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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 14, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1 and 10-11 are objected to because of the following informalities:
Claim 1, line 6 and line 8 both end in “and” however, only one is necessary before the final limitation
Similar issue in claims 10 and 11
Appropriate correction is required.
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.
Claim 1 (and claims 2-9 for inheriting and failing to cure the deficiency of claim 1) are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 repeatedly claims “a hardware processor” which performs actions. It is unclear if the “hardware processor” is intended to be the same processor each time, or a different processor. If the same processor, to maintain antecedent basis, the initial recitation of the hardware processor should read “a hardware processor” and the additional recitations should state “the hardware processor” or claim “a processor configured to:” then list all actions. If it is intended to be different hardware processors, the claim needs to be clarified, such as “a first hardware processor” and “a second hardware processor”.
Claims 2-9 are rejected for inheriting the rejection of claim 1, while failing to cure the rejection.
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 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. See MPEP 2106 and 2106.03 for guidance. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter of a process, machine, manufacture, or composition of matter. “A computer-readable recording medium” (or “computer program product”, “computer readable media”) as recited is not patent eligible subject matter because it is “software/data per se”. Furthermore, it is not a remedy when such “software/data” are claimed as a product without any structural recitations. “Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a ‘means plus function’ limitation) has no physical or tangible form, and thus does not fall within any statutory category.” MPEP 2106.03(I). A recommended remedy for claiming a computer program is to have it embodied within a “non-transitory” computer readable medium. See also USPTO Published 2019 Patent Eligibility Guidance.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-3, 5 and 10-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publication No. 2021/0042916 to Zhang et al. (hereinafter Zhang).
Regarding independent claim 1, Zhang discloses An image determination apparatus (abstract, “Disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches.” Paragraph 0004, “The present disclosure solves these technical problems with existing computer systems carrying out image analysis by providing improved systems and techniques that do not require substantial intervention by an expert to generate the classifiers. ”) comprising:
a hardware processor (paragraph 0013, “In certain embodiments, the present disclosure relates to a computer-implemented system comprising: a digital processing device comprising: at least one processor, an operating system configured to perform executable instructions, a memory, and a computer program including instructions executable by the digital processing device to create an application for providing a medical diagnosis”) that acquires a dynamic image obtained by capturing a site including a diagnosis target region of a patient (paragraph 0008, “a) obtaining a medical image of a lung;” lungs move throughout imaging, thus they are read as a dynamic image);
a hardware processor that extracts a feature amount through a first process based on the dynamic image (paragraph 0022, “In various embodiments, CNNs are composed of multiple processing layers to which image analysis filters, or convolutions, are applied. In some embodiments, the abstracted representation of images within each layer is constructed by systematically convolving multiple filters across the image, producing a feature map which is used as input to the following layer. CNNs learn representations of images with multiple levels of increasing understanding of the image contents, which is what makes the networks deep.” … “This architecture makes it possible to process images in the form of pixels as input and to give the desired classification as output.”);
a hardware processor that makes a determination related to diagnosis through a second process based on a result of machine learning based on the feature amount (paragraph 0081, “ b) evaluating the medical image using a predictive model trained using a machine learning procedure; and c) determining, by the predictive model, whether or not the medical image is indicative of a disease or disorder of the lung,”); and
a hardware processor that generates explanation data based on the feature amount and the determination related to the diagnosis (paragraph 0045, “According to one aspect of the present disclosure, an occlusion test to identify the areas of greatest importance used by the model in assigning diagnosis is performed. The greatest benefit of an occlusion test is that it reveals insights into the decisions of neural networks, which are sometimes referred to as “black boxes” with no transparency. Since this test is performed after training is completed, it demystifies the algorithm without affecting its results. The occlusion test also confirms that the network makes its decisions using accurate distinguishing features. In some embodiments, various platforms, systems, media, and methods recited herein comprise providing one or more of the areas of greatest importance identified by the occlusion test to a user or subject. In some embodiments, the one or more areas are provided in the form of a report (analog or electronic/digital).”… “In some embodiments, the report is annotated with medical insight such as descriptions or explanations of how the one or more areas are relevant to the diagnosis. This has the benefit of instilling greater trust and confidence in the methodology.”); and
an outputter or a communicator that outputs the explanation data to outside (paragraph 0045, “In some embodiments, the report is annotated with medical insight such as descriptions or explanations of how the one or more areas are relevant to the diagnosis. This has the benefit of instilling greater trust and confidence in the methodology.” paragraph 0056, “In some embodiments, the system provides the output of the analysis to the end user on the network. In some embodiments, the system sends the output to an electronic device of the end user such as a computer, smartphone, tablet or other digital processing device configured for network communications.”).
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Zhang further discloses wherein the first process is a process based on machine learning (paragraph 0013, “In some non-limiting embodiments, the machine learning procedure comprises a deep learning procedure.” paragraph 0022, “In various embodiments, CNNs are composed of multiple processing layers to which image analysis filters, or convolutions, are applied. In some embodiments, the abstracted representation of images within each layer is constructed by systematically convolving multiple filters across the image, producing a feature map which is used as input to the following layer. CNNs learn representations of images with multiple levels of increasing understanding of the image contents, which is what makes the networks deep.” … “This architecture makes it possible to process images in the form of pixels as input and to give the desired classification as output.”) or a process based on a rule.
Regarding dependent claim 3, the rejection of claim 1 is incorporated herein. Additionally, Zhang further discloses wherein the dynamic image is a radiographic image acquired by a radiography apparatus (abstract, “Deep learning algorithms enable the automated analysis of medical images such as X-rays to generate predictions of comparable accuracy to clinical experts for various diseases and conditions including those afflicting the lung such as pneumonia.” Paragraph 0008, “In some embodiments, the medical image of the lung is a chest X-ray.”).
Regarding dependent claim 5, the rejection of claim 1 is incorporated herein. Additionally, Zhang further discloses wherein the determination related to the diagnosis is determination of a disease level of a specific disease (paragraph 0038,” In some embodiments, the detection or diagnosis comprises a severity and/or stage of a disease or condition such as, for example, different stages of pneumonia”).
Regarding independent claim 10, the rejection of claim 1 applies directly. Additionally, Zhang further discloses An image determination method (abstract, “Disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches.” paragraph 0053, “Provided herein, in certain aspects, are platforms, systems, devices, and media for analyzing medical data according to any of the methods of the present disclosure. In some embodiments, the systems and electronic devices are integrated with a program including instructions executable by a processor to carry out analysis of medical data”) comprising:
acquiring a dynamic image obtained by capturing a site including a diagnosis target region of a patient (paragraph 0008, “a) obtaining a medical image of a lung;” lungs move throughout imaging, thus they are read as a dynamic image);
extracting a feature amount through a first process based on the dynamic image (paragraph 0022, “In various embodiments, CNNs are composed of multiple processing layers to which image analysis filters, or convolutions, are applied. In some embodiments, the abstracted representation of images within each layer is constructed by systematically convolving multiple filters across the image, producing a feature map which is used as input to the following layer. CNNs learn representations of images with multiple levels of increasing understanding of the image contents, which is what makes the networks deep.” … “This architecture makes it possible to process images in the form of pixels as input and to give the desired classification as output.”);
making a determination related to diagnosis through a second process based on a result of machine learning based on the feature amount (paragraph 0081, “ b) evaluating the medical image using a predictive model trained using a machine learning procedure; and c) determining, by the predictive model, whether or not the medical image is indicative of a disease or disorder of the lung,”); and
generating explanation data based on the feature amount and the determination related to the diagnosis (paragraph 0045, “According to one aspect of the present disclosure, an occlusion test to identify the areas of greatest importance used by the model in assigning diagnosis is performed. The greatest benefit of an occlusion test is that it reveals insights into the decisions of neural networks, which are sometimes referred to as “black boxes” with no transparency. Since this test is performed after training is completed, it demystifies the algorithm without affecting its results. The occlusion test also confirms that the network makes its decisions using accurate distinguishing features. In some embodiments, various platforms, systems, media, and methods recited herein comprise providing one or more of the areas of greatest importance identified by the occlusion test to a user or subject. In some embodiments, the one or more areas are provided in the form of a report (analog or electronic/digital).”… “In some embodiments, the report is annotated with medical insight such as descriptions or explanations of how the one or more areas are relevant to the diagnosis. This has the benefit of instilling greater trust and confidence in the methodology.”); and
outputting the explanation data to outside (paragraph 0045, “In some embodiments, the report is annotated with medical insight such as descriptions or explanations of how the one or more areas are relevant to the diagnosis. This has the benefit of instilling greater trust and confidence in the methodology.” paragraph 0056, “In some embodiments, the system provides the output of the analysis to the end user on the network. In some embodiments, the system sends the output to an electronic device of the end user such as a computer, smartphone, tablet or other digital processing device configured for network communications.”).
Regarding independent claim 11, the rejection of claim 1 applies directly. Additionally, Zhang further discloses A computer-readable recording medium storing an image determination program that causes a computer (paragraph 0053, “Provided herein, in certain aspects, are platforms, systems, devices, and media for analyzing medical data according to any of the methods of the present disclosure. In some embodiments, the systems and electronic devices are integrated with a program including instructions executable by a processor to carry out analysis of medical data”) to execute:
acquiring a dynamic image obtained by capturing a site including a diagnosis target region of a patient (paragraph 0008, “a) obtaining a medical image of a lung;” lungs move throughout imaging, thus they are read as a dynamic image);
extracting a feature amount through a first process based on the dynamic image (paragraph 0022, “In various embodiments, CNNs are composed of multiple processing layers to which image analysis filters, or convolutions, are applied. In some embodiments, the abstracted representation of images within each layer is constructed by systematically convolving multiple filters across the image, producing a feature map which is used as input to the following layer. CNNs learn representations of images with multiple levels of increasing understanding of the image contents, which is what makes the networks deep.” … “This architecture makes it possible to process images in the form of pixels as input and to give the desired classification as output.”);
making a determination related to diagnosis through a second process based on a result of machine learning based on the feature amount (paragraph 0081, “ b) evaluating the medical image using a predictive model trained using a machine learning procedure; and c) determining, by the predictive model, whether or not the medical image is indicative of a disease or disorder of the lung,”); and
generating explanation data based on the feature amount and the determination related to the diagnosis (paragraph 0045, “According to one aspect of the present disclosure, an occlusion test to identify the areas of greatest importance used by the model in assigning diagnosis is performed. The greatest benefit of an occlusion test is that it reveals insights into the decisions of neural networks, which are sometimes referred to as “black boxes” with no transparency. Since this test is performed after training is completed, it demystifies the algorithm without affecting its results. The occlusion test also confirms that the network makes its decisions using accurate distinguishing features. In some embodiments, various platforms, systems, media, and methods recited herein comprise providing one or more of the areas of greatest importance identified by the occlusion test to a user or subject. In some embodiments, the one or more areas are provided in the form of a report (analog or electronic/digital).”… “In some embodiments, the report is annotated with medical insight such as descriptions or explanations of how the one or more areas are relevant to the diagnosis. This has the benefit of instilling greater trust and confidence in the methodology.”); and
outputting the explanation data to outside (paragraph 0045, “In some embodiments, the report is annotated with medical insight such as descriptions or explanations of how the one or more areas are relevant to the diagnosis. This has the benefit of instilling greater trust and confidence in the methodology.” paragraph 0056, “In some embodiments, the system provides the output of the analysis to the end user on the network. In some embodiments, the system sends the output to an electronic device of the end user such as a computer, smartphone, tablet or other digital processing device configured for network communications.”).
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) 4 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang as applied to claims 1 and 5 respectively above, and further in view of Ohkura, Noriyuki, et al. "Dynamic-ventilatory digital radiography in air flow limitation: a change in lung area reflects air trapping." Respiration 99.5 (2020): 382-388. (hereinafter Ohkura).
Regarding dependent claim 4, the rejection of claim 1 is incorporated herein. Additionally, Zhang fails to explicitly disclose wherein the feature amount includes one or more still images making up the dynamic image.
However, Ohkura discloses wherein the feature amount includes one or more still images making up the dynamic image (abstract, “Sequential chest X-ray images were captured in 15 frames per second using a dynamic flat-panel imaging system.”).
Zhang is directed toward, “Disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches (abstract).” Ohkura is directed toward “determine the utility of dynamic-ventilatory digital radiography (DR) for pulmonary function assessment in patients with airflow limitation (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Zhang and Ohkura are directed toward similar methods of endeavor of medical diagnosis using medical images. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily understand the lung is a moving organ, thus acquiring a series of images as opposed to one single image can reveal data over time of the lung function. Further, some diseases may only be able to be determined based on changes over time, as opposed to individual image data. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Ohkura in order to ensure the system is applicable to multiple diseases including those that are diagnosed based on temporal changes.
Regarding dependent claim 6, the rejection of claim 5 is incorporated herein. Additionally, Zhang further discloses wherein the specific disease is COPD (paragraph 0038, “In some cases, the X-ray images are analyzed to detect lung diseases or conditions. Examples of lung diseases and conditions include chronic obstructive pulmonary disease, cystic fibrosis, lung cancer, pneumonia, interstitial lung disease, hiatal hernia, and pneumothorax.”);
wherein the disease level is a stage of the disease (paragraph 0038,” In some embodiments, the detection or diagnosis comprises a severity and/or stage of a disease or condition such as, for example, different stages of pneumonia”).
Zhang fails to explicitly disclose as further recited. However, Ohkura discloses wherein the feature amount is at least one of a lung field area, a change rate of the lung field area (abstract, “The relationship between the lung area and the rate of change in the lung area due to respiratory motion with respect to pulmonary function was analyzed.” figure 1A, “The rate of change in the lung area was calculated as follows: Rs(In to Ex) = (S_Ex – S_In)/S_In, Rs(Ex to In) = (S_In – S_Ex)/S_Ex.”), a trachea diameter, a change rate of the trachea diameter, a displacement amount of a diaphragm, a change amount of alveoli, an image density, a variance of each change amount, one or more still images making up the dynamic image, and one or more processed still images (the value is read as being calculated from the still image to determine the change over time (i.e. across multiple images)).
Zhang is directed toward, “Disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches (abstract).” Ohkura is directed toward “determine the utility of dynamic-ventilatory digital radiography (DR) for pulmonary function assessment in patients with airflow limitation (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Zhang and Ohkura are directed toward similar methods of endeavor of medical diagnosis using medical images. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily understand there are a multiple different feature values used to quantify different diseases. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Ohkura in order to ensure the system is applicable to multiple diseases by analyzing multiple different relevant feature values, without having to utilize different processes based on a disease in question.
Regarding dependent claim 7, the rejection of claim 5 is incorporated herein. Additionally, Zhang further discloses wherein the specific disease is COPD (paragraph 0038, “In some cases, the X-ray images are analyzed to detect lung diseases or conditions. Examples of lung diseases and conditions include chronic obstructive pulmonary disease, cystic fibrosis, lung cancer, pneumonia, interstitial lung disease, hiatal hernia, and pneumothorax.”);
wherein the disease level is a stage of the disease (paragraph 0038,” In some embodiments, the detection or diagnosis comprises a severity and/or stage of a disease or condition such as, for example, different stages of pneumonia”).
Zhang fails to explicitly disclose as further recited. However, Ohkura discloses wherein the feature amount is at least one of a ratio of a magnitude of a movement for each point of a lung field, an area of the entire lung field (abstract, “The relationship between the lung area and the rate of change in the lung area due to respiratory motion with respect to pulmonary function was analyzed. ” … “The rate of change in the lung area due to respiratory motion evaluated with dynamic DR reflects air trapping”), an area of the lung field with reduced movement, a ratio of the area of the lung field with reduced movement to the area of the entire lung field, one or more still images making up the dynamic image, and one or more processed still images (the value is read as being calculated from the still image to determine the change over time (i.e. across multiple images)).
Zhang is directed toward, “Disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches (abstract).” Ohkura is directed toward “determine the utility of dynamic-ventilatory digital radiography (DR) for pulmonary function assessment in patients with airflow limitation (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Zhang and Ohkura are directed toward similar methods of endeavor of medical diagnosis using medical images. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily understand there are a multiple different feature values used to quantify different diseases. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Ohkura in order to ensure the system is applicable to multiple diseases by analyzing multiple different relevant feature values, without having to utilize different processes based on a disease in question.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang as applied to claim 5 above, and further in view of Guibert, Romain, et al. "Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images." Medical image analysis 18.1 (2014): 63-82. (hereinafter Guibert).
Regarding dependent claim 8, the rejection of claim 5 is incorporated herein. Additionally, Zhang fails to explicitly disclose wherein the specific disease is tetralogy of Fallot;
wherein the disease level is a backflow rate; and
wherein the feature amount is at least one of a waveform of a pulmonary artery, a heartbeat waveform, one or more still images making up the dynamic image, and one or more processed still images.
However, Guibert discloses wherein the specific disease is tetralogy of Fallot (abstract, “This approach is applied to a data-set of 17 tetralogy of Fallot patients to simulate blood flow through the pulmonary artery under normal (healthy or synthetic valves with almost no backflow) and pathological (leaky or absent valve with backflow) conditions to better understand the impact of regurgitated blood on pressure and velocity at the outflow tracts.”);
wherein the disease level is a backflow rate (abstract, “This approach is applied to a data-set of 17 tetralogy of Fallot patients to simulate blood flow through the pulmonary artery under normal (healthy or synthetic valves with almost no backflow) and pathological (leaky or absent valve with backflow) conditions to better understand the impact of regurgitated blood on pressure and velocity at the outflow tracts.” Page 71, right column, “we investigated different degrees of pathological conditions, with 15% and 40% of backflow during diastole”); and
wherein the feature amount is at least one of a waveform of a pulmonary artery (Figure 9 pressure and flow graphed), a heartbeat waveform, one or more still images making up the dynamic image, and one or more processed still images.
Zhang is directed toward, “Disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches (abstract).” Guibert is directed toward “a data-set of 17 tetralogy of Fallot patients to simulate blood flow through the pulmonary artery under normal (healthy or synthetic valves with almost no backflow) and pathological (leaky or absent valve with backflow) conditions to better understand the impact of regurgitated blood on pressure and velocity at the outflow tracts (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Zhang and Guibert are directed toward similar methods of endeavor of medical diagnosis using medical images. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily understand there are a variety of diseases effecting a person’s lung health that can be analyzed through medical images. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Guibert in order to ensure the system is applicable to multiple diseases, without having to utilize multiple different processes based on a disease in question.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang as applied to claim 5 above, and further in view of Yamasaki, Yuzo, et al. "Efficacy of dynamic chest radiography for chronic thromboembolic pulmonary hypertension." Radiology 306.3 (2022): e220908. (hereinafter Yamasaki).
Regarding dependent claim 9, the rejection of claim 5 is incorporated herein. Additionally, Zhang fails to explicitly disclose wherein the specific disease is CTEPH;
wherein the disease level is a certainty factor; and
wherein the feature amount is at least one of a phase change amount and an amplitude change amount of a 2 blood flow image at each measurement position, one or more still images making up the dynamic image, and one or more processed still images.
However, Yamasaki discloses wherein the specific disease is CTEPH (abstract, “ detection of chronic thromboembolic PH (CTEPH).”);
wherein the disease level is a certainty factor (abstract, “ The sensitivity, specificity, and accuracy of DCR were 97%, 86%, and 92%,”); and
wherein the feature amount is at least one of a phase change amount and an amplitude change amount of a blood flow image at each measurement position (page 2, right column, “The temporal change in pixel value (x-ray translucency) for each pixel was calculated from these images. The end-diastolic phase was automatically estimated from the change in pixel value in the heart. Temporal changes in pixel values from the end-diastolic phase were color-coded and visualized as dynamic perfusion images. Because small (dark image in radiography, high x-ray translucency) and large (bright image in radiography, low x-ray translucency) pixel values represent low and high blood volume, respectively, they were considered to indicate a change in blood volume in the lung, representing pulmonary circulation.”), one or more still images making up the dynamic image, and one or more processed still images.
Zhang is directed toward, “Disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches (abstract).” Yamasaki is directed toward “to compare the performance of dynamic chest radiography (DCR) and lung V/Q scanning for detection of chronic thromboembolic PH (CTEPH) (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Zhang and Yamasaki are directed toward similar methods of endeavor of medical diagnosis using medical images. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily understand there are a variety of diseases effecting a person’s lung health that can be analyzed through medical images. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Yamasaki in order to ensure the system is applicable to multiple diseases, without having to utilize multiple different processes based on a disease in question.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2016/0022240 discloses, “The image storage memory stores data of a plurality of images in different respiratory phases. The calculation circuitry calculates a motion amount of a region between the plurality of images for each pixel or area. The level decision circuitry decides a level concerning a severity of chronic obstructive pulmonary disease for each pixel or area. The output interface circuitry outputs information concerning the decided level (abstract).”
U.S. Publication No. 2021/0304896 discloses, “ The method may further include generating a diagnosis result with respect to the subject based on the first feature information (abstract).”
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661
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