PATENT CLAIM ANALYSIS

Application Number: 16433192
Application Type: Utility
Filing Date: 2019-06
Publication Date: 2019-12
Patent Classification: ["703", "002000"]

Abstract:
A computing device provides distributed estimation of an empirical distribution function. A boundary cumulative distribution function (CDF) value is defined at a start of each region of a plurality of regions. An accuracy value is defined for each region. (a) First equal proportion bins are computed for a first sample of a first marginal variable using the defined boundary CDF value for each region. (b) Second equal proportion bins are computed for the first sample of the first marginal variable within each region based on the defined accuracy value for each region. (c) The computed second equal proportion bins are added as an empirical distribution function (EDF) for the first marginal variable. (d) (a) to (c) are repeated for each remaining sample of the first marginal variable. (e) (a) to (d) are repeated with each remaining marginal variable of a plurality of marginal variables as the first marginal variable.

Claim (Index 15):
The non-transitory computer-readable medium of  claim 11 , wherein the bin boundary computation convergence determination comprises:\n computing a count of values in each bin; when the computed count of values is less than a predefined minimum expected number of observations per bin, loading the entire sample in the device memory, sorting the sample, setting bin boundaries as sorted sample points, and determining that the bin boundary computation has converged; when the computed count of values is greater than or equal to the predefined minimum expected number of observations per bin, computing a p-value, wherein the p-value is a probability that a value of a random variable that follows a chi-squared distribution with B c \u22121 degrees of freedom is greater than a value computed using a predefined uniformity test statistic, wherein B c  is a number of bins that contain a nonzero computed count of values; and when the computed p-value is greater than a predefined minimum p-value,\n computing a first criterion based on a proportion of the computed count of values in each bin; \n computing a convergence parameter for each region of the plurality of regions by multiplying a predefined acceptable tolerance value by the defined accuracy value for each region of the plurality of regions; and \n when the computed first criterion is greater than the computed convergence parameter, determining that the bin boundary computation has converged.

Metadata:
- Claim Count in Document: 53.0
- Percentile: 100.0
- Lexical Diversity: 2.98333
- Patent Class: 703.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16433136', '16161339', '16033851', '15961373', '16140931']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2853096512389429
- 35 USC 102 Novelty (BERT): 0.5296481330976942
- Combined Prediction Score: 0.309743499424818
- Mean Citation Score: 219.618288
- Max Citation Score: 335.3869
- Similarity Product: 248.8334854344488

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

Dataset: test