PATENT CLAIM ANALYSIS

Application Number: 16147238
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-01
Patent Classification: ["382", "103000"]

Abstract:
A sequence layer in a machine-learning engine configured to learn from the observations of a computer vision engine. In one embodiment, the machine-learning engine uses the voting experts to segment adaptive resonance theory (ART) network label sequences for different objects observed in a scene. The sequence layer may be configured to observe the ART label sequences and incrementally build, update, and trim, and reorganize an ngram trie for those label sequences. The sequence layer computes the entropies for the nodes in the ngram trie and determines a sliding window length and vote count parameters. Once determined, the sequence layer may segment newly observed sequences to estimate the primitive events observed in the scene as well as issue alerts for inter-sequence and intra-sequence anomalies.

Claim (Index 6):
A non-transitory computer storage medium, which, when executed on a processor, performs an operation for evaluating objects detected in a video stream, comprising:\n detecting a plurality of foreground objects present in the video stream; for each foreground object of the plurality of foreground objects, building a trajectory characterizing each foreground object in a series of successive frames of the single video stream; storing each trajectory in a memory; identifying one or more patterns of behavior of objects in the video stream using the stored trajectories; detecting a successive foreground object in the video stream; building a trajectory of the successive foreground object; determining a probability distribution that the trajectory of the successive object is anomalous based on the stored trajectories; and if the trajectory of the successive object is determined to be anomalous, generating an alert.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 97.0
- Lexical Diversity: 1.77632
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13722812', '12543318', '12543307', '12543379', '13472214']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4324951450999997
- 35 USC 102 Novelty (BERT): 0.5933621818158035
- Combined Prediction Score: 0.4485818487715801
- Mean Citation Score: 456.414254
- Max Citation Score: 559.9193
- Similarity Product: 445.74742756435865

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test