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 8):
The non-transitory computer storage medium of  claim 7 , wherein the sequences of vectors are mapped to nodes of a self organizing map (SOM) and wherein nodes of the SOM are clustered using an adaptive resonance theory (ART) network to generate a sequence of SOM nodes.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 97.0
- Lexical Diversity: 1.77632
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['13722812', '12543318', '12543307', '12543379', '13472214']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4628810684241786
- 35 USC 102 Novelty (BERT): 0.5930885739721201
- Combined Prediction Score: 0.4759018189789727
- Mean Citation Score: 456.414254
- Max Citation Score: 559.9193
- Similarity Product: 435.85209853897095

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