Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
IDS
The information disclosure statements (IDS) submitted on April 10, 2024 and September 5, 2025 are being considered by the Examiner.
Drawing
The drawing filed on April 10, 2024 is accepted by the Examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim rejection – 35 U.S.C. §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.
In reference to claims 1-20: the claimed invention is directed to judicial exception (i.e., abstract idea) without significantly more.
The requirement for subject matter eligibility test for products and processes requires first, the claimed invention must be to one of the four statutory categories. 35 U.S.C. §101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. The latter three categories define "things" or "products" while the first category defines "actions" (i.e., inventions that consist of a series of steps or acts to be performed).
Second, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception. The judicial exceptions (also called "judicially recognized exceptions" or simply "exceptions") are subject matter that the courts have found to be outside of, or exceptions to, the four statutory categories of invention, and are limited to abstract ideas, laws of nature and natural phenomena (including products of nature).
In the first step, it is to be determined whether the patent claim under examination is directed to an abstract idea. If so, in the second step of analysis, it is to be determined whether the patent adds to the idea "something more" or "significantly more" that embodies an "inventive concept."
In the instant case, claim 1 is representative and it is reproduced here with the limitations that are part of the abstract idea in bold:
A method comprising:
receiving at least one set of natural disaster indicator data;
generating, by a machine learning (ML) model, natural disaster shed data based on the at least one set of natural disaster indicator data;
generating, by the ML model, at least one of a home vulnerability score or a hazard score based on the natural disaster shed data; and
determining recommendation information based on the at least one of the home vulnerability score or the hazard score.
Step 2A:
Prong I: The claim recites the steps of " receiving at least one set of natural disaster indicator data; generating..., natural disaster shed data based on the at least one set of natural disaster indicator data; generating, …, at least one of a home vulnerability score or a hazard score based on the natural disaster shed data; and determining recommendation information based on the at least one of the home vulnerability score or the hazard score”. These limitations could be carried out as purely mental process (at least in some relatively small sample situations, like determining recommendation information based on “score”). Therefore, the recited method falls in the abstract idea grouping of mental processes and/or mathematical/computational concepts at Prong I of the §101 analysis.
Prong II:
This abstract idea is not integrated into a practical application at Prong 2 of the §101 analysis because the claim does not recite sufficient additional elements to integrate the abstract idea into a practical application. The claim recites the method comprising the additional element steps of "[carrying each step using] a machine learning (ML) model”; however, these additional elements are merely citing a generic computer processing elements, like Machine Learning (ML) that are invoked as a tool to perform the abstract idea noted in Prong I. These additional elements do not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the abstract idea.
The courts have found that adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea (such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)) is not enough to integrate the abstract idea into a particular practical application or make the claim qualify as "significantly more" (see MPEP § 2106.05(g)).
As stated above, the machine learning used for the purposes of creating a prediction model that is invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea (see MPEP 2106.05(b)).
The claim does not recite applying the abstract idea with, or by use of, any particular machine, nor does the claim affect a real-world transformation or reduction of a particular article to a different state or thing. The claim amounts to manipulating data: determining recommendation information based on …the home venerability score or the hazard score. The claim does not recite any particular real-world actions that are taken as a result of the notification that is output. The claim characterizes a “recommendation information” as the general field-of-use, but does not recite a particular practical application being carried out within that field-of-use. Therefore, the claimed invention does not appear to be limited to the use of the mental process or math in a particular practical application, but instead the claim appears to monopolize the mental process or math itself, in any practical application where it might conceivably be used.
Step 2B:
Finally, at Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the abstract idea for the same reasons as discussed above with regard to Prong 2. Claim 1 is rejected as ineligible under 35 USC §101.
Claims 6 and 17 are directed to a system and a one or more non-transitory computer-readable media respectively and are analogous to claim 1; and further include on or more processors and non-transitory computer-readable media; need to be considered at Prong 2 of the §101 analysis. However, these additional elements are merely generic computer processing components that are invoked as a tool to perform the abstract idea, which does not cause the claim as a whole to integrate the abstract idea into a particular practical application or provide significantly more than the recited abstract idea. Claims 6 and 17 are therefore rejected as ineligible under 35 USC §101 as well.
Dependent claims 2, 7 and 18: the instant claims are directed to the generation of fire shed data based on topology region data, and are considered data analysis and gathering step.
Dependent claims 3, 8 and 19: the instant claims are directed to the generation of fire shed data based on topology region data, and are considered data analysis, gathering and characterizing of the collected data.
Dependent claims 4, 9 and 20: the instant claims are directed to aggregating of environmental indicator data are not considered significantly more than abstract idea, in fact it reads on human thought process and/or mathematical algorithm or computational analysis.
Dependent claims 5 and 10: the instant claims are directed to displaying the mitigation information on a display; however, this action is merely insignificant extra solution activity because outputting or displaying information or result a mental process (see MPEP 2106.05(g)).
Dependent claims 11-13: the instant claims are directed to characterizing the model through various parameter scores using a generic ML model, which would not be a significantly more than abstract idea because reads on a human thought process and/or mathematical algorithm.
Dependent claims 14-16: the instant claims are directed to plotting the risk or vulnerability scores into a map associating those values to a pixel to represent a vulnerability score; and would be considered a human thought process and/or a computational analysis or mathematical model thought plotting or graphing values.
Art of Interest
In reference to claims 1-20: Wani et al. (U.S. PAP 2019/0318440, hereon Wani) discloses methods, systems, and computer programs for flood-risk analysis and mapping. These systems, methods, and programs include operations for presenting, in a graphical user interface (GUI), options for calculating a flood risk map, and receiving, via the GUI, input identifying a geographical region and a weather scenario. Further, the method includes operations for dividing the geographical region into cells; calculating, utilizing a hydrological model, an inflow and an outflow of water between cells in the geographical region based on the weather scenario; and calculating, utilizing a hydraulic model, water depth in each cell based on the weather scenario and the inflow and outflow of water between cells. The flood risk map, generated based on the calculated water depth in each cell, shows the probability that each cell in the geographical region will be inundated with water under the weather scenario. The flood risk map is presented in the GUI (see Wani, Abstract).
The instant application differs from Wani in that it “generating, by a machine learning (ML) model, natural disaster shed data based on the at least one set of natural disaster indicator data; generating, by the ML model, at least one of a home vulnerability score or a hazard score based on the natural disaster shed data; and determining recommendation information based on the at least one of the home vulnerability score or the hazard score,” in combination with the rest of the claim limitations as claimed and defined by the Applicants.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cook (U.S. PAP 2024/0168198) discloses systems and methods disclosed herein implement machine/deep learning tools to provide skillful weather forecasts around the world. These severe weather outlooks forecast the following severe weather perils at time ranges extending out to 12-13 months: tornadoes and significant tornadoes, hail (one to two inches in diameter) and significant hail; and thunderstorm wind gusts exceeding severe thresholds (50 knots or greater, just to name a few.
McIntyre et al. (U.S. Patent No. 12,164,595) discloses methods and computing systems which may include examples of calculating risk scores for certain natural disasters perils based on machine learning model outputs. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure.
Karli et al. (U.S. Patent No. 11,436,777) discloses a hazard visualization system that can use artificial intelligence to identify locations at which hazards have occurred and a cause therein and to predict locations at which hazards may occur in the future is described herein. As a result, the hazard visualization system may reduce the likelihood of structural damage and/or loss of life that could otherwise occur due to natural disasters or other hazards. For example, the hazard visualization system can train an artificial intelligence model to predict the date, time, type, severity, path, and/or other conditions of a hazard that may occur at a geographic location. As another example, the hazard visualization system can train an artificial intelligence model to identify equipment or other infrastructure depicted in geographic images.
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/ELIAS DESTA/
Primary Examiner, Art Unit 2857
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