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 16):
A computer program product for identifying a target object in a digital image, the computer program product comprising:\n a non-transitory memory having stored thereon a code for execution by at least one hardware processor of a computing device, the code comprising:\n instructions for extracting a plurality of query descriptors from respective a plurality of locations in the digital image; \n instructions for comparing each one of said plurality of query descriptors with a plurality of training descriptors for identifying a plurality of matching training descriptors, each one of the plurality of training descriptors is associated with one of a plurality of reference object identifiers and with relative location data comprising an estimated distance and an estimated direction from a center point of a reference object indicated by the respective associated reference object identifier from the plurality of reference object identifiers; \n instructions for computing a plurality of 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 of the plurality of target objects of the digital image, each object-region is associated with another common center point of said common center points ,\n wherein each of the plurality of object-regions is computed independently of the respective reference object identifier associated with each of the plurality of object-regions, and \n \n instructions for classifying the bound target object of each object-region of the plurality of object-regions according to the reference object identifier of the respective cluster according to a statistically significant correlation requirement between the common center point of a respective cluster and the center point of the reference object associated with the reference object identifier of the respective cluster; \n wherein the comparing is performed by finding a set of Euclidean nearest neighbors of the respective extracted query descriptors, wherein each member of the set of Euclidean nearest neighbors is one of the plurality of matching training descriptors; wherein the set of Euclidean nearest neighbors are identified for a first subset of the extracted query descriptors, wherein a second subset of extracted query descriptors are unmatched, wherein for each member of the second subset of extracted query descriptors that are unmatched, a matching training descriptor is computed such that the difference between the center point of the relative location data of the identified matching training descriptors and the center point of the relative location data of the computed training descriptor matched to the unmatched second subset of query descriptors is equal to the difference between the location relative location of the query descriptor matched to the identified matching training descriptor and the location relative location of the unmatched second subset of query descriptors for which the matching training descriptor is computed.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 98.0
- Lexical Diversity: 2.38235
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15594611', '15647292', '13665480', '12324241', '14785045']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3561127883092009
- 35 USC 102 Novelty (BERT): 0.5644084211841013
- Combined Prediction Score: 0.376942351596691
- Mean Citation Score: 274.372942
- Max Citation Score: 435.78305
- Similarity Product: 432.82572720134857

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