PATENT CLAIM ANALYSIS

Application Number: 15909658
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["382", "128000"]

Abstract:
An ophthalmic image diagnostic tool and method submits a test image to a neural network trained to identify abnormal regions of an ophthalmic image, to distinguish between multiple types of abnormalities, and to associate an abnormality type with each identified potentially abnormal region. Each potentially abnormal region in the test image is highlighted, and in response to a user-selection of a highlighted region, a previously diagnosed sample (e.g., from a library of samples) of the abnormality type associated with the selected highlighted region is displayed.

Claim (Index 1):
An ophthalmic image diagnostic method, comprising:\n submitting a test ophthalmic image to a neural network trained to identify potentially abnormal regions of an ophthalmic image, the neural network distinguishing between a plurality of abnormality types, wherein the neural network associates at least one abnormality type with each identified potentially abnormal region; and displaying the test ophthalmic image on a display and highlighting on the display any region of the test ophthalmic image identified as a potentially abnormal region by the neural network.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 90.0
- Lexical Diversity: 1.77358
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15810694', '15879486', '15429806', '15680584', '15799129']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2995603292898162
- 35 USC 102 Novelty (BERT): 0.491584548119823
- Combined Prediction Score: 0.3187627511728168
- Mean Citation Score: 143.425428
- Max Citation Score: 147.84746
- Similarity Product: 87.98577498359683

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