PATENT CLAIM ANALYSIS

Application Number: 16226496
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-04
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 7):
The computer-implemented method of  claim 3 , wherein the object is classified as an unknown object, and wherein the input data further comprises behavior engaged in by the unknown object that includes one or more of appearing, moving, turning and stopping.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 1.6
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14584967', '15921595', '15494010', '15338072', '14992973']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4356754757098967
- 35 USC 102 Novelty (BERT): 0.5867915783495571
- Combined Prediction Score: 0.4507870859738627
- Mean Citation Score: 486.784816
- Max Citation Score: 491.6356
- Similarity Product: 434.2509741394997

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