DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Final Office Action is in response to the Amendment and Remarks filed 07/28/2026, wherein:
Claims 1, 15, 20, 73-74 and 81 are amended. Claims 1-6, 8, 10, 13, 15, 18, 20, 68-71, 73 and 80-81 are pending review herein.
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, 8, 10, 11, 13, 15, 18, 20, 68-71, 73 and 80-81 are rejected under 35 U.S.C. §101 because they recite an abstract idea without significantly more.
Claim 1 recites, wherein the abstract idea is not emboldened:
A computer-implemented method for performing image quality review of medical images in a mammography medical imaging system, wherein the method is performed by at least one processor and the method comprises: receiving an electronic request from a user to perform the image quality review for a selected time period; retrieving image quality data from a database associated with a plurality ofby inputting covariates into a predictive model trained to predict probability of image quality error events, the one or more image quality scores indicating mammography positioning and acquisition adequacy including at least positioning and compression during image acquisition by the mammographic machine; accessing a predictive classifier stored in a memory, wherein the predictive classifier is configured to generate a technical recall classification based on the image quality data, and wherein the predictive classifier comprises a statistical model that is developed based on historical image quality results associated with technical recall and no technical recall outcomes; applying the predictive classifier to the retrieved image quality data to generate a predicted probability that an image study should undergo visual quality review (VQR), the predicted probability being a VQR score of the image study; comparing the VQR score to a VQR threshold, wherein the VQR threshold is algorithmically determined to separate technical recall patients from no technical recall patients; generating a VQR recommendation based on the comparison of the VQR score to the VQR threshold and receiving reviewer feedback associated with the VQR recommendation; retraining the predictive model used to generate the one or more image quality scores based on the reviewer feedback; and responsive to the VQR recommendation and the reviewer feedback, implementing and tracking at least one corrective action associated with the positioning and compression during image acquisition to improve image quality performance of the mammography medical imaging system.
Claim 20 recites substantially similar limitations (and includes the additional elements of “An electronic device for providing image quality review of medical images in a mammography medical imaging system, wherein the electronic device comprises: a memory unit that includes software instructions for visualizing image quality data; a network unit for communicating with other devices and software programs in the mammography medical imaging system; a database that stores image quality data; and a processor unit in communication with the memory unit, the network unit and the database, the processor unit having at least one processor. The claimed invention is broadly directed to the abstract idea of collecting medical image information, analyzing the information, and determining feedback quality related to the medical image for display based on the analyses,” which are generic computer devices used to apply the abstract idea, without more).
The limitations of “receiving a request from a user to perform the image quality review for a selected time period; retrieving image quality data associated with a plurality of mammography images for one or more image studies acquired by a mammographic machine in the mammography medical imaging system, wherein the image quality data comprises one or more image quality scores that are generated from the mammography images, the one or more image quality scores indicating mammography positioning and acquisition adequacy including at least positioning and compression during image acquisition by the mammographic machine; generate a technical recall classification based on the image quality data, and a statistical model that is developed based on historical image quality results associated with technical recall and no technical recall outcomes; generate a predicted probability that an image study should undergo visual quality review (VQR), the predicted probability being a VQR score of the image study; comparing the VQR score to a VQR threshold, wherein the VQR threshold is determined to separate technical recall patients from no technical recall patients; generating a VQR recommendation based on the comparison of the VQR score to the VQR threshold and receiving reviewer feedback associated with the VQR recommendation; and responsive to the VQR recommendation and the reviewer feedback, implementing and tracking at least one corrective action associated with the positioning and compression during image acquisition to improve image quality performance of the mammography medical imaging system,” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic recitation of an “electronic device” in a “medical imaging system,” “a memory unit that includes software instructions for visualizing image quality data,” “a network unit for communicating with other devices and software programs in the medical imaging system,” “a processor unit in communication with the memory unit and the network unit, the processor unit having at least one processor,” a “database” a “graphical user interface (GUI),” “a predictive model trained to predict probability” and “a predictive classifier” and training/retraining a model, analyzing medical image data and determining quality and/or positioning information for display based on the analyses, in the context of this claim, is an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations allowing users to access medical image data, analyze the data, and generate a relevant categorization of the medical image data based on the analyses. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people including a patient and clinician. Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic “electronic device” in a “medical imaging system,” “a memory unit that includes software instructions for visualizing image quality data,” “a network unit for communicating with other devices and software programs in the medical imaging system,” “a processor unit in communication with the memory unit and the network unit, the processor unit having at least one processor,” a “database,” a “graphical user interface (GUI),” “a predictive model trained to predict probability” and “a predictive classifier” and training/retraining a model, does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of being implemented by a generic “electronic device” in a “medical imaging system,” “a memory unit that includes software instructions for visualizing image quality data,” “a network unit for communicating with other devices and software programs in the medical imaging system,” “a processor unit in communication with the memory unit and the network unit, the processor unit having at least one processor,” a “database” a “graphical user interface (GUI),” “a predictive model trained to predict probability” and “a predictive classifier” and training/retraining a model, for the sending and receiving and display of information related to a patient and/or relevant medical images. The devices in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying or sending selected information, or as mathematical concepts) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient medical image analysis and general diagnostic techniques between a physician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of being implemented by a generic “electronic device” in a “medical imaging system,” “a memory unit that includes software instructions for visualizing image quality data,” “a network unit for communicating with other devices and software programs in the medical imaging system,” “a processor unit in communication with the memory unit and the network unit, the processor unit having at least one processor,” a “database” a “graphical user interface (GUI),” “a predictive model trained to predict probability” and “a predictive classifier” and training/retraining a model, amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea and do not overcome the rejection under 35 U.S.C. §101. Claims 2, 3, 4, 6, 10, 68, 69, 70 and 73-74 further describe the GUI, and/or image study ID or related data, which is recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the image quality GUI and image study ID and additional data do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 5 and 71 describe presenting the user with follow-up tasks and further limits the abstract idea. Claims 8 details displaying a subwindow and image quality data comprising errors, which is recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the GUI subwindows and information does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claim 13 details a visual quality review and further limits the abstract idea. Claim 15 describes a user selection and further limits the abstract idea. Claims 75-80 detail quality components, remedial actions, sending notifications or additions, or image quality categories and further limit the abstract idea. Claim 81 further describes a processor unit and at a high level of generality such that it amounts to mere instructions to apply the judicial exception using a generic computing component. Even in combination, the processor unit does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself.
Therefore, the claims are not patent eligible.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 20, 76-77 and 79-81 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2020/227794 A1 to Lotufo et al., hereinafter “Lotufo,” in view of in view of in view of U.S. 2020/0222023 A1 to Wong et al., hereinafter “Wong,” in view of U.S. 2014/0072192 A1 to Reiner, hereinafter “Reiner.”
Regarding claim 1, Lotufo discloses A computer-implemented method for performing image quality review of medical images in a mammography medical imaging system, wherein the method is performed by at least one processor (See Lotufo at least at Abstract; Paras. [0054]-[0055]; Fig. 1) retrieving image quality data from a database associated with a plurality of mammography images for one or more image studies acquired by a mammographic machine in the mammography medical imaging system, wherein the image quality data comprises one or more image quality scores that are generated from the mammography images by inputting covariates into a predictive model trained to predict probability of image quality error events, the one or more image quality scores indicating mammography positioning and acquisition adequacy including at least positioning and compression during image acquisition by the mammographic machine (See Lotufo at least at Abstract; Paras. [0005], [0009], [0018]-[0021] (deep learning, mammography positioning, errors, image evaluations (i.e., scores)), [0034], [0046]-[0050], [0061]-[0068] (database, predictive model “[F]oresee the need for a new image capture with a better positioning.”); Claims 1-14; Fig. 1); accessing a predictive classifier stored in a memory, wherein the predictive classifier is configured to generate a technical recall classification based on the image quality data, and wherein the predictive classifier comprises a statistical model that is developed based on historical image quality results associated with technical recall and no technical recall outcomes (See id. at least at Paras. [0005] (reschedule), [0022]-[0034] (trained classifier), [0042]-[0043] (repeat the exam or not), [0061]-[0068]; Claims 1-14; Fig. 1); applying the predictive classifier to the retrieved image quality data to generate a predicted probability that an image study should undergo visual quality review (VQR), the predicted probability being a VQR score of the image study (See id. at least at Abstract; Paras. [0005], [0009], [0018]-[0021] (deep learning, mammography positioning, errors, image evaluations (i.e., scores)), [0034], [0046]-[0050], [0061]-[0068] (database, predictive model “[F]oresee the need for a new image capture with a better positioning.”); Claims 1-14; Fig. 1); comparing the VQR score to a VQR threshold, wherein the VQR threshold is algorithmically determined to separate technical recall patients from no technical recall patients (See id. at least at Abstract; Paras. [0005] (reschedule the scanning, need for new image capture), [0009], [0018]-[0022] (deep learning, mammography positioning, errors, image evaluations (i.e., scores)), [0034], [0042]-[0043], [0046]-[0050], [0061]-[0068] (database, predictive model “[F]oresee the need for a new image capture with a better positioning.”); Claims 1-14; Fig. 1).
The reference may not specifically describe but Wong teaches generating a VQR recommendation based on the comparison of the VQR score to the VQR threshold and receiving reviewer feedback associated with the VQR recommendation (See Wong at least at Paras. [0012]-[0016], [0105]-[0110] (“If the centralized system determined that the technologist's or facility's scores are below a particular threshold, the technologist or members of the facility may be recommended for patient positioning education or quality control improvements. In one example, a look-up table or algorithm determines whether corrective action is needed and what type of action to recommend based in part on the score[…] the report may be automatically generated on a periodic basis that includes information such as the score information for that facility, the number of times corrective procedures were taken, the number of times that corrective measures such as education and quality control measures were recommended and were taken.”); Claims 1-8; Figs. 1-9); retraining the predictive model used to generate the one or more image quality scores based on the reviewer feedback (See Wong at least at Abstract; Paras. [0005]-[0008] (corrective action), [0012]-[0016] (machine learning models trained and retrained to identify patterns based on positioning scores reviewed by a technician), [0105]-[0110]; Claims 1-8; Figs. 1-9); responsive to the VQR recommendation and the reviewer feedback, implementing and tracking at least one corrective action associated with the positioning and compression during image acquisition to improve image quality performance of the mammography medical imaging system (See id.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo to incorporate the teachings of Wong and provide generating reviewer feedback for a mammograph imaging procedure and training and improving a machine learning model. Wong is directed to an image quality compliance tool. Incorporating the image quality compliance and machine learning as in Wong with the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
The references may not specifically describe but Reiner teaches receiving an electronic request from a user to perform the image quality review for a selected time period (See Reiner at least at Paras. [0013]-[0021], [0031]-[0032], [0070]-[0074], [0078]-[0082], [0108]-[0114]; Claim 3; Figs. 2, 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo and Wong to incorporate the teachings of Reiner and provide various data for a relevant medical image study. Reiner relates to quality assurance in medical imaging. Incorporating the quality assurance assessment for medical imaging as in Reiner with the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 2, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 1, and Lotufo further discloses wherein the image quality GUI that is displayed comprises a window that includes an image quality metric that summarizes the image quality of the image study (See Lutofo at least at Paras. [0018]-[0022], [0035], [0041]-[0043]; Claims 1-14; Fig. 1).
Regarding claim 20, claim 20 recites substantially the same limitations as included in independent claim 1. Thus, claim 20 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 1.
Regarding claim 15, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 1 and Reiner further teaches receiving a first user selection of an institution, a department for the institution, or one or more Medical Imaging Technologists (MITs) (See Reiner at least at Paras. [0078]-[0084] (“The technologist could simply save the image(s) of interest, annotate them accordingly, and provide an overall image quality score to the imaging dataset.”), [0092]-[0095], [0102] (“The application of these data-derived EBM guidelines can take place at the level of the individual operator (e.g., technologist, radiologist), department, or institution and be made available for access by medical imaging consumers (e.g., patients, referring clinicians, payers).”), [0108]-[0114] (technologists, departments, institutions, manual selection)); receiving a second user selection of an image quality parameter, an image quality parameter feature or an image quality category (See id. at least at Paras. [0078], [0084], [0092]-[0113]); retrieving selected image quality data from the database that corresponds to the user selections for a plurality of image studies over the selected time period (See id. at least at Abstract; Paras. [0013]-[0032], [0078]-[0083], [0092], [0102], [0108]-[0114] (defined time period of review); computing statistical metrics of variation of error rate for the retrieved selected image quality data over the selected time period; and display the error rates for the selected time period (See id. at least at Paras. [0028]-[0032], [0084], [0100]-[0114] (statistical analysis of image quality database, and over a time period of review), [0171], [0199]); and determining a remedial action based on a pattern of an observed image quality problem (See Reiner at least at Paras. [0030]-[0032], [0178], [0198]; Claims 3, 18; Figs. 2, 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo and Wong to incorporate the teachings of Reiner and provide image quality data and scores and requests. Reiner relates to quality assurance in medical imaging. Incorporating the quality assurance assessment for medical imaging as in Reiner with the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 76, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 15, and Reiner further teaches wherein the remedial action includes i) scheduling a follow-up visual quality review of the image studies; and/or ii) sending an electronic request message to another user to review the image studies to provide a second assessment (See Reiner at least at Paras. [0008], [0083]-[0086], [0095]-[0097], [0180]-[0111]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo and Wong to incorporate the teachings of Reiner and provide image quality data and scores and requests. Reiner relates to quality assurance in medical imaging. Incorporating the quality assurance assessment for medical imaging as in Reiner with the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 77, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 15, and Reiner further teaches sending automated notifications and/or automated additions to imaging study worklists based on criteria that are set using image quality features or a study level quality score where the criteria are pre-0determined and user-configurable (See id. at least at Paras. [0072]-[0078], [0085]-[0087], [0104]-[0112], [0188]-[0204]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo and Wong to incorporate the teachings of Reiner and provide image quality data and notificaitons and requests. Reiner relates to quality assurance in medical imaging. Incorporating the quality assurance assessment for medical imaging as in Reiner with the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claims 79 and 80, claims 79 and 80 recite substantially the same limitations as included in claims 76 and 77, respectively. Thus, claims 79 and 80 are rejected under the same grounds of rejection and for the same reasoning as applied to claims 76 and 77.
Regarding claim 81, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 20, and Reiner further teaches wherein the at least one processor unit is further configured to: receive a first user selection of an institution, a department for the institution, or one or more Medical Imaging Technologists (MITs) (See Reiner at least at Paras. [0078]-[0084] (“The technologist could simply save the image(s) of interest, annotate them accordingly, and provide an overall image quality score to the imaging dataset.”), [0092]-[0095], [0102] (“The application of these data-derived EBM guidelines can take place at the level of the individual operator (e.g., technologist, radiologist), department, or institution and be made available for access by medical imaging consumers (e.g., patients, referring clinicians, payers).”), [0108]-[0114] (technologists, departments, institutions, manual selection)); receive a second user selection of an image quality parameter, an image quality parameter feature or an image quality category (See id. at least at Paras. [0078], [0084], [0092]-[0113]); retrieve image quality data from the database that corresponds to the user selections for a plurality of image studies over the selected time period (See id. at least at Abstract; Paras. [0013]-[0032], [0078]-[0083], [0092], [0102], [0108]-[0114] (defined time period of review); compute statistical metrics of variation of error rate for the retrieved image quality data over the selected time period; and display the error rates for the selected time period (See id. at least at Paras. [0028]-[0032], [0084], [0100]-[0114] (statistical analysis of image quality database, and over a time period of review), [0171], [0199]); and determine a remedial action based on a pattern of an observed image quality problem (See Reiner at least at Paras. [0030]-[0032], [0178], [0198]; Claims 3, 18; Figs. 2, 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo and Wong to incorporate the teachings of Reiner and provide image quality data and selections and requests. Reiner relates to quality assurance in medical imaging. Incorporating the quality assurance assessment for medical imaging as in Reiner with the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Claims 3, 8, 13, 18, 75 and 78 are rejected under 35 U.S.C. 103 as being unpatentable over Lofuto, in view of Wong, in view of Reiner and further in view of U.S. 2019/0287241 A1 to Hill et al., hereinafter “Hill.”
Regarding claim 3, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 2. The references may not specifically describe but Hill teaches displaying additional measures in the image quality GUI where the additional measures are related to one or more images of the image study and comprise: breast density data, cancer risk data, a priority score or any combination thereof (See Hill at least at Paras. [0014], [0075], [0103]-[0109]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lotufo, Wong and Reiner to incorporate the teachings of Hill and provide image quality data including breast density data, cancer risk and scores. Hill is directed to systems for clinical decision optimization and feedback on soft tissue image quality. Incorporating the processing of medical images and studies as in Hill with the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 8, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 6. The references may not specifically describe but Hill teaches wherein the image quality data shown in the subwindow comprises names of image parameter feature scores and scores or image quality symbols for the image parameter feature scores, wherein the image quality symbols comprise error symbols or pass symbols and when the user selects an edit function in the image quality GUI the method further includes displaying an opposite image quality symbol for any image quality symbols selected by the user (See Hill at least at Paras. [0001] (“The present invention relates to a system and apparatus comprising automated analysis and feedback on soft tissue imaging quality, including analysis and feedback in real time and in conformance with key parameters and metrics which affect the quality of an image. In particular it relates to system which adjusts the parameters via statistical counter-data to enhance interpretation of imaging physics data, anticipate error and suggest remedies.”), [0009]-[0010] (“Even if the position of the breast appears optimal at image acquisition, many errors cannot be anticipated or overcome even with the skill and experience of a system operator or clinician, and images are routinely rejected and repeated because of unanticipated defects in imaging quality.”), [0014], [0049]-[0052] (“The system further records and presents guidance on imaging parameters e.g. force, physiological features, patient context and historical treatment criteria, error appraisal, pre-acceptance and post-acceptance prompt (e.g. reject, repeat vis-à-vis individual); reason and resolution of basis for rejection […] the image quality checks may not occur until all of the images have been acquired and the images have been quality-checked at the close of study.” This is being done in computer display windows.), [0075], [0103]-[0109], [0117] (“Once the radiographic technologist is satisfied with the quality of each individual image based on her/his observation of the images and the ‘before acceptance’ and/or ‘after acceptance’ scores, the radiographic technologist indicates ‘close of study’, which indicates that there is no intention of taking additional images—the patient is dismissed at this time. In one embodiment the images may then be sent to a ‘close of study’ image quality checking software module for complete analysis”), [0165]-[0166], [0180]-[0184]; Figs. 1-4, 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lotufo, Wong and Reiner to incorporate the teachings of Hill and provide image quality data and parameter scores. Hill is directed to systems for clinical decision optimization and feedback on soft tissue image quality. Incorporating the processing of medical images and studies as in Hill with the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 18, Lotufo as modified by Wong, Reiner and Hill teaches all the limitations of claim 3, and Hill further teaches generating the priority score using the image quality data, the breast density data and the cancer risk data; and wherein the priority score is generated using a decision tree having a first level where the cancer risk data is stratified between a standard risk score and a priority risk score based on comparing a priority score value to a priority score threshold (See Hill at least at Paras. [0014], [0075], [0103]-[0109]), a second level where the breast density data is stratified between high density or low density based on comparing a breast density value to a breast density threshold and a third level where the image quality data is stratified between high quality and poor quality based on comparing an overall image quality value for the image study to an image quality threshold (See id. at least at Paras. [0011], [0070]-[0075], [0097]-[0109], [0168]-[0172], [0178]-[0182], [0196]-[0210]; Figs. 3, 4, 5, 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lotufo, Wong and Reiner to incorporate the teachings of Hill and provide image quality data and parameter scores. Hill is directed to systems for clinical decision optimization and feedback on soft tissue image quality. Incorporating the processing of medical images and studies as in Hill with the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 75, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 15. The references may not specifically describe but Hill further teaches wherein the image quality category comprises positioning, compression, exam ID, artifacts, exposure, contrast, sharpness and noise (See Hill at least at Paras. [0005]-[0008], [0020]-[0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lotufo, Wong and Reiner to incorporate the teachings of Hill and provide image quality data and parameter details. Hill is directed to systems for clinical decision optimization and feedback on soft tissue image quality. Incorporating the processing of medical images and studies as in Hill with the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 78, claim 78 recites substantially the same limitations as included in claim 75. Thus, claim 78 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 75.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Lotufo, in view of Wong, in view of Reiner, in view of Hill and further in view of U.S. 9,449,380 B2 to Mehta, hereinafter “Mehta.”
Regarding claim 13, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 10. The references may not specifically describe but Mehta teaches wherein upon receiving a command from the user to send the image study to VQR, the method comprises electronically documenting that the image study is to be sent for VQR (See id. at least at Col. 2, ln. 28 – Col. 4, ln. 25; Col 4. ln. 65 – Col. 5, ln. 24; Claims 12, 15).
The references may not specifically describe but Hill teaches wherein upon receiving a command from the user to send the image study to VQR, the method comprises updating the image quality GUI to display that the image study is to be sent for VQR (See Hill at least at Paras. [0008]-[0010], [0220]-[0226], [0234]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lotufo, Wong and Reiner to incorporate the teachings of Hill and provide image quality data and parameter details. Hill is directed to systems for clinical decision optimization and feedback on soft tissue image quality. Incorporating the processing of medical images and studies as in Hill with the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Claims 4-5 and 70-71 are rejected under 35 U.S.C. 103 as being unpatentable over Lotufo, in view of Wong, in view of Reiner, in view of Hill and further in view of U.S. 2108/0197288 A1 to Nunes et al., hereinafter “Nunes.”
Regarding claim 4, Lotufo as modified by Wong, Reiner and Hill teaches all the limitations of claim 3. The references may not specifically describe but Nunes teaches wherein the image quality GUI is generated and displayed when the image quality metric indicates the image study is inadequate for making a correct diagnosis by comparing the image quality metric with an image quality criterion (See Nunes at least at Paras. [0004], [0026]-[0028], [0042]-[0044], [0051]-[0056]; Figs. 4, 5, 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo, Wong and Reiner and Hill to incorporate the teachings of Nunes and provide an image study is inadequate because of bad quality. Nunes is directed to techniques for image analysis for assessing image data. Incorporating the image analysis techniques of Nunes with the processing of medical images and studies as in Hill, the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 5, Lotufo as modified by Wong, Reiner, Hill and Nunes teaches all the limitations of claim 4, and Nunes further teaches presenting the user with a list of follow-up tasks including any combination of: (a)displaying an enhanced view of the image quality data for the image study; (b) scheduling a follow-up visual quality review of the image study; (c) sending an electronic message with the image study ID for electronic documentation and creation of a report of a review of the image study; (d) sending an electronic notification message to prioritize the image study for review; (e) sending an electronic notification message to perform a follow-up action on a patient from whom the image study was obtained; and (f) sending an electronic request message to another user to review the image study to provide a second assessment (See Nunes at least at Paras. [0007], [0026]-[0028], [0042], [0068], [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lotufo, Wong and Reiner and Hill to incorporate the teachings of Nunes and provide various tasks. Nunes is directed to techniques for image analysis for assessing image data. Incorporating the image analysis techniques of Nunes with the processing of medical images and studies as in Hill, the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claims 70-71, claims 70-71 recite substantially the same limitations as included in claims 4-5. Thus, claims 70-71 are rejected under the same grounds of rejection and for the same reasoning as applied to claims 4-5, respectively.
Claims 73-74, 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Lotufo, in view of Wong, in view of Reiner and further in view of U.S. 2018/0060512 A1 to Sorenson et al., hereinafter “Sorenson.”
Regarding claim 73, Lotufo as modified by Wong and Reiner teaches all the limitations of claim 1. The references may not specifically describe but Sorenson teaches receiving an indication that an image study is being retrieved for viewing at the computer device by the user and an image study ID for the image study (See Sorenson at least at Abstract; Paras. [0039]-[0041], [0072]-[0073], [0125]-[0126], [0141], [0149]; Figs. 1-6, 8-9). Reiner further discloses retrieving image quality data that corresponds to the image study based on the image study ID, the image quality data being retrieved from a database (See Reiner at Paras. [0073]-[0075], [0083]-[0086], [0098]-[0108]; Figs. 2-5). Lotufo discloses generating and displaying the image quality GUI along with at least some of the image quality data for the image study corresponding to the image study ID at the computing device (See Lutofo at least at Paras. [0018]-[0022], [0035], [0041]-[0043]; Claims 1-14; Fig. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lotufo, Wong and Reiner to incorporate the teachings of Sorenson and provide image studies being retrieved and study IDs. Sorenson is directed to medical imaging informatics and review techniques. Incorporating the medical image review techniques of Sorenson with the quality assurance assessment for medical imaging as in Reiner, the image quality compliance and machine learning as in Wong and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 74, claim 74 recites substantially the same limitations as included in claim 73. Thus, claim 74 is rejected under the same grounds of rejection and for the same reasoning as applied to claim 73.
Regarding claim 6, Lotufo as modified by Wong, Reiner and Sorenson teaches all the limitations of claim 73, and Wong further teaches wherein the image quality GUI that is displayed comprises a subwindow having a plurality of image quality data for different images of the image study; and the subwindow further includes images of the image study (See Wong at least at Paras. [0015], [0041], [0050], [0058], [0073], [0086], [0103]-[0104]; Figs. 1-15).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lutofo, Reiner and Sorenson to incorporate the teachings of Wong and provide GUI subwindows. Mehta is directed to a medical image quality monitoring and improvement system. Incorporating the image quality compliance and machine learning as in Wong with the medical image review techniques of Sorenson, the quality assurance assessment for medical imaging as in Reiner and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Regarding claim 10, Lotufo as modified by Wong, Reiner and Sorenson teaches all the limitations of claim 6, and Wong further teaches wherein the method comprises displaying an input button in the image quality GUI for allowing the user to select that the image study is to be sent for Visual Quality Review (VQR) and the method comprises flagging the image study for VQR upon receipt of the input button being selected by the user (See id.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Lutofo, Reiner and Sorenson to incorporate the teachings of Wong and provide various display of information. Mehta is directed to a medical image quality monitoring and improvement system. Incorporating the image quality compliance and machine learning as in Wong with the medical image review techniques of Sorenson, the quality assurance assessment for medical imaging as in Reiner and the automatic mammograph image and deep learning analysis as in Lotufo would thereby increase the functionality and effectiveness of implementing image quality review of medical images.
Response to Arguments
Applicant’s remarks filed July 28, 2026 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The basis for the previous rejection under 35 U.S.C. §101 is still operative and the claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under the relevant sections of the MPEP. The arguments at pages 14-20 of Applicant’s Remarks are not persuasive. At pages 14-16 the Examiner disagrees that there is not an abstract idea and there is a technological improvement in the claims. The claims are directed to the abstract idea of methods of organizing human activity (judicial exception), discussed above. At pages 16-19 the Examiner disagrees there is significantly more than the abstract idea or there is a practical application that is integrated in the claims. Here the image quality review techniques using computer-implementation/generic electronic computing device act as a tool used to employ the abstract idea. That the claims include a self-described “standardized” image review does not integrate into a practical application, when there are “no standardized mechanisms for providing feedback on image quality,” as a stipulated by Applicant at page 17. That it may be tedious or laborious to perform analyses in the mind or manually is not of consequence in the eligibility analysis, and it is still a method of organizing human activity, if not a mental process. The claims amount to collecting patient data for patient images and performing quality review and recommendations based on analyzing the data, a process that can be done manually and that is also a method of organizing human activity of following rules or instructions between a patient and physician/healthcare group. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the predictive learning model, and training and retraning the model, in the claims are recited at a high level, and amount to applying the judicial exception using a generic computer (see e.g. Updated PEG Example 47, claim 2, where the “detecting” and “analyzing” were mental processes, and “using the trained ANN” amounted to generic computer implementation). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. For at least these reasons and those stated in the rejection above, the claims are not patent eligible.
Applicant’s arguments pertaining to prior art rejections are not persuasive. The claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments pertaining to prior art references of the Applicant’s Remarks are not persuasive. The arguments at pages 22-24 are moot at least in light of new references Lutofo and Wong, discussed above. As such, it is submitted that the cited prior art, including those identified by Applicant, in the same field of endeavor, i.e., techniques for patient image data analysis and review, teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. 2022/0323043 A1 to Duric et al., U.S. 2022/0287687 A1 to Liao et al., U.S. US 20200360312 A1 to Hall et al., U.S. 10,037,601 B1 to Ben-Ari, U.S. 2022/0004863 A1 to Park et al., U.S. 2019/0313992 A1 to Buelow et al., U.S. 2020/0360312 A1 to Hall et al.
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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/WILLIAM T. MONTICELLO/ Examiner, Art Unit 3682
/FONYA M LONG/ Supervisory Patent Examiner, Art Unit 3682
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