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 Amendment
The office action is in response to Applicant’s amendment filed 05/26/2026 which has been entered and made of record. Claims 1, 12, and 16-17 have been amended. No claim has been newly added or canceled. Claims 1-20 are pending in the application.
Response to Arguments
Applicant’s arguments filed 05/26/2026, with respect to the rejections under 35 U.S.C. 102(a)(1) and 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Shi and Abdal as fully explained below.
Applicant argues Shi does not teach the newly added limitations.
Examiner agrees. However, a new ground of rejection is made in view of Abdal as fully explained below.
Conclusions: The rejections set in the previous Office Action are shown to have been proper, and the claims are rejected below. New citations and parenthetical remarks can be considered new grounds of rejection and such new grounds of rejection are necessitated by the Applicant's amendments to the claims. Therefore, the present Office Action is made final.
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.
Claims 1-4, 6, 8-9, and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning (Shi et al, hereinafter Shi) as modified by Abdal et al (US 11640684 B2, hereinafter Abdal).
Regarding claim 1, Shi teaches method comprising: obtaining an input prompt (Sect 3.1, Par 2 “We first inject a unique identifier ˆV to the input prompt to represent the object concept, then use a learnable image encoder to map the input images to a concept textual embedding.”),
a reference image (Sect 3.1, Par 1 “Given a few images of a concept, the goal is to generate new high-quality images of this concept from text description p. The generated image variations should preserve the identity of the input concept.”, Sect 4.4 Ablation Study, Par 1 “Single image as input. Since our model is flexible for the number of input images, we evaluate our model using a single image as the input image condition, i.e., N = 1.”),
wherein the input prompt describes a scene (Introduction, Par 1 “generate new scenes or styles of the concept from input prompts”),
the reference image depicts an object (Sect 3.1, Par 1 “Given a few images of a concept, the goal is to generate new high-quality images of this concept from text description p. The generated image variations should preserve the identity of the input concept. As DreamBooth [34] summarized, the variations include changing the concept’s location, property or style, modifying the subject’s pose, structure, expression or material, etc.” where the subject is the depicted object),
generating, using an object encoder of an image generation model, an object embedding based on the reference image wherein the image generation model takes the input prompt, and the reference image as inputs (Figure 2, Sect 3.2 Concept Embedding Learning Par 1-2 “we adopt an image encoder Ec to map the images to a compact concept feature vector fc in the textual space. Specifically, fc is the average feature vector of the global features of all input images. We have: N fc = i=1 Ec(xi s)/N (2). To obtain the final textual embeddings of the input prompt, we first obtain the CLIP [28] Text embeddings cs of the modified prompt cs = CLIP(ps), then replace the embedding of identifier ˆ V with the concept feature fc to obtain the concept injected textual embedding c. This final embedding will be the condition in the cross-attention layers of the text-to-image diffusion model.”);
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and generating, using the image generation model, a synthetic image based on the input prompt and the object embedding, wherein the synthetic image depicts the object in the scene from the input prompt (Sect 3.1, par 1 “Given a few images of a concept, the goal is to generate new high-quality images of this concept from text description p. The generated image variations should preserve the identity of the input concept.”).
Shi fails to explicitly teach a transform input separate from the input prompt, which indicates a transformation of the object, wherein the image generation model takes the transform input as input, generating an object embedding based on the reference image and the transform input wherein the object embedding represents the object with the transformation indicated by the transform input, and wherein the synthetic image depicts the object with the transformation indicated by the transform input. In related field of endeavor, Abdal teaches a transform input separate from the input prompt, which indicates a target transformation of the object, wherein the image generation model takes the transform input as input (Col 5 Line 18-21 “The image editing application 310 receives input from a user 300 indicating the image to be edited, along with a target attribute value to be changed”) generating an object embedding based on the reference image and the transform input wherein the object embedding represents the object with the transformation indicated by the transform input (Col 10 Line 4-20 “At operation 600, the system identifies an original image including a set of original attributes, where the original attributes include semantic features of the original image” … “At operation 605, the system identifies a target attribute value for modifying the original image, where the target attribute value represents a target attribute different from a corresponding original attribute of the original image.” … “At operation 610, the system computes a modified feature vector based on the target attribute value, where the modified feature vector represents the target attribute and at least one preserved attribute of the original attributes”) and wherein the synthetic image depicts the object with the transformation indicated by the transform input (Col 10 Line 24-27 “At operation 615, the system generates a modified image based on the modified feature vector, where the modified image includes the target attribute and the at least one preserved attribute.”)
It would have been obvious to one of ordinary skill in the art prior to the time of filing to have modified Shi to include a transform input separate from the input prompt, which indicates a target transformation of the object, wherein the image generation model takes the transform input as input, wherein the object embedding represents the object with the transformation indicated by the transform input, and wherein the synthetic image depicts the object with the transformation indicated by the transform input as taught by Abdal. Doing so would present a high-quality identity-preserving edit at high quality (Col 8 Line 32-34 “the described methods are used to perform edits on human faces, and present a range of high-quality identity-preserving edits at an unmatched quality.”)
Regarding claim 2, Shi as modified by Abdal teaches the method of claim 1. Shi further teaches wherein obtaining the reference image comprises obtaining a preliminary image depicting the object; and removing a background from the preliminary image to obtain the reference image. (Sect 3.2, Par 1 “Since the object of the input concept in the images may not be large enough, we crop out the object from each image to obtain a set of conditional image Xs = {xi s}N 1 . To further enforce the model to focus on the exact object, we mask out the background of each cropped image” where the cropped image is the preliminary image.)
Regarding claim 3, Shi as modified by Abdal teaches the method of claim 1. Shi further teaches wherein generating the object embedding comprises: generating a preliminary embedding representing the object (Sect 3.2 Concept Embedding Learning: “To this end, we convert the input images into a textual concept embedding”). Abdal further teaches transforming the preliminary embedding based on the transform input to obtain the object embedding (Col 10 Line 16-20 “At operation 610, the system computes a modified feature vector based on the target attribute value, where the modified feature vector represents the target attribute and at least one preserved attribute of the original attributes”). It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Shi and Abdal to include transforming the preliminary embedding based on the transform input to obtain the object embedding as taught by Abdal. Doing so would provide edited images with the target attribute while best preserving the identity of the source image (Col 11 Line 52-55 “Attribute-controlled editing includes editing given images such that the edited images have the target attributes, while best preserving the identity of the source images.”)
Regarding claim 4, Shi as modified by Abdal teaches the method of claim 3, and Abdal further teaches further comprising: encoding the transform input to obtain a projection vector, wherein the preliminary embedding is transformed based on the projection vector (Col 10 Line 16-20 “At operation 610, the system computes a modified feature vector based on the target attribute value, where the modified feature vector represents the target attribute and at least one preserved attribute of the original attributes”). It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Shi and Abdal to include encoding the transform input to obtain a projection vector, wherein the preliminary embedding is transformed based on the projection vector as taught by Abdal. Doing so would provide edited images with the target attribute while best preserving the identity of the source image (Col 11 Line 52-55 “Attribute-controlled editing includes editing given images such that the edited images have the target attributes, while best preserving the identity of the source images.”)
Regarding claim 6, Shi teaches the method of claim 1, further comprising: encoding the input prompt to obtain a text embedding, wherein the synthetic image is generated based on the text embedding (Sect 3.2 Concept Embedding Learning “Since the identifier ˆV has indicated the location of the textual embedding, we adopt an image encoder Ec to map the images to a compact concept feature vector fc in the textual space. Specifically, fc is the average feature vector of the global features of all input images … To obtain the final textual embeddings of the input prompt, we first obtain the CLIP [28] Text embeddings cs of the modified prompt cs = CLIP(ps), then replace the embedding of identifier ˆV with the concept feature fc to obtain the concept injected textual embedding c”).
Regarding claim 8, Shi as modified by Abdal teaches the method of claim 1. Abdal further teaches wherein: the transform input includes a size parameter, an identity parameter, or both (Col 9 Line 28-32 “ At operation 505, the user selects one or more target attributes. For example, the user modifies the gender, age, glasses, orientation, facial hair, expression, and lighting of the original image using input elements of an image editing application”). It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Shi and Abdal to include the transform input includes a size parameter, an identity parameter, or both as taught by Abdal. Doing so would represent the target and preserved attributed in a complex way in the modified features (Col 9 Line 39-40 “The target attributes and the preserved attributes are represented in a complex way in the modified features.”)
Regarding claim 9, Shi as modified by Abdal teaches the method of claim 8. Abdal further teaches wherein: the identity parameter indicates a pose of the object, a view angle of the object, or both (Col 9 Line 28-32 “ At operation 505, the user selects one or more target attributes. For example, the user modifies the gender, age, glasses, orientation, facial hair, expression, and lighting of the original image using input elements of an image editing application”). It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Shi and Abdal to include wherein: the identity parameter indicates a pose of the object, a view angle of the object, or both as taught by Abdal. Doing so would represent the target and preserved attributed in a complex way in the modified features (Col 9 Line 39-40 “The target attributes and the preserved attributes are represented in a complex way in the modified features.”)
Regarding claim 11, Shi as modified by Abdal teaches the method of claim 1. Abdal further teaches wherein: the transform input indicates a target level of identity preservation for the object (Col 9 Line 1-5 “For example, a user edits the gender, age, glasses, orientation, facial hair, expression, and lighting of the original image. Attributes not selected or changed by the user are preserved during the change. In one example, as illustrated, sliders are bused to select the target attributes”) It would have been obvious to one of ordinary skill in the art prior to the time of filing to have further modified Shi and Abdal to include wherein: the transform input indicates a target level of identity preservation for the object. Doing so would allow a user to select which attributes to modify and which to preserve (Col 8 Line 66 – Col 9 Line 4 “allowing a user to make edits using attribute selectors 415, resulting in modified image 410. For example, a user edits the gender, age, glasses, orientation, facial hair, expression, and lighting of the original image. Attributes not selected or changed by the user are preserved during the change”)
Regarding claim 12, Shi teaches a method comprising: obtaining a training set including a training input prompt (Sect 4.1 Datasets and Metric – Metrics Par 4 “We construct various prompts ranging from background modifications (“A photo of ˆV [class noun] on the moon”), to style changes (“An oil painting of ˆV [class noun]”), and a compositional prompt (“ˆV [class noun] shaking hand with Biden”).”), a training input image (Sect 4.1 Datasets and Metric – Datasets Par 1 “We select 50 identity in the test split of PPR10k [22], where each selected identity is guaranteed to have more than 5 images and we only keep the first 5 images in naming order as our test input”), a training target image wherein the training target image depicts an object from the training input image (Section 4.1 Datasets and Metric – Metrics Par 2 “It is measured by the similarity of CLIP visual features between the input image and the generated image”, Metrics Par 4 “measure the vision-language alignment between the input prompt and the output image” , Sect 3.3 Model Training Par 1 “During training, we use heavy augmentation A to obtain variations of masked images Xs. The original image set Xt (without cropping out the object region or masking out the background) is regarded as the ground-truth.”)
and training, using the training set, an image generation model to generate an object embedding that represents the object (Sect 3.2 Concept Embedding Learning Par 1-2 “we adopt an image encoder Ec to map the images to a compact concept feature vector fc in the textual space. Specifically, fc is the average feature vector of the global features of all input images. We have: N fc = i=1 Ec(xi s)/N (2). To obtain the final textual embeddings of the input prompt, we first obtain the CLIP [28] Text embeddings cs of the modified prompt cs = CLIP(ps), then replace the embedding of identifier ˆ V with the concept feature fc to obtain the concept injected textual embedding c. This final embedding will be the condition in the cross-attention layers of the text-to-image diffusion model.”) with theindicated by the training transform input and to generate a synthetic image based on the object embedding (Sect 3.1, Par 1 “Given a few images of a concept, the goal is to generate new high-quality images of this concept from text description p. The generated image variations should preserve the identity of the input concept.”), wherein the image generation model takes the training input prompt, and the training input image as inputs (Sect 3.2 Prompt Creation “During training, for each object category, we first detect the corresponding nouns in the original prompt, then insert the identifier.”, Sect 3.3 Model Training “Since we do not have paired images of the same concept as training data, we simply use 1 image to train our model for each concept”)
Shi fails to explicitly teach a training set including a training transform input separate from the training input prompt, the training transform input indicates a transformation for the object; generating an object embedding that represents the object with the transformation indicated by the transform input, and wherein the image generation model takes the training transform input as input, and the synthetic image depicts the object with the transformation indicated by the training transform input. In related field of endeavor, Abdal teaches a training set including a training transform input separate from the training input prompt (Col 24 Line 1-6 “the mapping network is trained jointly based on a plurality of attributes, and wherein the original attributes correspond to the attributes used to train the mapping network. In some examples, the mapping network is configured to enable changing the target attributes while preserving the remaining subset of the original attributes”), wherein the training transform input indicates a transformation for the object (Col 24 Line 5-6 “the mapping network is configured to enable changing the target attributes while preserving the remaining subset of the original attributes”); generating an object embedding that represents the object with the transformation indicated by the transform input (Col 10 Line 4-20 “At operation 600, the system identifies an original image including a set of original attributes, where the original attributes include semantic features of the original image” … “At operation 605, the system identifies a target attribute value for modifying the original image, where the target attribute value represents a target attribute different from a corresponding original attribute of the original image.” … “At operation 610, the system computes a modified feature vector based on the target attribute value, where the modified feature vector represents the target attribute and at least one preserved attribute of the original attributes”), and wherein the image generation model takes the training transform input as input (Col 5 Line 18-21 “The image editing application 310 receives input from a user 300 indicating the image to be edited, along with a target attribute value to be changed”), and the synthetic image depicts the object with the transformation indicated by the training transform input (Col 10 Line 24-27 “At operation 615, the system generates a modified image based on the modified feature vector, where the modified image includes the target attribute and the at least one preserved attribute.”).
It would have been obvious to one of ordinary skill in the art prior to the time of filing to have modified Shi to include a training set including a training transform input separate from the training input prompt, the training transform input indicates a transformation for the object; generating an object embedding that represents the object with the transformation indicated by the transform input, and wherein the image generation model takes the training transform input as input, and the synthetic image depicts the object with the transformation indicated by the training transform input as taught by Abdal. Doing so would present a high-quality identity-preserving edit at high quality (Col 8 Line 32-34 “the described methods are used to perform edits on human faces, and present a range of high-quality identity-preserving edits at an unmatched quality.”)
Regarding claim 13, Shi as modified by Abdal teaches the method of claim 12, and Shi further teaches wherein training the image generation model comprises: jointly training an object encoder that generates the object embedding and a diffusion model that generates the synthetic image (Sect 4.2 Implementation Details “We utilize the Stable Diffusion [33] V1-4 as our pre trained text-to- image model, which is the current leading model available to the public. For all experiments of our model, we use “sks” as the unique identifier ˆV as suggested in DreamBooth [40]. For both the concept encoder Ec and the patch encoder Ep, we use the pre-trained CLIP image encoder as the backbone followed by a randomly initialized fully-connected layer. During training, we freeze the back bone of the image encoders and only update the FC layers and the adapter layers. The weights of CLIP text encoder and the original weights in the U-Net of the pre-trained text to-image model are also frozen.”)
Regarding claim 14, Shi as modified by Abdal teaches The method of claim 12, and Shi further teaches wherein obtaining the training set comprises: obtaining a preliminary image; and applying an image transformation to the preliminary image to obtain the training input image. (Sect 3.3 Model Training, Par 1 “During training, we use heavy augmentation A to obtain variations of masked images Xs. The original image set Xt (without cropping out the object region or masking out the background) is regarded as the ground-truth”, Section 3.4 Model Inference – Arbitrary Number of Input Images, Par 1 “During model’s inference, we still mask out the background of the cropped images, but do not perform any augmentations to the masked images, i.e., A = None. ”)
Regarding claim 15, Shi as modified by Abdal teaches the method of claim 12, and Shi further teaches wherein training the image generation model comprises: generating an intermediate output image, computing a reconstruction loss between the intermediate output image and the training target image (Sect 4.1 Dataset and Metric – Metrics Par 2 “Reconstruction is to evaluate whether the identity can be fully preserved by the default prompt “A photo of ˆV [class noun]”, where [class noun] can be person or cat. It is measured by the similarity of CLIP visual features between the input image and the generated image”);
and updating parameters of the image generation model based on the reconstruction loss (Sect 4.4 Ablation Study – Par 7 “Adjust the adapter weight β and concept renormalization factor α. Tab.4 shows different compositions of β and α. The results indicate that larger β or α can both contribute to better identity preservation but weaker language comprehension ability. We finally choose the model with β = 0.3, α = 0.4 as a trade-off.”).
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Regarding claim 16, the apparatus claim 16 is similar in scope to the method claim 1, and is rejected under similar rationale.
Regarding claim 17, Shi as modified by Abdal teaches the apparatus of claim 16. Shi further teaches wherein: the object encoder is trained to generate the object embedding. (Fig 2, Sect 3.1, Par 2 “We first inject a unique identifier ˆV to the input prompt to represent the object concept, then use a learnable image encoder to map the input images to a concept textual embedding. The pre-trained diffusion model takes the concept embedding along with the embedding of the original prompts to generate new images of the input concept. To enhance the identity of the generated images, we introduce adapter layers to the pre -trained model to take rich patch features extracted from the input images for better identity preservation”)
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Regarding claim 18, Shi as modified by Abdal teaches the apparatus of claim 16. Shi further teaches the image generation model comprises a diffusion model trained to generate the synthetic image (Sect 3.1, Par 2 “We first inject a unique identifier ˆV to the input prompt to represent the object concept, then use a learnable image encoder to map the input images to a concept textual embedding. The pre-trained diffusion model takes the concept embedding along with the embedding of the original prompts to generate new images of the input concept. To enhance the identity of the generated images, we introduce adapter layers to the pre-trained model to take rich patch features extracted from the input images for better identity preservation”).
Regarding claim 19, Shi as modified by Abdal teaches the apparatus of claim 16, and Shi further teaches further comprising: a text encoder configured to encode the input prompt to obtain a text embedding, wherein the synthetic image is generated based on the text embedding (Sect 3.2 Concept Embedding Learning "Since the identifier ˆV has indicated the location of the textual embedding, we adopt an image encoder Ec to map the images to a compact concept feature vector fc in the textual space. Specifically, fc is the average feature vector of the global features of all input images … To obtain the final textual embeddings of the input prompt, we first obtain the CLIP [28] Text embeddings cs of the modified prompt cs = CLIP(ps), then replace the embedding of identifier ˆV with the concept feature fc to obtain the concept injected textual embedding c")
Claims 5, 7, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shi and Abdal as applied to claim1 above, and further in view of eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers (Yogesh et al, hereinafter Yogesh).
Regarding claim 5, Shi as modified by Abdal teaches the method of claim 1, but fails to explicitly teach generating the synthetic image comprises: obtaining a noise map; and denoising the noise map based on the object embedding. In related field of endeavor, Yogesh teaches generating the synthetic image comprises: obtaining a noise map; and denoising the noise map based on the object embedding (Figure 2 Description: “Synthesis in diffusion models corresponds to an iterative denoising process that gradually generates images from random noise; a corresponding stochastic process is visualized for a one dimensional distribution. Usually, the same denoiser neural network is used throughout the entire denoising process. ”)
It would have been obvious to one of ordinary skill in the art to have further modified Shi as modified by Abdal to include generating a synthetic image comprises: obtaining a noise map and denoising the noise map based on the object embedding as taught by Yogesh. Doing so would allow synthetic images to be generated by iteratively denoising random noise (Figure 2 Description: “Synthesis in diffusion models corresponds to an iterative denoising process that gradually generates images from random noise”).
Regarding claim 7, Shi as modified by Abdal teaches the method of claim 1, but fails to explicitly teach obtaining an additional reference image depicting the scene; and encoding the additional reference image to obtain a reference embedding, wherein the synthetic image is generated based on the reference embedding. In related field of endeavor, Yogesh teaches obtaining an additional reference image depicting the scene; and encoding the additional reference image to obtain a reference embedding, wherein the synthetic image is generated based on the reference embedding (Figure 5 Description: “eDiff-I also allows the user to optionally provide an additional CLIP image embedding. This can enable detailed stylistic control over the output”).
It would have been obvious to one of ordinary skill in the art to have modified Shi to include obtaining an additional reference image depicting the scene; and encoding the additional reference image to obtain a reference embedding, wherein the synthetic image is generated based on the reference embedding as taught by Yogesh. Doing so would provide additional control over the styling of the output (Figure 5 Description: “This can enable detailed stylistic control over the output”).
Regarding claim 20, the apparatus claim 20 is similar in scope to claim 7 and is rejected under the same rationale.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Shi as modified by Abdal as applied to claim8 above, and further in view of US 20240157114 A1 (Yuan et al, hereinafter Yuan).
Regarding claim 10, Shi as modified by Abdal teaches the method of claim 8, but fails to explicitly teach the size parameter indicates a target scale of the object relative to the reference image. In related field of endeavor, Yuan teaches a size parameter indicates a target scale of the object relative to the reference image (Par 100 “a scale parameter indicative of a scale of the at least one first element in a 3D frame of reference in which the subject is positioned”).
It would have been obvious to one of ordinary skill in the art to have modified Shi to include a size parameter indicates a target scale of the object relative to the reference image as taught by Yuan. Doing so would allow the size of an element of to be adjusted (Par 100 “a scale parameter indicative of a scale of the at least one first element in a 3D frame of reference in which the subject is positioned”).
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.
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/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
/J.P.G./ Examiner, Art Unit 2611
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