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 .
Information Disclosure Statement
The IDS(s) dated 10/13/2025 has/have been considered and placed in the application file.
Claim Rejections - 35 USC § 103
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.
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) 1-5, 7-12, 14-18, and 20 is/are rejected under 35 U.S.C. 103 as obvious over Su et al (Su, X., Song, J., Meng, C., & Ermon, S. (2022). Dual diffusion implicit bridges for image-to-image translation. arXiv preprint arXiv:2203.08382., hereafter referred to as Su) in view of Ma et al (Ma, W., Liu, Q., Wang, J., Wang, A., Liu, Y., Kortylewski, A., & Yuille, A. (2023). Adding 3d geometry control to diffusion models. arXiv preprint arXiv:2306.08103., hereafter referred to as Ma).
Claim 1
Regarding Claim 1, Su teaches A computer-implemented method for semi-generative artificial intelligence modelling between domains, the method comprising:
using a number of processors to perform:
receiving a source image of an object in a first domain (Su in p. 2, Figure 1 discloses “Given a source image x(s)”; p.4, Algorithm 1 discloses receiving “data sample from source domain x(s) ∼ ps(x)”);
diffusing the source image through a source diffusion model to generate a first Gaussian distribution in the first domain (Su in pp. 3-4, §§2.1-3, Algorithm 1 discloses applying the source-domain probability-flow ODE from t=0 to t=1 to obtain the source model’s latent code; pp. 3-4, §§2.2 discloses that an SGM’s endpoint marginal p1 is the standard Gaussian prior; p.14 discloses, Appendix B.1, discloses modeling the diffusion conditionals as Gaussians and samples ɛt from N(0,I). Under BRI, the source model’s endpoint marginal is the first, source model associated Gaussian distribution);
feeding the embeddings into dual diffusion implicit bridges (Su in p.8, §4.4 discloses DDIB translation with a pretrained conditional diffusion model and guidance toward selected classes);
sampling from the first Gaussian distribution (Su in p. 4, Algorithm 1 discloses beginning with source sample x(s) ∼ ps(x) and deterministically transports it through the source probability-flow ODE to latent x(l); pp. 3-4, §§2.1-2.2 discloses that the probability-flow ODE carries the same marginals as the diffusion process and that the endpoint marginal is the standard Gaussian prior. x(l) is a sample having the source model’s Gaussian endpoint law);
mapping, through the dual diffusion implicit bridges, the sample from the first Gaussian distribution to a second Gaussian distribution in a second domain (Su in p. 4, §3, Algorithm 1 discloses the source latent code x(l) as the “target latent code at t=1” into the independently trained target model; pp. 3-5, §§2.2-3, Proposition 3.2 discloses that the endpoint prior of the SGM/DDIM is Gaussian and characterizes the source-to-latent and latent-to-target operations as concatenated Schrödinger Bridges. Under BRI, passing x(l) unchanged between compatible source-model and target-model Gaussian priors is an identity mapping/coupling to the second target model associated Gaussian distribution); and
reverse diffusing the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata (Su in pp. 2/4, Figure 1, §3, Algorithm 1 discloses solving the target model from t=1 to t=0 to construct target-domain image x(t) while preserving source content).
Su does not explicitly teach all of generating embeddings from metadata, wherein the metadata provides constraints for image reconstruction; feeding the embeddings into dual diffusion implicit bridges; and reverse diffusing the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata.
However, Ma teaches generating embeddings from metadata, wherein the metadata provides constraints for image reconstruction (Ma in p. 4, §3.1, Eq. 2 discloses preprocessing text prompt T with pretrained text encoder θ to produce representation θ(T), which is used as key/value information in U-Net cross-attention; pp. 5-6, Figure 2, §§3.2-3.3, forms the prompt from CAD class names, tags or keywords, and generated descriptions that constrain object identity, background, color, shape, pose, viewpoint, and distance. θ(T) is the embedding; the separate CAD-derived edge map is additional visual conditioning);
feeding the embeddings into dual diffusion implicit bridges (Ma in p. 4, §3.1 discloses feeding θ(T) into successive target-denoising U-Net cross-attention layers);
and reverse diffusing the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata (Ma in pp. 4-6, §§3.1-3.4, Figure 2, Algorithm 1 discloses reverse denoising conditioned on θ(T), optionally with a CAD-derived edge prompt, and decoding z0 into a target image constrained by that information).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Su by conditioning the target DDIB stage with metadata-derived text embeddings that is taught by Ma, since both reference are analogous art in the field of diffusion-based computer vision image generation and translation; thus, one of ordinary skilled in the art would be motivated to combine the references since Su’s source-latent-target DDIB process with Ma’s fixed cross-attention condition yields the predictable result of metadata-conditioned target reverse diffusion, thereby improving control over target object attributes, background, color, viewpoint, and geometry.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 2
Regarding Claim 2, Su in view of Ma teaches The method of claim 1, wherein the first domain comprises three-dimensional CAD (computer assisted drawing) image data (Ma in pp. 2/5-6, Figures 1-2, §3.2 discloses CAD models rendered from selected viewpoints and distances into two-dimensional CAD-derived sketches and compact edge-map images carrying the CAD object’s 3D shape and pose).
Claim 3
Regarding Claim 3, Su in view of Ma teaches The method of claim 1, wherein the second domain comprises real world image data (Su in p. 7, §4.3 discloses an image domain containing “real photos taken via a camera or a satellite” and translation in either direction).
Claim 4
Regarding Claim 4, Su in view of Ma teaches The method of claim 3, wherein the target image comprises a photorealistic image (Ma in pp. 1/5, Abstract, Introduction, Figure 2 discloses generating “photo-realistic images” using encoded text and CAD-derived visual conditions).
Claim 5
Regarding Claim 5, Su in view of Ma teaches The method of claim 1, wherein the metadata comprises at least one of:
clustering of data;
prompt embedding;
segmentation mask;
two-dimensional drawing information;
text description of the target object;
audio description of the target object;
graph representation of the target object;
material; or
background (Ma in p. 4, §3.1 discloses generating prompt embedding θ(T); pp. 5-6, §§3.2-3.3, Figure 2 discloses target-object descriptions and background descriptions).
Claim 7
Regarding Claim 7, Su in view of Ma teaches The method of claim 1, wherein the source diffusion model and target diffusion model comprise Schrödinger bridges (Su in pp. 1/4-5, Abstract, §§2.2-3, Proposition 3.2 discloses characterizing its source-to-Gaussian and Gaussian-to-target PF-ODE stages as two concatenated special Schrödinger Bridges).
Claim 8
Regarding Claim 8, Su teaches A system for semi-generative artificial intelligence modelling between domains, the system comprising:
a storage device that stores program instructions (Su in Abstract discloses “image translation with DDIBs relies on two diffusion models trained independently on each domain, and is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and then decode such encodings using the target model to construct target images”. The computer-implemented image processing would predictably be executed on conventional reliable hardware);
one or more processors operably connected to the storage device (Su in Abstract discloses “image translation with DDIBs relies on two diffusion models trained independently on each domain, and is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and then decode such encodings using the target model to construct target images”. The computer-implemented image processing would predictably be executed on conventional reliable hardware) and configured to execute the program instructions to cause the system to:
receive a source image of an object in a first domain (Su in p. 2, Figure 1 discloses “Given a source image x(s)”; p.4, Algorithm 1 discloses receiving “data sample from source domain x(s) ∼ ps(x)”);
diffuse the source image through a source diffusion model to generate a first Gaussian distribution in the first domain (Su in pp. 3-4, §§2.1-3, Algorithm 1 discloses applying the source-domain probability-flow ODE from t=0 to t=1 to obtain the source model’s latent code; pp. 3-4, §§2.2 discloses that an SGM’s endpoint marginal p1 is the standard Gaussian prior; p.14 discloses, Appendix B.1, discloses modeling the diffusion conditionals as Gaussians and samples ɛt from N(0,I). Under BRI, the source model’s endpoint marginal is the first, source model associated Gaussian distribution);
feed the embeddings into dual diffusion implicit bridges (Su in p.8, §4.4 discloses DDIB translation with a pretrained conditional diffusion model and guidance toward selected classes);
sample from the first Gaussian distribution (Su in p. 4, Algorithm 1 discloses beginning with source sample x(s) ∼ ps(x) and deterministically transports it through the source probability-flow ODE to latent x(l); pp. 3-4, §§2.1-2.2 discloses that the probability-flow ODE carries the same marginals as the diffusion process and that the endpoint marginal is the standard Gaussian prior. x(l) is a sample having the source model’s Gaussian endpoint law);
map, through the dual diffusion implicit bridges, the sample from the first Gaussian distribution to a second Gaussian distribution in a second domain (Su in p. 4, §3, Algorithm 1 discloses the source latent code x(l) as the “target latent code at t=1” into the independently trained target model; pp. 3-5, §§2.2-3, Proposition 3.2 discloses that the endpoint prior of the SGM/DDIM is Gaussian and characterizes the source-to-latent and latent-to-target operations as concatenated Schrödinger Bridges. Under BRI, passing x(l) unchanged between compatible source-model and target-model Gaussian priors is an identity mapping/coupling to the second target model associated Gaussian distribution); and
reverse diffuse the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata (Su in pp. 2/4, Figure 1, §3, Algorithm 1 discloses solving the target model from t=1 to t=0 to construct target-domain image x(t) while preserving source content).
Su does not explicitly teach all of generate embeddings from metadata, wherein the metadata provides constraints for image reconstruction; feed the embeddings into dual diffusion implicit bridges; and reverse diffuse the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata.
However, Ma teaches generate embeddings from metadata, wherein the metadata provides constraints for image reconstruction (Ma in p. 4, §3.1, Eq. 2 discloses preprocessing text prompt T with pretrained text encoder θ to produce representation θ(T), which is used as key/value information in U-Net cross-attention; pp. 5-6, Figure 2, §§3.2-3.3, forms the prompt from CAD class names, tags or keywords, and generated descriptions that constrain object identity, background, color, shape, pose, viewpoint, and distance. θ(T) is the embedding; the separate CAD-derived edge map is additional visual conditioning);
feed the embeddings into dual diffusion implicit bridges (Ma in p. 4, §3.1 discloses feeding θ(T) into successive target-denoising U-Net cross-attention layers);
and reverse diffuse the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata (Ma in pp. 4-6, §§3.1-3.4, Figure 2, Algorithm 1 discloses reverse denoising conditioned on θ(T), optionally with a CAD-derived edge prompt, and decoding z0 into a target image constrained by that information).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Su by conditioning the target DDIB stage with metadata-derived text embeddings that is taught by Ma, since both reference are analogous art in the field of diffusion-based computer vision image generation and translation; thus, one of ordinary skilled in the art would be motivated to combine the references since Su’s source-latent-target DDIB process with Ma’s fixed cross-attention condition yields the predictable result of metadata-conditioned target reverse diffusion, thereby improving control over target object attributes, background, color, viewpoint, and geometry.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 9
Regarding Claim 9, Su in view of Ma teaches The system of claim 8, wherein the first domain comprises three-dimensional CAD (computer assisted drawing) image data (Ma in pp. 2/5-6, Figures 1-2, §3.2 discloses CAD models rendered from selected viewpoints and distances into two-dimensional CAD-derived sketches and compact edge-map images carrying the CAD object’s 3D shape and pose).
Claim 10
Regarding Claim 10, Su in view of Ma teaches The system of claim 8, wherein the second domain comprises real world image data (Su in p. 7, §4.3 discloses an image domain containing “real photos taken via a camera or a satellite” and translation in either direction).
Claim 11
Regarding Claim 11, Su in view of Ma teaches The system of claim 10, wherein the target image comprises a photorealistic image (Ma in pp. 1/5, Abstract, Introduction, Figure 2 discloses generating “photo-realistic images” using encoded text and CAD-derived visual conditions).
Claim 12
Regarding Claim 12, Su in view of Ma teaches The system of claim 8, wherein the metadata comprises at least one of:
clustering of data;
prompt embedding;
segmentation mask;
two-dimensional drawing information;
text description of the target object;
audio description of the target object;
graph representation of the target object;
material; or
background (Ma in p. 4, §3.1 discloses generating prompt embedding θ(T); pp. 5-6, §§3.2-3.3, Figure 2 discloses target-object descriptions and background descriptions).
Claim 14
Regarding Claim 14, Su in view of Ma teaches The system of claim 8, wherein the source diffusion model and target diffusion model comprise Schrödinger bridges (Su in pp. 1/4-5, Abstract, §§2.2-3, Proposition 3.2 discloses characterizing its source-to-Gaussian and Gaussian-to-target PF-ODE stages as two concatenated special Schrödinger Bridges).
Claim 15
Regarding Claim 15, Su teaches A computer program product for semi-generative artificial intelligence modelling between domains, the computer program product comprising:
a computer-readable storage medium having program instructions embodied thereon to perform the steps of:
receiving a source image of an object in a first domain (Su in p. 2, Figure 1 discloses “Given a source image x(s)”; p.4, Algorithm 1 discloses receiving “data sample from source domain x(s) ∼ ps(x)”);
diffusing the source image through a source diffusion model to generate a first Gaussian distribution in the first domain (Su in pp. 3-4, §§2.1-3, Algorithm 1 discloses applying the source-domain probability-flow ODE from t=0 to t=1 to obtain the source model’s latent code; pp. 3-4, §§2.2 discloses that an SGM’s endpoint marginal p1 is the standard Gaussian prior; p.14 discloses, Appendix B.1, discloses modeling the diffusion conditionals as Gaussians and samples ɛt from N(0,I). Under BRI, the source model’s endpoint marginal is the first, source model associated Gaussian distribution);
feeding the embeddings into dual diffusion implicit bridges (Su in p.8, §4.4 discloses DDIB translation with a pretrained conditional diffusion model and guidance toward selected classes);
sampling from the first Gaussian distribution (Su in p. 4, Algorithm 1 discloses beginning with source sample x(s) ∼ ps(x) and deterministically transports it through the source probability-flow ODE to latent x(l); pp. 3-4, §§2.1-2.2 discloses that the probability-flow ODE carries the same marginals as the diffusion process and that the endpoint marginal is the standard Gaussian prior. x(l) is a sample having the source model’s Gaussian endpoint law);
mapping, through the dual diffusion implicit bridges, the sample from the first Gaussian distribution to a second Gaussian distribution in a second domain (Su in p. 4, §3, Algorithm 1 discloses the source latent code x(l) as the “target latent code at t=1” into the independently trained target model; pp. 3-5, §§2.2-3, Proposition 3.2 discloses that the endpoint prior of the SGM/DDIM is Gaussian and characterizes the source-to-latent and latent-to-target operations as concatenated Schrödinger Bridges. Under BRI, passing x(l) unchanged between compatible source-model and target-model Gaussian priors is an identity mapping/coupling to the second target model associated Gaussian distribution); and
reverse diffusing the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata (Su in pp. 2/4, Figure 1, §3, Algorithm 1 discloses solving the target model from t=1 to t=0 to construct target-domain image x(t) while preserving source content).
Su does not explicitly teach all of generating embeddings from metadata, wherein the metadata provides constraints for image reconstruction; feeding the embeddings into dual diffusion implicit bridges; and reverse diffusing the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata.
However, Ma teaches generating embeddings from metadata, wherein the metadata provides constraints for image reconstruction (Ma in p. 4, §3.1, Eq. 2 discloses preprocessing text prompt T with pretrained text encoder θ to produce representation θ(T), which is used as key/value information in U-Net cross-attention; pp. 5-6, Figure 2, §§3.2-3.3, forms the prompt from CAD class names, tags or keywords, and generated descriptions that constrain object identity, background, color, shape, pose, viewpoint, and distance. θ(T) is the embedding; the separate CAD-derived edge map is additional visual conditioning);
feeding the embeddings into dual diffusion implicit bridges (Ma in p. 4, §3.1 discloses feeding θ(T) into successive target-denoising U-Net cross-attention layers);
and reverse diffusing the second Gaussian distribution through a target diffusion model to generate a target image of the object in the second domain in accordance with the metadata (Ma in pp. 4-6, §§3.1-3.4, Figure 2, Algorithm 1 discloses reverse denoising conditioned on θ(T), optionally with a CAD-derived edge prompt, and decoding z0 into a target image constrained by that information).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Su by conditioning the target DDIB stage with metadata-derived text embeddings that is taught by Ma, since both reference are analogous art in the field of diffusion-based computer vision image generation and translation; thus, one of ordinary skilled in the art would be motivated to combine the references since Su’s source-latent-target DDIB process with Ma’s fixed cross-attention condition yields the predictable result of metadata-conditioned target reverse diffusion, thereby improving control over target object attributes, background, color, viewpoint, and geometry.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 16
Regarding Claim 16, Su in view of Ma teaches The computer program product of claim 15, wherein the first domain comprises three-dimensional CAD (computer assisted drawing) image data (Ma in pp. 2/5-6, Figures 1-2, §3.2 discloses CAD models rendered from selected viewpoints and distances into two-dimensional CAD-derived sketches and compact edge-map images carrying the CAD object’s 3D shape and pose).
Claim 17
Regarding Claim 17, Su in view of Ma teaches The computer program product of claim 15, wherein the second domain comprises real world image data (Su in p. 7, §4.3 discloses an image domain containing “real photos taken via a camera or a satellite” and translation in either direction).
Claim 18
Regarding Claim 18, Su in view of Ma teaches The computer program product of claim 15, wherein the metadata comprises at least one of:
clustering of data;
prompt embedding;
segmentation mask;
two-dimensional drawing information;
text description of the target object;
audio description of the target object;
graph representation of the target object;
material; or
background (Ma in p. 4, §3.1 discloses generating prompt embedding θ(T); pp. 5-6, §§3.2-3.3, Figure 2 discloses target-object descriptions and background descriptions).
Claim 20
Regarding Claim 20, Su in view of Ma teaches The computer program product of claim 15, wherein the source diffusion model and target diffusion model comprise Schrödinger bridges (Su in pp. 1/4-5, Abstract, §§2.2-3, Proposition 3.2 discloses characterizing its source-to-Gaussian and Gaussian-to-target PF-ODE stages as two concatenated special Schrödinger Bridges).
Claim(s) 6, 13, and 19 is/are rejected under 35 U.S.C. 103 as obvious over Su et al (Su, X., Song, J., Meng, C., & Ermon, S. (2022). Dual diffusion implicit bridges for image-to-image translation. arXiv preprint arXiv:2203.08382., hereafter referred to as Su) in view of Ma et al (Ma, W., Liu, Q., Wang, J., Wang, A., Liu, Y., Kortylewski, A., & Yuille, A. (2023). Adding 3d geometry control to diffusion models. arXiv preprint arXiv:2306.08103., hereafter referred to as Ma), further in view of Jin et al (Jin, L., Li, Z., & Tang, J. (2020). Deep semantic multimodal hashing network for scalable image-text and video-text retrievals. IEEE Transactions on Neural Networks and Learning Systems, 34(4), 1838-1851., hereafter referred to as Jin).
Claim 6
Regarding Claim 6, Su in view of Ma teaches The method of claim 1.
Su in view of Ma does not explicitly teach all of wherein the metadata comprises information provided by an artificial intelligence design parser that cross-references text and specifications against images and video.
However, Jin teaches wherein the metadata comprises information provided by an artificial intelligence design parser that cross-references text and specifications against images and video (The Examiner interprets under the broadest reasonable interpretation that ‘information provided by an artificial intelligence design parser that cross-references text and specifications against images and video' is given its BRI consistent with the spec ¶22 as metadata produced by a machine-implemented component that extract information from text or specification sources and associates that information with image or video data. The recitations limits the metadata by its source and does not require any particular parser architecture, training procedure, or accuracy. Jin in p. 1, Abstract discloses a deep semantic multimodal hashing network for both image-text and video-text retrieval, using 2-D CNN processing for image information and 3-D CNN processing for spatial and temporal video information while preserving intermodality similarity; pp. 3-4, §III.A-C discloses image/video modality X and text modality Y, using deep modality-specific networks to map new samples from those modalities into a common Hamming space, and measuring similarity for cross-modal text-visual pairs. Under BRI, the deep network is a machine-implemented parser that obtains information from text sources and associates that information with image or video data; the resulting associated textual/semantic information is parser-provided metadata).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Su in view of Ma by incorporating a cross-modal network that is taught by Jin, since both reference are analogous art in the field of learned computer vision image processing; thus, one of ordinary skilled in the art would be motivated to combine the references since Su in view of Ma’s image translation and metadata conditioning framework with Jin’s text-image/video common space association yields the predictable result of providing semantically matched parser-derived metadata to the target diffusion stage, thereby reducing mismatched reconstruction constraints and improving consistency between the source object and the metadata conditioned target image.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 13
Regarding Claim 13, Su in view of Ma teaches The system of claim 8.
Su in view of Ma does not explicitly teach all of wherein the metadata comprises information provided by an artificial intelligence design parser that cross-references text and specifications against images and video.
However, Jin teaches wherein the metadata comprises information provided by an artificial intelligence design parser that cross-references text and specifications against images and video (The Examiner interprets under the broadest reasonable interpretation that ‘information provided by an artificial intelligence design parser that cross-references text and specifications against images and video' is given its BRI consistent with the spec ¶22 as metadata produced by a machine-implemented component that extract information from text or specification sources and associates that information with image or video data. The recitations limits the metadata by its source and does not require any particular parser architecture, training procedure, or accuracy. Jin in p. 1, Abstract discloses a deep semantic multimodal hashing network for both image-text and video-text retrieval, using 2-D CNN processing for image information and 3-D CNN processing for spatial and temporal video information while preserving intermodality similarity; pp. 3-4, §III.A-C discloses image/video modality X and text modality Y, using deep modality-specific networks to map new samples from those modalities into a common Hamming space, and measuring similarity for cross-modal text-visual pairs. Under BRI, the deep network is a machine-implemented parser that obtains information from text sources and associates that information with image or video data; the resulting associated textual/semantic information is parser-provided metadata).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Su in view of Ma by incorporating a cross-modal network that is taught by Jin, since both reference are analogous art in the field of learned computer vision image processing; thus, one of ordinary skilled in the art would be motivated to combine the references since Su in view of Ma’s image translation and metadata conditioning framework with Jin’s text-image/video common space association yields the predictable result of providing semantically matched parser-derived metadata to the target diffusion stage, thereby reducing mismatched reconstruction constraints and improving consistency between the source object and the metadata conditioned target image.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 19
Regarding Claim 19, Su in view of Ma teaches The computer program product of claim 15.
Su in view of Ma does not explicitly teach all of wherein the metadata comprises information provided by an artificial intelligence design parser that cross-references text and specifications against images and video.
However, Jin teaches wherein the metadata comprises information provided by an artificial intelligence design parser that cross-references text and specifications against images and video (The Examiner interprets under the broadest reasonable interpretation that ‘information provided by an artificial intelligence design parser that cross-references text and specifications against images and video' is given its BRI consistent with the spec ¶22 as metadata produced by a machine-implemented component that extract information from text or specification sources and associates that information with image or video data. The recitations limits the metadata by its source and does not require any particular parser architecture, training procedure, or accuracy. Jin in p. 1, Abstract discloses a deep semantic multimodal hashing network for both image-text and video-text retrieval, using 2-D CNN processing for image information and 3-D CNN processing for spatial and temporal video information while preserving intermodality similarity; pp. 3-4, §III.A-C discloses image/video modality X and text modality Y, using deep modality-specific networks to map new samples from those modalities into a common Hamming space, and measuring similarity for cross-modal text-visual pairs. Under BRI, the deep network is a machine-implemented parser that obtains information from text sources and associates that information with image or video data; the resulting associated textual/semantic information is parser-provided metadata).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Su in view of Ma by incorporating a cross-modal network that is taught by Jin, since both reference are analogous art in the field of learned computer vision image processing; thus, one of ordinary skilled in the art would be motivated to combine the references since Su in view of Ma’s image translation and metadata conditioning framework with Jin’s text-image/video common space association yields the predictable result of providing semantically matched parser-derived metadata to the target diffusion stage, thereby reducing mismatched reconstruction constraints and improving consistency between the source object and the metadata conditioned target image.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
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/J.P.C./Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
Date: ***
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