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 .
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hsieh et al (Pub. No.:
US 2018/0144243)
Regarding claims 1, 8, 15, Hsieh et al disclose a method of performing ultrasound imaging, the
method comprising:
retrieving prior imaging data for a patient [see 0051, 0075] by disclosing deep learning machines
can be used to quantitatively measure qualitative aspects of images. For example, deep learning
machines can be utilized after an image has been acquired [see 0075];
segmenting relevant structures (observable features) from the prior imaging data [see 0051,
0065, 0067, 0082, 0262-0263] by disclosing medical image visualization software allows a clinician to segment, annotate, measure, and/or report functional or anatomical characteristics on various locations
of a medical image. In some examples, a clinician may utilize the medical image visualization software to
identify regions of interest with the medical image [see 0051];
extracting quantitative parameters (functional or anatomical characteristics) of the patient from
the segmented relevant structures [see 0051, 0054, 0058, 0067, 0262-0263] by disclosing a machine
provided with a large set of well classified data is better equipped to distinguish and extract the features
pertinent to successful classification of new data [see 0067];
applying a trained predictive model to the extracted quantitative parameters to determine
whether or not to apply a contrast agent to the patient [see 0055, 0058, 0065, 0215] by disclosing the
deployed learning device 2050 can determine how to scan the patient 1406, use or do not use contrast
injection (e.g., how fast, concentration, total injection volume, etc.), use or do not use dual energy, etc.
Settings can be evaluated and configured for a plurality of imaging modalities, such as CT, MICT, SPECT,
PET [see 0215].
Regarding claims 2, 16, Hsieh et al disclose before the retrieving, determining when prior
imaging data are available for the patient [see 0075].
Regarding claims 3, 13, Hsieh et al disclose wherein when the prior imaging data are not
available for the patient, instead of the segmenting, retrieving non-imaging data and extracting the
quantitative parameters from the non-imaging data [see 0164, 0176, 0193] by disclosing personalize
patient variables are input to the acquisition engine 1430. Personalized patient variables can include
patient height, patient weight, imaging type, reason for exam, patient health history [see 0193].
Regarding claims 4, 9, 14, 17, 20, Hsieh et al disclose wherein the non-imaging data comprises
demographic data, or clinical data from the patient, or both [see 0164, 0176, 0193] by disclosing
personalize patient variables are input to the acquisition engine 1430. Personalized patient variables can
include patient height, patient weight, imaging type, reason for exam, patient health history [see 0193].
Regarding claims 5, 10, 18, Hsieh et al disclose wherein the prior imaging data comprises one or
more of magnetic resonance imaging (MRI) data and computer tomography (CT) imaging data [see 0177,
0215].
Regarding claim 6, Hsieh et al disclose wherein the segmenting further comprises applying a
model containing anatomy-specific segmentation parameters [see 0259].
Regarding claims 7, 11, 19, Hsieh et al disclose wherein the relevant structures comprise rib
spacing [see 0259, 0263], or a thickness of subcutaneous fat, or both [see 0262].
Regarding claim 12, Hsieh et al disclose wherein the ultrasound imaging device comprises a
cardiac imaging device [see 0119, 0215-0216, 0257, 0259].
Response to Arguments
Applicant's arguments filed 9/2/2025 have been fully considered but they are not persuasive.
Applicant argues that Hsieh et al. fail to disclose the steps of segmenting structures from prior imaging data, then using those segmented structures to extract quantitative parameters, and then apply a trained model to those quantitative parameters to determine whether or not to apply a contrast agent to the patient. Hsieh et al. mention segmenting images using software, Para. [0051], and use of patient parameters, Para. [0143], but fail to contemplate extracting those patient parameters from the segmented relevant structures. Hsich et al. mention using a deployed learning device to determine whether or not to use contrast injection, Para. [0215], but utterly fail to contemplate making such determination from their extracted patient parameters.
The examiner disagrees because the same machine that does the extracting is the same machine that performs the analysis with the extracted data by disclosing extracting quantitative parameters (functional or anatomical characteristics) of the patient from
the segmented relevant structures [see 0051, 0054, 0058, 0067, 0262-0263] by disclosing a machine provided with a large set of well classified data is better equipped to distinguish and extract the features
pertinent to successful classification of new data [see 0067];
applying a trained predictive model to the extracted quantitative parameters to determine
whether or not to apply a contrast agent to the patient [see 0055, 0058, 0065, 0215] by disclosing the deployed learning device 2050 can determine how to scan the patient 1406, use or do not use contrast injection (e.g., how fast, concentration, total injection volume, etc.), use or do not use dual energy, etc. Settings can be evaluated and configured for a plurality of imaging modalities, such as CT, MICT, SPECT, PET [see 0215].
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOEL F BRUTUS whose telephone number is (571)270-3847. The examiner can normally be reached Mon-Sat, 11:00 AM to 7:00 PM.
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/JOEL F BRUTUS/ Primary Examiner, Art Unit 3797