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 2):
The method of  claim 1 , further comprising:\n providing access to a diagnostic library, the diagnostic library having at least one diagnosed ophthalmic sample associated with each of the plurality of abnormality types; and responding to a user-selection of a highlighted region of the displayed test ophthalmic image by displaying at least one diagnosed ophthalmic sample, from the diagnostic library, associated with the abnormality type corresponding to the selected highlighted region.

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): 0.2958874717028765
- 35 USC 102 Novelty (BERT): 0.5079633744561864
- Combined Prediction Score: 0.3170950619782076
- Mean Citation Score: 143.425428
- Max Citation Score: 147.84746
- Similarity Product: 74.07350547110082

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