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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/11/2026 has been entered.
Claim Rejections - 35 USC § 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 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 1-2, 5-6, 8, 11-13, 16-17, 19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Provost (WO 2023/245290 A1) and Zhang (2020, Scatterer Distribution Estimation).
Regarding claims 1 and 12, Provost teaches an acoustic imaging device for subwavelength imaging of an imaged object within a set of scattering elements with subwavelength dimension and movably positioned around the imaged object, the device comprising:
at least one acoustic transducer capturing an acoustic image of the imaged object and scatterers at an acoustic wavelength [[abstract] system for ultrasound imaging comprising at least one ultrasonic transmitter configured to transmit at least one ultrasonic wave toward at least one target… ultrasonic wave backscattered … ultrasonic receiver … reconstruct at least one image; [0012] at least one strong scatterer is imaged with an ultrasound probe; [0062] microbubbles are clinically approved contrast agents used routinely in ultrasound imaging to improve the detection of vasculature], the acoustic transducer having a point spread function [[0056] appreciated that equation (8) substantially corresponds to the backprojected data divided by the amplitude of a Point Spread Function (PSF) in each pixel]; and
an image processor executing a stored program to [[0095] processing unit 602 may comprise, for example, any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor]:
(a) acquire multiple acoustic images of the imaged object with the set of scattering elements in different unknown locations [[0050] interpolate … desirable for this to be done since in the context of a cloud of microbubbles the random position of scatterers cannot be controlled in a way in which they are in the center of the pixels to be reconstructed] in different images [[0088] in one embodiment, the calibration procedure performed at step 202 comprises using reference images 302 and associated signals 304 generated by imaging clouds of strong scatterers, for example microbubbles, with an ultrasound probe 610 to create a dictionary 306. During reconstruction 308, the dictionary 306 and the signals 31 0 output by the ergodic relay (and detected by the ultrasonic receiver) are used to generate reconstructed images 312]; and
(b) process the multiple acoustic images using the point spread function [[0055-0056] real time image reconstruction … point spread function in each pixel; [0089] to form images/volumes using such an ergodic relay 122, the direct matrix K was first built by experimentally acquiring codas (i.e., signals measured by the ergodic relay 122) associated with point-sources (e.g. a 20-um wire in a water tank) in each pixel to be reconstructed.] in a predefined assumption of sparsity of the imaged object to provide an acoustic image revealing subwavelength features of the imaged object smaller than the acoustic wavelength [[0063] By locating the centroids of sparse scatterers circulating in the vascular network, ULM allows to go beyond the limits of conventional ultrasound imaging fixed by diffraction, and to go down to a resolution of only a few microns, using microbubbles, or sono-activated nanodroplets; [0064] PSF of a microbubble … localize microbubbles centers with a subwavelength precision].
Provost does not explicitly teach and yet Zhang teaches acquire multiple acoustic images of the imaged object with the set of scattering elements as movably positioned around the imaged object in different unknown locations in different images [[abstract] realistic ultrasound image appearance with typical speckle texture can be modeled as convolution of a point spread function with point scatterers representing tissue microstructure. Such scatterer distribution, however, is in general not known and its estimation for a given tissue type is fundamentally an ill-posed inverse problem. In this paper, we demonstrate a convolutional neural network approach for probabilistic scatterer estimation from observed ultrasound data.; [pg. 2, col. 1] problem with using scatterers in simulations is that such tissue representations are not known a priori. Assuming it can be modeled as a PSF convolution, finding a representation from observed US data can then be posed as a blind deconvolution problem. There has been several approaches and approximations to this problem, including inverse problem based solutions; [pg. 3, col. 1] we aim to find a tissue representation from observed image, which can be used to simulate the same tissue with varying imaging conditions. Rather than estimating a deterministic scatterer locations and amplitudes by solving a large-scale inverse problem, we proposed to impose a statistical model on the scatterer distribution and infer corresponding parameters from the observation. We learn the mapping from a single US image to its parameter map in a supervised manner; [pg. 4, col. 2] treating scatterers as physical tissue-embedded entities, we herein keep their amplitudes fixed while their spatial locations may change, similarly to the works on displacement tracking and elastography.].
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention with a reasonable expectation of success to combine the ultrasound imaging with microbubbles as taught by Provot, with the identification of sub-wavelength tissue structures known as scatterers using ultrasound trained by a convolutional neural network as taught by Zhang because once trained the network can estimate scatterer maps in milliseconds at inference time for arbitrary input image size (as being a fully convolutional network architecture), whereas in contrast the inverse problem based approach takes approximately 2 hours for one image from a single view (Zhang) [[pg. 10, col. 2]].
Regarding claims 2 and 13, Provost teaches the acoustic imaging device of claim 1 and the method of claim 12 wherein the image processor executes the stored program to iteratively model the imaged object with subwavelength dimensions to match the model to the received multiple acoustic images [[0050] difference in position with respect to the center of a pixel can be taken into account using an interpolation term h(x), e.g., by using a phase shift term of, or by forming small pixels that are then used to interpolate to a regular grid. It is desirable for this to be done since, in the context of a cloud of microbubbles, the random position of scatterers cannot be controlled in a way in which they are in the center of the pixels to be reconstructed; [0055] L2 norm].
Regarding claims 5 and 16, Provost teaches the acoustic imaging device of claim 1 and the method of claim 12 wherein the imaged object and set of scattering elements are surrounded by a liquid in which the scattering elements flow independently [[0062] Microbubbles are clinically approved contrast agents used routinely in ultrasound 320 imaging to improve the detection of vasculature … ultrasonic probe was submerged in a degassed, 12-liter water tank containing 1.08 x 107 definity microbubbles (Lantheus Medical Imaging, USA) and was aiming at an ultrasound absorber laying at the bottom of the water tank; [0068] multiple microbubbles flowing freely through water were used as the input data of an 380 encoder network of the calibration unit 108, the encoder network capturing frames and temporal information into latent feature layers. An expanding decoder network was then used to detect positions of the microbubbles].
Regarding claims 6 and 17, Provost teaches the acoustic imaging device of claim 5 and the method of claim 16 further including microbubble scattering elements in the liquid [[0062] microbubbles].
Regarding claims 8 and 19, Provost teaches the acoustic imaging device of claim 1 and claim 12 wherein the scatterers have an average cross-sectional dimension less than 1/5 of the acoustic wavelength [[0050] term "strong scatterer'' refers to an object having dimensions comparable to or smaller than the wavelength of the transmitted ultrasound waves.].
Regarding claim 11, Provost teaches the acoustic imaging device of claim 1 wherein at least one acoustic transducer is an ultrasonic transducer [[0041] ultrasonic probes having fully populated transducer element arrays].
Regarding claim 21, Provost teaches the method of claim 12 wherein the location of the set of scattering elements is uncharacterized prior to processing each given acoustic image of the imaged object with the set of scattering elements to provide the acoustic image revealing subwavelength features [[0062] Microbubbles are clinically approved contrast agents used routinely in ultrasound 320 imaging to improve the detection of vasculature; [0068] multiple microbubbles flowing freely through water were used as the input data of an 380 encoder network of the calibration unit 108, the encoder network capturing frames and temporal information into latent feature layers. An expanding decoder network was then used to detect positions of the microbubbles].
Regarding claim 22, Provost teaches the acoustic imaging device of claim 1 wherein the location of the set of scattering elements is uncharacterized prior to processing each given acoustic image of the imaged object with the set of scattering elements to provide the acoustic image revealing subwavelength features [[0062]; [0068]].
Claims 3-4 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Provost (WO 2023/245290 A1) and Zhang (2020, Scatterer Distribution Estimation) as applied to claim 2 above, and further in view of Murray (US 2019/0234911 A1).
Regarding claims 3 and 14, Provost does not explicitly teach and yet Murray teaches the acoustic imaging device of claim 2 and the method of claim 13 wherein the processing employs a joint sparsity calculation using the point spread function as a parameter [[0017] ultrasound transducer may be configured to generate multiple photoacoustic responses of multiple photoacoustic signals generated by the absorption object in response to illumination with the different speckle patterns. The ultrasound transducer may have a PSF or a LSF. The processor may be configured to perform operations that include reconstructing an absorber distribution of the absorption object by exploiting joint sparsity of sound sources in the photoacoustic responses using the PSF or LSF of the ultrasound transducer].
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to implement the sparse processing as taught by Provost, with the exploitation of joint sparsity of sound sources in the plurality of photoacoustic responses as taught by Murray so that the object distribution may be reconstructed (Murray) [[abstract]].
Regarding claims 4 and 15, Provost does not explicitly teach and yet Murray teaches the acoustic imaging device of claim 3 and the method of claim 14 wherein the processing back projects the acquired multiple acoustic images to an object plane of the imaged object before applying the joint sparsity calculation [[0017]].
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to implement the sparse processing as taught by Provost, with the exploitation of joint sparsity of sound sources in the plurality of photoacoustic responses as taught by Murray so that the object distribution may be reconstructed (Murray) [[abstract]].
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Provost (WO 2023/245290 A1) and Zhang (2020, Scatterer Distribution Estimation) as applied to claim 5 above, and further in view of Ruland (2021, Ultrasound in Med. & Biol.).
Regarding claims 7 and 18, Provost does not explicitly teach and yet Ruland teaches the acoustic imaging device of claim 5 and the method of claim 16 further including composite materials scattering elements presenting concentric layers of material providing a resonance at the frequency of the acoustic wave from the transducer [[title] reference phantom for ultrasonic imaging of thin dynamic constructs; [pg. 2395, col. 2] moulds consisted of three concentric rings 3 mm in height, where the centre ring, 8 mm in inner diameter, contained the agarose hydrogel (Fig. 4a). The rings were cut out from an acrylic sheet using a laser engraver (No. PLS6MW, Universal Laser Systems, Scottsdale, AZ, USA). Moulds were encapsulated with film wrap, using Teflon tape to provide a tight fit between rings. Before the molten agarose PS mixture was poured into the mould, one side of the ring was closed with film wrap after inserting the middle ring; [pg. 2401, col. 2] a limitation of this approach is expected in biological constructs with stratified layers, such as thin layers developing a dense extracellular matrix. In those cases, the average sound speed may not represent the actual local sound speed. Different methodologies for the local estimation of sound speed have been proposed (Jakovljevic et al. 2018).; [pg. 2392, col. 2] the backscatter coefficient of the reference phantom, BSCR (1/sr¢cm), per depth as a function of frequency was determined with the equation].
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to combine the transducer as taught by Provost, with the tissue phantom made from concentric layers as taught by Ruland so that backscatter coefficients as a function of frequency from a tissue phantom may be used to calibrate an ultrasonic transducer (Ruland) [[pg. 2392, col. 2]].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Provost (WO 2023/245290 A1) and Zhang (2020, Scatterer Distribution Estimation) as applied to claim 1 above, and further in view of Nokolov (US 2015/0245812 A1).
Regarding claim 10, Provost does not explicitly teach and yet Nokolov teaches the acoustic imaging device of claim 1 wherein the at least one acoustic transducer provides multiple transducer elements providing an output acoustic wave directed at the imaged object and measuring phase and acoustic amplitude of a return acoustic wave at a variety of locations to generate the acoustic image [[0002] transducer elements of the transducer array to transmit an ultrasonic beam and receive echoes produced in response thereto, which are processed to generate an image(s) of the interior characteristics; [0010] transmitting, with a two-dimensional non-rectangular transducer array, an ultrasound signal into a field of view, receiving, with the two-dimensional non-rectangular transducer array, echoes produced in response to an interaction between the ultrasound signal].
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to combine the transducer as taught by Provost, beamforming as taught by Nokolov so that an accurate image can be formed.
Allowable Subject Matter
Claims 9 and 20 are allowed.
Response to Arguments
Applicant’s arguments, see pg. , filed 6, with respect to claims 9 and 20 have been fully considered and are persuasive.
Applicant’s arguments, see pg. 9, filed 6/11/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Zhang (2020, Scatterer Distribution Estimation).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN D ARMSTRONG whose telephone number is (571)270-7339. The examiner can normally be reached M - F 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Isam Alsomiri can be reached at 571-272-6970. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JONATHAN D ARMSTRONG/Examiner, Art Unit 3645
Prosecution Insights