DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102/103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-14 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Li-Arxiv(2021).
Regarding claim 1, Li teaches A computer-implemented method for beamforming of ultrasound channel data to obtain a beamformed image[1.3.3 is ultrasonic imaging with beamforming], the method comprising the steps of receiving channel data acquired by an ultrasound transducer in response to an ultrasound transmission[Page 20 Lines 6-18, Fig 1.7 has measurements Y which is received channel data];
determining an initial estimate of the beamformed image as intermediate beamformed image data[equations 1.20, 1.21 and page 20, Lines 29 to Page 21 Lines 9 has L0 and S0 which is initial estimate];
performing at least one iteration of a processing operation which comprises a data consistency step followed by a prior step [Equation 1.20 and 1.21 are an iteration of a processing operation], wherein
the data consistency step takes as input the channel data and the intermediate beamformed image data[Equations 1.19 minimization procedure between Y and calculated Y and Fig 1.7 have channel data Y and intermediate data Ll and Sl],
performs at least one processing step which is designed to improve the consistency of the intermediate beamformed image data with the channel data and outputs updated intermediate beamformed image data[minimization of eq. 1.19 by using iteration in Equations 1.20 and 1.21 and fig 1.7 have processing to improve data with the other terms in the equation brackets]; wherein the data consistency step includes the steps of:
processing the channel data(Y) and the intermediate beamformed image data(H1L+H2S) in a first processing step[Equation 1.20 has intermediate image data Ll and Sl and channel data Y being processed];
and adding the result of the first processing step to the intermediate beamformed image data to obtain the updated intermediate beamformed image data [everything repeated for second, third …. iterations Equation 1.20 has intermediate image data Ll and Sl and channel data Y being processed and Ll and Sl being added to];
the prior step takes as input the updated intermediate beamformed image data and performs at least one processing step, which uses a prior assumption on the beamformed image data, to improve the updated intermediate beamformed image data and outputs an improved updated intermediate image data[Page 20 Lines 35-37 has Tλ and Sλ1,2 for thresholding operators and are selected based on prior assumptions see Page 20 Lines 18-37; See also fig 1.7];
outputting the improved updated intermediate image data as the beamformed image [Fig 1.8 and Page 21, Lines 18-27 has output of data as image].
In the event that Li is not explicit in the adding the results to obtain updated data, it would have been obvious to one of ordinary skill in the art before the filing date to have an iterative process for further processing the data for a better output.
Regarding claim 11, Li A computer-implemented method for providing a trained algorithm and which includes trainable parameters, the method comprising: receiving input training data, the input training data comprising channel data acquired by an ultrasound transducer in response to an ultrasound transmission[Page 20 Lines 6-18, Fig 1.7 has measurements Y which is received channel data Page 21, Lines 12-17 has training data and network training meaning trained algorithm];
receiving output training data, the output training data comprising beamformed image data obtained from the input training data, preferably by a content-adaptive beamforming algorithm such as a minimum variance algorithm[ Page 21, Lines 12-17 has training data and network training meaning trained algorithm; page 32 as iterative algorithms with low variance];
training the algorithm by using the input training data and the output training data[Page 21, Lines 12-17 has training data and network training meaning trained algorithm; page 32 as iterative algorithms with low variance; Page 20 Lines 6-18, Fig 1.7 has measurements];
providing the trained algorithm[Page 21, Lines 12-17 has training data and network training meaning trained algorithm].
Regarding claim 14, Li teaches A system for beamforming of ultrasound channel data to obtain a beamformed image, the system comprising: a first interface, configured for receiving channel data acquired by an ultrasound transducer in response to an ultrasound transmission[Page 20 Lines 6-18, Fig 1.7 has measurements Y which is received channel data];
a computational unit configured for determining an initial estimate of the beamformed image as intermediate beamformed image data[equations 1.20, 1.21 and page 20, Lines 29 to Page 21 Lines 9 has L0 and S0 which is initial estimate];
and performing at least one iteration of a processing operation which comprises a data consistency step followed by a prior step[Equation 1.20 and 1.21 are an iteration of a processing operation], wherein
the data consistency step takes as input the channel data and the intermediate beamformed image data[Equations 1.19 minimization procedure between Y and calculated Y and Fig 1.7 have channel data Y and intermediate data Ll and Sl],
performs at least one processing step which is designed to improve the consistency of the intermediate beamformed image data with the channel data and outputs updated intermediate beamformed image data[minimization of eq. 1.19 by using iteration in Equations 1.20 and 1.21 and fig 1.7 have processing to improve data with the other terms in the equation brackets]; wherein the data consistency step includes the steps of:
processing the channel data (Y) and the intermediate beamformed image data(H1L+H2S) in a first processing step [Equation 1.20 has intermediate image data LL and SL and channel data Y being processed];
and adding the result of the first processing step to the intermediate beamformed image data to obtain the updated intermediate beamformed image data[everything repeated for second, third …. iterations Equation 1.20 has intermediate image data LL and SL and channel data Y being processed and LL and SL being added to];
the prior step takes as input the updated intermediate beamformed image data and performs at least one processing step, which uses a prior assumption on the beamformed image data, to improve the updated intermediate beamformed image data and outputs an improved updated intermediate image data[Page 20 Lines 35-37 has Tλ and Sλ1,2 for thresholding operators and are selected based on prior assumptions see Page 20 Lines 18-37; See also fig 1.7];
a second interface, configured for outputting the improved updated intermediate image data as the beamformed image[Fig 1.8 and Page 21, Lines 18-27 has output of data as image].
In the event that Li is not explicit in the adding the results to obtain updated data, it would have been obvious to one of ordinary skill in the art before the filing date to have an iterative process for further processing the data for a better output.
Regarding claim 2, Li teaches wherein the data consistency step includes at least one processing step, which is performed by a trained neural network.[Page 21, Lines 1-17 have algorithm with training data ad convolutional layers meaning neural network]
Regarding claim 3, Li teaches wherein the first processing step includes the steps of computing a residual by multiplying the intermediate beamformed image data pixel-by-pixel with a steering vector and subtracting the result from the channel data[Equation 1.20 and 1.21 has iterative processing of data with multiplication, subtraction of various factors]; processing the residual and the channel data by a second processing step.[Equation 1.20 and 1.21 has iterative processing of data]
Regarding claim 4, Li teaches wherein the method includes calculating a set of apodization weights from the channel data, preferably by a trained neural network, [Page 20 and 21 has convolutional layers meaning neural network and Page 25 has convolutional recurrent neural network with weights]and wherein the second processing step includes multiplying the residual by the apodization weights.[Equation 1.20 and 1.21 has iterative processing of data and Page 25 has convolutional recurrent neural network with weights]
Regarding claim 5, Li teaches wherein the first processing step includes a processing step which is performed by a trained neural network, wherein preferably the second processing step is performed by said trained neural network [Page 21, Lines 1-17 have algorithm with training data ad convolutional layers meaning neural network; Page 25 has convolutional recurrent neural network].
Regarding claim 6, Li teaches wherein the prior step is based on the prior assumption that the beamformed image is sparse.[Page 20 Lines 1-21 has Contrast enhanced ultrasound and matrix data as sparse].
Regarding claim 7, Li teaches wherein the prior step includes at least one processing step which is performed by a trained neural network.[Page 21, Lines 1-17 have algorithm with training data ad convolutional layers meaning neural network].
Regarding claim 8, Li teaches The method of claim lone of the preceding claims, wherein the prior step comprises a soft-thresholding step.[Page 20 Lines 35-37 has Tλ and Sλ1,2 for thresholding operators]
Regarding claim 9, Li teaches wherein a pre- determined number of iterations of the processing operation is carried out, preferably 1 to 20, more preferably 2 to 10 iterations.[Page 21, Lines 10-11 have output after L iterations meaning predetermined number of iterations]
It would have been obvious to one having ordinary skill in the art at the time the invention was made to select an arbitrary number of iterations, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233.
Regarding claim 10, Li teaches wherein the method is performed by an algorithm which uses parameters which have been trained from training data.[Page 21, Lines 1-17 have algorithm with training data].
Regarding claim 12, Li teaches wherein the trained algorithm comprises the steps of: performing at least one iteration of a processing operation which comprises a data consistency step followed by a prior step [Equation 1.20 and 1.21 are an iteration of a processing operation], wherein
the data consistency step takes as input the channel data and the intermediate beamformed image data[Equations 1.19 minimization procedure between Y and calculated Y and Fig 1.7 have channel data Y and intermediate data Ll and Sl],
performs at least one processing step which is designed to improve the consistency of the intermediate beamformed image data with the channel data and outputs updated intermediate beamformed image data[minimization of eq. 1.19 by using iteration in Equations 1.20 and 1.21 and fig 1.7 have processing to improve data with the other terms in the equation brackets]; wherein the data consistency step includes the steps of:
processing the channel data (Y) and the intermediate beamformed image data(H1L+H2S) in a first processing step[Equation 1.20 has intermediate image data LL and SL and channel data Y being processed];
and adding the result of the first processing step to the intermediate beamformed image data to obtain the updated intermediate beamformed image data[everything repeated for second, third …. iterations Equation 1.20 has intermediate image data LL and SL and channel data Y being processed and LL and SL being added to];
the prior step takes as input the updated intermediate beamformed image data and performs at least one processing step, which uses a prior assumption on the beamformed image data, to improve the updated intermediate beamformed image data and outputs an improved updated intermediate image data [Page 20 Lines 35-37 has Tλ and Sλ1,2 for thresholding operators and are selected based on prior assumptions see Page 20 Lines 18-37; See also fig 1.7];
and wherein at least one of steps includes parameters, preferably parameters of a trainable neural network, which are trained by using the input and output training data.[Page 20 Lines 6-18, Fig 1.7 has measurements Y which is received channel data Page 21, Lines 12-17 has training data and network training meaning trained algorithm; Page 25 has convolutional neural network].
In the event that Li is not explicit in the adding the results to obtain updated data, it would have been obvious to one of ordinary skill in the art before the filing date to have an iterative process for further processing the data for a better output.
Regarding claim 13, Li teaches which, when the program is executed by a computational unit, causes the computational unit to carry out a method according to claim 1[Ultrasound system is a computer].
Conclusion
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/VIKAS ATMAKURI/Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645
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