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 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, 3, 5, 7-8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Abdel-Rahman, Artificial Intelligence Modeling tools for Monitoring Desert Locust: breeding grounds, hatching time, population dynamics and spatio-temporal distribution, 2022, hereinafter referred to as ‘Abdel-Rahman’ and in further view of Pan et al. (CN103955606), hereinafter referred to as ‘Pan’.
Regarding Claim 1, Abdel-Rahman discloses a method for predicting a desert locust, comprising: acquiring environment factor data of a target area (pg. 2, item d), wherein the environment factor data comprises total precipitation data (“monthly rainfall”pg. 2, item d), soil temperature data (pg. 2, item d), soil water data (“soil moisture … at 0-2cm depth” pg. 2, item d), and vegetation index data (“vegetation variables” pg. 2, item d); extracting fluctuation features corresponding to the environment factor data through wavelet transform (“wavelet analysis” pg. 14, 7th row), wherein the fluctuation features comprise a precipitation fluctuation feature corresponding to the total precipitation data, a soil temperature fluctuation feature corresponding to the soil temperature data, a soil water fluctuation feature corresponding to the soil water data, and a vegetation fluctuation feature corresponding to the vegetation index data (pg. 3, item e, ); and predicting time when a desert locust presents in the target area based on the fluctuation features (pg. 3, item d).
However, Abdel-Rahman does not explicitly disclose wherein the fluctuation features comprise a precipitation fluctuation feature corresponding to the total precipitation data, a soil temperature fluctuation feature corresponding to the soil temperature data, a soil water fluctuation feature corresponding to the soil water data, and a vegetation fluctuation feature corresponding to the vegetation index data.
Nevertheless, Pan discloses the fluctuation features comprise a precipitation fluctuation feature corresponding to the total precipitation data (In the prediction method provided by this invention, during the third-instar correction process in step 2.3), the growth suitability index needs to be recalculated, and a rainfall factor (RF) is added to reflect the impact of rainfall on locust growth and development [0036]), a soil temperature fluctuation feature corresponding to the soil temperature data (The higher the soil moisture content, the greater the soil thermal inertia, and the smaller the soil temperature fluctuation; conversely, the drier and less water-scarce the soil, the smaller the soil thermal inertia, and the greater the soil temperature fluctuation [0110]), a soil water fluctuation feature corresponding to the soil water data (The higher the soil moisture content, the greater the soil thermal inertia, and the smaller the soil temperature fluctuation [0110]), and a vegetation fluctuation feature corresponding to the vegetation index data (In the formula, VFD is the vegetation cover factor during the locust growth stage, FVC2 is the vegetation cover during the locust growth stage retrieved from remote sensing data, which is dimensionless and takes a value of 0 to 1, and FVC2<sub> max </sub> and FVC2<sub> min </sub> represent the maximum and minimum values, i.e. vegetation fluctuation, of vegetation cover for each remote sensing pixel in the study area during the locust growth stage, respectively [0092]); and predicting time when a desert locust presents in the target area based on the fluctuation features (In the formula, VFD is the vegetation cover factor during the locust growth stage, FVC2 is the vegetation cover during the locust growth stage retrieved from remote sensing data, which is dimensionless and takes a value of 0 to 1, and FVC2<sub> max </sub> and FVC2<sub> min </sub> represent the maximum and minimum values, i.e. vegetation fluctuation, of vegetation cover for each remote sensing pixel in the study area during the locust growth stage, respectively [0092]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
Regarding Claim 3, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 2.
Abdel-Rahman discloses the predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period comprises (as discussed above); and predicting the time when the desert locust presents in the target area based on the adjusted second fluctuation feature (as discussed above).
However, Abdel-Rahman does not explicitly disclose the predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period comprises: adjusting the second fluctuation feature based on the lag period; and predicting the time when the desert locust presents in the target area based on the adjusted second fluctuation feature.
Nevertheless, Pan discloses the predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period comprises (as discussed above): adjusting the second fluctuation feature based on the lag period (as discussed above); and predicting the time when the desert locust presents in the target area based on the adjusted second fluctuation feature (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
Regarding Claim 5, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 1.
Abdel-Rahman discloses acquiring migration prediction information of the target area (pg. 5, item a), wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises (as discussed above): predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information (pg. 5, item a).
However, Abdel-Rahman does not explicitly disclose acquiring migration prediction information of the target area, wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information.
Nevertheless, Pan discloses wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises (as discussed above): predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
Regarding Claim 7, Abdel-Rahman discloses a device for predicting a desert locust, comprising: an acquiring module, configured to acquire environment factor data of a target area (pg. 2, item d), wherein the environment factor data comprises total precipitation data (“monthly rainfall”pg. 2, item d), soil temperature data (pg. 2, item d), soil water data (“soil moisture … at 0-2cm depth” pg. 2, item d), and vegetation index data (“vegetation variables” pg. 2, item d); an extracting module, configured to extract fluctuation features corresponding to the environment factor data through wavelet transform (“wavelet analysis” pg. 14, 7th row), wherein the fluctuation features comprise a precipitation fluctuation feature corresponding to the total precipitation data, a soil temperature fluctuation feature corresponding to the soil temperature data, a soil water fluctuation feature corresponding to the soil water data, and a vegetation fluctuation feature corresponding to the vegetation index data (pg. 3, item e, ); and predicting time when a desert locust presents in the target area (pg. 3, item d).
However, Abdel-Rahman does not explicitly disclose wherein the fluctuation features comprise a precipitation fluctuation feature corresponding to the total precipitation data, a soil temperature fluctuation feature corresponding to the soil temperature data, a soil water fluctuation feature corresponding to the soil water data, and a vegetation fluctuation feature corresponding to the vegetation index data; and a predicting module, configured to predict time when a desert locust presents in the target area based on the fluctuation features.
Nevertheless, Pan discloses the fluctuation features comprise a precipitation fluctuation feature corresponding to the total precipitation data (In the prediction method provided by this invention, during the third-instar correction process in step 2.3), the growth suitability index needs to be recalculated, and a rainfall factor (RF) is added to reflect the impact of rainfall on locust growth and development [0036]), a soil temperature fluctuation feature corresponding to the soil temperature data (The higher the soil moisture content, the greater the soil thermal inertia, and the smaller the soil temperature fluctuation; conversely, the drier and less water-scarce the soil, the smaller the soil thermal inertia, and the greater the soil temperature fluctuation [0110]), a soil water fluctuation feature corresponding to the soil water data (The higher the soil moisture content, the greater the soil thermal inertia, and the smaller the soil temperature fluctuation [0110]), and a vegetation fluctuation feature corresponding to the vegetation index data (In the formula, VFD is the vegetation cover factor during the locust growth stage, FVC2 is the vegetation cover during the locust growth stage retrieved from remote sensing data, which is dimensionless and takes a value of 0 to 1, and FVC2<sub> max </sub> and FVC2<sub> min </sub> represent the maximum and minimum values, i.e. vegetation fluctuation, of vegetation cover for each remote sensing pixel in the study area during the locust growth stage, respectively [0092]); and predicting time when a desert locust presents in the target area based on the fluctuation features (In the formula, VFD is the vegetation cover factor during the locust growth stage, FVC2 is the vegetation cover during the locust growth stage retrieved from remote sensing data, which is dimensionless and takes a value of 0 to 1, and FVC2<sub> max </sub> and FVC2<sub> min </sub> represent the maximum and minimum values, i.e. vegetation fluctuation, of vegetation cover for each remote sensing pixel in the study area during the locust growth stage, respectively [0092]) and predicting time when a desert locust presents in the target area based on the fluctuation features (The higher the soil moisture content, the greater the soil thermal inertia, and the smaller the soil temperature fluctuation; conversely, the drier and less water-scarce the soil, the smaller the soil thermal inertia, and the greater the soil temperature fluctuation [0110])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
Regarding Claim 8, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 7.
Abdel-Rahman discloses wherein the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time (pg. 2, item c), and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time (pg.2, item c), wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature (pg. 2, item d); and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period (pg. 3, item d).
However, Abdel-Rahman does not explicitly disclose the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time, and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time, wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature; and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period.
Nevertheless, Pan discloses the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time (as discussed above), and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time (as discussed above), wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature (as discussed above); and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises (as discussed above): predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
However, Ahmed-Rahman and Pan do not explicitly disclose the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time, and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time, wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature; and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period.
Nevertheless, Zhang discloses the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]), and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]), wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]); and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman and Pan with the teachings of Zhang to ability to retrieve environmental factors in any area of the world during historical periods and monitor current and future soil water changes while improving emergency response and disaster prevention of locust plague.
Regarding Claim 10, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 7.
Abdel-Rahman discloses a migration prediction information acquiring module, configured to acquire migration prediction information of the target area (pg. 5, item a), wherein the predicting module is configured to predict the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information (pg. 5, item a).
However, Abdel-Rahman does not explicitly disclose acquiring migration prediction information of the target area, wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information.
Nevertheless, Pan discloses wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises (as discussed above): predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ahmed-Rahman and Pan, and further in view of Zhang et al. (CN115906656) hereinafter referred to as ‘Zhang1’.
Regarding Claim 2, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 1.
Abdel-Rahman discloses wherein the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time (pg. 2, item c), and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time (pg.2, item c), wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature (pg. 2, item d); and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period (pg. 3, item d).
However, Abdel-Rahman does not explicitly disclose the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time, and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time, wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature; and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period.
Nevertheless, Pan discloses the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time (as discussed above), and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time (as discussed above), wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature (as discussed above); and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises (as discussed above): predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
However, Ahmed-Rahman and Pan do not explicitly disclose the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time, and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time, wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature; and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period.
Nevertheless, Zhang1 discloses the fluctuation features comprise a first fluctuation feature corresponding to the environment factor data of the target area for a past period of time (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]), and a second fluctuation feature corresponding to the environment factor data of the target area for a future period of time (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]), wherein the method further comprises: determining a lag period of the fluctuation features based on data of the desert locust presence in the target area for the past period of time and the first fluctuation feature (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]); and wherein the predicting time when the desert locust presents in the target area based on the fluctuation features comprises: predicting the time when the desert locust presents in the target area based on the second fluctuation feature and the lag period (This invention provides a method for retrieving soil water content based on meteorological and gravity satellite data, which solves the problems of traditional methods being time-consuming, labor-intensive, and expensive. It also has the ability to retrieve soil water content in any area of the world during historical periods and monitor current and future soil water changes [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman and Pan with the teachings of Zhang1 to ability to retrieve environment factors in any area of the world during historical periods and monitor current and future soil water changes while improving emergency response and disaster prevention of locust plague.
Claims 4 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed-Rahman and Pan, and further in view of Zhang et al. (CN103560587) hereinafter referred to as ‘Zhang’.
Regarding Claim 4, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 1.
Abdel-Rahman discloses classifying the environment factor data as a first type of data and a second type of data based on a correspondence between the environment factor data of the target area and data of the desert locust presence in the target area for a past period of time (pg. 7, item a); and performing differential processing on the second type of data, wherein the extracting fluctuation features corresponding to the environment factor data through wavelet transform comprises (as discussed above): extracting a fluctuation feature corresponding to the first type of data and a fluctuation feature corresponding to the processed second type of data through the wavelet transform (as discussed above).
However, Abdel-Rahman does not explicitly disclose classifying the environment factor data as a first type of data and a second type of data based on a correspondence between the environment factor data of the target area and data of the desert locust presence in the target area for a past period of time; and performing differential processing on the second type of data, wherein the extracting fluctuation features corresponding to the environment factor data through wavelet transform comprises: extracting a fluctuation feature corresponding to the first type of data and a fluctuation feature corresponding to the processed second type of data through the wavelet transform.
Nevertheless, Pan discloses classifying the environment factor data as a first type of data and a second type of data based on a correspondence between the environment factor data of the target area and data of the desert locust presence in the target area for a past period of time (as discussed above);, wherein the extracting fluctuation features corresponding to the environment factor data through wavelet transform comprises (as discussed above): extracting a fluctuation feature corresponding to the first type of data and a fluctuation feature corresponding to the processed second type of data through the wavelet transform (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
However, Abdel-Rahman and Pan do not explicitly disclose and performing differential processing on the second type of data (Model Testing and Evaluation: Substitute the test set data generated in step S4 into the optimal long short-term memory network model generated in step S7 to obtain the soil moisture content data simulated by the model. Then, based on the measured soil moisture content data in step S1, evaluate the performance of the soil moisture content obtained through model simulation [0017]).
Nevertheless, Zhang discloses and performing differential processing (Model Testing and Evaluation: Substitute the test set data generated in step S4 into the optimal long short-term memory network model generated in step S7 to obtain the soil moisture content data simulated by the model, i.e. differential processing. Then, based on the measured soil moisture content data in step S1, evaluate the performance of the soil moisture content obtained through model simulation [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman and Pan with the teachings of Zhang to evaluate the performance of the environmental factors obtained through model simulation while improving emergency response and disaster prevention of locust plague.
Regarding Claim 9, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 7.
Abdel-Rahman discloses classifying the environment factor data as a first type of data and a second type of data based on a correspondence between the environment factor data of the target area and data of the desert locust presence in the target area for a past period of time (pg. 7, item a); and performing differential processing on the second type of data, wherein the extracting fluctuation features corresponding to the environment factor data through wavelet transform comprises (as discussed above): extracting a fluctuation feature corresponding to the first type of data and a fluctuation feature corresponding to the processed second type of data through the wavelet transform (as discussed above).
However, Abdel-Rahman does not explicitly disclose classifying the environment factor data as a first type of data and a second type of data based on a correspondence between the environment factor data of the target area and data of the desert locust presence in the target area for a past period of time; and performing differential processing on the second type of data, wherein the extracting fluctuation features corresponding to the environment factor data through wavelet transform comprises: extracting a fluctuation feature corresponding to the first type of data and a fluctuation feature corresponding to the processed second type of data through the wavelet transform.
Nevertheless, Pan discloses classifying the environment factor data as a first type of data and a second type of data based on a correspondence between the environment factor data of the target area and data of the desert locust presence in the target area for a past period of time (as discussed above);, wherein the extracting fluctuation features corresponding to the environment factor data through wavelet transform comprises (as discussed above): extracting a fluctuation feature corresponding to the first type of data and a fluctuation feature corresponding to the processed second type of data through the wavelet transform (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Pan to construct an environment factor model while improving emergency response and disaster prevention of locust plague.
However, Abdel-Rahman and Pan do not explicitly disclose and performing differential processing on the second type of data (Model Testing and Evaluation: Substitute the test set data generated in step S4 into the optimal long short-term memory network model generated in step S7 to obtain the soil moisture content data simulated by the model. Then, based on the measured soil moisture content data in step S1, evaluate the performance of the soil moisture content obtained through model simulation [0017]).
Nevertheless, Zhang discloses and performing differential processing (Model Testing and Evaluation: Substitute the test set data generated in step S4 into the optimal long short-term memory network model generated in step S7 to obtain the soil moisture content data simulated by the model, i.e. differential processing. Then, based on the measured soil moisture content data in step S1, evaluate the performance of the soil moisture content obtained through model simulation [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman and Pan with the teachings of Zhang to evaluate the performance of the environmental factors obtained through model simulation while improving emergency response and disaster prevention of locust plague.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ahmed-Rahman and Pan, and further in view of Lu et al. (CN109389139) hereinafter referred to as ‘Lu’.
Regarding Claim 6, Abdel-Rahman and Pan discloses the claimed invention discussed in claim 5.
Abdel-Rahman discloses the predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information comprises (as discussed above): performing binarization processing on the migration prediction information to obtain locust source information (as discussed above); and predicting the time when the desert locust presents in the target area based on the fluctuation features and the locust source information (as discussed above).
However, Abdel-Rahman does not explicitly disclose the predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information comprises: performing binarization processing on the migration prediction information to obtain locust source information; and predicting the time when the desert locust presents in the target area based on the fluctuation features and the locust source information.
Nevertheless, Pan discloses the predicting the time when the desert locust presents in the target area based on the fluctuation features and the migration prediction information comprises (as discussed above): performing binarization processing on the migration prediction information to obtain locust source information (as discussed above); and predicting the time when the desert locust presents in the target area based on the fluctuation features and the locust source information (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman with the teachings of Panto plan the most effective path while minimizing drilling risks and control costs.
However, Abdel-Rahman and Pando not explicitly disclose performing binarization processing on the migration prediction information to obtain locust source information.
Nevertheless, Lu discloses performing binarization processing to obtain locust source information (S1, use the Meanshift algorithm to cluster the locust images, and perform binarization on the clustered locust images to obtain each target connected region in the locust images [0009]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Abdel-Rahman and Pan with the teachings of Lu to obtain each target connected region in the locust images while improving migration prediction.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/SHARAH ZAAB/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
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