Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 102 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claim(s) 1 -5, 8-14, 16-17, and 19-20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Zhang ( Zhang, Lvmin , Anyi Rao, and Maneesh Agrawala . "Adding conditional control to text-to-image diffusion models." Proceedings of the IEEE/CVF international conference on computer vision . 2023. ) Regarding claim 1 , Zhang teaches a method comprising: receiving a text prompt and an image prompt for generating a digital image (Figure 3: Prompt → Text Encoder → Each layer of stable diffusion, i.e. a text prompt, and Condition → … → zero convolution → a layer of SD Decoder, i.e. an image prompt, and Output, i.e. a digital image, see Figure 4) ; conditioning an upsampling layer of a neural network with an image vector representation of the image prompt (Figure 3: Any of zero convolution → a layer of SD Decoder ); conditioning an additional upsampling layer of the neural network with a text vector representation of the text prompt without the image vector representation of the image prompt (Figure 3: Any of the other SD Decoder blocks with a Text Encoder input. The Text Encoder input does not include the image vector representation) ; and generating, utilizing the neural network, the digital image from the image vector representation and the text vector representation (Figure 4) . Regarding claim 2 , Zhang teaches all of the limitations of claim 1, wherein conditioning the upsampling layer of the neural network comprises conditioning a high-resolution upsampling layer of the neural network with the image vector representation of the image prompt, wherein the high-resolution upsampling layer has a higher resolution than a low-resolution upsampling layer of the neural network (Figure 3(a), any of the 16x16, 32x32, or 64x64 Decoder blocks) . Regarding claim 3 , Zhang teaches all of the limitations of claim 2, wherein conditioning the additional upsampling layer of the neural network comprises conditioning the low-resolution upsampling layer with the text vector representation of the text prompt without the image vector representation of the image prompt (Figure 3(a), the 8x8 Decoder block or any of the 16x16 or 32x32 blocks if lower than that chosen in claim 2) . Regarding claim 4 , Zhang teaches all of the limitations of claim 2, further comprising conditioning the high-resolution upsampling layer of the neural network with the text vector representation of the text prompt (Figure 3(a)) . Regarding claim 5 , Zhang teaches all of the limitations of claim 1, wherein generating, utilizing the neural network, the digital image from the image vector representation and the text vector representation comprises utilizing the neural network in at least one denoising iteration of a diffusion neural network to generate the digital image (Figure 3(a), Decoder blocks are denoising iterations) . Regarding claim 8 , Zhang teaches all of the limitations of claim 1, further comprising conditioning a plurality of downsampling layers of the neural network with the text vector representation of the text prompt without the image vector representation of the image prompt (Figure 3(a), Encoder layers). Regarding claim 9 , Zhang teaches a system comprising: a memory component (§1, a computer with a processor and memory are used to run the disclosed models/algorithms, “ We also show that in some tasks like depth-to-image, training ControlNets on a personal computer (one Nvidia RTX 3090TI) can achieve competitive results to commercial models trained on large computation clusters with terabytes of GPU memory and thousands of GPU hours. ” ) ; and one or more processing devices coupled to the memory component ( §1, a computer with a processor and memory are used to run the disclosed models/algorithms, “ We also show that in some tasks like depth-to-image, training ControlNets on a personal computer (one Nvidia RTX 3090TI) can achieve competitive results to commercial models trained on large computation clusters with terabytes of GPU memory and thousands of GPU hours.” ) , the one or more processing devices to perform operations comprising: receiving a first prompt and a second prompt for generating a digital image (Figure 3, “ Condition ” and “ Prompt ”, see Figure 6) ; generating, from a noise representation utilizing a denoising iteration of a diffusion neural network, an additional noise representation (Figure 3: Input → Encoder/Middle /Decoder ) by: conditioning a first layer of a neural network of the denoising iteration with a first vector representation of the first prompt (Figure 3, Condition → zero convolution → second decoder block ) ; and conditioning a second layer of the neural network of the denoising iteration with a second vector representation of the second prompt (Figure 3, Text encoder → first decoder block or any of the first encoder blocks ) ; and generating, utilizing additional denoising iterations of the diffusion neural network, the digital image from the additional noise representation, the first vector representation, and the second vector representation (Figure 6) . Regarding claim 10 , Zhang teaches all of the limitations of claim 9, wherein conditioning the first layer of the neural network of the denoising iteration with the first vector representation comprises conditioning a high-resolution upsampling layer of the neural network with an image vector representation of an image prompt, wherein the high-resolution upsampling layer has a higher resolution than a low-resolution upsampling layer of the neural network (Figure 3, second decoder block) . Regarding claim 11 , Zhang teaches all of the limitations of claim 10 wherein conditioning the second layer of the neural network of the denoising iteration with the second vector representation comprises conditioning the low-resolution upsampling layer of the neural network with a text vector representation of a text prompt without the image vector representation of the image prompt ( Figure 3: The first SD Decoder blocks with a Text Encoder input. The Text Encoder input does not include the image vector representation ). Regarding claim 12 , Zhang teaches all of the limitations of claim 9, wherein conditioning the first layer of the neural network of the denoising iteration with the first vector representation comprises conditioning a low-resolution upsampling layer of the neural network with an image vector representation of an image prompt, wherein the low-resolution upsampling layer has a lower resolution than a high-resolution upsampling layer of the neural network (Figure 3, the second decoder block can be considered a low-resolution upsampling layer) . Regarding claim 13 , Zhang teaches all of the limitations of claim 12, wherein conditioning the second layer of the neural network of the denoising iteration with the second vector representation comprises conditioning the high-resolution upsampling layer of the neural network with a text vector representation of a text prompt without the image vector representation (Figure 3, the third decoder block takes in a text vector representation of a text prompt that does not include the image vector representation) . Regarding claim 14 , Zhang teaches all of the limitations of claim 9, wherein: conditioning the first layer of the neural network comprises conditioning a downsampling layer of the neural network with a text vector representation of a text prompt (Figure 3, any of the encoder blocks) ; and conditioning the second layer of the neural network comprises conditioning an upsampling layer of the neural network with an image vector representation of an image prompt (Figure 3, any of the decoder blocks) . Regarding claim 16 , Zhang according to claim 1 teaches all of the limitations of claim 16, see §1 “ We also show that in some tasks like depth-to-image, training ControlNets on a personal computer (one Nvidia RTX 3090TI) can achieve competitive results to commercial models trained on large computation clusters with terabytes of GPU memory and thousands of GPU hours. ” Regarding claim 17 , Zhang teaches all of the limitations of claim 16, wherein: conditioning the upsampling layer of the neural network comprises conditioning a high-resolution upsampling layer of the neural network with the image vector representation of the image prompt (Figure 3, any of the 16x16, 32x32, or 64x64 decoder blocks) ; and conditioning the additional upsampling layer of the neural network comprises conditioning a low-resolution upsampling layer of the neural network with the text vector representation of the text prompt, wherein the high-resolution upsampling layer has a higher resolution than the low-resolution upsampling layer (Figure 3, Decoder block 8x8) . Regarding claim 19 , Zhang teaches all of the limitations of claim 16, wherein generating, utilizing the neural network, the digital image from the image vector representation and the text vector representation comprises: generating a first noise representation utilizing a first neural network of a first denoising iteration of a diffusion neural network; and generating a second noise representation utilizing a second neural network of a second denoising iteration of the diffusion neural network (Figure 3, Encoder and Decoder) . Regarding claim 20 , Zhang teaches all of the limitations of claim 16, wherein the operations further comprise conditioning a plurality of downsampling layers of the neural network with the text vector representation of the text prompt (Figure 3: Encoder layers) . Allowable Subject Matter Claims 6-7, 15, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT SCHYLER S SANKS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-6125 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 06:30 - 15:30 Central Time, M-F . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SCHYLER S SANKS/ Primary Examiner, Art Unit 2129