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 Arguments
Applicant's arguments filed 05/19/2026 have been fully considered but they are not persuasive.
Arguments Regarding 35 U.S.C. 112(f):
Applicant argues on pages 8 and 9 that the rejection relies on a misapplication of the Broad Reasonable Interpretation standard. Examiner does not find this argument persuasive. Applicant states that “the examiner asserts that because 35 U.S.C. 112(f) is not invoked the specification is irrelevant”. Examiner admits this specific verbiage was incorrect and the specification is relevant. However, examiner maintains the interpretation of rendering parameters. Applicant cites MPEP 211, stating “the BRI must be consistent with the ordinary and customary meaning of the term and must be consistent with the use of the term in the disclosure” and “the described embodiments pertain to volume rendering and image synthesis, where rendering parameters (such as camera parameters, clipping parameters, transfer functions and light presets) are variables utilized by a renderer to synthesize a 2D image from an underlying 3D or 4D medical image dataset.”
This description of rendering parameters is not inconsistent with the Examiner’s interpretation of rendering parameters. Per MPEP 2173.01, “under broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification.” And the plain meaning of a rendering parameter, consistent with the specification, is a variable utilized by a renderer to synthesize a 2D image. The cited cropping size of Nicklaus is a variable utilized to synthesize a 2D image. Therefore it is consistent the plain meaning of the term and is not inconsistent with the specification.
Arguments Regarding 35 U.S.C. 103:
Applicant additionally makes the argument on page 9, with respect to rendering parameters, that “cropping an existing, already rendered 2D pixel array is an image editing or data augmentation operation, not a ‘rendering’ operation, and the size of the crop is not a ‘rendering parameter’”.
Examiner does not find this argument persuasive, the cropping size is utilized for training a model, because this training is further utilized to interpolate a rendered image, and a change in the cropping size parameter would cause a change in the rendered image, it is a rendering parameter.
Applicant argues on pages 9 and 10 that “by equating the claimed keyframe to a standard 2D image (such as the video frames in Niklaus), the rejection improperly reads the clause ‘comprises predetermined values of a set of rendering parameters’”.
Examiner does not find this argument persuasive for the reasons described above in relation to cropping size being a rendering parameter. In other words, because the video frames include information on cropping size (which is an aspect ratio) the video frame can be considered a keyframe. Applicant additionally argues that the specification “explicitly notes that each frame contains the rendering parameters, scene description, actions and any further information needed to render an image”, however this description of a keyframe is not what is claimed. If applicant wishes the examiner to interpret a key frame as including scene description, actions and any further information needed to render an image, it would need to be claimed, as is required by MPEP 2173 “Claims Must Particularly Point Out and Distinctly Claim the Invention”. Therefore Niklaus does teach “wherein a keyframe comprises predetermined values of a set of rendering parameters”
Furthermore one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references (MPEP 2145, subsection IV). Claim 1 was rejected in view of Teramura and Li. Li clearly describes a keyframe in the same term as the claim. This keyframe is used in an animation interpolation pipeline, taking into account data such as an occlusion mask, contour map and including parameters like an SSIM threshold and FPS in data preparation, which can be considered rendering parameters. Therefore, even if applicant’s argument were to be accepted, the claim would remain rejected in view of the same references.
Additionally, Petkov (US 10,692,267 B1), previously included in the applicant's IDS, describes a keyframe animation where each keyframe has a collection of values of rendering parameters (Col. 6 lines 11-24) using the same verbiage of the claim. In other words, if applicant’s arguments with respect to keyframes and their rendering parameters were to be found persuasive, the limitations are not novel.
Applicant argues on pages 10 and 11, that Nicklaus fails to teach “wherein optimizing the perceptual metric comprises optimizing the perceptual metric as a function of values of the set of rendering parameters”.
Applicant is correct that Nicklaus optimizes neural network weights and not specifically rendering parameters. However, examiner disagrees that the limitation is not taught as Nicklaus teaches both optimizing a perceptual metric and a set of rendering parameters, and it would be obvious to one of ordinary skill in the art to substitute a neural weight with rendering parameters, and optimize the perceptual metric to improve the quality of the outputted image frame.
Applicant argues on pages 11 and 12 with respect to the limitation “wherein generating the intermediary frames is based on optimizing a perceptual metric” that the Niklaus generates frames using a model that was previously trained using perceptual loss, and examiner’s position that this is analogous to the claim language “rewrites the claim and ignores the difference between machine learning training and inference”. Examiner respectfully disagrees. The claim does not state that optimization of the perceptual loss is done at inference or runtime. The claim merely states that “generating the intermediary frames is based on optimizing a perpetual metric”. Applicant further argues “The described embodiments are directed to an active optimization loop used during storyboard/animation generation process”. Again, this is not what is claimed. The term “based on” is extremely broad, and in order to claim the described function of optimizing a perceptual at inference time or to claim an active optimization loop, more specific language than “based on” is needed.
In other words, applicant’s assertion that Nicklaus evaluates perceptual loss only against historic training data to update network weights and doesn’t do so during the actual generation is correct. However, utilizing this network which is trained on a perceptual metric is still generating frames based on optimizing a perceptual metric. Applicant’s arguments in regards to claim 2 utilize the same arguments challenged above and are not found to be persuasive.
Applicant argues, with respect to claim 11, that “because Tasse does not generate synthesized intermediary frames between two existing keyframes, it is logically impossible for Tasse to teach ‘promoting’ a generated intermediary frame into a keyframe”. Examiner does not find this argument persuasive.
The previous office action asserted that Tasse teaches “promoting one or more intermediary frames to keyframes, which are added to the selected set of keyframes to form an expanded set of keyframes”, in this rejection there is no mapping of the keyframes of Tasse to the generated intermediary keyframes of the applicant’s argument. This is because the claim language does not recite generated intermediary keyframes in the context of promotion. Therefore applicant’s argument that Tasse does not teach this limitation because it is logically impossible for Tasse to teach “promoting” a generated intermediary frame into a keyframe is moot.
Applicant further argues with respect to the Blender reference, that “a human animator manually deciding an animation is ‘a bit too fast’ and dragging keyframes further apart on a screen does not teach or suggest a computer-implemented method where the generation step itself comprises extending the time interval and expanding the set of keyframes”.
Examiner does not find this argument persuasive. The blender reference does not just describe a “a human animator manually deciding an animation is ‘a bit too fast’ and dragging keyframes further apart on a screen”. Blender describes the functionality of keyframe interpolation present on the Blender platform at the time which involves extending a length of a time interval between at least one pair of consecutive keyframes within the selected set of keyframes(“You can aslo select all of your KFs, hit the A key to select all, and press S to scale your KFs further apart, or closer together (which will speed up/slow down the animations)”) and wherein generating the intermediary frames in the temporal sequence is repeated for the expanded set of keyframes (KF interpolation).
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.
Claim(s) 1-5, 10, 13, 15, 16, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Niklaus, Simon, Long Mai, and Feng Liu. "Video frame interpolation via adaptive separable convolution." Proceedings of the IEEE international conference on computer vision. 2017. (hereinafter "Niklaus") in view of Teramura (EP 4093025) and Li X, Zhang B, Liao J, Sander PV. Deep sketch-guided cartoon video inbetweening. IEEE Transactions on Visualization and Computer Graphics. 2021 Jan 5;28(8):2938-52. (Hereinafter “Li”).
Regarding claim 1, Nicklaus teaches A computer-implemented method for rendering animations, the method comprising:
receiving at least one input, wherein each of the at least one input comprises an image dataset (page 2, section 3, paragraphs 1 & 2 – video frames );
selecting a set of keyframes associated with the received at least one input, wherein a keyframe comprises predetermined values of a set of rendering parameters (page 2, section 3, paragraph 2 first sentence – two input video frames, page 4 section “data augmentation”);
generating intermediary frames in a temporal sequence between two consecutive ones of the keyframes (page 2, section 3, paragraph 2, first sentence: “interpolate a frame temporally in the middle”), wherein the consecutive keyframes are consecutive according to a temporal ordering of the keyframes within the selected set of keyframes (page 8, left-hand column, last paragraph - right-hand column first paragraph), wherein generating the intermediary frames is based on optimizing a perceptual metric associated with the selected set of keyframes and the generated intermediary frames (page 3 left column, page 4, left column lines 12-20, equation 3), and wherein optimizing the perceptual metric comprises optimizing the perceptual metric as a function of values of the set of rendering parameters associated with each of the intermediary frames (page 3 left column, last sentence of section 3; page 4, left column lines 12-20, section “data augmentation”, eq 3); and
Nicklaus describes a method of interpolating intermediate keyframes given input frames. This method includes optimizing a perceptual metric, using input video frames which include the parameters used in rendering. Nicklaus fails to teach rendering animations of medical images, and wherein the at least one input comprises a medical image dataset; rendering an animation using the generated intermediary frames and the selected set of keyframes.
However, teaches Teramura teaches rendering animations of medical images (paragraph [0037] - moving image is analogous to animation), and at least one input comprises a medical image dataset (paragraph [0024]); Teramura is considered analogous to the claimed invention as it is in the same field of medical imaging and image processing. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Teramura with Nicklaus to implement the animation methodology of Nicklaus in a medical imaging context to help improve visual quality of intermediate frame interpolation..
Nicklaus in view of Teramura fail to teach rendering an animation using the generated intermediary frames and the selected set of keyframes
However, Li teaches rendering an animation using the generated intermediary frames and the selected set of keyframes (Figs 1 & 2, section 5.1, section 3.4 – “generated video” is a rendered animation).
Li is considered analogous to the claimed invention as it is in the same field of animation frame synthesis. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Li with Nicklaus in view of Teramura
Regarding claim 2, Nicklaus in view of Teramura and Li teaches the method according to claim 1. Nicklaus further teaches wherein the perceptual metric is selected from the group consisting of: perceptual hash, pHash; structural similarity; visual entropy; and blend reference image spatial quality evaluator, BRISQUE (Page 4 col 1 lines 18-20, SSIM is a structural similarity metric).
Regarding claim 3, Nicklaus in view of Teramura and Li teaches the method according to claim 1. Nicklaus further teaches wherein the at least one input comprises a two-dimensional image dataset (Page 2 section 3 paragraph 2 first sentence - a video frame is two dimensional). Nicklaus fails to teach a two-dimensional medical image dataset.
However Teramura teaches a two-dimensional medical image dataset (paragraph [0029]).
Regarding claim 4, Nicklaus in view of Teramura and Li, teaches the method according to claim 1. Teramura further teaches wherein the medical image dataset comprised in at least one input is received from a medical scanner (paragraph [0037]).
Regarding claim 5, Nicklaus in view of Teramura and Li, teaches the method according to claim 1. Teramura further teaches wherein the medical image dataset comprises at least two different medical image datasets obtained from at least two different medical scanners (paragraph [0037]).
Regarding claim 10, Nicklaus in view Teramura and Li teaches the method according to claim 1. Nicklaus further teaches wherein a length of a time interval between consecutive intermediary frames and/or an intermediary frame rate is constant between two consecutive keyframes (page 8, left-hand column, last paragraph - right-hand column first paragraph).
Regarding claim 13, Nicklaus in view of Teramura and Li teaches the method according to claim 1. Nicklaus further teaches wherein the input comprises one or more animations, and wherein the one or more animations are comprised in the rendered animation (age 2, section 3, second paragraph, first sentence & figure 2).
Regarding claim 15, Nicklaus in view of Teramura and Li teaches the method according to claim 1. Nicklaus further teaches wherein the method is performed by a neural network and/or using artificial intelligence (Page 3, Fig 2).
Apparatus claim(s) 16 is/are drawn to the method of using as claimed in claim(s) 1. Therefore, the apparatus claim(s) 16 correspond(s) to the method claim(s) 1, and is/are rejected for the same reasons of obviousness as used above.
CRM claim(s) 19 is/are drawn to the method of using as claimed in claim(s) 1. Therefore, the CRM claim(s) 19 correspond(s) to the method claim(s) 1, and is/are rejected for the same reasons of obviousness as used above.
Claim(s) 6, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicklaus in view of Teramura and Li and in further view of S. Weiss and R. Westermann, "Differentiable Direct Volume Rendering," IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 562-572, 2021. (Hereinafter “Weiss”).
Regarding claim 6, Nicklaus in view of Teramura and Li teach the method according to claim 1. Nicklaus in view of Teramura fails to teach wherein the set of rendering parameters comprises at least one rendering parameter selected from the group consisting of: camera parameter; clipping parameter; classification parameter; and lighting preset parameter.
However, Weiss teaches wherein the set of rendering parameters comprises at least one rendering parameter selected from the group consisting of: camera parameter; clipping parameter; classification parameter; and lighting preset parameter. (Section 1, Paragraph 2 – “For surface rendering, one objective is on the optimization of scene parameters like material properties, lighting conditions, or even geometric shape”, Section 5.1 Paragraph 1 – “The camera is parameterized by longitude and latitude. AD is used to optimize the camera parameters to determine the viewpoint that maximized the selected cost function.”). Weiss describes surface rendering consisting of parameters such as lighting parameters, which is analogous to light preset parameters. Weiss also suggests the use of camera parameters. Weiss is considered analogous to the claimed invention as it is in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the rendering parameter teachings of Weiss with the animation system of Nicklaus in view of Teramura and Li to determine optimal parameters and improve the generation of synthetic images.
Regarding claim 14, Nicklaus in view of Teramura and Li teaches the method according to claim 1. Nicklaus in view of Teramura and Li fails to teach teaches wherein rendering comprises differentiable rendering.
However, Weiss teaches wherein rendering comprises differentiable rendering (title, introduction, conclusion). The motivation to combine Weiss with Nicklaus in view of Teramura and Li would have been the same as in claim 6.
Claim(s) 7, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicklaus in view of Teramura and Li and in further view of Grabli (US 2011/0205233 A1).
Regarding claim 7, Nicklaus in view of Teramura and Li teaches the method according to claim 1. Nicklaus in view of Teramura and Li fails to teach temporally ordering the keyframes within the selected set of keyframes.
However, Grabli teaches temporally ordering the keyframes within the selected set of keyframes (paragraph [0009]). Grabli describes ordering strokes of a stroke-based animation. This process partially orders strokes for each of the frames and then based on this selects a “temporally coherent” sequence of frames which is an ordered set of frames and analogous to the temporally ordered keyframes described in the limitation.
Grabli is considered analogous to the claimed invention as it is in the same field of image processing and animation. Therefore it would have been obvious to one of ordinary skill in the art to combine the teachings of Grabli with Nicklaus in view of Teramura and Li to implement a method of temporal ordering and improve cohesion of animation.
Regarding claim 8, Nicklaus in view of Teramura and Li and in further view of Grabli teach the method according to claim 7. Nicklaus further teaches a perceptual metric (page 7, line 1 “loss function that optimizes for perceptual quality”). Nicklaus in view of Teramura fails to teach temporally ordering is based on optimizing a perceptual metric.
However, Grabli further teaches wherein temporally ordering comprises temporally ordering is based on optimizing a metric (paragraphs [0005], [0006]). Grabli describes using geometric considerations in ordering. These geometric considerations can be considered metrics. The motivation to combine is the same as claim 8.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicklaus in view of Teramura, Li and Grabli and in further view of Tasse (US 2023/0290036 A1) and Knoll (US 9,734,615 B1).
Regarding claim 9, Nicklaus in view of Teramura, Li and Grabli teach the method according to claim 8. Teramura further teaches exceeds a predetermined threshold value (paragraph [0068]).
Nicklaus in view of Teramura, Li and Grabli fails to teach wherein an initial temporal ordering is modified when a value of the perceptual metric for the temporal ordering is indicative of a perceptual dissimilarity.
However Tasse teaches a value of the perceptual metric exceeds a predetermined threshold value indicative of a perceptual dissimilarity (paragraph [0058]). Tasse describes determining the differences present in a 3D mesh and whether or not the dissimilarity meets a threshold to determine which keyframes are to be added to a list of visible keyframe. This is then used to determine texture information about the mesh object. This process is analogous to determining where a value of a perceptual metric exceeds a predetermined threshold indicative of perceptual dissimilarity. Tasse is considered analogous to the claimed invention as it is in the same field of computer graphics. Therefore it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tasse with Nicklaus in view of Teramura, Li and Grabli in order to implement a determination of dissimilarity and make sure the changes sufficiently warrant an update which can be computationally expensive (paragraph [0059]) .
Nicklaus in view of Teramura, Li and Grabli and in further view of Tasse fail to teach an initial temporal ordering is modified.
However, Knoll teaches an initial temporal ordering is modified (Col. 1 lines 25-30). Knoll describes an editing program which can modify clips along a timeline to create a time-ordered sequence of clips. This is analogous to modifying an initial temporal ordering. Knoll is considered analogous to the claimed invention as it is in the same field of image processing and animation. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Knoll with Nicklaus in view of Teramura, Li and Grabli and in further view of Tasse to incorporate modification of the temporal ordering and reduce processing requirement (Col 8, lines 43-45).
Claim(s) 11, 12, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicklaus in view of Teramura and Li and in further view of Tasse, ANONYMOUS; “Increase Space Between Keyframes – Artwork/Animations – Blender Artists Community”, 1 October 2012 (hereinafter "Blender").
Regarding claim 11, Nicklaus in view of Teramura and Li teaches the method according to claim 1. Nicklaus further teaches one or more intermediary frames are associated with the at least one pair of consecutive keyframes (page 2, section 3, paragraph 2, first sentence: “interpolate a frame temporally in the middle”).
Nicklaus in view of Teramura and Li fails to teach wherein generating the intermediary frames in the temporal sequence further comprises extending a length of a time interval between at least one pair of consecutive keyframes within the selected set of keyframes and expanding the selected set of keyframes by promoting one or more intermediary frames to keyframes, which are added to the selected set of keyframes to form an expanded set of keyframes, and wherein generating the intermediary frames in the temporal sequence is repeated for the expanded set of keyframes.
However, Tasse teaches expanding the selected set of keyframes by promoting one or more intermediary frames to keyframes, which are added to the selected set of keyframes to form an expanded set of keyframes (paragraph [0053]-[0055]). Tasse describes adding keyframes to a keyframe queue (analogous to set of keyframes) and in turn expanding the set of keyframes. While Tasse doesn’t specifically describe “promoting” an intermediate keyframe, it serves the same function of expanding the keyframe set and incorporating more keyframes to “cover as much of the physical scene depicted as possible”. Therefore it would have been obvious to one of ordinary skill in the art to combine Tasse with Nicklaus in view of Teramura and Li to improve the keyframe animation’s representation of a scene.
Nicklaus in view of Teramura and Li and in further view of Tasse fails to teach extending a length of a time interval between at least one pair of consecutive keyframes within the selected set of keyframes and wherein generating the intermediary frames in the temporal sequence is repeated for the expanded set of keyframes.
However, Blender teaches extending a length of a time interval between at least one pair of consecutive keyframes within the selected set of keyframes and wherein generating the intermediary frames in the temporal sequence is repeated for the expanded set of keyframes (full page). The Blender reference is an internet article helping a user implement the functionality of increasing space between keyframes in the Blender application. The response from another user shows that in 2012 there existed teachings of extending a length of time interval between keyframes and further interpolating if needed. Blender is considered analogous to the claimed invention as it is in the same field of invention of computer graphics and keyframe editing and interpolation. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Blender with Nicklaus in view of Teramura and Li and in further view of Tasse to extend an interval of time and continue interpolation to help create smooth transitions.
Regarding claim 12, with Nicklaus in view of Teramura and Li and in further view of Tasse and Blender teaches the method according to claim 11. Tasse further teaches wherein the promoting of one or more intermediary frames to keyframes is performed when the perceptual metric between consecutive intermediary frames comprising the to-be-promoted one or more intermediary frames exceeds a predetermined threshold indicative of a perceptual dissimilarity (paragraph [0058]).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicklaus in view of Teramura and Li and in further view of Weiss and Grabli.
Regarding claim 17, Nicklaus in view of Teramura and Li teach the system according to claim 16. Nicklaus further teaches a perceptual metric (page 7, line 1 “loss function that optimizes for perceptual quality”). Nicklaus in view of Teramura and Li fails to teach wherein the set of rendering parameters comprises at least one rendering parameter selected from the group consisting of: camera parameter; clipping parameter; classification parameter; and lighting preset parameter; wherein the computer is configured to temporally order the keyframes within the selected set of keyframes based on optimization of the perceptual metric.
However, Weiss teaches wherein the set of rendering parameters comprises at least one rendering parameter selected from the group consisting of: camera parameter; clipping parameter; classification parameter; and lighting preset parameter. (Section 1, Paragraph 2 – “For surface rendering, one objective is on the optimization of scene parameters like material properties, lighting conditions, or even geometric shape”, Section 5.1 Paragraph 1 – “The camera is parameterized by longitude and latitude. AD is used to optimize the camera parameters to determine the viewpoint that maximized the selected cost function.”). Weiss describes surface rendering consisting of parameters such as lighting parameters, which is analogous to light preset parameters. Weiss also suggests the use of camera parameters. Weiss is considered analogous to the claimed invention as it is in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the rendering parameter teachings of Weiss with the animation system of Nicklaus in view of Teramura and Li to determine optimal parameters and improve the generation of synthetic images.
Nicklaus in view of Teramura and Li and Weiss fails to teach wherein the computer is configured to temporally order the keyframes within the selected set of keyframes based on optimization of the perceptual metric.
However Grabli teaches wherein the computer is configured to temporally order the keyframes within the selected set of keyframes based on optimization of the metric (paragraphs [0005], [0006], [0009]). Grabli describes ordering strokes of a stroke-based animation. This process partially orders strokes for each of the frames and then based on this selects a “temporally coherent” sequence of frames which is an ordered set of frames and analogous to the temporally ordered keyframes described in the limitation. Grabli describes using geometric considerations in ordering. These geometric considerations can be considered metrics.
Grabli is considered analogous to the claimed invention as it is in the same field of image processing and animation. Therefore it would have been obvious to one of ordinary skill in the art to combine the teachings of Grabli with Nicklaus in view of Teramura and Li to implement a method of temporal ordering and improve cohesion of animation.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nicklaus in view of Teramura, Li, Weiss and Grabli and in further view of Weiss and Grabli.
Regarding claim 18, Nicklaus in view of Teramura, Li, Weiss and Grabli teach the system of claim 17. Nicklaus further teaches wherein a length of a time interval between consecutive intermediary frames and/or an intermediary frame rate is constant between two consecutive keyframes (page 8, left-hand column, last paragraph - right-hand column first paragraph); one or more intermediary frames are associated with the at least one pair of consecutive keyframes (page 2, section 3, paragraph 2, first sentence: “interpolate a frame temporally in the middle”).
Nicklaus in view of Teramura and Li fails to teach wherein the intermediary frames are generated in the temporal sequence by extension of a length of a time interval between at least one pair of consecutive keyframes within the selected set of keyframes and expansion of the selected set of keyframes by promotion of one or more intermediary frames to keyframes, which are added to the selected set of keyframes to form an expanded set of keyframes, and wherein generation of the intermediary frames in the temporal sequence is repeated for the expanded set of keyframes.
However, Tasse teaches expansion of the selected set of keyframes by promotion of one or more intermediary frames to keyframes, which are added to the selected set of keyframes to form an expanded set of keyframes (paragraph [0053]-[0055]). Tasse describes adding keyframes to a keyframe queue (analogous to set of keyframes) and in turn expanding the set of keyframes. While Tasse doesn’t specifically describe “promoting” an intermediate keyframe, it serves the same function of expanding the keyframe set and incorporating more keyframes to “cover as much of the physical scene depicted as possible”. Therefore it would have been obvious to one of ordinary skill in the art to combine Tasse with Nicklaus in view of Teramura and Li to improve the keyframe animation’s representation of a scene.
Nicklaus in view of Teramura and Li and in further view of Tasse fails to teach extension of a length of a time interval between at least one pair of consecutive keyframes within the selected set of keyframes and wherein generation of the intermediary frames in the temporal sequence is repeated for the expanded set of keyframes.
However, Blender teaches extension of a length of a time interval between at least one pair of consecutive keyframes within the selected set of keyframes and wherein generation of the intermediary frames in the temporal sequence is repeated for the expanded set of keyframes. (full page). The Blender reference is an internet article helping a user implement the functionality of increasing space between keyframes in the Blender application. The response from another user shows that in 2012 there existed teachings of extending a length of time interval between keyframes and further interpolating if needed. Blender is considered analogous to the claimed invention as it is in the same field of invention of computer graphics and keyframe editing and interpolation. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Blender with Nicklaus in view of Teramura and Li and in further view of Tasse to extend an interval of time and continue interpolation to help create smooth transitions.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/AIDAN W MCCOY/ Examiner, Art Unit 2611
/TAMMY GODDARD/ Supervisory Patent Examiner, Art Unit 2611
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