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 § 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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A computer-implemented method for quantifying an extent and recurrence of a predetermined natural event in a geographical area, the method comprising the steps of:
providing, in a first memory element, a first probability data set associated with a plurality of locations in said geographical area, indicating probabilities of each of the plurality of locations being affected by said predetermined event based on remote sensing data;
providing, in a second memory element, a plurality of second probability data sets associated with the plurality of locations, wherein each of the second probability data set-data sets is further associated with a recurrence period or frequency of said predetermined natural event, and wherein each of the plurality of second probability data sets data indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event based on computer simulation results;
using computing means, computing for each of said plurality of locations a set of weights, wherein each of the weights indicates a similarity between the probability associated with said location in the first probability data set, and in one of the second probability data sets, respectively;
using computing means, computing, for each of said locations and using the corresponding set of weights,
a weighted combination of the probability data associated with the location in each of said second probability data sets,
a weighted combination of the corresponding recurrence periods or frequencies, and
storing results in a third memory element, thereby generating data indicative of the extent and recurrence of said predetermined natural event for each of the plurality of locations in said geographical area.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “computing for each of said plurality of locations a set of weights, wherein each of the weights indicates a similarity between the probability associated with said location in the first probability data set, and in one of the second probability data sets, respectively (mathematically determining weights for parameters); and
computing, for each of said locations and using the corresponding set of weights, a weighted combination of the probability data associated with the location in each of said second probability data sets, a weighted combination of the corresponding recurrence periods or frequencies (applying the weights in computations)” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “computing for each of said plurality of locations a set of weights, wherein each of the weights indicates a similarity between the probability associated with said location in the first probability data set, and in one of the second probability data sets, respectively (determining weights for different parameters);
storing results, thereby generating data indicative of the extent and recurrence of said predetermined natural event for each of the plurality of locations in said geographical area (recording and sharing results)” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claim 14.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A computer-implemented method for quantifying an extent and recurrence of a predetermined natural event in a geographical area, the method comprising the steps of:
providing, in a first memory element, a first probability data set (100)set associated with a plurality of locations in said geographical area, indicating probabilities of each of the plurality of locations being affected by said predetermined event based on remote sensing data;
providing, in a second memory element, a plurality of second probability data sets associated with the plurality of locations, wherein each of the second probability data set-data sets is further associated with a recurrence period or frequency of said predetermined natural event, and wherein each of the plurality of second probability data sets data indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event based on computer simulation results;
using computing means; a third memory element;
Claim 10: A non-transitory computer readable storage medium storing instructions that when executed by a computer, which includes a data processing means performs a method;
providing, in a first memory element, a first probability data set associated with a plurality of locations in said geographical area, indicating probabilities of each location being affected by said predetermined event based on remote sensing data;
providing, in a second memory element, a plurality of second probability data sets associated with the plurality of locations, wherein each of the second probability data sets is further associated with a recurrence period or frequency of said predetermined natural event,
and wherein each of the plurality of second probability data sets indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event based on computer simulation results.
The additional element in the preamble of “A computer-implemented method for quantifying an extent and recurrence of a predetermined natural event in a geographical area” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. Providing a first probability data set (100)set associated with a plurality of locations in said geographical area, indicating probabilities of each of the plurality of locations being affected by said predetermined event based on remote sensing data;
providing a plurality of second probability data sets associated with the plurality of locations, wherein each of the second probability data set-data sets is further associated with a recurrence period or frequency of said predetermined natural event, and wherein each of the plurality of second probability data sets data indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event based on computer simulation results represent mere data gathering steps and only adds insignificant extra-solution activity to the judicial exception. A non-transitory computer readable storage medium, first memory element, second memory element, and third memory element (generic memory), and a computer or computing means (generic processor) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-15, and 16 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-10, 12, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (US 20170083747 A1), hereinafter “Guan”, in view of Wani et al. (US 20190316309 A1), hereinafter “Wani”.
Regarding Claim 1, Guan teaches a computer-implemented (Guan [0056] FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate.) method for quantifying an extent and recurrence of a predetermined natural event in a geographical area, the method comprising the steps of:
providing, in a first memory element, a first probability data set associated with a plurality of locations in said geographical area (Guan [0056] In one embodiment, a user 102 owns, operates, or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields.), indicating probabilities of each of the plurality of locations being affected by said predetermined event based on remote sensing data (Guan [0112] The spectral analysis logic 171 analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water. In some embodiments, the spectral analysis logic 171 implements a classifier that has been trained on labeled imagery to generate the probability. For example, the spectral analysis logic 171 may employ a logistic regression model that uses the spectral bands of each pixel as features.);
providing, in a second memory element, a plurality of second probability data sets associated with the plurality of locations, wherein each of the second probability data set-data sets is further associated with a recurrence period or frequency of said predetermined natural event, based on computer simulation results (Guan [0113] The flow simulation logic 172 uses the measured level of precipitation from the previous rainfall, along with a geographic elevation map of the field and absorption properties of the soil within the field, to estimate regions where rainwater is likely to pool. For example, an iterative algorithm can be used to estimate from a starting position of water from the rainfall on the field, where the water is likely to flow and in what quantities based on the elevation of the region and absorption rate of the soil. After a number of iterations, areas in the simulation which still contain water are marked as potential ponding areas.);
using computing means, computing for each of said plurality of locations a set of weights, wherein each of the weights indicates a similarity between the probability associated with said location in the first probability data set, and in one of the second probability data sets (Guan [0115] The probability that each pixel represents water is based on a weighted combination of value of the water probability map for the pixel and the likelihood of water pooling in the area represented by the pixel based on the flow simulation.), respectively;
using computing means, computing, for each of said locations and using the corresponding set of weights, a weighted combination of the probability data associated with the location in each of said second probability data sets, (Guan [0115] The coupling logic 173 formalizes the aforementioned concepts into a concrete workable model. For example, the satellite image could be viewed as a graph, where each node represents a pixel and is connected to the neighboring pixels with an edge. The probability that each pixel represents water is based on a weighted combination of value of the water probability map for the pixel and the likelihood of water pooling in the area represented by the pixel based on the flow simulation.), and
storing results in a third memory element, thereby generating data indicative of the extent of said predetermined natural event for each of the plurality of locations in said geographical area (Guan [0114] The coupling logic 173 combines the results of the spectral analysis logic 171 with the results of the flow simulation logic 172. The coupling logic 173 works under the assumption that a pixel which accurately represents water has a greater likelihood to be surrounded by other water pixels, rather than by pixels representing dry land. As a result, a pixel which has a high probability of being a water pixel also increases the likelihood that neighboring pixels also represent water. In addition, the model assumes that pixels with a high probability of being water (as determined by the spectral analysis logic 171) and are in an area in which water is likely to pool (as determined by the flow simulation logic 172) has a higher probability of representing ponding water.).
Guan is not relied upon to explicitly teach wherein each of the plurality of second probability data sets data indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event; and
a weighted combination of the corresponding recurrence periods or frequencies.
Wani teaches wherein each of the plurality of second probability data sets data indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event (Wani [0135] The flood risk map 1602 represents the probabilities that locations in the map will be inundated within a certain period (e.g., within the next 10 years, within the next 50 years, within the next 100 years)).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Guan in view of Wani to explicitly teach wherein each of the plurality of second probability data sets data indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event, to include further analysis indicating possible future scenarios based on past events (Wani [0069] Additionally, the flood analysis system 200 may develop flood-risk maps 228 for an area based on the analysis of possible scenarios 222 that may occur in the future. The scenarios may be based on past events or may be created by the user.).
With the incorporation of probabilities of return periods Guan in view of Wani (as stated above) now also teaches a weighted combination of the corresponding recurrence periods or frequencies, as well as generating data indicative of recurrence of the event (Guan [0115] The probability that each pixel represents water is based on a weighted combination of value of the water probability map for the pixel and the likelihood of water pooling in the area represented by the pixel based on the flow simulation. The return period probabilities are simulated/modeled).
Regarding Claim 2, Guan in view of Wani (as stated above) further teaches wherein the predetermined natural event comprises a flooding event, and wherein the first and second probability data sets indicate probabilities that each of the plurality of locations is covered by water (Guan [0044] Aspects of this disclosure focus on the problem of detecting standing water within agricultural fields, particularly in early growing seasons when crops are more sensitive to being overwatered.).
Regarding Claim 3, Guan in view of Wani (as stated above) further teaches obtaining remote sensing data representing said geographical area using image processing means, detecting representations of said predetermined natural event in said remote sensing data (Guan [0046] The spectral analysis logic analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water.); and
associating a probability of detection of a representation of said predetermined natural event to the locations represented by said remote sensing data (Guan [0046] In some embodiments, the spectral analysis logic implements a classifier that has been trained on labeled imagery to generate the probability. Also see [0112] The spectral analysis logic 171 analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water. In some embodiments, the spectral analysis logic 171 implements a classifier that has been trained on labeled imagery to generate the probability.).
Regarding Claim 4, Guan in view of Wani (as stated above) further teaches wherein each of the plurality of locations is represented by one pixel of said remote sensing data (Guan [0046] The spectral analysis logic analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water.).
Regarding Claim 5, Guan in view of Wani (as stated above) further teaches wherein at least one of the locations is represented by a sub-area comprising a plurality of pixels in said remote sensing data (Guan [0046] The spectral analysis logic analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water. Each pixel represents an sub-area of a larger area).
Regarding Claim 6, Guan in view of Wani (as stated above) further teaches wherein the first and second probability data sets indicate probabilities that each of the locations is covered by water, and wherein boundaries of at least one of the sub-areas are determined using image processing means by the detection of a watershed area in a dataset describing said geographical area (Guan [0162] The pixels within the mask which correlate to known bodies of water can be removed, leaving the remaining portions of the mask as indicative of ponding water. Alternatively, the ponding water mask can be refined by looking at satellite imagery from other time periods. If a body of water appears in both the sample images and in the historical images of the area, there is a strong likelihood those bodies of water are permanent, rather than temporary ponding water. As an example source for the historical images, the National Agricultural Imagery Program (NAIP) provides images of agricultural fields which can be used for this purpose.).
Regarding Claim 7, Guan in view of Wani (as stated above) further teaches wherein the step of computing a set of weights for each of the locations comprises the computation of weights for sub-areas comprising a plurality of locations, wherein the weights computed for each location in the sub-area are aggregated into weights for the sub-areas (Guan The probability that each pixel represents water is based on a weighted combination of value of the water probability map for the pixel and the likelihood of water pooling in the area represented by the pixel based on the flow simulation. For example, if the pixel has both a high probability of being water and is in a position where flooding is likely to occur, the probability of that pixel being ponding water is strengthened. Weights are based on pixel resolution and are therefore tied to the area represented by the pixel whether large or small).
Regarding Claim 8, Guan in view of Wani (as stated above) further teaches wherein the first and second probability data sets are characterized by the same spatial resolution (Guan [0140] In some cases, the precipitation data is resampled to match the spatial resolution of the satellite images, which can make some of the calculations described later more efficient to perform. For example, the precipitation data when resampled to the same resolution as the satellite image could be used to identify the amount of rainfall represented by each pixel of the satellite image. Also see [0142] In some cases, the elevation data is resampled to match the spatial resolution of the satellite images, which can make some of the calculations described later more efficient to perform.).
Regarding Claim 9, Guan in view of Wani further teaches wherein said remote sensing data comprises synthetic aperture radar data (Guan [0087] In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. Also see [0098] Such sensors may include […] radar emitters and reflected radar energy detection apparatus.).
Regarding Claim 10, Guan in view of Wani (as stated above) further teaches wherein the probabilities comprised in each of the second probability data sets are either 0 or 1 (Guan [0168] For example, the labeled training set may consider the probability is 1 if labeled as water and 0 if labeled as dry-land. Also see [0181] For example, the map may be a bitmap of the regions or pixels that identifies ponding water using the value 1 for water and 0 for dry-land.).
Regarding Claim 12, Guan in view of Wani (as stated above) further teaches combining, using image processing means, the remote sensing data for each location in said geographical area with the generated data indicative of the extent and recurrence of said predetermined natural event for each location in said geographical area, to generate a map (Guan [0192] The end result is a ponding water map that identifies, for each pixel of the satellite image of the agricultural field, whether that pixel represents ponding water or dry land. The ponding water map is then stored in the model data and field data repository 160 for later use by the alert logic 174. Also see [0194] In some embodiments, when the coupling logic 173 produces the ponding water map, if the map shows areas of significant ponding water. And [0195] the message may contain or provide a link to map that displays the field or fields where ponding water has been detected and highlights the pixels determined to contain ponding water, such as by displaying them in a particular color.).
Regarding Claim 14, Guan teaches a non-transitory computer readable storage medium storing instructions that when executed by a computer, which includes a data processing means performs a method (Guan [0121] Computer system 400 also includes a main memory 406, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 402 for storing information and instructions to be executed by processor 404. Main memory 406 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400 into a special-purpose machine that is customized to perform the operations specified in the instructions.), the method comprising:
providing, in a first memory element, a first probability data set associated with a plurality of locations in said geographical area, indicating probabilities of each location being affected by said predetermined event based on remote sensing data (Guan [0112] The spectral analysis logic 171 analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water. In some embodiments, the spectral analysis logic 171 implements a classifier that has been trained on labeled imagery to generate the probability. For example, the spectral analysis logic 171 may employ a logistic regression model that uses the spectral bands of each pixel as features.);
providing, in a second memory element, a plurality of second probability data sets associated with the plurality of locations, based on computer simulation results (Guan [0113] The flow simulation logic 172 uses the measured level of precipitation from the previous rainfall, along with a geographic elevation map of the field and absorption properties of the soil within the field, to estimate regions where rainwater is likely to pool. For example, an iterative algorithm can be used to estimate from a starting position of water from the rainfall on the field, where the water is likely to flow and in what quantities based on the elevation of the region and absorption rate of the soil. After a number of iterations, areas in the simulation which still contain water are marked as potential ponding areas.);
computing for each of said plurality of locations a set of weights, wherein each of the weights indicates a similarity between the probability associated with said location in the first probability data set, and in one of the second probability data sets (Guan [0115] The probability that each pixel represents water is based on a weighted combination of value of the water probability map for the pixel and the likelihood of water pooling in the area represented by the pixel based on the flow simulation.), respectively;
computing, for each of said locations and using the corresponding set of weights,
a weighted combination of the probability data associated with the location in each of said second probability data sets, (Guan [0115] The coupling logic 173 formalizes the aforementioned concepts into a concrete workable model. For example, the satellite image could be viewed as a graph, where each node represents a pixel and is connected to the neighboring pixels with an edge. The probability that each pixel represents water is based on a weighted combination of value of the water probability map for the pixel and the likelihood of water pooling in the area represented by the pixel based on the flow simulation.), and
storing the results in a third memory element, thereby generating data indicative of the extent of said predetermined natural event for each location in said geographical area (Guan [0114] The coupling logic 173 combines the results of the spectral analysis logic 171 with the results of the flow simulation logic 172. The coupling logic 173 works under the assumption that a pixel which accurately represents water has a greater likelihood to be surrounded by other water pixels, rather than by pixels representing dry land. As a result, a pixel which has a high probability of being a water pixel also increases the likelihood that neighboring pixels also represent water. In addition, the model assumes that pixels with a high probability of being water (as determined by the spectral analysis logic 171) and are in an area in which water is likely to pool (as determined by the flow simulation logic 172) has a higher probability of representing ponding water.).
Guan is not relied upon to explicitly teach wherein each of the second probability data sets is further associated with a recurrence period or frequency of said predetermined natural event,
and wherein each of the plurality of second probability data sets indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event.
Wani teaches wherein each of the second probability data sets is further associated with a recurrence period or frequency of said predetermined natural event, and
wherein each of the plurality of second probability data sets indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event (Wani [0135] The flood risk map 1602 represents the probabilities that locations in the map will be inundated within a certain period (e.g., within the next 10 years, within the next 50 years, within the next 100 years)).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Guan in view of Wani to explicitly teach wherein each of the second probability data sets is further associated with a recurrence period or frequency of said predetermined natural event, and
wherein each of the plurality of second probability data sets indicates probabilities of each of the plurality of locations being affected at a corresponding recurrence by said predetermined event, to include further analysis indicating possible future scenarios based on past events (Wani [0069] Additionally, the flood analysis system 200 may develop flood-risk maps 228 for an area based on the analysis of possible scenarios 222 that may occur in the future. The scenarios may be based on past events or may be created by the user.).
With the incorporation of probabilities of return periods Guan in view of Wani (as stated above) now also teaches a weighted combination of the corresponding recurrence periods or frequencies, as well as generating data indicative of recurrence of the event (Guan [0115] The probability that each pixel represents water is based on a weighted combination of value of the water probability map for the pixel and the likelihood of water pooling in the area represented by the pixel based on the flow simulation. The return period probabilities are simulated/modeled).
Regarding Claim 15, Guan in view of Wani (as stated above) further teaches using image processing means, detecting representations of said predetermined natural event in said remote sensing data (Guan [0046] The spectral analysis logic analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water.);
associating a probability of detection of a representation of said predetermined natural event to locations represented by said remote sensing data (Guan [0046] In some embodiments, the spectral analysis logic implements a classifier that has been trained on labeled imagery to generate the probability. Also see [0112] The spectral analysis logic 171 analyzes spectral bands from a satellite image of the field and provides a water probability map. The water probability map indicates, for individual pixels of the satellite image, what the probability is that the pixel represents water. In some embodiments, the spectral analysis logic 171 implements a classifier that has been trained on labeled imagery to generate the probability.).
Regarding Claim 16, Guan in view of Wani further teaches wherein said remote sensing data comprises an electro-optical image (Guan [0087] In an embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. Also see [0098] Such sensors may include cameras with detectors effective for any range of the electromagnetic spectrum including visible light, infrared, ultraviolet, near-infrared (NIR), and the like).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan in view of Wani (as stated above), further in view of Dumas (US 20050222829 A1).
Regarding Claim 11, Guan in view of Wani (as stated above) is not relied upon to explicitly teach wherein said weighted combination is a weighted average.
Dumas teaches wherein said weighted combination is a weighted average (Dumas [0074] In one embodiment, a weighted average of these scores is taken to find the combined score across all features.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Guan in view of Wani (as stated above), further in view of Dumas, to explicitly teach wherein said weighted combination is a weighted average, to explain exactly how the models of Guan are combined, since “a weighted average of these scores can be taken to find the combined score across all features” (Dumas [0081]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Noda et al. (JP 2018092467 A) discloses a Disaster Occurrence Probability Calculating System And Method, Operation Control Apparatus, Program, And Recording Medium.
Wang (CN 107563455 A) discloses a Method And Device For Obtaining Information.
Wani et al. (US 20190318440 A1) discloses Flood Risk Analysis And Mapping.
Haas et al. (US 20160239750 A1) discloses Geographical Condition Prediction.
Liu et al. (CN 107729695 A) discloses A Method For Small Watershed Hydrology Model Rating Method Of Analogue.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863 09/26/2025