# US 2022/0044285 A1 · Machine learning systems and methods for determining home value

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- Number: US 2022/0044285 A1
- Document ID: US20220044285A1
- Status: Published application
- Kind: A1
- Application number: 17/510,248
- Filed: 2021-10-25
- Published: 2022-02-10
- Earliest claimed: 2019-10-15
- Assignees: Orchard Technologies, Inc.
- Applicants: Orchard Technologies, Inc.
- Inventors: Mark Jayne; Gergely Svigruha; Raymond Liang; Gregory Aponte
- Web page: https://patentgrep.com/patent/US20220044285A1

## CPC

- G06Q 30/0278: {Product appraisal}
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/254: {of classification results, e.g. of results related to same input data}
- G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 3/091: Active learning
- G06N 3/096: Transfer learning
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/20: Ensemble learning
- G06Q 50/16: Real estate
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
- G06V 10/809: {of classification results, e.g. where the classifiers operate on the same input data}
- G06V 10/82: using neural networks
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Techniques for determining value of a home by applying one or more neural network models to images of spaces in the home. The techniques include: obtaining at least one image of a first space inside or outside of a home; determining a type of the first space by processing the at least one image of the first space with a first neural network model; identifying at least one feature in the first space by processing the at least one image with a second neural network model different from the first neural network model and trained using images of spaces of a same type as the first space; and determining a value of the home at least in part by using the at least one feature as input to a machine learning model different from the first neural network model and the second neural network model.

## Claims

### Claim 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 at least one first image of a first room inside a home and at least one second image of a second room inside the home;
determining a type of the first room by processing the at least one first image of the room with a first neural network model;
determining a type of the second room by processing the at least one second image of the second room with the first neural network model;
identifying at least one first feature in the first room by processing the at least one first image with a second neural network model different from the first neural network model and trained using images of rooms of a same type as the first room;
identifying at least one second feature in the second room by processing the at least one second image with a third neural network model different from the first neural network model and the second neural network model, the third neural network model trained using images of rooms of a same type as the second room; 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.

### Claim 2 (depends on 1)

The system of claim 1, wherein the first neural network model comprises one or more convolutional layers.

### Claim 3 (depends on 2)

The system of claim 2, wherein the first neural network comprises two neural network sub-models including a first sub-model having an average pooling layer and a second sub-model having a max pooling layer instead of the average pooling layer.

### Claim 4 (depends on 3)

The system of claim 3, wherein processing the at least one first image of the first room with the first neural network model comprises:
processing the at least one first image using the first sub-model to obtain first results;
processing the least one first image using the second sub-model to obtain second results; and
combining the first and second results to obtain a first output result for the first neural network model.

### Claim 5 (depends on 4)

The system of claim 4, wherein processing the at least one second image of the second room with the first neural network model comprises:
processing the at least one second image using the first sub-model to obtain third results;
processing the least one second image using the second sub-model to obtain fourth results; and
combining the third and fourth results to obtain a second output result for the first neural network model.

### Claim 6 (depends on 1)

The system of claim 1, wherein the first neural network model is a deep neural network.

### Claim 7 (depends on 1)

The system of claim 1, wherein the first neural network model comprises multiple convolutional layers.

### Claim 8 (depends on 1)

The system of claim 1, wherein the first neural network model is trained to determine the types for the first room and the second room using one or more images of the same types of rooms using a transfer learning technique.

### Claim 9 (depends on 1)

The system of claim 1, wherein the at least one first image consists of a single first image of the first room.

### Claim 10 (depends on 9)

The system of claim 9, wherein the at least one second image consists of a single second image of the second room.

### Claim 11 (depends on 1)

The system of claim 1, wherein determining the type of the first room is selected from the group consisting of: garage, living room, bedroom, kitchen, bathroom, dining room, family room, basement, attic, closet, laundry room, and mud room.

### Claim 12 (depends on 1)

The system of claim 1, wherein the processor-executable instructions further cause the at least one computer hardware processor to perform:
processing multiple images using the first neural network model to identify images for which the first neural network model output differs from labels produced by manual classification;
obtain new labels for at least some of the multiple images; and
update one or more parameters of the first neural network model by using the at least some of the multiple images with the new labels.

### Claim 13 (depends on 1)

The system of claim 1, wherein the second neural network model is a deep neural network model.

### Claim 14 (depends on 13)

The system of claim 13, wherein the second neural network model comprises one or more residual connections.

### Claim 15 (depends on 13)

The system of claim 13, wherein the second neural network model uses a bank of convolution kernels having different resolutions.

### Claim 16 (depends on 13)

The system of claim 13, wherein the second neural network model includes one or more convolutional layers.

### Claim 17 (depends on 13)

The system of claim 13, wherein the second neural network model is trained at least in part by using transfer learning.

### Claim 18 (depends on 1)

The system of claim 1, wherein the first room is a kitchen, and wherein identifying the at least one feature comprises identifying a type of material of a countertop in the kitchen.

### Claim 19 (depends on 1)

The system of claim 1, wherein the first room is a kitchen, and wherein identifying the least one feature comprises identifying a finish of an appliance in the kitchen.

### Claim 20 (depends on 1)

The system of claim 1, wherein the machine learning model is a random forest model.

### Claim 21 (depends on 1)

The system of claim 1, wherein the first plurality of layers of the first neural network model includes at least one million parameters, and wherein the second plurality of layers of the second neural network model includes at least one million parameters.

### Claim 22 (depends on 1)

The system of claim 1, wherein the processor executable instructions, when executed by the at least one computer hardware processor, further cause the at least one computer hardware processor to perform:
obtaining the plurality of images including at least one third image of an outdoor space outside the home;
determining a type of the outdoor space by processing the at least one third image with the first neural network model;
identifying at least one third feature of the outdoor space by processing the at least one third image with a fourth neural network model different from the first neural network, the second neural network model and the third neural network model, the fourth neural network model trained using images of a same type as the outdoor space; and
determining the value of the home at least in part by using the at least one first feature, the at least one second feature and the at least one third feature as input to the machine learning model different from the first neural network model, the second neural network model, the third neural network model and the fourth neural network model.

### Claim 23 (depends on 22)

The system of claim 22, wherein determining the type of the outside space is selected from the group consisting of: front yard, back yard, side yard and porch.