PATENT CLAIM ANALYSIS

Application Number: 15860395
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-07
Patent Classification: ["382", "224000"]

Abstract:
A computer-implemented method for identifying character traits associated with a target subject includes acquiring image data of a target subject from an image data source, rendering a 3D image data set, comparing each of a plurality of regions of interest within the 3D image set to a historical image data set to identify active regions of interest, grouping subsets of the regions of interest into one or more convolutional feature layers, wherein each convolutional feature layer probabilistically maps to a pre-identified character trait, and applying a convolutional neural network model to the convolutional feature layers to identify a pattern of active regions of interest within each convolutional feature layer to predict whether a target subject possesses the pre-identified character trait.

Claim (Index 11):
A system for identifying character traits associated with a target subject, the system comprising:\n a characteristic recognition server, an image data source, a user interface, and a data store, wherein the characteristic recognition server comprises a processor and a non-transitory medium with computer executable instructions embedded thereon, the computer executable instructions configured to cause the processor to: acquire image data of a target subject from the image data source; render a textured or colored 3D image data set; compare each of a plurality of regions of interest within the 3D image set to a historical image data set to identify active regions of interest; group subsets of the regions of interest into one or more convolutional feature layers, wherein convolutional feature layers probabilistically map to pre-identified character traits; and apply, with a prediction and learning engine, a convolutional neural network model to the convolutional feature layers to identify and train a pattern of active regions of interest within each convolutional feature layer to predict whether a target subject possesses the pre-identified character trait.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 86.0
- Lexical Diversity: 2.1
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15172826', '14835736', '15452076', '15194541', '15225597']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3302140015452362
- 35 USC 102 Novelty (BERT): 0.4893891857421472
- Combined Prediction Score: 0.3461315199649273
- Mean Citation Score: 202.509954
- Max Citation Score: 207.36055
- Similarity Product: 143.75292386011182

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test