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 .
Response to Amendment
The amendment filed on 06/09/2026 has been entered.
Claims 1, 5, 12, 15, 17, 20, 23, 26 were amended and claims 28-30 were newly added.
Claims 6, 7, 10, 13, 14, 16, 18, 24 was/were cancelled.
Claims 1-5, 8, 9, 11, 12, 15, 17, 19-23, 25-30 remain pending in the application.
Response to Arguments
Applicant’s arguments (in Remarks filed on 06/09/2026) have been considered but are not fully persuasive.
Applicants argues on pg 11, first full paragraph, reproduced below:
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. The underlined portioned is the argument summary being addressed; another argument is also shown in the image above, which will be addressed later.
Upon further review of the reference and in light of applicant's argument, the examiner respectfully disagrees as follows: first of all, the claims are broad enough to read on using a region to find sampling locations within. Further, the claims do not disavow or prohibit the use of a region to find sampling locations; applicant also has the interpretation later on that “the claimed method may identify locations for further sampling in different regions”, showing that an interpretation of the amended claims does read on using a region to get locations. Secondly, the usage of location and region may appear to have specific meaning, but may still lead to ambiguity. For example, Stumpf uses the specific GPS location to point to a region used for the study (Section 2.1, lines 1-3), which is visualized in Figure 2; the specific GPS location points to this region. Then in the Stumpf study, more regions are identified for sampling locations. Further, Stumpf discloses gathering physical soil samples at specific locations using a 40 cm x 40 cm square, and then using further specific locations of the 4 corners of that square and the center. The specificity of location and region may be ambiguous depending on the context. Finally, Applicant states that Stumpf is limited to only identifying additional sampling locations in a single region; however, Figure 2a and 2b (Examiner recommends viewing the color version) shows multiple different regions with uncertainty-guided sampling (the black squares show the uncertainty guided sampling locations). Additionally, Stumpf also teaches “quartiles breaks of the combined uncertainty (greater than 25%, greater than 50%, and greater than 75%)” (Section 3.1, first paragraph), which shows low uncertainty of around 25%, is possible. Further, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., low uncertainty sampling) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant further argues in the above image (paraphrased as “Stumpf not disclosing an iterative process”), followed by arguments from page 11 (last paragraph) to page 12 (first partial paragraph to the second paragraph), reproduced below:
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Upon further review of the reference and in light of applicant's argument, the examiner respectfully disagrees as follows: first of all, Applicant states that Stumpf does not disclose iteration, but provides no specific evidence. Secondly, the teaching against iteration does not appear to specifically disavow iteration, the Stumpf citations is reproduced below (pg 36, column 1, lines 5-8):
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; instead, this is in regards to resampling the calibration sample set or spatial modeling. Thirdly, Stumpf does initial RF approaches (first iteration), and then a second iteration (new uncertainty map). See the new claim mapping in the rejection below. Finally, the claims are broad enough that the “second iteration” does not necessarily link up with the first iteration, and simply exists.
The remaining rejected dependent claims depend on the independent claims, and remain rejected for similar reasons.
Accordingly, the claims, as they are currently written (other than claims 23 and 25), remain rejected.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 8, 9, 11, 12, 19-21, 26-27, 29 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stumpf (“"Uncertainty-guided sampling to improve digital soil maps", 2017; as cited in IDS Filed 03/10/2025).
Regarding claim 1, Stumpf teaches A method for training a soil mapping model (Stumpf, pg 31, column 1, 2 paragraphs before the last two lines, reproduced below:
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. DSM is Digital Soil Mapping, from Abstract. Digital Soil Mapping is being interpreted as involving “soil mapping model”), the method performed by a processor and comprising:
Training (Stumpf, pg 31, column 2, Section 2.2, ¶1, reproduced below:
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. “Learner” shows “training” occurs for learning to happen) a set of initial parameters (Stumpf, see Section 2.3 image below, “two initial RF approaches” are being interpreted as “set of initial parameters”) for a soil mapping model relating one or more soil characteristics (Stumpf, see pg 31, column 1 image below: “silt and clay contents” is being interpreted as “one or more soil characteristics”) to one or more covariates over an area of interest (Stumpf, pg 31, column 1, paragraph before Section 2.3, reproduced below:
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. “Terrain attributes as covariates (Table 1)” is being interpreted as “one or more covariates over an area of interest”) based on an initial training dataset (Stumpf, pg 31, column 2, Section 2.1, ¶3, reproduced below:
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. “Calibration set (LD)” is being interpreted as “initial training dataset”.);
determining a first uncertainty map (Stumpf, pg 31, column 1, paragraph before Section 2.3, image provided above, “compute uncertainty maps for both approaches”, which is being interpreted to involve “a first uncertainty map”) for the soil mapping model over at least a portion of the area of interest (Stumpf, pg 31, column 1, paragraph before Section 2.3, reproduced below:
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. “Compute uncertainty maps for both [initial] approaches” which is being interpreted to contain at least a portion of the area of interest) based on the initial parameters (Stumpf, pg 31, column 1, paragraph before Section 2.3, reproduced below:
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. “Compute uncertainty maps for both [initial] approaches”. “Initial approaches” are being interpreted having “initial parameters”), the first uncertainty map (Stumpf, pg 31, column 1, paragraph before Section 2.3, image provided above, “compute uncertainty maps for both approaches”, which is being interpreted to involve “a first uncertainty map”) mapping each of a plurality of locations in the area of interest to a measure of uncertainty (Stumpf, see most recent image above: “compute uncertainty maps for both approaches”. Uncertainty maps are being interpreted to involve a measure of uncertainty) in at least one of the one or more soil characteristics (Stumpf, see most recent image above: “predict silt and clay contents” is being interpreted as “one or more soil characteristics”);
determining one or more primary sampling locations (Stumpf, see image directly below: “to identify areas relevant to acquire additional soil data” is being interpreted to involve “determining one or more primary sampling locations”) in the area of interest based on the first uncertainty map (Stumpf, pg 31, column 1, second to last paragraph, reproduced below:
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. “Spatial uncertainty analysis” is being interpreted to involve “the first uncertainty map”, as can be seen in Section 2.2 and 2.3, in the area of interest) comprising selecting a first one of a plurality of candidate sampling locations (Stumpf, pg 32, paragraph before Section 2.3, “We set up two initial RF approaches... We used err_var to compute uncertainty maps for both approaches”. The uncertainty maps are being interpreted to have a plurality of candidate sampling locations), the first candidate sampling location (Stumpf, pg 33, Figure 2a, an example of what an uncertainty map looks. The colors shows the plurality candidate sampling locations, which includes the first candidate sampling location. Examiner notes Figure 2a appears to be the combined iteration, and it being used as an illustrative example of what an uncertainty map looks like) being mapped to a measure of uncertainty (Stumpf, pg 33, Figure 2a, which shows an uncertainty map with measures of uncertainty) by the first uncertainty map (Stumpf, pg 32, paragraph before Section 2.3, “We set up two initial RF approaches... We used err_var to compute uncertainty maps for both approaches”. The initial RF approaches are being interpreted to include a first uncertainty map) at least as great as measures of uncertainty mapped (Stumpf, pg 33, Fig 2a, the heatmap color coding shows the levels of uncertainty, as indicated by the top left legend showing a range that includes high, medium, low. “at least as great as measure of uncertainty mapped” is being interpreted as an uncertainty having a minimum value that other values should be equal to or greater. For example, if the first candidate sampling location is low, then another sampling location that is also low would have at least as great as measure of uncertainty mapped. Similarly, if the first candidate sampling location is high, as seen by the reddish/orange color, and if another location that is selected is also high, then that second candidate location would be at least as great as the first one) to by any other candidate sampling location of the plurality of candidate sampling locations (Stumpf, pg 33, Fig 2a, the heatmap color coding shows the levels of uncertainty, as indicated by the top left legend showing a range that includes high, medium, low. “at least as great as measure of uncertainty mapped” is being interpreted as an uncertainty having a minimum value that other values should be equal to or greater. For example, if the first candidate sampling location is low, then another sampling location that is also low would have at least as great as measure of uncertainty mapped. Similarly, if the first candidate sampling location is high, as seen by the reddish/orange color, and if another location that is selected is also high, then the other candidate location would be at least as great as the first one.);
receiving one or more sample measurements corresponding to the first one of the plurality of candidate sampling locations (Stumpf, pg 32, Section 2.3, ¶1, partial reproduction to underline the relevant portion:
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. “Additional samples” is being interpreted as “receiving one or more sample measurements”. The RFLD_silt and RFLD_clay with asterisks are the uncertainty maps, as seen in the paragraph before Section 2.3. These uncertainty maps are being interpreted as having the first one of the plurality of candidate sampling locations);
refining the set of initial parameters (Stumpf, see image below, “We refined the initial approaches”. “Initial approaches” are being interpreted as “initial parameters”) of the soil mapping model (Stumpf, see image below, the initial RF approaches for the silt and clay are being interpreted as coming from a digital soil mapping model) based on the one or more sample measurements (Stumpf, see image below: “Sampling design” shows that “one or more sample measurements” were used in refining as they are using the high uncertainty areas) to generate a first set of refined parameters for the soil mapping model (Stumpf, pg 32, starts in column 1, Section 2.3, ¶1, reproduced below:
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.”We refined the initial approaches”);
determining a second uncertainty map (Stumpf, pg 31, column 1, paragraph before Section 2.3, image provided above, “compute uncertainty maps for both approaches”, which is being interpreted to involve “a second uncertainty map”), the second uncertainty map mapping each of a plurality of locations in the area of interest to a second measure of uncertainty (Stumpf, see most recent image above: “compute uncertainty maps for both approaches”. Uncertainty maps are being interpreted to involve a measure of uncertainty in a plurality of locations in the area of interest) in at least one of the one or more soil characteristics (Stumpf, see most recent image above: “predict silt and clay contents” is being interpreted as “one or more soil characteristics”);
determining one or more secondary sampling locations (Stumpf, see image directly below: “to identify areas relevant to acquire additional soil data” is being interpreted to involve “determining one or more secondary sampling locations” using the second uncertainty map) in the area of interest based on the second uncertainty map (Stumpf, pg 31, column 1, second to last paragraph, reproduced below:
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. “Spatial uncertainty analysis” is being interpreted to involve “the second uncertainty map” as well, as can be seen in Section 2.2 and 2.3, in the area of interest) comprising selecting a second one of a plurality of candidate sampling locations (Stumpf, pg 32, paragraph before Section 2.3, “We set up two initial RF approaches... We used err_var to compute uncertainty maps for both approaches”. The uncertainty maps are being interpreted to have a plurality of candidate sampling locations for the second uncertainty map, as part of the two initial approaches), the second candidate sampling location (Stumpf, pg 33, Figure 2a, an example of what an uncertainty map looks. The colors shows the plurality candidate sampling locations, which includes the second candidate sampling location, as part of the two initial approaches. Examiner notes Figure 2a appears to be the combined iteration, and it being used as an illustrative example of what an uncertainty map looks like) being mapped to a measure of uncertainty (Stumpf, pg 33, Figure 2a, which shows an uncertainty map with measures of uncertainty) by the second uncertainty map (Stumpf, pg 32, paragraph before Section 2.3, “We set up two initial RF approaches... We used err_var to compute uncertainty maps for both approaches”. The initial RF approaches are being interpreted to include a second uncertainty map) at least as great as measures of uncertainty mapped (Stumpf, pg 33, Fig 2a, the heatmap color coding shows the levels of uncertainty, as indicated by the top left legend showing a range that includes high, medium, low. “at least as great as measure of uncertainty mapped” is being interpreted as an uncertainty having a minimum value that other values should be equal to or greater. For example, if the first candidate sampling location is low, then another sampling location that is also low would have at least as great as measure of uncertainty mapped. Similarly, if the first candidate sampling location is high, as seen by the reddish/orange color, and if another location that is selected is also high, then that second candidate location would be at least as great as the first one) to by any other candidate sampling location of the plurality of candidate sampling locations (Stumpf, pg 33, Fig 2a, the heatmap color coding shows the levels of uncertainty, as indicated by the top left legend showing a range that includes high, medium, low. “at least as great as measure of uncertainty mapped” is being interpreted as an uncertainty having a minimum value that other values should be equal to or greater. For example, if the first candidate sampling location is low, then another sampling location that is also low would have at least as great as measure of uncertainty mapped. Similarly, if the first candidate sampling location is high, as seen by the reddish/orange color, and if another location that is selected is also high, then the other candidate location would be at least as great as the first one.);
receiving one or more sample measurements corresponding to the second one of the plurality of secondary sampling locations (Stumpf, pg 32, Section 2.3, ¶1, partial reproduction to underline the relevant portion:
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. “Additional samples” is being interpreted as “receiving one or more sample measurements”. The RFLD_silt and RFLD_clay with asterisks are the uncertainty maps, as seen in the paragraph before Section 2.3. These uncertainty maps are being interpreted as having the second one of the plurality of candidate sampling locations as part of the two initial approaches); and
refining the first set of refined parameters of the soil mapping model ((Stumpf, section 2.3, ¶1: “We refined the initial approaches by augmenting LD”, the legacy samples. The initial refinement is being interpreted to involve “the first set of refined parameters of the soil mapping model”) based on the one or more measurements (Stumpf, see nearest image below, RFLD_silt and RFLD_clay are being interpreted as having the “one or more measurements” that results in the stacked “normalized uncertainty maps”) to generate a second set of refined parameters for the soil mapping model (Stumpf, pg 32, column 2, first full paragraph, reproduced below:
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. The “new uncertainty map RFLD_silt/clay” is being interpreted as involving a second set of refined parameters as it is used to “determine a subarea of increased uncertainty, and thus to obtain additional samples”).
Regarding claim 8, Stumpf teaches The method according to claim 1 wherein:
the plurality of locations mapped by the uncertainty map (Stumpf, see pg 32 image below: “identifying an area of high uncertainties”. “Area” is being interpreted to involves mapped locations. “Area of high uncertainties” is being interpreted as involving “the uncertainty map”) comprises a plurality of regions in the area of interest (Stumpf, ); and
determining the one or more sampling locations (Stumpf, see pg 32 image below, “sampling design for the additional samples is based on identifying an area of high uncertainties…”. “Area” is being interpreted as involving locations that are determined for sampling) comprises at least one of:
random sampling (Stumpf, see pg 31 image below: “simple random sampling”), uniform sampling, stratified sampling (Stumpf, pg 31, column 2, Section 2.1, ¶3, reproduced below:
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. “Stratified random sampling”), and conditioned Latin hypercube sampling (Stumpf, pg 32, column 2, lines 1-4, reproduced below:
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), and orthogonal sampling in the at least one region.
Regarding claim 9, Stumpf teaches The method according to claim 8 wherein the method determining the one or more sampling locations comprises determining (Stumpf, see pg 32 image below, “determine a subarea of increased uncertainty, and thus to obtain additional samples”. Which is being interpreted as determining one or more sampling locations), for each one of the plurality of regions (Stumpf, see pg 32 image below, “we defined four potential sampling areas” is being interpreted as “each one of the plurality of regions”), a measure of aggregate uncertainty for the region (Stumpf, see pg 32 image below, “> 50%” involves the median, as one with ordinary skill in the art would know, median is being interpreted as involving aggregate uncertainty); and
selecting the at least one region based on the measures of aggregate uncertainty (Stumpf, pg 32, column 2, first 3 paragraphs:
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. “determine a subarea of increased uncertainty, and thus to obtain additional samples” is being interpreted to involve region selection based on aggregate uncertainty).
Regarding claim 11, Stumpf teaches The method according to claim 1 wherein receiving the one or more sample measurements (Stumpf, see pg 31 image below, “acquire additional soil data” is being interpreted to involve receiving the one or more sample measurements”) corresponding to the one or more sampling locations (Stumpf, see pg 31 image below, “to identify areas relevant to acquire additional soil data”. “Areas” are being interpreted as involving “one or more sampling locations”) comprises generating a request for the one or more sample measurements of soil at the one or more sampling locations (Stumpf, pg 31, column 1, two paragraphs before the end, reproduced below:
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. “identify areas relevant to acquire additional soil data” is being interpreted as involving “generating a request” as the additional soil data does not just suddenly appear).
Regarding claim 12, Stumpf teaches The method according to claim 1 comprising measuring soil at the one or more sampling locations (Stumpf, see image below, “to identify areas relevant”) to generate the one or more sample measurements (Stumpf, pg 31, column 1, two paragraphs before the end, reproduced below:
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. “acquire additional soil data” is being interpreted as involving “generate the one or more sample measurements”):
Regarding claim 19, Stumpf teaches The method according to claim 1 wherein:
the soil mapping model (Stumpf, pg 31, column 1, 3rd to last paragraph, reproduced below:
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. “DSM” is “Digital Soil Mapping”, as shown in the Abstract.) comprises a machine learning model (Stumpf, see image below, “RF” is the acronym for “Random Forest” regression algorithm, as seen in Section 2.2, line 1 on pg 31, which is being interpreted as a machine learning model), the machine learning model (Stumpf, see image below, “RF” is the acronym for “Random Forest” regression algorithm, as seen in Section 2.2, line 1 on pg 31, which is being interpreted as a machine learning model) configured to receive the one or more covariates as input (Stumpf, see image below, “terrain attributes as covariates”) and to generate predictions for the one or more soil characteristics as output (Stumpf, see image below, “predict silt and clay contents”), the predictions (Stumpf, see image below, “predict silt and clay contents”) associated with a measure of confidence (Stumpf, pg 32, column 1, paragraph before Section 2.3, reproduced below:
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err_var is being interpreted as a measure of confidence.); and
determining an uncertainty map for the soil mapping model comprises (Stumpf, see image above, “we used err_var to compute uncertainty maps”), for each of the plurality of locations (Stumpf, pg 35, Figure 4, which shows the predicted silt and clay contents for a plurality of locations), generating a prediction for the location and determining the measure of uncertainty for the location (Stumpf, see image above: “to computer uncertainty maps”, which is being interpreted to include locations on a map) based on the measure of confidence for the prediction (Stumpf, see image above: “we use err_var to compute uncertainty maps”. Err_var is being interpreted as “measure of confidence”).
Regarding claim 20, Stumpf teaches The method according to claim 19 wherein the machine learning model comprises at least one of:
a linear estimation model, a least-squares model, a neural network and a random forest (Stumpf, pg 31, column 2, Section 2.2, Line 1: “We used the Random Forest (RF) regression algorithm”. “At least one of: a neural network and a random forest” is being interpreted as “a neural network or a random forest”. “Random forest” is being interpreted as a “machine learning model”. This statement is part of an “at least one of”, and is being interpreted as an “or” statement, so a minimum of one item needs to be addressed).
Regarding claim 21, Stumpf teaches The method according to claim 1 wherein:
the soil mapping model comprises an ensemble of soil sub-models (Stumpf, pg 31, column 2, Section 2.2, ¶1, reproduced below:
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. “Multiple randomized decision tree models” are being interpreted as including “an ensemble of soil sub-models”);
determining an uncertainty map for the soil mapping model (Stumpf, pg 32, column 1, paragraph before Section 2.3, reproduced below:
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. “Computer uncertainty maps”) comprises,
for each of the plurality of locations (Stumpf, see pg 32 image above, “uncertainty maps” are being interpreted to include the plurality of locations),
generating a prediction for the one or more soil characteristics at the location (Stumpf, see pg 32 image above, “predict silt and clay contents”) by each of the soil sub-models (Stumpf, see pg 31 image above, “Multiple randomized decision tree models” are being interpreted as including “an ensemble of soil sub-models”),
thereby generating a plurality of predictions for the location (Stumpf, see pg 32 image above, “predict silt and clay contents”; pg 33, Figure 2—which shows predictions for the location), and
determining the measure of uncertainty for the location (Stumpf, see pg 32 image above, “compute uncertainty maps”, which is being interpreted as involving determine “the measure of uncertainty”) based on the plurality of predictions (Stumpf, see pg 32 image above, “predict silt and clay contents” is being interpreted as involving “plurality of predictions”).
Regarding claim 26, Stumpf teaches A method for mapping soil characteristics (Stumpf, pg 32, starts in column 1, Section 2.3, ¶1, reproduced below:
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.”We refined the initial approaches”. “Initial RF approaches” and “soil properties” are being interpreted as “method for mapping soil characteristics” because of the “digital soil mapping” from the prior art), the method (Stumpf, see image above, “RF approaches”, or Random Forest approaches) performed by a processor (Stumpf, see image above, Random Forest is being interpreted as an algorithm that is performed by a processor) and comprising:
mapping at least one location in an area of interest (Stumpf, see image above, “identifying an area” is being interpreted to involve “mapping at least one location in an area of interest”) to at least one predicted value of at least one soil characteristics (Stumpf, see image below, “observations of silt and clay contents” are being interpreted as “at least one predicted value of at least one soil characteristics”) by a soil mapping model (Stumpf, pg 31, column 2, Section 2.2, ¶1, reproduced below:
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”Random Forest regression algorithm” is being interpreted as part of a soil mapping model),
the at least one predicted value based on a set of at least first and second refined parameters (Stumpf, see Section 2.3 image above: “we refined the initial approaches” is being interpreted as “refined parameters”. “Initial approaches” is being interpreted as “at least one predicted value based on a set of at least first and second refined parameters”, which are being interpreted to be involved from the RFLD_silt and RFLD_clay approaches) for the soil mapping model trained according to claim 1 (Stumpf, pg 31, column 1, 2 paragraphs before the last two lines, reproduced below:
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. DSM is Digital Soil Mapping, from Abstract. Digital Soil Mapping is being interpreted as involving “soil mapping model”).
Regarding claim 27, Stumpf teaches A computer system comprising:
one or more processors (Stumpf, pg 31, column 2, Section 2.2, line 1: “We used the Random Forest (RF) regression algorithm”. Algorithm is being interpreted to use one or more processors); and
a memory storing instructions which cause the one or more processors to perform operations comprising the method of claim 1 (Stumpf, pg 31, column 2, Section 2.2, line 1: “We used the Random Forest (RF) regression algorithm”. Algorithm is being interpreted as using a memory storing instructions, the instructions being involved with the algorithm).
Regarding claim 29, Stumpf teaches The method according to claim 12, wherein measuring soil at the one or more sampling locations comprises (Stumpf, see nearest image below, “All soil samples…were obtained”, which is being interpreted to involve “measuring soil at the one or more sampling locations”) generating the one or more sample measurements by proximal sensing (Stumpf, Section 2.1, last paragraph, reproduced below:
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. “Analyzed identically” and the Sedigraph III 5120 are being interpreted as generating the one or more sample measurements by proximal sensing. The Sedigraph III 5120 is being interpreted as involving proximal sensing to generate the one or more sample measurements.).
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.
Claim(s) 2-5, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stumpf, in view of Taghizadeh-Mehrjardi (“Bio-inspired hybridization of artificial neural networks: An application for mapping the spatial distribution of soil texture fractions”, 2021; as cited in IDS filed 5/14/2024).
Regarding claim 2, Stumpf teaches The method according to claim 1 comprising mapping at least one location (Stumpf, pg 31, column 1, second to last paragraph, reproduced below:
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. “Spatial uncertainty analysis” is being interpreted to involve “the uncertainty map”, as can be seen in Section 2.2 and 2.3, in the area of interest that contains at least one location) in the area of interest (Stumpf, see previous image, “Spatial uncertainty analysis” is being interpreted to involve “the uncertainty map”, as can be seen in Section 2.2 and 2.3, in the area of interest that contains at least one location) to at least one predicted value of at least one of the one or more soil characteristics, the at least one predicted value based on at least one of:
However, Stumpf does not appear to specifically teach the first set of refined parameters and a further set of refined parameters based on the first set of refined parameters for the soil mapping model.
Pertaining to the same field of endeavor, Taghizadeh-Mehrjardi teaches
the first set of refined parameters (Taghizadeh-Mehrjardi, see image below, “Each iteration” shows that a first set of refined parameters occurs after the first PSF [particle size fraction] map generated during the first iteration) and a further set of refined parameters based on the first set of refined parameters for the soil mapping model (Taghizadeh-Mehrjardi, pg 10, paragraph before Section 3, reproduced below:
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. “Each iteration” shows that subsequent iterations use a further set of refined parameters, otherwise no iteration would be done. As stated in the Abstract: “soil texture and particle size fractions (PSFs) are a critical characteristic of soil”, which are being interpreted to involve parameters.).
Stumpf and Taghizadeh-Mehrjardi are considered to be analogous art because they are directed to digital soil mapping with uncertainty-guided refinement. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for digital soil mapping with uncertainty-guided refinement (or a first iteration) (as taught by Stumpf) to include multiple iterations of refinement (as taught by Taghizadeh-Mehrjardi) because the combination provides a reduction in uncertainty (Taghizadeh-Mehrjardi, Abstract).
Regarding claim 3, Stumpf teaches The method according to claim 1 comprising iteratively generating one or more further sets of refined parameters (Stumpf, pg 32, starts in column 1, Section 2.3, ¶1, reproduced below:
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.”We refined the initial approaches”. Which was done with at least one iteration), each set of refined parameters generated by:
However, Stumpf does not appear to specifically teach determining a further uncertainty map.
Pertaining to the same field of endeavor, Taghizadeh-Mehrjardi
determining a further uncertainty map (Taghizadeh-Mehrjardi, pg 10, paragraph before Section 3, reproduced below:
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. “PSF maps generated by each iteration of the models” is being interpreted to involve “a further uncertainty map”) for the soil mapping model (Taghizadeh-Mehrjardi, see image above, PSF is being interpreted as part of the “soil mapping model”) based on at least one of the first set of refined parameters (Taghizadeh-Mehrjardi, see image above, the second iteration and beyond would refine based on at least one of the first set of refined parameters, the parameters involving what creates the PSF maps) and the one or more further sets of refined parameters (Taghizadeh -Mehrjardi, see image above, the second iteration and beyond would refine based on at least one of the first set of refined parameters, the parameters involving what creates the PSF maps);
determining one or more further sampling locations in the area of interest (Taghizadeh-Mehrjardi, pg 7, Section 2.4.2, ¶1, reproduced below:
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”select the parents” is being interpreted as “determining one or more further sampling locations”. The genetic algorithm is used as part of the digital soil mapping model, as seen in pg 5, Section 2.4, lines 1-2) based on the further uncertainty map (Taghizadeh-Mehrjardi, see pg 10 image above, uncertainty analysis is being interpreted as uncertainty map);
receiving one or more further sample measurements (Taghizadeh-Mehrjardi, see pg 10 image above, each iteration a PSF map is generated, which requires further sample measurements to create the PSF map) corresponding to the one or more further sampling locations (Taghizadeh-Mehrjardi, see Section 2.4.2 image above, the parent selection is being interpreted as involving a sampling location as each sampling location allows for the creation of a PSF map); and
refining at least one of the first set of refined parameters (Taghizadeh-Mehrjardi, see pg 10 image above. Each iteration, the PSF maps are refined. After the first iteration, a first set of refined parameters are refined) and at least one further sets of refined parameters based on the one or more sample measurements (Taghizadeh-Mehrjardi, see pg 10 image above, each iteration a PSF map is generated, which requires further sample measurements to create the PSF map. Figure 4 contains the covariates used to create the PSF maps) to generate a further set of refined parameters for the soil mapping model (Taghizadeh-Mehrjardi, see pg 10 image above. Each iteration, the parameters are refined that are used to create the PSF maps which are part of the soil mapping model).
Stumpf and Taghizadeh-Mehrjardi are considered to be analogous art because they are directed to digital soil mapping with uncertainty-guided refinement. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for digital soil mapping with uncertainty-guided refinement (or a first iteration) (as taught by Stumpf) to include determining a further uncertainty map (as taught by Taghizadeh-Mehrjardi) because the combination provides a reduction in uncertainty (Taghizadeh-Mehrjardi, Abstract).
Regarding claim 4, Stumpf teaches The method according to claim 3
However, Stumpf does not appear to specifically teach further sets of refined parameters for the soil mapping model.
Pertaining to the same field of endeavor, Taghizadeh-Mehrjardi
comprising mapping at least one location in the area of interest to at least one predicted value (Taghizadeh-Mehrjardi, see pg 10 image below, PSF maps are being interpreted as involving predicting, as a hybridized ANN is involved that does predictions for at least one location in the area of interest. As pg 4, Figure 1 shows a sampling points for areas of interest in a map) of at least one of the one or more soil characteristics (Taghizadeh-Mehrjardi, Abstract: “soil texture and particle size fractions (PSFs) are a critical characteristic of soil”), the at least one predicted value (Taghizadeh-Mehrjardi, pg 10, paragraph before Section 3, reproduced below:
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. “PSF maps generated by each iteration of the models” are being interpreted to involve “at least one predicted value”) based on at least one of the further sets of refined parameters for the soil mapping model (pg 7, Section 2.4.2, reproduced below:
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. “Optimizing the weights and bias of neural network connections” is being interpreted as “further sets of refined parameters” that occur after a number of iterations).
Stumpf and Taghizadeh-Mehrjardi are considered to be analogous art because they are directed to digital soil mapping with uncertainty-guided refinement. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for digital soil mapping with uncertainty-guided refinement (or a first iteration) (as taught by Stumpf) to include further sets of refined parameters for the soil mapping model (as taught by Taghizadeh-Mehrjardi) because the combination provides a reduction in uncertainty (Taghizadeh-Mehrjardi, Abstract).
Regarding claim 5, Stumpf teaches The method according to claim 3 wherein iteratively generating the one or more further sets of refined parameters (Taghizadeh-Mehrjardi, pg 10, paragraph before Section 3, reproduced below:
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. “PSF maps generated by each iteration of the models” is being interpreted as “iteratively generating the one or more further sets of refined parameters”) comprises determining a measure of model uncertainty based on at least one of the uncertainty maps (Taghizadeh-Mehrjardi, pg 12, last paragraph, reproduced below:
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. “Evaluation of the uncertainty estimates” is being interpreted as “measure of model uncertainty”); and halting generation of further refined parameters (pg 7, Section 2.4.1, ¶1: “for adjusting the weights and bias until at least one stopping criteria is reached”. “weights and bias” are being interpreted as “refined parameters”) based on the measure of model uncertainty (pg 7, Section 2.4.1., ¶1: “The maximum number of epochs and the MSE of the network output for each target PSFs were the two stopping criteria”. MSE, mean squared error, is being interpreted as being based on model uncertainty).
Stumpf and Taghizadeh-Mehrjardi are considered to be analogous art because they are directed to digital soil mapping with uncertainty-guided refinement. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for digital soil mapping with uncertainty-guided refinement (or a first iteration) (as taught by Stumpf) to include determining a measure of model uncertainty based on at least one of the uncertainty maps (as taught by Taghizadeh-Mehrjardi) because the combination provides a reduction in uncertainty (Taghizadeh-Mehrjardi, Abstract).
Regarding claim 17, Stumpf teaches The method according to claim 1 wherein the one or more covariates (Stumpf, pg 32, Table 1, “Covariate”) comprise at least one of:
soil type (Stumpf, pg 31, column 2, Section 2.1, ¶2: “According to SCORPAN, the soil property (S_a) or soil class (S_c) is a function of other soil properties (s), climate (c), organisms (o), relief (r), parent material (p), age (a), and the space or spatial position (n)”. “Soil class” is being interpreted as “soil type”), soil elevation (Stumpf, pg 32, Table 1, Covariate column, “Elevation”), one or more climate measurements (Stumpf, pg 31, column 2, Section 2.1, ¶2: “According to SCORPAN, the soil property (S_a) or soil class (S_c) is a function of other soil properties (s), climate (c), organisms (o), relief (r), parent material (p), age (a), and the space or spatial position (n)”. “Climate” is being interpreted as involving “one or more climate measurements”), one or more land uses, one or more farming practices, and one or more optical measurements,
However, Stumpf does not appear to explicitly teach satellite imagery (Figure 2 does teach what appears to be at least aerial photography).
Pertaining to the same field of endeavor, Taghizadeh-Mehrjardi teaches
one or more satellite imagery measurements (Taghizadeh-Mehrjardi, pg 4, last 2 lines: “The 41 satellite-derived covariates were acquired based on the median values of optical satellite data from Landsat 8 (Operational Land Imager) and Sentinel-2 imagery taken”. “Optical satellite data” is being interpreted as “satellite imagery”. This statement contains “at least one of” and is being treated as an “or” statement, so a minimum of one item needs to be addressed.), one or more aerial imagery measurements, one or more aircraft imagery measurements, one or more drone imagery measurements, one or more terrestrial imagery measurements, one or more visual spectrum imagery measurements, and one or more infrared spectrum imagery measurements.
Stumpf and Taghizadeh-Mehrjardi are considered to be analogous art because they are directed to digital soil mapping. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for digital soil mapping (as taught by Stumpf) to include satellite imagery (as taught by Taghizadeh-Mehrjardi) because the combination provides a reduction in uncertainty (Taghizadeh-Mehrjardi, Abstract).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stumpf, in view of Ringrose-Voase (“Four Pillars of digital land resource mapping to address information and capacity shortages in developing countries”; as cited in IDS filed 03/10/2025).
Regarding claim 15, Stumpf teaches The method according to claim 1
However, Stumpf does not appear to explicitly teach soil carbon content.
Pertaining to the same field of endeavor, Ringrose-Voase teaches
wherein the one or more soil characteristics (Ringrose-Voase, see image below, “range of soil parameters”) comprise a measure of soil carbon content (Ringrose-Voase, see pg 306, Section 3.3 ¶1 image below, “organic carbon” is being interpreted as “soil carbon content”);
wherein the measure of soil carbon content comprises at least one of a measure of soil organic carbon content, a (Ringrose-Voase, pg 306, Section 3.3 ¶1, reproduced below:
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”Organic carbon” is being interpreted as “a measure of soil organic carbon content”, as “Organic C” is a “Soil property in Table 5 on the same page. More broadly speaking, this is also “a measure of soil carbon content”), a measure of soil organic matter content (Ringrose-Voase, pg 306, Table 5, column “Soil Property”, P, or Phosphorus), a measure of soil inorganic carbon content (Ringrose-Voase, pg 306, Table 6, column “Soil Property”, Sand, silt, clay, are being interpreted as examples of “a measure of soil inorganic carbon content”), a measure of soil mineral-associated organic carbon content (Ringrose-Voase, see nearest image above, “clay content and organic carbon”), and a measure of soil particulate organic carbon content.
Stumpf and Ringrose-Voase are considered to be analogous art because they are directed to digital soil mapping. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for digital soil mapping (as taught by Stumpf) to include soil carbon content (as taught by Ringrose-Voase) because the combination provides an improvement to soil surveys (Ringrose-Voase, Abstract).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stumpf, in view of Kulkarni (“Efficient learning of random forest classifier using disjoint partitioning approach”, 2013).
Regarding claim 22, Stumpf teaches The method according to claim 21 wherein a first soil sub-model of the ensemble (Stumpf, pg 31, column 2, Section 2.2, reproduced below:
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. “Randomized decision tree models” is being interpreted to include “a first soil sub-model of the ensemble”) comprises initial parameters trained over a first training dataset (Stumpf, see image below: “form the calibration set” is being interpreted as “a first training dataset”. Where the random forest regression algorithm would have initial parameters, as one with ordinary skill in the art would know) and a second soil sub-model of the ensemble (Stump, see image above, “multiple randomized decision tree models” that is part of the “non-parametric ensemble learner” is being interpreted to include a second soil sub-model that uses initial parameters) comprises initial parameters trained over a second training dataset (Stumpf, pg 31, column 2, Section 2.1, ¶3, reproduced below:
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. “Second calibration set”),
However, Stumpf does not appear to explicitly disjoint datasets.
Pertaining to the same field of endeavor, Kulkarni teaches
the first and second datasets being disjoint (Kulkarni, pg 2, column 2, Section III, ¶1, partially reproduced below:
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“Disjoint sets of data samples” are being interpreted to include a first and second dataset).
Stumpf and Kulkarni are considered to be analogous art because they are directed to ensemble learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for random forest regression algorithm for digital soil mapping (as taught by Stumpf) to include disjoint datasets (as taught by Kulkarni) because the combination provides an improvement to learning time of Random Forest (Kulkarni, Abstract).
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stumpf, in view of Zhang (“Efficient learning of random forest classifier using disjoint partitioning approach”, 2016).
Regarding claim 28, Stumpf teaches The method according to claim 5, wherein the measure of model uncertainty (Stumpf, see nearest image below, “aggregated measure” is being interpreted as involving an example of “measure of model uncertainty”) comprises an average of measures of uncertainty (Stumpf, see nearest image below, “Average ERR_var” is the average uncertainty, which is being interpreted as “an average of measures of uncertainty”.) for the plurality of locations in the area of interest (Stumpf, Section 2.4, ¶2, reproduced below:
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. The uncertainty map is being interpreted to have “plurality of locations in the area of interest”) and
However, Stumpf does not appear to explicitly teach below an uncertainty threshold. Although Stumpf does teach halting refinement, as one with ordinary skill in the art would know, machine learning algorithms such as Random Forest has stopping criteria.
Pertaining to the same field of endeavor, Zhang teaches
wherein halting generation of further refined parameters (Zhang, see nearest image below, “Repeat the above process till the overall uncertainty is below a certainty threshold”. Which shows the repetition stops of the further refinement of parameters, the “update the prediction uncertainty map”) based on the measure of model uncertainty (Zhang, see nearest image below, “update the prediction uncertainty map” is being interpreted to involve the measure of model uncertainty) comprises determining that the measure of model uncertainty (Zhang, see nearest image below, “prediction uncertainty map” is being interpreted as “determining that the measure of model uncertainty”) is less than a threshold uncertainty value (Zhang, pg 124, column 2, ¶3, reproduced below:
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. “below a certainty threshold” is being interpreted as “is less than a threshold uncertainty value”).
Stumpf and Zhang are considered to be analogous art because they are directed to digital soil mapping with uncertainty measures. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for digital soil mapping with uncertainty measures (as taught by Stumpf) to include uncertainty measure below a threshold (as taught by Zhang) because the combination provides an improvement to digital soil mapping accuracy (Zhang, Abstract).
Claim(s) 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stumpf, in view of Le (“Field and model investigation of flow and sediment transport in the Lower Mekong River”, 2020).
Regarding claim 30, Stumpf teaches The method according to claim 29, wherein generating the one or more sample measurements by proximal sensing (Stumpf, Section 2.1, last paragraph, reproduced below:
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. “Analyzed identically” and the Sedigraph III 5120 are being interpreted as generating the one or more sample measurements by proximal sensing. The Sedigraph III 5120 is being interpreted as involving proximal sensing to generate the one or more sample measurements.)
However, Stumpf does not appear to explicitly teach inserting a proximal sensor. Although Stumpf does soil analysis using a proximal sensor (Sedigraph III 5120). One with ordinary skill in the art would be aware that portable soil analysis exists.
Pertaining to the same field of endeavor, Zhang teaches
comprises inserting a proximal sensor (Le, pg 24, Section 2.3.2, ¶1: “We utilize the LISST-Portable XR instrument to measure sediment particle sizes during mixing”. One with ordinary skill in the art would know the portable device contains a probe) into soil at the one or more sampling locations (Le, pg 24, Section 2.3.2, ¶1: “We utilize the LISST-Portable XR instrument to measure sediment particle sizes during mixing”. “Sediment” is being interpreted to involve “soil”. Measuring sediment particle sizes is being interpreted to involve one or more sampling locations, one with ordinary skill in the art would know sample collections happens at multiple locations).
Stumpf and Zhang are considered to be analogous art because they are directed to soil analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system for soil analysis with uncertainty measures (as taught by Stumpf) to inserting a proximal sensor (as taught by Zhang) because the combination provides an improvement to soil analysis (Zhang, Section 3.4, Conclusion). Further, one with ordinary skill in the art would know having a portable sensor would increase efficiency in data collection.
Allowable Subject Matter
Claims 23 and 25 is/are allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 23: The cited prior art fails to disclose, teach, or suggest: “The method according to claim 22 wherein the first training dataset comprises images of the area of interest captured from at least one of: an altitude of no more than 100 km, and an aircraft; and wherein the second training dataset comprises images of the area of interest captured from at least one of: an altitude of no less than 100 km, and a satellite” in the context of the claim as a whole.
Regarding claim 25: The cited prior art fails to disclose, teach, or suggest: “The method according to claim 22 wherein the first training dataset comprises images having a first spatial density and the second training dataset comprises images having a second spatial density less than the first spatial density, such that an element of the second training dataset corresponds spatially to a plurality of elements of the first training dataset” in the context of the claim as a whole.
The cited prior art includes Gray et al (“Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments”, 2018) discloses combining greater than 100 km altitude imagery (satellite, which may capture a different spatial resolution, or density, than the other) and less than 100 km imagery (UAS or drone or unoccupied aircraft systems; which may capture ultra-high spatial resolution or high spatial density) for habitat monitoring. However, Gray does not appear to specifically teach disjoint datasets for training as they are combined and soil mapping. Further, there appears to be no motivation to combine with the cited prior art.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kienast-Brown et al (“Digital Soil Mapping”, 2017) discloses digital soil mapping with uncertainty (interpreted from confusion index) with refinement (pg 346, Summary, as one with ordinary skill in the art would know, these are possible in loops or machine learning algorithms).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNY B DUONG whose telephone number is (571)272-1358. The examiner can normally be reached Monday - Thursday 10a-9p (ET).
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/J.B.D./Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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