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 embodiment 1 (claims 1-12) in the reply filed on 09/24/2025 is acknowledged.
Claim Objections
Claim is objected to because of the following informalities:
In claim 2, line 2 the term “patent’s age” should be changed to “patient’s age”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 1, the step of “performing whole body MRI screening for the patient comprising the personalized set of MR sequences” is unclear since the personalized set of MR sequences determined by the step after are not specifically directed to the whole body, but rather just an MRI sequence personalized for a patient; which can merely to for example the heart of the patient alone; but then a whole body MRI screening is performed comprising such sequences. It appears, the first AI model, needs to determine the MRI sequences for the whole body of the patient in the step of “determining”.
In claim 1 it is unclear what is meant by “interpreting the results” is vague and unclear. For example it can merely imply that the images have been taken without any error by the MR magnet based on the MR sequences, without entering into the merits of the actual patients data being collected.
In claim 2, it is set forth to tailor the MR sequences based on…”risk factors”; risk factors of what characteristics? What is considered a risk factor in the claims.
In claim 3, it is set forth that the whole body MRI is adjusted based on real time analysis; is the AI model is performed such analysis, the claim is unclear.
In claim 4,5 it is set forth identifying… a “vascular anomaly” or “sign of degenerative disease”; what would be considered an “anomaly” or “sign” of degenerative diseases by the method; they appear to be relative terms.
In claim 5, the term “potential concern” it appears to be a relative term.
In claim 8, it is set forth to “generate a report” based on results; it is unclear what does it means; a report of what specifically; would the MR images being displayed can be considered a report since it showing the results?
In claim 9, the term “simple” is a relative term.
Claim Rejections - 35 USC § 102
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, 3-5, 7-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Odry et al. (US 2019/0320934, hereinafter Odry).
Odry discloses a method for Whole Body MRI Screening using AI powered models, the method comprising (see Fig. 3):
determining, by a first AI model, a personalized set of MR sequences for a patient based on initial images and available clinical data (See steps A120 or A210 in which a scout acquisition sequence (determine using machine learning) is determined and image is acquired (see para. 0032, 0033, 0038, 0039);
performing whole body MRI screening for the patient comprising the personalized set of MR sequences (see para. 0022, 0023, 0062, 0038, 0039 0033, “whole body” coil for imaging);
and interpreting the results of the whole body MRI screening using one or more second AI models (see step A260 in which anomalies of the patient are identified using deep learning and machine learning, see para. 0067).
PNG
media_image1.png
404
742
media_image1.png
Greyscale
With respect to claim 3, Odry further discloses adjusting the whole body MRI screening based on real-time analysis of real-time results of one or more of the personalized set of MR sequences (see para. 0004-0008).
With respect to claim 4, Odry further discloses wherein interpreting comprises:identifying one or more areas of potential concern for the patient, wherein the one or more areas related to a tumor, vascular anomaly, or sign of degenerative diseases (see Fig. 3 step A260).
With respect to claim 5, Odry further discloses identifying one or more areas of potential concern for the patient based on results from an initial sequence of the personalized set of MR sequences, wherein the one or more areas related to a tumor, vascular anomaly, or sign of degenerative diseases; and selecting a new sequence not included in the personalized set of MR sequences based on the identification (see para. 0073, Fig. 3 step A270.
With respect to claim 7, Odry further discloses wherein comparing is performed using an autoencoder-based anomaly detection method (see para. 00062).
With respect to claim 8, Odry further discloses generating a report based on the interpreted results (see para 0052, 0074, 0084).
With respect to claim 9, Odry further discloses wherein the report includes simple language and simple visuals for the patient (para. 0052, 0074, 0084).
With respect to claim 10, Odry further discloses wherein the first Al model and/or the one or more second Al models comprise machine trained neural networks (see para. 0039).
With respect to claim 11, Odry further discloses wherein the one or more second Al models comprise at least one machine trained network trained for segmenting image data for a specific region of the patient (see para. 0039, 0041).
With respect to claim 12, Odry further discloses wherein the at least one machine trained network comprises a machine trained segmentation network (see para. 0058).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2, 6 are is/are rejected under 35 U.S.C. 103 as being unpatentable over Odry et al. (US 2019/0320934, hereinafter Odry) in view of Lin et al. (US 2018/0228857, hereinafter Lin)
With respect to claim 2, Odry discloses the method as discloses above, but fails to teach to further discloses wherein determining comprises:tailoring the personalized set of MR sequences based on the patent's age, risk factors, and prior medical history.
In the same field of endeavor in the subject of smart image acquisition protocoling using machine learning, Lin discloses that medical history, lab results, prior images, deomographic information, can be used to generate an imaging protocal by the machine learning algorithm (see abstract, para. 0037, 0041).
It would have been obvious to one skilled in the art before the effective filing date to tailor the personalized set of MR sequences based on the patent's age, risk factors, and prior medical history as disclosed by Lin because doing so will allow for smart imaging protocoling (see Lin para. 0005).
With respect to claim 6, Lin further discloses wherein interpreting comprises: comparing results of the personalized set of MR sequences with prior MRIs, CTs, or other images from the patient's history; and identifying any changes or developments (see para. 0060). It would have been obvious to one skilled in the art before the effective filing date to compare results of the personalized set of MR sequences with prior MRIs, CTs, or other images from the patient's history; and identifying any changes or developments as disclosed by Lin because doing so will allow for smart imaging protocoling (see Lin para. 0005).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M SANTOS RODRIGUEZ whose telephone number is (571)270-7782. The examiner can normally be reached Monday-Friday 8:30am to 5:30pm.
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, Anne M. Kozak can be reached at 571-270-0552. 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.
/JOSEPH M SANTOS RODRIGUEZ/Primary Examiner, Art Unit 3797