DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . It is responsive to the submission dated 03/26/2025. Claims 1-20 are presented for examination. Claims 1, 8 and 16 are independent claims.
Information Disclosure Statement
2. The information disclosure statements (IDSs) submitted on 03/26/2025 are in compliance with the provisions of 37 CFR 1.97 and are being considered by the Examiner.
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
4. Claims 1-14, 16-17 and 19-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 1, the limitations reciting: “performing, using an image generation model, an attention operation …; and generating, using the image generation model, a synthetic image …” render the claim indefinite because the details of how the image generation model is used to perform the abovementioned tasks are lacking. The claim, at the preamble, merely recites “A method comprising” the limitations in question however, no detail is provided that ties the method claim to a particular structure-explaining how the functions of the method are being implemented. The cited use of an “image generation model” is not sufficient detail explaining how the method is implemented. Typically, a particular non-generic training mechanism is required for training a model. In the present case, the use of said “image generation model” is divorced from any training platform that facilitates the training or learning of targeted statistical parameters/patterns fed into it to create the synthetic image. Thus, the limitations fail to limit the claim.
Furthermore, claim 1 is also deemed indefinite because none of the functions, particularly, the “obtaining an input prompt” or “generating a plurality of image tokens”, etc … is tied to a specific actor that makes the claim sequence (e.g., the obtaining, generating, performing and generating steps) concrete and self-contained, but not a concatenation of block box experiments.
In claim 8, the limitation reciting “generating, using the image generation model, a synthetic image … image generation stage” renders the claim indefinite because it is unclear how the image generation model is used to perform the abovementioned task. The cited use of an “image generation model” is not sufficient detail explaining how the method is implemented. Typically, a particular non-generic training mechanism is required for training a model. In the present case, the use of said “image generation model” is divorced from any means for training the model to achieve the stated result. Thus, the limitation fails to limit the claim.
Claim 16 is indefinite because the limitations reciting: “performing, using an image generation model, an attention operation …; and generating, using the image generation model, a synthetic image …” lack the necessary details explaining the training platform used by the “image generation model” to train or learn the targeted statistical parameters/patterns fed into it to create the synthetic image.
Claims 17 and 19 are indefinite because no particular non-generic training mechanism is provided for training the router model and the diffusion transformer model, respectively.
As such, the ordinary skill in the art would not be able to draw a clear boundary between what is and is not covered by the claim.
The claims not specifically cited in this rejection are rejected as being dependent upon their rejected base claims.
Claim Rejections - 35 USC § 101
5. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
6. Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a program per se.
Claim 1 recites: “A method comprising: obtaining an input prompt; generating a plurality of image tokens based on the input prompt, wherein each of the plurality of image tokens represents an image region; performing, using an image generation model, an attention operation on a subset of the plurality of image tokens at an image generation stage to obtain an attention output, wherein the subset of the plurality of image tokens is determined based on the image generation stage; and generating, using the image generation model, a synthetic image based on the attention output”.
Based on the plain meaning of the words in the claim, the broadest reasonable interpretation of claim 1 is a method that receives and processes one mathematical representation associated with a known image, transforms it via computation of a mathematical algorithm and then uses the result of the computation to generate a desired image.
As no tangible form of generating the image using the mathematical representations is provided, it is submitted that the method of claim 1 is construed as being implemented as a software component. Thus, claim 1 is rejected under 35 USC 101 as being directed to a program, as evidenced by recitation of the generation of a synthetic image using an image generation model, which encompasses the implementation of running computer program processes.
As such, claim 1 is not patent eligible under 35 USC 101.
Claims 2-7 fail to remedy the deficiencies of parent claim 1 above, because they merely further detail the claimed elements shown above to be implemented as a software component by running additional computer program processes using different mathematical representations to compute additional image generation stages. Thus, claims 2-7 are rejected for at least the same rationale as applied to parent claim 1 above and incorporated herein.
7. Claims 16-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 16 recites “A system comprising: a memory component; a processing device coupled to the memory component, the processing device configured to perform operations comprising: obtaining an input prompt; generating a plurality of image tokens based on the input prompt, wherein each of the plurality of image tokens represents an image region; performing, using an image generation model, an attention operation on a subset of the plurality of image tokens determined based on an image generation stage to obtain an attention output; and generating, using the image generation model, a synthetic image based on the attention output”.
Based on the plain meaning of the words in the claim, the broadest reasonable interpretation of claim 16, as a whole, is to use a computer to receive and process mathematical representations (image tokens) associated with a known image, transform the mathematical representations by means/computation of a mathematical algorithm (image generation model) and then uses the result of the computation to generate a desired image.
This is considered an abstract idea because it merely represents mathematical relationships between image data, but for the citation of generic computer components. This judicial exception is not integrated into a practical application because the only additional element of the claim besides the recitation of using an image generation model is the recitation of a processing device coupled to a memory component.
This is construed to merely apply the mathematical relationship using reciting generic computer components. This is not considered sufficient to integrate an abstract idea into a practical application, because the use of the image generation model via a processing device is cited at high-level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f). These elements appear to be mere tangential additions to the abstract idea(s) and amount to extra-solution activity concerning mere data gathering. The addition of an insignificant extra-solution activity limitation does not impose meaningful limits on the claim in that it is not nominally or tangentially related to the invention.
In the claimed context, the use of a processor and a memory for using an image generation model to generate and output image data, together, only presents the idea of a solution, without expressly describing how the processor and the memory are used as a training platform to train the “image generation model” to learn the targeted statistical parameters/patterns fed into it to create the synthetic image. The vague citation of a processor and a memory together with using an image generation model are mere tangential addition to the abstract idea(s) and amount to extra-solution activity concerning mere data gathering and mathematical computations. They do not impose meaningful limits on the claim such that they are not nominally or tangentially related to the invention. Accordingly, the additional elements do not integrate the abstract idea into a practical application of the invention.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an invention concept (i.e., whether the additional elements amount to significantly more than the exception itself), as discussed in MPEP 2106.05.
The judicial exception is not integrated into a practical application, because the processor, memory and image generation (as the additional elements) are merely incidental or token additions to the claims. No particular non-generic training mechanism is provided for training the image generation model. By merely providing an output of the recited mathematical computations, as cited in the claims, they do not alter or affect how the functions in the abstract idea(s) are being performed. Therefore, the cited processor and memory for using the image generation model are construed as insignificant extra-solution activity limitation that do not impose any meaningful limits on practicing the mental processes beyond a general linking to a technological environment. See: MPEP 2106.05(h).
A processing device coupled to a memory, such as a mobile phone, tablet or desk top computer is a well-known commodity for everyday use for image generation. Using a mobile device or desktop to process an image generation model, is also well-known, routine, and conventional in the art of image processing and artificial intelligence. Thus, they do not qualify as inventive concepts, even when applied together.
As such, it is submitted that claim 16 is not patent eligible under 35 USC 101.
Claims 17 and 19-20 fail to remedy the deficiencies of parent claim 16 above, because they merely further detail the claimed elements shown above to be implemented as a software component by running additional computer program processes using different mathematical representations to compute additional image generation stages. Thus, claims 17 and 19-20 are rejected for at least the same rationale as applied to parent claim 16 above and incorporated herein. In addition, no particular non-generic training mechanism(s) is/are provided for training the router model of claim 17 and the diffusion transformer model of claim 19.
Claim Rejections - 35 USC § 102
8. 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.
9. Claims 1-2, 5-6, 16-17 and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Khu et al. (20260094428).
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Considering claim 1, Khuc discloses a method comprising: obtaining an input prompt (e.g., obtaining a text prompt describing an image element. See paras. 3 and 21);
generating a plurality of image tokens (e.g., image embeddings or visual features) based on the input prompt, wherein each of the plurality of image tokens represents an image region (e.g., Khuc discloses generating, using a transformer prior model, an image embedding based on the text prompt, where the image embedding represents visual features of the image element. See para. 3. Wherein the transformer prior model tokenizes the text prompt to obtain a plurality of text tokens and generate a plurality of text token embeddings based on the plurality of text tokens. See para. 21);
performing, using an image generation model, an attention operation on a subset of the plurality of image tokens at an image generation stage to obtain an attention output, wherein the subset of the plurality of image tokens is determined based on the image generation stage (e.g., Khuc discloses the image generation model performs a reverse diffusion process, which iteratively refines the noisy image by predicting and removing noise at each diffusion timestep. The reverse diffusion process gradually transforms the random noise into a coherent image (e.g., the synthetic image 630). The reverse diffusion process is guided based on a conditional guidance embedding. For example, the image generation model 625 receives the image embedding 615 to guide the reverse diffusion process. In one aspect, the image embedding 615 is added to the U-Net of the image generation model through a cross-attention mechanism to guide the reverse diffusion process, so that the output aligns with the visual features encoded in the image embedding 615. See paras. 109-110. In addition, Zhuc discloses transformer prior model 700 is trained to generate image embedding 750 based on a text prompt 705. … In some embodiments, a tokenization component 710 tokenizes the text prompt 705 into a set of text tokens 715. For example, the first text token (e.g., Text Token 1 depicted in FIG. 7) may include the word “closeup”, and the second text token (e.g., Text Token 2) may include the word “of”, and the last text token (e.g., Text Token L) may include the word “highway”. According to some embodiments, the tokenization component 710 may tokenize the text prompt 705 based on parts of the sequence that are more important relative to each words or phrases within the text prompt 705. For example, the tokenization component may identify the object “two hedgehogs” as the most important, the scene “side of a busy highway” as the second most important, and so on. Then, the tokenization component 710 may tokenize the text prompt 705 into a plurality of text tokens 715. For example, the first text token may include the group of words “two hedgehogs”, the second text token may include the group of words “side of a busy highway”, and the last text token may include the words “walking along” or “closeup of”. See paras. 114-115, wherein the parts of the sequence of words or phrases within the text prompt that are tokenized into a set of text tokens, namely, Text Token 1, Text Token 2 and/or Text Token L to generate the position embeddings to assist the model to understand the words in a sequence, corresponds to the subset of the plurality of image tokens on which an attention operation is performed during an image generation stage); and
generating, using the image generation model, a synthetic image based on the attention output (e.g., the system generates, using an image generation model, a synthetic image depicting the image element based on the image embedding, where the image embedding represents visual features of the image element. See para. 69. In addition, Khuc discloses the transformer prior model 700 includes a plurality of neural network layers (e.g., a first neural network layer 720, a second neural network layer, and a third neural network layer 740) that are trained to convert the text tokens 715 into the image embedding 750. Each of the first, the second and the third neural network layers generates a first, second and third intermediate embeddings, respectively, based on the text token embeddings. For example, each of the intermediate embeddings represents a combination of partial textual feature and partial image feature of the corresponding text token. In some embodiments, a second intermediate embedding among the intermediate embeddings 735 is weighted more than the first intermediate embedding among the intermediate embeddings 735….. Accordingly, the image embedding 750 is provided to an image generation model to generate a synthetic image See paras. 116-118).
As per claim 2, Khuc discloses computing an importance score for each of the plurality of image tokens and ordering the plurality of image tokens based on the importance score (see paras. 127-129 of Khluc); and
selecting the subset of the plurality of image tokens based on the ordering (e.g., The cross-attention block then normalizes the attention scores to obtain attention weights that determine how much information from each value element is incorporated into the final attended representation. By attending to different parts of the key-value sequence simultaneously, the cross-attention block captures relationships and dependencies across the input sequences, allowing the machine learning model to understand the context and generate more accurate and contextually relevant outputs. See para. 129, wherein, by determining how much information from each value element is incorporated into the final attended representation and by causing the cross-attention block to capture relationships and dependencies across the input sequences, allowing the machine learning model to understand the context and generate more accurate and contextually relevant outputs, the Khluc reference inherently select the subset of the plurality of image tokens based on the ordering).
As per claim 5, Khuc discloses the input prompt comprises an image with an inpainting region (see paras. 64-65, wherein the synthetic image depicts generated content in the inpainting region (see paras. 69 and 121-127).
As per claim 6, Khuc discloses generating the synthetic image comprises: performing a diffusion denoising process to obtain the synthetic image. See paras. 109-110.
The subject-matter of independent claim 16 is similar in scope to that of claim 1, and the rationale raised above to reject the later also apply, mutatis mutandis, to the former.
As per claim 17, Khuc discloses a router model configured to compute an importance score for each of the plurality of image tokens. See paras. 127-129, wherein the cross-attention block being trained to perform image sharing corresponds with the router model)
As per claim 19, Khuc discloses the image generation model comprises a diffusion transformer (DiT) model. See paras. 109-110, 120-124.
Claim 20 is rejected under the same rationale as claim 5.
Allowable Subject Matter
10. Claims 3-4, 7 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable, upon resolution of the indefiniteness issues raised above, if rewritten in independent form including all of the limitations of the base claim and any intervening claims, because the prior art of record fail to teach the method of claim 1 comprising: determining a dynamic selectivity value based on an image generation stage, wherein the subset of the plurality of image tokens is determined based on the dynamic selectivity value (as recited in claims 3 and 7); and system of claim 17, wherein: the router model is trained to determine a dynamic selectivity value based on the image generation stage (as recited in claim 18).
11. Claims 8-15, upon resolution of the indefiniteness issues raised above, would be allowed over the prior art, because, while the prior art of record discloses substantial features of the claimed invention, as exemplified above with respect to the Khluc reference, the prior arts of record fail to particularly teach determining a dynamic selectivity value based on an image generation stage; selecting a subset of the plurality of image tokens based on the scoring and the dynamic selectivity value.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Azarian et al. (US 12626420) discloses techniques for generating an output image based on a text prompt. A method may include receiving the text prompt; providing a user interface comprising one or more input elements associated with one or more words of the text prompt; receiving input corresponding to at least one of the one or more input elements, the input indicating a semantic importance for each of at least one of the one or more words associated with the at least one of the one or more input elements; and generating the output image based on the text prompt and the input.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESNER SAJOUS whose telephone number is (571) 272-7791. The examiner can normally be reached on M-F 10:00 TO 7:30 (ET).
Examiner interviews are available via telephone 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 or email the Examiner directly at wesner.sajous@uspto.gov.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Said Broome can be reached on 571-272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WESNER SAJOUS/Primary Examiner, Art Unit 2612
WS
10/02/2026
