Patent Issued for Apparatus and method for remote determination of architectural feature elevation and orientation (USPTO 11852728): Assurant Inc.

2024 JAN 17 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Assurant Inc. (New York, New York, United States) has been issued patent number 11852728, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Brusky, Ron (Miami, FL, US), Kambhatla, Prasanth (Atlanta, GA, US), Matta, Rajiv (Atlanta, GA, US), Schmitt, Mathew (Atlanta, GA, US).

This patent was filed on September 16, 2022 and was published online on December 26, 2023.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Effectively preparing for the damage to land, buildings, and other structures caused by floods has challenged individuals and communities for centuries. As the populations of coastal regions and other areas susceptible to flood events have grown, so too has the need to effectively assess the risks to people and property posed by such flood events. The inventors of the invention disclosed herein have identified technical challenges associated with efficiently determining the elevation and orientation of architectural features of structures erected on particular parcels of land, and developed the solutions described and otherwise referenced herein.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “An apparatus, computer program product, and method are therefore provided in accordance with an example embodiment in order permit the efficient determination of an elevation position of an architectural feature of a structure. In this regard, the method, apparatus and computer program product of an example embodiment provide for the creation of a predicted feature position data set that can be stored within a renderable object and otherwise presented to a user via an interface of a client device. Moreover, the method, apparatus, and computer program product of an example embodiment provide for use of the machine learning model in connection with the determination and retrieval of a predicted feature elevation position set determined based at least in part on location context data associated with a target geographic location and location context data associated with one or more other geographic locations.

“In an example embodiment, an apparatus is provided, the apparatus comprising a processor and a memory, the memory comprising instructions that configure the apparatus to: receive a message request data object from a client device associated with a user; extract, from the message request data object, a location identification data set, wherein the location identification data set is associated with a first geographic location; receive a first location context data object, wherein the first location context data object is associated with the first geographic location; receive a second location context data object, wherein the second location context data object is associated with a second geographic location; retrieve a predicted feature elevation position data set, wherein retrieving the predicted feature elevation position data set comprises applying the location identification data set, the first location context data object, and the second location context data object to a first model; and generate a control signal causing a renderable object comprising the predicted feature position data set to be displayed on a user interface of the client device associated with the user.

“In some example implementations of such an apparatus, the message request data object comprises an authenticated indication of the identity of the user. In some such example implementations, and in other example implementations, the location identification data set comprises a geographic coordinates set for the first geographic location. In some such example implementations, the geographic coordinates set define a location of an architectural feature of a structure located at the first geographic location.

“In some example implementations of such an apparatus, the first location context data object comprises a light detection and ranging (LiDAR) data set associated with the first geographic location. In some such example implementations, and in other example implementations, the first location context data object further comprises a land use data set associated with the first geographic location. In some such example implementations, the first location context data object further comprises a property characteristic dataset, which may include information regarding the foundation type, date of construction, construction materials used, and/or other information that may be obtained from databases associated with permitting, taxation, and/or other sources, for example. In some such example implementations, the first location context data object further comprises a land coverage data set associated with the first geographic location. In some such example implementations, the first location context data object further comprises a census data set associated with the first geographic location.

“In some example implementations of such an apparatus, the second location context data object comprises an identification of an elevation position of an architectural feature of a structure located at the second geographic location. In some such example implementations, and in other example implementations, the first model is a geospatial machine learning model. In some such example implementations, and in other example implementations, retrieving a predicted feature position data set comprises: applying the location identification data set, the first location context data object, and the second location context data object to the geospatial machine learning model; and determining, by the geospatial machine learning model, a predicted location of a finished floor of the structure located at the first geographic location.”

The claims supplied by the inventors are:

“1. An apparatus for predicting an elevation position of an architectural feature of a structure, the apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least: receive a message request data object from a client device, the message request data object comprising at least a location identification data set associated with a first geographic location; receive a first location context data object associated with the first geographic location, wherein the first location context data object comprises at least one of ranging data or topographical data; apply the location identification data set and the first location context data object to a model to predict the elevation position associated with the location identification data set; and cause transmission, to the client device, of the predicted elevation position.

“2. The apparatus according to claim 1, wherein the first location context data object is indicative of returned reflection of ranging signals transmitting toward one or more structures.

“3. The apparatus according to claim 1, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least: receive a second location context data object, wherein the second location context data object is associated with a second geographic location, and wherein the second location context data object is applied with the location identification data set and the first location context data object to predict the elevation position.

“4. The apparatus according to claim 1, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least: generate a control signal causing a renderable object comprising the elevation position to be displayed on a user interface of the client device.

“5. The apparatus according to claim 1, wherein the message request data object is received from the client device via an application programming interface (API), and the predicted elevation position is transmitted to the client device via the API.

“6. The apparatus according to claim 1, wherein the model is a machine learning model trained with location context data comprising at least one of ranging data or topographical data and known associated elevation positions.

“7. The apparatus according to claim 1, wherein predicting the elevation position associated with the location identification data set comprises predicting a predicted finished floor elevation position.

“8. A method for predicting an elevation position of an architectural feature of a structure, the method comprising: receiving a message request data object from a client device, the message request data object comprising at least a location identification data set associated with a first geographic location; receiving a first location context data object associated with the first geographic location, wherein the first location context data object comprises at least one of ranging data or topographical data; applying the location identification data set and the first location context data object to a model to predict the elevation position associated with the location identification data set; and causing transmission, to the client device, of the predicted elevation position.

“9. The method according to claim 8, wherein the first location context data object is indicative of returned reflection of ranging signals transmitting toward one or more structures.

“10. The method according to claim 8, further comprising: receiving a second location context data object, wherein the second location context data object is associated with a second geographic location, and wherein the second location context data object is applied with the location identification data set and the first location context data object to predict the elevation position.

“11. The method according to claim 8, further comprising: generating a control signal causing a renderable object comprising the elevation position to be displayed on a user interface of the client device.

“12. The method according to claim 8, wherein the message request data object is received from the client device via an application programming interface (API), and the predicted elevation position is transmitted to the client device via the API.

“13. The method according to claim 8, wherein the model is a machine learning model trained with location context data comprising at least one of ranging data or topographical data and known associated elevation positions.

“14. The method according to claim 8, wherein predicting the elevation position associated with the location identification data set comprises predicting a predicted finished floor elevation position.

“15. A computer program product for predicting an elevation position of an architectural feature of a structure, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions to: receive a message request data object from a client device, the message request data object comprising at least a location identification data set associated with a first geographic location; receive a first location context data object associated with the first geographic location, wherein the first location context data object comprises at least one of ranging data or topographical data; apply the location identification data set and the first location context data object to a model to predict the elevation position associated with the location identification data set; and cause transmission, to the client device, of the predicted elevation position.

“16. The computer program product according to claim 15, wherein the first location context data object is indicative of returned reflection of ranging signals transmitting toward one or more structures.

“17. The computer program product according to claim 15, wherein the computer-executable program code instructions further comprise program code instructions to: receive a second location context data object, wherein the second location context data object is associated with a second geographic location, and wherein the second location context data object is applied with the location identification data set and the first location context data object to predict the elevation position.

“18. The computer program product according to claim 15, wherein the computer-executable program code instructions further comprise program code instructions to: generate a control signal causing a renderable object comprising the elevation position to be displayed on a user interface of the client device.

“19. The computer program product according to claim 15, wherein the message request data object is received from the client device via an application programming interface (API), and the predicted elevation position is transmitted to the client device via the API.

“20. The computer program product according to claim 15, wherein the model is a machine learning model trained with location context data comprising at least one of ranging data or topographical data and known associated elevation positions.

“21. The computer program product according to claim 15, wherein predicting the elevation position associated with the location identification data set comprises predicting a predicted finished floor elevation position.”

For the URL and additional information on this patent, see: Brusky, Ron. Apparatus and method for remote determination of architectural feature elevation and orientation. U.S. Patent Number 11852728, filed September 16, 2022, and published online on December 26, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11852728)&db=USPAT&type=ids

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