PATENT CLAIM ANALYSIS

Application Number: 15952626
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-10
Patent Classification: ["702", "108000"]

Abstract:
A system and method for selecting a test vector for use in classification of electronic components. The system and method can quickly and automatically select a test vector that, when applied to a set of training electronic components, causes the electronic components to produce side channel information that can be used to build a classifier that accurately classifies a set of test electronic components.

Claim (Index 1):
A method for automated test vector generation comprising;\n selecting a set of one or more training test vectors; iteratively, installing a plurality of training components into an electronic component interface; iteratively, applying the set of one or more test vectors to the training components; iteratively, sensing, with a sensor, side channel information from each of the training components for each of the one or more test vectors; iteratively, comparing the side channel information from the at least one training component for each of the one or more test vectors to the side channel information from one or more of the other components for each of the one or more test vectors; iteratively, changing, with a machine learning module, the set of one or more test vectors based on the comparison; and selecting at least one of the one or more test vectors as a trained test vector set.

Metadata:
- Claim Count in Document: 56.0
- Percentile: 91.0
- Lexical Diversity: 1.7
- Patent Class: 702.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15696667', '15178708', '12890789', '15223283', '15473980']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1995805078926744
- 35 USC 102 Novelty (BERT): 0.489017760975112
- Combined Prediction Score: 0.2285242332009182
- Mean Citation Score: 204.446454
- Max Citation Score: 229.14627
- Similarity Product: 167.404171334365

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