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 21):
A computing device comprising:\n a processor; and a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, 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 \n \u03bc 1 = \u2211 i = 1 N 1 \ue89e \ue89e w i \ue89e x i 1 W \u2003, 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 \n output the computed Gaussian bandwidth parameter value and the defined set of support vectors for determining if a new observation vector is an outlier.

Metadata:
- Claim Count in Document: 10.0
- Percentile: 88.0
- Lexical Diversity: 2.34921
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15390236', '15096552', '15583067', '15185277', '15335530']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4828548130197975
- 35 USC 102 Novelty (BERT): 0.554604908809959
- Combined Prediction Score: 0.4900298225988136
- Mean Citation Score: 420.564896
- Max Citation Score: 465.09488
- Similarity Product: 340.49410889929766

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