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 1):
A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:\n compute a mean pairwise distance value between a plurality of observation vectors, wherein each observation vector of the plurality of observation vectors includes a variable value for each variable of a plurality of variables, wherein the mean pairwise distance value is computed using D _ 2 = 2 \ue89e \ue89e N ( N - 1 ) \ue89e \u2211 j = 1 p \ue89e \ue89e \u03c3 j 2 , , where  D  is the mean pairwise distance value, N is a number of the plurality of observation vectors, p is a number of the plurality of variables, and \u03c3 j 2  is a variance of each variable of the plurality of variables;\n compute a scaling factor value based on a number of the plurality of observation vectors and a predefined tolerance value; \n compute a Gaussian bandwidth parameter value using the computed mean pairwise distance value and the computed scaling factor value; \n compute an optimal value of an objective function that includes a Gaussian kernel function that uses the computed Gaussian bandwidth parameter value, wherein the objective function defines a support vector data description (SVDD) model using the plurality of observation vectors to define a set of support vectors and a set of Lagrange constants, wherein a Lagrange constant is defined for each support vector of the defined set of support vectors; \n output the computed Gaussian bandwidth parameter value, the defined set of support vectors, and the set of Lagrange constants; \n receive a new observation vector; \n compute a distance value using the defined set of support vectors, the defined set of Lagrange constants, and the received new observation vector; and \n when the computed distance value is greater than a computed threshold, identify the received new observation vector as an outlier.

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): 0.5115423396212839
- 35 USC 102 Novelty (BERT): 0.5590700486228729
- Combined Prediction Score: 0.5162951105214428
- Mean Citation Score: 420.564896
- Max Citation Score: 465.09488
- Similarity Product: 354.79315974172584

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

Dataset: test