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
Claims 15-16 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected apparatus, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 02/13/2026
Applicant's election with traverse of Group I in the reply filed on 02/13/2026 is acknowledged. The traversal is on the ground(s) that “the wording of the processor function of Claim 15 is essentially identical to the Claim 1 method recititation [sic]. Applicant also respectfully submits that the mere presence of an input/output in the Claim 15 device is insufficient to warrant a Restriction because the method Claim 1 also recites an inputting step and an outputting step”. This argument does not present an argument on the merits of a restriction. Additionally, Applicant failed to address that the method performed by the device of Group II is distinct from that of Group I, including but not limited to the plane-mapping.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The disclosure does not support the functionality of the claimed disease detection module or how it is capable of detecting disease using these features without undue experimentation. The disclosure does not support the claimed plane-mapping module or how it is able to identify 2D slices.
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.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are: There is no connection, especially no structural connection, between a “trained algorithm” and the claimed elements applicant proports to further limit.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because computer code can encompass a signal per se.
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) 1-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (US 2014/0039318 A1).
Regarding claim 1, Zhang et al. (‘318) teach a computer-implemented method, comprising: receiving as input from an ultrasound acquisition system a 3D ultrasound image dataset comprising at least one 3D image frame spanning a 3D field (see [0039]); applying a disease detection module adapted for processing input 3D image frames and detecting one or more suspected disease features therein of a pre-defined set of possible disease features (see [0043]); applying a plane-mapping module adapted to determine for each 3D image frame a set of one or more 2D slices through the 3D field of the image frame based on an output from the disease detection module, for imaging one or more disease regions associated with the detected one or more disease features (see [0052], [0054]-[0055]); and generating control instructions for output to the ultrasound acquisition system for causing automated acquisition of the set of one or more 2D slices (see [0042]).
Regarding claim 2, Zhang et al. (‘318) teach the method of claim 1, wherein the input 3D ultrasound image dataset comprises a stream of 3D image frames, and wherein the steps of the method are performed in real time with receipt of each 3D image frame (see [0042]).
Regarding claim 3, Zhang et al. (‘318) teach the method of claim 1, wherein the plane-mapping module is adapted to use a look-up table to select a pre-determined plane based on the one or more detected disease features (see [0044]).
Regarding claim 4, Zhang et al. (‘318) teach the method of claim 1, wherein the disease detection module is adapted to generate for the at least one 3D image frame a 3D spatial map of disease regions within the 3D field of the image frame corresponding to the disease features; and wherein the plane-mapping module is adapted for receiving as input a 3D map of detected disease regions within a 3D field, and is adapted for determining a set of one or more 2D slices through the 3D field intersecting with the disease regions, in dependence upon the map (see [0042], [0052], [0054]-[0055]).
Regarding claim 5, Zhang et al. (‘318) teach the method of claim 4, wherein, the plane mapping module is adapted to perform a spatial fitting of planes to the one or more disease regions, to determine a set of one or more 2D slices which intersect all of the regions, and optionally which meet a further one or more constraints (see [0042], [0044]).
Regarding claim 6, Zhang et al. (‘318) teach the method of claim 4, wherein, for at least a subset of the pre-defined set of disease features, the detection of the disease feature by the disease-detection module comprises segmenting and classifying one or more spatial regions as suspicious regions, and wherein the 3D spatial map output by the disease detection module comprises a map of the segmented suspicious regions (see [0042], [0044], [0052], [0054]-[0055]).
Regarding claim 7, Zhang et al. (‘318) teach the method of claim 4, wherein, for at least a subset of the pre-defined set of disease features, the detection of the respective disease feature by the disease-detection module comprises computing a 3D saliency map spanning the 3D field in relation to the disease feature, and deriving a discrete classification of the 3D image in relation to the feature based on the saliency map, and wherein the saliency map is used as the 3D spatial map of disease regions (see [0042], [0044], [0052], [0054]-[0055]).
Regarding claim 8, Zhang et al. (‘318) teach the method of claim 7, wherein the plane mapping module is adapted to determine the set of one or more 2D slices based on fitting planes of maximum saliency through the saliency map (see [0042], [0044], [0052], [0054]-[0055]).
Regarding claim 9, Zhang et al. (‘318) teach the method of claim 1, wherein the control instructions are adapted to cause the ultrasound acquisition system to interleave acquisition of the set of one or more 2D slices with any other acquisition sequence which the ultrasound acquisition system is currently performing (see [0042], [0044], [0052], [0054]-[0055]).
Regarding claim 10, Zhang et al. (‘318) teach the method of claim 1, wherein the disease detection module comprises at least one trained machine learning algorithm, and preferably a convolutional neural network (CNN) (see [0042], [0044], [0052], [0054]-[0055]).
Regarding claim 11, Zhang et al. (‘318) teach the method of claim 1, wherein the control instructions are adapted to control acquisition of 2D slices which are of higher spatial resolution than the input 3D ultrasound image data (see [0042], [0044], [0052], [0054]-[0055]).
Regarding claim 12, Zhang et al. (‘318) teach the method of claim 1, wherein the method further comprises receiving the acquired set of one or more 2D slices; and controlling a user interface to generate a visual output representative thereof (see [0042])
Regarding claim 13, Zhang et al. (‘318) teach the method of claim 1, wherein the method further comprises, after determining the set of 2D slices, controlling a user interface to generate a user-perceptible prompt requesting approval to acquire the set of 2D slices, and wherein the generating of the control instructions is performed only responsive to receipt from the user interface of a user input indicative of approval (see [0042]).
Regarding claim 14, Zhang et al. (‘318) teach a computer program product comprising code means configured, when run on a processor which is operatively coupled with an ultrasound acquisition system, to cause the processor to perform a method in accordance with claim 1 (see [0039], [0042]-[0043], [0052], [0054]-[0055]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK REMALY whose telephone number is (571)270-1491. The examiner can normally be reached Mon - Fri 9:00 - 6:00.
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/MARK D REMALY/Primary Examiner, Art Unit 3797