PATENT CLAIM ANALYSIS

Application Number: 15880339
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-07
Patent Classification: ["706", "020000"]

Abstract:
During training mode, first input data is provided to a first neural network to generate first output data indicating that the first input data is classified in a first cluster. The first input data includes at least one of a continuous feature or a categorical feature. Second input data is generated and provided to at least one second neural network to generate second output data. The at least one second neural network corresponds to a variational autoencoder. An aggregate loss corresponding to the second output data is determined, including at least one of evaluating a first loss function for the continuous feature or evaluating a second loss function for the categorical feature. Based on the aggregate loss, at least one parameter of at least one neural network is adjusted. During use mode, the neural networks are used to determine cluster identifications and anomaly likelihoods for received data samples.

Claim (Index 3):
The method of  claim 1 , further comprising adjusting the at least one parameter associated with the third neural network based on the aggregate loss.

Metadata:
- Claim Count in Document: 51.0
- Percentile: 86.0
- Lexical Diversity: 2.56452
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15793890', '15489564', '15794980', '15832050', '15600696']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3662580784086224
- 35 USC 102 Novelty (BERT): 0.4717490055473057
- Combined Prediction Score: 0.3768071711224908
- Mean Citation Score: 156.754692
- Max Citation Score: 161.75179
- Similarity Product: 115.52960084358511

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

Dataset: test