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 .
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.
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.
Claims 1-2, 6, 11-12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al (12,700,181) in view of Gangadharan et al (12,530,572)
Regarding method claim 1 and claim 11 Zhu discloses,
A method for generating aligned image data through an aligning parameter generated by an image transformation artificial intelligence (AI) model, the method comprising: through an encoder (note fig. 2 block 206b, image encoder) of the image transformation AI model (note fig. 2, 204 machine learning model),
Generating at least one aligning parameter from a first camera property and a second camera property related to a first camera and a second camera respectively (note col. 10 lines 34-40, lines cite camera properties);
Through an image transformer of the image transformation AI model, transforming (note col. 10 lines 51-55, neural network transforms) based on the at least one aligning parameter and a brightness parameter, first image data photographed by the first camera to be aligned with second image data photographed by the second camera.
Zhu does not clearly disclose training the encoder and a discriminator of the image transformation AI model by adversarial training, wherein the image transformation AI model discriminates between the transformed first image data and the second image data. Gangadharan discloses training the encoder and a discriminator of the image transformation AI model by adversarial training, wherein the image transformation AI model discriminates between the transformed first image data and the second image data (note col. 19 lines 13-35, lines cited discriminator and adversarial training). Zhu and Gangadharan are combinable because they are from the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include training the encoder and a discriminator of the image transformation AI model by adversarial training, wherein the image transformation AI model discriminates between the transformed first image data and the second image data in the system of Zhu as evidenced by Gangadharan. The suggestion/motivation for doing so provides it is desirable to be able to render the scene from any given viewpoint such that the end user is able to view the scene from the user's own perspective (note Gangadharan, col. 1 lines 35-38).
Regarding method claim 2 and device claim 12 Zhu and Gangadharan discloses,
Wherein the first camera property and the second camera property include at least one of an intrinsic parameter or a distortion coefficient of each of the first camera and the second camera (note Zhu, col. 10 lines 34-40, cites intrinsic parameter of the camera).
Regarding method claim 6 and device claim 16 Zhu and Gangadharan discloses,
Wherein the brightness parameter is a parameter learnable based on a loss of the discriminator caused by the brightness parameter, independently of a loss of the discriminator caused by the encoder (note Gangadharan col. 13 lines 19-35).
Claims 3, 7, 10, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu and Gangadharan as applied to claim 1 above, and further in view of Zhang et al (11,734,855).
Regarding method claim 3 and device claim 13 Zhu and Gangadharan does not clearly disclose aligning parameter includes at least one of a crop parameter for removing a predetermined area. Zhang discloses aligning parameter includes at least one of a crop parameter for removing a predetermined area (note col. 12 lines 37-47, cites cropping augmentation). Zhu, Gangadharan and Zhang are combinable because they are from the same field of endeavor. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include crop parameter for removing predetermined area in the system of Zhang as evidenced by Zhu and Gangadharan. The suggestion/motivation provides an accurate finding of the position and orientation of the input camera sensor with respect to the known environment an image (note col. 1 lines 12-15). It would have been obvious to combine Zhang with Zhu and Gangadharan to obtain the invention as specified by claim 3 and 13.
Regarding method claim 7 Zhu , Gangadharan and Zhang discloses,
Subordinating the crop parameter and the projection matrix to the trained encoder; and determining the crop parameter and the projection matrix by regression (note Zhang col. 12, 37-47, crop parameter).
Regarding method claim 10 and device claim 20 Zhu, Gangadharan and Zhang discloses,
Performing, by the encoder, normalization for the first camera property and the second camera property being input and the crop parameter and the projection matrix being output, to a predetermined range (note Zhang, col. 12 lines 37-47, crop parameter).
Allowable Subject Matter
Claims 4-5, 8-9, 14-15 and 17-19 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.
The following is a statement of reasons for the indication of allowable subject matter for dependent claims 4-5, 9, 14-15 and 19.
Regarding dependent claims 4 and 14, prior art could not be found for the features projecting the first image data based on the projection matrix; removing the predetermined area by reflecting the crop parameter in the projected first image data; adjusting brightness by applying the brightness parameter to the first image data with the predetermined area being removed; and performing a resizing operation to match a size of the first image data with the brightness to a size of the second image data. These features in combination with other features could not be found in the prior art. Claims 8 and 18 depend on claims 4 and 14, respectfully. Therefore, are also objected.
Regarding dependent claims 5 and 15, prior art could not be found for the features removing the predetermined area of the first image data based on the crop parameter; projecting the first image data by reflecting the projection matrix onto the first image data with the predetermined area being removed; adjusting brightness by applying the brightness parameter to the projected first image data; and performing a resizing operation to match a size of the first image data with the brightness to a size of the second image data. These features in combination with other features could not be found in the prior art. Claim 17 depends on claim 15. Therefore, are also objected.
Regarding dependent claims 9 and 19, prior art could not be found for the features discriminating, by the discriminator, between truth and falsehood regarding whether the transformed first image data is photographed by the second camera; training the discriminator to determine falsehood; and learning the encoder and the brightness parameter to be determined as truth by the discriminator. These features in combination with other features could not be found in the prior art.
Related Prior Art
Daniilidis et al (11,288,818) Through an image transformer of the image transformation AI model (neural network of fig. 2) transforming (note col. 6 lines 21-20, neural network transforms) based on the at least one aligning parameter and a brightness parameter, first image data photographed by the first camera to be aligned with second image data photographed by the second camera (col. 22 lines 28-32, cites cropping).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am-3:00pm.
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G.D.
October 9, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676
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