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 02/14/2025. Claims 1-20 are presented for examination. Claims 1, 12 and 20 are independent claims.
Information Disclosure Statement
2. The information disclosure statements (IDSs) submitted on 03/04/2025 are in compliance with the provisions of 37 CFR 1.97 and are being considered by the Examiner.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1 recites the followings:
1. “A method for generating an image, comprising: obtaining a first machine learning model, the first machine learning model being obtained based on a reference image having a first resolution; fine-tuning the first machine learning model to a second machine learning model by a fine-tuning plug-in, the fine-tuning plug-in being obtained based on a reference image having a second resolution; and generating, by the second machine learning model, a target image based on a target prompt, the target image having a resolution and image content specified by the target prompt.”
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/computes one mathematical representation of a known image data, transformed it to a different mathematical algorithm and then use 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 target image using a machine learning model, which encompasses the implementation of running computer program processes. As such, claim 1 is not patent eligible under 35 USC 101.
Claims 2-11, 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 generate additional image formats. Thus, claims 2-11 are rejected for at least the same rationale as applied to parent claim 1 above and incorporated herein.
Claim Rejections - 35 USC § 112
5. 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.
6. Claims 1-20 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.
The claimed limitations, as recited in claim 1, render the claim indefinite as they merely provide a series of steps written with broadly functional claimed language that only describes the function of the invention as opposed to how the invention is carried out. These steps appear to represent a concatenation of block box experiments, of which only inputs and outputs are specified to achieve the desired result. Particularly, the claimed subject-matters provide no details explaining how the first and second machine learning models are trained using the different reference images and the target prompt to achieve the expected solution of generating the target image having a resolution and image content specified by the target prompt.
For examples, paragraph 33 of the original disclosure merely describes: obtaining and training a first machine learning model based on a conventional resolution. Paragraph 34 describes training the fine-tuning plug-in for first machine learning model with a plurality of reference images to obtain a second machine learning model to generate images of other resolutions. And paragraphs 36-37 further describe each of the first and second machine learning models to include a plurality of diffusion models.
However, the original disclosure fails to provide the sufficient details explaining how the first machine learning model and the fine-tuning plug-in are obtained and by what means are they implemented to apply and integrate the stated tasks. In addition, the disclosure fails also to describe the processes and/or framework by which the first and second machine learning models, including the diffusion models, are implemented to train these models in association with the reference images and target prompt to generate the target image.
Thus, claim 1, as drafted, merely attempt to define the subject matters in terms of the result to be achieved, but in so doing merely states the problem to be solved, without indicating the technical features necessary for achieving the result, such that one skilled in the art would recognize that the applicant has possession of the claimed features at the time of filing.
In claims 2 and 13, the limitations reciting “the fine-tuning plug-in is obtained by: injecting the fine-tuning plug-in into the first machine learning model; and updating the injected fine-tuning plug-in based on the reference image having the second resolution” render the claims indefinite, because the method by which the fine-tuning plug-in is obtained appears redundant since the same fine-tuning plug-in is being used in connection with the first machine learning model to obtain itself. Thus, the limitations fail to limit the claims.
The limitations of claims 12 and 20 render the claim indefinite, because it is unclear as to the first machine learning model and the fine-tuning plug-in are obtained and the framework by which they are implemented to process and integrate the stated tasks in connection with the reference images. Also, the framework by which the first second machine learning model is implemented in association with the reference images and target prompt to generate the target image is not made clear by the claims.
The claims not specifically cited in this rejection are rejected as being dependent upon their rejected base claims.
Allowable Subject Matter
7. Claims 1-20 have no art rejection but are rejected for patent inegibility under 35 USC 101 and for clarity reasons under 35 U.S.C. § 112. As the technical features of the wording of dependent claims 2-9 and 11-18 are indefinite for the reasons discussed above, the technical effect of the subject-matters of these claims is indeterminate and thus, they cannot be agreed that a problem is solved by each of these diverging claims.
A final determination of patentability of the claims will be made upon resolution of the above claim rejections. The lack of an art rejection with this Office action is not an indication of allowable subject matter (i.e., even though the claims are rewritten or amended to overcome the rejection under 35 U.S.C. 112 as discussed above). The claimed language is written as such that a reasonable search of the prior art by the examiner could not be thoroughly conducted.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Karpman (US 12499519) discloses a method receives a text prompt. A text encoder is executed on the text prompt to generate a representation. The method generates a set of images based on the representation and a set of parameters of an image generation model. The set of images is ranked using reward values that are generated by a reward model. The reward model is trained using human input that provided feedback on a quality of generated images using the image generation model. The method outputs one or more images based on the ranking in response to the text prompt.
9. 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).
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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
08/21/2026
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