DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see p.7-12, filed 05/08/2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive. Applicant argues that it is not correct to conclude that each independent claim as a whole is directed to a judicial exception because whatever the Examiner has identified as an abstract idea clearly is integrated into a practical application. Furthermore, the Applicant identifies improvements that the specification describes, and how the claims reflect those improvements. Lastly, Applicant argues that the specification portions and claim language that Applicant cited track closely to the Board’s precedential reasoning in Desjardins with regards to improvements in AI technology. Examiner respectfully disagrees because the limitations “b) performing optical character recognition (OCR) to extract the identified text ;c) converting the identified text to text image formatted characters; d) converting the text image formatted characters to pseudo images; e) providing said pseudo images to a diffusion model to train said diffusion model” are only the extraction of text using OCR, converting the text to text image formatted characters, converting the text image formatted characters to pseudo images, and providing those images to a diffusion model for training” are considered to be insignificant extra-solution activities. Therefore, the judicial exception is not integrated into a practical application because the claim only recites these insignificant extra-solution activities wherein other additional recited elements in certain other claims are just only generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it is a field-of-use limitation that does not impose any meaningful limits on practicing the abstract idea due to the claim limitation only reciting simply “providing” the images to the diffusion model for training as opposed to properly imposing meaningful limits on practicing the idea by providing further details on the training of the model such as how the model learns to permute and rearrange the character features for different regions as recited in Paragraph 86 of the Specification. Furthermore, Examiner respectfully disagrees that the claims recite additional elements that amount to significantly more than the judicial exception such as through improvements to the functioning of a computer, technology, or technical field. The additional elements/steps amount to no more than insignificant extra-solution activities and are therefore not sufficient to amount to significantly more than the judicial exception. Therefore, the claim as a whole, recites an abstract idea.
Applicant’s arguments, see p.12-16, filed 05/08/2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 103 have been fully considered but are not persuasive. Firstly, Examiner appreciates and acknowledges the Applicant pointing out the typographical errors for the rejections of claims 6, 7, 8, 9, 16, 17, 18, and 19 for the total number of references relied upon not being recited which has been corrected for this action. Furthermore, Applicant argues that the Examiner does not specify what training Yoffe allegedly improves, with respect to any reasonable combination of Yoffe and Vamshi. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Yoffe, Para. 29, teaches the motivation of combination to be giving rise to synthetic images improving training of the deep learning model. Therefore, a person of ordinary skill in the art would be motivated to combine the teachings of Vamshi with the teachings of Yoffe to convert the text image formatted characters to pseudo images so that the training of the deep learning model could be improved by the pseudo images.
Additionally, Applicant argues that the Examine uses a similar motivation to apply Kumari to the combination of references of Vamshi in view of Yoffe, but the Examiner does not specify what improvement Kumari allegedly provides that would be relevant to any reasonable combination. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Kumari, Para. 51, teaches the motivation of combination to be to improve performance of the diffusion model wherein the selected parameters of the diffusion model are updated based on the training set. Therefore, a person of ordinary skill in the art would be motivated to combine the teachings of Vamshi and Yoffe wherein text is identified in input images, OCR is used to extract the text, the text is converted to image formatted characters, and the image formatted characters are converted to pseudo images with the teachings of Kumari to provide those pseudo images to the diffusion model to train the diffusion model in order to improve the performance of the diffusion model by updating those selected parameters.
Furthermore, Applicant argues that there is no basis for imputing the repetition in Kansy to elements a) to e) in claims 1 and 11, no matter what the ”unsatisfactory” condition of the diffusion model outputs in these claims are. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Kansy, Para. 72, teaches the motivation of combination to be to lower the reconstruction loss to below a threshold. Therefore, a person of ordinary skill in the art would be motivated to combine the teachings of Vamshi, Yoffe, and Kumari wherein the steps included comprise text is identified in input images, OCR is used to extract the text, the text is converted to image formatted characters, the image formatted characters are converted to pseudo images, and the pseudo images are provided to a diffusion model for training with the teachings of Kansy to repeat steps in response to the outputs of the diffusion model being above a threshold of loss in order to improve the loss of the diffusion model thereby improving the performance of the model.
Additionally, regarding Claims 2, 4, 12, and 14, Applicant argues that it would have not been obvious to combine Zhu with the combination of references of Vamshi in view of Yoffe, Kumari, and Kansy due to the claims having nothing to do with improving optical character recognition. Examiner respectfully disagrees due to claim 1 explicitly reciting the performance of optical character recognition to extract identified text, and it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi, Yoffe, Kumari, and Kansy wherein the model is a diffusion model and the images are form images by including the text image conversion on pseudo images, converting the pseudo images to text in bounding boxes, identifying text in bounding boxes, applying font characteristics to the text, and generating further images taught by Zhu. One of ordinary skill in the art would be motivated to combine the references since it increases the accuracy of the OCDR (Zhu, Para. 64, teaches the motivation of combination to be to increase accuracy for OCDR).
Furthermore, regarding Claims 3 and 13, Applicant argues that it would not have been obvious to add Zhang to the combination of references due to Zhang using pre-trained models and data obfuscation. Examiner respectfully disagrees due to Zhang only being directed to teach the application of font style characteristics to text prior to the generation of form images. It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi, Yoffe, Kumari, Kansy, and Zhu by including the application of font type, size, and style characteristics are added before the form images are generated taught by Zhang. One of ordinary skill in the art would be motivated to combine the references since it reduces processing requirements (Zhang, Para. 9, teaches the motivation of combination to be to reduce processing requirements and replace sensitive data).
Additionally, regarding claims 5 and 15, Applicant argues that nothing in Wen remotely resembles the formula in claims 5 and 15, and that the formula is missing from Wen. Examiner respectfully disagrees, and for further clarification, Wen, Paras. 18-33, teaches performing maximum normalization of the cumulative calculation of the histogram with the max cumulative count C(255) and multiplying this maximum normalization by 255 to obtain an enhancement gray scale table out with calculation and looking up the table to obtain a new output gray scale value, i.e., grayscale value of pixels is output from a lookup table by multiplying a numerical value of an entry being the cumulative calculation of the histogram divided by its max number of entries being the maximum cumulative value by 255. Accordingly, this action is made FINAL.
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 without significantly more, and the claimed invention is directed to non-statutory subject matter as follows. The claims recite identifying text in input form images, performing OCR to extract the identified text, converting the text to text image formatted characters, converting the text image formatted characters to pseudo images, providing the pseudo images to a diffusion model for training, and repeating the steps if the diffusion model is not satisfied.
Step 1:
With regard to Step 1, the instant claims are directed to a method, which is among the statutory categories of invention.
Step 2A – Prong 1:
With regard to Step 2A – Prong 1, for example in Claim 1, the limitations of "a) responsive to input form images, identifying text in the input form images; and f) responsive to outputs of said diffusion model being unsatisfactory, repeating a)-e)", as drafted only involves mental processes or mathematical calculations, such as identifying text on a form image and repeating steps so the model is satisfied. That is, nothing in the above-described claim elements preclude the steps from practically being performed in the mind or on a piece of paper. If a claim limitation, under its broadest reasonably interpretation covers performance of the limitation in the mind or through mathematical calculations, but for the recitation of a generic apparatus components, such as a processor, computer program, or machine-readable media, then it falls within the "mental processes", which include concepts performed in the human mind, including an observation, evaluation, judgement, opinion, or mathematical calculations groupings of the abstract idea. Accordingly, the claim recites an abstract idea.
Step 2A – Prong 2:
The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional elements in the claims do not apply, rely on, or use the judicial exception.
This judicial exception is not integrated into a practical application because the claim only recites the following additional steps "b) performing optical character recognition (OCR) to extract the identified text; c) converting the identified text to text image formatted characters; d) converting the text image formatted characters to pseudo images; e) providing said pseudo images to a diffusion model to train said diffusion model", i.e., insignificant extra-solution activity comprising routine and conventional image processing steps. The other additional recited elements in certain other claims are just a non-transitory computer-readable storage medium with a processor, which are generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it is a field-of-use limitation that does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim as a whole, recites an abstract idea.
Step 2B:
Because the claim fails under Step 2A, the claims are further evaluated under Step 2B. The claim herein does not include additional steps that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into practical application, the additional elements/steps amount to no more than insignificant extra-solution activities. Mere instructions to apply an exception using generic apparatus component, such as a processor, cannot provide an inventive concept. The claim is not patent eligible. It should be noted that a similar analysis may be performed with respect to independent Claims 6 and 11.
Further, with regard to dependent Claims 2-5, 7-10, and 12-20 viewed individually, these additional steps are under their broadest reasonable interpretation, cover performance of the limitation in the mind and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims’ limitations amount to significantly more than the abstract idea itself. For example, applying style characteristics including font type and font size before generating the image as recited in Claim 3 or defining the pseudo images as grayscale or RGB as recited in Claim 4 are only examples of routine and conventional image processing steps and do not amount to significantly more to consider as inventive steps. Accordingly, Claims 1-20 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Vamshi et al. (US 20240362939 A1) in view of Yoffe et al. (US 20230298338 A1), Kumari et al. (US 20240185588 A1), and Kansy et al. (US 20230377214 A1).
Regarding Claim 1, Vamshi teaches "A computer-implemented method comprising: a) responsive to input form images, identifying text in the input form images"; (Vamshi, Paras. 2 and 30, teaches the text detection/mining module is configured to receive the input in form of image data wherein the module uses text extraction techniques such as optical character recognition which depends on identifying text and its correctness, i.e., identify text in input form images in response to the input of form images);
"b) performing optical character recognition (OCR) to extract the identified text"; (Vamshi, Para. 30, teaches text extraction based on the input document format to include optical character recognition, i.e., perform OCR to extract identified text);
"c) converting the identified text to text image formatted characters"; (Vamshi, Para. 30, teaches the text detection/mining module determines textual information from the input image by converting the document image into the readable text image to determine text characters for each row of the document, i.e., convert identified text to text image formatted characters being the conversion into the readable text image to determine text characters).
However, Vamshi does not explicitly teach "d) converting the text image formatted characters to pseudo images; e) providing said pseudo images to a diffusion model to train said diffusion model; and f) responsive to outputs of said diffusion model being unsatisfactory, repeating a)-e)”.
In an analogous field of endeavor, Yoffe teaches "d) converting the text image formatted characters to pseudo images"; (Yoffe, Paras. 23-37, teaches the processing circuitry is configured to perform OCR on at least one image of the plurality of images and utilize data derivative of the performing the OCR to train the deep learning model and enable the user to modify the respective image thereby giving rise to one or more synthetic images wherein the synthetic images are utilized to train the deep learning model and wherein the modifying of the respective image includes overlaying or modifying image text, i.e., converting the text image formatted characters to pseudo images being the rise of synthetic images from the modification of the respective image).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi wherein the respective image is a converted identified text to text image formatted characters by including the conversion of text image formatted characters to pseudo images taught by Yoffe. One of ordinary skill in the art would be motivated to combine the references since it improves the training (Yoffe, Para. 29, teaches the motivation of combination to be to improve the training of the deep learning model).
However, the combination of references of Vamshi in view of Yoffe does not explicitly teach “e) providing said pseudo images to a diffusion model to train said diffusion model; and f) responsive to outputs of said diffusion model being unsatisfactory, repeating a)-e)”.
In an analogous field of endeavor, Kumari teaches "e) providing said pseudo images to a diffusion model to train said diffusion model"; (Kumari, Para. 3, teaches an image generation system including a diffusion model may receive an image of the new concept, generate a synthetic image, compare the received image to the synthetic image, and train the diffusion model by updating selected parameters based on the comparison, i.e., provide pseudo images to a diffusion model to train the diffusion model being the input of the synthetic images to train the diffusion model).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi and Yoffe by including the providing of pseudo images to a diffusion model for training taught by Kumari. One of ordinary skill in the art would be motivated to combine the references since it improves performance of the model (Kumari, Para. 51, teaches the motivation of combination to be to improve performance of the diffusion model).
However, the combination of references of Vamshi in view of Yoffe and Kumari does not explicitly teach “and f) responsive to outputs of said diffusion model being unsatisfactory, repeating a)-e)”.
In an analogous field of endeavor, Kansy teaches "and f) responsive to outputs of said diffusion model being unsatisfactory, repeating a)-e)"; (Kansy, FIG. 4 and Para. 72, teaches the training engine determining that the diffusion neural network should continue to be trained using the reconstruction loss until one or more conditions are met and wherein while training for reconstruction continues, training engine repeats steps, i.e., responsive to outputs of diffusion model being unsatisfactory being that conditions are not met and training continues so that steps are repeated).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi, Yoffe, and Kumari wherein the steps comprise identifying text in the input form images, performing OCR to extract the text, converting the text to text image formatted characters, converting the text image formatted characters to pseudo images, and providing the pseudo images to the diffusion model for training by including the repeating of steps if the output of the model is not satisfactory taught by Kansy. One of ordinary skill in the art would be motivated to combine the references since it lowers loss to below a threshold (Kansy, Para. 72, teaches the motivation of combination to be to lower the reconstruction loss to below a threshold).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 11 recites an apparatus with elements corresponding to the steps recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, and Kansy references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, and Kansy references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claims 2, 4, 6, 9, 12, 14, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Vamshi in view of Yoffe, Kumari, Kansy, and Zhu et al. (US 20240020863 A1).
Regarding Claim 2, the combination of references of Vamshi in view of Yoffe, Kumari, and Kansy does not explicitly teach “The method of claim 1, further comprising :g) responsive to outputs of said diffusion model being satisfactory: i. performing text image conversion on said pseudo images; ii. converting said pseudo images to text within bounding boxes; iii. identifying text in said bounding boxes; iv. applying font characteristics to said identified text; and v. generating further form images”.
In an analogous field of endeavor, Zhu teaches "The method of claim 1, further comprising: g) responsive to outputs of said diffusion model being satisfactory: i. performing text image conversion on said pseudo images"; (Zhu, Paras. 62 and 86, teaches a synthetic image with described variations randomly assigned is used to train one or more neural networks to further identify text from images and convert into a machine-readable form wherein the untrained neural network is trained until it achieves a desired accuracy, i.e.., when output of model is satisfactory being the network is trained at a desired accuracy the synthetic image is converted to a text image);
"ii. converting said pseudo images to text within bounding boxes"; (Zhu, FIG. 2 and Paras. 62-64, teaches a synthetic image with described variations randomly assigned is used to train one or more neural networks to further identify text from images and convert into a machine-readable form wherein a DNN is used to identify locations of textual information from one or more images and output one or more bounding polygons for each group of text, i.e., convert pseudo images to text within bounding boxes being the conversion of synthetic images to a machine readable text form and outputting bounding polygons for each group of text);
"iii. identifying text in said bounding boxes"; (Zhu, FIG. 2 and Paras. 62-64, teaches a DNN is used to identify locations of textual information from one or more images and output one or more bounding polygons for each group of text, i.e., text is identified in bounding boxes);
"iv. applying font characteristics to said identified text"; (Zhu, FIG. 2 and Paras. 62-64, teaches a synthetic image output varies from an inputted image by differing fonts wherein text identified in images include font and other textual features located in an image using an identification method, i.e., apply font characteristics to identified text by differing fonts);
"and v. generating further form images"; (Zhu, FIG. 3 and Para. 68, teaches using a real image or synthetic image as an original image wherein augmented images are output, i.e., generating further form images).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi, Yoffe, Kumari, and Kansy wherein the model is a diffusion model and the images are form images by including the text image conversion on pseudo images, converting the pseudo images to text in bounding boxes, identifying text in bounding boxes, applying font characteristics to the text, and generating further images taught by Zhu. One of ordinary skill in the art would be motivated to combine the references since it increases the accuracy of the OCDR (Zhu, Para. 64, teaches the motivation of combination to be to increase accuracy for OCDR).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 4, the combination of references Vamshi in view of Yoffe, Kumari, Kansy, and Zhu teaches "The method of claim 1, wherein the pseudo images are one of grayscale or RGB images"; (Zhu, Para. 61, teaches data interpreted form an image is altered to create a synthetic image output which is generated using a color model including an RGB color model, i.e., pseudo images are RGB images).
The proposed combination as well as the motivation for combining the Vamshi, Yoffe, Kumari, Kansy, and Zhu references presented in the rejection of Claim 2, applies to claim 4. Thus, the method recited in claim 4 is met by Vamshi in view of Yoffe, Kumari, Kansy, and Zhu.
Regarding Claim 6, the combination of references Vamshi in view of Yoffe, Kumari, Kansy, and Zhu teaches "The method of claim 1, further comprising: i. performing text image conversion on pseudo images output from the (Zhu, FIG. 3 and Paras. 62 and 86, teaches a synthetic image with described variations randomly assigned is used to train one or more neural networks to further identify text from images and convert into a machine-readable form wherein the untrained neural network is trained until it achieves a desired accuracy, i.e., the synthetic image output from a trained model is converted to a text image);
"the diffusion model"; (Kumari, Para. 3, teaches an image generation system including a diffusion model may receive an image of the new concept and generate a synthetic image, i.e., pseudo images output from a diffusion model).
"ii. converting said pseudo images to text within bounding boxes"; (Zhu, FIG. 2 and Paras. 62-64, teaches a synthetic image with described variations randomly assigned is used to train one or more neural networks to further identify text from images and convert into a machine-readable form wherein a DNN is used to identify locations of textual information from one or more images and output one or more bounding polygons for each group of text, i.e., convert pseudo images to text within bounding boxes being the conversion of synthetic images to a machine readable text form and outputting bounding polygons for each group of text);
"iii. identifying text in said bounding boxes"; (Zhu, FIG. 2 and Paras. 62-64, teaches a DNN is used to identify locations of textual information from one or more images and output one or more bounding polygons for each group of text, i.e., text is identified in bounding boxes);
"and iv. generating further form images"; (Zhu, FIG. 3 and Para. 68, teaches using a real image or synthetic image as an original image wherein augmented images are output, i.e., generating further form images).
The proposed combination as well as the motivation for combining the Vamshi in view of Yoffe, Kumari, Kansy, and Zhu references presented in the rejection of Claim 2, applies to claim 6. Thus, the method recited in claim 6 is met by Vamshi in view of Yoffe, Kumari, Kansy, and Zhu.
Regarding Claim 9, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, and Zhu teaches "The method of claim 6, wherein the pseudo images are one of grayscale or RGB images";(Zhu, Para. 61, teaches data interpreted form an image is altered to create a synthetic image output which is generated using a color model including an RGB color model, i.e., pseudo images are RGB images).
The proposed combination as well as the motivation for combining the Vamshi in view of Yoffe, Kumari, Kansy, and Zhu references presented in the rejection of Claim 2, applies to claim 9. Thus, the method recited in claim 9 is met by Vamshi in view of Yoffe, Kumari, Kansy, and Zhu.
Claim 12 recites an apparatus with elements corresponding to the steps recited in Claim 2. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, Kansy, and Zhu references, presented in rejection of Claim 2, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, Kansy, and Zhu references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claim 14 recites an apparatus with elements corresponding to the steps recited in Claim 4. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, Kansy, and Zhu references, presented in rejection of Claim 4, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, Kansy, and Zhu references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claim 16 recites an apparatus with elements corresponding to the steps recited in Claim 6. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, Kansy, and Zhu references, presented in rejection of Claim 6, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, Kansy, and Zhu references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claim 19 recites an apparatus with elements corresponding to the steps recited in Claim 9. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Zhu and Kumari references, presented in rejection of Claim 9, apply to this claim. Finally, the combination of the Zhu and Kumari references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claims 3, 7, 8, 13, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vamshi in view of Yoffe, Kumari, Kansy, Zhu, and Zhang et al. (US 20230040974 A1).
Regarding Claim 3, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, and Zhu does not explicitly teach "The method of claim 2, further comprising applying style characteristics to said identified text; wherein said generated further form images have applied font type and font size, and said style characteristics are added before said further form images are generated".
In an analogous field of endeavor, Zhang teaches "The method of claim 2, further comprising applying style characteristics to said identified text; wherein said generated further form images have applied font type and font size, and said style characteristics are added before said further form images are generated"; (Zhang, FIGS. 6A-6C and Paras. 9-14 and Para. 50, teaches the generating the artificial image data comprises identifying the visual format of the structure data in the one or more image portions and generating one or more artificial image portions corresponding to the one or more image portions based on the visual format of the structured data and modifying the image data to replace the image portions with artificial image portions in which different visual formats at different image portions can be taken into consideration for obscuring the sensitive data wherein the visual format comprises one or more of text length, text font, text color, the size of the text, and any special style characteristics, i.e., apply style characteristics to text wherein the generated further images being the generation of artificial image data have applied font type and size and style characteristics are added before further images are generated.
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi, Yoffe, Kumari, Kansy, and Zhu by including the application of font type, size, and style characteristics are added before the form images are generated taught by Zhang. One of ordinary skill in the art would be motivated to combine the references since it reduces processing requirements (Zhang, Para. 9, teaches the motivation of combination to be to reduce processing requirements and replace sensitive data).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Regarding Claim 7, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, Zhu, and Zhang teaches "The method of claim 6, further comprising applying font characteristics to said identified text"; (Zhang, FIGS. 6A-6C and Paras. 9-14 and Para. 50, teaches the generating the artificial image data comprises identifying the visual format of the structure data in the one or more image portions and generating one or more artificial image portions corresponding to the one or more image portions based on the visual format of the structured data and modifying the image data to replace the image portions with artificial image portions in which different visual formats at different image portions can be taken into consideration for obscuring the sensitive data wherein the visual format comprises one or more of text length, text font, text color, the size of the text, and any special style characteristics, i.e., apply font style characteristics to identified text ).
The proposed combination as well as the motivation for combining the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references presented in the rejection of Claim 3, applies to claim 7. Thus, the method recited in claim 7 is met by Vamshi in view of Yoffe, Kumari, Kansy, Zhu, and Zhang.
Regarding Claim 8, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, Zhu, and Zhang teaches "The method of claim 7, further comprising applying style characteristics to said identified text, wherein said generated further form images have applied font type and font size, and said style characteristics are added before said further form images are generated"; (Zhang, FIGS. 6A-6C and Paras. 9-14 and Para. 50, teaches the generating the artificial image data comprises identifying the visual format of the structure data in the one or more image portions and generating one or more artificial image portions corresponding to the one or more image portions based on the visual format of the structured data and modifying the image data to replace the image portions with artificial image portions in which different visual formats at different image portions can be taken into consideration for obscuring the sensitive data wherein the visual format comprises one or more of text length, text font, text color, the size of the text, and any special style characteristics, i.e., apply style characteristics to text wherein the generated further images being the generation of artificial image data have applied font type and size and style characteristics are added before further images are generated).
The proposed combination as well as the motivation for combining the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references presented in the rejection of Claim 3, applies to claim 8. Thus, the method recited in claim 8 is met by Vamshi in view of Yoffe, Kumari, Kansy, Zhu, and Zhang.
Claim 13 recites an apparatus with elements corresponding to the steps recited in Claim 3. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references, presented in rejection of Claim 3, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claim 17 recites an apparatus with elements corresponding to the steps recited in Claim 7. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references, presented in rejection of Claim 7, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claim 18 recites an apparatus with elements corresponding to the steps recited in Claim 8. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references, presented in rejection of Claim 8, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, Kansy, Zhu, and Zhang references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Vamshi in view of Yoffe, Kumari, Kansy, Zhu, Shukla et al. (US 20240320820 A1), and Wen (US 20170169548 A1).
Regarding Claim 5, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, and Zhu does not explicitly teach "The method of claim 4, wherein each of said pseudo images comprises a plurality of grayscale pseudo pixels; wherein a grayscale value Ri of each of said plurality of pseudo pixels are obtained according to the following: Ri = Ni / M * 255 where Ri = Grayscale Pseudo Pixel Value M= Number of entries in a lookup table comprising an alphabet Ni = Numerical Value of entry in said lookup table”.
In an analogous field of endeavor, Shukla teaches "The method of claim 4, wherein each of said pseudo images comprises a plurality of grayscale pseudo pixels"; (Shukla, Para. 65, teaches a grayscale is applied on the set of synthetic images to obtain a set of grayscale images which includes setting the color of each pixel in the synthetic image to a monochromatic format, i.e., pseudo images comprise a plurality of grayscale pseudo pixels being the converted synthetic pixels to grayscale).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi, Yoffe, Kumari, Kansy, and Zhu by including the pseudo image comprising a plurality of grayscale pseudo pixels taught by Shukla. One of ordinary skill in the art would be motivated to combine the references since it improves analysis of the images.
However, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, Zhu, and Shukla does not explicitly teach "wherein a grayscale value Ri of each of said plurality of pseudo pixels are obtained according to the following: Ri = Ni / M * 255 where Ri = Grayscale Pseudo Pixel Value M= Number of entries in a lookup table comprising an alphabet Ni = Numerical Value of entry in said lookup table".
In an analogous field of endeavor, Wen teaches "wherein a grayscale value Ri of each of said plurality of pseudo pixels are obtained according to the following: Ri = Ni / M * 255 where Ri = Grayscale Pseudo Pixel Value M= Number of entries in a lookup table comprising an alphabet Ni = Numerical Value of entry in said lookup table"; (Wen, Paras. 18-33, teaches performing maximum normalization of the cumulative calculation of the histogram with the max cumulative count C(255) and multiplying this maximum normalization by 255 to obtain an enhancement gray scale table out with calculation and looking up the table to obtain a new output gray scale value, i.e., grayscale value of pixels is output from a lookup table by multiplying a count of entries divided by its max entries by 255).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi, Yoffe, Kumari, Kansy, Zhu, and Shukla wherein pixels are pseudo pixels by including the determination of grayscale value of each pixel according to a normalization of the number of an entry in a table divided by the max number of entries in the table multiplied by 255 taught by Wen. One of ordinary skill in the art would be motivated to combine the references since it adjusts the gray scale distribution (Wen, Para. 3, teaches the motivation of combination to be to adjust the gray scale distribution of the image and increase distribution range).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 15 recites an apparatus with elements corresponding to the steps recited in Claim 5. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi, Yoffe, Kumari, Kansy, Zhu, Shukla, and Wen references, presented in rejection of Claim 5, apply to this claim. Finally, the combination of the Vamshi, Yoffe, Kumari, Kansy, Zhu, Shukla, and Wen references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Vamshi in view of Yoffe, Kumari, Kansy, Zhu, Shukla, Wen, and Yang et al. (US 20210019352 A1).
Regarding Claim 10, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, Zhu, Shukla, and Wen teaches "the pseudo images comprising pseudo pixels each having a gray scale value"; (Shukla, Para. 65, teaches a grayscale is applied on the set of synthetic images to obtain a set of grayscale images which includes setting the color of each pixel in the synthetic image to a monochromatic format, i.e., pseudo images comprise a plurality of grayscale pseudo pixels being the converted synthetic pixels to grayscale);
"said text characters being determined according to the following: Ni = Ri / 255 * M where Ni = Numerical Value of a character in a lookup table M = Total size of said lookup table Ri = Grayscale Pseudo Pixel Value"; (Wen, Paras. 18-33, teaches performing maximum normalization of the cumulative calculation of the histogram with the max cumulative count C(255) and multiplying this maximum normalization by 255 to obtain an enhancement gray scale table out with calculation and looking up the table to obtain a new output gray scale value, i.e., cumulative count of entries is equal to a grayscale output from the lookup table divided by 255 and multiplied by the maximum cumulative).
The proposed combination as well as the motivation for combining the Vamshi, Yoffe, Kumari, Kansy, Zhu, Shukla, and Wen references presented in the rejection of Claim 5, applies to claim 10.
However, the combination of references of Vamshi in view of Yoffe, Kumari, Kansy, Zhu, Shukla, and Wen does not explicitly teach "The method of claim 9, wherein in said converting, said text comprises text characters from a lookup table having a numerical value for each of said text characters".
In an analogous field of endeavor, Yang teaches "The method of claim 9, wherein in said converting, said text comprises text characters from a lookup table having a numerical value for each of said text characters"; (Yang, Paras. 33 and 41, teaches a lookup table includes an index value and the different characters respectively corresponding to each of the index values wherein the index value is an integer from zero to an integer to less than the predetermined value, i.e., text comprises text characters from a lookup table having a numerical value for each of said text characters).
It would have been obvious to one having ordinary skill in the art before the effective filing date to modify the invention of Vamshi in view of Yoffe, Kumari, Kansy, Zhu, Shukla, and Wen wherein the converting is converting pseudo image to text by including the text comprising text characters from a lookup table having a numerical value for each character taught by Yang. One of ordinary skill in the art would be motivated to combine the references since it provides a product capable of maintaining privacy (Yang, Para. 2, teaches the motivation of combination to be to provide a text comparison method and product capable of maintaining privacy).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
Claim 20 recites an apparatus with elements corresponding to the steps recited in Claim 10. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Vamshi in view of Yoffe, Kumari, Kansy, Zhu, Shukla, Wen, and Yang references, presented in rejection of Claim 10, apply to this claim. Finally, the combination of the Vamshi in view of Yoffe, Kumari, Kansy, Zhu, Shukla, Wen, and Yang references discloses a processor and a memory (for example, see Vamshi, Paragraph 5).
Conclusion
THIS ACTION IS MADE FINAL. 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. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW STEVEN BUDISALICH whose telephone number is (703)756-5568. The examiner can normally be reached Monday - Friday 8:30am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571) 272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx
for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDREW S BUDISALICH/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
