| CPC G06Q 30/0278 (2013.01) [G06F 18/214 (2023.01); G06N 3/042 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 5/01 (2023.01); G06N 20/20 (2019.01); G06Q 50/16 (2013.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/809 (2022.01); G06V 10/82 (2022.01)] | 22 Claims |
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1. A system, comprising:
at least one computer hardware processor; and
at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
obtaining a plurality of images, the plurality of images including a first image of a first room inside a home and a second image of a second room inside the home;
determining a type of the first room by processing the first image of the first room with a first neural network model, the first neural network model having a first plurality of layers comprising at least a convolutional layer, a pooling layer, a fully connected layer, or a softmax layer, the first plurality of layers including at least one million parameters, wherein the first image of the first room has a first resolution and wherein processing the first image of the first room with the first neural network model comprises:
generating, from the first image, a second image of the first room having a second resolution lower than the first resolution; and
processing the second image of the first room with the first neural network model;
determining a type of the second room by processing the second image of the second room with the first neural network model;
identifying at least one first feature in the first image of the first room by processing the first image with a second neural network model different from the first neural network model and trained using a first plurality of training images of rooms of a same type as the first room, the first plurality of training images including training images augmented by one or more transformations, the second neural network model having a second plurality of layers comprising at least first deep neural network layers, a reduction layer, second deep neural network layers, an average pooling layer, a fully connected layer, a dropout layer, or a softmax layer, the second plurality of layers including at least one million parameters;
identifying at least one second feature in the second image of the second room by processing the second image of the second room with a third neural network model different from the first neural network model and second neural network model, the third neural network model trained using a second plurality of training images of rooms of a same type as the second room, the second plurality of training images including training images augmented by one or more transformations, the third neural network model having a third plurality of layers comprising at least first deep neural network layers, a reduction layer, second deep neural network layers, an average pooling layer, a fully connected layer, a dropout layer, or a softmax layer, the third plurality of layers including at least one million parameters; and
determining a value of the home at least in part by using the at least one first feature and the at least one second feature as input to a machine learning model different from the first neural network model, the second neural network model, and the third neural network model.
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