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 .
Claim Status
The status of claims 1-18 is:
Claims 1-18 were restricted as of the Requirement for Restriction mailed 05/08/2026.
Applicant elected claims 1-9 and 13-18 with traverse in a response received 07/08/2026.
Claims 1-9 and 13-18 are pending.
Claims 10-12 are withdrawn.
Election/Restrictions
Applicant's election with traverse of Group I (claims 1-9 and 13-18) in the reply filed on 07/08/2026 is acknowledged. The traversal is on the ground(s) that Examiner has not articulated an undue burden in searching both groups of the invention. This is not found persuasive because Examiner identified that the different groups are classified in different CPCs and that a different field of search would be required for each group.
The requirement is still deemed proper and is therefore made FINAL.
Claim Objections
Claim 18 is objected to because of the following informalities: the claim states “the received medical image data comprise” when it should state “the received medical image comprises”. Appropriate correction is required.
Claim Interpretation
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.
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) are:
“input interface” in claim 13
“estimation unit” in claim 13
“comparison unit” in claim 13
“adoption unit” in claim 13.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they 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 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 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 them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does not fall within at least one of the four categories of patent eligible subject matter because the BRI of “A computer program product” includes software per se. Examiner suggests amending the claim language to state “A non-transitory computer program product”.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2 and 13-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (U.S. Patent Publication No 2022/0366540, hereinafter “Wang”).
Regarding claim 1, Wang discloses a method for AI-assisted generating an adapted medical image processing chain (Wang Abstract: “The medical imaging system . . . configured to optimize the learning network based on generated images sent to the learning network module, wherein the generated images comprise the post-processed image previously obtained by the learning network module”), the method comprising:
receiving medical image data representing only a piece of information of a complete matching pair of medical image data related to a target flavor (Wang [0069]: “In step 410, obtain an original image acquired by an X-ray imaging system”), wherein a missing piece of information of the complete matching pair is missing from the received medical image data (Wang [0069]: “In step 410, obtain an original image acquired by an X-ray imaging system”, in this scenario the target image is not recieved);
generating an estimated medical image by applying the medical image processing chain to a raw projection medical image related to the received medical image data (Wang [0071]: “In step 420, post-process the original image based on a trained network to obtain an optimized image after processing. Specifically, the original image is inputted to the trained network, so that the optimized image after post-processing can be obtained (outputted), where the optimized image can also be displayed in the display apparatus of the X-ray imaging system”);
determining a result of a comparison based on the estimated medical image and a target medical image related to the received medical image data (Wang [0072]: “In some embodiments, the one or a plurality of networks are trained based on UNet or other well-known models, where the network is trained based on a sample original image set and a target image set”; Wang [0074]: “In step 2, obtain an optimized image after post-processing corresponding to each image in the sample original image set to serve as a target image set. In some embodiments, the post-processing includes one or a plurality of noise reduction, detail enhancement, and contrast adjustment processes. In some embodiments, the optimized image is obtained based on a conventional post-processing method, for example, obtained by manually adjusting one or a plurality of post-processing parameters and making judgment based on experience. However, those skilled in the art can understand that the obtaining of the optimized image is not limited to the aforementioned representation manner, and may also adopt any suitable manner”); and
generating an adapted medical image processing chain by adapting the medical image processing chain based on the result of the comparison (Wang [0075]: “In step 3, train a network by using the sample original image set as an input and the target image set as an output, so as to obtain the trained network”),
wherein the missing piece of information is generated based on the received medical image data using an AI-based model (Wang [0075]: “In step 3, train a network by using the sample original image set as an input and the target image set as an output, so as to obtain the trained network”, target image is generated by model).
Regarding claim 13, it is rejected under the same analysis as claim 1 above along with Wang’s disclosure of an input interface (Wang [0031]: “The operator workstation 126 may include a user interface (or a user input apparatus), such as a keyboard, a mouse, a voice activation controller, or any other suitable input apparatus in the form of an operator interface”), an estimation unit (Wang [0035]: “The post-processing module 220 is configured to post-process the original image based on a trained network to obtain an optimized image after processing”), a comparison unit (Wang [0035]: “The post-processing module 220 is configured to post-process the original image based on a trained network to obtain an optimized image after processing”), and an adaption unit (Wang [0035]: “The post-processing module 220 is configured to post-process the original image based on a trained network to obtain an optimized image after processing”).
Regarding claim 2, Wang discloses the method, wherein the generating the estimated model, the determining the result of the comparison and the generating the adapted medical image processing chain are automatically iteratively repeated using the adapted medical image processing chain as a subsequent medical image processing chain for the generating the estimated medical image (Wang [0059]: “Afterwards, the created network output is compared with the expected output of the data set, and then a difference between the created and expected outputs is used to iteratively update network parameters (weight and/or bias). A stochastic gradient descent (SGD) method may usually be used to update network parameters. However, those skilled in the art should understand that other methods known in the art may also be used to update network parameters”).
Regarding claim 14, Wang discloses a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of claim 1 (Wang [0084]: “The present invention may further provide a non-transitory computer-readable storage medium for storing an instruction set and/or a computer program. When executed by a computer, the instruction set and/or computer program causes the computer to perform the aforementioned medical imaging method”).
Regarding claim 15, Wang discloses a non-transitory computer program comprising instructions which, when executed by a computer, cause the computer to perform the method of claim 1 (Wang [0084]: “The present invention may further provide a non-transitory computer-readable storage medium for storing an instruction set and/or a computer program. When executed by a computer, the instruction set and/or computer program causes the computer to perform the aforementioned medical imaging method”).
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.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Salomon et al. (U.S. Patent Publication No 2024/0185485, hereinafter “Salomon”).
Regarding claim 3, Wang discloses the method, wherein the generating the estimated model, the determining the result of the comparison and the generating the adapted medical image processing chain are automatically iteratively repeated (Wang [0059]: “Afterwards, the created network output is compared with the expected output of the data set, and then a difference between the created and expected outputs is used to iteratively update network parameters (weight and/or bias). A stochastic gradient descent (SGD) method may usually be used to update network parameters. However, those skilled in the art should understand that other methods known in the art may also be used to update network parameters”).
Wang does not explicitly disclose the method, wherein the process is iteratively repeated until a predetermined quality criteria or optimum criteria for the result of the comparison is achieved.
However, Salomon teaches the method, wherein the process is iteratively repeated until a predetermined quality criteria or optimum criteria for the result of the comparison is achieved (Salomon [0150]: “If the error is not negligible, that is, it exceeds the threshold used in step S470, the process flow continues on into the next iteration cycle, and so on”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the error threshold as taught by Salomon with the method of Wang because it would improve the method by minimizing the error rate and allowing the method to automatically know when to stop the iterative process. This motivation for the combination of Wang and Salomon is supported by KSR exemplary rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Claim(s) 4-5 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Karbhari et al. (Karbhari, Y., Basu, A., Geem, Z. W., Han, G. T., & Sarkar, R. (2021). Generation of synthetic chest X-ray images and detection of COVID-19: A deep learning based approach. Diagnostics, 11(5), 895., hereinafter “Karbhari”).
Regarding claim 4, Wang discloses the method, wherein the received medical image data comprises a raw projection medical image (Wang [0069]: “In step 410, obtain an original image acquired by an X-ray imaging system”), and
the determining the result of the comparison is performed by comparing the estimated medical image with the target medical image being used as the target medical image (Wang [0072]: “In some embodiments, the one or a plurality of networks are trained based on UNet or other well-known models, where the network is trained based on a sample original image set and a target image set”; Wang [0074]: “In step 2, obtain an optimized image after post-processing corresponding to each image in the sample original image set to serve as a target image set. In some embodiments, the post-processing includes one or a plurality of noise reduction, detail enhancement, and contrast adjustment processes. In some embodiments, the optimized image is obtained based on a conventional post-processing method, for example, obtained by manually adjusting one or a plurality of post-processing parameters and making judgment based on experience. However, those skilled in the art can understand that the obtaining of the optimized image is not limited to the aforementioned representation manner, and may also adopt any suitable manner”).
Wang does not explicitly disclose the method, wherein the generation of the missing piece of information comprises the generation of a synthetic target medical image by applying a first trained AI-based model to the raw projection medical image.
However, Karbhari discloses the generation of the missing piece of information comprises the generation of a synthetic target medical image by applying a first trained AI-based model to the raw projection medical image (Karbhari Pages 5-6: “In particular, GANs are a useful tool to generate high-quality synthetic data irrespective of the domains, and researchers across the world have been utilizing this where there is a lack of required data. These synthetic samples are better than conventional samples produced by vanilla data augmentation techniques, such as rotation, cropping, brightness modification, etc. However, there are some challenges. The input data must be varied enough, otherwise the generated images will be similar in nature with almost identical texture, and lack variety. The training of GANs can also be unstable at times, which can lead to mode collapse. Despite their limitations, GANs are a useful tool for medical image generation, and active research is ongoing to improve the quality of images as well as the stability of GANs”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GAN generating synthetic target data as taught by Karbhari with the method of Wang because it would improve the method by allowing for the generation of data instead of needing an actual target image which may not be available (Karbhari Page 5-6). This motivation for the combination of Wang and Salomon is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention and rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding claim 5, Wang discloses the method, wherein the received medical image data comprises a target medical image (Wang [0037]: “In some embodiments, the medical imaging system 200 further includes a training module (not shown in the figure) for training the network 300 based on a sample original image set (known input) and a target image set (expected output)”),
the generating the estimated model is performed by applying the medical image processing chain to the raw projection medical image being used as the raw projection medical image (Wang [0071]: “In step 420, post-process the original image based on a trained network to obtain an optimized image after processing. Specifically, the original image is inputted to the trained network, so that the optimized image after post-processing can be obtained (outputted), where the optimized image can also be displayed in the display apparatus of the X-ray imaging system”), and
the determining the result of the comparison is performed by comparing the estimated medical image with the target medical image (Wang [0072]: “In some embodiments, the one or a plurality of networks are trained based on UNet or other well-known models, where the network is trained based on a sample original image set and a target image set”; Wang [0074]: “In step 2, obtain an optimized image after post-processing corresponding to each image in the sample original image set to serve as a target image set. In some embodiments, the post-processing includes one or a plurality of noise reduction, detail enhancement, and contrast adjustment processes. In some embodiments, the optimized image is obtained based on a conventional post-processing method, for example, obtained by manually adjusting one or a plurality of post-processing parameters and making judgment based on experience. However, those skilled in the art can understand that the obtaining of the optimized image is not limited to the aforementioned representation manner, and may also adopt any suitable manner”).
Wang does not explicitly disclose the method, wherein the generation of the missing piece of information comprises the generation of a synthetic raw projection medical image by applying a second trained AI-based model to the target medical image (however, Wang does disclose that the original images can be obtained a variety of ways at [0039]: “In some other embodiments, the sample original images are obtained after being acquired and reconstructed by X-ray imaging systems of different models and normalized”).
However, Karbhari teaches the method, wherein the generation of the missing piece of information comprises the generation of a synthetic raw projection medical image by applying a second trained AI-based model to the target medical image (Karbhari Pages 5-6: “In particular, GANs are a useful tool to generate high-quality synthetic data irrespective of the domains, and researchers across the world have been utilizing this where there is a lack of required data. These synthetic samples are better than conventional samples produced by vanilla data augmentation techniques, such as rotation, cropping, brightness modification, etc. However, there are some challenges. The input data must be varied enough, otherwise the generated images will be similar in nature with almost identical texture, and lack variety. The training of GANs can also be unstable at times, which can lead to mode collapse. Despite their limitations, GANs are a useful tool for medical image generation, and active research is ongoing to improve the quality of images as well as the stability of GANs”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GAN generating synthetic original data as taught by Karbhari with the method of Wang because it would improve the method by allowing for the generation of data instead of needing an actual original image which may not be available (Karbhari Page 5-6). This motivation for the combination of Wang and Salomon is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention and rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding claim 16, Wang discloses the method, wherein the received medical image data comprises a raw projection medical image (Wang [0069]: “In step 410, obtain an original image acquired by an X-ray imaging system”), and
the determining the result of the comparison is performed by comparing the estimated medical image with the target medical image being used as the target medical image (Wang [0072]: “In some embodiments, the one or a plurality of networks are trained based on UNet or other well-known models, where the network is trained based on a sample original image set and a target image set”; Wang [0074]: “In step 2, obtain an optimized image after post-processing corresponding to each image in the sample original image set to serve as a target image set. In some embodiments, the post-processing includes one or a plurality of noise reduction, detail enhancement, and contrast adjustment processes. In some embodiments, the optimized image is obtained based on a conventional post-processing method, for example, obtained by manually adjusting one or a plurality of post-processing parameters and making judgment based on experience. However, those skilled in the art can understand that the obtaining of the optimized image is not limited to the aforementioned representation manner, and may also adopt any suitable manner”).
Wang does not disclose the method, wherein the generation of the missing piece of information comprises the generation of a synthetic target medical image by applying a first trained AI-based model to the raw projection medical image.
However, Karbhari teaches the method, wherein the generation of the missing piece of information comprises the generation of a synthetic target medical image by applying a first trained AI-based model to the raw projection medical image (Karbhari Pages 5-6: “In particular, GANs are a useful tool to generate high-quality synthetic data irrespective of the domains, and researchers across the world have been utilizing this where there is a lack of required data. These synthetic samples are better than conventional samples produced by vanilla data augmentation techniques, such as rotation, cropping, brightness modification, etc. However, there are some challenges. The input data must be varied enough, otherwise the generated images will be similar in nature with almost identical texture, and lack variety. The training of GANs can also be unstable at times, which can lead to mode collapse. Despite their limitations, GANs are a useful tool for medical image generation, and active research is ongoing to improve the quality of images as well as the stability of GANs”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GAN generating synthetic original data as taught by Karbhari with the method of Wang because it would improve the method by allowing for the generation of data instead of needing an actual original image which may not be available (Karbhari Page 5-6). This motivation for the combination of Wang and Salomon is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention and rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding claim 17, Wang discloses the method, wherein the received medical image data comprises a target medical image (Wang [0037]: “In some embodiments, the medical imaging system 200 further includes a training module (not shown in the figure) for training the network 300 based on a sample original image set (known input) and a target image set (expected output)”),
the generating the estimated model is performed by applying the medical image processing chain to the raw projection medical image being used as the raw projection medical image (Wang [0071]: “In step 420, post-process the original image based on a trained network to obtain an optimized image after processing. Specifically, the original image is inputted to the trained network, so that the optimized image after post-processing can be obtained (outputted), where the optimized image can also be displayed in the display apparatus of the X-ray imaging system”), and
the determining the result of the comparison is performed by comparing the estimated medical image with the target medical image (Wang [0072]: “In some embodiments, the one or a plurality of networks are trained based on UNet or other well-known models, where the network is trained based on a sample original image set and a target image set”; Wang [0074]: “In step 2, obtain an optimized image after post-processing corresponding to each image in the sample original image set to serve as a target image set. In some embodiments, the post-processing includes one or a plurality of noise reduction, detail enhancement, and contrast adjustment processes. In some embodiments, the optimized image is obtained based on a conventional post-processing method, for example, obtained by manually adjusting one or a plurality of post-processing parameters and making judgment based on experience. However, those skilled in the art can understand that the obtaining of the optimized image is not limited to the aforementioned representation manner, and may also adopt any suitable manner”).
Wang does not explicitly disclose the method, wherein the generation of the missing piece of information comprises the generation of a synthetic raw projection medical image by applying a second trained AI-based model to the target medical image (however, Wang does disclose that the original images can be obtained a variety of ways at [0039]: “In some other embodiments, the sample original images are obtained after being acquired and reconstructed by X-ray imaging systems of different models and normalized”).
However, Karbhari teaches the method, wherein the generation of the missing piece of information comprises the generation of a synthetic raw projection medical image by applying a second trained AI-based model to the target medical image (Karbhari Page 5-6: “In particular, GANs are a useful tool to generate high-quality synthetic data irrespective of the domains, and researchers across the world have been utilizing this where there is a lack of required data. These synthetic samples are better than conventional samples produced by vanilla data augmentation techniques, such as rotation, cropping, brightness modification, etc. However, there are some challenges. The input data must be varied enough, otherwise the generated images will be similar in nature with almost identical texture, and lack variety. The training of GANs can also be unstable at times, which can lead to mode collapse. Despite their limitations, GANs are a useful tool for medical image generation, and active research is ongoing to improve the quality of images as well as the stability of GANs”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the GAN generating synthetic original data as taught by Karbhari with the method of Wang because it would improve the method by allowing for the generation of data instead of needing an actual original image which may not be available (Karbhari Page 5-6). This motivation for the combination of Wang and Salomon is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention and rationale (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Allowable Subject Matter
Claims 6-9 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/AIDAN KEUP/ Examiner, Art Unit 2666 /Molly Wilburn/Primary Examiner, Art Unit 2666
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