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 11):
The non-transitory computer-readable medium of  claim 9 , wherein the classification for the object specifies that the object depicted in the scene depicts one of a vehicle object, a person object, or an unknown object.

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): 0.4335936026472717
- 35 USC 102 Novelty (BERT): 0.5915584813101732
- Combined Prediction Score: 0.4493900905135619
- Mean Citation Score: 486.784816
- Max Citation Score: 491.6356
- Similarity Product: 460.275091328168

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