PATENT CLAIM ANALYSIS

Application Number: 15893572
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-08
Patent Classification: ["714", "057000"]

Abstract:
Examples for detecting anomalies in a dataset are provided herein. A decision tree is trained using the data set and partitions of the data set produced by the trained decision tree are identified. Further, subsets of data based at least on the partitions of the data set are identified and z-scores are computed for the subsets of data. Based at least on the subsets of data, a subset of data with a highest z-score is identified as an anomalous subset of data, and the anomalous subset of data is provided for display.

Claim (Index 17):
One or more computer-readable storage media comprising computer-executable instructions for detecting anomalies in a data set, the computer-executable instructions when executed by one or more processors, cause the one or more processors to perform operations comprising:\n training a decision tree using the data set; identifying partitions of the data set produced by the trained decision tree; identifying subsets of data based at least on the partitions of the data set; computing z-scores for the subsets of data; based at least on the subsets of data, identifying a subset of data with a highest z-score as an anomalous subset of data; and providing the anomalous subset of data for display.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 88.0
- Lexical Diversity: 2.25
- Patent Class: 714.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14687848', '15069797', '12940432', '11825527', '15806265']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2693251201040774
- 35 USC 102 Novelty (BERT): 0.4570093216631062
- Combined Prediction Score: 0.2880935402599803
- Mean Citation Score: 144.725984
- Max Citation Score: 156.90282
- Similarity Product: 123.59726078183412

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