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 .
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-20 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-20 of copending Application No. 18964484 (reference application) (‘484). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
Regarding claim 1
Claim 1 of ‘484
1. A computer-implemented method comprising:
receiving a real image that includes a human, the real image comprising a photograph or a frame of a video;
1. A computer-implemented method comprising:
receiving a real image that includes a human, the real image comprising a photograph or a frame of a video;
creating a synthetic image corresponding to the real image, the synthetic image including a synthetic environment and a humanoid shape that correspond to the human;
creating a synthetic image corresponding to the real image, the synthetic image including a synthetic environment and a humanoid shape that correspond to the human;
predicting, using a trained viewpoint network and based on the synthetic image, a predicted viewpoint heatmap, the trained viewpoint network comprising a first trained convolutional neural network;
predicting, using a trained viewpoint network and based on the synthetic image, a predicted viewpoint heatmap, the trained viewpoint network comprising a first trained convolutional neural network;
predicting, using a trained pose network and based on the synthetic image, a predicted pose heatmap, the trained pose network comprising a second trained convolutional neural network;
predicting, using a trained pose network and based on the synthetic image, a predicted pose heatmap, the trained pose network comprising a second trained convolutional neural network;
providing, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap;
providing, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap;
creating a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment; and
creating a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment; and
classifying the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.
classifying the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.
Regarding claim 2
Claim 2 of ‘484
2. The computer-implemented method of claim 1, further comprising:
determining that at least one of the predicted viewpoint heatmap or the predicted pose heatmap specify a Gaussian heatmap that warps around a vertical edge or a horizontal edge of the synthetic image.
2. The computer-implemented method of claim 1, further comprising:
determining that at least one of the predicted viewpoint heatmap or the predicted pose heatmap specify a Gaussian heatmap that warps around a vertical edge or a horizontal edge of the synthetic image.
Regarding claim 3
Claim 3 of ‘484
3. The computer-implemented method of claim 1, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include more than three dimensions.
3. The computer-implemented method of claim 1, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include more than three dimensions.
Regarding claim 4
Claim 4 of ‘484
4. The computer-implemented method of claim 1, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include a time dimension.
4. The computer-implemented method of claim 1, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include a time dimension.
Regarding claim 5
Claim 5 of ‘484
5. The computer-implemented method of claim 1, wherein creating the reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment comprises:
decomposing a human pose of the human into bone vectors and bone lengths that are relative to a parent joint; and
transforming a camera position of the synthetic image from subject-centered coordinates to world coordinates.
5. The computer-implemented method of claim 1, wherein creating the reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment comprises:
decomposing a human pose of the human into bone vectors and bone lengths that are relative to a parent joint; and
transforming a camera position of the synthetic image from subject-centered coordinates to world coordinates.
Regarding claim 6
Claim 6 of ‘484
6. The computer-implemented method of claim 1, further comprising:
wrapping a matrix in a geometric formation including defining an encoding in which a seam line is at a back of the humanoid shape and opposite a forward vector.
6. The computer-implemented method of claim 1, further comprising:
wrapping a matrix in a geometric formation including defining an encoding in which a seam line is at a back of the humanoid shape and opposite a forward vector.
Regarding claim 7
Claim 7 of ‘484
7. The computer-implemented method of claim 1, wherein creating the reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment comprises:
transforming a camera’s position from subject-centered coordinates to world coordinates.
7. The computer-implemented method of claim 1, wherein creating the reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment comprises:
transforming a camera’s position from subject-centered coordinates to world coordinates.
Regarding claim 8
Claim 8 of ‘484
8. A computing device comprising:
one or more processors; and
a non-transitory memory device to store instructions executable by the one or more processors to perform operations comprising:
8. A computing device comprising:
one or more processors; and
a non-transitory memory device to store instructions executable by the one or more processors to perform operations comprising:
receiving a real image that includes a human, the real image comprising a photograph or a frame of a video;
receiving a real image that includes a human, the real image comprising a photograph or a frame of a video;
creating a synthetic image corresponding to the real image, the synthetic image including a synthetic environment and a humanoid shape that correspond to the human;
creating a synthetic image corresponding to the real image, the synthetic image including a synthetic environment and a humanoid shape that correspond to the human;
predicting, using a trained viewpoint network and based on the synthetic image, a predicted viewpoint heatmap, the trained viewpoint network comprising a first trained convolutional neural network;
predicting, using a trained viewpoint network and based on the synthetic image, a predicted viewpoint heatmap, the trained viewpoint network comprising a first trained convolutional neural network;
predicting, using a trained pose network and based on the synthetic image, a predicted pose heatmap, the trained pose network comprising a second trained convolutional neural network;
predicting, using a trained pose network and based on the synthetic image, a predicted pose heatmap, the trained pose network comprising a second trained convolutional neural network;
providing, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap;
providing, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap;
creating a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment; and
creating a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment; and
classifying the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.
classifying the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.
Regarding claim 9
Claim 9 of ‘484
9. The computing device of claim 8, wherein the trained pose network and the trained viewpoint network are created by:
randomly selecting a pose from a set of poses;
randomly selecting a viewpoint from a set of viewpoints;
generating the synthetic environment based at least in part on the pose and the viewpoint; and
deriving, from the synthetic environment, an abstract representation, a viewpoint heatmap, and a pose heatmap, wherein the viewpoint heatmap and the pose heatmap are used as supervised training targets.
9. The computing device of claim 8, wherein the trained pose network and the trained viewpoint network are created by:
randomly selecting a pose from a set of poses;
randomly selecting a viewpoint from a set of viewpoints;
generating the synthetic environment based at least in part on the pose and the viewpoint; and
deriving, from the synthetic environment, an abstract representation, a viewpoint heatmap, and a pose heatmap, wherein the viewpoint heatmap and the pose heatmap are used as supervised training targets.
Regarding claim 10
Claim 10 of ‘484
10. The computing device of claim 9, the operations further comprising:
extracting, using a first feature extraction neural network to extract first features from the synthetic environment and the abstract representation;
training a viewpoint network using the first features to create the trained viewpoint network;
extracting, using a second feature extraction neural network to extract second features from the synthetic environment and the abstract representation; and
training a pose network using the second features to create the trained pose network.
10. The computing device of claim 9, the operations further comprising:
extracting, using a first feature extraction neural network to extract first features from the synthetic environment and the abstract representation;
training a viewpoint network using the first features to create the trained viewpoint network;
extracting, using a second feature extraction neural network to extract second features from the synthetic environment and the abstract representation; and
training a pose network using the second features to create the trained pose network.
Regarding claim 11
Claim 11 of ‘484
11. The computing device of claim 10, the operations further comprising:
minimizing a viewpoint L2 loss for a viewpoint output of the viewpoint network; and
minimizing a pose L2 loss for a pose output of the pose network.
11. The computing device of claim 10, the operations further comprising:
minimizing a viewpoint L2 loss for a viewpoint output of the viewpoint network; and
minimizing a pose L2 loss for a pose output of the pose network.
Regarding claim 12
Claim 12 of ‘484
12. The computing device of claim 8, further comprising:
creating multiple tiles based on the synthetic image, wherein the multiple tiles include:
a limb tile for each limb of the humanoid shape; and
a torso tile for a torso of the humanoid shape.
12. The computing device of claim 8, further comprising:
creating multiple tiles based on the synthetic image, wherein the multiple tiles include:
a limb tile for each limb of the humanoid shape; and
a torso tile for a torso of the humanoid shape.
Regarding claim 13
Claim 13 of ‘484
13. The computing device of claim 9, further comprising:
adding perlin noise to the synthetic image to introduce granular missing patches.
13. The computing device of claim 9, further comprising:
adding perlin noise to the synthetic image to introduce granular missing patches.
Regarding claim 14
Claim 14 of ‘484
14. A non-transitory computer-readable memory device configured to store instructions executable by one or more processors to perform operations comprising:
receiving a real image that includes a human, the real image comprising a photograph or a frame of a video;
14. A non-transitory computer-readable memory device configured to store instructions executable by one or more processors to perform operations comprising:
receiving a real image that includes a human, the real image comprising a photograph or a frame of a video;
creating a synthetic image corresponding to the real image, the synthetic image including a synthetic environment and a humanoid shape that correspond to the human;
creating a synthetic image corresponding to the real image, the synthetic image including a synthetic environment and a humanoid shape that correspond to the human;
predicting, using a trained viewpoint network and based on the synthetic image, a predicted viewpoint heatmap, the trained viewpoint network comprising a first trained convolutional neural network;
predicting, using a trained pose network and based on the synthetic image, a predicted pose heatmap, the trained pose network comprising a second trained convolutional neural network;
predicting, using a trained viewpoint network and based on the synthetic image, a predicted viewpoint heatmap, the trained viewpoint network comprising a first trained convolutional neural network;
predicting, using a trained pose network and based on the synthetic image, a predicted pose heatmap, the trained pose network comprising a second trained convolutional neural network;
providing, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap;
providing, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap;
creating a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment; and
creating a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment; and
classifying the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.
classifying the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.
Regarding claim 15
Claim 15 of ‘484
15. The non-transitory computer-readable memory device of claim 14, further comprising:
determining that at least one of the predicted viewpoint heatmap or the predicted pose heatmap specify a fuzzy location.
15. The non-transitory computer-readable memory device of claim 14, further comprising:
determining that at least one of the predicted viewpoint heatmap or the predicted pose heatmap specify a fuzzy location.
Regarding claim 16
Claim 16 of ‘484
16. The non-transitory computer-readable memory device of claim 14, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include more than three dimensions.
16. The non-transitory computer-readable memory device of claim 14, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include more than three dimensions.
Regarding claim 17
Claim 17 of ‘484
17. The non-transitory computer-readable memory device of claim 14, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include a time dimension.
17. The non-transitory computer-readable memory device of claim 14, further comprising:
determining, that at least one of the predicted viewpoint heatmap or the predicted pose heatmap include a time dimension.
Regarding claim 18
Claim 18 of ‘484
18. The non-transitory computer-readable memory device of claim 14, wherein the trained pose network and the trained viewpoint network are created by:
randomly selecting a pose from a set of poses;
randomly selecting a viewpoint from a set of viewpoints;
generating the synthetic environment based at least in part on the pose and the viewpoint; and
deriving, from the synthetic environment, an abstract representation, a viewpoint heatmap, and a pose heatmap, wherein the viewpoint heatmap and the pose heatmap are used as supervised training targets.
18. The non-transitory computer-readable memory device of claim 14, wherein the trained pose network and the trained viewpoint network are created by:
randomly selecting a pose from a set of poses;
randomly selecting a viewpoint from a set of viewpoints;
generating the synthetic environment based at least in part on the pose and the viewpoint; and
deriving, from the synthetic environment, an abstract representation, a viewpoint heatmap, and a pose heatmap, wherein the viewpoint heatmap and the pose heatmap are used as supervised training targets.
Regarding claim 19
Claim 19 of ‘484
19. The non-transitory computer-readable memory device of claim 18, the operations further comprising:
extracting, using a first feature extraction neural network to extract first features from the synthetic environment and the abstract representation;
training a viewpoint network using the first features to create the trained viewpoint network;
extracting, using a second feature extraction neural network to extract second features from the synthetic environment and the abstract representation; and
training a pose network using the second features to create the trained pose network.
19. The non-transitory computer-readable memory device of claim 18, the operations further comprising:
extracting, using a first feature extraction neural network to extract first features from the synthetic environment and the abstract representation;
training a viewpoint network using the first features to create the trained viewpoint network;
extracting, using a second feature extraction neural network to extract second features from the synthetic environment and the abstract representation; and
training a pose network using the second features to create the trained pose network.
Regarding claim 20
Claim 20 of ‘484
20. The non-transitory computer-readable memory device of claim 14, wherein creating the reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment comprises:
transforming a camera’s position from subject-centered coordinates to world coordinates.
20. The non-transitory computer-readable memory device of claim 14, wherein creating the reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment comprises:
transforming a camera’s position from subject-centered coordinates to world coordinates.
Other Prior Art Cited
14. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
"3D Interpreter Networks for Viewer-Centered Wireframe Modeling" to Wu discloses estimation of keypoint in relation to heatmaps and poses (see Abstract).
US-20250173891 to Manzur is publication of application 18964484 (reference application).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENIYAM MENBERU whose telephone number is (571) 272-7465. The examiner can normally be reached on Monday-Friday, 10:00am-6:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached on (571) 270-3438. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the customer service office whose telephone number is (571) 272-2600. The group receptionist number for TC 2600 is (571) 272-2600.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only.
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.
For more information about the PAIR system, see <http://pair-direct.uspto.gov/>. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Patent Examiner
Beniyam Menberu
/BENIYAM MENBERU/Primary Examiner, Art Unit 2681
06/24/2026
Prosecution Insights