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 1-5 in the reply filed on 3 February 2026 is acknowledged. Claims 6-20 have been withdrawn.
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 1-5 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.
Claim 1 recites the limitation "the inputs" in line 8. There is insufficient antecedent basis for this limitation in the claim. The claim does not previously recite any inputs.
Claim 1 recites the limitation "the trained out" in line 12. There is insufficient antecedent basis for this limitation in the claim. The claim does not previously recite a trained output.
Claim 1 recites the limitation “the specified ultrasonic probe” in lines 9-10 which is considered indefinite. It is not clear to the examiner whether the specified ultrasonic probe is the same as or different from “a specification for an ultrasound probe” recited in line 2. For the purpose of examination and this office action they are being considered the same. Examiner recommends amending the claims to use consistent language throughout the claims in order to avoid confusion.
Claim 4 recites the limitation “the target anatomy” which is considered indefinite. It is not clear to the examiner whether the target anatomy recited in claim 4 is the same as or different from the anatomical target recited in claim 1. For the purpose of examination and this office action they are being interpreted as the same. Examiner recommends amending the claims to use consistent language throughout the claims in order to avoid confusion.
Claims dependent upon the rejected claims above, but not directly addressed, are also rejected because they inherit the indefiniteness of the claim(s) they respectively depend upon.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Navab et al. (US 20210103753, hereinafter Navab).
Regarding claim 1, Navab teaches a system for training a large vision model ([0171] and fig. 12 disclose system 10. [0008] discloses the invention trains an artificial neural network for finding landmarks. [0153] discloses the neural network is a convolutional neural network. [0015] of the present applications specification discloses the large vision model is a CNN (convolutional neural network), therefore the CNN of Navab represents a large vision model) comprising:
a specification for an ultrasound probe ([0023] discloses the use of different types of imaging devices for obtaining ultrasound images, thereby specifying the use of an ultrasound probe);
a database of volumetric anatomical scans ([0021] discloses the high dimensional images are received from a database. [0063] discloses the high dimensional image is a 3D ultrasound image, therefore the database comprises 3D (volumetric) anatomical scans);
a prompt string defining an anatomical target ([0139] discloses the high dimensional image includes a region of interest which defines an anatomical structure to be examined. [0028] additionally teaches the training data includes a training set that includes a collection of features (anatomical landmarks) to be used);
at least one computing device (the electronic circuitry of system 10 in fig. 12); and
an application ([0096] discloses a method executed by the system) executed by the at least one computing device that, when executed, causes the at least one computing device to at least:
train a large vision model ([0096] “a method for training or retraining an artificial neural network useful in finding landmarks in low dimensional images is provided”) where the inputs comprise the prompt string and data obtained by simulating a field produced by the specified ultrasonic probe and one of the volumetric anatomical scans ([0097]-[0098] “receiving input training data, namely synthetic low dimensional images of a region of interest simulated from a number of poses of a virtual low dimensional imaging device, from a high dimensional image of the region of interest, receiving output training data, namely synthetic low dimensional images of the region of interest and positions of landmarks within the low dimensional images”, where the landmarks represent the prompt string, the synthetic low dimensional images represent the simulated field produced by the specified ultrasonic probe [0062], and the high dimensional image represents the volumetric anatomical scans. Also see [0153]-[0156]); and
the trained output produces weights that generate a set of target locations ([0141] discloses determining the landmarks using the learning algorithm. Which corresponds to generating a set of target locations).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Navab in view of Pinkovich et al. (US 20210034921, hereinafter Pinkovich).
Regarding claim 2, Navab teaches the system of claim 1, as set forth above. Navab does not specifically teach the specified ultrasonic probe comprises a two-dimensional array of elements.
However,
Pinkovich in a similar field of generating simulated ultrasound field data discloses the specified ultrasonic probe comprises a two-dimensional array of elements ([0018] discloses the transducer array probe is a two-dimensional matrix transducer array probe).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the specified ultrasound probe of Navab for the specified ultrasonic probe comprising a two-dimensional array of elements of Pinkovich because it amounts to simple substitution of one known element for another to obtain the predictable results of training the large vision model.
Regarding claim 3, Navab teaches the system of claim 1, as set forth above. Navab does not specifically teach the specified ultrasonic probe comprises a one-dimensional array of elements.
However,
Pinkovich in a similar field of generating simulated ultrasound field data discloses the specified ultrasonic probe comprises a one-dimensional array of elements ([0018] discloses the transducer array probe is a one-dimensional transducer array probe).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the specified ultrasound probe of Navab for the specified ultrasonic probe comprising a one-dimensional array of elements of Pinkovich because it amounts to simple substitution of one known element for another to obtain the predictable results of training the large vision model.
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Navab in view of Rezai et al. (US 20230352141, hereinafter Rezai).
Regarding claim 4, Navab teaches the system of claim 1, as set forth above. Navab does not specifically teach the target anatomy is a structure within a human brain.
However,
Rezai in a similar field of training models teaches the target anatomy is a structure within a human brain ([0126] discloses the segmented (target) structure is a structure within the brain).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of having the target anatomy be a structure within the human brain of Rezai to the system of Navab to allow for the predictable results of making the system more versatile.
Regarding claim 5, Navab teaches the system of claim 1, as set forth above. Navab does not specifically teach the large vision model is trained on a database of cranial data.
However,
Rezai in a similar field of training models teaches the large vision model is trained on a database of cranial data ([0126] discloses the convolutional neural network is trained on a plurality of annotated image samples, which include annotations of the regions of the brain).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of having the large vision model be trained on a database of cranial data of Rezai to the system of Navab to allow for the predictable results of making the system more versatile.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW BEGEMAN whose telephone number is (571)272-4744. The examiner can normally be reached Monday-Thursday 8:30-5:00.
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/ANDREW W BEGEMAN/Examiner, Art Unit 3798