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 .
Status of the Application
Claims 1-20 are currently pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/30/2024, 01/03/2025, 03/28/2025, 05/09/2025, 07/01/2025, 10/10/2025, and 08/20/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,360,286 in view of Dhuvur et al. (Hereafter, “Dhuvur”) [US 2022/0051344 A1] in further view of Hedges et al. (Hereafter, “Hedges”) [US 2022/0405856 A1]. Although the claims at issue are not identical, they are not patentably distinct from each other because they cover mutually associated subject matter. Thus, a terminal disclaimer is required. An analysis of the claims can be seen in Table 1 below.
Table 1: Instant Application No. 18/789,427 vs. U.S. Patent No. 12,360,286
Instant Application No. 18/789,427 Claims (Difference Emphasis Added)
U.S. Patent No. 12,360,286 (Difference Emphasis Added)
1. A computer implemented method comprising: receiving, using one or more processors, a location; determining, using the one or more processors, a wind speed associated with the location using a first wind speed machine learning model; determining, using the one or more processors, a wind report frequency associated with the location using a first wind report frequency machine learning model; obtaining, using the one or more processors, first feature data associated with the location, the first feature data including the wind speed associated with the location, the wind report frequency associated with the location, and data describing a first set of features at the location; determining, using the one or more processors, a damage frequency metric associated with the location by applying a first damage frequency machine learning model to the first feature data; obtaining, using the one or more processors, second feature data associated with the location, the second feature data including data describing a second set of features at the location; and determining, using the one or more processors, a damage severity metric associated with the location by applying a first damage severity machine learning model to the second feature data.
1. A computer implemented method comprising: receiving, using one or more processors, a location; training, using the one or more processors, a first hail machine learning model that discounts older hail data; determine, using the one or more processors, a hail size associated with the location using the first hail machine learning model; determine, using the one or more processors, a hail frequency associated with the location using a first hail frequency machine learning model; obtain, using the one or more processors, first feature data associated with the location, the first feature data including the hail size associated with the location, the hail frequency associated with the location, and data describing a first set of features at the location; determine, using the one or more processors, a damage frequency associated with the location by applying a first damage frequency machine learning model to the first feature data; obtain, using the one or more processors, second feature data associated with the location, the second feature data including data describing a second set of features at the location; and determine, using the one or more processors, a damage severity associated with the location by applying a first damage severity machine learning model to the second feature data.
2. The computer implemented method of claim 1, wherein the location is represented by a latitude and longitude.
2. The computer implemented method of claim 1, wherein the location is represented by a latitude and longitude.
3. The computer implemented method of claim 1, wherein the wind speed represents an average wind speed associated with the location.
3. The computer implemented method of claim 1, wherein the hail size represents one or more of an average hail size associated with the location and a maximum hail size associated with the location.
4. The computer implemented method of claim 1, wherein wind report frequency represents one or more of a frequency of a wind report and a frequency of a wind report having an average wind speed that exceeds a threshold.
4. The computer implemented method of claim 1, wherein hail frequency represents one or more of a probability of a hail event and a probability of a hail event in which hail exceeds a size threshold.
5. The computer implemented method of claim 1, wherein one or more of the first set of features includes one or more of: a vegetation density, a roof resilience score, a number of roof penetrations, a roof quality, one or more reasons generated by a roof quality reasoning model, a land cover code, a temperature, and a precipitation metric.
10. The computer implemented method of claim 1, wherein the second set of features includes one or more of a building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, a roof shape, a temperature, a temperature variation, and a precipitation metric.
5. The computer implemented method of claim 1, wherein one or more of the first set of features and the second set of features include one or more of: a building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, presence of skylights, a portion of a roof covered by skylights, presence of a solar panel, a portion of the roof covered by solar panels, a number of roof facets, a roof shape, a land cover code, a temperature, a precipitation type or measure, and an elevation.
10. The computer implemented method of claim 1, wherein the second set of features includes one or more of a roof area, a building area, a vegetation density, a roof material, a roof quality, a roof pitch, a roof height, presence of skylights, a portion of a roof covered by skylights, presence of a solar panel, a portion of the roof covered by solar panels, a number of roof facets, a roof shape, a land cover code, a temperature, a precipitation type or metric, and an elevation.
6. The computer implemented method of claim 1, wherein the one or more features at the location include a first feature obtained by applying a feature model to an aerial image of the location.
6. The computer implemented method of claim 1, wherein the first set of features at the location includes a first feature that is obtained actively by applying a feature model to an aerial image of the location.
7. The computer implemented method of claim 6, wherein the feature model is a convolutional neural network.
7. The computer implemented method of claim 6, wherein the feature model is a convolutional neural network.
8. The computer implemented method of claim 1 further comprising: determining, based on one or more of the damage frequency metric and the damage severity metric, one or more of: a remedial action to reduce a wind damage metric; a determination of the wind damage metric; a warning to one or more of a property owner, a resident, and an entity associated with the location, the warning comprising the wind damage metric.
8. The computer implemented method of claim 1 further comprising: determining, based on one or more of the damage frequency and damage severity, one or more of: a remedial action to reduce a risk of hail; whether to approve or deny hail insurance coverage or an insurance claim; an insurance premium associated with the location, an adjustment to an insurance premium associated with location; and to warn one or more of a property owner, resident, financier and insurer associated with the location of a risk of damage posed by hail.
9. The computer implemented method of claim 1, wherein the first set of features at the location and the second set of features at the location are not mutually exclusive.
9. The computer implemented method of claim 1, wherein the first set of features at the location and the second set of features at the location are not mutually exclusive.
Claim 11 is the same as claim 1 in system form.
Claim 11 is the same as claim 1 in system form.
Claim 12 is the same as claim 2 in system form.
Claim 12 is the same as claim 2 in system form.
Claim 13 is the same as claim 3 in system form.
Claim 13 is the same as claim 3 in system form.
Claim 14 is the same as claim 4 in system form.
Claim 14 is the same as claim 4 in system form.
Claim 15 is the same as claim 5 in system form.
Claim 20 is the same as claim 10 in system form.
Claim 15 is the same as claim 5 in system form.
Claim 20 is the same as claim 10 in system form.
Claim 16 is the same as claim 6 in system form.
Claim 16 is the same as claim 6 in system form.
Claim 17 is the same as claim 7 in system form.
Claim 17 is the same as claim 7 in system form.
Claim 18 is the same as claim 8 in system form.
Claim 18 is the same as claim 8 in system form.
Claim 19 is the same as claim 9 in system form.
Claim 19 is the same as claim 9 in system form.
Some of the differences in the claim limitations in the U.S. Patent are narrower than the instant application, and thus it would have been obvious to make the claim limitations in the instant application broader by removing the specific language found in the U.S. Patent.
The U.S. Patent fails to explicitly disclose determining, using the one or more processors, a wind speed associated with the location using a first wind speed machine learning model; determining, using the one or more processors, a wind report frequency associated with the location using a first wind report frequency machine learning model; the first feature data including the wind speed associated with the location, the wind report frequency associated with the location, and data describing a first set of features at the location; determining, using the one or more processors, a damage severity metric; wherein the wind speed represents an average wind speed associated with the location; wherein wind report frequency represents one or more of a frequency of a wind report and a frequency of a wind report having an average wind speed that exceeds a threshold; wherein one or more of the first set of features includes one or more of: a roof resilience score, a number of roof penetrations, one or more reasons generated by a roof quality reasoning model; wherein the second set of features includes one or more of a temperature variation; a remedial action to reduce a wind damage metric; a determination of the wind damage metric, the warning comprising the wind damage metric.
Dhuvur discloses determining, using the one or more processors, a wind speed associated with the location using a first wind speed machine learning model ([0006] first climate risk model wherein the first climate risk is wind); determining, using the one or more processors, a wind report frequency associated with the location using a first wind report frequency machine learning model ([0027] a second climate model that provides a score for a location/property that indicates the risk/extent of damage to the property if it gets involved in a climate event [0082] location variable include wind speed and direction); the first feature data including the wind speed associated with the location, the wind report frequency associated with the location, and data describing a first set of features at the location ([0082] location variable include wind speed and direction); determining, using the one or more processors, a damage severity metric ([0006] determining a damage severity score representing a severity of damage to the property to be expected from an incident of the first climate risk); wherein the wind speed represents an average wind speed associated with the location ([0082] location variable include wind speed and direction); wherein the second set of features includes one or more of a temperature variation ([0124] Some property features may not be easily, or realistically, remediable (e.g., average precipitation, average temperature, elevation, building density, etc.) to lower the risk of an incident, risk of damage, or severity of damage, while other features (e.g., roof material, presence of overhanging vegetation, fuel types present on property, etc.) may be more easily changed or remediated by a property owner.); wherein one or more of the first set of features includes one or more of: a roof resilience score, a number of roof penetrations, one or more reasons generated by a roof quality reasoning model ([0077] The reduced set of variables may vary based on the climate risk being scored/assessed. For example, in some implementations, the variables for wildfire may be reduced to include one or more of vegetation variable(s), building variable(s), a roof material variable, a fire response variable (e.g., a distance to a fire station), location variable(s), and weather variables. As another example, in some implementations, the variables for hail may be reduced to include one or more of vegetation variable(s), building and parcel variable(s), location variable(s), and weather variables. As yet another example, in some implementations, the variables for flood may be reduced to include one or more of building and parcel variable(s), location variable(s), and weather variables. [0083] the variables for a level 2, or damage scorer 474, for hail may be reduced to include roof variables including roof surface area, roof material, roof shape, roof facets, roof quality/useful life, skylights (e.g., presence and/or percentage of roof), solar panels (e.g., presence and/or percentage of roof)); a remedial action to reduce a wind damage metric ([0006] determining a remedial action associated with the first climate risk (wind)); a determination of the wind damage metric, the warning comprising the wind damage metric ([0006] a first climate risk damage model, where the first score is an incident score representing a likelihood of first climate risk occurring at the location of the property, and where the second score is a damage score representing a likelihood of damage from the first climate risk to the property, wherein the first climate risk is wind).
It would have been obvious to one of ordinary skill in the art to modify the invention with the known climate risk of wind as taught by Dhuvur in order to accurately predict the risk posed by a climate event to a property that account the property-specific attributes of that property. [See Dhuvur].
Hedges discloses wherein wind report frequency represents one or more of a frequency of a wind report and a frequency of a wind report having an average wind speed that exceeds a threshold ([0074] Weather data (and/or hazard data) can include: dates of prior hazard events, the severity of prior hazard events (e.g., hail size, wind speeds, wildfire boundary, fire damage severity, flood magnitude, etc.), locations of prior hazard events (e.g., relative to the property location, hazard perimeter, etc.), regional hazard occurrence and/or severity information (e.g., frequency of hazard events, average severity of hazard events, etc.), general weather data (e.g., average wind speeds, temperatures, etc.), hazard scores (e.g., third-party regional exposure scores), and/or any other data associated with a location.).
It would have been obvious to one of ordinary skill in the art to modify the invention with the average wind speeds for a location as taught by Hedges in order to increase the accuracy of the hazard model [See Hedges].
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kaitlin A Retallick whose telephone number is (571)270-3841. The examiner can normally be reached Monday-Friday 8am-5pm.
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/KAITLIN A RETALLICK/Primary Examiner, Art Unit 2482
