PATENT CLAIM ANALYSIS

Application Number: 16199270
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-04
Patent Classification: ["382", "103000"]

Abstract:
There is provided a method of identifying objects in an image, comprising: extracting query descriptors from the image, comparing each query descriptor with training descriptors for identifying matching training descriptors, each training descriptor is associated with a reference object identifier and with relative location data (distance and direction from a center point of a reference object indicated by the reference object identifier), computing object-regions of the digital image by clustering the query descriptors having common center points defined by the matching training descriptors, each object-region approximately bounding one target object and associated with a center point and a scale relative to a reference object size, wherein the object-regions are computed independently of the identifier of the reference object associated with the object-regions, wherein members of each cluster point toward a common center point, and classifying the target object of each object-region according to the reference object identifier of the cluster.

Claim (Index 2):
The method of  claim 1 , wherein each object-region is further associated with a scale relative to a reference object size, and wherein each object-region of the plurality of object regions is computed by:\n aggregating the relative location data of the matching training descriptors to generate a Kernel Density Estimate (KDE) for a plurality of posterior probability maps of the center point and scale of each respective reference object of a plurality of reference object identifiers; aggregating the posterior probability maps into a plurality of probability map clusters; extracting each of the plurality of object-regions with inter-scale normalization and non-maximal suppression according to location of the center point and the scale of each respective cluster of the plurality of probability map clusters, wherein each of the plurality of object-regions is defined according to the center point and scale of the reference object of the plurality of reference objects associated with the respective cluster of the plurality of probability map clusters.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 98.0
- Lexical Diversity: 2.38235
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15594611', '15647292', '13665480', '12324241', '14785045']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.388473603184673
- 35 USC 102 Novelty (BERT): 0.557406303897411
- Combined Prediction Score: 0.4053668732559468
- Mean Citation Score: 274.372942
- Max Citation Score: 435.78305
- Similarity Product: 350.22425251515807

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