PATENT CLAIM ANALYSIS

Application Number: 15887037
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-02
Patent Classification: ["706", "012000"]

Abstract:
A computing device employs machine learning and determines a bandwidth parameter value for a support vector data description (SVDD). A mean pairwise distance value is computed between observation vectors. A scaling factor value is computed based on a number of the plurality of observation vectors and a predefined tolerance value. A Gaussian bandwidth parameter value is computed using the computed mean pairwise distance value and the computed scaling factor value. An optimal value of an objective function is computed that includes a Gaussian kernel function that uses the computed Gaussian bandwidth parameter value. The objective function defines a SVDD model using the plurality of observation vectors to define a set of support vectors. The computed Gaussian bandwidth parameter value and the defined a set of support vectors are output for determining if a new observation vector is an outlier.

Claim (Index 9):
The non-transitory computer-readable medium of  claim 1 , wherein the scaling factor value is computed using F=W/\u221a{square root over (Q\u00d7ln[2Q/(\u03b4 2 M)])}, where F is the scaling factor value, W=\u03a3 i=1 N1 w i , M=\u03a3 i=1 N1 w i 2 , Q=(W 2 \u2212M)/2, N1 is a number of distinct observation vectors included in the plurality of observation vectors, \u03b4 is the predefined tolerance value, and w i  is a repetition vector that indicates a number of times each observation vector of the distinct observation vectors is repeated.

Metadata:
- Claim Count in Document: 10.0
- Percentile: 88.0
- Lexical Diversity: 2.34921
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15390236', '15096552', '15583067', '15185277', '15335530']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5147719934275226
- 35 USC 102 Novelty (BERT): 0.552135410386653
- Combined Prediction Score: 0.5185083351234356
- Mean Citation Score: 420.564896
- Max Citation Score: 465.09488
- Similarity Product: 309.6830578797149

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