processing, textbook, May 2020 edition) hereinafter O’Haver.
Regarding claim 6, Li in view of Mei teaches the method according to claim 1, including the obtaining runoff time sequence data as set forth with respect to rejection of claim 1.
Li in view of Mei further teaches screening out the determined peak occurrence time from the initial peak occurrence time, and screening out the start and end time corresponding to the peak occurrence time from the initial start and end time as set forth with respect to rejection of claim 1.
Li in view of Mei candidate peaks are not found on the smoothed data. Instead, Li search the original, un-smoothed array y() to identify peaks using its first order backward difference y’(), (Li, step 2 and step 23). Smoothing is only introduced later in step 4 to find the start and end positions.
Li in view of Mei does not teach that its method of obtaining runoff time sequence data comprises: obtaining original runoff time sequence data; and smoothing the original runoff time sequence data to obtain the runoff time sequence data; wherein after screening out the determined peak occurrence time from the initial peak occurrence time, and screening out the start and end time corresponding to the peak occurrence time from the initial start and end time, the method further includes: obtaining partial original runoff data between the start time and the end time from the original runoff time sequence data; screening out a maximum runoff volume from the partial original runoff data; and if the maximum runoff volume is greater than or equal to the preset flood peak threshold, determining that the maximum runoff volume is the flood peak discharge, and correcting the peak occurrence time to time corresponding to the maximum runoff volume.
O’Haver defines raw time-series data sequence (x,y) as signal, where (y) represents original unsmoothed measurement amplitudes, (page 10). O’Haver teaches after data acquisition, applying digital smoothing to the raw signal to generate a smoothed sequence, (page 35) (obtaining original runoff time sequence data; and smoothing the original sequence data to obtain [smoothed sequence data]). O’Haver further teaches performing downward going zero-crossing checks on the smoothed first derivative to locate stable peak and valley boundaries without noise interference, (page 214). O’Haver teaches isolating a localized, raw segment of the original unsmoothed signal in the vicinity of the detected boundaries, (2nd ¶, page 43) (obtaining partial original data between the start time and the end time from the original time sequence data).
O’Haver teaches that one of the standard methods for extracting a peak from a raw data segment is to compute the “Max” value, which he defines as the highest individual Y value near the peak, (page 252). O’Haver teaches that the smoothing filter might lower the peak value (window 1, page 38), further stating that the algorithm uses smoothing only for peak detection; it performs measurements on the raw unsmoothed y data, (2nd ¶, page 316). (screening out a maximum from the partial original data; and if the maximum is greater than or equal to the preset peak threshold, determining that the maximum is the peak).
O’Haver further teaches how using the raw peak’s top segment with “RealTimeSmoothPeakDetectionGauss.m” corrects for temporal delay, ensuring the final reported peak coordinates are realigned with the physical crest rather than downstream of it, (page 328-329) (correcting the peak occurrence time to time corresponding to the maximum) .
It would have been obvious to a person having ordinary skill in the art before effective filling date of the claimed invention to modify Li in view of Mei automated streamflow segmentation with O’Haver’s teaching. Because using Li’s smoothed moving average sequence (ya) to calculate peak arrival times and crest magnitudes would introduce systematic phase delays and amplitude underestimations. Combining Li’s stable boundary-seeking steps with O’Haver’s “smooth for boundaries, raw for value” correction represents the predictable application of standard signal-conditioning principles to yield the expected benefit of securing highly accurate, noise-immune, and physically precise flood warning without introducing any computational complexity.
Conclusion
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/SAEEDE NAFOOSHE/ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857
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