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 § 101
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter. The instant invention encompasses a method in claims 1-17 (i.e., a process); a storage medium in claims 18-19 (i.e. a manufacture); an apparatus in claims 20 (i.e., a machine). All claims are directed to one of the four statutory categories and meet the requirements of step 1.
Step 2A
Prong One
The claimed invention is directed to an abstract idea without significantly more. The instant invention is broadly about generating an output based on mathematical manipulation.
Claim 1 recites the following (with emphasis added):
A method comprising:
determining, by a generative model, a conditional output and an unconditional output for denoising a noisy sample;
determining an update direction based on the conditional output and the unconditional output;
decomposing the update direction into a first component and a second component;
weighting one or more of the first component and the second component to generate a weighted update direction, wherein the weighted update direction is based on reducing a strength of the second component; and
determining a denoised output based on the conditional output and the weighted update direction, wherein the denoised output is used to generate a generative output by the generative model.
Claim 1 encompass the abstract idea, which is also encompassed by the dependent claims 2-17.
Claim 1 recites the steps for process of obtaining data, computing and manipulate data, which is directed to the mathematical relationships and calculations, a mathematical concept.
Independent claims 18 and 20 recite similar limitations as claim 1, thus are about mathematical concept.
Prong Two
Claim 1, 18 and 20 recites using a generative model, which could be a machine learning model to perform the abstract idea. Claim 18 and 20 recites using a computer device or processors to perform the abstract idea. This judicial exception is not integrated into a practical application because mere instruction to implement on a computer or a computer model, or merely using a computer or computer model as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment or field of use is not considered integration into a practical application. The using of the computer, a generic system and the neural network model does not add improvement to the functioning of a computer or to any other technology field, which failed to enable the abstract idea to integrate into a practical application. Claims 2-17, 19 are about more mathematical data, relationships and calculations, which are abstract idea. The claims do not include additional elements that are sufficient to enable the abstract idea to integrate into a practical application.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires 'more than simply stat[ing] the [abstract idea] while adding the words 'apply it."' Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include 'additional features' to ensure 'that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].'" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims include the additional elements other than the abstract idea which include a computer (e.g. processor and memory). These additional elements are merely conventional computer. Any potentially technical aspects of the claims are well-known generic computer components performing conventional functions (e.g., a processor performing generic data handling using mathematical concepts). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine and conventional in the field. Thus the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are generally linked to implement an abstract idea on a computer. When looked at individually and as a whole, the claim limitations are determined to be an abstract idea without "significantly more," and thus not patent eligible.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
The claim limitation of “decomposing the update direction into a first component and a second component;” is interpreted accordingly the Specification paragraphs [0020], where the first component and second component corresponds to the parallel component and orthogonal component respectively, where parallel component means the update direction is parallel to the conditional model output and the orthogonal component means the update direction is orthogonal to the conditional model output.
Regarding claim 1, Zhang et al. (US 2025/0292371 A1) teaches:
A method comprising:
determining, by a generative model, a conditional output and an unconditional output for denoising a noisy sample; (FIG. 2, step S1063 and S1062 generating conditional output and unconditional output.)
determining an update direction based on the conditional output and the unconditional output; (FIG. 2, step S1064 generating correction value delta x.)and
determining a denoised output based on the conditional output and the weighted update direction, wherein the denoised output is used to generate a generative output by the generative model.(FIG. 1, after the iteration ending condition is met at step S110, the generating denoised output are the final output.)
However, Zhang does not teach:
decomposing the update direction into a first component and a second component;
weighting one or more of the first component and the second component to generate a weighted update direction, wherein the weighted update direction is based on reducing a strength of the second component;
Claims 18 and 20 recite similar limitations of claim 1.
Other relevant references:
Voynov et al. (US 2025/0371678 A1) teaches denoising neural network, which uses classifier-free guidance at each reverse diffusion step. When using classifier-free guidance, the network processes the first denoising input for the reverse diffusion step using the denoising neural network but not conditioned on the respective conditioning input to generate another denoising output. The network then combines the conditional and unconditional denoising outputs in accordance with a guidance weight for the reverse diffusion step to generate a final denoising output.
Ho et al. (US 2024/0338936 A1) recites a method: receiving the input; initializing a current intermediate representation; generating an output video by updating the current intermediate representation at each of a plurality of iterations, wherein the updating comprises, at each iteration: processing an intermediate input for the iteration comprising the current intermediate representation using a diffusion model that is configured to process the intermediate input to generate a noise output; and updating the current intermediate representation using the noise output for the iteration. However, Ho does not teach the limitations of the claim 1 as a whole.
Conclusion
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/YANNA WU/Primary Examiner, Art Unit 2615
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