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 .
DETAILED ACTION
Response to Amendment
This is in response to Applicant’s Arguments/Remarks filed on 07/28/2026, which has been entered and made of record.
Response to Arguments
Rejections - 35 USC § 112
Claim rejections under 35 USC § 112 (b) are withdrawn, as necessitated by amendment.
Claim Rejections - 35 USC § 102
Applicant’s arguments regarding the current claim(s) have been fully considered. But, the arguments/remarks are directed to the claims as amended, and so are believed to be answered by and therefore moot in view of the new grounds of rejection presented below.
Status of Claims
Claims 1 – 5 and 7 – 20 are pending. Claims 1 – 5 and 7 – 20 are considered below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 of this title, 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.
Claim(s) 1 – 5 and 7 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Koger, Thomas Lee (US-20170039307-A1, hereinafter simply referred to as Lee) in view of Emison, Joseph Tierney Masters (US-20190205287-A1, hereinafter simply referred to as Emison).
Regarding independent claim 1, Lee teaches:
A method, comprising: for each property of a set of properties exposed to a hazard event: determining a first measurement (e.g., remotely sensed imagery of Lee) of the property, the first measurement sampled prior to the hazard event (See at least Lee, ¶ [0054]; FIG. 3; "…FIG. 3 is a method 50 to accurately estimate damage potential before a significant weather event and to accurately adjust insurance claims following the event…"); determining a second measurement (e.g., remotely sensed imagery of Lee) of the property, the second measurement sampled after the hazard event (See at least Lee, ¶ [Abstract, 0054]; FIG. 3; "…collecting the geospatial data occurs before and during a determined or simulated significant weather event…", "…FIG. 3 is a method 50 to accurately estimate damage potential before a significant weather event and to accurately adjust insurance claims following the event…").
Lee teaches the subject matter of the claimed invention as described above. But, Lee does not expressly disclose the concept of determining a first representation of the property comprising a first embedding based on the first measurement using a representation model; determining a second representation of the property comprising a second embedding based on the second measurement using the representation model; and detecting a rare change for the property based on the first representation embedding and the second embedding.
Nevertheless, Emison teaches the concept of determining a first representation of the property comprising a first embedding (e.g., data related to the target structure of Emison) based on the first measurement using a representation model (See at least Emison, ¶ [0022]; FIGS. 2, 5, 7; "…an Area Average Roof Age based on the data related to a plurality of structures in the vicinity of the target structure may be calculated according to methods described herein, and inputted into the generalized linear model to determine the Per-Property Modeled Roof Age of the structure…"); determining a second representation of the property comprising a second embedding based on the second measurement using the representation model (See at least Emison, ¶ [0022]; FIGS. 2, 5, 7; "…an Area Average Roof Age based on the data related to a plurality of structures in the vicinity of the target structure may be calculated according to methods described herein, and inputted into the generalized linear model to determine the Per-Property Modeled Roof Age of the structure…"); and detecting a rare change for the property based on the first representation embedding and the second embedding (See at least Emison, ¶ [0022, 0030, 0147-0148]; FIGS. 2, 5, 7;).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use and apply the known technique of determining a first representation of the property comprising a first embedding based on the first measurement using a representation model; determining a second representation of the property comprising a second embedding based on the second measurement using the representation model; and detecting a rare change for the property based on the first representation embedding and the second embedding as disclosed in the device of Emison to modify and improve the known and similar device of Lee for the desirable and advantageous purpose of meeting the need in the insurance industry for more accurate methods for estimating roof age of a property. To this end, methods of the invention aim to solve this business challenge by providing a more realistic roof age of a property based on a per-property modeled roof age, as discussed in Emison (See ¶ [0006]); thereby, achieving the predictable result of improving the overall efficiency and speed of the system with a reasonable expectation of success while enabling others skilled in the art to best utilize the invention along with various implementations and modifications as are suited to the particular use contemplated.
Regarding independent claim 11, Lee teaches:
A method (e.g., FIG. 3 of Lee), comprising: for each training property of a set of training properties exposed to a hazard event determining a first measurement (e.g., remotely sensed imagery of Lee) of the training property, the first measurement sampled prior to the hazard event (See at least Lee, ¶ [0054]; FIG. 3; "…FIG. 3 is a method 50 to accurately estimate damage potential before a significant weather event and to accurately adjust insurance claims following the event…"); " determining a second measurement (e.g., remotely sensed imagery of Lee) of the training property, the second measurement sampled after the hazard event (See at least Lee, ¶ [Abstract, 0054]; FIG. 3; "…collecting the geospatial data occurs before and during a determined or simulated significant weather event…", "…FIG. 3 is a method 50 to accurately estimate damage potential before a significant weather event and to accurately adjust insurance claims following the event…").
Lee teaches the subject matter of the claimed invention as described above. But, Lee does not expressly disclose the concept of: using a first model, detecting a rare change for the training property based on the first measurement and the second measurement by determining a first representation of the training property comprising a first embedding based on the first measurement; determining a second representation of the training property comprising a second embedding based on the second measurement; and determining the rare change for the training property based on the first representation and the second representation; training a second model based on training data comprising the detected rare changes detected for the set of training properties; and for a property: determining a measurement of the property; extracting a set of attribute values for the property based on the measurement; and using the second model, predicting a rare change probability for the property based on the set of attribute values.
Nevertheless, Emison teaches the concept of using a first model, detecting a rare change for the training property based on the first measurement and the second measurement by determining a first representation of the training property comprising a first embedding based on the first measurement (See at least Emison, ¶ [0022]; FIGS. 2, 5, 7; "…an Area Average Roof Age based on the data related to a plurality of structures in the vicinity of the target structure may be calculated according to methods described herein, and inputted into the generalized linear model to determine the Per-Property Modeled Roof Age of the structure…"); determining a second representation of the training property comprising a second embedding based on the second measurement (See at least Emison, ¶ [0022]; FIGS. 2, 5, 7; "…an Area Average Roof Age based on the data related to a plurality of structures in the vicinity of the target structure may be calculated according to methods described herein, and inputted into the generalized linear model to determine the Per-Property Modeled Roof Age of the structure…"); and determining the rare change for the training property based on the first representation and the second representation (See at least Emison, ¶ [0022, 0030, 0147-0148]; FIGS. 2, 5, 7;); training a second model based on training data comprising the detected rare changes detected for the set of training properties (See at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7;); and for a property: determining a measurement of the property; extracting a set of attribute values for the property based on the measurement (See at least Emison, ¶ [0030]; FIGS. 2, 5, 7;); and using the second model, predicting a rare change probability for the property based on the set of attribute values (See at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7;).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use and apply the known technique of the further limitations of the claimed invention (described immediately above) as disclosed in the device of Emison to modify and improve the known and similar device of Lee for the desirable and advantageous purpose of meeting the need in the insurance industry for more accurate methods for estimating roof age of a property. To this end, methods of the invention aim to solve this business challenge by providing a more realistic roof age of a property based on a per-property modeled roof age, as discussed in Emison (See ¶ [0006]); thereby, achieving the predictable result of improving the overall efficiency and speed of the system with a reasonable expectation of success while enabling others skilled in the art to best utilize the invention along with various implementations and modifications as are suited to the particular use contemplated.
Regarding dependent claim 2, Lee modified by Emison above teaches:
for each of the set of properties, inferring a claim event for the property based on the detected rare change (See at least Lee, ¶ [0028]; FIGS. 2, 3; "…FIG. 3 is a method to accurately estimate damage potential before a significant weather event and to accurately adjust insurance claims following the event…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 3, Lee modified by Emison above teaches:
wherein a model is trained to evaluate a new property using training data, the training data comprising the rare changes for the set of properties (See at least Lee, ¶ [0007, 0008]; FIG. 3; "…A SLOSH forecast is a model of predicted and then measured storm surge for a particular storm…", "…The SLOSH model takes several inputs including central pressure of a storm, storm size (e.g., diameter of the storm), forward motion of a storm, storm track, and highest sustained winds. Area topography, orientation of relevant bodies of water, depth of water, astronomical tides, and other physical features are also taken into account…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 4, Lee modified by Emison above teaches:
wherein the model comprises a vulnerability model, wherein the vulnerability model outputs a predicted vulnerability of the new property to a hazard event based on attribute values of the new property (See at least Lee, ¶ [0007, 0008, 0054]; FIG. 3; "…A SLOSH forecast is a model of predicted and then measured storm surge for a particular storm…", "…The SLOSH model takes several inputs including central pressure of a storm, storm size (e.g., diameter of the storm), forward motion of a storm, storm track, and highest sustained winds. Area topography, orientation of relevant bodies of water, depth of water, astronomical tides, and other physical features are also taken into account…", "…the data generated in the method 50 before an imminent significant weather event is used to prospectively warn insured parties and other stakeholders in a geographic area that is predicted to be affected…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 5, Lee modified by Emison above teaches:
wherein the training data further comprises claim data for a second set of properties (See at least Lee, ¶ [0040]; FIG. 3; "…the inventors have developed a computer automated storm damage estimation system and method that is arranged to automatically integrate very large quantities of input data, model a significant weather event, provide output data to simulate losses due to the significant weather event, and apply the provided output data to automate insurance claims processing…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 7, Lee modified by Emison above teaches:
for each of the set of properties, estimating a cost associated with the rare change based on a set of attribute values extracted from the second measurement (See at least Lee, ¶ [0040, 0041, 0065]; FIGS. 2, 3; "…the inventors have developed a computer automated storm damage estimation system and method that is arranged to automatically integrate very large quantities of input data, model a significant weather event, provide output data to simulate losses due to the significant weather event, and apply the provided output data to automate insurance claims processing…", "…These results have been combined with damage estimating models to calculate the individual and specific cost to repair or replace the resulting damage to individual and specific structures…", "…At 64, parcel data is collected and processed. The parcel data may include an electronic representation of a physical description of the real property, the precise geospatial location of structures within the property to an acceptable tolerance, structure outlines extracted from aerial photography or via cadastral maps for example, structure outlines derived from LiDAR data or other means, structure square footage, property value, and property condition. Many other attributes are also considered…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 8, Lee modified by Emison above teaches:
wherein the hazard event comprises a series of multiple hazard events (See at least Lee, ¶ [0033]; FIGS. 2, 3; "…significant weather events include but are not limited to natural disasters such as blizzards, hurricanes, tsunamis, earthquakes, landslides, volcano eruptions, pestilence, famine, drought, fires…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 9, Lee modified by Emison above teaches:
wherein the weather event comprises at least one of a hurricane event, a hail event, a storm event, or a wildfire event (See at least Lee, ¶ [0033]; FIGS. 2, 3; "…significant weather events include but are not limited to natural disasters such as blizzards, hurricanes, tsunamis, earthquakes, landslides, volcano eruptions, pestilence, famine, drought, fires…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 10, Lee modified by Emison above teaches:
wherein the rare change comprises at least one of a roof damage event, a roof repair event, or a roof replacement event (See at least Lee, ¶ [0169]; FIGS. 2, 3; "…e.g., damage to a south face of a roof…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 12, Lee modified by Emison above teaches:
wherein the rare change probability for the property comprises a probability of a claim for the property conditional on exposure to a hazard event (See at least Lee, ¶ [0007, 0008, 0054]; FIG. 3; "…A SLOSH forecast is a model of predicted and then measured storm surge for a particular storm…", "…The SLOSH model takes several inputs including central pressure of a storm, storm size (e.g., diameter of the storm), forward motion of a storm, storm track, and highest sustained winds. Area topography, orientation of relevant bodies of water, depth of water, astronomical tides, and other physical features are also taken into account…", "…the data generated in the method 50 before an imminent significant weather event is used to prospectively warn insured parties and other stakeholders in a geographic area that is predicted to be affected…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 13, Lee modified by Emison above teaches:
" determining historical weather data for the property (See at least Lee, ¶ [0050]; FIGS. 2, 3; "…In cases where prospective events are modeled, the data can be combined with historical weather data, for example, to assist in estimating, generating, or otherwise setting insurance premiums…"); and " predicting a hazard risk for the property based on the rare change probability and the historical weather data for the property (See at least Lee, ¶ [0007, 0008, 0054]; FIG. 3; "…A SLOSH forecast is a model of predicted and then measured storm surge for a particular storm…", "…The SLOSH model takes several inputs including central pressure of a storm, storm size (e.g., diameter of the storm), forward motion of a storm, storm track, and highest sustained winds. Area topography, orientation of relevant bodies of water, depth of water, astronomical tides, and other physical features are also taken into account…", "…the data generated in the method 50 before an imminent significant weather event is used to prospectively warn insured parties and other stakeholders in a geographic area that is predicted to be affected…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 14, Lee modified by Emison above teaches:
wherein the training data further comprises claim data for a second set of training properties (See at least Lee, ¶ [0008, 0040]; FIG. 3; "…The SLOSH model takes several inputs including central pressure of a storm, storm size (e.g., diameter of the storm), forward motion of a storm, storm track, and highest sustained winds. Area topography, orientation of relevant bodies of water, depth of water, astronomical tides, and other physical features are also taken into account…", "…the inventors have developed a computer automated storm damage estimation system and method that is arranged to automatically integrate very large quantities of input data, model a significant weather event, provide output data to simulate losses due to the significant weather event, and apply the provided output data to automate insurance claims processing…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 15, Lee modified by Emison above teaches:
for each of the set of training properties, extracting training attribute values for the training property based on the first measurement of the training property (See at least Lee, ¶ [0040, 0041, 0065]; FIGS. 2, 3; "…the inventors have developed a computer automated storm damage estimation system and method that is arranged to automatically integrate very large quantities of input data, model a significant weather event, provide output data to simulate losses due to the significant weather event, and apply the provided output data to automate insurance claims processing…", "…These results have been combined with damage estimating models to calculate the individual and specific cost to repair or replace the resulting damage to individual and specific structures…", "…At 64, parcel data is collected and processed. The parcel data may include an electronic representation of a physical description of the real property, the precise geospatial location of structures within the property to an acceptable tolerance, structure outlines extracted from aerial photography or via cadastral maps for example, structure outlines derived from LiDAR data or other means, structure square footage, property value, and property condition. Many other attributes are also considered…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7), wherein the training data further comprises the training attribute values (See at least Lee, ¶ [0008]; FIG. 3; "…The SLOSH model takes several inputs including central pressure of a storm, storm size (e.g., diameter of the storm), forward motion of a storm, storm track, and highest sustained winds. Area topography, orientation of relevant bodies of water, depth of water, astronomical tides, and other physical features are also taken into account…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 16, Lee modified by Emison above teaches:
wherein, the first representation and the second representation are determined using the first model (See at least Lee, ¶ [0065, 0091]; FIGS. 2, 3; "…At 64, parcel data is collected and processed. The parcel data may include an electronic representation of a physical description of the real property, the precise geospatial location of structures within the property to an acceptable tolerance, structure outlines extracted from aerial photography or via cadastral maps for example, structure outlines derived from LiDAR data or other means, structure square footage, property value, and property condition. Many other attributes are also considered…", "…The significant weather event modeling data 102 is consolidated along with other modeling information in a model stack 110. The other modeling information may include terrain modeling information 112. The model stack 110 includes any number of model data packages. Model data packages include information, algorithms, data points, data structures, templates, and other computer readable data arranged to direct a computing device to accept input data and produce output data according to a particular model…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7); wherein the first representation and the second representation comprise values for non-semantic features (See at least Lee, ¶ [0065, 0091]; FIGS. 2, 3; "…At 64, parcel data is collected and processed. The parcel data may include an electronic representation of a physical description of the real property, the precise geospatial location of structures within the property to an acceptable tolerance, structure outlines extracted from aerial photography or via cadastral maps for example, structure outlines derived from LiDAR data or other means, structure square footage, property value, and property condition. Many other attributes are also considered…", "…The significant weather event modeling data 102 is consolidated along with other modeling information in a model stack 110. The other modeling information may include terrain modeling information 112. The model stack 110 includes any number of model data packages. Model data packages include information, algorithms, data points, data structures, templates, and other computer readable data arranged to direct a computing device to accept input data and produce output data according to a particular model…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7); and wherein the training attribute values for the training property comprise values for semantic features (See at least Lee, ¶ [Abstract, 0052, 0067]; FIGS. 2, 3; "…collecting the geospatial data occurs before and during a determined or simulated significant weather event…", "…a statistical comparison between the measured and modeled results shows substantial correlation (R.sup.2=0.95) between the in situ measurement and the modeled estimate…", "…The hydrodynamic model employed at 58 is highly accurate when compared with carefully surveyed post storm benchmarks…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 17, Lee modified by Emison above teaches:
wherein the attribute values comprise values for at least one of: roof condition, roof geometry, or roof material (See at least Lee, ¶ [0169]; FIGS. 2, 3; "…e.g., damage to a south face of a roof…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 18, Lee modified by Emison above teaches:
wherein, for each of the set of training properties, the rare change comprises at least one of property damage or property construction (See at least Lee, ¶ [0169]; FIGS. 2, 3; "…e.g., damage to a south face of a roof…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 19, Lee modified by Emison above teaches:
wherein the measurement of the property comprises an aerial image of the property (See at least Lee, ¶ [0065]; FIGS. 2, 3; "…At 64, parcel data is collected and processed. The parcel data may include an electronic representation of a physical description of the real property, the precise geospatial location of structures within the property to an acceptable tolerance, structure outlines extracted from aerial photography or via cadastral maps for example, structure outlines derived from LiDAR data or other means, structure square footage, property value, and property condition. Many other attributes are also considered…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Regarding dependent claim 20, Lee modified by Emison above teaches:
wherein the weather event comprises at least one of a hurricane event, a hail event, a wind event, a tornado event, or a wildfire event (See at least Lee, ¶ [0033]; FIGS. 2, 3; "…significant weather events include but are not limited to natural disasters such as blizzards, hurricanes, tsunamis, earthquakes, landslides, volcano eruptions, pestilence, famine, drought, fires…" Also, see at least Emison, ¶ [0022, 0030, 0032, 0097, 0117, 0147-0148]; FIGS. 2, 5, 7).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office Action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: See the Notice of References Cited (PTO–892)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IDOWU O. OSIFADE whose telephone number is (571)272-0864. The Examiner can normally be reached on Monday-Friday 8:00am-5:00pm EST.
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/IDOWU O OSIFADE/
Primary Examiner, Art Unit 2675
Prosecution Insights