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 5):
The method of  claim 4  comprising determining to use the disambiguation deep neural network for an object from the plurality of objects when two of the corresponding confidence scores for the object each satisfy the threshold confidence score.

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): 0.3151528820000893
- 35 USC 102 Novelty (BERT): 0.529576759292944
- Combined Prediction Score: 0.3365952697293747
- Mean Citation Score: 238.63362200000003
- Max Citation Score: 358.05542
- Similarity Product: 255.4565013571167

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