18DETAILED 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 .
Election/Restrictions
Claims 7-10, 14-15, and 17-181 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 01/20/2026. As a result, claims 1-6, 11-13, 16, and 19-20 are examined below.
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 1-6, 11-13, 16, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 13 recites (additional elements crossed out):
The above limitations as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, and mental processes. That is, other than reciting the steps as being performed by a “lung volume estimation apparatus” comprised of “units”, and an “artificial intelligence model” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, and mental processes. For example, but for the recited “lung volume estimation apparatus” comprised of “units”, and an “artificial intelligence model”, the limitations describe a system for calculating a lung volume based on a received medical image and clinical information of a patient. The limitations describe the management of personal behavior, as well as actions that can be performed mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “lung volume estimation apparatus” comprised of “units”, and an “artificial intelligence model” to perform the steps. The “lung volume estimation apparatus” is recited at a high level of generality (see at least Para. [0060]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. Further, the “artificial intelligence model” is considered to be generic computer function and/or field-of-use/”general link” implementations and does not meaningfully limit the claim.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), particularly as it relates to the recited “lung volume estimation apparatus” comprised of “units”, and an “artificial intelligence model” elements. This is not sufficient to amount to significantly more than the judicial exception.
Claim 1 features limitations similar to those of claim 13, and is therefore rejected using the same rationale.
Claims 2-6 and 11-12 are dependent on claim 1, and include all the limitations of claim 1. Claims 16 and 19-20 are dependent on claim 13, and include all the limitations of claim 13. Therefore, they are also found to be directed to an abstract idea. Claim 5 recites the additional element of a “a region segmentation model. Claims 11, 16, and 19 recite a “first neural network including an encoder and a decoder” and a “second neural network” However these claims merely apply these additional elements to the judicial exception (“apply it”). The remaining dependent claims merely serve to further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more. Therefore, the claims are not found to be patent eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 13, 16, and 19-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5, the limitation “The method of claim 4, further comprising generating the segmented image of the lung region from the 2D medical image using a region segmentation model trained using training data including an X-ray image and a segmented image of a lung region” is indefinite. The claim is preceded by claim 4’s limitation “wherein the 2D medical image comprises a segmented image obtained by separating a lung region from an X-ray image”. Based on claim 4’s language, it appears as though the 2D medical image is the segmented image. It is unclear how the segmented image may be generated from the 2D medical image if the 2D medical image comprises the segmented image.
Claim limitations “an image unit configured to…”, “a clinical information input unit configured to…”, “a volume determination unit configured to…”, and “a training unit configured to…” (see claims 13 and 19) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The written description fails to provide any details regarding the structure of any of the claimed “units”. Therefore, the claims are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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) 1-4, 6, 13, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Young (KR 2025-0009639 A2) .
Regarding claim 1, Young discloses A lung volume estimation method, comprising:
receiving a two-dimensional (2D) medical image of a patient;
receiving clinical information of the patient; and
determining a lung volume of the patient by inputting the 2D medical image and the clinical information into an artificial intelligence model.
(See Abstract – “An artificial intelligence-based method for calculating respiratory volume according to an embodiment of the present invention may include a step of obtaining body measurement information of a subject, a step of obtaining a chest image of the subject, a step of inputting the body measurement information of the subject and the chest image of the subject into an artificial intelligence model that calculates an appropriate respiratory volume learned based on a correlation between the body measurement information and the chest image and a respiratory volume value according to a pulmonary function test, and a step of calculating an appropriate respiratory volume of the subject based on an output of the artificial intelligence model.”
Regarding claim 2, Young discloses The method of claim 1, wherein the 2D medical image comprises a chest X-ray image. (See page 2 – “The above medical data management server (200) can obtain not only the subject's body measurement information but also the subject's chest image (e.g., chest simple X-ray image) from a photographing device and transmit the obtained image data to the respiratory volume calculation device (100).”
Regarding claim 3, Young discloses The method of claim 1, wherein the clinical information comprises an age or gender of the patient. (See page 5 – “And the body measurement information according to an embodiment of the present invention may include items related to obesity. For example, the body measurement information may include at least one of height, weight, age, and gender. Furthermore, the body measurement information may additionally include waist circumference, body mass index, or body fat index.”)
Regarding claim 4, Young discloses The method of claim 1, wherein the 2D medical image comprises a segmented image obtained by separating a lung region from an X-ray image. (See page 10 – “In order to determine information about the characteristics of the lungs from the chest image in this way, the respiratory volume calculation unit (113b) can distinguish the lungs, which are the target object, from the chest image. In addition, the respiratory volume calculation unit (113b) can be controlled to measure the area of the lungs, which are the target object, based on the classification or segmentation work of pixels (measuring the area of the segment) for the target object.”
Regarding claim 6, Young discloses The method of claim 4, wherein the clinical information comprises an area of the lung region determined based on the segmented image. (See page 10 – “In order to determine information about the characteristics of the lungs from the chest image in this way, the respiratory volume calculation unit (113b) can distinguish the lungs, which are the target object, from the chest image. In addition, the respiratory volume calculation unit (113b) can be controlled to measure the area of the lungs, which are the target object, based on the classification or segmentation work of pixels (measuring the area of the segment) for the target object.”
Claims 13 and 20 features limitations similar to those of claim 1, and are therefore rejected using the same rationale.
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) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Han (US 2020/0272841).
Regarding claim 5, Young does not explicitly disclose The method of claim 4, further comprising generating the segmented image of the lung region from the 2D medical image using a region segmentation model trained using training data including an X-ray image and a segmented image of a lung region. (See Han, Para. [0157] – “A training image refers to an image of a sample object that has a known ROI (which is annotated in the image). In some embodiments, the training image may be of the same type of image as the target image, and the sample object may be of the same type of object as the object in the target image as described in connection with 510. The ROI in the training image may correspond to the same type of physical portion as the ROI in the target image to be segmented. For example, if the ROI segmentation model is used to segment a specific organ (e.g., a left kidney) on a target image of a patient, the training image may be an image of a sample patient, wherein a region representing the specific organ of the sample patient may be annotated in the training image. In some embodiments, a set of images may be annotated with different types of ROIs to generate different sets of training images, wherein the different sets of training images may be used to train different types of ROI segmentation models. For example, a set of chest CT images may be annotated with the heart to generate a set of training images used to train a heart segmentation model, and the set of chest CT images may be annotated with the lung to generate another set of training images used to train a lung segmentation model. In some embodiments, different sets of images may be annotated with different types of ROIs to generate different sets of training images, wherein the different sets of training images may be used to train different types of ROI segmentation models.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Young to utilize the teachings of Han since they are both within the same field of endeavor (i.e., medical imaging), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claim(s) 11-12, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Shen (US 2021/0393229).
Regarding claim 11, Young does not fully disclose The method of claim 1, wherein the determining of the lung volume comprises:
generating a reconstructed image by inputting the 2D medical image into a first neural network including an encoder and a decoder; and
predicting a lung volume by inputting, into a second neural network, data obtained by concatenating the reconstructed image with the clinical information.
(Young discloses the use of multiple neural networks. See page 8 – “The artificial intelligence model according to various embodiments of the present invention may be configured in a form that combines a CNN (Convolutional Neural Network), which is mainly used for extracting visual features for image data, with an FCNN (Fully Connected Neural Network) or RNN (Recurrent Neural Network) layer for processing measurement information.” Young also discloses inputting an image with clinical information into a neural network. See at least page 9 – “The above calculation performing unit (113) can input the subject's body measurement information and the subject's chest image into the artificial intelligence model.” However, Young does not explicitly disclose a generating a reconstructed image by inputting the 2D medical image into a first neural network including an encoder and a decoder. See Shen, Para. [0007] – “In one aspect, the invention provides a method for tomographic imaging comprising acquiring a set of one or more 2D projection images, e.g., with a computed tomography x-ray scan, and reconstructing a 3D volumetric image from the set of one or more 2D projection images using a residual deep learning network comprising an encoder network, a transform module and a decoder network, wherein the reconstructing comprises: transforming by the encoder network the set of one or more 2D projection images to 2D features; mapping by the transform module the 2D features to 3D features; and generating by the decoder network the 3D volumetric image from the 3D features.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Young to utilize the teachings of Shen since they are both within the same field of endeavor (i.e., medical imaging), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claim 12, Young discloses The method of claim 11, further comprising training the first neural network and the second neural network through a supervised learning method by using training data including 2D medical images, clinical information, and an actual lung volume. (See page 11 – “The artificial intelligence model according to an embodiment of the present invention may be characterized as a model in which supervised learning is performed by setting body measurement information and a chest image as input training data and setting respiratory volume values according to a pulmonary function test as output training data.”
Claim 16 features limitations similar to those of claim 11, and is therefore rejected using the same rationale.
Regarding claim 19, Young does not fully disclose The apparatus of claim 13, further comprising a training unit configured to train the artificial intelligence model including a first neural network and a second neural network using training data through a supervised learning method, wherein the training unit is configured to:
generate a reconstructed image by inputting a 2D medical image of the training data into the first neural network including an encoding process and a decoding process;
predict a lung volume by inputting, into the second neural network, information obtained by concatenating the reconstructed image with clinical information of the training data; and
train the first neural network and the second neural network so that a difference between the predicted lung volume and an actual lung volume of the training data is reduced.
Young discloses the use of multiple neural networks. See page 8 – “The artificial intelligence model according to various embodiments of the present invention may be configured in a form that combines a CNN (Convolutional Neural Network), which is mainly used for extracting visual features for image data, with an FCNN (Fully Connected Neural Network) or RNN (Recurrent Neural Network) layer for processing measurement information.” Young also discloses inputting an image with clinical information into a neural network. See at least page 9 – “The above calculation performing unit (113) can input the subject's body measurement information and the subject's chest image into the artificial intelligence model.” Young also discloses training the first and second neural network. See page 11 – “The artificial intelligence model according to an embodiment of the present invention may be characterized as a model in which supervised learning is performed by setting body measurement information and a chest image as input training data and setting respiratory volume values according to a pulmonary function test as output training data.” The Examiner notes that the language “so that a difference between the predicted lung volume and an actual lung volume of the training data is reduced” is a statement of intended result and fails to result in a manipulative difference between the claimed invention and the prior art.
Young does not explicitly disclose a generating a reconstructed image by inputting the 2D medical image into a first neural network including an encoder and a decoder. See Shen, Para. [0007] – “In one aspect, the invention provides a method for tomographic imaging comprising acquiring a set of one or more 2D projection images, e.g., with a computed tomography x-ray scan, and reconstructing a 3D volumetric image from the set of one or more 2D projection images using a residual deep learning network comprising an encoder network, a transform module and a decoder network, wherein the reconstructing comprises: transforming by the encoder network the set of one or more 2D projection images to 2D features; mapping by the transform module the 2D features to 3D features; and generating by the decoder network the 3D volumetric image from the 3D features.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Young to utilize the teachings of Shen since they are both within the same field of endeavor (i.e., medical imaging), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 EST.
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/MARK HOLCOMB/Primary Examiner, Art Unit 3685
1 Applicant appears to have erroneously included claims 17 and 18 in the elected species in response. Claims 17-18 were included in unelected Species 1 of the Requirement for Restriction/Election of 11/19/2025.
2 Translation provided by ip.com