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 1):
A method of generating multiple neural networks that are configured to perform anomaly detection, the method comprising:\n providing, by a processor of a computing device, first input data to a first neural network to generate first output data indicating that the first input data is classified in a first cluster of a plurality of clusters, wherein the first input data includes at least one of a continuous feature or a categorical feature; generating second input data, each input entry of the second input data including first features corresponding to the first input data and second features, each one of the second features indicating a respective one of the plurality of clusters; providing the second input data to at least one second neural network to generate second output data, wherein the at least one second neural network includes one or more encoding layers configured to dimensionally reduce the first features to a latent space and one or more decoding layers configured to generate a reconstruction of the first features based on sampled data from the latent space; providing third input data to a third neural network to generate third output data, the third output data including first mean values and first variance values for each feature in the latent space, wherein the one or more encoding layers are further configured to generate second mean values and second variance values for each feature in the latent space; determining an aggregate loss corresponding to the second output data, wherein determining the aggregate loss comprises at least one of evaluating a first loss function with respect to the continuous feature or evaluating a second loss function with respect to the categorical feature; and based on the aggregate loss, adjusting at least one parameter associated with the first neural network, at least one parameter associated with the at least one second neural network, at least one parameter associated with the third neural network, or a combination thereof, to train multiple neural networks to perform anomaly detection, the multiple neural networks including the first neural network, the at least one second neural network, and the third neural network.

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): 0.3697725449997783
- 35 USC 102 Novelty (BERT): 0.4575320812853861
- Combined Prediction Score: 0.3785484986283391
- Mean Citation Score: 156.754692
- Max Citation Score: 161.75179
- Similarity Product: 129.68582880646528

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