PATENT CLAIM ANALYSIS

Application Number: 15891175
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-06
Patent Classification: ["702", "025000"]

Abstract:
In various implementations, methods and systems are designed for predicting effluent total phosphorus (TP) concentrations in an urban wastewater treatment process (WWTP). To improve the efficiency of TP prediction, a particle swarm optimization self-organizing radial basis function (PSO-SORBF) neural network may be established. Implementations may adjust structures and parameters associated with the neural network to train the neural network. The implementations may predict the effluent TP concentrations with reasonable accuracy and allow timely measurement of the effluent TP concentrations. The implementations may further collect online information related to the estimated effluent TP concentrations. This may improve the quality of monitoring processes and enhance management of WWTP.

Claim (Index 17):
The method of  claim 16 , wherein the training the SORBF neural network comprises:\n adjusting a network structure of the SORBF neural network, the network structure indicating a number of hidden neurons; adjusting neural network parameters of the SORBF neural network, the neural network parameters comprising a center value, a width, a connection weights of the network structure; and training the SORBF neural network using the adjusted neural network parameters.

Metadata:
- Claim Count in Document: 17.0
- Percentile: 88.0
- Lexical Diversity: 1.64384
- Patent Class: 702.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['14668836', '13985482', '14234955', '15389755', '11996384']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1766794884114905
- 35 USC 102 Novelty (BERT): 0.5238957214641169
- Combined Prediction Score: 0.2114011117167531
- Mean Citation Score: 243.30053
- Max Citation Score: 318.0135
- Similarity Product: 216.6699661332965

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

Dataset: test