PATENT CLAIM ANALYSIS

Application Number: 16252353
Application Type: Utility
Filing Date: 2019-01
Publication Date: 2019-07
Patent Classification: ["381", "086000"]

Abstract:
A training system ( 10 ) for a vehicle control unit for detecting hazard sounds, in particular accident sounds, that has at least one interface ( 12 ) for inputting training data ( 15 ) containing an audio signal ( 16 ) and a target reaction signal ( 18 ) in each case, an evaluation unit ( 20 ) that forms an artificial neural network ( 22 ) and is configured for forward propagation of the artificial neural network ( 22 ) with training data ( 14 ) in order to calculate an actual reaction signal ( 24 ), and calculating weightings through backward propagation of the target reaction signal ( 18 ) in the artificial neural network ( 22 ), wherein the weightings are configured to be stored in the vehicle control unit for detecting accident sounds.

Claim (Index 9):
A vehicle control unit for detecting hazard sounds in driving situations, in particular accident sounds, comprising at least one microphone, preferably a directional microphone, for picking up driving situation sounds, and an evaluation unit, configured for forward propagation of an artificial neural network with the vehicle situation sounds that has been trained in accordance with the process according to  claim 8 , in order to assign the driving situation sounds to a reaction signal.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 99.0
- Lexical Diversity: 2.11765
- Patent Class: 381.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['11549699', '11953671', '13997123', '10151438', '14887564']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6603219056555025
- 35 USC 102 Novelty (BERT): 0.4938715280436902
- Combined Prediction Score: 0.6436768678943213
- Mean Citation Score: 149.6074
- Max Citation Score: 155.27223
- Similarity Product: 92.1254735350156

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

Dataset: test