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 .
Claim Objections
Claim 4 is objected to because of the following informalities:
In claim 4, line 2, change “the performing identification” to -wherein the performing
identification-. Appropriate correction is required.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process (claim 1, a method), which is statutory category.
However, evaluating claim 1, under Step 2A, Prong One, the claim is directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship/mental process. The limitations include:
S1, time series reconstruction and trough identification: obtaining annual radar time series backscatter data of a target area in vertical transmission and horizontal reception (VH) polarization; constructing time series backscatter S[t] based on the annual radar time series backscatter data, where t represents a normalized Julian date, and a value of t ranges from 0 to 1; performing time series harmonic fitting by using a formula 1 to obtain reconstructed time series backscatter:
S[t] = a +
∑
i
=
1
3
A
i
cos
2
π
i
t
-
φ
i
(formula 1) ,
where a is a constant term representing an average value of the time series backscatter S[t];
i
takes values 1, 2, and 3 successively, indicating an order of a cosine term;
A
i
represents an amplitude of an i-th order cosine term, and
φ
i
represents a phase of the i-th order cosine term; the process of performing time series harmonic fitting by using the formula 1 to obtain the reconstructed time series backscatter comprises: obtaining values of a,
A
i
, and
φ
i
by using least square fitting, and substituting the values of a,
A
i
and
φ
i
into the formula 1 to obtain the reconstructed time series backscatter; obtaining a first-order difference S'[t] of the reconstructed time series backscatter using a formula 2:
S'[t] =
∑
i
=
1
3
-
2
π
i
A
i
sin
2
π
i
t
-
φ
i
(formula 2) ; and
calculating values of S[t] and S'[t] for the normalized Julian date t in the range from 0 to 1 with a step size of 0.01; and when S'[t-1] <0, S'[t+1] >0, and S[t] <0.02, determining that the normalized Julian date t corresponds to the occurrence of a backscatter trough, wherein an actual Julian date d corresponding to the normalized Julian date t is calculated as d=365t, where the unit of d is in days;
S2, potential rice phenological phase estimation:
representing five phenological phases composed of a seedling phase, a transplanting phase, a vegetative phase, a reproductive phase, and a maturation phase, as DS, DT, DV, DR and DM, respectively; obtaining annual daily averaged temperature data of the target area; calculating a duration of an annual cold period PC of the target area based on the annual daily averaged temperature data, where PC is defined as a period with temperatures below 10 degrees Celsius (℃), and the unit of PC is in days; for the target area, when PC≠ 0, and d > 240, determining that d corresponds to DR, and in this case, determining that DS = d - 90, DT = d - 60, DV = d - 30, DR = d, and DM= d + 30; and when PC = 0 or d ≤ 240, determining that d corresponds to DT, and in this case, determining DS = d - 30, DT = d, DV = d + 30, DR = d + 60, and DM = d + 90; and
S3, temperature limitation of rice phenological phase:
obtaining a temperature ES corresponding to DS, a temperature ET corresponding to DT, a temperature EV corresponding to DV, a temperature ER corresponding to DR, and a temperature EM corresponding to DM, respectively;
for a target backscatter trough, when ES >10℃, ET >10℃, EV >18℃, ER >18℃, and EM >10℃, determining the target backscatter trough as a valid trough, otherwise determining the target backscatter trough as an invalid trough and removing the invalid trough; counting the number N of valid troughs;
determining a maximum rice-crop intensity suitability S for the target area according to the following regulation:
i
f
P
c
=
0
,
t
h
e
n
S
=
3
;
i
f
0
<
P
c
≤
120
,
t
h
e
n
S
=
2
;
i
f
120
<
P
c
≤
240
,
t
h
e
n
S
=
1
i
f
P
c
>
240
,
t
h
e
n
S
=
0
)
; and
for the target area, when N>S, determining that there is overestimation of rice-crop intensity; calculating a sum value Sum of the temperatures on the five phenological phases for each valid trough: Sum ＝ ES+ET+EV+ER+EM ; removing troughs with a lower Sum, where the number of troughs to be removed is N-S; and determining the remaining troughs as rice troughs and counting the number of rice troughs of the target area, which is a rice-crop intensity of the target area; and when N≤S, determining the number N of valid troughs as the number of rice troughs of the target area, which is a rice-crop intensity of the target area; and
S4, identification of rice-crop intensity:
obtaining digital elevation data of the target area, and extracting altitude and slope information of the target area from the digital elevation data; obtaining land use product of the target area, and extracting cropland distribution information from the land use product; and performing rice-crop intensity identification of the target area based on the altitude and slope information, the cropland distribution information, and the number of rice troughs of the target area.
The claim recites mathematical concepts and mental processes, including harmonic curve fitting using cosine series, lest-squares parameter estimation, derivative computation, threshold comparisons, conditional rule-based mappings between cold-period duration and suitability levels, temperature summation, and counting operations, which constitute mathematical relationships and evaluative logic.
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application.
The additional elements, i.e., obtaining radar backscatter data, temperature data, digital elevation data, and land use data, amount to mere data gathering and do not integrate the abstract mathematical analysis into a practical application, as the claim does not improve radar technology, signal acquisition, polarization processing, or computer functionality.
Therefore, the claims are directed to an abstract idea.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claim significantly more than the abstract idea.
The additional elements of “obtaining annual radar time series backscatter data of a target area in vertical transmission and horizontal reception (VH) polarization”, “obtaining a temperature ES corresponding to DS, a temperature ET corresponding to DT, a temperature EV corresponding to DV, a temperature ER corresponding to DR, and a temperature EM corresponding to DM” and “obtaining digital elevation data of the target area” are considered insignificant extra-solution activity of collecting data that is not sufficient to integrate the claim into a particular practical application. The act of data gathering is considered insufficient to make the claim significantly more than the abstract idea.
The additional elements, considered individually and as an ordered combination, merely apply the abstract calculations to conventional data sources using generic computational techniques to produce a crop-intensity classification result, and therefore do not amount to significantly more than the judicial exception.
Accordingly, the claim is not patent-eligible under 35 U.S.C. § 101.
Dependent claims 2-6 does not add more than additional filtering, software implementation details, presentation of data, intended use, or generic computer implementation. When considered individually and in combination with claim 1, these limitations do not integrate the abstract idea into practical application nor provide significantly more than the judicial exception.
Accordingly, claims 2-6 are not patent eligible under 35 U.S.C. § 101.
The additional elements (claim 6), the element of “processor” and “memory” are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system (Alice Corp. Pty. Ltd. v. CLS Bank Int’l 573 U.S. __, 134 S. Ct. 2347, 110 U.S.P.Q.2d 1976 (2014)).
Examiner’s Notes
Claim 1 distinguish over the prior art of record because the closest prior at of
record Sinha et al. (pub. No. US 2021/0110157) discloses estimating crop maturity and phenological development using remote sensing imagery, including synthetic aperture radar (SAR) imagery, in combination with weather-based growth indicators. Sinha et al. teaches acquiring remote sensing images of agricultural fields and using a response model to estimate crop maturity. It further teaches incorporating weather-related data such as growing degree days (GDD) and updating crop maturity estimates using recursive filtering as new imagery becomes available. The system predicts crop development stages based on image-derived features and temperature-driven growth modeling. However, Sinha et al. fails to anticipate or render obvious a rice-crop intensity identification method based on radar time series observation and temperature analysis, the method including the steps of: obtaining a first-order difference S'[t] of the reconstructed time series backscatter using a formula 2; and calculating values of S[t] and S'[t] for the normalized Julian date t in the range from 0 to 1 with a step size of 0.01; and when S'[t-1] <0, S'[t+1] >0, and S[t] <0.02, determining that the normalized Julian date t corresponds to the occurrence of a backscatter trough, wherein an actual Julian date d corresponding to the normalized Julian date t is calculated as d=365t, where the unit of d is in days; S2, potential rice phenological phase estimation: representing five phenological phases composed of a seedling phase, a transplanting phase, a vegetative phase, a reproductive phase, and a maturation phase, as DS, DT, DV, DR and DM, respectively; obtaining annual daily averaged temperature data of the target area; calculating a duration of an annual cold period PC of the target area based on the annual daily averaged temperature data, where PC is defined as a period with temperatures below 10 degrees Celsius (℃), and the unit of PC is in days; for the target area, when PC≠ 0, and d > 240, determining that d corresponds to DR, and in this case, determining that DS = d - 90, DT = d - 60, DV = d - 30, DR = d, and DM= d + 30; and when PC = 0 or d ≤ 240, determining that d corresponds to DT, and in this case, determining DS = d - 30, DT = d, DV = d + 30, DR = d + 60, and DM = d + 90; and S3, temperature limitation of rice phenological phase: obtaining a temperature ES corresponding to DS, a temperature ET corresponding to DT, a temperature EV corresponding to DV, a temperature ER corresponding to DR, and a temperature EM corresponding to DM, respectively; for a target backscatter trough, when ES >10℃, ET >10℃, EV >18℃, ER >18℃, and EM >10℃, determining the target backscatter trough as a valid trough, otherwise determining the target backscatter trough as an invalid trough and removing the invalid trough; counting the number N of valid troughs; determining a maximum rice-crop intensity suitability S for the target area according to the recites regulation; and for the target area, when N>S, determining that there is overestimation of rice-crop intensity; calculating a sum value Sum of the temperatures on the five phenological phases for each valid trough: Sum ＝ ES+ET+EV+ER+EM ; removing troughs with a lower Sum, where the number of troughs to be removed is N-S; and determining the remaining troughs as rice troughs and counting the number of rice troughs of the target area, which is a rice-crop intensity of the target area; and when N≤S, determining the number N of valid troughs as the number of rice troughs of the target area, which is a rice-crop intensity of the target area; and S4, identification of rice-crop intensity: obtaining digital elevation data of the target area, and extracting altitude and slope information of the target area from the digital elevation data; obtaining land use product of the target area, and extracting cropland distribution information from the land use product; and performing rice-crop intensity identification of the target area based on the altitude and slope information, the cropland distribution information, and the number of rice troughs of the target area, in combination with the rest of the claim limitations as claimed and defined by the applicant.
Prior art
The prior art made record and not relied upon is considered pertinent to applicant’s
disclosure:
Albrecht et al. [‘375] discloses a computer-implemented method for agricultural monitoring using vegetation index time-series data. Albrecht et al. teaches reconstructing time-series signals (e.g., NDVI-diff time series) using Fourier transform techniques to capture seasonal features such as local maxima, local minima, and crop intensity from reconstructed time-series data.
Xiao et al. [‘215] discloses a system and method utilizing a phenology-based algorithm which that identifies and maps particular types of crop fields from diverse crop types across local, state, and/or country scales. The method including the steps of: receiving first image data of a geographic region with one or more processor, calculating for particular real-world locations within the geographic region with the one or more processor, one or more vegetation index with combinations of the pixel information; generating at least one land cover mask with the one or more vegetation index with the one or more processor, the at least one land cover mask identifying first real-world locations within the geographic region having a water-related land cover type, a non-vegetated land cover type and an evergreen land cover type; classifying second real-world locations within the geographic region that are not classified as the water-related land cover type, the non-vegetated land cover type and the evergreen land cover type are identified as cropland via the one or more processor; and analyzing a time-series of image data with one or more processor depicting the second real-world locations within the geographic region with phenology metrics to identify at least one particular type of cropland within the second real-world locations.
Rikimaru et al. [‘738] discloses analyzing vegetation growth conditions at multiple times of a year accurately using radar images obtainable from a flying body such as artificial satellites, etc. A plurality of radar images of a ground surface of a same target area, which have been taken at multiple times of a year by a radar device mounted on a flying body, are acquired. The acquired plurality of radar images is stored in a map database. While using as a criterion image a radar image of the plurality of radar images stored in the map database, taken at a predetermined time in the multiple times of a year, other radar images than the criterion image of the plurality of radar images, taken at other times than the predetermined time in the multiple times of a year, are aligned with the criterion image, respectively. Then, backscatter coefficients of specified areas in the plurality of radar images are extracted. Based on a backscatter coefficient of a specified area in the criterion image of the plurality of radar images stored in the map database, backscatter coefficients of other radar images than the criterion image of the plurality of radar images are calibrated. And, based on a correlation between backscatter coefficients of radar images and growth values of vegetation shown in the radar images, growth values of vegetation shown in other radar images than the criterion image of the plurality of radar images, whose backscatter coefficients have been calibrated in the calibrating step, are calculated using the calibrated backscatter coefficients.
Contact information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213. The examiner can normally be reached Monday through Friday, from 9 am to 6 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (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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Mohamed Charioui
/MOHAMED CHARIOUI/Primary Examiner, Art Unit 2857