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 .
Response to Amendment
The amendments to claims 1 and 4 in the response filed on 01/23/2026 are acknowledged.
Claims 1-20 remain pending in the application
Claims 11-20 are withdrawn.
Claims 1-10 are examined.
Response to Arguments
Applicant’s arguments, see pages 7-10, filed 01/23/2026, with respect to claims 1-10 have been fully considered and are persuasive. The 35 U.S.C. 102 and 103 rejections of claims 1-10 has been withdrawn.
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) 1-4 and 6 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”) in view of U.S. Publication No. 2012/0007819 to Hewes et al. (hereinafter “Hewes”).
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]).
Durr fails to expressly teach wherein the image includes a stereographic image, and wherein the stereographic image includes a left image and a right image; calculate the depth information based on determining a horizontal disparity mismatch between the left image and the right image.
However, Hewes teaches of an analogous system wherein the image includes a stereographic image, and wherein the stereographic image includes a left image and a right image; calculate the depth information based on determining a horizontal disparity mismatch between the left image and the right image ([0015])
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 device of Durr so that the image includes a stereographic image, and the stereographic image includes a left image and a right image and to calculate the depth information based on determining a horizontal disparity mismatch between the left image and the right image, as taught by Hewes. It would have been advantageous to make the combination for the purpose of determining the shift that occurs at each point in a scene between the left and right images ([0015] of Hewes).
Regarding claim 2, Durr, in view of Hewes, teaches 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, in view of Hewes, teaches 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, in view of Hewes, teaches the system of claim 1.
Durr, in view of Hewes, fails to expressly teach wherein the depth information includes pixel depth.
However, Hewes further teaches wherein the depth information includes pixel depth (Hewes: [0015]).
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 device of Durr so that the depth information includes pixel depth, as taught by Hewes. It would have been advantageous to make the combination for the purpose of determining the shift that occurs at each point in a scene between the left and right images ([0015] of Hewes).
Regarding claim 6, Durr, in view of Hewes, teaches 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(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Durr in view of Hewes and further in view of U.S. Publication No. 2017/0105601 to Pheiffer et al. (hereinafter “Pheiffer”).
Regarding claim 5, Durr, in view of Hewes, teaches the system of claim 1.
Dur, in view of Hewes, 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, in view of Hewes, 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”) in view of Hewes and further in view of U.S. Publication No. 2022/0311784 to Vandikas et al. (hereinafter “Vandikas”).
Regarding claim 7, Durr, in view of Hewes, teaches the system of claim 1.
Durr, in view of Hewes, 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, in view of Hewes 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 Hewes and Vandikas, teaches the system of claim 7.
Durr, in view of Hewes and 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 Hewes and 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”) , in view of Hewes and further in view of U.S. Publication No. 2014/0243614 to Rothberg et al. (hereinafter “Rothberg”).
Regarding claim 9, Durr, in view of Hewes, teaches the system of claim 1.
Durr, in view of Hewes, 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, in view of Hewes 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 Hewes and Rothburg, teaches the system of claim 9.
Durr, in view of Hewes and 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 Hewes and 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
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/C.A.S./Examiner, Art Unit 3795
/MICHAEL J CAREY/Supervisory Patent Examiner, Art Unit 3795
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