PATENT CLAIM ANALYSIS

Application Number: 16003707
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-12
Patent Classification: ["345", "634000"]

Abstract:
Systems and methods for standardizing target lesion selection within cross-sectional medical images can include the acts of: (i) sending a plurality of cross-sectional images to a user device, where each cross-sectional image is a cross-sectional slice of digital medical image data captured at a first timepoint from a radiologic device; (ii) receiving a user input identifying a set of pixels corresponding to a target lesion within a cross-sectional image of the plurality of cross-sectional images; and (iii) generating a target lesion location file that includes a precise anatomical location of the cross-sectional image and a pixel location of the target lesion within the cross-sectional image. The systems and methods can additionally include the act of causing a digital marker to be displayed on the cross-sectional image and on each analogous cross-sectional image captured at a later timepoint.

Claim (Index 12):
The computer system of  claim 11 , wherein the computer-executable instructions additionally cause the computer system to receive an additional second user input comprising segmentation data for the target lesion and the additional target lesions within the set of analogous cross-sectional images.

Metadata:
- Claim Count in Document: 28.0
- Percentile: 94.0
- Lexical Diversity: 2.12857
- Patent Class: 345.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15797360', '15407662', '15797391', '11552516', '13305495']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6683016646766946
- 35 USC 102 Novelty (BERT): 0.5175481149373782
- Combined Prediction Score: 0.653226309702763
- Mean Citation Score: 266.72215800000004
- Max Citation Score: 318.0107
- Similarity Product: 223.4041044136644

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