PATENT CLAIM ANALYSIS

Application Number: 15757883
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-11
Patent Classification: ["348", "079000"]

Abstract:
Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a two-stage Likelihood pipeline analysis that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage pipeline may include a first stage including the application of approximate Likelihood functions in which one or more of the following assumptions or modifications may be applied: (i) the pattern of interest and background are at a specified position in a segment of the data set under examination; (ii) the SNR is low; and (iii) measurement noise can be represented in such a form that all non-position parameters of the representation are linear with respect to the derivative of the Log Likelihood versus lambda. The second stage may include a full Likelihood analysis.

Claim (Index 5):
The method of  claim 4 ,\n wherein calculating the first approximate MLE for the segment comprises representing measurement noise as a Poisson distribution; and wherein calculating the second approximate MLE for the segment comprises representing measurement noise as a Poisson distribution.

Metadata:
- Claim Count in Document: 9.0
- Percentile: 90.0
- Lexical Diversity: 1.77528
- Patent Class: 348.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['13084345', '11324503', '14242283', '12422878', '10603666']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6162626029649754
- 35 USC 102 Novelty (BERT): 0.5039025652079853
- Combined Prediction Score: 0.6050265991892764
- Mean Citation Score: 152.66268399999996
- Max Citation Score: 175.22546
- Similarity Product: 98.20754562562942

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

Dataset: test