PATENT CLAIM ANALYSIS

Application Number: 15959179
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-08
Patent Classification: ["356", "432000"]

Abstract:
The present invention provides a Voigt line shape fitting method, including step 1: Calculate a Gauss line shape function and a Lorentz line shape function, and calculate a Voigt line shape function. Step 2: For determined line shape parameters to be fitted, calculate partial derivatives of the Voigt line shape function with respect to the parameters, convert a partial derivative of the Voigt line shape function with respect to a parameter into a partial derivative of the Gauss line shape function or the Lorentz line shape function with respect to the parameter. Step 3: Substitute the Voigt line shape function and the partial derivative of the Voigt line shape function with respect to the parameter to be fitted, into a least squares algorithm step, perform least squares fitting calculation, and determine whether to terminate the least squares fitting calculation or return to step 1 to perform next iterative calculation.

Claim (Index 1):
A non-approximate Voigt line shape fitting method for absorption spectrum measurement, comprising the following steps:\n step 1: non-approximate calculation of a Voigt line shape function (1) separately calculating a normalized Gauss line shape function and a normalized Lorentz line shape function according to given line shape parameters comprising a center wavelength, a Gauss line width, and a Lorentz line width; (2) separately calculating Fourier transforms of the Gauss line shape function and the Lorentz line shape function; (3) by using Fourier transform properties of a convolution, obtaining a Fourier transform of the Voigt line shape function, which is equal to a product of the Fourier transforms of the Gauss line shape function and the Lorentz line shape function; (4) obtaining a normalized Voigt line shape function by using an inverse Fourier transform; step 2: non-approximate calculation of first-order partial derivatives of the Voigt line shape function with respect to the parameters (1) by using differential properties of the convolution, converting the first-order partial derivatives of the Voigt line shape function with respect to the line shape parameters into first-order partial derivatives of the Gauss line shape function or the Lorentz line shape function with respect to the line shape parameters; (2) calculating the first-order partial derivatives of the Gauss line shape function or the Lorentz line shape function with respect to the line shape parameters; (3) by using the Fourier transform properties of the convolution, calculating the first-order partial derivatives of the Voigt line shape function with respect to the line shape parameters; step 3: non-approximate least squares fitting of a Voigt line shape (1) setting initial values of line shape parameters to be fitted, and calculating the Voigt line shape function and first-order partial derivatives of the Voigt line shape function by using the initial values; (2) calculating an optimum factor SSE by using the calculated Voigt line shape function and a measured profile; (3) constructing a Hessian matrix and a gradient equation by using the calculated first-order partial derivatives of the Voigt line shape function, and obtaining an increment of parameters to be fitted; (4) recalculating the Voigt line shape function and optimum factor SSE by using the updated parameters to be fitted; and (5) comparing the new optimum factor with the old optimum factor, and determining whether to terminate the calculation or repeat steps (1) to (4) until fitting differences converge to an acceptable small value.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 91.0
- Lexical Diversity: 3.0
- Patent Class: 356.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['09838801', '11398696', '10573869', '13861284', '14986244']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6887676571293553
- 35 USC 102 Novelty (BERT): 0.4953034745899685
- Combined Prediction Score: 0.6694212388754166
- Mean Citation Score: 188.075492
- Max Citation Score: 220.92726
- Similarity Product: 147.04224496703625

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

Dataset: test