Abstract: A system and method for generating floor plans using a mobile device, comprising a memory and a processor in communication with the memory. The processor captures a plurality of walls in a room using one or more sensors of the mobile device. The processor then captures one or more openings in the room. The processor then generates a floor plan based on the walls and the openings.

Abstract: Machine learning systems and methods for property estimate anomaly detection are provided. The system receives property estimation data from a data source and processes the property estimation data to extract line item information from the property estimation data. The line item information, along with majority estimate information, is then processed by an automated anomaly detection process which performs majority estimate unit detection, line item quantity detection, and line item cluster detection on the extracted line item information using a plurality of machine learning models. The system then processes the majority estimate unit detection, line item quantity detection, and line item cluster detection to identify anomalous data in the line item information, and generates and displays a summary of the anomalous data in a graphical user interface screen of a claims estimation software application.

Abstract: Machine learning systems and methods for automated claims data and workflow processing are provided. The system includes a property claims processing platform which monitors for one or more automation trigger events in connection with an insurance claims property file. When the one or more automation trigger events is detected, the system identifies and executes one or more automated claims processing workflows in response to the detected trigger events. The workflows can be pre-defined and/or user-defined workflows that are selected in response to pre-defined logic or heuristic rules, and/or they could be selected by one or more machine learning algorithms.

Abstract: Systems and methods for automated software testing and test data set generation using workflow path monitoring and modeling are provided. The system includes a workflow path monitoring processor that executes a workflow monitoring software module, an artificial intelligence/machine learning (“AI/ML”) software module, and a test script generation software module. The workflow monitoring software module monitors one or more workflow paths taken by a user using one or more software applications or platforms. The AI/ML module processes the one or more workflow paths monitored by the workflow monitoring software module and develops a workflow model for the software application or platform. The test script generation software module automatically generates one or more test scripts and/or test data sets based on the one or more monitored workflow.

Abstract: Described in detail herein are systems and methods for generating computerized models of structures using geometry extraction and reconstruction techniques. The system includes a computing device coupled to a input device. The input device obtains raw data scanned by a sensor. The computing device is programmed to execute a data fusion process is applied to fuse the raw data, and a geometry extraction process is performed on the fused data to extract features such as walls, floors, ceilings, roof planes, etc. Large- and small-scale features of the structure are reconstructed using the extracted features. The large- and small-scale features are reconstructed by the system into a floor plan (contour) and/or a polyhedron corresponding to the structure. The system can also process exterior features of the structure to automatically identify condition and areas of roof damage.

Abstract: Machine learning systems and methods for automatic generation of rebuild estimates are provided. The system includes a data integration software layer which collects and pre-processes data generated from an insurance claims estimation software application; a machine learning (ML)/artificial intelligence (AI) software layer which extracts features from the data including details of damages, materials involved, labor costs, loss locations, and other information and trains and deploys one or more predictive machine learning models; a computer vision software layer which analyzes image or other visual data to detect, classify, and assess damage associated with a structure to be rebuilt; and an automated building estimate generation software layer which automatically generates a rebuild estimate for the structure using information generated by the data integration, ML/AI, and computer vision software layers.

Abstract: Machine learning systems and methods for automatic photo labeling and filing are provided. The system operates in conjunction with a claims estimation software application to rapidly and automatically label one or more photos associated with an insurance claim, and to automatically file such photos in one or more albums of a claim file, using machine learning techniques. The system retrieves a photo, processes the photo to detect one or more features depicted in the photo, generates a label for the photo based on the detected one or more features, assigns the label to the photo, determines a correct photo album of an insurance claim file, and stores the labeled photo in the correct photo album of the claim file.

Abstract: Artificial intelligence systems and methods are provided for automatic property damage estimate generation and contents pricing from textual and image data. The system generates an initial prompt for submission to an AI model and modifies the prompt to include additional information relating to historical claims data, property data, and/or other data. The system then submits the modified prompt to one or more AI models along with one or more of textual data and photo data. Output of the AI model(s) is then processed by the system to generate a damage estimate, which could be delivered in the form of natural language output, lists, and/or category and selector codes operable with a software-based insurance claims processing system. Additionally, the system can process textual and/or video (e.g., photo) information in order to automatically generate a list of contents and associated prices, using one or more custom-trained AI models.

Abstract: Described in detail herein are systems and methods for generating computerized models of structures using geometry extraction and reconstruction techniques. The system includes a computing device coupled to a input device. The input device obtains raw data scanned by a sensor. The computing device is programmed to execute a data fusion process is applied to fuse the raw data, and a geometry extraction process is performed on the fused data to extract features such as walls, floors, ceilings, roof planes, etc. Large- and small-scale features of the structure are reconstructed using the extracted features. The large- and small-scale features are reconstructed by the system into a floor plan (contour) and/or a polyhedron corresponding to the structure. The system can also process exterior features of the structure to automatically identify condition and areas of roof damage.

Abstract: Machine learning systems and methods for automatic generation of construction schedules and expense estimates are provided. The system includes a data integration software layer which collects and pre-processes data generated from an insurance claims estimation software application; a machine learning (ML)/artificial intelligence (AI) software layer which extracts features from the data and trains and deploys one or more predictive machine learning models for generating construction schedules and expense estimates; and an automated construction schedule generation software layer which automatically generates a construction schedule and associated expense estimates using information generated by the data integration and ML/AI layers.

Abstract: A system for aerial image detection and classification is provided herein. The system comprising an aerial image database storing one or more aerial images electronically received from one or more image providers, and an object detection pre-processing engine in electronic communication with the aerial image database, the object detection pre-processing engine detecting and classifying objects using a disparity mapping generation sub-process to automatically process the one or more aerial images to generate a disparity map providing elevation information, a segmentation sub-process to automatically apply a pre-defined elevation threshold to the disparity map, the pre-defined elevation threshold adjustable by a user, and a classification sub-process to automatically detect and classify objects in the one or more stereoscopic pairs of aerial images by applying one or more automated detectors based on classification parameters and the pre-defined elevation threshold.

Abstract: A system and method for construction estimation using aerial images is provided. The system receives at least one aerial image of a building. An estimation engine processes the aerial image at a plurality of angles to automatically identify a plurality (e.g., perimeter and interior) lines in the image corresponding to a plurality of features of a roof the building. The estimation engine allows users to generate two-dimensional and three-dimensional models of the roof by automatically delineating various roof features, and generates a report including information about the roof of the building.

Abstract: A computer vision system and method for detecting and modeling features of a building in a plurality of images is provided. The system includes a computer system in communication with a database of aerial imagery, and computer vision system code executed by the computer system which automatically detects contours and infers interior roof features of the building. The system first processes the plurality of images to identify a plurality of two-dimensional (2D) line segments in each image. Then, the system processes the plurality of 2D line segments to generate a plurality of three-dimensional (3D) line segments. The plurality of 2D line segments are then processed to detect a contour of the structure, and the contour of the structure is utilized by the system to infer interior roof lines from the structure. A model of the roof of the structure is finally generated using the detected contour and interior roof lines.

Abstract: A system for aerial image detection and classification is provided herein. The system comprising an aerial image database storing one or more aerial images electronically received from one or more image providers, and an object detection pre-processing engine in electronic communication with the aerial image database, the object detection pre-processing engine detecting and classifying objects using a disparity mapping generation sub-process to automatically process the one or more aerial images to generate a disparity map providing elevation information, a segmentation sub-process to automatically apply a pre-defined elevation threshold to the disparity map, the pre-defined elevation threshold adjustable by a user, and a classification sub-process to automatically detect and classify objects in the one or more stereoscopic pairs of aerial images by applying one or more automated detectors based on classification parameters and the pre-defined elevation threshold.

Abstract: A system and method for construction estimation using aerial images is provided. The system receives at least one aerial image of a building. An estimation engine processes the aerial image at a plurality of angles to automatically identify a plurality (e.g., perimeter and interior) lines in the image corresponding to a plurality of features of a roof the building. The estimation engine allows users to generate two-dimensional and three-dimensional models of the roof by automatically delineating various roof features, and generates a report including information about the roof of the building.

Abstract: A system and method for generating computerized floor plans is provided. The system comprises a mobile computing device, such as a smart cellular telephone, a tablet computer, etc. having an internal digital gyroscope and camera, and an interior modeling software engine interacts with the gyroscope and camera to allow a user to quickly and conveniently take measurements of interior building features, and to create computerized floor plans of such features from any location within a space, without requiring the user to stay in a single location while taking the measurements. The system presents the user with a graphical user interface that allows a user to quickly and conveniently delineate wall corner features using a reticle displayed within the user interface. As corners are identified, the system processes the corner information and information from the gyroscope to calculate wall features and creates a floor plan of the space with high accuracy.

Abstract: A computer vision system and method for detecting and modeling features of a building in a plurality of images is provided. The system includes at least one computer system in communication with a database of aerial imagery, and computer vision system code executed by the at last one computer system which automatically detects contours and infers interior roof features of the building. The system first processes the plurality of images to identify a plurality of two-dimensional (2D) line segments in each image. Then, the system processes the plurality of 2D line segments to generate a plurality of three-dimensional (3D) line segments. The plurality of 2D line segments are then processed to detect a contour of the structure, and the contour of the structure is utilized by the system to infer interior roof lines from the structure. A model of the roof of the structure is finally generated using the detected contour and interior roof lines.

Abstract: A system and method for generating computerized floor plans is provided. The system comprises a mobile computing device, such as a smart cellular telephone, a tablet computer, etc. having an internal digital gyroscope and camera, and an interior modeling software engine interacts with the gyroscope and camera to allow a user to quickly and conveniently take measurements of interior building features, and to create computerized floor plans of such features from any location within a space, without requiring the user to stay in a single location while taking the measurements. The system presents the user with a graphical user interface that allows a user to quickly and conveniently delineate wall corner features using a reticle displayed within the user interface. As corners are identified, the system processes the corner information and information from the gyroscope to calculate wall features and creates a floor plan of the space with high accuracy.

Abstract: Described in detail herein are systems and methods for generating computerized models of structures using geometry extraction and reconstruction techniques. The system includes a computing device coupled to a input device. The input device obtains raw data scanned by a sensor. The computing device is programmed to execute a data fusion process is applied to fuse the raw data, and a geometry extraction process is performed on the fused data to extract features such as walls, floors, ceilings, roof planes, etc. Large- and small-scale features of the structure are reconstructed using the extracted features. The large- and small-scale features are reconstructed by the system into a floor plan (contour) and/or a polyhedron corresponding to the structure. The system can also process exterior features of the structure to automatically identify condition and areas of roof damage.

Abstract: A system and method for construction estimation using aerial images is provided. The system receives at least one aerial image of a building. An estimation engine processes the aerial image at a plurality of angles to automatically identify a plurality (e.g., perimeter and interior) lines in the image corresponding to a plurality of features of a roof the building. The estimation engine allows users to generate two-dimensional and three-dimensional models of the roof by automatically delineating various roof features, and generates a report including information about the roof of the building.