PATENT CLAIM ANALYSIS

Application Number: 15916943
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["714", "026000"]

Abstract:
Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Claim (Index 1):
A distributed architecture system for detecting an anomaly in a power semiconductor device, the system comprising:\n a server computing device; and one or more local components communicatively coupled to the server computing device, each one of the one or more local components comprising one or more sensors positioned adjacent to the power semiconductor device for sensing one or more properties of the power semiconductor device, wherein:\n each one of the one or more local components receives data corresponding to one or more sensed properties of the power semiconductor device from the one or more sensors and transmits the data to the server computing device, \n the server computing device utilizes the data, via one or more of a machine learning algorithm, a fault diagnostic algorithm, and a fault prognostic algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors, and \n each one of the one or more local components conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 90.0
- Lexical Diversity: 2.2
- Patent Class: 714.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15408092', '15569222', '14537839', '14536477', '12577913']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2690081631335433
- 35 USC 102 Novelty (BERT): 0.4706929363465723
- Combined Prediction Score: 0.2891766404548462
- Mean Citation Score: 143.56622199999995
- Max Citation Score: 149.083
- Similarity Product: 103.24013673782348

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

Dataset: test