PATENT CLAIM ANALYSIS

Application Number: 16053994
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-02
Patent Classification: ["702", "183000"]

Abstract:
Disclosed is an apparatus for troubleshooting fault component in equipment and a method thereof. The device includes portions for building a fault-component-sensor Bayesian belief network model by acquiring component state-abnormal and data of fault maintenance of the equipment, and for calculating probabilities of actual component abnormality when a sensor connected with an component detects component abnormality based on the model; and arranging the probabilities in a descending order to obtain the arranged probabilities of actual component abnormality, and the top-arranged component being the one to be troubleshot first. Namely, a relational expression among the fault, the component and the sensor may be systematically built by adopting the method or the apparatus provided by the present disclosure, and the most-likely-failing component in an equipment may be quickly detected according to the expression, thereby improving troubleshooting efficiency.

Claim (Index 5):
The apparatus of  claim 1 , wherein the apparatus further comprises a display portion, configured to display the sequence of the plurality of the components ranked correspondingly according to the plurality of the probabilities of actual component abnormality.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 96.0
- Lexical Diversity: 1.90789
- Patent Class: 702.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['09758891', '14909788', '15823116', '13336153', '11868245']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2155240294877122
- 35 USC 102 Novelty (BERT): 0.495921468156823
- Combined Prediction Score: 0.2435637733546233
- Mean Citation Score: 186.220998
- Max Citation Score: 203.27025
- Similarity Product: 129.83995389068127

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