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 Nonfinal Office Action is in response to the RCE filed 12/04/2025, wherein:
Claims 1, 15, 20 and 73-80 are amended. Claim 81 is new. Claims 11 is cancelled. Claims 1-6, 8, 10, 13, 15, 18, 20, 68-71, 73 and 80-81 are pending review herein.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 20 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.
The independent claims include “the VQR score of the image study,” but there is no antecedent basis for “the VQR.” The acronym VQR should also be spelled out fully in the claims. Appropriate correction is required.
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 20 recites, wherein the abstract idea is not emboldened:
An electronic device for providing image quality review of medical images in a 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 medical imaging system; a database that stores image quality data; and a processor unit in communication with the memory unit, the network unit, the processor unit having at least one processor that is configured to: receive an electronic request from a user to perform the image quality review for a selected time period; retrieve image quality data from the database associated with a plurality of medical images acquired by a Medical Imaging Technologists (MIT), wherein the image quality data comprises one or more of a plurality of: (a) Image Quality Parameters, (b) Image Quality Parameter Features, (c) Image Quality Parameter Scores, (d) Image Quality Parameter Indices, (e) Study Quality Parameters, (f) Study Quality Parameter Scores, and (g) Study Quality Parameter Indices that are all extracted from the medical images of an image study; access 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; apply the predictive classifier to the retrieved image quality data to generate a predicted probability for the VQR score of the image study; compare the VQR score to a VQR threshold, wherein the VQR threshold is algorithmically determined to separate technical recall patients from no technical recall patients; and implement and track at least one corrective action for the MIT to improve image quality performance.
Claim 1 recites substantially similar limitations. 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.
The limitations to “request from a user to perform the image quality review for a selected time period; retrieve image quality data associated with a plurality of medical images acquired by a Medical Imaging Technologists (MIT), wherein the image quality data comprises one or more of a plurality of: (a) Image Quality Parameters, (b) Image Quality Parameter Features, (c) Image Quality Parameter Scores, (d) Image Quality Parameter Indices, (e) Study Quality Parameters, (f) Study Quality Parameter Scores, and (g) Study Quality Parameter Indices that are all extracted from the medical images of an image study; access a predictive classifier, 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; apply the predictive classifier to the retrieved image quality data to generate a predicted probability for the VQR score of the image study; compare the VQR score to a VQR threshold, wherein the VQR threshold is algorithmically determined to separate technical recall patients from no technical recall patients; and implement and track at least one corrective action for the MIT to improve image quality performance,” 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” and a “graphical user interface (GUI),” (dependent claims) analyzing medical image data and determining quality 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” and a “graphical user interface (GUI),” does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
The claims can also be classified as an abstract idea including mental processes. That is, other than 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” and a “graphical user interface (GUI),” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the generic computer components and GUI, receiving information related to a medical image and providing/retrieving quality review information based on analyzing the medical image(s), in the context of this claim, encompasses one skilled in the pertinent art to manually determine the details of the medical image for analysis and review by a clinician. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites 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” and a “graphical user interface (GUI),” 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” and a “graphical user interface (GUI),” 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 U.S. 2017/0372155 A1 to Odry et al., hereinafter “Odry,” in view of U.S. 2014/0072192 A1 to Reiner, hereinafter “Reiner” and further in view of U.S. 9,449,380 B2 to Mehta, hereinafter “Mehta.”
Regarding claim 1, Odry discloses A computer-implemented method for performing image quality review of medical images, wherein the method is performed by at least one processor (See Odry at least at Paras. [0069]-[0084]), wherein the image quality data comprises one or more of a plurality of: (a) Image Quality Parameters, (b) Image Quality Parameter Features, (c) Image Quality Parameter Scores, (d) Image Quality Parameter Indices, (e) Study Quality Parameters, (f) Study Quality Parameter Scores, and (g) Study Quality Parameter Indices that are all extracted from the medical images of an image study (See id. at least at Paras. [0003]-[0008] (“A scoring system assesses image quality after acquisition and helps determine whether enough significant clinical value may be extracted and therefore lead to correct diagnosis. The scoring system evaluates the extent and severity of artifacts.’), [0018]-[0023], [0032]-[0035], [0038]-[0046] (parameters); Claim 5; Figs. 1-5); 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 (See id. at least at Paras. [0007]-[0012], [0018]-[0023] (previously stored images), [0032]-[0035], [0041]-[0049] (“The difference between the ground truth or known scores for the training images and the prediction by the discriminative classifier is minimized […] The discriminative classifier is trained to output a score of image quality.”), [0055] (“The classifier 58 assigns the image quality score with the application of the probability map and application of the image to the discriminative machine-learnt classifier.”); Claim 5; Figs. 1-5); applying the predictive classifier to the retrieved image quality data to generate a predicted probability for the VQR score of the image study (See id. at least at Paras. [0037]-[0062]; Claims 1-18); 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 Paras. [0017], [0021]-[0034]; [0041]-[0042], [0059]-[0065], [0070]; Claim 5);
Odry 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); retrieving image quality data from a database associated with a plurality of medical images acquired by a Medical Imaging Technologists (MIT) (See id. at least at Paras. [0078]-[0083], [0092], [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)).
The references may not specifically describe but Mehta teaches implementing and tracking at least one corrective action for the MIT to improve image quality performance (See Mehta at least at Col. 1, ln. 19-61; Col. 2, ln. 28 – Col. 5, ln. 24 (“[A] record of a sequence of user interface actions occurring […] The user in step 210 activates a dissatisfaction button in response to a determination an image is of reduced quality […] a user imaging system workflow is identified in step 307 by system 10 by parsing user actions. Report generator 34 in step 310 determines if a workflow step affecting image quality is omitted by comparing the workflow steps performed against a predetermined list of steps for an identified imaging function determined from a map associating identified user actions with imaging system operation functions. In step 313 in response to identifying that a workflow step affecting image quality is omitted, report generator 34 presents an alert message to the user on display 19 and generates a log record that contains UI action history, acquisition parameters, image quality assessment and problem description and system configuration parameters and communicates the generated report to a remote service centre and notifies service personnel assigned to a particular site.”); Claims 12, 15; Figs. 1-5).
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 Odry to incorporate the teachings of Reiner and Mehta and provide various data for a relevant medical image study. Mehta is directed to a medical image quality monitoring and improvement system. Reiner relates to quality assurance in medical imaging. Incorporating the medical image quality studies as in Mehta with the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Regarding claim 2, Odry as modified by Reiner and Mehta teaches all the limitations of claim 1, and Mehta further teaches 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 Mehta at least at Col. 1, ln. 40-61; Col. 2, ln. 28-45 (“System comprises image acquisition device bidirectionally communicating with at least one medical image computer system, workstation, server or other processing device including, display, repository, display processor, user interface, image processor and report generator. Display processor generates data representing an image for display including a user selectable image element enabling a user to identify at least one medical image as having an image quality deficiency.”); Col. 6, ln. 7-28; Col. 7, ln. 15-52 (“A graphical user interface (GUI), as used herein, comprises one or more display elements, generated by a display processor and enabling user interaction with a processor or other device and associated data acquisition and processing functions. The UI also includes an executable procedure or executable application. The executable procedure or executable application conditions the display processor to generate signals representing the UI display images.”); Col. 3, ln. 17 – Col. 4, ln. 2; Col. 7, ln. 15-52; Claims 12, 15; Figs. 1-5).
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, Odry as modified by Reiner and Mehta 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 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 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 Odry and Mehta 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 medical image quality studies as in Mehta with the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Regarding claim 76, Odry as modified by Reiner and Mehta 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]).
Regarding claim 77, Odry as modified by Reiner and Mehta 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]).
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, Odry as modified by Reiner and Mehta 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).
Claims 3, 8, 13, 18, 75 and 78 are rejected under 35 U.S.C. 103 as being unpatentable over Odry in view of Reiner, in view of Mehta and further in view of U.S. 2019/0287241 A1 to Hill et al., hereinafter “Hill.”
Regarding claim 3, Odry as modified by Reiner and Mehta teaches all the limitations of claim 2. The references may not specifically describe but and 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 Odry, Reiner and Mehta 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 medical image quality studies as in Mehta, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Regarding claim 8, Odry as modified by Reiner and Mehta 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 Odry, Reiner and Mehta 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 medical image quality studies as in Mehta, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Regarding claim 13, Odry as modified by Reiner and Mehta teaches all the limitations of claim 10, and Mehta further 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 Odry and Reiner to incorporate the teachings of Hill and Mehta and provide image quality data and parameter scores. Mehta relates to medical image quality studies. 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 medical image quality studies as in Mehta, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Regarding claim 18, Odry as modified by Reiner, Mehta 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).
Regarding claim 75, Odry as modified by Reiner and Mehta 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 Odry, Reiner and Mehta 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 medical image quality studies as in Mehta, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for 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.
Claims 4-5 and 70-71 are rejected under 35 U.S.C. 103 as being unpatentable over Odry, in view of Reiner, in view of Mehta, in view of Hill and further in view of U.S. 2108/0197288 A1 to Nunes et al., hereinafter “Nunes.”
Regarding claim 4, Odry as modified by Reiner, Mehta 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 Odry, Reiner, Mehta 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 medical image quality studies as in Mehta, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Regarding claim 5, Odry as modified by Reiner, Mehta, 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]).
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 Odry, in view of Reiner, in view of Mehta and further in view of U.S. 2018/0060512 A1 to Sorenson et al., hereinafter “Sorenson.”
Regarding claim 73, Odry as modified by Reiner and Mehta 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). Mehta teaches 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 Mehta at least Col. 1, ln. 40-61; Col. 2, ln. 28-45; Col. 3, ln. 17 – Col. 4, ln. 2; Col. 6, ln. 7-28; Col. 7, ln. 15-52; Claims 12, 15; Figs. 1-5).
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 Odry, Reiner and Mehta 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 medical image quality studies as in Mehta, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for 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, Odry as modified by Reiner, Mehta and Sorenson teaches all the limitations of claim 73, and Mehta 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 Mehta at least at Col. 5, ln. 53 – Col. 6, ln. 28; Claims 12, 15; Figs. 1-5).
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 Odry, Reiner, Mehta and Sorenson to incorporate the teachings of Mehta and provide GUI subwindows. Mehta is directed to a medical image quality monitoring and improvement system. Incorporating the medical image quality studies as in Mehta with the medical image review techniques of Sorenson, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Regarding claim 10, Odry as modified by Reiner, Mehta and Sorenson teaches all the limitations of claim 6, and Mehta 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 Mehta at least at Col 2, ln. 40-61 (“A system enables a user to express dissatisfaction with image quality outcome via simple button press on a patient table side UI, computer workstation or other displayed image and in response acquires relevant information for investigation and examination assessment without manual intervention of service personnel or image quality experts.”); Claim 12).
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 Odry, Reiner and Sorenson to incorporate the teachings of Mehta and provide GUI subwindows. Mehta is directed to a medical image quality monitoring and improvement system. Incorporating the medical image quality studies as in Mehta with the medical image review techniques of Sorenson, the quality assurance assessment for medical imaging as in Reiner and the image quality score determination using machine learning as in Odry would thereby increase the functionality and effectiveness of implementing the claimed system and method for image quality review of medical images.
Response to Arguments
Applicant’s remarks filed December 4, 2025 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 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) and Updated PEG. The arguments at pages 13-19 of Applicant’s Remarks are not persuasive. At pages 13-16 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) and mental processes, 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. 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. Even if a user is performing any image selections and scores for image-quality review and inputting data into a computer, it is not precluded by and still reads on the broadly recited claims. Additionally, the arguments at pages 20-23 are moot at least in light of new references Odry, 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
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681