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 group I in the reply filed on 07/07/2025 is acknowledged.
Applicant asserts this election reads on claims 1-10.
Status of Claims
Claims 1-20 are pending, claims 11-20 have been withdrawn from consideration, and claims 1-10 are currently under consideration for patentability under 37 CFR 1.104.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/27/2022 and 01/13/2025 have been considered by the examiner.
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) 1-4 and 6 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publication No. 2015/0374210 to Durr et al. (hereinafter “Durr”).
Regarding claim 1, Durr discloses a system for object enhancement in endoscopy images, comprising:
a light source configured to provide light within a surgical operative site (1985, Fig. 20A, [0103]);
an imaging device configured to acquire images (1965, Fig. 20A, [0103]);
an imaging device control unit configured to control the imaging device ([0097]-[0103]), the imaging device control unit including:
a processor (1964, Fig. 20A, [0103]); and
a memory storing instructions ([0104]) which, when executed by the processor, cause the system to:
capture an image of an object within the surgical operative site, by the imaging device, the image including a plurality of pixels, wherein each of the plurality of pixels includes color information ([0103]-[0104]);
access the image ([0103]-[0114]);
access data relating to depth information about each of the pixels in the image ([0059]-[0060] and [0114]);
input the depth information to a machine learning algorithm ([0117]);
emphasize a feature of the image based on an output of the machine learning algorithm ([0117]-[0126]);
generate an augmented image based on the emphasized feature ([0126]); and
display the augmented image on a display ([0103]-[0114]).
Regarding claim 2, Durr disclose the system of claim 1, and Durr further discloses wherein emphasizing the feature includes at least one of: augmenting a 3D aspect of the image, emphasizing a boundary of the object, changing the color information of the plurality of pixels of the object, or extracting 3D features of the object ([0126]).
Regarding claim 3, Durr discloses the system of claim 1, and Durr further discloses wherein the instructions, when executed, further cause the system to perform real-time image recognition on the augmented image to detect an object and classify the object ([0113]-[0114]).
Regarding claim 4, Durr discloses the system of claim 1, and Durr further discloses wherein the image includes a stereographic image, and wherein the stereographic image includes a left image and a right image, wherein the instructions, when executed, further cause the system to calculate depth information based on determining a horizontal disparity mismatch between the left image and the right image, and wherein the depth information includes pixel depth ([0070]).
Regarding claim 6, Durr discloses the system of claim 1, and Durr further discloses wherein the machine learning algorithm includes at least one of a convolutional neural network, a feed forward neural network, a radial bias neural network, a multilayer perceptron, a recurrent neural network, or a modular neural network ([0161]).
Claim Rejections - 35 USC § 103
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 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) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2015/0374210 to Durr et al. (hereinafter “Durr”) and further in view of U.S. Publication No. 2017/0105601 to Pheiffer et al. (hereinafter “Pheiffer”).
Regarding claim 5, Durr discloses the system of claim 1.
Durr fails to expressly teach wherein the instructions, when executed, further cause the system to calculate depth information based on structured light projection, wherein the depth information includes pixel depth.
However, Pheiffer teaches of an analogous system wherein the instructions, when executed, further cause the system to calculate depth information based on structured light projection, wherein the depth information includes pixel depth (Pheiffer: [0014]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Durr to utilize instructions in the manner taught by Pheiffer. It would have been advantageous to make the combination for the purpose of acquiring the depth information for each time frame ([0014] of Pheiffer).
Claim(s) 7, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2015/0374210 to Durr et al. (hereinafter “Durr”) and further in view of U.S. Publication No. 2022/0311784 to Vandikas et al. (hereinafter “Vandikas”).
Regarding claim 7, Durr discloses the system of claim 1.
Durr fails to expressly teach wherein the machine learning algorithm is trained based on tagging objects in training images, and wherein the training further includes augmenting the training images to include at least one of adding noise, changing colors, hiding portions of the training images, scaling of the training images, rotating the training images, or stretching the training images.
However, Vandikas teaches of an analogous system wherein the machine learning algorithm is trained based on tagging objects in training images, and wherein the training further includes augmenting the training images to include at least one of adding noise, changing colors, hiding portions of the training images, scaling of the training images, rotating the training images, or stretching the training images (Vandikas: [0033]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Durr to utilize a machine learning algorithm in the manner taught by Vandikas. It would have been advantageous to make the combination for the purpose of training the algorithm (Vandikas: [0033]).
Regarding claim 8, Durr, in view of Vandikas, teaches the system of claim 7.
Durr, in view of Vandikas, fails to expressly teach wherein the training includes at least one of supervised, unsupervised, or reinforcement learning.
However, Vandikas further teaches wherein the training includes at least one of supervised, unsupervised, or reinforcement learning (Vandikas: [0033]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Durr, in view of Vandikas, to utilize a machine learning algorithm in the manner taught by Vandikas. It would have been advantageous to make the combination for the purpose of training the algorithm (Vandikas: [0033]).
Claim(s) 9, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2015/0374210 to Durr et al. (hereinafter “Durr”) and further in view of U.S. Publication No. 2014/0243614 to Rothberg et al. (hereinafter “Rothberg”).
Regarding claim 9, Durr discloses the system of claim 1.
Durr fails to expressly teach wherein the instructions, when executed, further cause the system to: process a time series of the augmented image based on at least one of a learned video magnification, phase-based video magnification, or Eulerian video magnification.
However, Rothburg teaches of an analogous system wherein the instructions, when executed, further cause the system to: process a time series of the augmented image based on at least one of a learned video magnification, phase-based video magnification, or Eulerian video magnification (Rothburg: [0465] and [0524]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Durr to utilize the instructions in the manner taught by Rothburg. It would have been advantageous to make the combination for the purpose of enhancing motion (Rothberg: [0524]).
Regarding claim 10, Durr, in view of Rothburg, teaches the system of claim 9.
Durr, in view of Rothburg, fails to expressly teach wherein the instructions, when executed, further cause the system to: perform tracking of the object based on an output of the machine learning algorithm.
However, Rothburg further teaches wherein the instructions, when executed, further cause the system to: perform tracking of the object based on an output of the machine learning algorithm (Rothburg: [0465] and [0524]).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Durr, in view of Rothberg, to utilize the instructions in the manner taught by Rothburg. It would have been advantageous to make the combination for the purpose of enhancing motion (Rothberg: [0524]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTEN A. SHARPLESS whose telephone number is (571)272-2387. The examiner can normally be reached Monday-Tuesday 6:00 AM - 2:00 PM, and Friday 6:00 AM - 10:00 AM.
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/C.A.S./Examiner, Art Unit 3795
/MICHAEL J CAREY/Supervisory Patent Examiner, Art Unit 3795
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