Yembo, Inc.

Devin Waltman

Zachary Rattner

Maciej Halber

Siddharth Mohan

Marc Eder

Anoop Jakka

Noel Kennebeck

Receiving image data of a location in the form of a video feed, pictures, and/or depth information, as examples, from a client device controlled by a user, and/or other data is described. The received data serves as an input to a model (e.g., an artificial intelligence (AI)-based model such as a machine learning model) configured to generate an electronic representation of the location enriched with spatially localized details about surfaces and contents of the location. The electronic representation can be used for various purposes. The present disclosure provides a systems, methods, and computer programs that resolve several impediments in existing three dimensional image data acquisition and/or visualization systems by anonymizing personally identifying information (PII) in the received image data.

Anonymizing personally identifying information in image data

The process of obtaining an insurance quote is currently based on information provided by filing online forms. Insurance companies use the consumer input data from online the forms to provide a quote for property insurance and for insurance underwriting. To complete the underwriting, the insurance companies have to make assumptions on various factors; for example, the structure and layout of a consumer's home, what type of content the consumer owns, its value, and its condition, among several others. It is desirable to get more accurate data on the consumer content, the structure and layout of the home, as well as other factors (e.g., hazards such as whether a house is on a canyon) in an automated fashion which would let insurance companies underwrite policies with a more accurate risk profile as well as save costs by not having to send an onsite inspector.

Capacity optimized electronic model based prediction of changing physical hazards and inventory items

Systems, methods, and non-transitory computer readable media are disclosed that include operations to generate a virtual representation of a physical location with spatially localized information related to elements within the location being embedded in the virtual representation. The operations includes receiving description data (e.g., a plurality of images and videos) of the location, the description data being generated via at least one of a camera, a user interface; receive metadata associated elements within the location; generating (e.g., offline or in real-time), via a machine learning model and/or a geometric model, a 3-dimensional (3D) model of the location and elements therein; and generating, based on the 3D model of the location, an information-rich virtual representation of the location by annotating the 3D model with spatially localized metadata associated with the elements within the location and semantic information of the elements.

Systems and methods for building a virtual representation of a location

Disclosed is a method for using a virtual representation of an indoor environment to identify contents that have been damaged (e.g., by flooding). A virtual representation of a physical scene of an indoor environment is processed to identify a list of contents in the physical scene. The virtual representation may include 2-dimensional representations of the physical scene (e.g., images or video) or a 3-dimensional representation of the physical scene (e.g., 3D digital model). A reference line is determined in the virtual representation that is indicative of a maximum vertical extent of the damage in the physical scene. The position of the reference line is compared with the position of the identified contents in the virtual representation to determine contents that are likely to be damaged. For example, the contents that are at or below a plane represented by the reference line in the virtual representation may be identified as damaged.

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San Diego, CA, United States of America

Devin Waltman