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 statement (IDS) submitted on 01/31/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 2, 3, 6, 10, 11, 14, 16, 17, and 20 objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “perplexity” in claims 2, 10, and 16 is used by the claim to mean “a parameter of an image generator,” while the accepted meaning is “a measure of uncertainty for a discrete probability distribution.” The term is indefinite because the specification does not clearly redefine the term.
Claims 2, 10, and 16 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.
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 rejected under 35 U.S.C. 101 because they are directed toward an abstract idea without significantly more.
Step 1 analysis:
Independent Claims 1 and 9 recite, in part, a method, therefore falling into the statutory category of process. Independent Claim 15 recites, in part, a system comprising a processor and a memory, therefore falling into the statutory category of manufacture.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“generating a first set of images from a first data subset of the plurality of data subsets, each image of the first set of images generated using a different configuration of an image generation process”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses generating images in different ways.
“forming a first composite image using the first set of images”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses combining images.
“generating a second set of images from the first data subset of the plurality of data subsets, each image of the second set of images generated using a different configuration of an image generation process, wherein the configurations for generation of the first set of images are different from the configurations for generation of the second set of images”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses generating images in different ways.
“forming a second composite image using the second set of images”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses combining images.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“inputting the first composite image to a machine learning (ML) model to obtain a first prediction”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
“inputting the second composite image to the ML model to obtain a second prediction”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
“training the ML model based on at least one of the first prediction or the second prediction”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (train a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “inputting the first composite image to a machine learning (ML) model to obtain a first prediction” and “inputting the second composite image to the ML model to obtain a second prediction” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
As discussed above, the additional element(s) of “training the ML model based on at least one of the first prediction or the second prediction” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein configurations of the image generation process differ by adjusting one or more of a distance metric and a perplexity value used during the image generation process”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (image generation) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein configurations of the image generation process differ by adjusting one or more of a distance metric and a perplexity value used during the image generation process” is/are directed to particular field(s) of use (image generation) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein each image of the first set of images represents a single color of a color model such that the composite image includes all of the colors of the color model”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (image generation) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein each image of the first set of images represents a single color of a color model such that the composite image includes all of the colors of the color model” is/are directed to particular field(s) of use (image generation) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“wherein the first prediction includes a prediction for a value in the first data subset”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses making a prediction about a value in a first dataset.
“training the ML model includes updating at least one parameter of the model based on a difference between the first prediction and the value of the first data subset”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses updating a parameter of a model.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 5:
Step 2A: Prong 1 analysis:
Claim 5 recites in part:
“wherein training the ML model based on at least one of the first prediction or the second prediction includes updating at least one parameter of the model based on a difference between the first prediction and the second prediction”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses updating a parameter of a model.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 6:
Step 2A: Prong 1 analysis:
Claim 6 recites in part:
“obtaining a second difference between the first prediction and the value of the first data subset”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses finding a difference between values.
“combining the difference between the first prediction and the second prediction with the second difference”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses combining values.
“updating at least one parameter of the model based on the combined differences”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses updating a parameter of a model.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 7:
Step 2A: Prong 1 analysis:
Claim 7 recites in part:
“wherein training the ML model based on at least one of the first prediction or the second prediction further includes updating at least one parameter of the model based on a comparison of the first composite image and the second composite image”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this limitation encompasses updating a parameter of a model based on a comparison.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“One or more non-transitory computer-readable media storing instructions that, in response to being executed by one or more processors, cause a system to perform the method of claim 1”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (storage and processor) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “One or more non-transitory computer-readable media storing instructions that, in response to being executed by one or more processors, cause a system to perform the method of claim 1” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (storage and processor) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Due to language similar to that of Claim 1, Claim 9 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 10:
Due to language similar to that of Claim 2, Claim 10 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 11:
Due to language similar to that of Claim 3, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 12:
Due to language similar to that of Claim 4, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 13:
Due to language similar to that of Claims 1 and 5, Claim 13 is rejected for the same reasons as presented above in the rejection of Claims 1 and 5.
Regarding Claim 14:
Due to language similar to that of Claim 6, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 15:
Due to language similar to that of Claims 1, 8, and 9, Claim 15 is rejected for the same reasons as presented above in the rejection of Claims 1, 8, and 9.
Regarding Claim 16:
Due to language similar to that of Claims 2 and 10, Claim 16 is rejected for the same reasons as presented above in the rejection of Claims 2 and 10.
Regarding Claim 17:
Due to language similar to that of Claims 3 and 11, Claim 17 is rejected for the same reasons as presented above in the rejection of Claims 3 and 11.
Regarding Claim 18:
Due to language similar to that of Claims 4 and 12, Claim 18 is rejected for the same reasons as presented above in the rejection of Claims 4 and 12.
Regarding Claim 19:
Due to language similar to that of Claims 1, 5, and 13, Claim 19 is rejected for the same reasons as presented above in the rejection of Claims 1, 5, and 13.
Regarding Claim 20:
Due to language similar to that of Claims 6 and 14, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 6 and 14.
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.
Claim(s) 1, 4, 5, 7-9, 12, 13, 15, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun et al (B. Sun et al., "SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 2973-2981, doi: 10.1109/CVPRW.2019.00360., hereinafter Sun), in view of Song et al (US 20250022099 A1, hereinafter Song), and in view of Quinton et al (US 20220327811 A1, hereinafter Quinton).
Regarding Claim 1:
Sun teaches
A method comprising: accessing a dataset including a plurality of data subsets, each of the data subsets including a plurality of tabular data values (Sun [Page 2975, Section 2, par. 3]: “SuperTML is composed of two steps, the first of which is two-dimensional embedding. This step projects features in the tabular data onto the generated images, which will be called the SuperTML images in this paper. The conversion of tabular training data to SuperTML image is illustrated in Figure 1, where a collection of samples containing four tabular features is being sorted”; (EN): it can be seen in Figure 1 that the tabular data is accessed in order to create the images);
generating a first set of images from a first data subset of the plurality of data subsets, each image of the first set of images generated using a different configuration of an image generation process (Sun [Page 2975, Section 2, par. 3]: “SuperTML is composed of two steps, the first of which is two-dimensional embedding. This step projects features in the tabular data onto the generated images, which will be called the SuperTML images in this paper. The conversion of tabular training data to SuperTML image is illustrated in Figure 1, where a collection of samples containing four tabular features is being sorted”; (EN): it can be seen in Figure 1 that each image file is made up of a different configuration of the tabular data);
generating a second set of images from the first data subset of the plurality of data subsets, each image of the second set of images generated using a different configuration of an image generation process, wherein the configurations for generation of the first set of images are different from the configurations for generation of the second set of images (Sun [Page 2975, Section 2, par. 3]: “SuperTML is composed of two steps, the first of which is two-dimensional embedding. This step projects features in the tabular data onto the generated images, which will be called the SuperTML images in this paper. The conversion of tabular training data to SuperTML image is illustrated in Figure 1, where a collection of samples containing four tabular features is being sorted”; (EN): it can be seen in Figure 1 that each image file is made up of a different configuration of the tabular data, therefore when the processes is repeated for a second set of images, the configurations must necessarily be different);
Sun does not distinctly disclose
forming a first composite image using the first set of images;
forming a second composite image using the second set of images;
However, Song teaches
forming a first composite image using the first set of images (Song [0196]: “At operation 920, the system generates a composite image based on the descriptive embedding and the first image using an image generation model, where the composite image depicts the target element from the second image at the target location of the first image. In some cases, the operations of this step refer to, or may be performed by, an image generation model”);
forming a second composite image using the second set of images (Song [0234]: “At operation 1305, the system obtains a second training image and a second image embedding for the second training image, where the second training image depicts a target element. In some cases, the operations of this step refer to, or may be performed by, a training component”);
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the methods of classification of tabular data of Sun with the systems and methods for image compositing of Song in order to provide a method for image compositing based on descriptive embeddings. The method presented in Song is beneficial for Sun in that it allows for a machine learning model to create composite images and to learn from parameters and make predictions (Song [0097]: “According to some aspects, machine learning model 515 comprises machine learning parameters stored in memory unit 510. Machine learning parameters are variables that provide a behavior and characteristics of a machine learning model. Machine learning parameters can be learned or estimated from training data and are used to make predictions or perform tasks based on learned patterns and relationships in the data.”)
Song + Sun does not distinctly disclose
inputting the first composite image to a machine learning (ML) model to obtain a first prediction;
inputting the second composite image to the ML model to obtain a second prediction;
and training the ML model based on at least one of the first prediction or the second prediction.
However, Quinton teaches
inputting the first composite image to a machine learning (ML) model to obtain a first prediction (Quinton [0063]: “Embodiments for generating a Desire Response Map may include visiting each Composite Fragment in the Composite Image; or, visiting a subset of Composite Fragments in the Composite Image, and assessing each visited Composite Fragment to determine a label for a corresponding location in the Desired Response Map.”; (EN): determining a label is analogous to making a prediction about the composite image)
inputting the second composite image to the ML model to obtain a second prediction (Quinton [0063]: “Embodiments for generating a Desire Response Map may include visiting each Composite Fragment in the Composite Image; or, visiting a subset of Composite Fragments in the Composite Image, and assessing each visited Composite Fragment to determine a label for a corresponding location in the Desired Response Map.”; (EN): assessing each of the composite fragments in the composite images is analogous to inputting a second image into the model);
and training the ML model based on at least one of the first prediction or the second prediction (Quinton [0070]: “The System 220 may include a Composite Training Engine 270 for generating Composite Data 272 (e.g. the Composite Training Data having been interpreted or converted back into a conventional Training Data format) for use during a Composite Training Process of a Machine Learning System, such as during the Composite Training Process of Machine Learning System 290. In other words, the Composite Training Engine 270 trains the Machine Learning Engine”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the methods of classification of tabular data of Sun + Song with the systems and methods for composite training of Quinton in order to provide a method for training a machine learning model with composite data. The method presented in Quinton is beneficial for Sun + Song in that it allows for a machine learning model to be trained on composite data and make predictions about said data (Quinton [0070]: “The System 220 may include a Composite Training Engine 270 for generating Composite Data 272 (e.g. the Composite Training Data having been interpreted or converted back into a conventional Training Data format) for use during a Composite Training Process of a Machine Learning System, such as during the Composite Training Process of Machine Learning System 290. In other words, the Composite Training Engine 270 trains the Machine Learning Engine”)
Regarding Claim 4:
Sun does not distinctly disclose
The method of claim 1, wherein the first prediction includes a prediction for a value in the first data subset and training the ML model includes updating at least one parameter of the model based on a difference between the first prediction and the value of the first data subset.
However Quinton teaches
The method of claim 1, wherein the first prediction includes a prediction for a value in the first data subset (Quinton [0063]: “Embodiments for generating a Desire Response Map may include visiting each Composite Fragment in the Composite Image; or, visiting a subset of Composite Fragments in the Composite Image, and assessing each visited Composite Fragment to determine a label for a corresponding location in the Desired Response Map.”; (EN): determining a label is analogous to making a prediction for a value)
and training the ML model includes updating at least one parameter of the model based on a difference between the first prediction and the value of the first data subset (Quinton [0005]: “In some cases, the Training Process proceeds iteratively with the Parameters being updated and the Cost Function evaluated until the training Cost (e.g. a measurement of deviation of one or more a given Predictions from one or more Labels; the Cost is calculated by the Cost Function) goal is achieved, the maximum number of allowed iterations have completed, or some other condition or constraint is met.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the methods of classification of tabular data of Sun with the systems and methods for composite training of Quinton in order to provide a method for training a machine learning model with composite data. The method presented in Quinton is beneficial for Sun in that it allows for a machine learning model to be trained on composite data and make predictions about said data (Quinton [0070]: “The System 220 may include a Composite Training Engine 270 for generating Composite Data 272 (e.g. the Composite Training Data having been interpreted or converted back into a conventional Training Data format) for use during a Composite Training Process of a Machine Learning System, such as during the Composite Training Process of Machine Learning System 290. In other words, the Composite Training Engine 270 trains the Machine Learning Engine”)
Regarding Claim 5:
Sun does not distinctly disclose
The method of claim 1, wherein training the ML model based on at least one of the first prediction or the second prediction includes updating at least one parameter of the model based on a difference between the first prediction and the second prediction.
However, Quinton teaches
The method of claim 1, wherein training the ML model based on at least one of the first prediction or the second prediction includes updating at least one parameter of the model based on a difference between the first prediction and the second prediction (Quinton [0005]: “In some cases, the Training Process proceeds iteratively with the Parameters being updated and the Cost Function evaluated until the training Cost (e.g. a measurement of deviation of one or more a given Predictions from one or more Labels; the Cost is calculated by the Cost Function) goal is achieved, the maximum number of allowed iterations have completed, or some other condition or constraint is met.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the methods of classification of tabular data of Sun with the systems and methods for composite training of Quinton in order to provide a method for training a machine learning model with composite data. The method presented in Quinton is beneficial for Sun in that it allows for a machine learning model to be trained on composite data and make predictions about said data (Quinton [0070]: “The System 220 may include a Composite Training Engine 270 for generating Composite Data 272 (e.g. the Composite Training Data having been interpreted or converted back into a conventional Training Data format) for use during a Composite Training Process of a Machine Learning System, such as during the Composite Training Process of Machine Learning System 290. In other words, the Composite Training Engine 270 trains the Machine Learning Engine”)
Regarding Claim 7:
Sun does not distinctly disclose
The method of claim 1, wherein training the ML model based on at least one of the first prediction or the second prediction further includes updating at least one parameter of the model based on a comparison of the first composite image and the second composite image.
However, Song teaches
The method of claim 1, wherein training the ML model based on at least one of the first prediction or the second prediction further includes updating at least one parameter of the model based on a comparison of the first composite image and the second composite image (Song [0250]: “In some cases, the training component determines the adapter loss by comparing the training composite image and the training image (e.g., the ground-truth training image). According to some aspects, the training component updates the adapter network parameters of the adapter network based on the adapter loss.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the methods of classification of tabular data of Sun with the systems and methods for image compositing of Song in order to provide a method for image compositing based on descriptive embeddings. The method presented in Song is beneficial for Sun in that it allows for a machine learning model to create composite images and to learn from parameters and make predictions (Song [0097]: “According to some aspects, machine learning model 515 comprises machine learning parameters stored in memory unit 510. Machine learning parameters are variables that provide a behavior and characteristics of a machine learning model. Machine learning parameters can be learned or estimated from training data and are used to make predictions or perform tasks based on learned patterns and relationships in the data.”)
Regarding Claim 8:
Sun does not distinctly disclose
One or more non-transitory computer-readable media storing instructions that, in response to being executed by one or more processors, cause a system to perform the method of claim 1.
However, Song teaches
One or more non-transitory computer-readable media storing instructions that, in response to being executed by one or more processors, cause a system to perform the method of claim 1 (Song [0008]: “An apparatus and system for image generation are described. One or more aspects of the apparatus and system include one or more processors; one or more memory components coupled with the one or more processor”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the methods of classification of tabular data of Sun with the systems and methods for image compositing of Song in order to provide a method for image compositing based on descriptive embeddings. The method presented in Song is beneficial for Sun in that it allows for a machine learning model to create composite images and to learn from parameters and make predictions (Song [0097]: “According to some aspects, machine learning model 515 comprises machine learning parameters stored in memory unit 510. Machine learning parameters are variables that provide a behavior and characteristics of a machine learning model. Machine learning parameters can be learned or estimated from training data and are used to make predictions or perform tasks based on learned patterns and relationships in the data.”)
Regarding Claim 9:
Due to language similar to that of Claim 1, Claim 9 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 12:
Due to language similar to that of Claim 4, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 13:
Due to language similar to that of Claims 1 and 5, Claim 13 is rejected for the same reasons as presented above in the rejection of Claims 1 and 5.
Regarding Claim 15:
Due to language similar to that of Claims 1, 8, and 9, Claim 15 is rejected for the same reasons as presented above in the rejection of Claims 1, 8, and 9.
Regarding Claim 18:
Due to language similar to that of Claims 4 and 12, Claim 18 is rejected for the same reasons as presented above in the rejection of Claims 4 and 12.
Regarding Claim 19:
Due to language similar to that of Claims 1, 5, and 13, Claim 19 is rejected for the same reasons as presented above in the rejection of Claims 1, 5, and 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Zhu, Y., Brettin, T., Xia, F. et al. Converting tabular data into images for deep learning with convolutional neural networks. Sci Rep 11, 11325 (2021). https://doi.org/10.1038/s41598-021-90923-y – a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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