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 11/21/2025 and supplemental amendment filed 01/28/2026
Claims 1, 4-5, 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.
Claim Objections
While the majority of the issues previously discussed in regards improper amendment have been fixed in the latest supplemental amendment, it should be noted that a few issues have been missed and the claims are objected to for failing to conform to the requirements for submission of amendments, see MPEP 714 and 37 C.F.R. 1.121 Manner of making amendments in application.
…
(c) Claims. Amendments to a claim must be made by rewriting the entire claim with all changes (e.g., additions and deletions) as indicated in this subsection, except when the claim is being canceled. Each amendment document that includes a change to an existing claim, cancellation of an existing claim or addition of a new claim, must include a complete listing of all claims ever presented, including the text of all pending and withdrawn claims, in the application. The claim listing, including the text of the claims, in the amendment document will serve to replace all prior versions of the claims, in the application. In the claim listing, the status of every claim must be indicated after its claim number by using one of the following identifiers in a parenthetical expression: (Original), (Currently amended), (Canceled), (Withdrawn), (Previously presented), (New), and (Not entered).
(2) When claim text with markings is required. All claims being currently amended in an amendment paper shall be presented in the claim listing, indicate a status of "currently amended," and be submitted with markings to indicate the changes that have been made relative to the immediate prior version of the claims. The text of any added subject matter must be shown by underlining the added text. The text of any deleted matter must be shown by strike-through except that double brackets placed before and after the deleted characters may be used to show deletion of five or fewer consecutive characters. The text of any deleted subject matter must be shown by being placed within double brackets if strike-through cannot be easily perceived. Only claims having the status of "currently amended," or "withdrawn" if also being amended, shall include markings. If a withdrawn claim is currently amended, its status in the claim listing may be identified as "withdrawn— currently amended."
Particularly, a few amendments to the claims have been made without proper notation, such as:
The addition of “wherein the optimal fractal generator and initiator comprises measured variables that enhance weather forecasting;” in the comparing limitation of claims 1, 11, and 16.
The addition of “to improve weather variable measurement and, in turn,…” to the providing feedback limitation of claim 1, 11, and 16.
Response to Arguments – 101
Applicant's arguments filed 11/21/ have been fully considered but they are not persuasive.
Applicant argues that the claims do not recite an abstract idea.
Examiner responds by explaining that the claims do in fact recite an abstract idea, particularly:
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 comprises an initiator and a generator, and wherein the initiator represents a first step of the fractal and the generator is an arranged collection of scaled copies of the initiator; 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, with a pencil and paper, 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; and further comprising performing a K-nearest neighbor algorithm to compare new terrains to existing fractal maps to determine an optimal fractal generator and initiator;
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.
Additionally, using a mathematic algorithm like k-nearest neighbor to numerically compare the data represented by the maps and fractals is a mathematic process, as analyzed below
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; and
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 estimated low accuracy, where a new station would best improve the overall accuracy of the system. Adjusting the accuracy is merely a matter of estimating how much this new station would improve the accuracy in the area.
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.
providing feedback, dynamically, into the trained machine learning model to predict an optimal fractal-based weather station placement layout.
Providing feedback is a mental process equivalent to making a judgment on something, for example after a poor meal a person could write a note, with a pen and paper, indicating a rating of 2/5 stars.
Should it be found that this is not a mental process, it is also an example of certain methods of organizing human activity
The claim also recites a mathematic process, in particular:
… and further comprising performing a K-nearest neighbor algorithm to compare new terrains to existing fractal maps to determine an optimal fractal generator and initiator;
Performing a mathematic algorithm such as K-nearest neighbor amounts to no more than a mathematic process. See the MPEP at (MPEP 2106.04(a)(2)(I)(C) v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979); )
The claim also recites a certain methods of organizing human activity in particular:
providing feedback, dynamically, into the trained machine learning model to predict an optimal fractal-based weather station placement layout.
Providing feedback in such a way is a social activity equivalent to voting or providing information to a person/system. See (MPEP 2106.04(a)(2)(II)(C) “An example of a claim reciting social activities is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 126 USPQ2d 1498 (Fed. Cir. 2018). The social activity at issue in Voter Verified was voting. … Another example of a claim reciting social activities is Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018). The social activity at issue was the social activity of "’providing information to a person without interfering with the person’s primary activity.’" 896 F.3d at 1344, 127 USPQ2d 1553 (citing Interval Licensing LLC v. AOL, Inc., 193 F. Supp.3d 1184, 1188 (W.D. 2014)).”
Applicant argues that the inclusion of “wherein the weather forecast model includes a linear regression model to determine a conditional probability distribution of a weather response;” to the step of receiving weather data integrates the claims into a practical application and/or provides significantly more.
Examiner responds by explaining that this limitation merely clarifies additional aspects about the system from which the initial weather forecast data is gathered; note that this additional limitation changes virtually nothing about the “meat” (i.e. portion of the claims to which they are directed) of the process. Particularly, whether the weather forecast data comes from a model that “includes a linear regression model…” or any other kind of model does not change how the abstract idea of mentally determining the performance of the forecast, generating a map based on that performance, generating fractals matched to the topography and forecast performance, comparing new map data to fractals to determine fractals for a new area, and making suggestions regarding where new stations should be put operates.
An analogy for this would be a mental process of drawing a picture based on observed imagery, to which these hypothetical claims are directed, with a first step of downloading that reference imagery from the internet. Ignoring the potential 112 issues with such a claim, whether or not the claims included language requiring that the reference image should be gathered specifically from Google Images does not change how the main inventive thrust of the claims (observing the imagery, drawing a picture of it with a pencil and paper) is performed.
As such, being merely additional details specified for the source from which data is gathered, specifying that “the weather forecast model includes a linear regression model to determine a conditional probability distribution of a weather response;” amounts to no more than mere data gathering.
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);)
Further, any alleged improvement to the field of weather station technology is not enabled by this generic gathering of data, rather it is due to the abstract process of matching fractals to the mentally generated performance map and providing suggestions as to where new weather stations should be placed.
The claims are not directed to a method of gathering forecast data, they are directed to an abstract process for mentally matching fractals to map and performance data and suggesting new weather station locations.
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, 4-5, 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 comprises an initiator and a generator, and wherein the initiator represents a first step of the fractal and the generator is an arranged collection of scaled copies of the initiator; 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, with a pencil and paper, 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; and further comprising performing a K-nearest neighbor algorithm to compare new terrains to existing fractal maps to determine an optimal fractal generator and initiator, wherein the optimal fractal generator and initiator comprises measured variables that enhance weather forecasting;
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.
Additionally, using a mathematic algorithm like k-nearest neighbor to numerically compare the data represented by the maps and fractals is a mathematic process, as analyzed below
based on receiving, from the trained machine learning model, automated suggestions for the weather station placement, adjusting weather forecasting accuracy based on learned optimal placement patterns; and
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 estimated low accuracy, where a new station would best improve the overall accuracy of the system. Adjusting the accuracy is merely a matter of estimating how much this new station would improve the accuracy in the area.
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.
providing feedback, dynamically, into the trained machine learning model to improve weather variable measurement and, in turn, predict the optimal fractal-based weather station placement layout.
Providing feedback is a mental process equivalent to making a judgment on something, for example after a poor meal a person could write a note, with a pen and paper, indicating a rating of 2/5 stars.
Should it be found that this is not a mental process, it is also an example of certain methods of organizing human activity
The claim also recites a mathematic process, in particular:
… and further comprising performing a K-nearest neighbor algorithm to compare new terrains to existing fractal maps to determine an optimal fractal generator and initiator, wherein the optimal fractal generator and initiator comprises measured variables that enhance weather forecasting;
Performing a mathematic algorithm such as K-nearest neighbor amounts to no more than a mathematic process. See the MPEP at (MPEP 2106.04(a)(2)(I)(C) v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979); )
The claim also recites a certain methods of organizing human activity in particular:
providing feedback, dynamically, into the trained machine learning model to predict an optimal fractal-based weather station placement layout.
Providing feedback in such a way is a social activity equivalent to voting or providing information to a person/system. See (MPEP 2106.04(a)(2)(II)(C) “An example of a claim reciting social activities is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 126 USPQ2d 1498 (Fed. Cir. 2018). The social activity at issue in Voter Verified was voting. … Another example of a claim reciting social activities is Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018). The social activity at issue was the social activity of "’providing information to a person without interfering with the person’s primary activity.’" 896 F.3d at 1344, 127 USPQ2d 1553 (citing Interval Licensing LLC v. AOL, Inc., 193 F. Supp.3d 1184, 1188 (W.D. 2014)).”
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, wherein the weather forecast model includes a linear regression model to determine a conditional probability distribution of a weather response; … 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 in a generic manner, such as weather or map data, is merely the act of gathering that data. Specifying that the source of the data “includes a linear regression model to determine a conditional probability distribution of a weather response;” merely clarifies details about the source of the data.
Post Solution Activity:
presenting a first fractal map comprising the first fractal overlaid on the weather forecast performance map, and wherein the first fractal map depicts which shape basis has nodes that are associated with a best forecast performance; … 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, depicting best forecast performance placement;
Presenting the maps merely displays 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, wherein the weather forecast model includes a linear regression model to determine a conditional probability distribution of a weather response; … 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 in a generic manner, such as weather or map data, is merely the act of gathering that data. Specifying that the source of the data “includes a linear regression model to determine a conditional probability distribution of a weather response;” merely clarifies details about the source of the 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, and wherein the first fractal map depicts which shape basis has nodes that are associated with a best forecast performance; … 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, depicting best forecast performance placement;
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); ]
Further, the method of continuously training a machine learning model is considered a well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d).
See below:
US 10445885 B1 ([Col 1 line 34-49])
US 20210311853 A1 ([Par 65])
US 20170353477 A1 ([Par 43-52])
A regression unsupervised incremental learning algorithm for solar irradiance prediction ([Abstract][Page 910 Col 1 Par 2])
As per MPEP § 2106.05(d), an additional element that is “no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality,” does not integrate a judicial exception into a practical application, nor provide significantly more.
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 to cause a method to be performed 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 non-transitory 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 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 a mental process equivalent to judging how expensive it would be to install a new station at a node of the drawn fractal map and judging if this is acceptable or if more fractals should be tried to find a better balance between accuracy and cost.
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.
Additionally, coming up with such a position enhancement is mental process equivalent to observing where a station is currently placed and judging where it would be more accurate. For example, a person might observe that a weather station is 500 feet below the earth’s surface and judge that it would be more accurate if it was placed aboveground.
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
Conclusion
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
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