PATENT CLAIM ANALYSIS

Application Number: 16010380
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2019-09
Patent Classification: ["705", "035000"]

Abstract:
The present disclosure relates to systems, methods, and computer readable media for processing an image including a vehicle using machine learning. The systems can include determining a location of the client device. The systems can further include receiving a first image of a vehicle from an image sensor of the client device and matching, using machine learning, the first image to one or more images of vehicles in a vehicle database to identify the vehicle. The vehicle database can list vehicles located at the determined location of the client device and images of the vehicles. The systems can include retrieving vehicle information from the vehicle database, based on the identified vehicle, and obtaining comparison information based at least in part on the vehicle information. The systems can include estimating a quote for the vehicle based on the comparison information and transmitting the estimated quote for display on the client device.

Claim (Index 40):
An image processing system comprising:\n at least one processor; and at least one storage medium containing instructions that, when executed by the at least one processor, cause the image processing system to perform operations comprising:\n scraping websites to obtain vehicle exterior images and associated vehicle attributes, scraping comprising:\n scraping images and associated metadata from websites; \n selecting images using the associated metadata; and \n classifying a subset of the selected images as the vehicle exterior images using a logistic regression classifier; \n \n training a second convolutional neural network using the vehicle exterior images and associated vehicle attributes, the training comprising:\n applying a vehicle exterior image to a first convolutional neural network to obtain an input vector; \n applying the input vector to the second convolutional neural network to generate an estimated output vector; \n generating an actual output vector using a vehicle attribute associated with the vehicle exterior image; \n updating the second convolutional neural network based on a comparison of the estimated output vector and the actual output vector; and \n \n generating an estimated quote based on a location of a client device of a user and a client device image of a vehicle, generation comprising:\n selecting a vehicle database including database images using the location; \n applying the database images to the first convolutional neural network to generate database image features; \n applying the client device image to the first convolutional neural network to generate client device image features; \n obtaining vehicle information from the vehicle database by comparing the client device image features and the database image features; \n obtaining comparison information based at least in part on the vehicle information; and \n estimating a quote for the vehicle based on the comparison information.

Metadata:
- Claim Count in Document: 59.0
- Percentile: 94.0
- Lexical Diversity: 2.57143
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15915998', '15916218', '15916137', '15916124', '15915947']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1216631116855573
- 35 USC 102 Novelty (BERT): 0.5599065188696165
- Combined Prediction Score: 0.1654874524039632
- Mean Citation Score: 374.45159800000016
- Max Citation Score: 439.2632
- Similarity Product: 355.4872271386623

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test