PATENT CLAIM ANALYSIS

Application Number: 16124977
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-03
Patent Classification: ["706", "020000"]

Abstract:
Computer systems and methods generate a stochastic categorical autoencoder learning network (SCAN). The SCAN is trained to have an encoder network that outputs, subject to one or more constraints, parameters for parametric probability distributions of sample random variables from input data. The parameters comprise measures of central tendency and measures of dispersion. The one or more constraints comprise a first constraint that constrains a measure of a magnitude of a vector of the measures of central tendency as compared to a measure of a magnitude of a vector of the measures of dispersion. Thereafter, the sample random variables are generated from the parameters and a decoder is trained to output the input data from the sample random variables.

Claim (Index 26):
The method of  claim 25 , wherein:\n the measures of central tendency comprise means; and the measures of dispersion comprise standard deviations.

Metadata:
- Claim Count in Document: 72.0
- Percentile: 97.0
- Lexical Diversity: 2.09836
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14120522', '15454845', '15782725', '15822462', '16037454']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3957830301543112
- 35 USC 102 Novelty (BERT): 0.4651938361302931
- Combined Prediction Score: 0.4027241107519094
- Mean Citation Score: 148.344148
- Max Citation Score: 160.89807
- Similarity Product: 124.05038328215716

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