PATENT CLAIM ANALYSIS

Application Number: 15921595
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-07
Patent Classification: ["382", "103000"]

Abstract:
A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Claim (Index 10):
The non-transitory computer-readable medium of  claim 9 , further comprising, applying a singular value decomposition (SVD) to the first vector representation to generate a second vector representation from the first vector representation, wherein the second vector representation reduces the dimensionality of the first vector representation.

Metadata:
- Claim Count in Document: 59.0
- Percentile: 90.0
- Lexical Diversity: 1.6
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14584967', '15494010', '15338072', '14992973', '12170268']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4482394204340479
- 35 USC 102 Novelty (BERT): 0.5899905923596276
- Combined Prediction Score: 0.4624145376266059
- Mean Citation Score: 489.41119400000014
- Max Citation Score: 497.9642
- Similarity Product: 490.89736660698645

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