CTFR 17/850,512 CTFR 86506 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to communications: Amendment filed on 1/16/2026. Claims 1-25 are pending. Claims 1, 9, 16, and 21 are independent. The previous rejection of claims 1-25 under 35 USC § 103 have been withdrawn in view of the amendment. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: first means for training, second means for training, and means for implementing in claim 16-20. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-4, 7, 8, 16, 18-21, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (“ Few-Shot Text Style Transfer via Deep Feature Similarity ”) in view of Luo et al. (“ SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text ”) and Wang et al. (“ Generalizing from a Few Examples: A Survey on Few-shot Learning ”) as made of reference in IDS dated 10/23/2025 . In regards to claim 1, Zhu et al. discloses an apparatus to generate digitized handwriting with user style adaptations, the apparatus comprising: at least one memory ( Zhu et al. pg6932 Section I para2 ); machine readable instructions ( Zhu et al. pg6932 Section I para2 ); and at least one processor circuit to at least one of instantiate or execute the machine readable instructions to: train a machine learning model to generate a first digitized handwriting sequence based on a stored handwriting sample, to train the machine learning model, the one or more processor circuit is to at least one instantiate or execute the machine readable instructions to: cause a parameterization of a first portion of the machine learning model ( Zhu et al. pg6935 section III.A para5 , train model to get f.sub.s); and cause a reparameterization of a second portion of the machine learning model ( Zhu et al. pg6935 section III.A para2 , the content encoder aims to extract the feature of CT which we specified to generate). Zhu et al. does not explicitly disclose re-train the trained machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample; and facilitate at least one of instantiation, deployment, or execution of the re- trained machine learning model. However Luo et al. discloses re-train the trained machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample ( Luo et al. Algorithm 1 pg5 section III.A.4 , parameterize the selected handwriting styles by jointly training the generator and the style bank); and facilitate at least one of instantiation, deployment, or execution of the re- trained machine learning model ( Luo et al. pg11 section IV.J para4 , machine learning model initialized for volunteers to test) It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the text style transfer method of Zhu et al. with the Handwriting style synthesis of Luo in order to efficiently synthesize new handwriting styles from a printed style image ( Luo pg2 section1 para6 ). Zhu et al. does not explicitly disclose train a machine learning model on a sample at a first time; Re-train a model on a second sample at a second time after the first. However Wang et al. substantially discloses train a machine learning model on a sample at a first time ( Wang et al. pg21 section5.1.1 , a CNN pre-trained on the ImageNet for image classification is tuned using a large data set for foreground segmentation); Re-train a model on a second sample at a second time after the first ( Wang et al. pg21 section5.1.1 para1 , then further finetuned using a single shot of segmented object for object segmentation). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined text style transfer method of Zhu et al. with the refinement method of Wang et al. in order to adapt a machine learning model to a specific task in a few iterations ( Wang et al. pg21 section5.1 para1 ). In regards to claim 2, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 1, wherein the reparameterization is a first reparameterization, wherein, to re-train the machine learning model, one or more of the at least one processor circuit is to at least one of instantiate or execute the machine readable instructions to cause a second reparameterization of the second portion of the machine learning model utilizing gradient descent ( Zhu et al. pg6933 section II.A para3 , a texture model based on multi-scale oriented filter responses and used gradient descent to improve synthesized results). In regards to claim 3, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 1, wherein, to re-train the machine learning model, one or more of the at least one processor circuit is to at least one of instantiate or execute the machine readable instructions to: adjust the second portion of the machine learning model ( Zhu et al. pg6935 section III.A para2 , adjusts portion of model for specific content); and maintain the first portion of the machine learning model ( Zhu et al. pg6935 section III.A para6 , the extracted style features f s of IR is then mixed with the content feature f c of CT by concatenating directly). In regards to claim 4, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 1, wherein the second portion of the machine learning model determines the parameterization and a third portion of the machine learning model determines the reparameterization, wherein the first portion of the machine learning model is a mixture density network layer, the second portion of the machine learning model utilizes at least one long short-term memory, and the third portion of the machine learning model includes a reparameterization layer (Zhu et al. pg6937 section V.A para1 , The content encoder and individual style feature extracting DNNs have the same structure including three convolution blocks and six ResNet blocks). In regards to claim 7, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 1, wherein the user handwriting sample includes twenty or fewer words ( Luo et al. pg8 section IV.F para1 , we find that our method can significantly imitate a handwriting style by learning from approximately 20 samples). It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the text style transfer method of Zhu et al. with the Handwriting style synthesis of Luo in order to efficiently synthesize new handwriting styles from a printed style image ( Luo pg2 section1 para6 ). In regards to claim 8, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 1, wherein one or more of the at least one processor circuit is to at least one of instantiate or execute the machine readable instructions to execute the machine learning model to generate a third digitized handwriting sequence corresponding to an input in response to training and re-training the machine learning model ( Zhu et al. pg6942 section V.F para3 , To robust train the style classifier, we set the style category as 20 and repeat this experiment with different training and test sets five times). In regards to claim 16, Zhu et al. discloses an apparatus to generate digitized handwriting with user style adaptations, the apparatus comprising: first means for training a machine learning model to generate a first digitized handwriting sequence based on a stored handwriting sample ( Zhu et al. pg6935 section III.A para5 , train model to get f.sub.s). Zhu et al. does not explicitly disclose second means for training the machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample, the second means for training to maintain a first portion of the machine learning model configured by the first means for training and modify a second portion of the machine learning model using model-agnostic meta-learning; and means for implementing the machine learning model. However Luo et al. discloses second means for training the machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample, the second means for training to maintain a first portion of the machine learning model configured by the first means for training and modify a second portion of the machine learning model using model-agnostic meta-learning ( Luo et al. Algorithm 1 pg5 section III.A.4 , parameterize the selected handwriting styles by jointly training the generator and the style bank); and means for implementing the machine learning model ( Luo et al. pg11 section IV.J para4 , machine learning model initialized for volunteers to test). It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the text style transfer method of Zhu et al. with the Handwriting style synthesis of Luo in order to efficiently synthesize new handwriting styles from a printed style image ( Luo pg2 section1 para6 ). Zhu et al. does not explicitly disclose train a machine learning model on a sample at a first time; Re-train a model on a second sample at a second time after the first. However Wang et al. substantially discloses train a machine learning model on a sample at a first time ( Wang et al. pg21 section5.1.1 , a CNN pre-trained on the ImageNet for image classification is tuned using a large data set for foreground segmentation); Re-train a model on a second sample at a second time after the first ( Wang et al. pg21 section5.1.1 para1 , then further finetuned using a single shot of segmented object for object segmentation). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined text style transfer method of Zhu et al. with the refinement method of Wang et al. in order to adapt a machine learning model to a specific task in a few iterations ( Wang et al. pg21 section5.1 para1 ). In regards to claim 18, Zhu et al. as modified by Luo et al. and Wang et al. the apparatus of claim 16, wherein the first means for training causes a third portion of the machine learning model to backpropagate modifications to the second portion of the machine learning model and causes the second portion of the machine learning model to backpropagate modifications to the first portion of the machine learning model ( Zhu et al. pg6936 section III.C para3 , In backpropagation, the error only passes back through the individual style feature extracting side but not the side for individual content feature extraction). In regards to claim 19, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 18, wherein the first portion of the machine learning model utilizes at least one long short-term memory, the second portion of the machine learning model utilizes a mixture density network, and the third portion of the machine learning model utilizes a reparameterization layer (Zhu et al. pg6937 section V.A para1 , The content encoder and individual style feature extracting DNNs have the same structure including three convolution blocks and six ResNet blocks). In regards to claim 20, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 16, wherein the means for implementing the machine learning model is to execute the machine learning model to generate a third digitized handwriting sequence corresponding to an input in response to training and re-training the machine learning model ( Zhu et al. pg6942 section V.F para3 , To robust train the style classifier, we set the style category as 20 and repeat this experiment with different training and test sets five times). In regards to claim 21, Zhu et al. substantially discloses a method to generate digitized handwriting with user style adaptations the method comprising: training a machine learning model to generate a first digitized handwriting sequence based on a reference handwriting sample in a dataset at ( Zhu et al. pg6935 section III.A para5 , train model to get f.sub.s); fixing a first portion of the machine learning model in response to the training ( Zhu et al. pg6935 section III.A para2 , adjusts portion of model for specific content); and modifying a second portion of the machine learning model utilizing gradient descent ( Zhu et al. pg6933 section II.A para3 , a texture model based on multi-scale oriented filter responses and used gradient descent to improve synthesized results). Zhu et al. does not explicitly disclose re-training the machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample, the re-training including: facilitating at least one of instantiation, deployment, or execution of the machine learning model. Luo et al. discloses re-training the machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample ( Luo et al. Algorithm 1 pg5 section III.A.4 , parameterize the selected handwriting styles by jointly training the generator and the style bank), the re-training including: facilitating at least one of instantiation, deployment, or execution of the machine learning model ( Luo et al. pg11 section IV.J para4 , machine learning model initialized for volunteers to test). It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the text style transfer method of Zhu et al. with the handwriting style synthesis of Luo in order to efficiently synthesize new handwriting styles from a printed style image ( Luo pg2 section1 para6 ). Zhu et al. does not explicitly disclose train a machine learning model on a sample at a first time; Re-train a model on a second sample at a second time after the first. However Wang et al. substantially discloses train a machine learning model on a sample at a first time ( Wang et al. pg21 section5.1.1 , a CNN pre-trained on the ImageNet for image classification is tuned using a large data set for foreground segmentation); Re-train a model on a second sample at a second time after the first ( Wang et al. pg21 section5.1.1 para1 , then further finetuned using a single shot of segmented object for object segmentation). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined text style transfer method of Zhu et al. with the refinement method of Wang et al. in order to adapt a machine learning model to a specific task in a few iterations ( Wang et al. pg21 section5.1 para1 ). In regards to claim 23, Zhu et al. as modified by Luo et al. and Wang et al. discloses the method of claim 21, wherein the training includes: performing a parameterization of the first portion of the machine learning model ( Zhu et al. pg6935 section III.A para5 , train model to get f.sub.s); and performing a reparameterization of the second portion of the machine learning model ( Zhu et al. pg6935 section III.A para2 , the content encoder aims to extract the feature of CT which we specified to generate) . 07-21-aia AIA Claim (s) 5-6, 9-15, 17, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. in view of Luo et al., Wang et al. and Graves (“ Stochastic Backpropagation through Mixture Density Distributions ”) as made of reference in IDS dated 10/19/2023 . In regards to claim 5, Zhu et al. as modified by Luo et al. and Wang et al. discloses the apparatus of claim 1. Zhu et al. does not explicitly disclose wherein the reparameterization is based on an error computed using a loss function, the loss function including a mixture density network loss and a mean squared error between the first digitized handwriting sequence and the stored handwriting sample or between the second digitized handwriting sequence and the user handwriting sample. However Graves et al. discloses wherein the reparameterization is based on an error computed using a loss function, the loss function including a mixture density network loss and a mean squared error between the first digitized handwriting sequence and the stored handwriting sample or between the second digitized handwriting sequence and the user handwriting sample (Graves et al. pg2 section General Results para2 , Let h be the expectation over f of an arbitrary differentiable function g of a loss function and denote by Q(u) the sample from f returned by the quantile transform applied to U) It would have been obvious to one ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the backpropagation method of Graves in order to train variational autoencoders ( Graves pg1 section Abstract para1 ) . In regards to claim 6, Zhu et al. as modified by Luo et al., Wang et al., and Graves et al. discloses the apparatus of claim 5, wherein the loss function includes a hyperparameter that applies a first weight to the mixture density network loss and a second weight to the mean squared error, the first weight greater than the second weight ( Graves et al. pg3 section Application to Mixture Density Weights para2 , We seek the derivatives of h with respect to the mixture weights, after the weights have been normalized). It would have been obvious to one ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the backpropagation method of Graves in order to train variational autoencoders ( Graves pg1 section Abstract para1 ). In regards to claim 9, Zhu et al. discloses at least one non-transitory machine readable storage medium comprising instructions that, when executed, cause processor circuitry to at least: train a machine learning model to generate a first digitized handwriting sequence based on a handwriting sample in a handwriting dataset at a first time, to train the machine learning model, the instructions, when executed, cause the processor circuitry to: compute a mean squared error between at least the first digitized handwriting sequence and the handwriting sample ( Zhu et al. pg6936 section III.C para1 , The GL loss defined in Eq. (6) uses both a mean square error (MSE) and a L1 regularizer to minimize the difference between generated character and its ground truth); and modify the machine learning model based on the overall error ( Zhu et al. pg6936 section III.C para3 , In backpropagation, the error only passes back through the individual style feature extracting side but not the side for individual content feature extraction). Zhu et al. does not explicitly disclose re-train the machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample at a second time after the first; and facilitate at least one of instantiation, deployment, or execution of the machine learning model. However Luo et al. discloses re-train the machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample ( Luo et al. Algorithm 1 pg5 section III.A.4 , parameterize the selected handwriting styles by jointly training the generator and the style bank); and facilitate at least one of instantiation, deployment, or execution of the machine learning model ( Luo et al. pg11 section IV.J para4 , machine learning model initialized for volunteers to test). It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the text style transfer method of Zhu et al. with the Handwriting style synthesis of Luo in order to efficiently synthesize new handwriting styles from a printed style image ( Luo pg2 section1 para6 ). Zhu et al. does not explicitly disclose train a machine learning model on a sample at a first time; Re-train a model on a second sample at a second time after the first. However Wang et al. substantially discloses train a machine learning model on a sample at a first time ( Wang et al. pg21 section5.1.1 , a CNN pre-trained on the ImageNet for image classification is tuned using a large data set for foreground segmentation); Re-train a model on a second sample at a second time after the first ( Wang et al. pg21 section5.1.1 para1 , then further finetuned using a single shot of segmented object for object segmentation). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined text style transfer method of Zhu et al. with the refinement method of Wang et al. in order to adapt a machine learning model to a specific task in a few iterations ( Wang et al. pg21 section5.1 para1 ). Zhu et al. does not explicitly disclose compute a mixture density network loss between the first digitized handwriting sequence and the handwriting sample; determine an overall error of the first digitized handwriting sequence with respect to the handwriting sample based on the mixture density network loss and the mean squared error. However Graves et al. discloses compute a mixture density network loss between the first digitized handwriting sequence and the handwriting sample ( Graves et al. pg2 section General Results para2 , Let h be the expectation over f of an arbitrary differentiable function g of a loss function and denote by Q(u) the sample from f returned by the quantile transform applied to U); determine an overall error of the first digitized handwriting sequence with respect to the handwriting sample based on the mixture density network loss and the mean squared error ( Graves et al. pg2 section General Results para2 , Let h be the expectation over f of an arbitrary differentiable function g of a loss function and denote by Q(u) the sample from f returned by the quantile transform applied to U). It would have been obvious to one ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the backpropagation method of Graves in order to train variational autoencoders ( Graves pg1 section Abstract para1 ). In regards to claim 10, Zhu et al. as modified Luo et al., Wang et al., and Graves et al. discloses the at least one non-transitory machine readable storage medium of claim 9, wherein the instructions, when executed, cause the processor circuitry to: apply a first weight to the mixture density network loss ( Graves pg3 section Application to Mixture Density Weights, para2 , We seek the derivatives of h with respect to the mixture weights π j , after the weights have been normalized.); apply a second weight to the mean squared error ( Zhu et al. pg6936 section III.C para1 , the GL loss defined in Eq. (6) uses both a mean square error (MSE) and a L 1 regularizer to minimize the difference between generated character and its ground truth); and compute the overall error based on a sum of the mixture density network loss with the first weight and the mean squared error with the second weight ( Graves et al. pg2 section General Results para2 , Let h be the expectation over f of an arbitrary differentiable function g of a loss function and denote by Q(u) the sample from f returned by the quantile transform applied to U). It would have been obvious to one ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the backpropagation method of Graves in order to train variational autoencoders ( Graves pg1 section Abstract para1 ). In regards to claim 11, Zhu et al. as modified by Luo et al., Wang et al., and Graves et al. discloses the at least one non-transitory machine readable storage medium of claim 9, wherein to modify the machine learning model, the instructions, when executed, cause the processor circuitry to: cause a parameterization of a first portion of the machine learning model ( Zhu et al. pg6935 section III.A para5 , train model to get f.sub.s); and cause a reparameterization of a second portion of the machine learning model ( Zhu et al. pg6935 section III.A para2 , the content encoder aims to extract the feature of CT which we specified to generate). In regards to claim 12, Zhu et al. as modified by Luo et al., Wang et al. and Graves et al. discloses the at least one non-transitory machine readable storage medium of claim 11, wherein the reparameterization is a first reparameterization, wherein to re-train the machine learning model, the instructions, when executed, cause the processor circuitry to: cause the first portion of the machine learning model to remain fixed ( Zhu et al. pg6935 section III.A para2 , adjusts portion of model for specific content); and cause a second reparameterization of the second portion of the machine learning model ( Zhu et al. pg6935 section III.A para6 , the extracted style features f s of IR is then mixed with the content feature f c of CT by concatenating directly). In regards to claim 13, Zhu et al. as modified by Luo et al., Wang et al., and Graves et al. discloses the at least one non-transitory machine readable storage medium of claim 11, wherein the instructions, when executed, cause the processor circuitry to re-train the machine learning model using few-shot learning ( Zhu et al. pg6934 section III para1 , we focused on generating the uppercase character from ‘A’ to ‘Z’ using only a few observed characters). In regards to claim 14, Zhu et al. as modified by Luo et al., Wang et al., and Graves et al. discloses the at least one non-transitory machine readable storage medium of claim 9, wherein the user handwriting sample includes twenty or fewer words ( Luo et al. pg8 section IV.F para1 , we find that our method can significantly imitate a handwriting style by learning from approximately 20 samples). It would have been obvious to one of ordinary skill in the art before the filing date of the application to have combined the text style transfer method of Zhu et al. with the Handwriting style synthesis of Luo in order to efficiently synthesize new handwriting styles from a printed style image ( Luo pg2 section1 para6 ). In regards to claim 15, Zhu et al. as modified by Luo et al., Wang et al., and Graves et al. discloses the at least one non-transitory machine readable storage medium of claim 9, wherein the instructions, when executed, cause the processor circuitry to execute the machine learning model to generate a third digitized handwriting sequence corresponding to an input in response to training and re-training the machine learning model ( Zhu et al. pg6942 section V.F para3 , To robust train the style classifier, we set the style category as 20 and repeat this experiment with different training and test sets five times). In regards to claims 17, Zhu et al. as modified by Luo et al., Wang et a. discloses the apparatus of claim 16, wherein the first means for training is to: compute a mean squared error between the first digitized handwriting sequence and the stored handwriting sample ( Zhu et al. pg6936 section III.C para1 , The GL loss defined in Eq. (6) uses both a mean square error (MSE) and a L1 regularizer to minimize the difference between generated character and its ground truth); and modify the first portion of the machine learning model and the second portion of the machine learning model based on the error ( Zhu et al. pg6936 section III.C para3 , In backpropagation, the error only passes back through the individual style feature extracting side but not the side for individual content feature extraction). Zhu et al. does not disclose compute a mixture density network loss between the first digitized handwriting sequence and the stored handwriting sample; determine an error of the first digitized handwriting sequence with respect to the stored handwriting sample based on the mixture density network loss and the mean squared error. Graves et al. discloses compute a mixture density network loss between the first digitized handwriting sequence and the stored handwriting sample ( Graves et al. pg2 section General Results para2 , Let h be the expectation over f of an arbitrary differentiable function g of a loss function and denote by Q(u) the sample from f returned by the quantile transform applied to U); determine an error of the first digitized handwriting sequence with respect to the stored handwriting sample based on the mixture density network loss and the mean squared error ( Graves pg3 section Application to Mixture Density Weights, para2 , We seek the derivatives of h with respect to the mixture weights π j , after the weights have been normalized.). It would have been obvious to one ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the backpropagation method of Graves in order to train variational autoencoders ( Graves pg1 section Abstract para1 ). In regards to claim 22, Zhu et al. as modified by Luo et al., and Wang et al. discloses the method of claim 21, wherein training the machine learning model further includes: computing a mean squared error between the first digitized handwriting sequence and the reference handwriting sample( Zhu et al. pg6936 section III.C para1 , The GL loss defined in Eq. (6) uses both a mean square error (MSE) and a L1 regularizer to minimize the difference between generated character and its ground truth). Zhu et al. does explicitly disclose computing a mixture density network loss between the first digitized handwriting sequence and the reference handwriting sample; determining an error of the first digitized handwriting sequence with respect to the reference handwriting sample based on a weighted sum of the mixture density network loss and the mean squared error. However Graves et al. discloses computing a mixture density network loss between the first digitized handwriting sequence and the reference handwriting sample ( Graves pg3 section Application to Mixture Density Weights, para2 , We seek the derivatives of h with respect to the mixture weights π j , after the weights have been normalized.); determining an error of the first digitized handwriting sequence with respect to the reference handwriting sample based on a weighted sum of the mixture density network loss and the mean squared error ( Graves et al. pg2 section General Results para2 , Let h be the expectation over f of an arbitrary differentiable function g of a loss function and denote by Q(u) the sample from f returned by the quantile transform applied to U). It would have been obvious to one ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the backpropagation method of Graves in order to train variational autoencoders ( Graves pg1 section Abstract para1 ) . 07-21-aia AIA Claim (s) 24-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. in view of Luo et al., Wang et al., and Cottle ( US2022/0300702 ) . In regards to claim 24, Zhu et al. as modified by Luo et al., and Wang et al. discloses the method of claim 21. Zhu et al. does not explicitly disclose wherein the first portion of the machine learning model is to generate digital ink coordinates in the first digitized handwriting sequence and the second digitized handwriting sequence. However Cottle discloses wherein the first portion of the machine learning model is to generate digital ink coordinates in the first digitized handwriting sequence and the second digitized handwriting sequence ( Cottle para[0115] , the digital inking application converts the output from the text renderer to a series of coordinates, which the digital ink application saves to memory (rather than rendering the lines on a screen). When the text renderer is complete, the digital ink application converts the coordinates into ink points and ink strokes). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the conversion method of Cottle in order to efficiently create and edit handwritten digital text ( Cottle para[0004] ). In regards to claim 25, Zhu et al. as modified by Luo et al., Wang et al. and Cottle discloses the method of claim 24, wherein the second portion of the machine learning model is to determine a distribution of the digital ink coordinates for the first digitized handwriting sequence and the second digitized handwriting sequence ( Cottle para[0114] , the digital inking can generate the digital ink by generating or rendering an image of the text and then tracing characters within the image to produce ink points that are oriented in the same manner as characters of the text). It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the text style transfer method of Zhu et al. with the conversion method of Cottle in order to efficiently create and edit handwritten digital text ( Cottle para[0004] ). Response to Arguments Applicant’s arguments with respect to claims 1-25 have been considered but are moot because the arguments do not apply the current rejection. Conclusion 07-40-01 Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on 10/23/2025 prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 609.04(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /N.H/Examiner, Art Unit 2141 /TAN H TRAN/Primary Examiner, Art Unit 2141
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