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 .
Priority
Application is a continuation of Provisional Application No. 63/513,838, filed
on July 14, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/16/2024, 02/27/2025, and 06/25/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 an abstract idea (mental process and mathematical concept) without significantly more.
Regarding claim 1, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim recites A computer-implemented method for analyzing a generative output of a generative model, the comprising method. An method is one of the four statutory categories.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a [ mental process/mathematical concept] but for recitation of generic computer components:
determining a first latent representation of the generative output and a plurality of latent representations of a plurality of data samples associated with the generative model; (A person can mentally determine a latent representation for both a generative output and a plurality of data samples by as process of simply evaluating the output and the data and making a judgement on what their latent representations should be.)
computing a plurality of similarities between the first latent representation and the plurality of latent representations; (Computing a plurality of similarities is both a mathematical concept of mathematical calculations and a mental process. A person can mentally compute a plurality of similarities between a first latent representation and a plurality of latent representations by a process of simply evaluating the latent representations and making a judgement on how similar they are.)
determining that a first similarity that is included in the plurality of similarities and computed between the first latent representation and a second latent representation included in the plurality of latent representations exceeds a threshold; (A person can mentally determine if a first similarity exceeds a threshold by a process of simply evaluating the similarity score and a threshold value and make a judgement on if the similarity is above the threshold.)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a [ mental process/mathematical concept] but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
and in response to determining that the first similarity exceeds the threshold, causing output to be generated that indicates a high similarity between the generative output and a first data sample that is included in the plurality of data samples and corresponds to the second latent representation. (Adding insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
and in response to determining that the first similarity exceeds the threshold, causing output to be generated that indicates a high similarity between the generative output and a first data sample that is included in the plurality of data samples and corresponds to the second latent representation. (Adding insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g)) , Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
Claims 11 and 20 are rejected on the same grounds as Claim 1.
Regarding claim 2 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites determining that a second similarity that is included in the plurality of similarities and computed between the first latent representation and a third latent representation does not exceed the threshold; (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally make a determination by a process of simply evaluating the first latent representation and a third latent representation and making a judgement if their similarity does not exceed a threshold. (MPEP 2106).)
and in response to determining that the second similarity does not exceed the threshold, causing additional output to be generated that indicates a lack of high similarity between the generative output and a second data sample that is included in the plurality of data samples and corresponds to the third latent representation. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data output, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites sending data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more.)
Claim 12 are rejected on the same grounds as Claim 2.
Regarding claim 3 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites generating a plurality of clusters of the plurality of similarities; and determining that a representative similarity associated with a cluster that includes the first similarity exceeds the threshold. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a plurality of clusters of similarities and make a determination if one of the clusters exceeds a threshold by a process of simply evaluating the similarities and making a judgement on how they are clustered and if the clusters exceed a threshold. (MPEP 2106).)
Regarding claim 4 it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 3 recites wherein the representative similarity comprises at least one of an aggregate similarity associated with a subset of the plurality of similarities included in the cluster or a second similarity included in the subset of the plurality of similarities. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 5 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites wherein computing the plurality of similarities comprises: computing the first similarity between a first portion of the first latent representation and a corresponding first portion of the second latent representation; and computing a second similarity between a second portion of the first latent representation and a corresponding second portion of the second latent representation. (Computing a similarity is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106).)
Regarding claim 6 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites wherein the plurality of similarities comprises at least one of a cosine similarity, a Euclidean distance, a style similarity, or a multidimensional similarity. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing what calculations to use is not indicative of significantly more.)
Regarding claim 7 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites wherein the plurality of data samples is included in a training dataset for the generative model. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 8 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 8 recites wherein the output comprises at least one of an attribution associated with the first data sample, a compensation associated with the first data sample, or filtering of the generative output. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 9 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 9 recites wherein the first latent representation and the plurality of latent representations are generated using one or more components of the generative model. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 10 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 10 recites wherein the one or more components comprise at least one of an encoder, a U-Net, or an embedding model. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 13 it is dependent upon claim 12, and thereby incorporates the limitations of, and corresponding analysis applied to claim 12. Further, claim 13 recites wherein the additional output comprises at least one of a lack of attribution associated with the second data sample or a lack of compensation associated with the second data sample. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 14 it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Further, claim 11 recites wherein the instructions further cause the one or more processors to perform the steps of: determining that a second similarity that is included in the plurality of similarities and computed between the first latent representation and a third latent representation exceeds an additional threshold; (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally make a determination by a process of simply evaluating the first latent representation and a third latent representation and making a judgement if their similarity exceeds a threshold. (MPEP 2106).)
and in response to determining that the second similarity exceeds the additional threshold, causing additional output associated with the additional threshold to be generated. (In step 2A, prong 2, this recites insignificant extra solution activity of mere data output, which is not indicative of integration into a practical application (MPEP 2106.05(g)). In step 2B, this recites sending data over a network which is a well-understood, routine and conventional activity, which is not indicative of significantly more.)
Regarding claim 15 it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 15. Further, claim 14 recites wherein the additional threshold is associated with at least one of a different attribution than the threshold or a different level of compensation than the threshold. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 16 it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis applied to claim 11. Further, claim 16 recites wherein the first latent representation is computed based on at least one of the generative output or a prompt associated with the generative model. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 17 it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis applied to claim 11. Further, claim 17 recites wherein the plurality of data samples is included in a set of restricted content. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
Regarding claim 18 it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis applied to claim 11. Further, claim 18 recites wherein the generative model comprises a diffusion model. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 19 it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis applied to claim 11. Further, claim 19 recites wherein the first latent representation and the plurality of latent representations are generated using one or more components of a feature extractor model. (In step 2A prong 2, this recites mere instructions to apply an exception (MPEP 2106.05(f)). In step 2B, merely describing instructions to apply an exception to use is not indicative of significantly more.)
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 6, 8-16, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gulsun et al. Pub. No.: US 20220076053 A1.
Regarding claim 1 Gulsun teaches A computer-implemented method for analyzing a generative output of a generative model, the method comprising:
determining a first latent representation of the generative output and a plurality of latent representations of a plurality of data samples associated with the generative model; computing a plurality of similarities between the first latent representation and the plurality of latent representations; (Gulsun, paragraphs 0041-0044, teaches a generative machine learning model producing a generative output and then another model that takes the generative output calculates its similarity to another data which can be an input or other data that was used in the training of the model )
determining that a first similarity that is included in the plurality of similarities and computed between the first latent representation and a second latent representation included in the plurality of latent representations exceeds a threshold; and in response to determining that the first similarity exceeds the threshold, causing output to be generated that indicates a high similarity between the generative output and a first data sample that is included in the plurality of data samples and corresponds to the second latent representation. (Gulsun, paragraphs 0041-0044, teaches the comparison of the similarities to a similarity threshold and determing if the similarity is above or below that threshold. If the similarity is above the threshold the model will output an indication that the it is highly similar and therefore a normal output and if it is below a the threshold the model will output that it has a low similarity and is an abnormal output.)
Regarding claim 2 Gulsun teaches The computer-implemented method of claim 1, further comprising: determining that a second similarity that is included in the plurality of similarities and computed between the first latent representation and a third latent representation does not exceed the threshold; and in response to determining that the second similarity does not exceed the threshold, causing additional output to be generated that indicates a lack of high similarity between the generative output and a second data sample that is included in the plurality of data samples and corresponds to the third latent representation. (Gulsun, paragraphs 0041-0044, teaches the comparison of the similarities to a similarity threshold and determing if the similarity is above or below that threshold. If the similarity is above the threshold the model will output an indication that it is highly similar and therefore a normal output and if it is below a threshold the model will output that it has a low similarity and is an abnormal output.)
Regarding claim 6 Gulsun teaches The computer-implemented method of claim 1, wherein the plurality of similarities comprises at least one of a cosine similarity, a Euclidean distance, a style similarity, or a multidimensional similarity. (Gulsun, paragraphs 0041-0044, teaches the Siamese model that is used for similarity analysis as a replacement for a perceptual loss function which is a style similarity meaning that the model is able to better perform style similarity than a perceptual loss.)
Regarding claim 8 Gulsun teaches The computer-implemented method of claim 1, wherein the output comprises at least one of an attribution associated with the first data sample, a compensation associated with the first data sample, or filtering of the generative output. (Gulsun, paragraphs 0041-0044, teaches the filtering or attribution of the generative outputs into normal and abnormal categories based on their similarity.)
Regarding claim 9 Gulsun teaches The computer-implemented method of claim 1, wherein the first latent representation and the plurality of latent representations are generated using one or more components of the generative model (Gulsun, paragraphs 0041-0044, teaches the latent representation being generated by both the encoder of the generative model and the Siamese model of the generative model.)
Regarding claim 10 Gulsun teaches The computer-implemented method of claim 9, wherein the one or more components comprise at least one of an encoder, a U-Net, or an embedding model. (Gulsun, paragraphs 0041-0044, teaches the generative model being able to be an encoder or a series of encoders.)
Regarding claim 11 Gulsun teaches One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: determining a first latent representation of a generative output of a generative model and a plurality of latent representations of a plurality of data samples associated with the generative model; computing a plurality of similarities between the first latent representation and the plurality of latent representations; (Gulsun, paragraphs 0041-0044, teaches a generative machine learning model producing a generative output and then another model that takes the generative output calculates its similarity to another data which can be an input or other data that was used in the training of the model )
determining that a first similarity that is included in the plurality of similarities and computed between the first latent representation and a second latent representation included in the plurality of latent representations exceeds a threshold; and in response to determining that the first similarity exceeds the threshold, causing output to be generated that indicates a high similarity between the generative output and a first data sample that is included in the plurality of data samples and corresponds to the second latent representation. (Gulsun, paragraphs 0041-0044, teaches the comparison of the similarities to a similarity threshold and determing if the similarity is above or below that threshold. If the similarity is above the threshold the model will output an indication that it is highly similar and therefore a normal output and if it is below a the threshold the model will output that it has a low similarity and is an abnormal output.)
Regarding claim 12 Gulsun teaches The one or more non-transitory computer-readable media of claim 11, wherein the instructions further cause the one or more processors to perform the steps of: determining that a second similarity that is included in the plurality of similarities and computed between the first latent representation and a third latent representation does not exceed the threshold; and in response to determining that the second similarity does not exceed the threshold, causing additional output to be generated that indicates a lack of high similarity between the generative output and a second data sample that is included in the plurality of data samples and corresponds to the third latent representation. (Gulsun, paragraphs 0041-0044, teaches the comparison of the similarities to a similarity threshold and determing if the similarity is above or below that threshold. If the similarity is above the threshold the model will output an indication that it is highly similar and therefore a normal output and if it is below a threshold the model will output that it has a low similarity and is an abnormal output.)
Regarding claim 13 Gulsun teaches The one or more non-transitory computer-readable media of claim 12, wherein the additional output comprises at least one of a lack of attribution associated with the second data sample or a lack of compensation associated with the second data sample. (Gulsun, paragraphs 0041-0044, teaches the filtering or attribution of the generative outputs into normal and abnormal categories based on their similarity. If the similarity is below a threshold, then it is not similar and is declared abnormal (i.e. lacks attribution))
Regarding claim 14 Gulsun teaches The one or more non-transitory computer-readable media of claim 11, wherein the instructions further cause the one or more processors to perform the steps of: determining that a second similarity that is included in the plurality of similarities and computed between the first latent representation and a third latent representation exceeds an additional threshold; and in response to determining that the second similarity exceeds the additional threshold, causing additional output associated with the additional threshold to be generated. (Gulsun, paragraphs 0075, teaches after the similarity is determined to be below a first threshold it is then sent to a classification model that uses its own second threshold to determine a classification for the low similarity and produces the classification as an output.)
Regarding claim 15 Gulsun teaches The one or more non-transitory computer-readable media of claim 14, wherein the additional threshold is associated with at least one of a different attribution than the threshold or a different level of compensation than the threshold. (Gulsun, paragraphs 0075, teaches the classification further classifying the already determined abnormal similarities meaning that it gives a different attribution to the samples than when it was assigned the abnormal attribution.)
Regarding claim 16 Gulsun teaches The one or more non-transitory computer-readable media of claim 11, wherein the first latent representation is computed based on at least one of the generative output or a prompt associated with the generative model. (Gulsun, paragraphs 0041-0044, teaches a first latent representation being based on the generative output of an encoder machine learning model.)
Regarding claim 19 Gulsun teaches The one or more non-transitory computer-readable media of claim 11, wherein the first latent representation and the plurality of latent representations are generated using one or more components of a feature extractor model. (Gulsun, paragraphs 0041-0044, teaches the use of an encoder to extract the feature set of an input to create a latent representation (i.e. feature extractor model).)
Regarding claim 20 Gulsun teaches A system, comprising: one or more memories that store instructions, and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to perform the steps of: generating a latent representation of an output of a machine learning model and a plurality of latent representations of a plurality of data samples associated with the machine learning model; computing a plurality of similarities between the latent representation and the plurality of latent representations; (Gulsun, paragraphs 0041-0044, teaches a generative machine learning model producing a generative output and then another model that takes the generative output calculates its similarity to another data which can be an input or other data that was used in the training of the model )
determining that a first similarity that is included in the plurality of similarities and computed between the latent representation and a first data sample included in the plurality of data samples exceeds a threshold; and in response to determining that the first similarity exceeds the threshold, causing additional output indicating a high similarity between the output and the first data sample to be generated. (Gulsun, paragraphs 0041-0044, teaches the comparison of the similarities to a similarity threshold and determing if the similarity is above or below that threshold. If the similarity is above the threshold the model will output an indication that the it is highly similar and therefore a normal output and if it is below a the threshold the model will output that it has a low similarity and is an abnormal output.)
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Gulsun et al. Pub. No.: US 20220076053 A1 in view of Cazzanti et al. “Generative models for similarity-based classification”.
Regarding claim 3 Gulsun teaches The computer-implemented method of claim 1,
Gulsun does not teach wherein determining that the first similarity exceeds the threshold comprises: generating a plurality of clusters of the plurality of similarities; and determining that a representative similarity associated with a cluster that includes the first similarity exceeds the threshold. However, Cazzanti teaches this limitation in analogous art (Cazzanti, page 2290, section 2, teaches the use of nearest neighbor similarity which is a clustering method in which it determines if data points are within a threshold distance to be clustered together.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Cazzanti teaching of determining a similarity using a clustering method with Gulsun’s teaching of determing the similarities of generative outputs to other non-generated data. The motivation to do so would be to determine how similar multiple outputs are to multiple other data points all at the same time in a visual human interpretable manner.
Regarding claim 4, the combination of Gulsun and Cazzanti teaches The computer-implemented method of claim 3, wherein the representative similarity comprises at least one of an aggregate similarity associated with a subset of the plurality of similarities included in the cluster or a second similarity included in the subset of the plurality of similarities. (Cazzanti, page 2290, section 2, teaches the use of nearest centroid clustering which would cluster the multiple similarities into one centroid to be used in the similarity comparison.)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Gulsun et al. Pub. No.: US 20220076053 A1 in view of Balan et al. “EKILA: Synthetic Media Provenance and Attribution for Generative Art” June 17 2023.
Regarding claim 5 Gulsun teaches The computer-implemented method of claim 1,
Gulsun does not teach wherein computing the plurality of similarities comprises: computing the first similarity between a first portion of the first latent representation and a corresponding first portion of the second latent representation; and computing a second similarity between a second portion of the first latent representation and a corresponding second portion of the second latent representation. However, Balan teaches this limitation in analogous art (Balan, page 917, section 4, teaches the use of patch images, which are portions of the latent representation of images. A patch image of the first latent representation is compared to a patch image of a second latent representation and a similar is calculated based on those patch images.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Balan teaching of Comparing patch images of latent representations to get a similarity with Gulsun’s teaching of determing the similarities of generative outputs to other non-generated data. The motivation to do so would be to determine the similarity of specific features of a generated output to another piece of data instead of comparing just the whole image.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Gulsun et al. Pub. No.: US 20220076053 A1 in view of Adato et al. Pub No.: US 20230154153 A1.
Regarding claim 7 Gulsun teaches The computer-implemented method of claim 1,
Gulsun does not teach wherein the plurality of data samples is included in a training dataset for the generative model. However, Adato teaches this limitation in analogous art (Adato, paragraph 0065, teaches the data samples that are being compared to an output being part of the training data set for a generative model.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Adato’s teaching of the comparison of training data to a generative output with Gulsun’s teaching of determing the similarities of generative outputs to other non-generated data. The motivation to do so would be to determine if the model is properly generating outputs that are similar to the training data in order to determine the effectiveness of the model.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Gulsun et al. Pub. No.: US 20220076053 A1 in view of Padgett et al. Pub No.: US 20240160902 A1.
Regarding claim 17 Gulsun teaches The one or more non-transitory computer-readable media of claim 11,
Gulsun does not teach wherein the plurality of data samples is included in a set of restricted content. However, Padgett teaches this limitation in analogous art (Padgett, paragraph 0102 – 0105, teaches copyrighted and trademarked content (i.e. restricted content) being used in a plurality of data used in a similarity determination of a generative output)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Padgett’s teaching of using copyrighted materials in a similarity comparison to generated materials with Gulsun’s teaching of determing the similarities of generative outputs to other non-generated data. The motivation to do so would be to enable the model to filter out material that is too similar to copyrighted or trademarked material that might cause a lawsuit.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Gulsun et al. Pub. No.: US 20220076053 A1 in view of Liew et al. Pub No.: US 20240144544 A1.
Regarding claim 18 Gulsun teaches The one or more non-transitory computer-readable media of claim 11,
Gulsun does not teach wherein the generative model comprises a diffusion model. However, Liew teaches this limitation in analogous art (Liew, Paragraph 0045, teaches a text-to-image diffusion based generative model that is made to maximize the similarity between the text prompt and the generated image.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Liew’s teaching of diffusion generative model Gulsun’s teaching of determing the similarities of generative outputs to other non-generated data. The motivation to do so would be to test out the similarities of different generative models to determine which models produce the highest similarity results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7:20am-5:20pm; F: Out of Office.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MARIELA REYES can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS BERNARD LANE/ Examiner, Art Unit 2142
/HAIMEI JIANG/ Primary Examiner, Art Unit 2142
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