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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-3, 5-13, 16-21 in the reply filed on 05/19/2026 is acknowledged.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 5-13, 16-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yasuno et al. (US2023/0169627) in view of Pernisa et al. (US2014/0155748).
To claim 1, Yasuno teach a system for characterising tissues, the system comprising:
a processor; and a memory comprising instructions which when executed by the processor cause the processor to:
receive raw data corresponding to a dense two-dimensional (2D) image or signals arising from a scan of a tissues within a system of interest; generate a three-dimensional (3D) data set and representation from the dense 2D image data or signals (obvious in paragraph 0040, OCT imaging, paragraph 0123, ultrasound tomography imaging, wherein such 3D data reconstruction process from received raw data is a well-known practice in the art, hence Official Notice is taken); and
input the 3D data set into a convolutional network having a plurality of filters, said convolutional network configured to: reduce the 3D data set to a one-dimensional (1D) array corresponding to a frequency domain of the 3D data set (Fig. 4; paragraph 0087, input a 3D signal into a CNN and output a 1D characteristic values in frequency domain, such as signal intensity, resolution, or SNR); and
extract features from the 1D array and classify the 1D array into a tissue pathology classification based on the extracted features (obvious in paragraphs 0123, extracted characteristic values of tissue sample would correlate to pathological diagnosis).
In furthering said obviousness, Pernisa teach using quantitative ultrasound in pathological classification of region (paragraph 0203).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Pernisa into the system of Yasuno, in order to further tissue diagnosis with extracted characteristic values.
To claim 12, Yasuno and Pernisa teach a computer-implemented method of characterising tissues (as explained in response to claim 1 above).
To claim 2, Yasuno and Pernisa teach claim 1.
Yasuno and Pernisa teach wherein the raw data comprises at least one of ultrasound data and/or high-resolution microscopy and/or histopathology data (as explained in response to claim 1 above).
To claims 3 and 13, Yasuno and Pernisa teach claims 2 and 12.
Yasuno and Pernisa teach wherein the raw data further comprises at least one of patient demographic data, bmode data, and/or biomarker data (obvious as b-mode data of ultrasound correspond to 2D imaging data).
To claims 5 and 16, Yasuno and Pernisa teach claims 1 and 12.
Yasuno and Pernisa teach wherein to generate the 3D data set from the dense 2D image, the processor is configured to: discretize the 2D space through patches; and transform a frequency domain information in a third dimension (such reconstruction or transformation process is well-known in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply by design preference, hence Official Notice is taken).
To claims 6 and 17, Yasuno and Pernisa teach claims 5 and 16.
Yasuno and Pernisa teach wherein the transformation is one of: a Fast-Fourier transformation, Laplace transformation, Wavelet transformation, Z-transformation (as explained in response to claim 5 above, wherein specific 2D to 3D transformations are well-known in the art, hence Official Notice is taken).
To claims 7 and 18, Yasuno and Pernisa teach claims 1 and 12.
Yasuno and Pernisa teach wherein the processor is configured to at least one of:
divide the 3D data set into 3D segments; obtain a power spectrum from raw radio frequency (RF) data corresponding to each 3D segment; for each 3D segment: reduce the RF data in that 3D segment into a one-dimensional (1D) array; identify features of the tissue in that 1D array; and populate the 1D array into an RF data matrix such that a spatial relationship of the 3D segment is maintained with respect to neighbour 3D segments; receive a one-dimensional (1D) array representation of an image of a tissue; or inject one or more additional clinical values in the 1D array or at other steps of a convolutional neural network (in view of optional limitations, Yasuno and Pernisa teach segmenting 3D data in feature extraction of neural network of Fig. 4).
To claims 8 and 19, Yasuno and Pernisa teach claims 7 and 18.
Yasuno and Pernisa teach wherein the one or more additional clinical values include a Prostate Specific Antigen (PSA) value and/or additional biomarkers (since Yasuno and Pernisa teach one of optional limitations in claims 7 and 18, extension of other optional limitations would be considered).
To claims 9 and 20, Yasuno and Pernisa teach claims 1 and 12.
Yasuno and Pernisa teach wherein the identified features are obtained in a three dimensional (3D) matrix comprising spatial information along two planes and ultrasound power spectrums along a third plane (despite lack of disclosure, such feature is well-known in 3D representation of ultrasound, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate, hence Official Notice is taken).
To claims 10 and 21, Yasuno and Pernisa teach claims 9 and 20.
Yasuno and Pernisa teach wherein a 3D convolutional neural network is configured to capture spatio-frequency features from the ultrasound image, and reduce the features to a one dimension spectrum for final layers (as explained in response to claim 1 above, Yasumo, paragraphs 0110-0111).
To claims 11, Yasuno and Pernisa teach claim 1.
Yasuno and Pernisa teach wherein the tissue is one of several types found in: a liver, a thyroid, a breast, a kidney, a prostate, a bowel, a pancreas, an ovary, a musculoskeletal, skin and wounds, or other organs or glands (obvious in Pernisa, paragraph 0033).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 September 11, 2026
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