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 .
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.
Claim(s) is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 (U.S. PG-PUB NO. 2021/0074016) in view of D2 (U.S. PG-PUB NO. 2022/0044441).
-Regarding claim 1, D1 discloses a method, comprising: generating a first two-dimensional joint position estimate relative to a first camera in a first camera position (the first view of the hand replicates a left eye view of the hand, [0049]); generating a second two-dimensional joint position estimate relative to a second camera in a second camera position (the second view of the hand replicates a right eye view of the hand, [0049]); generating an estimated three-dimensional joint position based at least on: the first two-dimensional joint position estimate, and the second two-dimensional joint position estimate (post-processing system 206 converts the joint location coordinates computed by the machine learning system 204 to estimate joint location coordinates in the three-dimensional world space, [0032]); and transmitting the generated three-dimensional joint position estimate (identifies a 3D hand pose based on the plurality of sets of joint location coordinates, [0051]).
D1 is silent to teaching that a rotation transformation between the first camera position and the second camera position, a generated three-dimensional translational transformation between the first camera position and the second camera position. However, the claimed limitation is well known in the art as evidenced by D2.
In the same field of endeavor, D2 teaches a rotation transformation between the first camera position and the second camera position (each of the support cameras 30, e.g., first support camera 30a, second support camera 30b, and third support camera 30c, captures a different view of the objects 22 from a different view point (e.g., a first viewpoint, a second viewpoint, and a third viewpoint, respectively, [0069]), a generated three-dimensional translational transformation between the first camera position and the second camera position (the pose estimator 100 estimates a pose of the current object, represented by rotation transformation Ro and translation transformation To with respect to a global coordinate system (e.g., a coordinate system defined with respect to the main camera 10), [0138]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of D1 with the teaching of D2 in order to improve the performance (e.g., accuracy of the segmentation maps).
-Regarding claim 10, D1 discloses a system, comprising: one or more processors (processors 702, [0060]); and a memory storing instructions which (memory 704, [0060]), when executed by the one or more processors, cause performance of: generating a first two-dimensional joint position estimate relative to a first camera in a first camera position (the first view of the hand replicates a left eye view of the hand, [0049]); generating a second two-dimensional joint position estimate relative to a second camera in a second camera position (the second view of the hand replicates a right eye view of the hand, [0049]); generating an estimated three-dimensional joint position based at least on: the first two-dimensional joint position estimate, and the second two-dimensional joint position estimate (post-processing system 206 converts the joint location coordinates computed by the machine learning system 204 to estimate joint location coordinates in the three-dimensional world space, [0032]); and transmitting the generated three-dimensional joint position estimate (identifies a 3D hand pose based on the plurality of sets of joint location coordinates, [0051]).
D1 is silent to teaching that a rotation transformation between the first camera position and the second camera position, a generated three-dimensional translational transformation between the first camera position and the second camera position. However, the claimed limitation is well known in the art as evidenced by D2.
In the same field of endeavor, D2 teaches a rotation transformation between the first camera position and the second camera position (each of the support cameras 30, e.g., first support camera 30a, second support camera 30b, and third support camera 30c, captures a different view of the objects 22 from a different view point (e.g., a first viewpoint, a second viewpoint, and a third viewpoint, respectively, [0069]), a generated three-dimensional translational transformation between the first camera position and the second camera position (the pose estimator 100 estimates a pose of the current object, represented by rotation transformation Ro and translation transformation To with respect to a global coordinate system (e.g., a coordinate system defined with respect to the main camera 10), [0138]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of D1 with the teaching of D2 in order to improve the performance (e.g., accuracy of the segmentation maps).
-Regarding claim 19, D1 discloses a system, comprising: means for processing (processors 702, [0060]); and a memory storing instructions which (memory 704, [0060]), when executed by the means for processing, cause performance of: generating a first two-dimensional joint position estimate relative to a first camera in a first camera position (the first view of the hand replicates a left eye view of the hand, [0049]); generating a second two-dimensional joint position estimate relative to a second camera in a second camera position (the second view of the hand replicates a right eye view of the hand, [0049]); generating an estimated three-dimensional joint position based at least on: the first two-dimensional joint position estimate, and the second two-dimensional joint position estimate (post-processing system 206 converts the joint location coordinates computed by the machine learning system 204 to estimate joint location coordinates in the three-dimensional world space, [0032]); and transmitting the generated three-dimensional joint position estimate (identifies a 3D hand pose based on the plurality of sets of joint location coordinates, [0051]).
D1 is silent to teaching that a rotation transformation between the first camera position and the second camera position, a generated three-dimensional translational transformation between the first camera position and the second camera position. However, the claimed limitation is well known in the art as evidenced by D2.
In the same field of endeavor, D2 teaches a rotation transformation between the first camera position and the second camera position (each of the support cameras 30, e.g., first support camera 30a, second support camera 30b, and third support camera 30c, captures a different view of the objects 22 from a different view point (e.g., a first viewpoint, a second viewpoint, and a third viewpoint, respectively, [0069]), a generated three-dimensional translational transformation between the first camera position and the second camera position (the pose estimator 100 estimates a pose of the current object, represented by rotation transformation Ro and translation transformation To with respect to a global coordinate system (e.g., a coordinate system defined with respect to the main camera 10), [0138]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of D1 with the teaching of D2 in order to improve the performance (e.g., accuracy of the segmentation maps).
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 (U.S. PG-PUB NO. 2021/0074016) in view of D2 (U.S. PG-PUB NO. 2022/0044441) and further in view of D3 (U.S. PG-PUB NO. 2023/0162391).
-Regarding claim 6, the combination is silent to teaching that the generating of the first two-dimensional joint position estimate relative to the first camera position comprises utilizing a machine learning model, the model comprising: an object detection backbone; and a joint position estimation and camera parameter estimation block. However, the claimed limitation is well known in the art as evidenced by D3.
In the same field of endeavor, D3 teaches the generating of the first two-dimensional joint position estimate relative to the first camera position comprises utilizing a machine learning model (machine learning, [0040]), the model comprising: an object detection backbone (bounding box detection, [0040]); and a joint position estimation (landmark regression, [0040]) and camera parameter estimation block (camera focal length, [0040]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of the combination with the teaching of D3 in order to provide accurate interaction.
-Regarding claim 15, the combination further discloses the generating of the first two-dimensional joint position estimate relative to the first camera position comprises utilizing a machine learning model (D3, machine learning, [0040]), the model comprising: an object detection backbone (D3, bounding box detection, [0040]); and a joint position estimation (D3, landmark regression, [0040]) and camera parameter estimation block (D3, camera focal length, [0040]).
Allowable Subject Matter
Claims 2-5, 7-9, 11-14, 16-18 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 6, 10, 15 and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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/PING Y HSIEH/Primary Examiner, Art Unit 2664
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