PATENT CLAIM ANALYSIS

Application Number: 16030142
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2019-03
Patent Classification: ["708", "207000"]

Abstract:
A Gaussian similarity matrix is computed between observation vectors. An inverse Gaussian similarity matrix is computed from the Gaussian similarity matrix. A row sum vector is computed that includes a row sum value computed from each row of the inverse Gaussian similarity matrix. (a) A new observation vector is selected. (b) An acceptance value is computed for the new observation vector using the set of boundary support vectors, the row sum vector, and the new observation vector. (c) (a) and (b) are repeated when the computed acceptance value is less than or equal to zero. (d) An incremental vector is computed from the inverse Gaussian similarity matrix and the new observation vector when the computed acceptance value is greater than zero. (e) the selected new observation vector is output as an outlier observation vector when a maximum value of the incremental vector is less than a first predefined tolerance value.

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 Gaussian similarity matrix 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; compute an inverse Gaussian similarity matrix from the computed Gaussian similarity matrix; compute a row sum vector that includes a row sum value computed from each row of the computed inverse Gaussian similarity matrix; select a set of boundary support vectors from the plurality of observation vectors; (a) select a new observation vector from an event stream or from an input dataset; (b) compute an acceptance value for the selected new observation vector using the selected set of boundary support vectors, the computed row sum vector, and the new observation vector; (c) when the computed acceptance value is greater than zero, compute an incremental vector from the computed inverse Gaussian similarity matrix and the selected new observation vector; (d) when the computed acceptance value is greater than zero and when a maximum value of the computed incremental vector is less than a first predefined tolerance value, output an indicator that the selected new observation vector is an abnormal observation vector relative to the selected set of boundary support vectors; and (e) repeat (a) to (d) until the event stream is stopped or a last observation vector is selected from the input dataset in (a).

Metadata:
- Claim Count in Document: 80.0
- Percentile: 95.0
- Lexical Diversity: 3.0
- Patent Class: 708.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15583067', '15390236', '15185277', '15096552', '15911882']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5406547791810022
- 35 USC 102 Novelty (BERT): 0.5444667397853465
- Combined Prediction Score: 0.5410359752414367
- Mean Citation Score: 362.52844
- Max Citation Score: 383.9613
- Similarity Product: 284.78883113315106

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

Dataset: test