DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-6, 12-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Robert et al (US20210113182A1) in view of Chen et al (WO2024087406A1).
Regarding claims 1, 13 and 20, D1 teaches an ultrasonic imaging method, comprising:
acquiring a plurality sets of channel data of a target part;
(D1, Fig. 2; "A transducer array 12 is provided in an ultrasound probe 10 for transmitting ultrasonic waves and receiving echo signals.", [0015]; "For multiline reception the echo signals received by elements of the array 12 are beamformed in parallel by appropriately delaying them and then combining them in multiline sub-beamformers BF1, BF2, . . . BFN", [0016]; D1 teaches an ultrasonic imaging method that acquires pluralities of echo signals or channel data from a target part for parallel processing)
performing phase compensations on the plurality sets of the channel data to obtain a plurality sets of compensated channel data corresponding to the plurality sets of the channel data respectively; and
(D1, Fig. 2a; "In accordance with the principles of the present invention, the multiline signals used to form synthetically focused ultrasound scanlines are adjusted prior to being combined to account for speed of sound variation in the transmission medium.", [0007]; "The compensated delays are then used by delay lines 118 a . . . 118 n to remove the phase aberration during the synthetic transmit focusing process.", [0028]; D2, "The present invention directly establishes a network model of wrapped phase image input and Zernike polynomial output in advance, automatically compensates for most of the distortion components in the wrapped phase image before phase unwrapping", "Compared with the traditional phase distortion compensation method, the present invention does not require any manual intervention, input of initial parameters and restriction of sample types, thus improving the efficiency of phase distortion compensation operation.", [p4]; D1 teaches performing phase compensations on the plurality of signals/channel data by calculating differential delays to remove phase aberrations; D2 teaches performing phase distortion compensation using a trained neural network to improve efficiency over traditional methods; together D1 and D2 teach performing phase compensations on the plurality sets of the channel data to obtain compensated channel data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the teachings of D2 into the system or method of D1 in order to improve the calculation speed, accuracy, and efficiency of the phase aberration correction without requiring manual intervention. The combination of D1 and D2 also teaches other enhanced capabilities.
The combination of D1 and D2 further teaches:
obtaining an ultrasonic image of the target part according to the plurality sets of the compensated channel data.
(D1, Fig. 2a; "The delayed signals are combined by a summer 120 and the resultant image signals are coupled to the image processor 122.", [0021]; "The processed signals are coupled to an image processor 122 for processing such as B mode detection and scan conversion, and the resultant images are displayed on a display 124.", [0017]; obtaining an ultrasonic image by combining and processing the phase-compensated signals)
Regarding claims 2 and 14, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 1, wherein performing the phase compensations on the plurality sets of the channel data to obtain the plurality sets of the compensated channel data corresponding to the plurality sets of the channel data respectively comprises:
inputting the plurality sets of the channel data into a pre-trained phase compensation model to obtain a plurality sets of phase compensation data for the corresponding channel data; and
(D2, "S1: ... calculate the wrapped phase diagram of the object light complex amplitude U and input it into the trained neural network model, and output the Zernike polynomial coefficient Ac ;", [p3]; inputting channel data into a pre-trained neural network (phase compensation model) to obtain Zernike polynomial coefficients (phase compensation data))
determining the plurality sets of the compensated channel data according to the corresponding phase compensation data and the corresponding channel data.
(D2, "S2: Use Zernike polynomial coefficients Ac to fit the phase distortion ... Multiplying the complex amplitude U of the object light to compensate for most of the phase distortion in the complex amplitude U of the object light", [p3]; determining compensated channel data by combining the original data with the phase distortion data fitted from the coefficients)
Regarding claims 3 and 15, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 2, further comprising training the phase compensation model, wherein training the phase compensation model comprises:
acquiring sample channel data, the sample channel data comprising training channel data and validation channel data;
(D2, "The training set with a large number of simulated wrapped phase images φ and corresponding Zernike polynomial coefficients A as network input and label is calculated and generated", [p5]; acquiring sample data containing training data. It is standard machine learning practice to partition sample data into training and validation sets)
training a neural network model using the training channel data to obtain an initial phase compensation model; and
(D2, "train the neural network model, and obtain a trained neural network model.", [p3]; training the neural network using the training data)
adjusting model parameters of the initial phase compensation model using the validation channel data to obtain the phase compensation model.
(D2, "The more data in the training set, the less likely the network is to overfit.", [p5]; while D2 teaches using data to prevent overfitting, it is notoriously standard in the art to adjust hyperparameters/model parameters using validation channel data during training to ensure optimal generalization)
Regarding claim 4, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 3, wherein the sample channel data further comprises test channel data, and the ultrasonic imaging method further comprises:
evaluating a generalization ability of the phase compensation model based on the test channel data.
(D2, "The network only needs to be trained once, and then the trained network can be used to perform unlimited regression analysis on unknown samples.", [p4]; D2 implies applying the model to unknown samples. Evaluating generalization ability with test channel data is an obvious and standard machine learning practice)
Regarding claims 5 and 16, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 3, wherein training the neural network model using the training channel data to obtain the initial phase compensation model comprises:
inputting the training channel data into the neural network model to obtain training phase compensation data corresponding to the training channel data;
(D2, "use the simulated wrapped phase map φ as the input of the neural network model", [p3]; inputting the training data into the neural network model to generate phase compensation predictions)
determining a compensation loss according to the training phase compensation data; and
(D2, "the loss function is the root mean square error function", [p5]; determining a compensation loss for the training data)
adjusting the model parameters of the neural network model based on the compensation loss until the compensation loss is less than a preset threshold, to obtain the initial phase compensation model.
(D2, "the optimizer is Adam", [p5]; using an optimizer to iteratively adjust model parameters based on the loss to reduce it)
Regarding claim 6, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 5, wherein determining the compensation loss according to the training phase compensation data comprises:
acquiring target phase compensation data corresponding to the training channel data; and
(D2, "use the corresponding Zernike polynomial coefficient A as the label of the neural network model", [p3]; acquiring target labels corresponding to the training data)
determining the compensation loss according to the training phase compensation data and the target phase compensation data.
(D2, "the loss function is the root mean square error function", [p5]; determining the RMSE loss based on the predicted and target phase compensation data)
Regarding claim 12, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 1, wherein obtaining the ultrasonic image of the target part according to the plurality sets of the compensated channel data comprises:
performing beam synthesis on the plurality sets of the compensated channel data to obtain image data; and
(D1, Fig. 2a; "The echoes from each line are weighted by the multipliers 116 a-116 n and delayed by delay lines 118 a-118 n. In general, these delays will be related to the location of the transmit beam center to the receive line location.", [0021]; performing beam synthesis by weighting and delaying the phase-compensated signals)
obtaining the ultrasonic image according to the image data.
(D1, Fig. 2a; "The delayed signals are combined by a summer 120 and the resultant image signals are coupled to the image processor 122.", [0021]; obtaining the ultrasonic image by combining the beam-synthesized signals)
Claim(s) 7-8, 11, 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Robert et al (US20210113182A1) in view of Chen et al (WO2024087406A1) and further in view of Yamamoto (US20150080731A1).
Regarding claims 7 and 17, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 1, wherein the ultrasonic imaging method further comprises:
determining parameter values of at least one of phase distortion parameters according to the plurality sets of the compensated channel data; and
(D1, "the phase discrepancy of the received multilines caused by speed of sound variation in the medium is estimated in the frequency domain", [0007]; "The receive phase correction operates on multiline data which has already undergone dynamic focusing by the circuitry of FIG. 2a , its expected phase terms, in the absence of speed of sound aberration, should be flat at all depths", [0027]; "The phase term for receive aberration is then computed and used to correct the data", [0026]; determining a phase term/discrepancy (parameter value) from multiline data that has already been dynamically focused (compensated channel data))
The combination does not expressly disclose but D3 teaches:
evaluating a quality of the ultrasonic image according to the parameter values.
(D3, "an image quality determination unit configured to determine image qualities of the ultrasound images each corresponding to each of the set reception aperture levels and select an ultrasound image having a predetermined image quality from among the ultrasound images", [0015]; "the image quality determination unit 52 determines the image quality of each ultrasound image based on the brightness value, sharpness, or the like", [0065]; D1 teaches the parent claim and estimation of phase distortion; D3 teaches a quality determination unit that evaluates image parameters to select a high-quality image)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate D3’s evaluation logic into the system or method of D1 and D2 in order to assess if the phase compensation resulted in a qualified image based on the estimated distortion parameters. The combination of D1, D2 and D3 also teaches other enhanced capabilities.
Regarding claim 8, the combination of D1, D2 and D3 teaches it/their respective base claim(s).
The combination further teaches ultrasonic imaging method according to claim 7, wherein the phase distortion parameters comprise at least one of distortion intensity, correlation length of phase distortion, energy level fluctuation, and coherence coefficient.
(D1, "which averages the speckle effect on the correlation coefficient over N multilines", [0025]; D2, "input it into the trained neural network model, and output the Zernike polynomial coefficient Ac”, [p3]; “characterize the phase distortion", [p5]; D1 teaches a correlation coefficient, which corresponds to a coherence coefficient; D2 teaches Zernike coefficients which represent the magnitude (intensity) of the phase distortion; together with D3’s quality unit, it would be obvious to use these specific coefficients as the parameters for quality evaluation)
Regarding claims 11 and 19, the combination of D1 and D2 teaches it/their respective base claim(s).
The combination further teaches the ultrasonic imaging method according to claim 7, wherein evaluating the quality of the ultrasonic image according to the parameter values comprises:
comparing each of the parameter values with a corresponding preset parameter value threshold range; and
determining that the quality of the ultrasonic image is qualified on a condition that each of the parameter values falls within the corresponding preset parameter value threshold range.
(D3, "the image quality determination unit 52 selects an ultrasound image having a value equal to or greater than a threshold value with respect to the brightness value, sharpness, or the like corresponding to an ultrasound image having a predetermined image quality", [0065]; comparing image parameters to a threshold value to determine if an image is qualified (has a predetermined quality); it would be obvious to apply this thresholding method to the phase parameters determined in D1 and D2 to automate the quality validation process)
Allowable Subject Matter
Claim(s) 9, 10 and 18 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening Claim(s).
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 9, 10 and 18 recite(s) limitation(s) related to calculating the coherence coefficient parameter using beam synthesized data and corresponding mean value data; and calculating distortion intensity parameter values using delay error data extracted from beam synthesized data. There are no explicit teachings to the above limitation(s) found in the prior art cited in this office action and from the prior art search.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 8/22/2026
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