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 .
Response to Arguments
The reply filed on 23 April 2026 has been entered. Applicant’s arguments with respect to claims 1, 4-10, 13-15 and 17-20 have been considered but are moot in view of new ground(s) of rejection caused by the amendments. An applicant interview IS recommended in this case.
Claims 1, 4-10, 13-15 and 17-20 are pending in this application and have been considered below. Claims 2-3, 11-12 and 16 are canceled by the applicant.
Information Disclosure Statement
The IDSs dated 8 April 2024 and 12 August 2025 that have been previously considered remain placed in the application file.
1st Claim Interpretation
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Claims 6, 7, 17 and 18 recite “at least one of.” Since “at least one of” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
2nd 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 claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f), except as otherwise indicated. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in this Office action.
Such claim limitation(s) is/are:
“means for determining image acquisition parameters” in claim 10;
“means for receiving one or more PCCT images” in claim 10;
“means for performing one or more medical imaging analysis tasks” in claim 10;
“means for outputting results “ in claim 10; and
“means for determining the image acquisition parameters” in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), 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 this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
Claims 11 and 16, which were previously rejected under 35 USC 112(b) are canceled. The rejection is withdrawn.
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 1-9 and 15-20 were rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 15 have been amended. The rejection of claims 1 and 15 as an abstract idea is withdrawn.
However, claims 1-9 and 15-20 are now rejected under 35 USC 101 as being directed to a mental process. All of the claims are method claims (1-9) or apparatus/machine claims (15-20) under (Step 1), but under Step 2A all of these claims recite abstract ideas and specifically mental processes—concepts performed in the human mind including observation, evaluation, judgement and opinion which are generally described as a human visually observing a label to judge the locations and dimensions of empty regions in order to insert content into these empty regions; furthermore these mental processes are more particularly:
Recited in claims 1 and 15 as:
determining image acquisition parameters of a PCCT (photon-counting computed tomography) image acquisition device…
acquiring a plurality of candidate PCCT images using varying image acquisition parameters…
identifying, using one or more machine learning based models, one of the plurality of candidate PCCT images as having a highest analytical accuracy for performing one or more medical imaging analysis tasks…
determining the image acquisition parameters as parameters corresponding to the identified candidate PCCT image…
receiving one or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters…
performing one or more medical imaging analysis tasks analyzing the anatomical object based on the one or more PCCT images using one or more machine learning based models…
outputting results of the one or more medical imaging analysis tasks.
Consider also that “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea” as per MPEP 2106.04(a)(2)(III)(B). See also footnotes 14 and 15 of the Federal Register Notice. As detailed above, the steps of determining parameters, identifying images, performing image analysis tasks, etc. may be practically performed in the human mind with the use of a physical aid such as a pen and paper (marking the label on the package with a pen).
Under Step 2B, this judicial exception is not integrated into a practical application because each of claims 1-9 and 15-20 do not recite additional elements that integrate the exception into a practical application. The only additional elements {machine learning based tools, acquiring images and outputting results} are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract task which are not indicative of integration into a practical application as per MPEP 2106.05(f). See also MPEP 2106.04(a)(2)(III) with respect to Mental Processes: “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer”. See also MPEP 2106.04(a)(2)(III)(C)(3) Using a computer as tool to perform a mental process and MPEP 2106.04(a)(2)(III)(D) as well as the case law cited therein.
In other words, the additional elements and/or are recited at a high level of generality that does not amount to significantly more and/ such that they could practically be performed in the human mind.
For all of the above reasons, taken alone or in combination, claims 1-9 and 15-20 recite a non-statutory mental process.
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4-10, 13-15 and 17-20 (all claims) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2019 0122073 A1, (Ozdemir et al.) in view of US Patent Publication 2019 0050987 A1, (Hsieh et al.). The references are listed in a PTO-892 from the Office Action in which they are first used. If a reference is not identifiable (e.g., due to a typo), it can be identified by searching for the quoted text.
Claim 1
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Regarding Claim 1, Ozdemir et al. teach a computer-implemented method ("a system and method for detecting and/or characterizing a property of interest in a multi-dimensional space," paragraph [0009])comprising:
determining image acquisition parameters of a PCCT (photon-counting computed tomography) image acquisition device for acquiring PCCT images ("the medium can be energy emitted by a (e.g.) Positron Emission Tomography (PET) scan tracer particle, or photons emitted due to optically or electrically excited molecules, as occurs in Raman spectroscopy. All forms of electromagnetic, particle and/or photonic energy can characterize the medium measured herein," paragraph [0031] and "iteratively adjusts at least one image acquisition parameter (e.g. camera focus, exposure time, radar power level, frame rate, etc.) in a manner that optimizes or enhances the confidence level" paragraph [0013]) by;
acquiring a plurality of candidate PCCT images using varying image acquisition parameters ("This system and method acquires a first set of images, analyzes the first set of images to detect the property of interest and a confidence level associated with the detection," paragraph [0013] where the first set of images are the candidate images), and
determining the image acquisition parameters as parameters corresponding to the identified candidate PCCT image ("iteratively adjusts at least one image acquisition parameter (e.g. camera focus, exposure time, radar power level, frame rate, etc.) in a manner that optimizes or enhances the confidence level," paragraph [0013]);
receiving one or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters ("the acquired data is medical image data, including at least one of CT scan images, MM images, or targeted contrast ultrasound images of human tissue," paragraph [0011]);
performing one or more medical imaging analysis tasks analyzing the anatomical object based on the one or more PCCT images using one or more machine learning based models ("The image data can be preprocessed as appropriate to include edge information, blobs, etc. (for example based on image analysis conducted using appropriate, commercially available machine vision tools)," paragraph [0032] where machine vision is machine learning); and
outputting results of the one or more medical imaging analysis tasks ("A GUI process(or) 156 organizes and displays (or otherwise presents ( e.g. for storage)) the analyzed data results in a graphical and/or textual format for a user to employ in performing a related task," paragraph [0033]).
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Ozdemir et al. is not relied upon to explicitly teach all of highest analytical accuracy.
However, Hsieh et al. teach identifying, using one or more machine learning based models, one of the plurality of candidate PCCT images as having a highest analytical accuracy for performing one or more medical imaging analysis tasks ("Deep learning input includes labeled images and/or unlabeled images, for example. The labeled images can be classified based on clinical applications, human anatomy, and/or other important attribute. The labeled images have also undergone image quality evaluation, and an IQI can be assigned to each labeled image. The labeled images are rated based on a confidence level for making clinical decisions using the image. For example, a level 3 indicates sufficient confidence to make a decision based on the image, while a level 5 indicates the highest level of confidence in making the decision based on the image. A level 1, on the other hand, indicates that such image cannot be used for diagnosis. The labeled images are used to train the deep learning image quality algorithm initially," paragraph [0225], where confidence in making the decision is having a highest analytical accuracy).
Therefore, taking the teachings of Ozdemir et al. and Hsieh et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “quantifying Uncertainty in reasoning about 2D and 3D Spatial features with a computer machine learning architecture” as taught by Ozdemir et al. to use “Deep Learning Medical Systems” as taught by Hsieh et al., showing that Ozdemir et al. and Hsieh et al. are analogous art because both are medical systems using machine learning. The suggestion/motivation for combination is that, “Physicians have more patients, less time, and are inundated with huge amounts of data, and they are eager for assistance.” as noted by the Hsieh et al. disclosure in paragraph [0004], which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that physicians will need faster and better methods to look at results; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 4
Regarding claim 4, Ozdemir et al. teach the computer-implemented method of claim 1, wherein determining image acquisition parameters of a PCCT (photon-counting computed tomography) image acquisition device for acquiring PCCT images comprises:
determining the image acquisition parameters of the PCCT image acquisition device for acquiring PCCT images optimized for performing the one or more medical imaging analysis tasks ("This system and method acquires a first set of images, analyzes the first set of images to detect the property of interest and a confidence level associated with the detection," paragraph [0013] where the first set of images are the candidate images).
Claim 5
Regarding claim 5, Ozdemir et al. teach the computer-implemented method of claim 1, wherein the image acquisition parameters comprise a number of energy bands and associated energy thresholds ("Early detection of pulmonary nodules is crucial for early diagnosis of lung cancer. CADe of pulmonary nodules using low-dose computed tomography (CT)," paragraph [0006] where low dose teaches energy bands and thresholds).
Claim 6
Regarding claim 6, Ozdemir et al. teach the computer-implemented method of claim 1, wherein the image acquisition parameters comprise at least one of reconstructed image spacing, slice thickness, reconstruction kernels, or dose ("iteratively adjusting one or more image acquisition parameter(s) (e.g. camera focus, exposure time, X-ray/RADAR/ SONAR/LIDAR power level, frame rate, etc.) in a manner that optimizes/enhances the confidence level associated with detection of the property of interest," paragraph [0013]).
Claim 7
Regarding claim 7, Ozdemir et al. teach the computer-implemented method of claim 1, wherein the one or more medical imaging analysis tasks comprise at least one of detection, segmentation, size quantification, typology classification, or malignancy assessment of the anatomical object of the patient ("preprocessing for reduction of artifacts, image noise reduction, leveling (harmonization) of image quality (increased contrast) for clearing the image parameters (e.g. different exposure settings), and filtering; (b) segmentation for differentiation of different structures in the image (e.g. heart, lung, ribcage, blood vessels, possible round lesions, matching with anatomic database, and sample gray-values in volume of interest); (c) structure/ROI (Region of Interest) analysis, in which a detected region is analyzed individually for special characteristics, which can include compactness, form, size and location, reference to close-by structures/ ROIs, average grey level value analysis within the ROI, and proportion of grey levels to the border of the structure inside the ROI," paragraph [0005]).
Claim 8
Regarding claim 8, Ozdemir et al. teach the computer-implemented method of claim 1, wherein the one or more machine learning based models are trained using annotated PCCT training images ("Lung Image Database Consortium image collection (LIDC-IDRI), which consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions," paragraph [0051]).
Claim 9
Regarding claim 9, Ozdemir et al. teach the computer-implemented method of claim 1, wherein the anatomical object comprises a pulmonary nodule of the patient ("Lung Image Database Consortium image collection (LIDC-IDRI), which consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions," paragraph [0051]).
Claim 10
Regarding claim 10, Ozdemir et al. teach an apparatus ("a system and method for detecting and/or characterizing a property of interest in a multi-dimensional space," paragraph [0009]) comprising:
means for determining image acquisition parameters of a PCCT (photon-counting computed tomography) image acquisition device for acquiring PCCT images ("the medium can be energy emitted by a (e.g.) Positron Emission Tomography (PET) scan tracer particle, or photons emitted due to optically or electrically excited molecules, as occurs in Raman spectroscopy. All forms of electromagnetic, particle and/or photonic energy can characterize the medium measured herein," paragraph [0031] and "iteratively adjusts at least one image acquisition parameter (e.g. camera focus, exposure time, radar power level, frame rate, etc.) in a manner that optimizes or enhances the confidence level" paragraph [0013]) by;
acquiring a plurality of candidate PCCT images using varying image acquisition parameters ("This system and method acquires a first set of images, analyzes the first set of images to detect the property of interest and a confidence level associated with the detection," paragraph [0013] where the first set of images are the candidate images), and
determining the image acquisition parameters as parameters corresponding to the identified candidate PCCT image ("iteratively adjusts at least one image acquisition parameter (e.g. camera focus, exposure time, radar power level, frame rate, etc.) in a manner that optimizes or enhances the confidence level," paragraph [0013]);
means for receiving one or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters ("the acquired data is medical image data, including at least one of CT scan images, MM images, or targeted contrast ultrasound images of human tissue," paragraph [0011]);
means for performing one or more medical imaging analysis tasks analyzing the anatomical object based on the one or more PCCT images using one or more machine learning based models ("The image data can be preprocessed as appropriate to include edge information, blobs, etc. (for example based on image analysis conducted using appropriate, commercially available machine vision tools)," paragraph [0032] where machine vision is machine learning); and
means for outputting results of the one or more medical imaging analysis tasks ("A GUI process(or) 156 organizes and displays (or otherwise presents ( e.g. for storage)) the analyzed data results in a graphical and/or textual format for a user to employ in performing a related task," paragraph [0033]).
Ozdemir et al. is not relied upon to explicitly teach all of highest analytical accuracy.
However, Hsieh et al. teach identifying, using one or more machine learning based models, one of the plurality of candidate PCCT images as having a highest analytical accuracy for performing one or more medical imaging analysis tasks ("Deep learning input includes labeled images and/or unlabeled images, for example. The labeled images can be classified based on clinical applications, human anatomy, and/or other important attribute. The labeled images have also undergone image quality evaluation, and an IQI can be assigned to each labeled image. The labeled images are rated based on a confidence level for making clinical decisions using the image. For example, a level 3 indicates sufficient confidence to make a decision based on the image, while a level 5 indicates the highest level of confidence in making the decision based on the image. A level 1, on the other hand, indicates that such image cannot be used for diagnosis. The labeled images are used to train the deep learning image quality algorithm initially," paragraph [0225], where confidence in making the decision is having a highest analytical accuracy).
Ozdemir et al. and Hsieh et al. are combined as per claim 1.
Claim 13
Regarding claim 13, Ozdemir et al. teach the apparatus of claim 10, wherein the means for determining image acquisition parameters of a PCCT (photon-counting computed tomography) image acquisition device for acquiring PCCT images comprises:
means for determining the image acquisition parameters of the PCCT image acquisition device for acquiring PCCT images optimized for performing the one or more medical imaging analysis tasks ("This system and method acquires a first set of images, analyzes the first set of images to detect the property of interest and a confidence level associated with the detection," paragraph [0013] where the first set of images are the candidate images).
Claim 14
Regarding claim 14, Ozdemir et al. teach the apparatus of claim 10, wherein the image acquisition parameters comprise a number of energy bands and associated energy thresholds ("Early detection of pulmonary nodules is crucial for early diagnosis of lung cancer. CADe of pulmonary nodules using low-dose computed tomography (CT)," paragraph [0006] where low dose teaches energy bands and thresholds).
Claim 15
Regarding claim 15, Ozdemir et al. teach a non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out operations ("a system and method for detecting and/or characterizing a property of interest in a multi-dimensional space," paragraph [0009]) comprising:
determining image acquisition parameters of a PCCT (photon-counting computed tomography) image acquisition device for acquiring PCCT images ("the medium can be energy emitted by a (e.g.) Positron Emission Tomography (PET) scan tracer particle, or photons emitted due to optically or electrically excited molecules, as occurs in Raman spectroscopy. All forms of electromagnetic, particle and/or photonic energy can characterize the medium measured herein," paragraph [0031] and "iteratively adjusts at least one image acquisition parameter (e.g. camera focus, exposure time, radar power level, frame rate, etc.) in a manner that optimizes or enhances the confidence level" paragraph [0013]) by;
acquiring a plurality of candidate PCCT images using varying image acquisition parameters ("This system and method acquires a first set of images, analyzes the first set of images to detect the property of interest and a confidence level associated with the detection," paragraph [0013] where the first set of images are the candidate images), and
determining the image acquisition parameters as parameters corresponding to the identified candidate PCCT image ("iteratively adjusts at least one image acquisition parameter (e.g. camera focus, exposure time, radar power level, frame rate, etc.) in a manner that optimizes or enhances the confidence level," paragraph [0013]);
receiving one or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters ("the acquired data is medical image data, including at least one of CT scan images, MM images, or targeted contrast ultrasound images of human tissue," paragraph [0011]);
performing one or more medical imaging analysis tasks analyzing the anatomical object based on the one or more PCCT images using one or more machine learning based models ("The image data can be preprocessed as appropriate to include edge information, blobs, etc. (for example based on image analysis conducted using appropriate, commercially available machine vision tools)," paragraph [0032] where machine vision is machine learning); and
outputting results of the one or more medical imaging analysis tasks ("A GUI process(or) 156 organizes and displays (or otherwise presents ( e.g. for storage)) the analyzed data results in a graphical and/or textual format for a user to employ in performing a related task," paragraph [0033]).
Ozdemir et al. is not relied upon to explicitly teach all of highest analytical accuracy.
However, Hsieh et al. teach identifying, using one or more machine learning based models, one of the plurality of candidate PCCT images as having a highest analytical accuracy for performing one or more medical imaging analysis tasks ("Deep learning input includes labeled images and/or unlabeled images, for example. The labeled images can be classified based on clinical applications, human anatomy, and/or other important attribute. The labeled images have also undergone image quality evaluation, and an IQI can be assigned to each labeled image. The labeled images are rated based on a confidence level for making clinical decisions using the image. For example, a level 3 indicates sufficient confidence to make a decision based on the image, while a level 5 indicates the highest level of confidence in making the decision based on the image. A level 1, on the other hand, indicates that such image cannot be used for diagnosis. The labeled images are used to train the deep learning image quality algorithm initially," paragraph [0225], where confidence in making the decision is having a highest analytical accuracy).
Ozdemir et al. and Hsieh et al. are combined as per claim 1.
Claim 17
Regarding claim 17, Ozdemir et al. teach the non-transitory computer-readable storage medium of claim 15, wherein the image acquisition parameters comprise at least one of reconstructed image spacing, slice thickness, reconstruction kernels, or dose ("iteratively adjusting one or more image acquisition parameter(s) (e.g. camera focus, exposure time, X-ray/RADAR/ SONAR/LIDAR power level, frame rate, etc.) in a manner that optimizes/enhances the confidence level associated with detection of the property of interest," paragraph [0013]).
Claim 18
Regarding claim 18, Ozdemir et al. teach the non-transitory computer-readable storage medium of claim 15, wherein the one or more medical imaging analysis tasks comprise at least one of detection, segmentation, size quantification, typology classification, or malignancy assessment of the anatomical object of the patient ("preprocessing for reduction of artifacts, image noise reduction, leveling (harmonization) of image quality (increased contrast) for clearing the image parameters (e.g. different exposure settings), and filtering; (b) segmentation for differentiation of different structures in the image (e.g. heart, lung, ribcage, blood vessels, possible round lesions, matching with anatomic database, and sample gray-values in volume of interest); (c) structure/ROI (Region of Interest) analysis, in which a detected region is analyzed individually for special characteristics, which can include compactness, form, size and location, reference to close-by structures/ ROIs, average grey level value analysis within the ROI, and proportion of grey levels to the border of the structure inside the ROI," paragraph [0005]).
Claim 19
Regarding claim 19, Ozdemir et al. teach the non-transitory computer-readable storage medium of claim 15, wherein the one or more machine learning based models are trained using annotated PCCT training images ("Lung Image Database Consortium image collection (LIDC-IDRI), which consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions," paragraph [0051]).
Claim 20
Regarding claim 20, Ozdemir et al. teach the non-transitory computer-readable storage medium of claim 15, wherein the anatomical object comprises a pulmonary nodule of the patient ("Lung Image Database Consortium image collection (LIDC-IDRI), which consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions," paragraph [0051]).
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 20219 0021677 A1 to Grbic et al. discloses a method for assessing a patient include determining scan parameters of the patient using deep learning, scanning the patient using the determining scan parameters to generate at least one three dimensional (3D) image, detecting an injury from the 3D image using the deep learning, classifying the detected injury using the deep learning and assessing a criticality of the detected injury based on the classifying using the deep learning.
US Patent Publication 2016 0364862 A1 to Reicher et al. discloses performing image analytics using graphical reporting associated with clinical images. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/H.E.W/Examiner, Art Unit 2664
Date: 29 July 2026
/PING Y HSIEH/Primary Examiner, Art Unit 2664
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