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 4):
The computer system of  claim 3 , wherein the first constraint is that the measure of the magnitude of the vector of the measures of central tendency must be less than or equal to a first threshold value and the measure of the magnitude of the vector the measures of dispersion must be greater than or equal to a second threshold value.

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): 0.3925924634343418
- 35 USC 102 Novelty (BERT): 0.4777748171749462
- Combined Prediction Score: 0.4011106988084022
- Mean Citation Score: 148.344148
- Max Citation Score: 160.89807
- Similarity Product: 111.66163115437388

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