PATENT CLAIM ANALYSIS

Application Number: 16007629
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2019-01
Patent Classification: ["382", "156000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing object sub-types in images. One of the methods includes receiving training data; selecting training data for an image; determining whether to randomly permute a value of a property of the selected image; providing, to a deep neural network, the particular training data or the randomly permuted particular training data; receiving, from the deep neural network, output data indicating a predicted label for an object sub-type for an object depicted in the selected image, and a confidence score that represents a likelihood that the object has the object sub-type; updating one or more weights in the deep neural network using an expected output value, the predicted label, and the confidence score; and providing the deep neural network to a mobile device for use detecting whether one or more images depict objects having the particular object sub-type.

Claim (Index 17):
An object recognition system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:\n providing, to a deep neural network, image data for an image that depicts a plurality of objects to cause the deep neural network to predict object sub-type labels for each of the plurality of objects; receiving, from the deep neural network for each of the plurality of objects, a) a confidence score that indicates a likelihood that the object is of an object sub-type, and b) object location data that indicates a likely location of the object in the image; determining, for at least some of the objects using the corresponding confidence scores, whether to use a disambiguation deep neural network, different from the deep neural network, to determine an object sub-type label for the object; and generating, using the object sub-types and the object location data, a representation of the image that indicates, for some of the plurality of objects, a likely location of the object in the image and a sub-type label that indicates the object sub-type for the object.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 94.0
- Lexical Diversity: 2.30137
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15692180', '14821128', '14528815', '15643453', '15001417']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3142461425327887
- 35 USC 102 Novelty (BERT): 0.527795725450773
- Combined Prediction Score: 0.3356011008245872
- Mean Citation Score: 238.63362200000003
- Max Citation Score: 358.05542
- Similarity Product: 314.89031080951816

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

Dataset: test