Patent Application 17340212 - WEATHER STATION LOCATION SELECTION USING

Title: WEATHER STATION LOCATION SELECTION USING ITERATION WITH FRACTALS

Application Information

Invention Title: WEATHER STATION LOCATION SELECTION USING ITERATION WITH FRACTALS
Application Number: 17340212
Submission Date: 2025-05-14T00:00:00.000Z
Effective Filing Date: 2021-06-07T00:00:00.000Z
Filing Date: 2021-06-07T00:00:00.000Z
National Class: 703
National Sub-Class: 006000
Examiner Employee Number: 98832
Art Unit: 2187
Tech Center: 2100

Rejection Summary

102 Rejections: 0
103 Rejections: 2

Cited Patents

No patents were cited in this rejection.

Office Action Text

DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 02/26/2025
Claims 1, 3-5, 7-11, 14-16, 19-20 are presented for examination

Information Disclosure Statement
The IDS dated 06/07/2021 has been reviewed. See attached.
Drawings
The drawings dated 06/07/2021 have been reviewed. They are accepted.
Specification
The abstract dated 06/07/2021 has been reviewed. It has 147 words and 11 lines and no legal phraseology. It is accepted.

Finality

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 extension fee 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.

Response to Arguments- 35 U.S.C. 101
Applicant's arguments filed 02/26/2025 have been fully considered but they are not persuasive.

Applicant argues that the newly amended claim elements overcome the rejection under 101.

Examiner responds by addressing each new limitation individually.

generating fractals based on topographical features of terrain that influence local weather patterns; iteratively matching the fractals to the weather forecast performance map to identify a first fractal of the fractals that most closely matches a layout of the current locations of the first weather stations while optimizing coverage of distinct microclimates created by the topographical features;
Generating fractals and matching fractals can be practically done in the human mind using pencil and paper, i.e., drawing a fractal pattern on the paper. Doing so based on terrain features is a mental process that involves observing the terrain features, such as on a map, and drawing fractals that best match the layout of those features. Several different fractal configurations can be tried until one that matches the terrain best is found.

comparing the map data with existing fractal maps to determine a second fractal best suited for the new geographical area based on topographical features that create distinct microclimates in the new geographical area;
Comparing this map and fractal data to determine a new fractal for a second area is a mental process equivalent to observing the first map of a first area with the overlaid fractal, then looking at the second area to judge if the same or a different kind of fractal suits it better. For example, maybe a the first area is a flat plain with little variation and thus a coarse square grid fractal is sufficient to accurately capture the weather patterns of the region, while the second may include a series of widely varying mountain ranges with a large flat region at the center in which something like a sierpinski triangle better matches the topography of the region.

training a machine learning model with the first fractal map and with the second fractal map, to learn optimal weather station placement patterns; continuously updating the trained machine learning model based on new weather data and forecast accuracy measurements to dynamically provide optimize weather station placement over time; and
Training and continuously updating a generic machine learning model is equivalent to applying a generic computer to perform generic training operations.
Applying a computer to perform generic training at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that training, which is merely an instruction to apply a computer to the judicial exception. The specification lists a great many possible forms of the machine learning model without specifying a particular embodiment to be used, either in itself or the claims ([Par 26] “Deep learning machine learning models which may be considered artificial intelligence have also been used for weather forecasting. A deep learning model may include a neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), long short-term memory (LSTM) nodes, gated recurrent units (GRU), ConvLSTM networks which include a combination of a normal CNN with LSTM, variational auto-encoders (VAE), generative adversarial networks (GAN), combinations of VAE and CNN layers, combinations of GAN and CNN layers, multi-layer perceptron architectures, boosted decision trees, dynamic Gaussian Process models, deep belief networks that include restricted Boltzman machines, stochastic adversarial video predictions, and other systems.”), which evidences the generic nature of the application of a general purpose computer.
Mere instructions to apply a judicial exception do not integrate a judicial exception into a practical application nor provide significantly more.
Additionally, obtaining new data such as weather data is merely the process of gathering that new data.

based on receiving, from the trained machine learning model, automated suggestions for the weather station placement design enhance, adjusting weather forecasting accuracy based on learned optimal placement patterns.
Suggesting weather station placement positions based on fractals is a mental process equivalent to observing the fractals overlaid on the weather performance map and judging, based on the points of the fractal and areas of low accuracy, where a new station would best improve the overall accuracy of the system.
Doing so in an “automated” manner based on data received from the trained machine learning model is equivalent to merely applying a generic computer to perform these operations.

Applicant argues that the machine learning model training elements, as newly amended, are not mental processes, provide an improvement to technology and amount to significantly more

Examiner responds by firstly acknowledging that training the machine learning model is indeed not considered to be a mental process, however it was also never alleged to have been such. As to the limitations of training and continuously updating the model providing an improvement to technology or amounting to significantly more, these elements are still considered mere instructions to apply a judicial exception on a computer. The actual functioning of the training is extremely generic, does not recite a specific training method in the claims, nor disclose an improvement to the training method itself.
Such a generic machine learning training method is equivalent to applying a generic computer to perform generic training operations.
Applying a computer to perform generic training at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that training, which is merely an instruction to apply a computer to the judicial exception. The specification lists a great many possible forms of the machine learning model without specifying a particular embodiment to be used, either in itself or the claims ([Par 26] “Deep learning machine learning models which may be considered artificial intelligence have also been used for weather forecasting. A deep learning model may include a neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), long short-term memory (LSTM) nodes, gated recurrent units (GRU), ConvLSTM networks which include a combination of a normal CNN with LSTM, variational auto-encoders (VAE), generative adversarial networks (GAN), combinations of VAE and CNN layers, combinations of GAN and CNN layers, multi-layer perceptron architectures, boosted decision trees, dynamic Gaussian Process models, deep belief networks that include restricted Boltzman machines, stochastic adversarial video predictions, and other systems.”), which evidences the generic nature of the application of a general purpose computer.
Mere instructions to apply a judicial exception does not integrate a judicial exception into a practical application or provide significantly more.
Further, as expanded upon below, the alleged “improvement” to the technology, or inventive thrust of the system, does not arise from a generic method of training a neural network, rather it arises from the application of fractals to a weather location selection system, i.e. the generating, iterating, and comparing of the fractals based on weather performance data and terrain features. The increase in accuracy reportedly afforded by the system arises from these steps. However, these steps are abstract ideas, either mental process or mathematic concepts, and thus cannot provide a technical improvement.

Applicant argues that the automated suggestions for station placement goes beyond a generic computer implementation, and provides a technical improvement

Examiner responds by explaining that, in the currently amended form of the claims, the step of “based on receiving, from the trained machine learning model, automated suggestions weather station placement, adjusting weather forecasting accuracy based on learned optimal placement patterns” is a mental process applied on a computer. Suggesting weather station placement positions based on fractals is a mental process equivalent to observing the fractals overlaid on the weather performance map and judging, based on the points of the fractal and areas of low accuracy, where a new station would best improve the overall accuracy of the system. Doing so in an “automated” manner based on data received from the trained machine learning model is equivalent to merely applying a generic computer to perform these operations. Being a mental process and thus an abstract idea, this operation cannot provide a technical improvement; for a claim element to provide an improvement, it must be an additional element examined under Step 2A – Prong 2 or Step 2B.

Applicant argues that the alleged improvement of increasing weather forecasting accuracy provides a technical improvement

Examiner responds by explaining that the inventive thrust of the claimed invention, and thus the hypothetical “improvement,” is in the application of fractals to a weather location selection system, i.e. the generating, iterating, and comparing of the fractals based on weather performance data and terrain features. The increase in accuracy reportedly afforded by the system arises from these steps. However, these steps are mental processes, and thus cannot provide a technical improvement; for a claim element to provide an improvement, it must be an additional element examined under Step 2A – Prong 2 or Step 2B.

Response to Arguments- 35 U.S.C. 103
Applicant's arguments filed 02/26/2025 have been fully considered but they are not persuasive.

Applicant argues that none of the previously cited references teach the newly amended elements, namely: "...generating fractals based on topographical features of terrain that influence local weather patterns, wherein each fractal is a respective self-similar subset of Euclidean space whose fractal dimension strictly exceeds its topological dimension ... iteratively matching the fractals to the weather forecast performance map to identify a first fractal of the fractals that most closely matches a layout of the current locations of the first weather stations while optimizing coverage of distinct microclimates created by the topographical features... comparing the map data with existing fractal maps to determine a second fractal best suited for the new geographical area based on topographical features that create distinct microclimates in the new geographical area..."

Examiner responds by noting that, upon further consideration, the previously cited prior art references teach the newly amended claim limitation

Firstly, Flade makes obvious generating fractals based on topographical features of terrain ([Fig. 8] Note first fractal overlaid on a map [Examiner’s note: “a respective self-similar subset of Euclidean space whose fractal dimension strictly exceeds its topological dimension” is the basic definition of fractal, and thus is an essential characteristic of any fractal, including the one shown in Fig. 8]
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[Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining… The road map is divided into fractal cells A, B, C, D on a first segmentation level. The second segmentation level divides each fractal cell A, B, C, D of the first segmentation level into four fractal cells on the second segmentation level as depicted in FIG. 8. The fractal cell A is segmented into the four fractal cells A1, A2, A3, and A4. The marker of a vehicle depicted as travelling on a road segment of the road map in the upper portion of FIG. 8 corresponds to a fractal cell with the cell identifier D1 on the second segmentation level in the lower portion of FIG. 8.” [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded. ])

Any significant topological feature has an effect on the airflow in that area, and thus an effect on the local weather patterns. The roads and buildings in Fig. 8, for example, have an effect on the local weather conditions such as wind speed at certain locations. The only cell that does not contain anything that would significantly affect the local weather conditions is empty cell c1, which is excluded from the cell listing

Further, Hakim more clearly teaches identifying topological features of terrain that influence local weather patterns ([Page 864 Col 2 Par 1] “However, these two points are distant, separated by significant topography, and have very different weather regimes on short time scales (i.e., coastal vs plateau) [Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “We define the optimal location as one that maximizes the reduction in total variance of a given spatial field” [Fig. 1] Shows the terrain height map considered by the system [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] Shows examples of selected locations [Examiner’s note: the system identifies geographically diverse station locations that increase the accuracy in that region by determining where accuracy is lacking, i.e. it identifies distinct local microclimates based on the topographical features such as terrain height and determines whether the localized climate is distinct enough to warrant the selection of a new station location there.])

Flade makes obvious iteratively matching the fractals to([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining… The road map is divided into fractal cells A, B, C, D on a first segmentation level. The second segmentation level divides each fractal cell A, B, C, D of the first segmentation level into four fractal cells on the second segmentation level as depicted in FIG. 8. The fractal cell A is segmented into the four fractal cells A1, A2, A3, and A4. The marker of a vehicle depicted as travelling on a road segment of the road map in the upper portion of FIG. 8 corresponds to a fractal cell with the cell identifier D1 on the second segmentation level in the lower portion of FIG. 8.” [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded. ])

Hakim makes obvious the weather forecast performance map, ([Fig. 9] [Fig. 10] [Page 868, Col 2, Par. 2] “ Here we present results for the second metric described in section 2d: 24-h forecast errors in 2-m temperature. Again, we use a covariance localization length scale of 3000 km, and consider the same three cases as in the previous section: no existing network, new locations conditional on the CD90 subset, and new locations conditional on the CD75 subset. The regions highlighted to reduce forecast errors (Fig. 10)”) finding locations of the first weather stations ([Page 859, Col 2, Par. 3] “We consider the locations of weather stations that are assimilated into WRF as defined in Bumbaco et al. (2014). Namely, we use the locations of the weather stations…”) while optimizing coverage of distinct microclimates created by the topographical features ([Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “We define the optimal location as one that maximizes the reduction in total variance of a given spatial field” [Fig. 1] Shows the terrain height map considered by the system [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] Shows examples of selected locations [Examiner’s note: the system identifies geographically diverse station locations that increase the accuracy in that region by determining where accuracy is lacking, i.e. it identifies distinct local microclimates based on the topographical features such as terrain height and determines whether the localized climate is distinct enough to warrant the selection of a new station location there.])

Flade makes obvious ([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining… The road map is divided into fractal cells A, B, C, D on a first segmentation level. The second segmentation level divides each fractal cell A, B, C, D of the first segmentation level into four fractal cells on the second segmentation level as depicted in FIG. 8. The fractal cell A is segmented into the four fractal cells A1, A2, A3, and A4. The marker of a vehicle depicted as travelling on a road segment of the road map in the upper portion of FIG. 8 corresponds to a fractal cell with the cell identifier D1 on the second segmentation level in the lower portion of FIG. 8.” [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded. ])
Hakim makes obvious comparing the map data ([Fig 4] “Analysis of preferred regions for the first three optimally sited stations for monitoring 0000 UTC 2-m temperature over the entire continent, assuming no observation network currently exists. Cells that are colored indicate that the location was chosen at least once, and the color indicates the frequency with which that location is chosen over 10 000 iterations. The grid points shaded on the plots were made larger for easy viewing;” [Examiner’s note: note the comparison of proposed station locations to other existing and proposed station locations over 1000s of iterations in Fig. 4]) ([Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “We define the optimal location as one that maximizes the reduction in total variance of a given spatial field” [Fig. 1] Shows the terrain height map considered by the system [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] Shows examples of selected locations [Examiner’s note: the system identifies geographically diverse station locations that increase the accuracy in that region by determining where accuracy is lacking, i.e. it identifies distinct local microclimates based on the topographical features such as terrain height and determines whether the localized climate is distinct enough to warrant the selection of a new station location there.])
Maquaire makes obvious map data for a second fractal ([Fig. 2-3] [Fig. 5C-5F] [Par 62] “At step 940, a second fractal map comprising a second plurality of related articles to the received user selection is displayed.”)

Flade makes obvious ([Fig. 8])
Hakim makes obvious ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica.”)
Macquaire ([Fig. 2-3] [Fig. 5C-5F] [Par 62] “At step 940, a second fractal map comprising a second plurality of related articles to the received user selection is displayed.”)
Gryech makes obvious training a machine learning model to learn ([Page 18 Par 7] “Random Forest is one of the most popular Ensemble Learning techniques. Random Forest is based on an ensemble of decision tree predictors. It uses a modified tree learning algorithm which selects a random subset of the available features (feature bagging) to reduce the correlations between the trees; for a dataset with p features, pp features are used in each split. Moreover, each decision tree is trained…”)

Flade makes obvious ([Par 28] “This may be done based on trigger data that can be generated based on, e.g. Traffic Message Channels (TMC), weather station forecasts or -warnings, or even individual road users.” [Par 33] “Generating the trigger data of an advantageous exemplary embodiment bases on at least one of TMC data, disaster or weather warning”)
Hakim makes obvious([Page 862 Col 1 Par 6 – Col 2 Par 1] “To summarize, the general procedure for finding optimal locations uses the following algorithm:
1) Choose a vector metric that quantifies the desired
aspects of the system of interest.
2) Calculate the total variance for the metric (trace of
the metric covariance matrix).
3) Calculate the change in the total variance for all
possible stations.
4) Choose as the optimal measurement location the one
that maximizes the change in total variance from noi.
5) Update the metric and state to reflect the chosen
measurement using (l l ).
6) Repeat the procedure for the next measurement, using the updated state and metric to find the next location conditional on the previous measurement (i.e., repeat steps 3-5).”
[Examiner’s note: the system evaluates the forecast accuracy, selects a station location, and then updates the state of the system to include this new station as well as calculated accuracy metrics again, then repeats the process to continuously update and select optimal positions])
Gryech makes obvious the trained machine learning model ([Page 18 Par 7] “Random Forest is one of the most popular Ensemble Learning techniques. Random Forest is based on an ensemble of decision tree predictors. It uses a modified tree learning algorithm which selects a random subset of the available features (feature bagging) to reduce the correlations between the trees; for a dataset with p features, pp features are used in each split. Moreover, each decision tree is trained…” [Page 17 Par 1-7] “The building of the dataset is an ongoing work. Indeed, for the machine learning models to generalize well, the dataset must include diverse meteorological conditions; thus, the data collection must cover the four seasons. While building our dataset, we have tested our approach on data gathered by one nomadic sensor node placed in the neighborhood of Agdal over a period of one month. Since only one sensor node is considered, we skipped the use of spatial data and only kept meteorological components and traffic to predict the air quality measured by the nomadic node. We applied a 10-min average to the measurements of PM10 and PM2.5 which were initially taken every 5 s. For the meteorological features, we use temperature and humidity measurements that were also obtained with the nomadic sensor node”)

Hakim makes obvious ([Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica. We define the optimal location as one that maximizes the reduction in total variance of a given spatial field. Using WRF Model forecast output from the Antarctic Mesoscale Prediction System (AMPS), we identify the best locations for observations across the continent by considering two spatial fields: (i) the daily 0000 UTC 2-m temperature analysis field and (ii) the daily 0000 UfC 2-m air temperature 24-h forecast field” [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] “Analysis of preferred regions for the first three optimally sited stations for monitoring 0000 UTC 2-m temperature over the entire continent, assuming no observation network currently exists. Cells that are colored indicate that the location was chosen at least once, and the color indicates the frequency with which that location is chosen over 10 000 iterations. The grid points shaded on the plots were made larger for easy viewing;”)
Gryech makes obvious obtaining information based on receiving, from the trained machine learning model, learned information ([Page 18 Par 7] “Random Forest is one of the most popular Ensemble Learning techniques. Random Forest is based on an ensemble of decision tree predictors. It uses a modified tree learning algorithm which selects a random subset of the available features (feature bagging) to reduce the correlations between the trees; for a dataset with p features, pp features are used in each split. Moreover, each decision tree is trained…” [Abstract] “Another key feature is the use of machine learning to perform prediction.” [Table 4] Shows a table of prediction results obtained from the machine learning model based on its training.)

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, 3-5, 7-11, 14-16, and 19-20 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.

Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill): “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”

A method for weather station placement design, the method comprising: … determining forecast performance by the weather forecast model by comparing the weather data to the weather forecast data and so that first weather stations where the weather forecast model had best forecast performance are identified;

Comparing sets of data representing the performance of something and using that data to determine which performed better is a mental process that involves observing the data and judging which outperforms the other. For example, if a person has a piece of paper that states a first runner finished a marathon in 25 minutes, a second runner in 30, and a third in an hour, a person could easily judge that the runner who finished in 25 minutes was the fastest runner.

generating a weather forecast performance map based on the identified first weather stations, wherein the weather forecast performance map is a heat map that includes indicators that correspond to accuracy levels for weather prediction that was performed by the weather forecast model;
Generating a performance map can be done by a human mind with the use of pencil and paper, i.e., drawing a map of an area and including certain performance indications on said map.

generating fractals based on topographical features of terrain that influence local weather patterns wherein each fractal is a respective self-similar subset of Euclidean space whose fractal dimension strictly exceeds its topological dimension; iteratively matching the fractals to the weather forecast performance map to identify a first fractal of the fractals that most closely matches a layout of the current locations of the first weather stations while optimizing coverage of distinct microclimates created by the topographical features;
Generating fractals and matching fractals can be practically done in the human mind using pencil and paper, i.e., drawing a fractal pattern on the paper. Doing so based on terrain features is a mental process that involves observing the terrain features, such as on a map, and drawing fractals that best match the layout of those features. Several different fractal configurations can be tried until one that matches the terrain best is found.

comparing the map data with existing fractal maps to determine a second fractal best suited for the new geographical area based on topographical features that create distinct microclimates in the new geographical area;
Comparing this map and fractal data to determine a new fractal for a second area is a mental process equivalent to observing the first map of a first area with the overlaid fractal, then looking at the second area to judge if the same or a different kind of fractal suits it better. For example, maybe a the first area is a flat plain with little variation and thus a coarse square grid fractal is sufficient to accurately capture the weather patterns of the region, while the second may include a series of widely varying mountain ranges with a large flat region at the center in which something like a sierpinski triangle better matches the topography of the region.

based on receiving, from the trained machine learning model, automated suggestions for the weather station placement design enhance, adjusting weather forecasting accuracy based on learned optimal placement patterns.
Suggesting weather station placement positions based on fractals is a mental process equivalent to observing the fractals overlaid on the weather performance map and judging, based on the points of the fractal and areas of low accuracy, where a new station would best improve the overall accuracy of the system.
Doing so in an “automated” manner based on data received from the trained machine learning model is equivalent to merely applying a generic computer to perform these operations.
Additionally, “receiving” data from the machine learning model is merely the act of gathering the output data from the model.

Step 2A – Prong 2: Integrated into a Practical Solution?
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.

Data Gathering:
… receiving weather data measured at weather stations, current location data regarding current locations of the respective weather stations, and weather forecast data generated by a weather forecast model; … receiving map data regarding a new geographical area that lacks weather stations; … new weather data and forecast accuracy measurements… receiving, from the trained machine learning model
Receiving data, such as weather or map data, is merely the act of gathering data.

Post Solution Activity:

presenting a first fractal map comprising the first fractal overlaid on the weather forecast performance map … presenting a second fractal map, wherein the second fractal map comprises the second fractal being overlaid on a second map and comprises points for new weather stations corresponding to nodes of the second fractal;
Presenting the map is just displaying the results of the previous steps, and is therefore merely post solution activity.

Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
training a machine learning model with the first fractal map and with the second fractal map, to learn optimal weather station placement patterns; continuously updating the trained machine learning model based on new weather data and forecast accuracy measurements to dynamically provide optimize weather station placement over time; and
Training and continuously updating a generic machine learning model is equivalent to applying a generic computer to perform generic training operations.
Applying a computer to perform generic training at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that training, which is merely an instruction to apply a computer to the judicial exception. The specification lists a great many possible forms of the machine learning model without specifying a particular embodiment to be used, either in itself or the claims ([Par 26] “Deep learning machine learning models which may be considered artificial intelligence have also been used for weather forecasting. A deep learning model may include a neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), long short-term memory (LSTM) nodes, gated recurrent units (GRU), ConvLSTM networks which include a combination of a normal CNN with LSTM, variational auto-encoders (VAE), generative adversarial networks (GAN), combinations of VAE and CNN layers, combinations of GAN and CNN layers, multi-layer perceptron architectures, boosted decision trees, dynamic Gaussian Process models, deep belief networks that include restricted Boltzman machines, stochastic adversarial video predictions, and other systems.”), which evidences the generic nature of the application of a general purpose computer.

Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are mere data gathering or post solution activity (Insignificant Extra-Solution Activity) and a general purpose computer and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.

Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.

Data Gathering:
… receiving weather data measured at weather stations, current location data regarding current locations of the respective weather stations, and weather forecast data generated by a weather forecast model; … receiving map data regarding a new geographical area that lacks weather stations; … new weather data and forecast accuracy measurements … receiving, from the trained machine learning model
Receiving data, such as weather or map data, is merely the act of gathering data. The courts have found that merely gathering data in a generic manner does not integrate a judicial exception into a practical application, nor does it amount to an inventive concept. ([MPEP 2106.05(g)(Mere Data Gathering)(iv) Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);)

Post Solution Activity:

presenting a first fractal map comprising the first fractal overlaid on the weather forecast performance map … presenting a second fractal map, wherein the second fractal map comprises the second fractal being overlaid on a second map and comprises points for new weather stations corresponding to nodes of the second fractal;
Presenting the map is just displaying the results of the previous steps, and is therefore merely post solution activity.
The courts have found that this type of limitation that merely produces an output based on abstract steps does not integrate a judicial exception into a practical application, nor does it amount to an inventive concept ([MPEP 2106.05(g)(Insignificant application)(i-ii) Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential); Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55.

Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere Instructions to Apply:
training a machine learning model with the first fractal map and with the second fractal map, to learn optimal weather station placement patterns; continuously updating the trained machine learning model based on new weather data and forecast accuracy measurements to dynamically provide optimize weather station placement over time; and
Training and continuously updating a generic machine learning model is equivalent to applying a generic computer to perform generic training operations.
Applying a computer to perform generic training at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that training, which is merely an instruction to apply a computer to the judicial exception. The specification lists a great many possible forms of the machine learning model without specifying a particular embodiment to be used, either in itself or the claims ([Par 26] “Deep learning machine learning models which may be considered artificial intelligence have also been used for weather forecasting. A deep learning model may include a neural network, a convolutional neural network (CNN), a recurrent neural network (RNN), long short-term memory (LSTM) nodes, gated recurrent units (GRU), ConvLSTM networks which include a combination of a normal CNN with LSTM, variational auto-encoders (VAE), generative adversarial networks (GAN), combinations of VAE and CNN layers, combinations of GAN and CNN layers, multi-layer perceptron architectures, boosted decision trees, dynamic Gaussian Process models, deep belief networks that include restricted Boltzman machines, stochastic adversarial video predictions, and other systems.”), which evidences the generic nature of the application of a general purpose computer.
See (MPEP 2016.05(f)(2)(i)) “A commonplace business method or mathematical algorithm being applied on a general purpose computer,” [Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ]

Moreover, the additional computer elements of claim 1 “based on receiving, from the trained machine learning model, automated“ are rejected for simply applying a general purpose computer. (MPEP 2106.05(f))

Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.

The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.

Claims 11 and 16 recite substantially the same elements as claim 1 and are rejected for the same reasons under 35 U.S.C. 101.

Moreover, the additional computer elements of claim 11 “. A computer system for weather station placement design, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising: based on receiving, from the trained machine learning model, automated” and the additional computer elements of claim 16 “A computer program product for weather station placement design, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, wherein the program instructions are executable by a computer system to cause the computer system to perform a method comprising: based on receiving, from the trained machine learning model, automated” are rejected for simply applying a general purpose computer. (MPEP 2106.05(f))
Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.

Claim 3 recites “wherein the comparing the map data with the existing fractal maps further comprises performing a K-nearest neighbor algorithm.”
Being an algorithm, performing a K-nearest neighbor algorithm is a mathematic process.

Claim 4 recites “receiving new topological data regarding the new geographical area;”
Receiving data is merely data gathering.

Claim 4 also recites “comparing the new topological data with topological data for the existing fractal maps to determine the second fractal best suited for the new geographical area.”
Performing a comparison between two things and making a judgement based on that comparison is practically performable in the human mind, and is therefore a mental process.

Claim 5 recites “further comprising receiving a cost parameter as input: wherein the cost parameter is also used to determine the second fractal best suited for the new geographical area.”
Receiving data that indicates a cost parameter is mere data gathering. Determining the best suited fractal by factoring in the cost parameter is mere extra-solution activity

Claim 7 recites “receiving, as output from the trained machine learning model, at least one position enhancement for at least one of the first weather stations;”
This merely specifies the form of the output from the machine learning model, and is thus an extension of the abstract idea and mere instructions to apply. Further receiving output data is merely the act of gathering that output data.

Claim 7 also recites “presenting the at least one position enhancement.”
Presenting the position enhancement is just displaying the results of the previous steps, and is therefore merely post solution activity.

Claim 8 recites “the at least one position enhancement is based on a distance between a node of a generator of the first fractal and a map point for a first weather station, wherein the distance exceeds a first threshold.”
This is a mathematic concept, comprising a mathematic relationship between two sets or coordinates representing the locations of a first weather station and a fractal node, and a value representing a minimum distance between them.

Claim 9 recites “the iterative matching comprises determining a respective distance from map points of the first weather stations to nodes of generators of the fractals.”
Determining the distance between two points is a mathematic calculation, making this a mathematic concept.

Claim 10 recites “the nodes of the generators comprise at least one member selected from the group consisting of a center of the generator, an apex of the generator, or a central base point of the generator.”
This is a merely a clarification of the form of the nodes of the generator, and is therefore merely an extension of the mental process

Claims 14-15 recite substantially the same elements as claims 9-10 and are rejected for the same reasons under 35 U.S.C. 101

Claims 19-20 recite substantially the same elements as claims 9-10 and are rejected for the same reasons under 35 U.S.C. 101

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.

(1) Claims 1, 4-5, 7-11 14-16, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Flade (US 20220139213 A1) in view of Optimal Network Design Applied to Monitoring and Forecasting Surface Temperature in Antarctica (Hereinafter Hakim), in further view of Maquaire(US 20180300419 A1) and MoreAir: A Low-Cost Urban Air Pollution Monitoring System (Hereinafter Gryech) as well as General Software for Multimodal Signal Modeling and Optimal Sensor Placement (Hereinafter Yamamoto)

Claim 1. Flade makes obvious ([Par 96] “ Implicit triggers provide indirect hints, e.g., on the occurrence of a thunderstorm in a wooded area that may lead to fallen trees partially or entirely obstructing the road. The weather station 15 in the lower portion of FIG. 2 may provide further complementary data 13 including weather information.”) ([Fig. 8] Note first fractal overlaid on a map [Examiner’s note: “a respective self-similar subset of Euclidean space whose fractal dimension strictly exceeds its topological dimension” is the basic definition of fractal, and thus is an essential characteristic of any fractal, including the one shown in Fig. 8]
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[Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining… The road map is divided into fractal cells A, B, C, D on a first segmentation level. The second segmentation level divides each fractal cell A, B, C, D of the first segmentation level into four fractal cells on the second segmentation level as depicted in FIG. 8. The fractal cell A is segmented into the four fractal cells A1, A2, A3, and A4. The marker of a vehicle depicted as travelling on a road segment of the road map in the upper portion of FIG. 8 corresponds to a fractal cell with the cell identifier D1 on the second segmentation level in the lower portion of FIG. 8.” [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded. ]) iteratively matching the fractals to([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining… The road map is divided into fractal cells A, B, C, D on a first segmentation level. The second segmentation level divides each fractal cell A, B, C, D of the first segmentation level into four fractal cells on the second segmentation level as depicted in FIG. 8. The fractal cell A is segmented into the four fractal cells A1, A2, A3, and A4. The marker of a vehicle depicted as travelling on a road segment of the road map in the upper portion of FIG. 8 corresponds to a fractal cell with the cell identifier D1 on the second segmentation level in the lower portion of FIG. 8.” [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded. ])
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([Fig. 8] Shows a presented first fractal map) ([Fig.8]) to determine a ([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining… The road map is divided into fractal cells A, B, C, D on a first segmentation level. The second segmentation level divides each fractal cell A, B, C, D of the first segmentation level into four fractal cells on the second segmentation level as depicted in FIG. 8. The fractal cell A is segmented into the four fractal cells A1, A2, A3, and A4. The marker of a vehicle depicted as travelling on a road segment of the road map in the upper portion of FIG. 8 corresponds to a fractal cell with the cell identifier D1 on the second segmentation level in the lower portion of FIG. 8.” [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded. ]) ([Fig. 8]) and([Par 28] “This may be done based on trigger data that can be generated based on, e.g. Traffic Message Channels (TMC), weather station forecasts or -warnings, or even individual road users.” [Par 33] “Generating the trigger data of an advantageous exemplary embodiment bases on at least one of TMC data, disaster or weather warning”)

Flade does not explicitly teach A method for weather station placement design, current location data regarding current locations of the respective weather stations, and weather forecast data generated by a weather forecast model; determining forecast performance by the weather forecast model by comparing the weather data to the weather forecast data and so that first weather stations where the weather forecast model had best forecast performance are identified; generating a weather forecast performance map based on the identified first weather stations, wherein the weather forecast performance map is a heat map that includes indicators that correspond to accuracy levels for weather prediction performed by the weather forecast model; identifying topological features of terrain that influence local weather patterns; the weather forecast performance map; finding locations of the first weather stations while optimizing coverage of distinct microclimates created by the topographical features; presenting the weather forecast performance map; receiving map data regarding a new geographical area that lacks weather stations: comparing the map data for a second fractal based on topographical features that create distinct microclimates in the new geographical area; presenting a second fractal map, wherein the second fractal map comprises the second fractal being overlaid on a second map and comprises points for new weather stations corresponding to nodes of the second fractal; training a machine learning model with the second fractal map to learn optimal weather station placement patterns; continuously updating the trained machine learning model based on forecast accuracy measurements to dynamically optimize weather station placement over time; based on receiving, from the trained machine learning model, automated suggestions for weather station placement adjusting weather forecasting accuracy based on learned optimal placement patterns.

Hakim makes obvious A method for weather station placement design, ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica”) current location data regarding current locations of the respective weather stations, ([Page 859, Col 2, Par. 3] “We consider the locations of weather stations that are assimilated into WRF as defined in Bumbaco et al. (2014). Namely, we use the locations of the weather stations…”) and weather forecast data generated by a weather forecast model; ([Abstract] “Using WRF Model forecast output from the Antarctic Mesoscale Prediction System {AMPS), we identify the best locations for observations across the continent by considering two spatial fields: (i) the daily 0000 UTC 2-m temperature analysis field and (ii) the daily 0000 UfC 2-m air temperature 24-h forecast field.” [Page 859, Col 1, Par. 3] “The primary data used in this study are output from the Advanced Research version of the Weather Research and Forecasting (WRF) Model”) determining forecast performance by the weather forecast model by comparing the weather data to the weather forecast data and so that first weather stations where the weather forecast model had best forecast performance are identified; ([Fig. 2] [Fig. 9] [Fig. 10] [Page 862 Col 2, Par. 5] “Our vector metrics J in this work are defined for two separate network-design objectives: (i) the model analysis 0000 UTC 2-m temperature and (ii) the 24-h 2-m forecast temperature errors. Forecast errors for the second objective are defined as the difference between AMPS forecasts and analyses (i.e., 0-h forecasts) valid at the same date and hour.”)
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generating a weather forecast performance map based on the identified first weather stations, ([Fig. 9] [Fig. 10] [Page 868, Col 2, Par. 2] “ Here we present results for the second metric described in section 2d: 24-h forecast errors in 2-m temperature. Again, we use a covariance localization length scale of 3000 km, and consider the same three cases as in the previous section: no existing network, new locations conditional on the CD90 subset, and new locations conditional on the CD75 subset. The regions highlighted to reduce forecast errors (Fig. 10)”) wherein the weather forecast performance map is a heat map ([Fig. 9] [Fig. 10]) ([Page 868, Col 2, Par. 2] “ Here we present results for the second metric described in section 2d: 24-h forecast errors in 2-m temperature. Again, we use a covariance localization length scale of 3000 km, and consider the same three cases as in the previous section: no existing network, new locations conditional on the CD90 subset, and new locations conditional on the CD75 subset. The regions highlighted to reduce forecast errors (Fig. 10)”)

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identifying topological features of terrain that influence local weather patterns; ([Page 864 Col 2 Par 1] “However, these two points are distant, separated by significant topography, and have very different weather regimes on short time scales (i.e., coastal vs plateau) [Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “We define the optimal location as one that maximizes the reduction in total variance of a given spatial field” [Fig. 1] Shows the terrain height map considered by the system [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] Shows examples of selected locations [Examiner’s note: the system identifies geographically diverse station locations that increase the accuracy in that region by determining where accuracy is lacking, i.e. it identifies distinct local microclimates based on the topographical features such as terrain height and determines whether the localized climate is distinct enough to warrant the selection of a new station location there.])
the weather forecast performance map;([Fig. 9] [Fig. 10] [Page 868, Col 2, Par. 2] “ Here we present results for the second metric described in section 2d: 24-h forecast errors in 2-m temperature. Again, we use a covariance localization length scale of 3000 km, and consider the same three cases as in the previous section: no existing network, new locations conditional on the CD90 subset, and new locations conditional on the CD75 subset. The regions highlighted to reduce forecast errors (Fig. 10)”) finding locations of the first weather stations ([Page 859, Col 2, Par. 3] “We consider the locations of weather stations that are assimilated into WRF as defined in Bumbaco et al. (2014). Namely, we use the locations of the weather stations…”) while optimizing coverage of distinct microclimates created by the topographical features; ([Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “We define the optimal location as one that maximizes the reduction in total variance of a given spatial field” [Fig. 1] Shows the terrain height map considered by the system [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] Shows examples of selected locations [Examiner’s note: the system identifies geographically diverse station locations that increase the accuracy in that region by determining where accuracy is lacking, i.e. it identifies distinct local microclimates based on the topographical features such as terrain height and determines whether the localized climate is distinct enough to warrant the selection of a new station location there.]) presenting the weather forecast performance map; ([Fig. 9] [Fig. 10] [Page 868, Col 2, Par. 2] “ Here we present results for the second metric described in section 2d: 24-h forecast errors in 2-m temperature. Again, we use a covariance localization length scale of 3000 km, and consider the same three cases as in the previous section: no existing network, new locations conditional on the CD90 subset, and new locations conditional on the CD75 subset. The regions highlighted to reduce forecast errors (Fig. 10)”) receiving map data regarding a new geographical area that lacks weather stations; ([Fig. 7] [Page 866, Col 2, Par. 1] “ With a covariance localization of 3000 km, the results assuming that no stations currently exist on the continent are shown in Fig. 7”) comparing the map data for ([Fig 4] “Analysis of preferred regions for the first three optimally sited stations for monitoring 0000 UTC 2-m temperature over the entire continent, assuming no observation network currently exists. Cells that are colored indicate that the location was chosen at least once, and the color indicates the frequency with which that location is chosen over 10 000 iterations. The grid points shaded on the plots were made larger for easy viewing;” [Examiner’s note: note the comparison of proposed station locations to other existing and proposed station locations over 1000s of iterations in Fig. 4])

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([Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “We define the optimal location as one that maximizes the reduction in total variance of a given spatial field” [Fig. 1] Shows the terrain height map considered by the system [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] Shows examples of selected locations [Examiner’s note: the system identifies geographically diverse station locations that increase the accuracy in that region by determining where accuracy is lacking, i.e. it identifies distinct local microclimates based on the topographical features such as terrain height and determines whether the localized climate is distinct enough to warrant the selection of a new station location there.]) ([Fig 4] “Analysis of preferred regions for the first three optimally sited stations for monitoring 0000 UTC 2-m temperature over the entire continent, assuming no observation network currently exists. Cells that are colored indicate that the location was chosen at least once, and the color indicates the frequency with which that location is chosen over 10 000 iterations. The grid points shaded on the plots were made larger for easy viewing;”) ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica.”) continuously updating ([Page 862 Col 1 Par 6 – Col 2 Par 1] “To summarize, the general procedure for finding optimal locations uses the following algorithm:
1) Choose a vector metric that quantifies the desired
aspects of the system of interest.
2) Calculate the total variance for the metric (trace of
the metric covariance matrix).
3) Calculate the change in the total variance for all
possible stations.
4) Choose as the optimal measurement location the one
that maximizes the change in total variance from noi.
5) Update the metric and state to reflect the chosen
measurement using (l l ).
6) Repeat the procedure for the next measurement, using the updated state and metric to find the next location conditional on the previous measurement (i.e., repeat steps 3-5).”
[Examiner’s note: the system evaluates the forecast accuracy, selects a station location, and then updates the state of the system to include this new station as well as calculated accuracy metrics again, then repeats the process to continuously update and select optimal positions])
([Page 858 Col 2 Par 3- Page 859 Col 1 Par 1 “The optimal network design approach here uses an ensemble of existing deterministic model analyses and forecasts (not output from forecast ensembles) to find the best locations for stations by identifying the location that reduces the climatological variance the most in the chosen metric.” [Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica. We define the optimal location as one that maximizes the reduction in total variance of a given spatial field. Using WRF Model forecast output from the Antarctic Mesoscale Prediction System (AMPS), we identify the best locations for observations across the continent by considering two spatial fields: (i) the daily 0000 UTC 2-m temperature analysis field and (ii) the daily 0000 UfC 2-m air temperature 24-h forecast field” [Page 864 Col 1 Par 2] “For placement of a third station, three different areas are frequently chosen: Queen Maud Land closer to the coast in East Antarctica, the Antarctic Plateau near South Pole, and the Siple Coast location again. The plateau location (subsequently called "South Pole," though not directly over the pole) was chosen most frequently, representing geographical diversity in station locations that are most impactful for monitoring 2-m temperature across the continent.” [Fig 4] “Analysis of preferred regions for the first three optimally sited stations for monitoring 0000 UTC 2-m temperature over the entire continent, assuming no observation network currently exists. Cells that are colored indicate that the location was chosen at least once, and the color indicates the frequency with which that location is chosen over 10 000 iterations. The grid points shaded on the plots were made larger for easy viewing;”)

Hakim is analogous art because it is within the field of analyzing meteorological data. It would have been obvious to one of ordinary skill in the art to combine Hakim with Flade before the filing date.
One of ordinary skill in the art would have been motivated to make this combination to improve the forecasting capabilities of the weather station system present in Flade by presenting a new complex with optimally placed sensing stations to provide more accurate data. While Hakim is directed to operations specifically in antarctica, one of ordinary skill in the art would have recognized that the improved accuracy provided by the situation would be applicable to any location, with particular benefit for locations with a low coverage of meteorological data, such as locations with harsh natural conditions and limited infrastructure, as suggested in Hakim ([Abstract] “As harsh weather conditions in Antarctica make it difficult to support a dense weather observing network there, it is critical to place new weather stations in locations that are optimal for a given monitoring goal. Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica. We define the optimal location as one that maximizes the reduction in total variance of a given spatial field.” [Page 870, Col 2, Par. 2 – 871, Col 1, Par. 1] “Accurate atmospheric observations are crucial for supporting science and operations over the Antarctic continent. Beyond assisting in basic logistical support, a robust weather observing network in Antarctica supports scientific campaigns across the region. An augmented observing network on the Antarctic continent could improve forecasts…”)

The combination of Hakim and Flade does not explicitly teach a heat map that that includes indicators that correspond to accuracy levels; a second fractal; presenting a second fractal map, wherein the second fractal map comprises the second fractal being overlaid on a second map with nodes of the second fractal; training a machine learning model with the second fractal map to learn; the trained machine learning model; obtaining information based on receiving, from the trained machine learning model, learned information;

Maquaire makes obvious a second fractal; presenting a second fractal map, wherein the second fractal map comprises the second fractal being overlaid on a second map ([Fig. 2-3] [Fig. 5C-5F] [Par 62] “At step 940, a second fractal map comprising a second plurality of related articles to the received user selection is displayed.”) with nodes of the second fractal; ([Fig. 2-3] Clearly show a node labeled 1 in the center of the generator. [Fig. 5c] Clearly shows that generator after being iterated upon.) with the second fractal map ([Fig. 2-3] [Fig. 5C-5F] [Par 62] “At step 940, a second fractal map comprising a second plurality of related articles to the received user selection is displayed.”)

Maquaire is analogous art because it is within the field of the practical application of fractals and their use in mapping. It would have been obvious before the filing date to one ordinary skill in the art to combine it with Hakim and Flade. One of ordinary skill in the art would have been motivated to make this combination to allow for a better selection of the final location(s) for the proposed station(s). By processing a multiplicity of different maps with different fractals, specific station configurations for the specific features and layouts of each map can be determined. See [Fig. 4] of Hakim, the caption of which reads “Analysis of preferred regions for the first three optimally sited stations for monitoring 0000 UTC 2-m temperature over the entire continent, assuming no observation network currently exists. Cells that are colored indicate that the location was chosen at least once, and the color indicates the frequency with which that location is chosen over 10 000 iterations. Generating a plurality of potential new locations and comparing it with, and incorporating the data of, previously suggested or already existing station locations also allows for much more informed final selection, being able to compare it with other proposed options, as suggested by Hakim ([Page 862, Col 2, Par. 4] “To account for uncertainty in the network calculation, we use a Monte Carlo bootstrap approach where we repeat the calculation for a different, randomly drawn, 250-member ensemble. For each 250-member ensemble, the 20 locations for the idealized network are found as described above. This process is repeated 10000 times, providing 10000 sets of 20 locations, allowing for statistics on station location.”) Moreover, one of ordinary skill in the art would have been further motivated to combine Maquaire with Flade and Hakim to produce fractal maps with a greater capability for adaptive fractal generation tuned to specific geographic topologies, allowing for more dynamic placement suggestions. As can be seen in [Fig. 6] of Maquaire, the fractal generation system presented therein allows for simultaneous multiresolution, in other words different levels of fractal generation in different areas. One of ordinary skill in the art would have recognized that applying this feature to the fractal generation system of Flade would allow for increased fractal generation, and thus increased granularity, in areas of interest. For example, the evaluation of a busy block might benefit from being fractally broken up over several iterations, allowing for more granularity and thus more accuracy to the analysis, while a sparsely populated area of the same city may only require a single “chunk” to be modeled sufficiently, thus reducing processing power. One can clearly see how this concept could be applied to weather station placement; in mountainous regions where airflow and conditions are unpredictable as a result of the chaotic geography, higher levels of granularity may be desired. The conditions a mile north of a mountain may be vastly different than those a mile south with both being different from the conditions on the mountain itself, and multiple stations may ultimately be required to capture a complete picture of the region. On the other hand, an area of mostly flat plains or tundra may have consistent conditions for miles, requiring less granularity and only a few stations to cover an entire region. Overall, one of ordinary skill in the art would have recognized that combining Maquaire with Flade and Hakim would produce a more adaptable and efficient system that further adapts to topology, making the generated suggestions based on this topology even more dynamic and terrain specific.

The combination of Flade, Hakim, and Maquaire does not explicitly teach a heat map that includes indicators that correspond to accuracy levels; training a machine learning model to learn; the trained machine learning model; obtaining information based on receiving, from the trained machine learning model, learned information;

Gryech makes obvious ([Page 18 Par 7] “Random Forest is one of the most popular Ensemble Learning techniques. Random Forest is based on an ensemble of decision tree predictors. It uses a modified tree learning algorithm which selects a random subset of the available features (feature bagging) to reduce the correlations between the trees; for a dataset with p features, pp features are used in each split. Moreover, each decision tree is trained…”) obtaining information based on receiving, from the trained machine learning model, learned information; ([Page 18 Par 7] “Random Forest is one of the most popular Ensemble Learning techniques. Random Forest is based on an ensemble of decision tree predictors. It uses a modified tree learning algorithm which selects a random subset of the available features (feature bagging) to reduce the correlations between the trees; for a dataset with p features, pp features are used in each split. Moreover, each decision tree is trained…” [Abstract] “Another key feature is the use of machine learning to perform prediction.” [Table 4] Shows a table of prediction results obtained from the machine learning model based on its training.)

Gryech is analogous art because it is within the field of environmental sensing and determining ideal locations for sensors. It would have been obvious before the filing date to one ordinary skill in the art to combine it with Hakim, Flade, and Maquaire. One of ordinary skill in the art would have been motivated to make this combination to make the system as a whole much lower cost. As suggested by Gryech, individual sensing stations can be prohibitively expensive, limiting larger sensor networks to more affluent and well-funded areas, and making it difficult to establish them in poorer areas. ([Page 2, Par 1] “the deployment and maintenance of a high number of these fixed air monitoring networks are very expensive. One can expect at least $10K per station, excluding installation and maintenance costs[7]. Furthermore, these monitoring stations are generally not located in regions where anthropogenic activities and populations are concentrated; roadsides and major traffic congestion areas are also often very far from the measuring stations, which may significantly affect the accuracy of the pollutants’ spatial distribution estimation in urban areas [8]. To address these challenges, attention has recently been redirected towards using small low-cost sensing units.”) One of ordinary skill in the art would have recognized that integrating the technology of Gryech would produce a system that would be cost effective enough to be implemented virtually anywhere in the world.

Yamamoto makes obvious a heat map that includes indicators that correspond to accuracy levels; ([Fig. 4] “Performance prediction of a single IR sensor tower located 10 m AGL at blue box for detecting a human. Large red areas indicate sensor occlusion, where the sensor has no line of sight to the target, and the green-to-yellow-to-red transitions represent reduced probability of detection due to
diminished target apparent area and atmospheric attenuation. Bottom: Performance prediction in the same area using three networked IR sensor towers located 10 m AGL at the blue boxes. Green and red represent high and low probabilities, respectively, that at least one IR sensor would detect a human at that location” [Examiner’s note: if an area has a higher detection hit-rate/probability due to sensor placement, it means the detection mechanism is more accurate in that area])

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Yamamoto is analogous art because it is within the field of sensor placement optimization. It would have been obvious to one of ordinary skill in the art that combining Hakim, Flade, Maquaire and Gryech with Yamamoto before the effective filing date. One or ordinary skill in the art would have been motivated to make this combination to allow the system to integrate with a wider variety of sensor types, each of which may require different placement strategies, to generate an optimal placement of an entire multi-modal sensor network. ([Page 66 Col 1 Par 1 – Col 2 Par 2] “Effective use of the latest sensor technologies, however, often requires expert knowledge of sensor performance in complex environments, which is typically unavailable to small, forwardly deployed combat units and other potential beneficiaries of the technology. Specifically, the performance of sensors depends strongly on the modality (visible, infrared, acoustic, seismic, radio-frequency, chemical, biological, etc.) and the environmental conditions (terrain and weather) in which the sensors operate. For example, acoustic signals are most affected by wind and temperature profiles, while propagation of seismic waves depends primarily on local subsurface properties. Since terrain and weather effects on sensor performance are typically complex and sometimes counterintuitive, computational simulations are necessary. The Environmental Awareness for Sensor and Emitter Employment (EASEE) software has been developed to simulate realistic environmental effects on target signatures, signal propagation, and diverse sensor systems to predict sensor performance for a broad spectrum of signal modalities and to evaluate how sensing in different modalities may be best exploited to achieve surveillance objectives within given environmental constraints”) Overall, one of ordinary skull in the art would have recognized that combining Hakim, Flade, Maquaire and Gryech with Yamamoto would allow for wider sensor compatibility, allowing a more diverse set of metrics to be gathered and resulting in more accurate, well-informed decisions by the model when considering and selecting new station locations.

Claims 11 and 16 recite substantially the same elements as claim 1 and are rejected for the same reasons under 35 U.S.C. 103

Moreover, the additional computer elements of Claim 11 are rejected by Flade: the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors to cause a method to be performed comprising: ([Par 133] “A central server stores complementary information 11 in form of map data.” [Examiner’s Note: One of ordinary skill in the art would have recognized that a server would include one or more processors and one or more computer-readable storage devices/mediums])
Further, Hakim makes obvious A computer system for weather station placement design ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica”)

Moreover, the additional computer elements of Claim 16 are rejected by Flade: the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, wherein the program instructions are executable by a computer system to cause the computer system to perform a method comprising: ([Par 133] “A central server stores complementary information 11 in form of map data.” [Examiner’s Note: One of ordinary skill in the art would have recognized that a server would include one or more processors and one or more computer-readable storage devices/mediums that contain instructions])
Further, Hakim makes obvious A computer program product for weather station placement design, ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica”)

Claim 4. Flade makes obvious ([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining.”) to determine the ([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded. ])
Hakim makes obvious further comprising: receiving new topological data regarding the new geographical area; and comparing the new topological data with other topological data ([Fig. 2] “Antarctica with referenced stations (red dots) and regions (green text). Gray dots show locations considered for potential station locations in the network-design calculation, and blue dots show locations that are evaluated for the performance metrics. [Examiner’s note: note the plurality of regions and topographical areas from which data is measured and considered] [Fig. 7] [Page 866, Col 2, Par. 1] “ With a covariance localization of 3000 km, the results assuming that no stations currently exist on the continent are shown in Fig. 7” [Fig 4] [Examiner’s note: note the comparison of proposed station locations to other existing and proposed station locations over 1000s of iterations in Fig. 4]) and a

Maquaire makes obvious a second fractal map ([Fig. 2-3] [Fig. 5C-5F] [Par 62] “At step 940, a second fractal map comprising a second plurality of related articles to the received user selection is displayed.”)

Claim 5. Flade makes obvious further comprising receiving a cost parameter as input; wherein the cost parameter is also used to determine the ([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining.” [Par 83] “The evaluation processor 4 may comprise one or multiple servers as hardware, on which software for processing input data, in particular the image data 8, the trigger data 11 and possibly also complementary data 12 in order to generate the enhanced traffic data according to a traffic evaluation task is running” [Examiner’s note: it would have been obvious to one of ordinary skill in the art that the complementary data input to the system could include a cost parameter])
Maquaire makes obvious the second fractal ([Fig. 2-3] [Fig. 5C-5F] [Par 62] “At step 940, a second fractal map comprising a second plurality of related articles to the received user selection is displayed.”)

Claim 7. Hakim makes obvious stations; and presenting the at least one position enhancement. ([Page 860, Col 1, Par. 2] “To determine a second location, the impact of the first new measurement must be taken into account” [Fig. 8a][Abstract] “We find optimal locations assuming that no stations exist on the continent (blank slate) and conditional on existing stations (CD90). [Page 867, Col 1, Par 2] “Figure 8 shows the results for the monitoring metric after removing the variance explained by including the CD90 (Fig. 8a--c) and the CD75 (Fig. 8d--f) subsets.” [Examiner’s note: Fig 8 shows enhanced positions based on existing station locations. As supported by [Page 860, Col 1, Par. 2], one of ordinary skill in the art would have recognized that if optimal positions for new stations can be generated based on an existing sensor network, that same process could be applied to existing sensor stations])
Gryech makes obvious further comprising receiving, as output from the trained machine learning model, data. ([Page 18 Par 7] “Random Forest is one of the most popular Ensemble Learning techniques. Random Forest is based on an ensemble of decision tree predictors. It uses a modified tree learning algorithm which selects a random subset of the available features (feature bagging) to reduce the correlations between the trees; for a dataset with p features, pp features are used in each split. Moreover, each decision tree is trained…” [Page 18 Par 2] “The results of these prediction algorithms are presented in Table 4”)
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Claim 8. Flade makes obvious wherein([Fig. 8]) ([Par 134] “Each traffic package is assigned to the closest road segment by the central server… with a distance function d(x, y).” [Examiner’s note: one of ordinary skill in the art would have recognized that if distance is being measured, an arbitrary number representing a certain distance value could be chosen as a threshold])
Hakim makes obvious the at least one position enhancement is based on a distance, ([Fig. 8a][Abstract] “We find optimal locations assuming that no stations exist on the continent (blank slate) and conditional on existing stations (CD90). [Page 867, Col 1, Par. 2] “Figure 8 shows the results for the monitoring metric after removing the variance explained by including the CD90 (Fig. 8a--c) and the CD75 (Fig. 8d--f) subsets.” [Examiner’s note: Fig 8 shows enhanced positions based on existing station locations. Additionally, one of ordinary skill in the art would have recognized that if optimal positions for new stations can be generated based on an existing sensor network, that same process could be applied to existing sensor stations] [Page 864 Col 2 Par 2] “In F1i. 5a, the lettered regions on the map represent seven 450 km-radius areas of preferred locations that were chosen for each of the length scales ranging from 1000 to 8000 km m. These regions are also identified as areas of interest for potential station locations in Fig. 4. For the first station chosen over various length scales (hg. 5b), the Siple Coast region (B) was chosen most often for length scales less than 2000 km, while the Megadunes region (A)..”)
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([Fig 2] “Antarctica with referenced stations (red dots)” [Fig. 8a][Abstract] “We find optimal locations assuming that no stations exist on the continent (blank slate) and conditional on existing stations (CD90). [Page 867, Col 1, Par. 2] “Figure 8 shows the results for the monitoring metric after removing the variance explained by including the CD90 (Fig. 8a--c) and the CD75 (Fig. 8d--f) subsets.”)
Maquaire makes obvious a node of a generator of a fractal ([Fig. 2-3] Clearly show a node labeled 1 in the center of the generator. [Fig. 5c] Clearly shows that generator after being iterated upon. [Examiner’s note: the generator of a fractal is the base shape that is iterated upon and modified by the fractal process. In the case of Figs 2-5c, this process is reducing the size of the previous iteration by 50%, the first iteration, or iteration 0, being the generator])

Claim 9. Flade makes obvious wherein the iterative matching ([Fig. 8] [Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining… The road map is divided into fractal cells A, B, C, D on a first segmentation level. The second segmentation level divides each fractal cell A, B, C, D of the first segmentation level into four fractal cells on the second segmentation level as depicted in FIG. 8. The fractal cell A is segmented into the four fractal cells A1, A2, A3, and A4. The marker of a vehicle depicted as travelling on a road segment of the road map in the upper portion of FIG. 8 corresponds to a fractal cell with the cell identifier D1 on the second segmentation level in the lower portion of FIG. 8.” [Examiner’s note: see Fig. 8 for a good example of the iterative matching. As mentioned in the cited paragraph each cell is iteratively subdivided into smaller subcells which better depict and more closely represent the local topology of each cell. Note empty subcell C1 is excluded from the cell listing at the bottom of Fig. 8, exemplifying the actual matching process as opposed to blind subdivision. Since C1 is not part of the topology of interest, it is excluded.]) comprises determining a respective distance from ([Par 134] “Each traffic package is assigned to the closest road segment by the central server… with a distance function d(x, y).”
Hakim makes obvious map points of the first weather stations to ([Fig 2] “Antarctica with referenced stations (red dots)” [Fig. 8a][Abstract] “We find optimal locations assuming that no stations exist on the continent (blank slate) and conditional on existing stations (CD90). [Page 867, Col 1, Par. 2] “Figure 8 shows the results for the monitoring metric after removing the variance explained by including the CD90 (Fig. 8a--c) and the CD75 (Fig. 8d--f) subsets.”)
Maquaire makes obvious nodes of generators of the fractals ([Fig. 2-3] Clearly show a node labeled 1 in the center of the generator. [Fig. 5c] Clearly shows that generator after being iterated upon. [Examiner’s note: the generator of a fractal is the base shape that is iterated upon and modified by the fractal process. In the case of Figs 2-5c, this process is reducing the size of the previous iteration by 50%, the first iteration, or iteration 0, being the generator])

Claims 14 and 19 recite substantially the same elements as claim 9 and are rejected for the same reasons under 35 U.S.C. 103.

Moreover, the additional computer elements of Claim 11 as inherited by Claim 14 are rejected by Flade: the computer system comprising :one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors to cause a method to be performed comprising: ([Par 133] “A central server stores complementary information 11 in form of map data.” [Examiner’s Note: One of ordinary skill in the art would have recognized that a server would include one or more processors and one or more computer-readable storage devices/mediums])
Further, Hakim makes obvious A computer system for weather station placement design ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica”)

Moreover, the additional computer elements of Claim 16 as inherited by Claim 19 are rejected by Flade: the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, wherein the program instructions are executable by a computer system to cause the computer system to perform a method comprising: ([Par 133] “A central server stores complementary information 11 in form of map data.” [Examiner’s Note: One of ordinary skill in the art would have recognized that a server would include one or more processors and one or more computer-readable storage devices/mediums that contain instructions])
Further, Hakim makes obvious A computer program product for weather station placement design, ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica”)

Claim 10. Maquaire makes obvious wherein the nodes of the generators comprise at least one member selected from the group consisting of a center of the generator, an apex of the generator, or a central base point of the generator. ([Fig. 2-3] Clearly show a node labeled 1 in the center of the generator. [Fig. 5c] Clearly shows that generator after being iterated upon. [Examiner’s note: the generator of a fractal is the base shape that is iterated upon and modified by the fractal process. In the case of Figs 2-5c, this process is reducing the size of the previous iteration by 50%, the first iteration, or iteration 0, being the generator])

Claims 15 and 20 recite substantially the same elements as claim 9 and are rejected for the same reasons under 35 U.S.C. 103.

Moreover, the additional computer elements of Claim 11 as inherited by Claim 15 are rejected by Flade: the computer system comprising :one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors to cause a method to be performed comprising: ([Par 133] “A central server stores complementary information 11 in form of map data.” [Examiner’s Note: One of ordinary skill in the art would have recognized that a server would include one or more processors and one or more computer-readable storage devices/mediums])
Further, Hakim makes obvious A computer system for weather station placement design ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica”)

Moreover, the additional computer elements of Claim 16 as inherited by Claim 20 are rejected by Flade: the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, wherein the program instructions are executable by a computer system to cause the computer system to perform a method comprising: ([Par 133] “A central server stores complementary information 11 in form of map data.” [Examiner’s Note: One of ordinary skill in the art would have recognized that a server would include one or more processors and one or more computer-readable storage devices/mediums that contain instructions])
Further, Hakim makes obvious A computer program product for weather station placement design, ([Abstract] “Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica”)

(2) Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Flade (US 20220139213 A1) in view of Optimal Network Design Applied to Monitoring and Forecasting Surface Temperature in Antarctica (Hereinafter Hakim), in further view of Maquaire(US 20180300419 A1) and MoreAir: A Low-Cost Urban Air Pollution Monitoring System (Hereinafter Gryech) as well as General Software for Multimodal Signal Modeling and Optimal Sensor Placement (Hereinafter Yamamoto) and Mehtab (US 20220035073 A1)

Claim 3. Flade makes obvious ([Par 135] “ FIG. 8 illustrates an embodiment, in which the road map M is divided into fractal cells applying a quad-tree technique. This approach avoids computing the distance d to all known road points, and accordingly has advantageous computing characteristics. Based on using a position of the traffic package, a lowest level of the tree is found in a few iterations of the processing. At the lowest level of the road map M, for example representing a cell of 100×100 m cell size in real world in the road map M, only a few possible road segments are remaining.”)
Hakim makes obvious wherein the comparing the map data further comprises ([Fig. 2] “Antarctica with referenced stations (red dots) and regions (green text). Gray dots show locations considered for potential station locations in the network-design calculation, and blue dots
show locations that are evaluated for the performance metrics. [Examiner’s note: note the plurality of regions and topographical areas from which data is measured and considered] [Fig. 7] [Page 866, Col 2, Par. 1] “ With a covariance localization of 3000 km, the results assuming that no stations currently exist on the continent are shown in Fig. 7” [Fig 4] “Analysis of preferred regions for the first three optimally sited stations for monitoring 0000 UTC 2-m temperature over the entire continent, assuming no observation network currently exists. Cells that are colored indicate that the location was chosen at least once, and the color indicates the frequency with which that location is chosen over 10 000 iterations. The grid points shaded on the plots were made larger for easy viewing;” [Examiner’s note: note the comparison of proposed station locations to other existing and proposed station locations over 1000s of iterations in Fig. 4])

The combination of Flade, Hakim, Maquaire, Yamamoto, and Gryech does not explicitly teach performing a K-nearest neighbor algorithm.

Mehtab makes obvious performing a K-nearest neighbor algorithm. ([Par 60] “In some embodiments, the cloud segmentation algorithm involves the use of a non-parametric method, preferably a k-NN algorithm, i.e., a k-Nearest Neighbours algorithm.”)

Mehtab is analogous art because it is withing the field of weather forecasting. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine it with Flade, Hakim, Maquaire, Yamamoto, and Gryech. One of ordinary skill in the art would have been motivated to make this combination to improve forecasting capabilities and make more capable predictions of future conditions. The accuracy of forecasts and the quality of weather data can vary widely between regions, which can be a problem for people who need this information. This person of ordinary skill in the art would have recognized that a system that can help fill in these gaps and weak points in global weather-sensing system would be of vital importance and significant benefit to people whose lives and careers rely on weather information, such as farmers, as suggested in Mehtab ([Par 7 – 10] Even though the weather forecasts provided are impressive, they still do not serve the purpose of farmers whose land holdings are much smaller. Further, weather drives various physiological responses of plants, such as crops, making the importance of accurate information vital to the farmer. Furthermore, existing weather feeds all have differing geographical variances in predictive accuracy. Weather feeds typically supply averages, but will not divulge that region X is not as accurate as region Y. Therefore, farmers relying on a highly rated weather feed, but operating in a “weak” spot, may require a weather feed that is less accurate overall but more accurate at their specific location. There remains a need to further increase the accuracy and resolution of weather forecasts. The present methods and systems address these needs. The present invention particularly deals with downscaling the weather forecasts provided at kilometre resolution to the size of the farmer's land.”) Overall, one of ordinary skill in the art would have recognized that combining Flade, Hakim, Maquaire, Yamamoto, and Gryech with Mehtab would result in a more accurate system.

Conclusion

The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A Fractal Perspective on Scale in Geography teaches iteratively matching fractals to coastline geography in [Fig. 2]

New Methods for Estimating Fractal Dimensions of Coastlines expands on this idea, and is especially embodied in ([Fig 4-10] [Fig 4-11] [Fig-13])

Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael P Mirabito whose telephone number is (703)756-1494. The examiner can normally be reached M-F 10:30 am - 6:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at (571) 272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.P.M./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187