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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/11/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings submitted on 10/11/2023 are accepted.
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 mathematical concepts without significantly more. The claim(s) recite(s) a mathematical (denoising) operation. This judicial exception is not integrated into a practical application because the claimed limitations arrive at an answer, inherent to a mathematical operation, where nothing further is generated for any claimed utilization. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claimed limitations do not integrate the mathematical result into a practical application.
Dependent claims 2-4, 10,12-15 and 19 are rejected based on their dependency to their respective independent claim.
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?” to determine whether the additional elements transform the nature of the claim into a patent eligible application.
Under the 2019 Revised Patent Subject Matter Eligibility Guidance, the first step / Prong One of Step One (Step 2A) to determine patent eligibility requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept.
Prong (1) requires the determination of (a) the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and (b) determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated.
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles / practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules / instructions) and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Prong (2) asks does the claim recite additional elements that integrate the judicial exception into a practical application? For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981.
The second step (Step 2B) is to determine whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
Claim 1 is directed to a computer-implemented method:
A - performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item; and
B - performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item,
C - wherein the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.
Steps A and B are each a mathematical operation (calculation) in gathered data (input) that generates an answer (content item). Step C merely defines the mathematical operation parameters for step A and step B.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 2 is directed to the computer-implemented method of claim 1:
that recites: wherein the input includes an input text, and the method further comprises encoding the input text using a plurality of text encoders to generate a plurality of text embeddings, wherein the one or more first denoising operations and the one or more second denoising operations are based on the plurality of text embeddings.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 3 is directed to the computer-implemented method of claim 1:
that recites: wherein the input includes an input content item, and the method further comprises encoding the input content item using a content item encoder to generate a content item embedding, wherein the one or more first denoising operations and the one or more second denoising operations are based on the content item embedding.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 4 is directed to the computer-implemented method of claim 1:
that recites: wherein the input includes an input text and an input mask, and the method further comprises modifying an attention map based on the input mask, wherein the one or more first denoising operations and the one or more second denoising operations are based on the attention map.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 5 is directed to the computer-implemented method of claim 1:
that recites: wherein each of the one or more first denoising operations and the one or more second denoising operations includes one or more denoising diffusion operations.
This limitation of claim 5 defines a type of mathematical operation (calculation) of steps A and B.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 6 is directed to the computer-implemented method of claim 1:
D - performing one or more third denoising operations based on the input and the second content item using a third machine learning model to generate a third content item,
E - wherein the third machine learning model is trained to denoise content items having an amount of corruption within a third corruption range that is lower than the second corruption range.
Step D is a mathematical operation (calculation) in gathered data (input) and the generated result of step B. Step E merely defines the mathematical operation parameters for step D.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 7 is directed to the computer-implemented method of claim 1:
that recites: performing one or more denoising operations based on the input and the second content item using one or more additional machine learning models to generate a third content item, wherein the third content item has a higher resolution than the second content item.
The limitation of claim 7 performs an additional mathematical operation (calculation) on the result of step B (second content) to further generate an additional result.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 8 is directed to the computer-implemented method of claim 1:
that recites: wherein the second content item includes less corruption than the first content item.
The limitation of claim 8 describes a metric of the generated result of step B.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 9 is directed to the computer-implemented method of claim 1:
that recites: wherein the one or more first denoising operations are performed until the first content item is generated that includes an amount of corruption that is less than the first corruption range.
The limitation of claim 9 describes the metric of the generated result of step C.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 10 is directed to the computer-implemented method of claim 1:
that recites: training a third machine learning model to denoise content items having an amount of corruption within a third corruption range that includes the first corruption range and the second corruption range; retraining the third machine learning model to denoise content items having an amount of corruption within the first corruption range to generate the first machine learning model; and retraining the third machine learning model to denoise content items having an amount of corruption within the second corruption range to generate the second machine learning model.
Step 1 – yes, the claim is directed to a statutory category of a process.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 11 is directed to a non-transitory computer-readable media:
A - performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item; and
B - performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item,
C - wherein the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.
Steps A and B are each a mathematical operation (calculation) in gathered data (input) that generates an answer (content item). Step C merely defines the mathematical operation parameters for step A and step B.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application. A mere nominal recitation of a generic processor does not take the claim limitation out of mathematical concept grouping.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 12 is directed to the media of claim 11:
that recites: , wherein the input includes an input text, and the instructions, when executed by the at least one processor, further cause the at least one processor to perform the step of encoding the input text using a plurality of text encoders to generate a plurality of text embeddings, wherein the one or more first denoising operations and the one or more second denoising operations are based on the plurality of text embeddings.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 13 is directed to the media of claim 11:
that recites: wherein the input includes an input content item, and the instructions, when executed by the at least one processor, further cause the at least one processor to perform the step of encoding the input content item using a content item encoder to generate a content item embedding, wherein the one or more first denoising operations and the one or more second denoising operations are based on the content item embedding.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 14 is directed to the media of claim 11:
that recites: wherein the input includes an input mask, and the instructions, when executed by the at least one processor, further cause the at least one processor to perform the step of modifying an attention map based on the input mask, wherein the one or more first denoising operations and the one or more second denoising operations are based on the attention map.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 15 is directed to the media of claim 11:
that recites: wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform the step of receiving, via a user interface, the input mask and a specification of at least one portion of the input text that corresponds to at least one portion of the input mask.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 16 is directed to the media of claim 11:
D - performing one or more third denoising operations based on the input and the second content item using a third machine learning model to generate a third content item,
E - wherein the third machine learning model is trained to denoise content items having an amount of corruption within a third corruption range that is lower than the second corruption range.
Step D is a mathematical operation (calculation) in gathered data (input) and the generated result of step B. Step E merely defines the mathematical operation parameters for step D.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 17 is directed to the media of claim 11:
that recites: wherein the second content item includes less corruption than the first content item.
The limitation of claim 17 describes the data of step B.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 18 is directed to the media of claim 11:
that recites: wherein the one or more first denoising operations are performed until the first content item is generated that includes an amount of corruption that is less than the first corruption range.
The limitation of claim 18 describes a metric of the generated result of step B.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
Claim 19 is directed to the media of claim 11:
that recites: wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform the steps of: training a third machine learning model to denoise content items having an amount of corruption within a third corruption range that includes the first corruption range and the second corruption range; retraining the third machine learning model to denoise content items having an amount of corruption within the first corruption range to generate the first machine learning model; and retraining the third machine learning model to denoise content items having an amount of corruption within the second corruption range to generate the second machine learning model.
Step 1 – yes, the claim is directed to a statutory category of a product.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – yes, there are additional elements that integrate the abstract idea into a practical application.
Step 2B – N/A.
Claim 20 is directed to a system:
A - one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions,
B - performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item; and
C - performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item,
D - wherein the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.
Step A recites generic computer components. Steps B and C are each a mathematical operation (calculation) in gathered data (input) that generates an answer (content item). Step D merely defines the mathematical operation parameters for step B and step C.
Step 1 – yes, the claim is directed to a statutory category of a machine.
Step 2A Prong 1 – yes, the claim is directed to an abstract idea.
Step 2A Prong 2 – no, there are no additional elements that integrate the abstract idea into a practical application. A mere nominal recitation of a generic computer components does not take the claim limitation out of mathematical concept grouping.
Step 2B – no, the claim does not recite additional elements that amount to an inventive concept more than the recited judicial exceptions.
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.
Specifically, the Examiner found neither prior art cited in its entirety, nor based on the prior art of record, found any motivation to combine any of the said prior art that teaches in claim 1: “… performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item; and performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item, wherein the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.”
Independent claims 11 and 20 are similarly cited as claim 1 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Karras et al., US Patent No. 12423781, is directed to generating content items using denoising machine learning diffusion models.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARBARA D REINIER whose telephone number is (571)270-5082. The examiner can normally be reached M-Tu 10am - 6pm.
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/BARBARA D REINIER/Primary Examiner, Art Unit 2682
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