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 . This action is responsive to claims filed 3/23/2026. Claims 1, 3-9, 11-19 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 5-9, 11-14, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over FEMA as disclosed in FEMA.gov ("Expected annual loss", published 8/18/2021) and James ("FEMA Releases new National Risk Index Measuring natural disaster risk, vulnerability", published 11/24/2020) and Technical Documentation (“National Risk Index: Technical Documentation”, published December 2020), in view of Meyers (US 20190370894 A1).
For claim 1, FEMA discloses: a computer-based method (James p.1 discloses a National Risk Index website accessible via a computer browser ) comprising:
identifying a real estate asset file including geolocation data and asset values of real estate assets of a first geographic region (Technical Documentation §5.3.2: Consequence Types: Buildings (p.5-8) contemplates acquiring building exposure dollar value as recorded in Hazus, which is based on 2018 valuations of census data, hence, real estate assets including geolocation data and asset values for real estate assets are identified for a geographic regions; further, Hazus is a known platform that allows for importing of asset files for estimating building value, see Hazus ("Hazus Comprehensive Data Management System (CDMS) User Guide", published 4/2019) fig.1-2 (p.1-4) showing user input data from various files (e.g., MS Excel) being imported into Hazus data) comprising a first subset of a first plurality of real estate assets in a second geographic region and a second subset of a second plurality of real estate assets in a third geographic region (ibid: as building asset values are tied to geolocation data of various regions, the first geographic region, e.g., state., have various sub-regions, e.g., counties);
generating, by a computer, a first graphical user interface dashboard configured to display, on a client computing device, a climate hazard selection map encompassing a selectable region of a first geographic region, the first geographic region comprising a plurality of geo-regions (James 1:12 shows dashboard displaying selectable first region (states), i.e, either via mouse or via zooming in for more detailed vies, comprising county subregions which are in turn selectable for further information, see James 2:18);
receiving, by the computer, a selection of one of the plurality of geo-regions of the first graphical user interface, wherein the geo-region selection defines a boundary of a second geographic region within the first geographic region, the boundary abutting at least the third geographic region (James 1:24: selection of the Horry County geo-region, which abuts various other counties being third geographic regions);
determining a first risk score for the second geographic region and a second risk score for the third geographic region (James 1:47: determining expected annual loss scores to color the various regions),
the first risk score based on one more overlaps between (Documentation §4.3 discloses calculation of Expected Annual Loss (EAL), with §4.3.1 contemplating calculation at a census block level, 4-8 “Representation of Hazards as Spatial Polygons” disclosing converting hazard data to vector polygons and intersecting with the census block or tract data, see “Intersection” (4-8), fig.5 (4-9), hence, calculating EAL based on hazard and census block overlaps):
geolocation data for the first subset of real estate assets of the second geographic region (Documentation 4-7 “Base Calculation and Aggregation” ¶2 discloses determining building value data at census block level, hence, determining census block geolocation data and associated geographic region real estate values; §5.3.2: geolocation data as determined from Hazus of a plurality of building assets are determined for calculation); and
a first plurality of climate hazard regions of the second geographic region corresponding to a plurality of second geographic region occurrence probabilities of the climate hazard parameter and geolocation data (Documentation 4-8 “Representation of Hazards as Spatial Polygons” ¶1: frequency and exposure calculations data is associated with vector polygons for intersection with census blocks, via probability calculations such as described in 5-3:¶2-3, 5-2:Eq.5, hence, climate hazard regions corresponding to probability of occurrence of a climate hazard); and
the second risk score based on one or more overlaps between:
geolocation data for the second plurality of real estate assets of the third geographic region (Documentation 4-7 “Base Calculation and Aggregation” ¶2 discloses determining building value data at census block level, hence, determining census block geolocation data and associated geographic region real estate values; §5.3.2: geolocation data as determined from Hazus of a plurality of building assets are determined for calculation);
a second plurality of climate hazard regions of the second geographic region corresponding to a plurality of second geographic region occurrence probabilities of the climate hazard parameter and geolocation data (4-8, 4-9, fig.5 as described above, with the same vector intersecting multiple census tract regions); and
generating, by the computer, a second graphical user interface dashboard configured to display on the client computing device, a climate hazard risk map comprising a visual representation of the first risk score and the second risk score (James 1:47), the visual representation comprising:
a first element of an overlay corresponding to the first risk score overlaid over, and bounded by, the second geographic region (ibid: color overlay for a first region); and
a second element of the overlay corresponding to the second risk score overlaid over, and bounded by, the third geographic region (ibid: color overlay for second region).
FEMA does not expressly disclose: wherein the real estate assets are mortgages for real estate assets; wherein the receiving is receiving a climate hazard parameter selection, wherein the climate hazards is the selected climate hazard parameter.
Meyers discloses: wherein the real estate assets are mortgages for real estate assets (0031 contemplates providing exposure data as mortgage exposure data , e.g., UPB (unpaid principle balance), property value data, hence, combination with FEMA yielding a technique where exposure values for calculating EAL includes various mortgage asset values, e.g., UPB, total value); wherein the receiving is receiving a climate hazard parameter selection (0025, 0030, 0038; fig.2, 0042: receiving of a particular type of catastrophic event for a particular region for loss assessment, such as via UI), wherein the climate hazards is the selected climate hazard parameter (ibid).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of FEMA by incorporating the selection and mortgage value of Meyers. Both concern the art of climate hazard mapping, and the incorporation would have, according to Meyers, allow greater ease and efficiency in determining risk and liability for real estate assets, such as for various companies, e.g., insurance (0004).
Regarding claim 3 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: wherein the climate hazard parameter selection comprises one of a plurality of climate hazard types (Meyers 0025, 0030, 0038; fig.2, 0042), and a probability that the one of the plurality of climate hazard types will happen over a selected time frame (Technical Documentation 4-5 eq.3 includes computation of hazard frequency which is a measure of probability, see Table 4 (p.4-5): expected events per year; Meyers 0026 contemplates users (e.g., insurance providers) providing event loss tables include period loss tables, year loss tables, hence, user selection of probabilities of climate hazards over a user-selected time-frame as determined by the uploaded data, see also fig.2: upload interface).
For claim 5 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: wherein the visual representation within the second geographic region comprises a spatial visualization of geo-regions having relatively high probability of climate hazard (James 1:47: colorization of high-risk areas as darker).
For claim 6 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: wherein the visual representation comprises metadata describing climate hazard risks associated with given geographic regions or geo-locations within the second geographic region associated with one or more of the first plurality of real estate assets and the second of the plurality of real estate assets (FEMA “Expected Annual Loss” p.1 shows expected annual loss as various discrete levels as a visual representation of building climate hazard risks).
Regarding claim 7 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: inputting or selecting a real estate asset file including geolocation data extracted from a real estate assets portfolio of an enterprise (FEMA §5.3.2: asset files including geolocations data via Hazus; Meyers 0026, 0031, 0037).
Regarding claim 8 FEMA modified by Meyers discloses the method of claim 7, as described above. FEMA modified by Meyers further discloses: wherein the inputting or selecting the real estate asset file further comprises data enrichment of the geolocations data extracted from the real estate assets portfolio database of the enterprise (Meyers 0026, 0031: portfolio asset files are imported into a data preparation system for data enrichment, such as via Hazus, see Technical Documentation §5.3.2, Hazus fig.1-2).
Regarding claim 9 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: the step, in response to receiving the geo-region selection and the climate hazard parameter selection of generating a third graphical user interface dashboard configured to display on the client computing device a climate hazard estimate map comprising a visual representation within the second geographic region corresponding to the geo-region selection and the climate hazard parameter selection (James 1:47: shows third graphical user interface dashboard displaying various climate hazard estimates (risk index, social vulnerability, community resilience, etc.), combination, with Meyers 0025, 0030, 0038; fig.2, 0042 disclosing hazard selection).
Regarding claim 11 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: wherein the geolocation data for the plurality of real estate assets comprises one or more of geo-coordinates, geolocation data that define geographic boundaries, and geolocation data including latitude and longitude values (James 1:47: county boundaries; Meyers fig.6B, 7B: geo-coordinates, latitude and longitude values on a map).
Regarding claim 12 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: wherein the geolocation data for the plurality of real estate assets comprises one or more of geolocation data associated with postal codes, geolocation data associated with collections of proximate postal zones, geo-coordinates associated with centers of postal zone, and geo-coordinates defining boundaries of postal zones (James: 1:47: county boundaries constitute postal zones and postal zone boundaries; Meyers 0025, 0042, 0047: postal codes, collections of postal codes such as counties, states).
Regarding claim 13, FEMA discloses: a system, comprising:
a non-transitory machine-readable memory that stores a plurality of real estate asset files including geolocation data for a plurality of real estate assets (Technical Documentation §5.3.2: Consequence Types: Buildings (p.5-8) contemplates acquiring building exposure dollar value as recorded in Hazus, which is based on 2018 valuations of census data, hence, real estate assets including geolocation data and asset values for real estate assets are identified for a geographic regions; further, Hazus is a known platform that allows for importing of asset files for estimating building value, see Hazus ("Hazus Comprehensive Data Management System (CDMS) User Guide", published 4/2019) fig.1-2 (p.1-4) showing user input data from various files (e.g., MS Excel) being imported into Hazus data, this data being stored in non-transitory memory for future access and processing, such as via the web), and climate hazard data (James 1:12, 1:47 shows various climate hazard data (risk index, expected annual loss, social vulnerability, etc.)), wherein the real estate assets correspond to a first subset of real estate of a second geographic region and a second subset of real estate in a third geographic region (Technical Documentation §5.3.2, §4.3: as building asset values are tied to geolocation data of various regions, the first geographic region, e.g., state., have various sub-regions, e.g., counties); and
a processor, wherein the processor in communication with the non-transitory, machine-readable memory executes a set of instructions instructing the processor (James p.1 discloses a National Risk Index website accessible via a computer browser) to:
generate a first graphical user interface dashboard configured to display on a client computing device a climate hazard selection map encompassing a selectable region of a first geographic region, the first geographic region comprising a plurality of geo-regions (James 1:12 shows dashboard displaying selectable first region (states), i.e, either via mouse or via zooming in for more detailed vies, comprising county subregions which are in turn selectable for further information, see James 2:18);
receive a geo-region selection of one of the plurality of geo-regions of the first graphical user interface, wherein the geo-region selection defines a boundary of the second geographic region within the first geographic region, the boundary abutting at least the third geographic region (1:24: selection of the Horry County geo-region, which abuts various other counties being third geographic regions); and
determining a first risk score for the second geographic region and a second risk score for the third geographic region (1:47: determining expected annual loss scores to color the various regions), the first risk score based on one or more first overlaps between ((Documentation §4.3 discloses calculation of Expected Annual Loss (EAL), with §4.3.1 contemplating calculation at a census block level, 4-8 “Representation of Hazards as Spatial Polygons” disclosing converting hazard data to vector polygons and intersecting with the census block or tract data, see “Intersection” (4-8), fig.5 (4-9), hence, calculating EAL based on hazard and census block overlaps):
geolocation data for the first subset of real estate assets of the second geographic region (Documentation 4-7 “Base Calculation and Aggregation” ¶2 discloses determining building value data at census block level, hence, determining census block geolocation data and associated geographic region real estate values)
a first plurality of climate hazard regions of the second geographic region corresponding to a plurality of second geographic region occurrence probabilities of the climate hazard parameter and geolocation data (Documentation 4-8 “Representation of Hazards as Spatial Polygons” ¶1: frequency and exposure calculations data is associated with vector polygons for intersection with census blocks, via probability calculations such as described in 5-3:¶2-3, 5-2:Eq.5, hence, climate hazard regions corresponding to probability of occurrence of a climate hazard); and
the second risk score based on the real estate asset values (Technical Documentation §5.3.2) one or more second overlaps between:
geolocation data for the second plurality of real estate assets of the third geographic region (Documentation 4-7 “Base Calculation and Aggregation” ¶2 discloses determining building value data at census block level, hence, determining census block geolocation data and associated geographic region real estate values; §5.3.2: geolocation data as determined from Hazus of a plurality of building assets are determined for calculation);
a second plurality of climate hazard regions of the second geographic region corresponding to a plurality of second geographic region occurrence probabilities of the climate hazard parameter and geolocation data (4-8, 4-9, fig.5 as described above, with the same vector intersecting multiple census tract regions); and
generating a second graphical user interface dashboard configured to display on the client computing device, a climate hazard risk map comprising a visual representation of the first risk score and the second risk score (James 1:47), the visual representation comprising:
a first element of an overlay corresponding to the first risk score overlaid over, and bounded by, the second geographic region (ibid: color overlay for a first region); and
a second element of the overlay corresponding to the second risk score overlaid over, and bounded by, the third geographic region (ibid: color overlay for second region).
FEMA does not disclose: wherein the asset files include asset values of mortgages for the plurality of real estate asset; wherein the receive includes receiving a climate hazard parameter selection; the first risk score based on asset values of the mortgages.
Meyers discloses: wherein the asset files include asset values of mortgages for the plurality of real estate asset (0031 contemplates providing exposure data as mortgage exposure data , e.g., UPB (unpaid principle balance), property value data, hence, combination with FEMA yielding a technique where exposure values for calculating EAL includes various mortgage asset values, e.g., UPB, total value); wherein the receiving is receiving a climate hazard parameter selection (0025, 0030, 0038; fig.2, 0042: receiving of a particular type of catastrophic event for a particular region for loss assessment, such as via UI), wherein the first risk score is based on asset values of the mortgages (0031, as above).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of FEMA by incorporating the selection and mortgage value of Meyers. Both concern the art of climate hazard mapping, and the incorporation would have, according to Meyers, allow greater ease and efficiency in determining risk and liability for real estate assets, such as for various companies, e.g., insurance (0004).
Regarding claim 17 FEMA modified by Meyers discloses the system of claim 13, as described above. FEMA modified by Meyers further discloses: a climate hazard dashboard configured to generate one or both a dashboard and a report (FEMA 1:47, 2:18 shows dashboard and report) including the visual representation of climate hazard risk (FEMA 2:18 shows report data corresponding to or including the visual representation).
Claim(s) 14, 16 disclose systems analogous to the above claims 3, 5 and hence are rejected for the same reasons.
Claim(s) 4 are rejected under 35 U.S.C. 103 as being unpatentable over FEMA as disclosed in FEMA.gov ("Expected annual loss", published 8/18/2021) and James ("FEMA Releases new National Risk Index Measuring natural disaster risk, vulnerability", published 11/24/2020) and Technical Documentation (“National Risk Index: Technical Documentation”, published December 2020) in view of Meyers (US 20190370894 A1) in view of Eby (US 20220415155 A1).
Regarding claim 4 FEMA modified by Meyers discloses the method of claim 1, as described above. FEMA modified by Meyers further discloses: wherein climate hazard risks corresponding to the climate hazard parameters comprise flood hazard risks, wherein the climate hazard parameter selection comprises flooding (Pierre 0015, fig.17, 0320).
FEMA modified by Meyers does not disclose: wherein the flooding comprises pluvial flooding. Eby discloses: wherein the flooding comprises pluvial flooding (0036 discloses consideration of pluvial flooding in forecasting loss and risk, see fig.10).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of FEMA modified by Pierre by incorporating the pluvial flooding calculation technique of Eby. Both concern the art of climate hazard prediction and visualization, and the incorporation would have, according to Eby, provide additional and more fine-grained analysis of weather hazards to better inform the user (0003, 0036).
Claim(s) 15 disclose systems analogous to the above claims and hence are rejected for the same reasons.
Claim(s) 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over FEMA as disclosed in FEMA.gov ("Expected annual loss", published 8/18/2021) and James ("FEMA Releases new National Risk Index Measuring natural disaster risk, vulnerability", published 11/24/2020) and Technical Documentation (“National Risk Index: Technical Documentation”, published December 2020) in view of Meyers (US 20190370894 A1) in view of Franklin ("Enabling Geospatial Business Intelligence", published 9/30/2009).
Regarding claim 18 FEMA modified by Meyers discloses the system of claim 13, as described above. FEMA modified by Meyers does not disclose the limitations of claim 18.
Franklin discloses: a cloud data warehouse and a business intelligence (BI) analytics component (p.1 gives introduction to business intelligence with integrated GIS including data lake; see also p.3 fig.3 giving overview of geospatial integration; p.4 ¶2: cloud deployment; p.3 ¶1-2: connecting to data warehouse via a SOLAP client, map-driven dashboards, hence, cloud data warehouse).
It would have been obvious before the effective filing date to one of ordinary skill in the art to modify the method of FEMA modified by Meyers by incorporating GIS business intelligence technique of Franklin. Both concern the art of GIS data visualization, and the incorporation would have, according to Franklin, allow for the data mining of otherwise difficult to answer spatial queries (p.2: “Merging BI and GIS Software”).
Regarding claim 19 FEMA modified by Meyers modified by Franklin discloses the system of claim 18, as described above. FEMA modified by Meyers modified by Franklin further discloses: a climate hazard dashboard configured to generate one or both a dashboard and a report including the visual representation of climate hazard risk (FEMA 2:18 shows dashboard including report of the visual representation), wherein the BI analytics component acts as intermediary between the climate hazard dashboard and the cloud data warehouse (Franklin fig.3 shows BI products including Spatial Business Intelligence components intermediating between right dashboard and central cloud data warehouse).
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, Applicant argues:
1. The mapping to states and counties is inconsistent with the later reliance on census blocks and gridded subsections thereof; the cited references do not teach, suggest, or motivate a determination of any intersections between geolocation data for the first subset of real estate assets and a first plurality of climate hazard regions. Cited references fail to contemplate determining whether particular geographic data associated with mortgages for real estate actually overlaps with climate hazard regions.
Examiner respectfully disagrees, based on §5.3.2 disclosing determining value exposure data from Hazus, which allows importation of real estate asset data (Hazus fig.1-2 (p.1-4)). Hence, FEMA contemplation of particular geolocation data for real estate assets overlapping with climate hazard regions in the EAL calculation (§4.3), with Meyers is relied upon for contemplation of mortgage associations.
2. The depicted states are not selectable.
Examiner respectfully disagrees, states are selectable as particular regions are selectable, e.g., counties; furthermore, zooming in on a map to a particular sate constitutes selection for display; see rejection above.
3. General references to a census block fail to contemplate an intersection between the geolocation data for real estate assets; Figs.5-6 depicts overlap with area and not assets and is not capable of determining asset overlap.
Examiner respectfully disagrees for the cited portions above, i.e., §5.3.2, Hazus figs.1-2, p.1-4.
4. Office conflates counties with Census blocks.
Examiner respectfully disagrees; production of county-level EAL visualizations as disclosed by James necessarily relies on (aggregate) overlap of county areas.
5. Pierre does not contain any reference to geographic regions in referencing portfolio data, and only recites a grid tool.
Applicant’s arguments are moot in view of newly applied art.
6. 5 references are applied.
Examiner respectfully disagrees. Multiple references are cited for the FEMA’s risk visualization interface, with Hazus being additionally cited as disclosing data importation to the visualization. Furthermore, Meyers is cited for UI and data ingestion elements including hazard selection, mortgage portfolio ingestion, and so forth. Hence, at a high level, only 2 references are applied to the independent claims, with the secondary reference relied upon for disclosing additional UI features and additional data ingestion features, which Examiner submits a POSITA would be readily able to combine with the central visualization technique.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Elbl (US 11948212 B1) discloses calculation of fire risk zones on a map; Green (US 20140229420 A1) discloses use of flood risks per property, see, e.g., fig.48.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIANG LI/
Primary examiner AU 2143
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