DETAILED ACTION
This nonfinal action is in response to application 17/948,639 filed on 09/20/2022 with priority to provisional application 63/246,015 filed on 09/20/2021.
Claims 1-29 are pending in the application. Claims 1, 15, and 29 are independent claims.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) filed 09/01/2023 have been fully considered by the examiner.
Claim Objections
Claims 13, 14, 27 and 29 are objected to because of the following informalities:
In claims 13 and 27, “convolution neural network (CNN)” should read “convolutional neural network (CNN)” to correct an apparent typographical error.
In claims 14 and 28, “assess whether a location…; assess whether ground on…; and/or calculate a depth….” should read “perform at least one of: assessing whether a location…; assessing whether ground on…; and calculating a depth…” or be likewise amended to make clearer the intended scope of the alternative limitation construction.
In claim 29, “A method for reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset” should read “A method for reducing at least one of vertical bias and root mean square error (RMSE) of an elevation dataset” or be likewise amended to make clearer the intended scope of the alternative limitation construction.
Appropriate corrections are required.
Applicant is advised that should claim 16 be found allowable, claim 25 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim (see MPEP § 608.01(m)).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-29 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, it recites the limitation “iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation” and “output a predicted elevation based on the analysis of the input data”,. Although the terms “actual elevation” and “predicted elevation” imply measurement of height of some object or region, the claim does not actually state what object or region the recited elevations are in reference to. It is therefore unclear what the relationship is between the recited elevations and the “input data”, because the claim does not further specify the received input data as comprising elevation data, or any particular type of data with respect to an object or region, and yet later recites outputting predicted elevations based on analysis of the input data, thereby implying some type of correlation that is left unclear. Consequently, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination and as best understood in light of the specification, the limitation “iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation” is interpreted as “for each location of a plurality of locations, iteratively analyze the input data and learn nonlinear relationships between the input data and an actual elevation of the location”, and the limitation “output a predicted elevation based on the analysis of the input data” is interpreted as “for each location of a plurality of locations, output a predicted elevation of the location based on the analysis of the input data”.
Regarding claim 6, it recites the limitation “using data from a NASA ICESat-2 mission as ground truth”. As is commonly understood in the art, “ground truth” is interpreted as referring to known, “correct” values for a set of training data, which are used to evaluate outputted model predictions and calculate error (e.g., via a loss function). However, the claim simply recites training a neural network “using” ground truth data, and does not, for example, recite training the model via calculating a difference between a predicted elevation and an actual elevation (i.e., ground truth), or otherwise provide adequate antecedent basis to clearly illustrate how the recited “ground truth” is interrelated with the existing claim elements (input data, actual elevation, etc). It is thereby unclear if the “ground truth” data is drawn from to determine “actual elevation” values, or is being used in some other manner. Consequently, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination and as best understood in light of the specification, the limitation is interpreted as “using data from a NASA ICESat-2 mission as the actual elevations of the locations”.
Regarding claim 7, it recites the limitation “wherein the NN is configured to predict error corrections for pixels on land between a minimum and maximum elevation”. As is commonly understood in the art, “pixels” is interpreted as referring to the smallest discrete units that make up an image displayed on a screen. However, the claim does not recite any type of digital display, and instead simply recites “pixels on land” without providing an explicit definition for the claim term, or adequately explaining the intended scope of a land pixel in the specification. It is thereby further unclear what is meant by the “minimum elevation” or “maximum elevation” of a land pixel. Consequently, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination and as best understood in light of the specification, the limitation is interpreted as “wherein the NN is configured to, for areas on land between a minimum and maximum elevation, predict error corrections”.
Regarding claim 8, it recites the limitation “The system according to claim 6, wherein the minimum elevation is -10 m, and the maximum elevation is 120 m”. However, the terms “minimum elevation” and “maximum elevation” have insufficient antecedent basis in the claim, because parent claim 6 does not previously recite a minimum or maximum elevation. Consequently, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination, claim 8 is interpreted as being dependent on claim 7 instead of claim 6.
Regarding claim 11, it recites the limitations “The system according to claim 9” and “generate the graphical map”. However, the term “graphical map” has insufficient antecedent basis in the claim, because parent claim 9 does not previously recite a graphical map. Consequently, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination, claim 11 is interpreted as being dependent on claim 10 instead of claim 9.
Regarding claim 12, it recites the limitation “further comprising a plurality of remote devices, each remote device configured to display a graphical map generated based on user input sent from the remote device”. However, parent claim 10 already recites an outputted graphical map – it is thereby unclear if claim 12 is referring to the same graphical map with further limitations, or is referencing a separate graphical map element. It is further unclear what is meant by “user input sent from the remote device”, because the claim does not specify to what element the user input is sent to for triggering generation of the graphical map, leaving the interrelation of the limitation with existing claim elements (e.g., one or processors) uncertain. Consequently, one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination, the limitation is interpreted as “further comprising a plurality of remote devices, wherein for each remote device: the remote device is configured to send user input to the one or more processors, and display the graphical map outputted by the one or more processors, wherein the graphical map was generated based on the user input”.
Regarding claim 14, it recites the limitations “compare each data element of the digital elevation model to a water height or elevation; and for each data element, assess..”. However, the term “each data element” has insufficient antecedent basis in the claim, as parent claim 1 does not previous recite the digital elevation model comprising a plurality of data elements.
Claim 14 further recites “assess whether a location represented by the data element is at or below an elevation expected to flood or be inundated based on the water height or elevation”. However, the claim already previously recites comparing each data element to “a[n] elevation”; it is therefore unclear if the recited “elevation expected to flood” is referencing the previously recited elevation or a separate elevation element. The following limitations “assess whether ground on which an installed infrastructure or environment or planned infrastructure or environment at a location represented by the data element is at or below an elevation expected to flood or be inundated based on the water height or elevation” and “calculate a depth of a flood at a location represented by the data element based on the water height or elevation whether such water height or elevation is the result of a measurement, prediction, or flood model” share similar deficiencies. Further, these limitations appear to have substantial grammatical deficiencies (e.g., “ground on which an installed infrastructure or environment or planned infrastructure or environment at a location represented by the data element is at or below an elevation”, “a depth of a flood at a location represented by the data element based on the water height or elevation whether such water height or elevation is the result of a measurement”) that result in a lack of clarity with respect to the intended interrelation of claim elements, such that one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
For purposes of examination and as best understood in light of the specification, claim 14 is interpreted as follows:
The system according to claim 1, wherein the one or more processors are further configured to:
for each data element of a plurality of data elements within the digital elevation model, compare the data element to a particular water height or elevation; and perform at least one of:
assessing, based on the particular water height or elevation, whether a location represented by the data element is at or below an elevation expected to flood or be inundated,
assessing, based on the particular water height or elevation, whether the ground on which an installed infrastructure, environment, or planned infrastructure at a location represented by the data element is built, is at or below an elevation expected to flood or be inundated; and
calculating, based on the particular water height or elevation, wherein the particular water height or elevation is the result of a measurement, prediction, or flood model, a depth of a flood at a location represented by the data element.
Regarding claims 15, 21-22 and 28-29, they have substantially similar deficiencies to those found in claims 1, 6-7, and 14 above. Consequently, they are rejected for the same reasons and are interpreted as detailed above.
Regarding claims 2-5, 9-10, 13, 16-20, and 23-27, they inherit the deficiencies of their parent claims. Consequently, they are also rejected under 35 U.S.C. 112(b) as being indefinite for depending on an indefinite parent claim.
Applicant is advised to consider other parts of the disclosure, including the specification and drawings, for clarity and indefiniteness issues similar to those detailed above. The examiner notes that any amendments made to the specification, claims, or drawings must only contain subject matter that is supported by the originally filed disclosure in order to avoid being rejected under U.S.C. 112(a) as new matter (see MPEP § 608.04). Additionally, any interpretations of indefinite claim language detailed above are made solely for the purpose of examining the instant application on the merits, and are not attested as being adequately supported by the disclosure or adequately resolving all indefiniteness issues raised.
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 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Independent Claims (Claim 1, Claim 15, Claim 29)
Step 1: Claim 1 is drawn to a system/apparatus, claim 15 is drawn to a method, and claim 29 is drawn to a method. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/apparatus, manufacture/product, or composition of matter).
Step 2A Prong 1: Claims 1, 15, and 29 each recite a judicially recognized exception of an abstract idea.
Claim 1 recites, inter alia:
A system for creating [an] elevation model, comprising: iteratively analyze the input data; output a predicted elevation based on the analysis of the input data; and generate [an] elevation model based on the predicted elevation – These limitations amount to an abstract procedure of observing elevation data, using reasoning to draw conclusions from the observed data, making predictions on elevation levels based on the drawn conclusions, and then drawing a model of predicted elevation levels. They therefore recite a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
learn nonlinear relationships between the input data and actual elevation – Within the established abstract procedure of analyzing data detailed above, this limitation further amounts to a procedural step of manipulating data through mathematical correlations (nonlinear relationships) between variables (input data and actual elevation), and thereby recites a mathematical relationship.
Claims 15 and 29 recite substantially similar abstract idea limitations to those found in claim 1, and therefore recite the same judicial exception.
Step 2A Prong 2: Claims 1, 15, and 29 do not recite any further additional elements besides those recited in the independent claims, and the following additional elements recited in claims 1, 15, and 29 also do not integrate the recited judicial exceptions into a practical application.
Claim 1 additionally recites:
[A system for creating] a digital elevation model; [generate] a digital elevation model – These limitations amount to mere instructions to implement an abstract idea “digitally”, i.e., on a computer or computer components.
one or more processors configured to: [receive/provide/generate] – This limitation amounts to mere instructions to implement an abstract idea on a computer or computer components.
receive input data; provide the input data to [a neural network] – These limitations amount to steps of merely gathering data to enable further analysis, and therefore recite insignificant extra-solution activity.
a neural network (NN), the NN comprising: an input layer; a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to [iteratively analyze]; an output layer connected to the plurality of hidden layers, the output layer configured to [output] – These limitations amount to mere instructions to apply an exception, as they do no more than generically invoke a neural network and its components as tools to perform an existing abstract process of analyzing data to draw conclusions and make predictions.
Claim 15 recites substantially similar additional elements to those recited in claim 1, and further recites:
[generate a digital elevation model] for one or more geographic locations – This limitation amounts to no more than generally linking a judicial exception to the technological environment of geographic modeling, as it simply ties the drawn model to being somehow representative of an existing geographic location without providing anything more.
Claim 29 recites substantially similar additional elements to those recited in claim 1, and further recites:
A method for reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset, comprising: – The elements recited in the preamble simply state a purpose or intended use of the claimed procedure (reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset) without having any clear correspondence to, or providing a distinct definition for, any of the limitations present within the body of the claim; they are thereby not considered to be of patentable significance (see MPEP § 2111.02) when considering eligibility of the claim.
Step 2B: The additional elements recited in claims 1, 15, and 29, viewed individually or as an ordered combination, do not provide an inventive concept or otherwise amount to significantly more than the recited abstract ideas themselves.
Claim 1 additionally recites:
[A system for creating] a digital elevation model; [generate] a digital elevation model – Mere instructions to implement an abstract idea “digitally”, i.e., on a computer or computer components, do not provide an inventive concept or significantly more to the recited abstract idea.
one or more processors configured to: [receive/provide/generate] – Mere instructions to implement an abstract idea on a computer or computer components do not provide an inventive concept or significantly more to the recited abstract idea.
receive input data; provide the input data to [a neural network] – Receiving and transmitting data is well-understood, routine, and conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
a neural network (NN), the NN comprising: an input layer; a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to [iteratively analyze]; an output layer connected to the plurality of hidden layers, the output layer configured to [output] – Generically invoking a neural network and its components as tools to perform an existing abstract process does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 15 recites substantially similar additional elements to those recited in claim 1, and further recites:
[generate a digital elevation model] for one or more geographic locations – Generally linking a judicial exception to the technological environment of geographic modeling does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 29 recites substantially similar additional elements to those recited in claim 1, and further recites:
A method for reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset, comprising: – Elements recited in the preamble that have no clear correspondence to, and do not provide a distinct definition for, any of the limitations present within the body of the claim, do not provide an inventive concept or significantly more to the recited abstract idea.
Even when considered as an ordered combination, the additional elements recited in the claims ultimately do no more than place the claims in the context of generically invoking a neural network to perform an abstract procedure.
As such, claims 1, 15, and 29 are not patent eligible.
Dependent Claims (Claims 2-14, Claims 16-28)
Dependent claims 2-14 and 16-28 narrow the scope of independent claims 1, 15, and 29, and thus merely narrow the recited judicial exceptions. With respect to the independent claims, the recited judicial exceptions are not meaningfully integrated into a practical application, and also do not amount to significantly more than the recited abstract ideas themselves. The dependent claims recite abstract idea limitations similar to those recited within the independent claims, as they also do not provide anything more than mathematical concepts or mental processes that are capable of being performed in the human mind and/or using pen and paper. The dependent claims also do not recite any further additional elements that successfully integrate the recited judicial exceptions into a practical application or amount to significantly more than the recited abstract ideas themselves. Consequently, claims 2-14 and 16-28 are also rejected under 35 U.S.C. 101.
Step 1: Claims 2-14 are drawn to a system/apparatus, and claims 16-28 are drawn to a method. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/apparatus, manufacture/product, or composition of matter).
Step 2A Prong 1: Claims 2-14 and 16-28 each recite a judicially recognized exception of an abstract idea.
Claims 2-6 recite the same judicial exception as claim 1.
Claim 7 recites, inter alia:
predict error corrections for pixels on land between a minimum and maximum elevation – This limitation amounts to a process of using reasoning to update and improve elevation predictions based on observation of data, and therefore recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
Claim 8 recites, inter alia:
wherein the minimum elevation is -10 m, and the maximum elevation is 120 m – Wherein parent claim 7 already recites a process of updating elevation predictions, this limitation merely sets a range of possible elevation levels, and therefore further recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
Claim 9 recites the same judicial exception as claim 1.
Claim 10 recites, inter alia:
output a graphical map based on the digital elevation model – This limitation amounts to generating some type of graphical representation of spatial information from observing a drawn model, and therefore further recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
Claim 11 recites, inter alia:
receive user input, and based on the user input, [generate] – This limitation amounts to merely listening to a user request and performing an action in response, and therefore recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
generate the graphical map, where the graphical map shows root mean square error (RMSE) of the digital elevation model – This limitation amounts to manipulating data through mathematical methods (root mean square error) to produce output values, and therefore recites mathematical calculation.
Claim 12 recites, inter alia:
display a graphical map generated based on user input – This limitation amounts to, in response to a user request, creating some type of graphical representation of spatial information for display based on observation of a model, and therefore further recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
Claim 13 recites the same judicial exception as claim 1.
Claim 14 recites, inter alia:
compare each data element of the digital elevation model to a water height or elevation – This limitation amounts to a process of generically drawing comparisons between data points based on observation, and therefore recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
and for each data element,
assess whether a location represented by the data element is at or below an elevation expected to flood or be inundated based on the water height or elevation – This limitation amounts to making a prediction based on observation of data, and therefore recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
assess whether ground on which an installed infrastructure or environment or planned infrastructure or environment at a location represented by the data element is at or below an elevation expected to flood or be inundated based on the water height or elevation – This limitation amounts to making a prediction based on observation of data, and therefore recites a process of evaluation that a human could reasonably perform in the mind or using pen and paper.
calculate a depth of a flood at a location represented by the data element based on the water height or elevation whether such water height or elevation is the result of a measurement, prediction, or flood model – This limitation amounts to using mathematical methods (calculate based on the water height or elevation) to determine an output variable (a depth of a flood), and therefore recites mathematical calculation.
Claim 16 recites substantially similar abstract idea limitations to those found in claim 10, and therefore recites the same judicial exception.
Claims 17-26 and 27-28 recite substantially similar abstract idea limitations to those found in claims 2-11 and 13-14 therefore recite the same judicial exceptions.
Step 2A Prong 2: Claims 7-8, 10, 14, 16, 22-23, 25, and 28 do not recite any further additional elements besides those recited in the independent claims, and the following additional elements recited in claims 2-6, 9, 11-13, 17-21, 24, and 26-27 also do not integrate the recited judicial exceptions into a practical application.
Claim 2 additionally recites:
wherein input data includes vegetation, architecture, and population density information for a plurality of locations – This limitation merely specifies a type of data to manipulated, and therefore recites insignificant extra-solution activity.
Claim 3 additionally recites:
wherein the plurality of hidden layers comprises at least a thousand hidden units – This limitation amounts to an insignificant step with regard to implementation of a neural network, wherein the neural network is merely being invoked as a tool to perform an abstract idea. It therefore recites insignificant extra-solution activity.
Claim 4 additionally recites:
wherein the input layer comprises at least 10 units corresponding to at least 2,000 values of the input data – This limitation amounts to an insignificant step with regard to implementation of a neural network, wherein the neural network is merely being invoked as a tool to perform an abstract idea. It therefore recites insignificant extra-solution activity.
Claim 5 additionally recites:
wherein the output layer comprises one unit – This limitation amounts to an insignificant step with regard to implementation of a neural network, wherein the neural network is merely being invoked as a tool to perform an abstract idea. It therefore recites insignificant extra-solution activity.
Claim 6 additionally recites:
wherein the NN is trained using data from a NASA ICESat-2 mission as ground truth – This limitation merely specifies a type of data to manipulated, and therefore recites insignificant extra-solution activity.
Claim 9 additionally recites:
wherein the input data comprises one or more datasets stored on a database operably coupled to at least one of the one or more processors – This limitation amounts to steps of merely gathering data to enable further analysis, and therefore recite insignificant extra-solution activity.
Claim 11 additionally recites:
where the graphical map shows predicted flood locations, vertical bias of the digital elevation model – This limitation amounts to generally linking a judicial exception to the fields of use of flood risk assessment and digital elevation modeling, as it does no more than specify an intended purpose of the produced graphical map without providing anything more.
Claim 12 additionally recites:
further comprising a plurality of remote devices, each remote device configured to [display]; [based on user input] sent from the remote device – This limitation does no more than provide a generic means (remote device) for obtaining user input, i.e., gathering data, and displaying a result to the user, i.e., outputting data; it therefore recites insignificant pre-solution and post-solution activity.
Claim 13 additionally recites:
wherein the NN is a convolution neural network (CNN) – This limitation amounts to mere instructions to apply an exception, as it does no more than generically invoke a convolutional neural network as a tool to perform an existing abstract process.
Claims 17-21, 24, 26, and 27 recite substantially similar additional elements to those found in claims 2-6, 9, 11, and 13, and therefore also do not integrate the recited judicial exception into a practical application.
Step 2B: The additional elements recited in claims 2-6, 9, 11-13, 17-21, and 24, viewed individually or as an ordered combination, do not provide an inventive concept or otherwise amount to significantly more than the recited abstract ideas themselves.
Claim 2 additionally recites:
wherein input data includes vegetation, architecture, and population density information for a plurality of locations – Considering topographic and demographic variables of a geographical region in evaluating sea-level rise exposure (i.e., flood risk assessment) is well-understood, routine, and conventional activity (see McMichael et al., “A review of estimating population exposure to sea-level rise and the relevance for migration”, [pages 1-2 Introduction]) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 3 additionally recites:
wherein the plurality of hidden layers comprises at least a thousand hidden units – Neural networks comprising thousands of neurons (i.e., units) is well-understood, routine, and conventional activity (e.g., deep networks) (see Liu et al., “Towards Better Analysis of Deep Convolutional Neural Networks”, [page 91 Introduction]) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 4 additionally recites:
wherein the input layer comprises at least 10 units corresponding to at least 2,000 values of the input data – Neural networks utilizing large amounts of input data (e.g., common benchmark dataset CIFAR10 – see Liu et al., “Towards Better Analysis of Deep Convolutional Neural Networks”, [page 96 Base CNN]), wherein the number of neurons (i.e., units) of the input layer corresponds to the dimensionality of the input data (see Kamali, “Deep Learning (Part 1) – Feedforward neural networks (FNN)”, [page 2, pages 8-9]), is well-understood, routine, and conventional activity, and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 5 additionally recites:
wherein the output layer comprises one unit – The number of neurons (i.e., units) of the output layer corresponding to the type of learning being performed (e.g., one output neuron for binary classification or regression task) is well-understood, routine, and conventional activity (see Kamali, “Deep Learning (Part 1) – Feedforward neural networks (FNN)”, [page 2, pages 8-9, page 18]) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 6 additionally recites:
wherein the NN is trained using data from a NASA ICESat-2 mission as ground truth – Using lidar data (e.g., ICESat-2 data) for flood risk assessment is well-understood, routine, and conventional activity (see Muhadi et al., “The Use of LiDAR-Derived DEM in Flood Applications: A Review” [Abstract]) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 9 additionally recites:
wherein the input data comprises one or more datasets stored on a database operably coupled to at least one of the one or more processors – Storing and retrieving information in memory (e.g., database) is well-understood, routine, and conventional activity (see MPEP § 2106.05(d); “Storing and retrieving information in memory”) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 11 additionally recites:
where the graphical map shows predicted flood locations, vertical bias of the digital elevation model – Generally linking a judicial exception to the fields of use of flood risk assessment and digital elevation modeling does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 12 additionally recites:
further comprising a plurality of remote devices, each remote device configured to [display]; [based on user input] sent from the remote device – Receiving and transmitting data over a network (i.e., across devices) is well-understood, routine, and conventional activity (see MPEP § 2106.05(d); “Receiving or transmitting data over a network”) and therefore does not provide an inventive concept or significantly more to the recited abstract idea.
Claim 13 additionally recites:
wherein the NN is a convolution neural network (CNN) – Generically invoking a convolutional neural network as a tool to perform an existing abstract process does not provide an inventive concept or significantly more to the recited abstract idea.
Claims 17-21, 24, 26, and 27 recite substantially similar additional elements to those found in claim 2-6, 9, 11, and 13, and therefore also do not provide an inventive concept or significantly more to the recited abstract idea.
Even when considered as an ordered combination, the additional elements recited in the claims ultimately do no more than place the claims in the context of generically applying an abstract procedure on a neural network to perform flood risk assessment.
As such, claims 2-14 and 16-28 also are not patent eligible.
Claim Rejections - 35 USC § 102
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 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.
Claims 1, 4-5, 7, 9-10, 15-16, 19-20, 22, 24-25, and 29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kulp et al. (“CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network”, available online 29 December 2017, cited in IDS filed 09/01/2023), hereinafter Kulp.
Regarding claim 1, Kulp discloses A system for creating a digital elevation model, (“This paper seeks to substantially advance the techniques introduced in (Wendi et al., 2016) by building a neural network that improves 1-arcsecond (roughly 30 m) horizontal resolution coastal SRTM elevations by employing a more comprehensive selection of inputs and far larger training and testing sets…We assess the post-correction error of the resulting digital elevation model, CoastalDEM, in both the US and Australia using lidar data from both countries that was not used for training, and use ICESat data to assess improvements worldwide (Section 3)” [Kulp page 232 Introduction]) comprising: one or more processors (“In this report, we chose a classical multilayer perceptron neural network model to prove the effectiveness of such a method in improving a DEM…Further, due to computational resource limitations, we chose a relatively small number of hidden layers and nodes in our network” [Kulp page 238 Discussion; It is implicit that the CoastalDEM model is generated on a computer with adequate processing capabilities for performing the disclosed functions) configured to:
receive input data; (“We use data from SRTM, NASA's ICESat mission, two vegetation indices, and the Landscan population database - introducing altogether a total of 23 variables - as input into a multi layer perceptron neural network” [Kulp page 232 Introduction])
provide the input data to a neural network (NN) ([Kulp page 232 Introduction] as detailed above), the NN (“Loosely inspired by the human brain, a traditional multilayer perceptron (MLP) neural network is made up of layers of nodes, or units, arranged in a directed weighted graph. The units in one layer are fully connected to the units in the next layer” [Kulp page 232 Neural network algorithm and architecture]) comprising:
an input layer; (“The sizes of the input…layers are strictly decided by the dimensionality of data (our system uses a 23-unit input layer for each of the 23 variables)” [Kulp page 232 Neural network algorithm and architecture])
a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; (“An artificial neural network (ANN, or NN) is a nonparametric computational model that can be used to fully automatically learn complex, highly nonlinear relationships between any number of different variables…The units in one layer are fully connected to the units in the next layer, starting with a single input layer and ending with a single output layer, and some number of intermediate (“hidden”) layers between them…The sizes of the input and output layers are strictly decided by the dimensionality of the data…while the number and sizes of hidden layers are hyperparameters that largely depend on the complexity
of the problem. Weights between units are learned through iterative backpropagation using a training set of samples with known answers, with the goal of minimizing the difference between predictions and actual answers” [Kulp page 232 Neural network algorithm and architecture]) and
an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data; (“For each sample, we compute a desired output, SRTM elevation
error” [Kulp page 234 Training and testing with lidar]; “The units in one layer are fully connected to the units in the next layer, starting with a single input layer and ending with a single output layer… The sizes of the input and output layers are strictly decided by the dimensionality of the data (our system uses…a 1-unit output layer for predicted SRTM error)” [Kulp page 232 Neural network algorithm and architecture]) and
generate a digital elevation model based on the predicted elevation (“We assess the post-correction error of the resulting digital elevation model, CoastalDEM, in both the US and Australia using lidar data from both countries that was not used for training” [Kulp page 232 Introduction]; “We apply the trained neural network to every coastal pixel in the 1–20 m SRTM domain in the world” [Kulp page 234 Global correction and assessment]; see Fig. 3 including Original SRTM and Adjusted SRTM – “Fig. 3. Samples of lidar elevation, original SRTM, and corrected SRTM elevation in Charleston, SC and Brisbane, Australia” [Kulp page 235]; It is implicit that the SRTM elevation errors output by the neural network, wherein the neural network is trained on every pixel within the SRTM domain, naturally generate an adjusted elevation model drawn from corrections to the original SRTM model).
Regarding claim 4, Kulp discloses the limitations of parent claim 1 and wherein the input layer comprises at least 10 units corresponding to at least 2,000 values of the input data (“(our system uses a 23-unit input layer for each of the 23 variables)” [Kulp page 232 Neural network algorithm and architecture]; “Training and testing datasets are derived from the coasts of the contiguous United States and Australia… Each pixel has a 1/100 probability of being chosen as a sample, and the resulting 59.3 million US data samples are randomly split into training (70%), validation (15%) and testing (15%)” [Kulp page 234 Training and testing with lidar])
Regarding claim 5, Kulp discloses the limitations of parent claim 1 and wherein the output layer comprises one unit. (“our system uses…a 1-unit output layer for predicted SRTM error)” [Kulp page 232 Neural network algorithm and architecture])
Regarding claim 7, Kulp discloses the limitations of parent claim 1 and wherein the NN is configured to predict error corrections for pixels on land between a minimum and maximum elevation (“Training and testing datasets are derived from the coasts of the contiguous United States and Australia. We must limit pixels targeted for adjustment, as we have empirically found that both training time and optimal NN architecture size grow considerably as the range of possible inputs widens. Here, we concentrate on 1-arcsecond land pixels >=1 m and <=20 m elevation which, given published SRTM error (< 10 m), should cover the vast majority of the low elevation coastal zone (land below 10 m)” [Kulp page 234 Training and testing with lidar]).
Regarding claim 9, Kulp discloses the limitations of parent claim 1 and wherein the input data comprises one or more datasets stored on a database operably coupled to at least one of the one or more processors (“We use data from SRTM, NASA's ICESat mission, two vegetation indices, and the Landscan population database - introducing altogether a total of 23 variables - as input into a multi layer perceptron neural network, training with ground truth elevation data from US coastal lidar data curated by NOAA (2015) (Section 2; Fig. 1)” [Kulp page 232 Introduction]).
Regarding claim 10, Kulp discloses the limitations of parent claim 1 and wherein the one or more processors is further configured to output a graphical map based on the digital elevation model (“Samples from the final improved DEM in Charleston, SC and Brisbane, Australia (Fig. 3) qualitatively suggest that the neural network flattens high frequency error in SRTM (reduces noise) and substantially reduces elevation in areas of large, positive error. A corresponding elevation error map of Charleston (Fig. 4) again suggests that SRTM struggles the most with areas of high population density (downtown Charleston, see yellow area in top left panel of Fig. 5 for location), and places of dense and tall vegetation (northeast of Charleston)” [Kulp page 236 Sample test cases]; see Fig. 4 including map of elevation error in original SRTM (top) and corrected SRTM (bottom) – “Fig. 4. Elevation error in original SRTM (top), and corrected SRTM (bottom) in Charleston, SC and Brisbane, Australia at elevations between 1 m and 20 m” [Kulp page 236])
Regarding claim 15, it is a method claim that largely corresponds to the system/apparatus of claim 1, which is already disclosed by Kulp as detailed above. Kulp further discloses generating a digital elevation model based on the predicted elevation for one or more geographic locations (see Fig. 3 – “Fig. 3. Samples of lidar elevation, original SRTM, and corrected SRTM elevation in Charleston, SC and Brisbane, Australia” [Kulp page 235]). Consequently, claim 15 is rejected for the same reasons as claim 1.
Regarding claims 16 and 25, they are method claims that largely correspond to the system/apparatus of claim 10, which is already disclosed by Kulp as detailed above. Consequently, they are rejected for the same reasons.
Regarding claims 19-20, 22, and 24, they are method claims that largely correspond to the systems/apparatuses of claims 4-5, 7, and 9, which are already disclosed by Kulp as detailed above. Consequently, they are rejected for the same reasons.
Regarding claim 29, it is a method claim that largely corresponds to the system/apparatus of claim 1, which is already disclosed by Kulp as detailed above. Kulp further discloses reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset (“Our adjustment system reduces mean vertical bias in the coastal US from 3.67 m to less than 0.01 m, and in Australia from 2.49 m to 0.11 m. RMSE is cut by roughly one-half at both locations, from 5.36 m to 2.39 m in the US, and from 4.15 m to 2.46 in Australia” [Kulp Abstract]). Consequently, claim 29 is rejected for the same reasons as claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kulp, as applied to claims 1 and 15 above, further in view of Kim et al. ("Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling", published 14 Mar 2020), hereinafter Kim.
Regarding claim 2, Kulp teaches the limitations of parent claim 1 and wherein the input data includes vegetation and population density information for a plurality of locations (“Given a (1-arcsecond) SRTM target pixel, we encode a 23-dimensional vector as input into our neural network. This vector is made up of 9 values from SRTM 3.0 (1 arcsecond), 9 values from SRTM 2.1 (3 arcsecond), estimated slope, population density, a local ICESat error rate, tree canopy height, and vegetation density, each described below” [Kulp page 232 Neural network input layer]; “Training and testing datasets are derived from the coasts of the contiguous United States and Australia” [Kulp page 234 Training and testing with lidar]; see Fig. 3 – “Fig. 3. Samples of lidar elevation, original SRTM, and corrected SRTM elevation in Charleston, SC and Brisbane, Australia” [Kulp page 235]).
However, Kulp does not expressly teach wherein the input data includes architecture information for a plurality of locations (The examiner notes that under broadest reasonable interpretation in light of the specification (“The CNN was trained on high-quality global elevation data, using data from NASA's recent ICESat-2 mission, which covers land across the entire world. This choice was aimed at further improving performance in other countries where architecture and population density can be very different than what exists in the US” [¶ 0042]), Kulp may be reasonably interpreted as teaching the limitations of the claims on its own merits, given that the disclosed neural network is similarly trained on global elevation data (see Fig. 1 including Global Datasets – “Fig. 1. Flowchart visualization of adjustment system architecture. Global datasets, including SRTM 3.0, 2.1, Landscan, ICESat, and two vegetation indices, are used to make input samples to the neural network. Local lidar DEM's in the US and Australia are used to build training, validation, and testing sets with known actual SRTM error” [Kulp page 232]) wherein different countries (e.g., US and Australia) inherently have differences in architecture. Nevertheless, for the sake of completeness, the explicit teachings of an additional reference are further relied upon to cover the scope of the claim).
In the same field of endeavor, Kim teaches a means of applying a neural network to generate a digital elevation model (DEM) that improves accuracy over existing DEMs (“DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the artificial neural network (ANN) to improve the quality of SRTM’s DEM… The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available” [Kim Abstract]) wherein the data input to the ANN includes architecture information for a plurality of locations (In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore” [Kim -Abst