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 .
Priority
Priority is acknowledged from Provisional application 63/371,122 with a filing date of 08/11/2022.
Drawings
The 19-page drawings have been considered and placed on record in the file.
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.
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.
Claims 1-4, 6, 15-20, 22, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica et al. (US 20230003894 A1) in view of Velten et al. (US 20230351551 A1).
Regarding Claim 1, Zlokolica teaches "An apparatus for processing image data, comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: obtain a first image of an object at a first position in an environment"; (Zlokolica, Abstract and Paras. 37, 65, and 109, teaches a processor and image sensor which obtains first image data indicative of a scene which contains an object wherein a first image feature in the first image data is determined and wherein a position of the first image feature is determined, i.e., obtain a first image of an object and a first position of the object in an environment);
"obtain a second image of the object at a second position in the environment"; (Zlokolica, Abstract and Paras. 37, 65, and 109, teaches a processor and image sensor which obtains second image data indicative of a scene which contains an object wherein a second image feature in the second image data is determined and wherein a position of the second image feature is determined, i.e., obtain a second image of an object and a second position of the object in an environment).
However, Zlokolica does not explicitly teach "and determine movement of the object in the first image and the second image at least in part using an optical flow engine; wherein the optical flow engine is trained based on augmented training data generated using at least one of noise associated with low ambient lighting conditions, noise associated with motion blur due to exposure of an image sensor in low ambient lighting conditions, or brightness variations".
In an analogous field of endeavor, Velten teaches "and determine movement of the object in the first image and the second image at least in part using an optical flow engine"; (Velten, Paras. 8 and 28, teaches deducing motion using optical flow from a sequence of high-light images including an imaged object, i.e., determine movement of the object in a first and second image using an optical flow engine);
"wherein the optical flow engine is trained based on augmented training data generated using at least one of noise associated with low ambient lighting conditions, noise associated with motion blur due to exposure of an image sensor in low ambient lighting conditions, or brightness variations"; (Velten, Paras. 7, 10, and 53, teaches training the neural network with a teaching set of pairs of low-light image frames with respectively higher and lower levels of noise with respect to a common imaged object as well as using blurring from a combination of successive frames as nosie for training, i.e., optical flow engine trained based on augmented training data generated using noise associated with low ambient lighting conditions and noise associated with motion blur from the image sensor in the low ambient lighting conditions).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica by including the use of an optical flow engine trained using noise from low lighting conditions and motion blurring to determine movement of an object from two images taught by Velten. One of ordinary skill in the art would be motivated to combine the references since it helps increase the signal to noise ratio (Velten, Para. 7, teaches the motivation of combination to be to increase signal-to-noise ratio, improve alignment, and reduce motion blur).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 2, the combination of references of Zlokolica in view of Velten teaches "The apparatus of claim 1, wherein a part of the object is illuminated in the first image and a different part of the object is illuminated in the second image"; (Zlokolica, Fig. 6 and Paras. 73 and 151, teaches a spotted light source which illuminates the object in a matter that with each illumination cycle a different part of the object may be illuminated to simulate movement wherein the first and second image features correspond to light spots, i.e., a different part of the object is illuminated in the first image and a different part of the object is illuminated in the second image).
Regarding Claim 3, the combination of references of Zlokolica in view of Velten teaches "The apparatus of claim 1, wherein the at least one processor is configured to: generate the augmented training data using the noise associated with the low ambient lighting conditions; and train the optical flow engine based on the augmented training data"; (Velten, Paras. 7, 10, and 53, teaches training the neural network with a teaching set of pairs of low-light image frames with respectively higher and lower levels of noise with respect to a common imaged object, i.e., optical flow engine trained based on augmented training data generated using noise associated with low ambient lighting conditions and noise associated with motion blur from the image sensor in the low ambient lighting conditions).
The proposed combination as well as the motivation for combining the Zlokolica and Velten references presented in the rejection of Claim 1, applies to claim 3. Thus, the apparatus recited in claim 3 is met by Zlokolica in view of Velten.
Regarding Claim 4, the combination of references of Zlokolica in view of Velten teaches "The apparatus of claim 3, wherein, to generate the augmented training data using the noise associated with the low ambient lighting conditions, the at least one processor is configured to: apply first noise to a training image pair based on the low ambient lighting conditions"; (Velten, Paras. 38 and 53, teaches training the neural network with a set of noisy fluorescent frames in pairs with corresponding ground truth fluorescent frames wherein the fluorescent frames are low-light images and wherein the noisy fluorescent frames are prepared by reducing signal strength and introducing noise of a type expected for the particular detector and other types of noise such as spatial distortion, blurring, and quantization noise, i.e., apply first noise to a training image pair based on the low ambient lighting conditions);
"and apply second noise to the training image pair based on thermal conditions"; (Velten, Paras. 53 and 55, teaches preparing the noisy fluorescent frames of the training image pairs by reducing signal strength and introducing noise of a type expected for the particular detector wherein the same principle can be applied to thermal imaging where there are different received illumination signals with substantially different flux so that the stronger signal can allow motion tracking to permit integration of the weaker signal to improve its signal-to-noise ratio, i.e., training is performing on thermal images to improve signal-to-noise ration wherein noise is added based on the thermal detector indicating second noise may be applied to the training image pair based on thermal conditions).
The proposed combination as well as the motivation for combining the Zlokolica and Velten references presented in the rejection of Claim 1, applies to claim 4. Thus, the apparatus recited in claim 4 is met by Zlokolica in view of Velten.
Regarding Claim 6, the combination of references of Zlokolica in view of Velten teaches "The apparatus of claim 3, wherein the at least one processor is configured to: generate the augmented training data using the noise associated with the motion blur; and train the optical flow engine based on the augmented training data"; (Velten, Paras. 7, 10, and 53, teaches training the neural network with a teaching set of pairs of low-light image frames with respectively higher and lower levels of noise with respect to a common imaged object as well as using blurring from a combination of successive frames as noise for training, i.e., optical flow engine trained based on augmented training data generated using noise associated with motion blur from the image sensor in the low ambient lighting conditions).
The proposed combination as well as the motivation for combining the Zlokolica and Velten references presented in the rejection of Claim 1, applies to claim 6. Thus, the apparatus recited in claim 6 is met by Zlokolica in view of Velten.
Regarding Claim 15, the combination of references of Zlokolica in view of Velten teaches "The apparatus of claim 1, wherein the second position is different from the first position"; (Zlokolica, Para. 65, teaches determining a position of the first and second image features wherein the different positions may then be compared to estimate motion, i.e., the second position is different from the first position).
Regarding Claim 16, the combination of references of Zlokolica in view of Velten teaches "The apparatus of claim 1, wherein the first image corresponds to a first position of the apparatus and the second image corresponds to a second position of the apparatus"; (Zlokolica, Paras. 24 and 139-141, teaches two or more consecutive measurements from different positions based on a motion of the image sensor wherein motion estimation is performed based on the sensor position and movement, i.e., first image captured by the sensor at a first position and a second image captured by the sensor at a second position).
Claim 17 recites a method with steps corresponding to the elements of the apparatus recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica and Velten references, presented in rejection of Claim 1, apply to this claim.
Claim 18 recites a method with steps corresponding to the elements of the apparatus recited in Claim 2. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica and Velten references, presented in rejection of Claim 1, apply to this claim.
Claim 19 recites a method with steps corresponding to the elements of the apparatus recited in Claim 3. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica and Velten references, presented in rejection of Claim 1, apply to this claim.
Claim 20 recites a method with steps corresponding to the elements of the apparatus recited in Claim 4. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica and Velten references, presented in rejection of Claim 1, apply to this claim.
Claim 22 recites a method with steps corresponding to the elements of the apparatus recited in Claim 6. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica and Velten references, presented in rejection of Claim 1, apply to this claim.
Claim 30 recites a method with steps corresponding to the elements of the apparatus recited in Claim 15. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica and Velten references, presented in rejection of Claim 1, apply to this claim.
Claims 5 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica in view of Velten and Persson et al. (US 20120169936 A1).
Regarding Claim 5, the combination of references of Zlokolica in view of Velten does not explicitly teach "The apparatus of claim 4, wherein the first noise comprises a photon shot noise associated with the low ambient lighting conditions and the second noise comprises thermal readout noise associated with the image sensor".
In an analogous field of endeavor, Persson teaches "The apparatus of claim 4, wherein the first noise comprises a photon shot noise associated with the low ambient lighting conditions and the second noise comprises thermal readout noise associated with the image sensor"; (Persson, Para. 55, teaches CMOS image sensors suffering from noise such as photon shot noise which is particularly noticeable with short exposure times and low light conditions as well as thermal and flicker noise created by vibrations of a large number of electrons, i.e., first noise comprises photon shot noise associated with low ambient lighting conditions and second noise comprising thermal readout noise associated with the image sensor).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica and Velten by including the photon shot noise and thermal noise of the image sensor taught by Persson. One of ordinary skill in the art would be motivated to combine the references since it improves the reduction of noise (Persson, Para. 18, teaches the motivation of combination to be to improve the reduction of noise in an input stream of image signals).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 21 recites a method with steps corresponding to the elements of the apparatus recited in Claim 5. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica, Velten, and Persson references, presented in rejection of Claim 5, apply to this claim.
Claims 7-9 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica in view of Velten and Shi et al. (US 20240169498 A1).
Regarding Claim 7, the combination of references of Zlokolica in view of Velten does not explicitly teach "The apparatus of claim 6, wherein, to generate the augmented training data using the noise associated with the motion blur, the at least one processor is configured to: apply at least one motion blur kernel to a training image pair based on at least one parameter".
In an analogous field of endeavor, Shi teaches "The apparatus of claim 6, wherein, to generate the augmented training data using the noise associated with the motion blur, the at least one processor is configured to: apply at least one motion blur kernel to a training image pair based on at least one parameter"; (Shi, Paras. 72 and 82, teaches training image data including a set of blurred images and deblurred images wherein estimated motion blur can be used to generate a motion blur kernel which may be a one-dimensional kernel and processed with one or more polynomial filters to generate a sharpening kernel to augment the image data to generate augmented image data, i.e., apply a motion blur kernel to training image pair based on a parameter).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica and Velten wherein the training images are in pairs by including the application of a motion blur kernel to the training images based on a parameter taught by Shi. One of ordinary skill in the art would be motivated to combine the references since it improves deblurring of the image (Shi, Para. 6, teaches the motivation of combination to be to improve deblurring of an image).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 8, the combination of references of Zlokolica in view of Velten and Shi teaches "The apparatus of claim 7, wherein the at least one parameter is associated with a point spread function (PSF) to emulate motion blur"; (Shi, Para. 49, teaches estimating a blur kernel using a point spread function from the input frame, i.e., at least one parameter associated with a PSF to emulate motion blur).
The proposed combination as well as the motivation for combining the Zlokolica, Velten, and Shi references presented in the rejection of Claim 7, applies to claim 8. Thus, the apparatus recited in claim 8 is met by Zlokolica in view of Velten and Shi.
Regarding Claim 9, the combination of references of Zlokolica in view of Velten and Shi teaches "The apparatus of claim 8, wherein the at least one parameter comprises at least one of a motion blur kernel size, an intensity, a linear direction, or a non-linear direction"; (Shi, Paras. 55 and 58, teaches the use of a 1D kernel with a size of 30 and a constraint of a maximum kernel size of 30 and the implementation of motion blur kernels involving motion blur masking which involves adjusting frame motion to follow the specific direction of the motion blur, i.e., parameters comprising kernel size and blur direction).
The proposed combination as well as the motivation for combining the Zlokolica, Velten, and Shi references presented in the rejection of Claim 7, applies to claim 9. Thus, the apparatus recited in claim 9 is met by Zlokolica in view of Velten and Shi.
Claim 23 recites a method with steps corresponding to the elements of the apparatus recited in Claim 7. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica, Velten, and Shi references, presented in rejection of Claim 7, apply to this claim.
Claim 24 recites a method with steps corresponding to the elements of the apparatus recited in Claim 8. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica, Velten, and Shi references, presented in rejection of Claim 7, apply to this claim.
Claim 25 recites a method with steps corresponding to the elements of the apparatus recited in Claim 9. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica, Velten, and Shi references, presented in rejection of Claim 7, apply to this claim.
Claims 10 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica in view of Velten and Peng et al. (US 20150117703 A1).
Regarding Claim 10, the combination of references of Zlokolica in view of Velten does not explicitly teach "The apparatus of claim 3, wherein the at least one processor is configured to: generate the augmented training data using the brightness variations; and train the optical flow engine based on the augmented training data".
In an analogous field of endeavor, Peng teaches "The apparatus of claim 3, wherein the at least one processor is configured to: generate the augmented training data using the brightness variations; and train the optical flow engine based on the augmented training data"; (Peng, Paras. 41 and 45-46, teaches estimating optical flow and using an optic-flow-based tracked in order to train an object detector wherein the training data includes large variation in illumination, i.e., train the optical flow engine based on generated training data using brightness variations).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica and Velten by including the training data for an optic flow model including brightness variations taught by Peng. One of ordinary skill in the art would be motivated to combine the references since it increases robustness of the model (Peng, Para. 46, teaches the motivation of combination to be to provide robustness of the tracker in each segment of the video).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 26 recites a method with steps corresponding to the elements of the apparatus recited in Claim 10. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica, Velten, and Peng references, presented in rejection of Claim 10, apply to this claim.
Claims 11 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica in view of Velten, Peng, Lee (US 20210073953 A1), and Oosake (US 20210216823 A1).
Regarding Claim 11, the combination of references of Zlokolica in view of Velten and Peng does not explicitly teach “The apparatus of claim 10, wherein, to generate the augmented training data using the brightness variations, the at least one processor is configured to: obtain a mask; modify a brightness of regions in a training image based on the mask to generate a modified training image; input the training image and the modified training image into a neural network; determine a first loss associated with the training image and an output of the neural network based on processing the training image; determine a second loss associated with the modified training image input and an output of the neural network based on processing the modified training image; and train the neural network based on the first loss and the second loss”.
In an analogous field of endeavor, Lee teaches "The apparatus of claim 10, wherein, to generate the augmented training data using the brightness variations, the at least one processor is configured to: obtain a mask"; (Lee, Para. 79, teaches generating a segmentation mask, i.e., obtain a mask);
"modify a brightness of regions in a training image based on the mask to generate a modified training image"; (Lee, Para. 79, teaches generating a segmentation mask corresponding to the hair region from the input image wherein a module may change the color space of the region corresponding to the segmentation mask into black and white and generate a histogram of the brightness of the changed black and white region as well as preparing and storing sample hair colors having various brightness for other changes as desired, i.e., modify a brightness of regions in a training image to generate a modified training image).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica, Velten, and Peng wherein the training image pairs include a training image and a modified training image by including the use of a mask to modify brightness of regions in an image for generating a training image taught by Lee. One of ordinary skill in the art would be motivated to combine the references since it improves recognition accuracy (Lee, Para. 106, teaches the motivation of combination to be to improve recognition accuracy of a part of the object).
However, the combination of references of Zlokolica in view of Velten, Peng, and Lee does not explicitly teach “input the training image and the modified training image into a neural network; determine a first loss associated with the training image and an output of the neural network based on processing the training image; determine a second loss associated with the modified training image input and an output of the neural network based on processing the modified training image; and train the neural network based on the first loss and the second loss”.
In an analogous field of endeavor, Oosake teaches "input the training image and the modified training image into a neural network"; (Oosake, Abstract, teaches the first image and the second image are used as input images for the CNN, i.e., input the training images into a neural network);
"determine a first loss associated with the training image and an output of the neural network based on processing the training image"; (Oosake, Para. 15, teaches calculating a first loss value based on comparing a first feature map output from the neural network in response to input of the first image as the input image with first mask data associated with the first image, i.e., determine a first loss associated with the training image and an output of the neural network based on processing the image);
"determine a second loss associated with the modified training image input and an output of the neural network based on processing the modified training image"; (Oosake, Para. 15, teaches calculating a second loss value based on comparing a second feature map output from the neural network in response to input of the second image as the input image with second mask data associated with the second image, i.e., determine a second loss associated with the training image and an output of the neural network based on processing the image);
"and train the neural network based on the first loss and the second loss"; (Oosake, Para. 15, teaches a parameter control unit that updates the parameters of the neural network on the basis of the first loss value and the second loss value, i.e., train the neural network based on the first loss and the second loss).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica, Velten, Peng, and Lee wherein the training images include a training image and a modified training image by including the input of the images into a neural network to determine first and second losses respectively for the training image pairs and train the neural network based on the losses taught by Oosake. One of ordinary skill in the art would be motivated to combine the references since it helps prevent overtraining (Oosake, Para. 14, teaches the motivation of combination to be to prevent overtraining).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 27 recites a method with steps corresponding to the elements of the apparatus recited in Claim 11. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica, Velten, Peng, Lee, and Oosake references, presented in rejection of Claim 11, apply to this claim.
Claims 12 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica in view of Velten, Peng, Lee, Oosake, and Rao et al. (US 20210012093 A1).
Regarding Claim 12, the combination of references of Zlokolica in view of Velten, Peng, Lee, and Oosake does not explicitly teach "The apparatus of claim 11, wherein the at least one processor is configured to: sum the first loss and the second loss to generate a total loss, wherein the neural network is trained based on the total loss".
In an analogous field of endeavor, Rao teaches "The apparatus of claim 11, wherein the at least one processor is configured to: sum the first loss and the second loss to generate a total loss, wherein the neural network is trained based on the total loss"; (Rao, Para. 46, teaches updating the network based on a total loss obtained based on a weighted sum of the first loss and the second loss, i.e., sum the first and second loss to generate a total loss to train the neural network).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica, Velten, Peng, Lee, and Oosake by including the generation of a total loss from the sum of a first and second loss for training the network taught by Rao. One of ordinary skill in the art would be motivated to combine the references since it improves image generation efficiency and image quality (Rao, Para. 8, teaches the motivation of combination to be to improve image generation efficiency and improve image quality).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 28 recites a method with steps corresponding to the elements of the apparatus recited in Claim 12. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Zlokolica, Velten, Peng, Lee, Oosake, and Rao references, presented in rejection of Claim 12 apply to this claim.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica in view of Velten and Cole et al. (US 20240242366 A1).
Regarding Claim 13, the combination of references of Zlokolica in view of Velten does not explicitly teach "The apparatus of claim 1, wherein the optical flow engine comprises a Recurrent All-Pairs Field Transforms (RAFT) neural network".
In an analogous field of endeavor, Cole teaches "The apparatus of claim 1, wherein the optical flow engine comprises a Recurrent All-Pairs Field Transforms (RAFT) neural network"; (Cole, Para. 47, teaches optical flow being determined using the RAFT model, i.e., optical flow engine comprises RAFT neural network).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica and Velten by including the use of a RAFT neural network for optical flow taught by Cole. One of ordinary skill in the art would be motivated to combine the references since it improves accuracy and consistency of images (Cole, Para. 51, teaches the motivation of combination to be to improve accuracy and consistency of generated depth images).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claims 14 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Zlokolica in view of Velten and Simon (US 20210233257 A1).
Regarding Claim 14, the combination of references of Zlokolica in view of Velten does not explicitly teach "The apparatus of claim 1, wherein the at least one processor is configured to: determine at least one of a direction or a velocity of the object based on an output from the optical flow engine, wherein the direction and velocity comprises one of a float value or a vector value".
In an analogous field of endeavor, Simon teaches "The apparatus of claim 1, wherein the at least one processor is configured to: determine at least one of a direction or a velocity of the object based on an output from the optical flow engine, wherein the direction and velocity comprises one of a float value or a vector value"; (Simon, Para. 8, teaches an optical flow vector of a vehicle or object of the pixel from the first camera image to the assigned pixel in the second camera image to ascertain a movement direction and a movement velocity, i.e., determine a direction and velocity as output of the optical flow as a vector value).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Zlokolica and Velten by including the determination of a direction and velocity from the output of an optical flow model comprising a vector value taught by Simon. One of ordinary skill in the art would be motivated to combine the references since it enables an increase in the recording rate of cameras by pixels being rapidly and efficiently obtained (Simon, Para. 17, teaches the motivation of combination to be to increase the recording rate of the cameras due to pixels being more rapidly and efficiently ascertained).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW STEVEN BUDISALICH whose telephone number is (703)756-5568. The examiner can normally be reached Monday - Friday 8:30am-5:00pm EST.
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, Amandeep Saini can be reached on (571) 272-3382. 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.
/ANDREW S BUDISALICH/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662