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 .
Response to Amendment
In the amendment filed on February 9, 2026, the following has occurred: claim(s) 1, 6, 11, 15-19, 21 have been amended. Now, claim(s) 1-2, 4-21 are pending.
Claim Objections
Claim 11 objected to because of the following informalities: “the packaging application” in p. 5, ll. 16. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the examiner will interpret the claimed portion as “a packaging application”.
Claim 17 objected to because of the following informalities: “the packaging application” in p. 7, ll. 14. This appears to be a typographical error. Appropriate correction is required. For examination purposes, the examiner will interpret the claimed portion as “a packaging application”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-2, 4-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-2, 4-10: Step 2A Prong One
Claim 1 recite(s)
predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time;
determining that a number corresponding to a plurality of exams is greater than the threshold number of exams;
dynamically flagging, based on the number being greater than the threshold number of exams, an exam of the plurality of exams for transmission to an external institution;
packaging the exam with additional data based on user input;
transmitting the packaged exam to the external institution;
gather the user input and user preferences over time and to utilize the user input and the user preferences gathered over time to automatically perform the acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time;
determining that the number corresponding to the plurality of exams is greater than the threshold number, dynamically flagging, based on the number being greater than the threshold number of exams, an exam of the plurality of exams for transmission to an external institution; and
packaging the exam with additional data based on user input
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships between people (including following rules or instructions) which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “a processor-based device storing or accessing a packaging application, wherein the packaging application, when executed by the processor-based device, causes acts to be performed comprising:” “…without user intervention…”, “…via a display, a graphical user interface (GUI) comprising…”, “…via the display” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following rules or instructions to manage the workflow for radiologists. For example, the claim encompasses a user following instructions to predict a threshold number of exams that a plurality of reviewers of an institution can review over a period of time, a user following instructions to determine a number corresponding to a plurality of exams is greater than the threshold number of exams, a user following instructions to flag an exam of the plurality of exams for transmission to an external institution, a user following instructions to combine an exam with additional data based on a user’s input, a user following instructions to send the packaged exam to the external institution, a user following instructions to gather the user input and user preferences over time to follow instructions to predict the threshold number of exams that the plurality of receivers can review over the period of time, a user following instructions to determine the number corresponding to the plurality of exams is greater than the threshold number, and flagging an exam of the plurality of exams for transmission to an external institution, and a user following instructions to package the exam with additional data.
Claims 2, 4-10 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, claim 2 describes utilizing generic computer components to associate the report or diagnosis with the exam. Similarly, claims 4-5 further describe receiving user input to select an exam from the plurality of exams. Similarly, claim 6 further describes the routing rules. Similarly, claims 7-8 describes maintaining a list of exams that have been sent, and adjusting the delivery of the exams based on an issue. Similarly, claim 9 further describes the schedule. Finally, claim 10 further describes the exam and to reassign if identified to be a part of a long-term study. Such steps encompass Certain Methods of Organizing Human Activity.
Claims 1-2, 4-10: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform
the abstract idea and adding insignificant extra-solution activity.
Claims 1-2, 4-10, directly or indirectly, recite the following generic computer components, “a processor-based device storing or accessing a packaging application, wherein the packaging application, when executed by the processor-based device, causes acts to be performed comprising:”, “…via a display, a graphical user interface (GUI) comprising…”, “…via the display”, “a second GUI” (i.e., “The processor 14 may be any type of computer processor or microprocessor capable of executing computer-executable code. For example, the processor 14 may be configured to receive user input, such as actions performed by the operator, indications to send the package to the third-party reviewer 34, adjustments or readjustments the worklist, identifications of relevant priors and reports, scanning parameters, or the like. The user may select exams for viewing on the workstation 10 or perform one or more other actions. Thus, the operator may select image data for viewing on the workstation 10, perform one or more actions (e.g., identify priors, select exam), verify exams, assign exams, or otherwise operation the workstation 10.” in Specification in Paragraph [0031]), claims 3, 5-8 recite “a graphical user interface (GUI)” (e.g., “At block 62, the application 26 may display the worklist 60. For example, the application 26 may populate a GUI with the worklist 60 for display on the display 22 of the workstation 10.” in Specification in Paragraph [0047].) As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Additionally, claim 1 recites “displaying, the exam and an indication of the flagging proximate to the exam;” that amounts to insignificant extra-solution selecting a particular data source or type of data to be manipulated activity (See MPEP 2106.05(g)).
Additionally, the claims recite “…without user intervention…”, “utilizing a machine learning model…”, “train and to update the packaging application to”, “…and without user intervention…” are similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-2, 4-10: Step 2B
The claim(s) does/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 using a computer
configured to perform above identified functions amounts to no more than mere instructions to
apply the exception using generic computer components. Mere instructions to apply an exception
using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at
223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Insignificant, extra solution, data gathering activity and selecting a particular data source
or type of data to be manipulated has been found to not amount to significantly more than an
abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination,
the additional elements do not amount to significantly more than the abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Claims 11-16, 21: Step 2A Prong One
predicting, a threshold number of exams that a plurality of reviewers of an institution can review over a period of time;
determining, that a number corresponding to a plurality of exams is greater than the threshold number of exams;
dynamically flagging, an exam for transmission to an external institution based on the number being greater than the threshold number of exams;
packaging, the exam with additional data based on user input;
transmitting, the exam of the plurality of exams to an external institution;
gather the user input and user preferences over time and to utilize the user input and the user preferences gathered over time to perform acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time;
determining that the number corresponding to the plurality of exams is greater than the threshold number, dynamically flagging, based on the number being greater than the threshold number of exams, an exam of the plurality of exams for transmission to an external institution, and
packaging the exam with additional data based on user input
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships between people (including following rules or instructions) which is a subgrouping of Certain Methods of Organizing Human Activity. That is, other than reciting, “via a processor”, “a graphical user interface (GUI)”, “…to train and to update a packaging application…”, “…via the display” to perform these functions, nothing in the claim precludes the limitations from practically being performed by a person following rules or instructions to manage the workflow for radiologists. For example, the claim encompasses a user following instructions to predict a threshold number of exams that a plurality of reviewers of an institution can review over a period of time, a user following instructions to determine that a number corresponding to a plurality of exams is greater than the threshold number of exams, a user following instructions to flag an exam of the plurality of exams for transmission to an external institution, a user following instructions to package the exam with additional data based on user input, a user following instructions to send the packaged exam to the external institution, a user following instructions to gather the user input and user preferences over time to follow instructions to predict the threshold number of exams that the plurality of receivers can review over the period of time, a user following instructions to determine the number corresponding to the plurality of exams is greater than the threshold number, and flagging an exam of the plurality of exams for transmission to an external institution, and a user following instructions to package the exam with additional data.
Claims 12-16, 21 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, claims 12-13 describe identifying relevant priors associated with the exam. Similarly, claim 14 further describes a user following instructions to send an exam to another user. Similarly, claims 15 and 21 further describes generic computer components. Finally, claim 16 describes receiving a preference and a weight for each of the routing rules. Such steps encompass Certain Methods of Organizing Human Activity.
Claims 11-16, 21: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining
elements amount to no more than general purpose computer components programmed to perform
the abstract idea and adding insignificant extra-solution activity.
Claims 11-16, 21, directly or indirectly, recite the following generic computer components, “a processor-based device storing or accessing a packaging application, wherein the packaging application, when executed by the processor-based device, causes acts to be performed comprising:”, “…via a display, a graphical user interface (GUI) comprising…”, “…via the display”, “an additional graphical user interface (GUI)” (i.e., “The processor 14 may be any type of computer processor or microprocessor capable of executing computer-executable code. For example, the processor 14 may be configured to receive user input, such as actions performed by the operator, indications to send the package to the third-party reviewer 34, adjustments or readjustments the worklist, identifications of relevant priors and reports, scanning parameters, or the like. The user may select exams for viewing on the workstation 10 or perform one or more other actions. Thus, the operator may select image data for viewing on the workstation 10, perform one or more actions (e.g., identify priors, select exam), verify exams, assign exams, or otherwise operation the workstation 10.” in Specification in Paragraph [0031]), claims 3, 5-8 recite “a graphical user interface (GUI)” (e.g., “At block 62, the application 26 may display the worklist 60. For example, the application 26 may populate a GUI with the worklist 60 for display on the display 22 of the workstation 10.” in Specification in Paragraph [0047].) As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Additionally, claim 11 recites “instructing, via the processor, a display to display a graphical user interface (GUI) comprising the exam and an indication of the flagging proximate to the exam;” that amounts to insignificant extra-solution selecting a particular data source or type of data to be manipulated activity (See MPEP 2106.05(g)).
Additionally, the claims recite “…without user intervention…”, “utilizing, via the processor, a machine learning model to…”, “train and to update the packaging application to”, “…and without user intervention…” are similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Claims 11-16, 21: Step 2B
The claim(s) does/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 using a computer
configured to perform above identified functions amounts to no more than mere instructions to
apply the exception using generic computer components. Mere instructions to apply an exception
using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at
223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Insignificant, extra solution, data gathering activity and selecting a particular data source
or type of data to be manipulated has been found to not amount to significantly more than an
abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination,
the additional elements do not amount to significantly more than the abstract idea.
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)).
Claims 17-20 recite the same functions as claims 1-16, 21, but in non-transitory computer-readable medium form. Therefore, these claims also recite abstract ideas that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas as explained above. These claims also do not integrate the abstract idea into a practical application for the same reasons as explained above because they merely include instructions to implement the abstract idea on a computer.
Therefore, whether considered alone or in combination, the additional elements do not
amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-10-13, 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Sargent et al. (U.S. Patent Pre-Grant Publication No. 2023/0154592) in view of Chung et al. (U.S. Patent Pre-Grant Publication No. 2015/0347694).
As per independent claim 1, Sargent discloses a system comprising:
a processor-based device storing or accessing a packaging application, wherein the packaging application, when executed by the processor-based device (See [0021]: The computer device 24 may include an electronic processor 30 (e.g., a microprocessor, application-specific integrated circuit (ASIC), or another suitable electronic device), a memory 40 (e.g., a non-transitory, computer-readable storage medium) that stores machine learning models 44, including, for example, a recurrent neural network 46 and a convolutional neural network 48), causes acts to be performed comprising:
predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time (See [0029]-[0031], [0046]: The machine learning algorithm can be configured to predict a review score for each candidate imaging exam (e.g., using training information including completed reviews of other imaging exams) and uses the predict scores to select at least a subset of the candidate medical image exams for review, which the Examiner is interpreting the predict scores to select at least a subset of the candidate medical image exams for review to encompass predicting a threshold number of exams that a plurality of reviewers, and interpreting the machine learning algorithm analyzes other environment features including time of day and exam details for other medical exams to provide trends to the reading physician to encompass a plurality of reviewers of an institution can review over a period of time);
dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution (See [0035]-[0036]: The machine learning algorithm is configured to analyze various categories of candidate medical imaging exams and to determine the types of medical imaging exams that are more prone to errors, which the Examiner is interpreting determine the types of medical imaging exams that are more prone to errors to encompass dynamically flagging, without user intervention, an exam of the plurality of exams, and the assigning of the medical imaging exams to the selected peer reviewers includes assigning the medical imaging exam to at least one peer reviewer most competent for that type of medical imaging exam, which the Examiner is interpreting at least one peer reviewer to encompass an external institution as the peer reviewer does not have to be within the reading physician’s institution);
displaying, via a display, a graphical user interface (GUI) comprising the exam and an indication of the flagging proximate to the exam (See [0020], [0027]-[0028], [0035]: The computer device generates a random peer review worklist from a database table of available peer reviewers (e.g., on request), and the machine learning algorithm is configured to analyze various categories of candidate medical imaging exams and to determine the types of medical imaging exams that are more prone to errors, which the Examiner is interpreting the determined types of medical imaging exams that are more prone to errors to encompass an indication of the flagging proximate to the exam as the medical imaging exams with high scores will be selected for peer review based on a threshold value ([0051]));
packaging the exam with additional data based on user input via the display (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting the review score and text for the selected medical imaging exam to encompass packaging the exam with additional data based on user input via the display ( [0052]));
transmitting the packaged exam to the external institution (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting a quality assurance (QA) lead person to encompass the external institution);
utilizing a machine learning model to gather the user input and user preferences over time (See [0050]: Once trained the machine learning algorithms can be used to select a subset of candidate completed imaging exam for review and can assign a selected exam to a particular reviewer, the machine learning algorithms used by the system can learn specialties, preferences, which the Examiner is interpreting the machine learning algorithms used by the system can learn specialties, preferences to encompass a machine learning model to gather the user input and user preferences over time) and to utilize the user input and the user preferences gathered over time to train and to update the packaging application (See [0050]-[0052]: The input data, and ground truth score are used to update the online machine learning algorithm, if the online machine learning algorithm is a neural network, the update will be provided to the network weights using gradient descent, and the updating of the machine learning algorithm is an online machine learning update, which the Examiner is interpreting input data, and ground truth score are used to update the online machine learning algorithm to encompass the user input and the user preferences gathered over time to train and to update the packaging application) to automatically perform the acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time (See [0048]-[0049]: Both machine learning algorithms generated a predicted review score and the scores can be combined, such as by averaging, to determine a final predicted review score for an imaging exam, which the Examiner is interpreting determine a final predicted review score for an imaging exam to encompass automatically perform the acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time when combined Chung’s teachings on “determining that a number corresponding to a plurality of exams is greater than the threshold number of exams” described below); and
packaging the exam with additional data based on user input via the display (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting the review score and text for the selected medical imaging exam to encompass packaging the exam with additional data based on user input via the display ([0052]).)
While Sargent teaches the system as described above, Sargent may not explicitly teach determining that a number corresponding to a plurality of exams is greater than the threshold number of exams; and
determining that the number corresponding to the plurality of exams is greater than the threshold number, dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution.
Chung teaches a system for determining that a number corresponding to a plurality of exams is greater than the threshold number of exams (See [0150]-[0151], [0165]: The ranking of the readers for which the new order can be inserted in their respective schedule is performed as a function of at least one parameter, the parameter can include the minimum due-in-time requirement, the maximum due-in-time requirement, the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, and conditions where a reader may be overloaded with work can be detected, which the Examiner is interpreting conditions where a reader may be overloaded with work can be detected to encompass determining that a number corresponding to a plurality of exams is greater than a threshold number of exams); and
determining that the number corresponding to the plurality of exams is greater than the threshold number, dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution (See [0175]-[0178]: Various adequate methods or criteria for measuring the quality of a reader schedule can be used, the reader schedule quality is a function of at least one parameter, examples include the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, and the method is configurable for different optimization criteria based on site, client, or system state such as when overload conditions are detected for readers, which the Examiner is interpreting overload conditions to encompass dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution as the overload condition identifies a number corresponding to the plurality of exams is greater than the threshold number, and the reassignment policies to encompass transmission to an external institution.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Sargent to include determining that a number corresponding to a plurality of exams is greater than the threshold number of exams; and determining that the number corresponding to the plurality of exams is greater than the threshold number, dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 4, Sargent/Chung discloses the system of claim 1 as described above. Sargent further teaches wherein the packaging application, when executed, is configured to package the exam of the plurality of exams with the additional data by:
identifying a patient history associated with the exam, wherein the patient history comprises a plurality of priors (See [0028]-[0029]: A review can be recommended for a current medical imaging exam when a relevant prior exam (for the same patient) was viewed for the current exam, which the Examiner is interpreting a relevant prior exam (for the same patient) to encompass the patient history comprises a plurality of priors); and
determining one or more relevant priors of the plurality of priors based on one or more attributes of the exam (See [0028]-[0029]: The medical imaging exam selections selected via AI can be based on exam specifics (e.g., medical images, report text using natural language processing (NLP), and reader characteristics), reviewer characteristics, or combinations thereof, which the Examiner is interpreting the exam specifics to encompass one or more attributes of the exam.)
As per claim 5, Sargent/Chung discloses the system of claims 1 and 4 as described above. Sargent further teaches wherein the packaging application, when executed, is configured to:
display, via the display, a second GUI displaying the one or more relevant priors (See [0028]-[0029]: Ease of use is provided for an assigned peer reviewer using an automatically populated interface by a single click to agree with the reading physician data provided by the reading physician, which the Examiner is interpreting the automatically populated interface to encompass a second GUI); and
adjust the one or more relevant priors based on additional user input (See [0028]-[0029]: The peer reviewer provides text when the peer reviewer does not agree with the reading physician data provided by the reading physician for a completed medical imaging exam, which the Examiner is interpreting the peer reviewer provides text when the peer reviewer does not agree with the reading physician data provided to encompass adjust the one or more relevant priors based on additional user input.)
As per claim 6, Sargent/Chung discloses the system of claim 1 as described above. Sargent further teaches wherein the packaging application, when executed, is configured to:
assign a score to each exam of the plurality of exams based on routing rules (See [0040]-[0042]: The computer device can train a classifier to convert input data (exam, reading physician, and review data (scores)) into a feature presentation and predict a review score for each reader, and the medical imaging exam and the reading physician data as feature vectors, which the Examiner is interpreting the medical imaging exam as a feature vector to encompass assigning a score to each of the plurality of exams.)
While Sargent teaches the system as described above, Sargent may not explicitly teach
wherein the packaging application, when executed, is configured to:
display, via the display, a second GUI comprising the plurality of exams from a highest score to a lowest score, routing rules, and the indication of the flagging, and wherein the routing rules comprise a priority, a patient class, a type of exam, and a maximum number of priors; and
assign a weight to each of the routing rules based on additional user input.
Chung teaches a system wherein the packaging application, when executed, is configured to:
display, via the display, a second GUI comprising the plurality of exams from a highest score to a lowest score, routing rules, and the indication of the flagging, and wherein the routing rules comprise a priority, a patient class, a type of exam, and a maximum number of priors (See [0087], [0151]: Examples of parameters that may be used for the ranking of the readers comprise the total expected reading time for the reader schedule, the average expected reading time, the minimum expected reading time, the maximum expected reading time, the variance in expected reading time, the total RVU value for the reader schedule, the average RVU value, the minimum RVU value, the maximum RVU value, the variance in RVU value, the total slack value, the minimum slack value, the maximum slack value, the average slack value, the variance in slack value, the minimum due-in-time requirement, the maximum due-in-time requirement, the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, which the Examiner is interpreting the number of orders having a routine priority to encompass a priority, the number of orders having a stat priority to encompass a patient class as the stat priority is a high priority or critical priority, the number of orders having a stat priority to encompass a type of exam, and the maximum RVU value to encompass a maximum number of priors as the priors could affect the measure of work effort ([0116]), and the determined match scores may be transmitted to a display unit to be displayed to encompass display, via the display, a second GUI comprising the plurality of exams from a highest score to a lowest score, routing rules, and the indication of the flagging when combined with Sargent’s teachings of the medical imaging exam as a feature vector); and
assign a weight to each of the routing rules based on additional user input (See [0194]: A weight factor is assigned to each criterion according to the relative importance of the criteria.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Sargent to include wherein the packaging application, when executed, is configured to: display, via the display, a second GUI comprising the plurality of exams from a highest score to a lowest score, routing rules, and the indication of the flagging, and wherein the routing rules comprise a priority, a patient class, a type of exam, and a maximum number of priors; and assign a weight to each of the routing rules based on additional user input as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 7, Sargent/Chung discloses the system of claim 1 as described above. Sargent further teaches wherein the packaging application, when executed, is configured to: maintain an outgoing list of one or more transmitted exams transferred to the external institution (See [0053]: The review score and text can be provided to a database or other memory for export to a RADPEER database, which the Examiner is interpreting the review score and text can be provided to a database to encompass an outgoing list as the machine learning algorithm utilizes the provided feedback from the QA lead person.)
While Sargent discloses a system wherein the packaging application, when executed, is configured to: maintain an outgoing list of one or more transmitted exams transferred to the external institution, Sargent may not explicitly teach wherein the packaging application, when executed, is configured to: display, via the display, a second GUI displaying the outgoing list, a service level agreement time, and a turn-around time.
Chung teaches a system wherein the packaging application, when executed, is configured to:
display, via the display, a second GUI displaying the outgoing list, a service level agreement time, and a turn-around time (See [0160]-[0161], [0170]: The system is adapted to output a non-ordered list of readers who are able to analyze all of their assigned orders including the new order by their respective due-in-time requirement, and the choice of orders to be reassigned can be based on factors such as the order priority, Service Level Agreement (SLA) penalties, other cost functions, and/or the like.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Sargent to include 70. display, via the display, a second GUI displaying the outgoing list, a service level agreement time, and a turn-around time as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 8, Sargent/Chung discloses the system of claims 1 and 7 as described above. Sargent may not explicitly teach wherein the packaging application, when executed, is configured to:
determine a received exam matches a transmitted exam of the one or more transmitted exams of the outgoing list;
flag the exam on the outgoing list;
display, via the display and the second GUI a notification in response to flagging the exam;
transmit an additional notification to the external institution to stop reviewing the exam; and
assign the exam to an in-house reader.
Chung teaches a system wherein the packaging application, when executed, is configured to:
determine a received exam matches a transmitted exam of the one or more transmitted exams of the outgoing list (See [0177]-[0178]: A notification indicative of an at-risk order may be sent to a PACS administrator, an ATC, and/or the reader assigned to the at-risk order for follow up actions, such as promotion of the at-risk order up a reader's schedule or reassignment to another reader with greater reading capacity or having more slack in his or her schedule, which the Examiner is interpreting an at-risk order to encompass a received exam matches a transmitted exam of the one or more transmitted exams of the outgoing list);
flag the exam on the outgoing list (See [0177]-[0178]: A notification indicative of an at-risk order may be sent to a PACS administrator, an ATC, and/or the reader assigned to the at-risk order for follow up actions, such as promotion of the at-risk order up a reader's schedule or reassignment to another reader with greater reading capacity or having more slack in his or her schedule, which the Examiner is interpreting a notification indicative of an at-risk order to encompass flag the exam on the outgoing list);
display, via the display and the second GUI a notification in response to flagging the exam (See [0177]-[0179]: A notification indicative of an at-risk order may be sent to a PACS administrator, an ATC, and/or the reader assigned to the at-risk order for follow up actions, such as promotion of the at-risk order up a reader's schedule or reassignment to another reader with greater reading capacity or having more slack in his or her schedule, which the Examiner is interpreting a notification indicative of an at-risk order to encompass display, via the display and the second GUI a notification ([0179]: Pop-up window warning messages, etc.));
transmit an additional notification to the external institution to stop reviewing the exam (See [0177]-[0179]: A notification indicative of an at-risk order may be sent to a PACS administrator, an ATC, and/or the reader assigned to the at-risk order for follow up actions, such as promotion of the at-risk order up a reader's schedule or reassignment to another reader with greater reading capacity or having more slack in his or her schedule, which the Examiner is interpreting a notification indicative of an at-risk order to encompass an additional notification to the external institution to stop reviewing the exam ([0179]: Pop-up window warning messages, etc.)); and
assign the exam to an in-house reader (See [0176]-[0177]: The optimization criteria can be based on at least one parameter such as SLA penalties, order priority, order RVU, order ER, order location, or the like, which the Examiner is interpreting the order location to encompass an in-house reader as the optimization criteria can vary from site to site, and client to client ( [0173]).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Sargent to include the packaging application, when executed, is configured to: determine a received exam matches a transmitted exam of the one or more transmitted exams of the outgoing list; flag the exam on the outgoing list; display, via the display and the second GUI a notification in response to flagging the exam; transmit an additional notification to the external institution to stop reviewing the exam; and assign the exam to an in-house reader as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 9, Sargent/Chung discloses the system of claim 1 as described above. Sargent further teaches wherein the packaging application, when executed, is configured to predict the threshold number based on a plurality of work schedules associated with a number of in-house readers working over a period of time and a specialty of each of the in-house readers (See [0040]-[0041], [0048]: This information can be stored in the system (e.g., a PACS database), various logs maintained by the system or other systems, completed reports (e.g., analyzed using natural language processor), characteristics of a reader and a reviewer can include a user's specialty, modalities, shift schedules, etc., and the computer device can train a classifier to convert input data (exam, reading physician, and review data (scores)) into a feature presentation and predict a review score for each reader, which the Examiner is interpreting a review score for each reader to encompass the threshold number ( [0048]), and interpreting characteristics of a reader and a reviewer can include a user's specialty, modalities, shift schedules, etc. to encompass a plurality of work schedules associated with a number of in-house readers working over a period of time and a specialty of each of the in-house readers.)
As per claim 10, Sargent/Chung discloses the system of claim 1 as described above. Sargent may not explicitly teach wherein the packaging application, when executed, is configured to:
identify the exam of the plurality of exams as part of a long-term study; and
reassign the exam for an in-house reader based on the identification.
Chung teaches a system wherein the packaging application, when executed, is configured to:
identify the exam of the plurality of exams as part of a long-term study (See [0044]: The information about the medical image may comprise an identification of the imaging method/technology used for generating the medical image, referred hereinafter as the medical image modality, an identification of the body part that has been imaged, a due-in-time requirement for analyzing the medical image, i.e. the deadline for completing the analysis of the medical image, a study description which usually comprises a short description of the procedure used to capture the medical image(s), comments from the technician, a priority status for the analysis of the radiology order such as low priority, normal priority, critical priority, or stat or statim priority, and/or the like, which the Examiner is interpreting a study description to encompass identify the exam of the plurality of exams as a part of a long-term study); and reassign the exam for an in-house reader based on the identification (See [0168]-[0169]: The order reassignment is performed between groups instead of between individual readers to encompass reassign the exam for an in-house reader based on the identification.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Sargent to include identify the exam of the plurality of exams as part of a long-term study; and reassign the exam for an in-house reader based on the identification as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per independent claim 11, Sargent discloses a method comprising:
predicting, via a processor (See [0021]: The computer device 24 may include an electronic processor 30 (e.g., a microprocessor, application-specific integrated circuit (ASIC), or another suitable electronic device), a memory 40 (e.g., a non-transitory, computer-readable storage medium) that stores machine learning models 44, including, for example, a recurrent neural network 46 and a convolutional neural network 48), a threshold number of exams that a plurality of reviewers of an institution can review over a period of time (See [0029]-[0031], [0046]: The machine learning algorithm can be configured to predict a review score for each candidate imaging exam (e.g., using training information including completed reviews of other imaging exams) and uses the predict scores to select at least a subset of the candidate medical image exams for review, which the Examiner is interpreting the predict scores to select at least a subset of the candidate medical image exams for review to encompass predicting a threshold number of exams that a plurality of reviewers, and interpreting the machine learning algorithm analyzes other environment features including time of day and exam details for other medical exams to provide trends to the reading physician to encompass a plurality of reviewers of an institution can review over a period of time);
dynamically flagging, via the processor, an exam for transmission to an external institution without user intervention based on the number being greater than the threshold number of exams (See [0035]-[0036]: The machine learning algorithm is configured to analyze various categories of candidate medical imaging exams and to determine the types of medical imaging exams that are more prone to errors, which the Examiner is interpreting determine the types of medical imaging exams that are more prone to errors to encompass dynamically flagging, an exam for transmission to an external institution without user intervention, and the assigning of the medical imaging exams to the selected peer reviewers includes assigning the medical imaging exam to at least one peer reviewer most competent for that type of medical imaging exam, which the Examiner is interpreting at least one peer reviewer to encompass an external institution as the peer reviewer does not have to be within the reading physician’s institution);
instructing, via the processor, a display to display a graphical user interface (GUI) comprising the exam and an indication of the flagging proximate to the exam (See [0020], [0027]-[0028], [0035]: The computer device generates a random peer review worklist from a database table of available peer reviewers (e.g., on request), and the machine learning algorithm is configured to analyze various categories of candidate medical imaging exams and to determine the types of medical imaging exams that are more prone to errors, which the Examiner is interpreting the determined types of medical imaging exams that are more prone to errors to encompass an indication of the flagging proximate to the exam as the medical imaging exams with high scores will be selected for peer review based on a threshold value ([0051]));
packaging, via the processor, the exam with additional data based on user input via the display (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting the review score and text for the selected medical imaging exam to encompass packaging the exam with additional data based on user input via the display (Paragraph [0052]));
transmitting, via the processor, the packaged exam to an external institution (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting a quality assurance (QA) lead person to encompass an external institution);
utilizing, via the processor, a machine learning model to gather the user input and user preferences over time (See [0050]: Once trained the machine learning algorithms can be used to select a subset of candidate completed imaging exam for review and can assign a selected exam to a particular reviewer, the machine learning algorithms used by the system can learn specialties, preferences, which the Examiner is interpreting the machine learning algorithms used by the system can learn specialties, preferences to encompass a machine learning model to gather the user input and user preferences over time) and to utilize the user input and the user preferences gathered over time to train and to update a packaging application (See [0050]-[0052]: The input data, and ground truth score are used to update the online machine learning algorithm, if the online machine learning algorithm is a neural network, the update will be provided to the network weights using gradient descent, and the updating of the machine learning algorithm is an online machine learning update, which the Examiner is interpreting input data, and ground truth score are used to update the online machine learning algorithm to encompass the user input and the user preferences gathered over time to train and to update the packaging application) to automatically perform acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time (See [0048]-[0049]: Both machine learning algorithms generated a predicted review score and the scores can be combined, such as by averaging, to determine a final predicted review score for an imaging exam, which the Examiner is interpreting determine a final predicted review score for an imaging exam to encompass automatically perform the acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time when combined Chung’s teachings on “determining, via the processor, that a number corresponding to a plurality of exams is greater than the threshold number of exams” described below); and
packaging the exam with additional data based on user input via the display (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting the review score and text for the selected medical imaging exam to encompass packaging the exam with additional data based on user input via the display ([0052]).)
While Sargent teaches the method as described above, Sargent may not explicitly teach determining, via the processor, that a number corresponding to a plurality of exams is greater than the threshold number of exams; and
determining that the number corresponding to the plurality of exams is greater than the threshold number of exams, dynamically flagging the exam for transmission to the external institution without user intervention based on the number being greater than the threshold number of exams.
Chung teaches a method for determining, via the processor, that a number corresponding to a plurality of exams is greater than the threshold number of exams (See [0150]-[0151], [0165]: The ranking of the readers for which the new order can be inserted in their respective schedule is performed as a function of at least one parameter, the parameter can include the minimum due-in-time requirement, the maximum due-in-time requirement, the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, and conditions where a reader may be overloaded with work can be detected, which the Examiner is interpreting conditions where a reader may be overloaded with work can be detected to encompass determining, via the processor, that a number corresponding to a plurality of exams is greater than the threshold number of exams); and
determining that the number corresponding to the plurality of exams is greater than the threshold number of exams, dynamically flagging the exam for transmission to the external institution without user intervention based on the number being greater than the threshold number of exams (See [0175]-[0178]: Various adequate methods or criteria for measuring the quality of a reader schedule can be used, the reader schedule quality is a function of at least one parameter, examples include the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, and the method is configurable for different optimization criteria based on site, client, or system state such as when overload conditions are detected for readers, which the Examiner is interpreting overload conditions to encompass dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution as the overload condition identifies a number corresponding to the plurality of exams is greater than the threshold number, and the reassignment policies to encompass transmission to an external institution.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Sargent to include determining, via the processor, that a number corresponding to a plurality of exams is greater than the threshold number of exams; and determining that the number corresponding to the plurality of exams is greater than the threshold number of exams, dynamically flagging the exam for transmission to the external institution without user intervention based on the number being greater than the threshold number of exams as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 12, Sargent/Chung discloses the method of claim 11 as described above. Sargent further teaches comprising: identifying, via the processor, one or more priors associated with the exam (See [0028]-[0029]: A review can be recommended for a current medical imaging exam when a relevant prior exam (for the same patient) was viewed for the current exam, which the Examiner is interpreting a relevant prior exam (for the same patient) to encompass the patient history comprises a one or more priors); and
determining, via the processor, a relevant prior of the one or more priors to transmit with the exam (See [0028]-[0029]: The medical imaging exam selections selected via AI can be based on exam specifics (e.g., medical images, report text using natural language processing (NLP), and reader characteristics), reviewer characteristics, or combinations thereof, which the Examiner is interpreting the exam specifics to encompass a relevant prior of the one or more priors to transmit with the exam.)
As per claim 13, Sargent/Chung discloses the method of claims 11-12 as described above. Sargent further teaches wherein determining, via the processor, the relevant prior comprises:
assigning, via the processor, one or more additional score to the one or more priors based on one or more attributes of the exam (See [0040]-[0042], [0048]: The computer device can train a classifier to convert input data (exam, reading physician, and review data (scores)) into a feature presentation and predict a review score for each reader, and the medical imaging exam and the reading physician data as feature vectors, details of the imaging exam can include a body part, impressions, findings, annotations, measurements, anatomy segmentation, modality, procedure, priors, number of slices, computer-aided diagnosis (CAD) results, raw image data, or the like, which the Examiner is interpreting the medical imaging exam as a feature vector to encompass assigning one or more additional score to the one or more priors based on one or more attributes of the exam);
creating, via the processor, a list of the one or more priors based on the one or more additional scores (See [0042], [0048]: The exam details can be stored in the system, various logs maintained by the system or other systems, and completed reports); and
receiving, via the processor, additional user input identifying a relevant prior from the list (See [0028]-[0029]: A review can be recommended for a current medical imaging exam when a relevant prior exam (for the same patient) was viewed for the current exam, which the Examiner is interpreting a relevant prior exam (for the same patient) to encompass additional user input identifying a relevant prior from the list.)
As per claim 15, Sargent/Chung discloses the method of claim 11 as described above. Sargent further teaches comprising: instructing, via the processor, the display to display the GUI comprising a worklist and the threshold number (See [0028], [0041], [0048]: The computer device generates a random peer review worklist from a database table of available peer reviewers (e.g., on request), and the computer device can train a classifier to convert input data (exam, reading physician, and review data (scores)) into a feature presentation and predict a review score for each reader, which the Examiner is interpreting a review score for each reader to encompass the threshold number [0048])); and
adjusting, via the processor, the worklist in response to additional user input (See [0028]-[0029]: After a medical imaging exam is selected for review and a reviewer is assigned, embodiments described herein can provide a user interface that allows a reviewer to efficiently and effectively provide feedback on the medical imaging exam.)
As per claim 16, Sargent/Chung discloses the method of claim 11 as described above. Sargent further teaches comprising: assigning, via the processor, a score to each exam of the plurality of exams based on routing rules (See [0040]-[0042]: The computer device can train a classifier to convert input data (exam, reading physician, and review data (scores)) into a feature presentation and predict a review score for each reader, and the medical imaging exam and the reading physician data as feature vectors, which the Examiner is interpreting the medical imaging exam as a feature vector to encompass assigning a score to each of the plurality of exams.)
While Sargent teaches the method as described above, Sargent may not explicitly teach comprising: instructing, via the processor, the display to display an additional graphical user interface (GUI) displaying the plurality of exams from a highest score to a lowest score, the indication of the flagging proximate to the exam, and the routing rules; and
receiving, via the processor, a preference and a weight for each of the routing rules.
Chung teaches a method comprising: instructing, via the processor, the display to display an additional graphical user interface (GUI) displaying the plurality of exams from a highest score to a lowest score, the indication of the flagging proximate to the exam, and the routing rules (See [0087], [0151]: Examples of parameters that may be used for the ranking of the readers comprise the total expected reading time for the reader schedule, the average expected reading time, the minimum expected reading time, the maximum expected reading time, the variance in expected reading time, the total RVU value for the reader schedule, the average RVU value, the minimum RVU value, the maximum RVU value, the variance in RVU value, the total slack value, the minimum slack value, the maximum slack value, the average slack value, the variance in slack value, the minimum due-in-time requirement, the maximum due-in-time requirement, the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, which the Examiner is interpreting the number of orders having a routine priority to encompass a priority, the number of orders having a stat priority to encompass a patient class as the stat priority is a high priority or critical priority, the number of orders having a stat priority to encompass a type of exam, and the maximum RVU value to encompass a maximum number of priors as the priors could affect the measure of work effort ([0116]), and the determined match scores may be transmitted to a display unit to be displayed to encompass display, via the display, an additional GUI comprising the plurality of exams from a highest score to a lowest score, the indication of the flagging, and routing rules, when combined with Sargent’s teachings of the medical imaging exam as a feature vector); and
receiving, via the processor, a preference and a weight for each of the routing rules (See [0194]: A weight factor is assigned to each criterion according to the relative importance of the criteria.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Sargent to include instructing, via the processor, the display to display an additional graphical user interface (GUI) displaying the plurality of exams from a highest score to a lowest score, the indication of the flagging proximate to the exam, and the routing rules; and receiving, via the processor, a preference and a weight for each of the routing rules as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 21, Sargent/Chung discloses the method of claims 11 and 15 as described above. Sargent further teaches comprising: learning, via the processor and the machine learning model, routing rules over time via the additional user input adjusting the worklist (See [0029]-[0030]: The review information, the reviewer's agreement, disagreement, and any explanation text can be used to train (e.g., further update) the machine learning algorithms, and the review information (and reviewee feedback) can also be used to learn trends for readers, reviewers, or both, which allows the AI system to confidentially inform readers of their common errors or situations that lead to errors, which the Examiner is interpreting the review information, the reviewer's agreement, disagreement, and any explanation text can be used to train (e.g., further update) the machine learning algorithms to encompass learning, via the processor and a machine learning model, routing rules over time via the user input adjusting the worklist); and
creating, via the processor and the machine learning model, an additional worklist comprising the plurality of exams based on the routing rules (See [0029]-[0031]: Collected review information can also be summarized or aggregated and uploaded to one or more accrediting bodies, such as RADPEER, which the Examiner is interpreting the collected review information to encompass an additional worklist comprising the plurality of exams based on the routing rules.)
Claims 2, 14, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sargent et al. (U.S. Patent Pre-Grant Publication No. 2023/0154592) in view of Chung et al. (U.S. Patent Pre-Grant Publication No. 2015/0347694) in further view of Khorasani et al. (U.S. Patent Pre-Grant Publication No. 2010/0080476).
As per claim 2, Sargent/Chung discloses the system of claim 1 as described above. Sargent further teaches wherein the packaging application, when executed, is configured to: associate the report or the diagnosis with the exam (See [0002]: Peer review involves reviewers (also referred to as “colleagues” herein) reviewing images and associated reports from exams completed by a reading physician.)
While Sargent/Chung teaches a system wherein the packaging application, when executed, is configured to: associate the report or the diagnosis with the exam, Sargent/Chung may not explicitly teach wherein the packaging application, when executed, is configured to: decompress a file from the external institution, wherein the file comprises a report or a diagnosis.
Khorasani teaches a system wherein the packaging application, when executed, is configured to: decompress a file from the external institution, wherein the file comprises a report or a diagnosis (See [0026]-[0027]: These proxies may intercept communication data transmitted between a PACS Server and a Viewer Application in order to compress DICOM images on the PACS side and decompress them on the Viewer side, which the Examiner is interpreting decompress DICOM images on the Viewer side to encompass decompress a file from the external institution and the diagnostic quality can be maintained.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the system of Sargent/Chung to include decompress a file from the external institution, wherein the file comprises a report or a diagnosis as taught by Khorasani. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent/Chung with Khorasani with the motivation of enhance the efficiency of transferring large volumes of Medical Images to remote stations (See Background of Khorasani in Paragraph [0012]).
As per claim 14, Sargent/Chung discloses the method of claims 11-13 as described above. Sargent further teaches comprising: wherein transmitting, via the processor, comprises transmitting the package to the third-party reader in response to the additional user input (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting a quality assurance (QA) lead person to encompass a third-party reader.)
While Sargent/Chung teaches a method comprising: wherein transmitting, via the processor, comprises transmitting the package to the third-party reader in response to user input, Sargent/Chung may not explicitly teach comprising: compressing, via the processor, the relevant prior and the exam into a package.
Khorasani teaches a method comprising: compressing, via the processor, the relevant prior and the exam into a package (See [0009]: Employing a compression engine or encoder 215 and a DICOM Enabled Server Application 217 in a PACS server 202 within a PACS Station 203 to compress medical images prior to storing them.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Sargent/Chung to include compressing, via the processor, the relevant prior and the exam into a package as taught by Khorasani. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent/Chung with Khorasani with the motivation of enhance the efficiency of transferring large volumes of Medical Images to remote stations (See Background of Khorasani in Paragraph [0012]).
As per independent claim 17, Sargent discloses a non-transitory, computer-readable medium comprising computer-readable code, that when executed by one or more processors, causes the one or more processors to perform operations comprising:
predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time (See [0029]-[0031], [0046]: The machine learning algorithm can be configured to predict a review score for each candidate imaging exam (e.g., using training information including completed reviews of other imaging exams) and uses the predict scores to select at least a subset of the candidate medical image exams for review, which the Examiner is interpreting the predict scores to select at least a subset of the candidate medical image exams for review to encompass predicting a threshold number of exams that a plurality of reviewers, and interpreting the machine learning algorithm analyzes other environment features including time of day and exam details for other medical exams to provide trends to the reading physician to encompass a plurality of reviewers of an institution can review over a period of time);
dynamically flagging, without user intervention, an exam of the plurality of exams for transmission to an external institution to review based on the number being greater than the threshold number of exams (See [0035]-[0036]: The machine learning algorithm is configured to analyze various categories of candidate medical imaging exams and to determine the types of medical imaging exams that are more prone to errors, which the Examiner is interpreting determine the types of medical imaging exams that are more prone to errors to encompass dynamically flagging, without user intervention, an exam of the plurality of exams for transmission, and the assigning of the medical imaging exams to the selected peer reviewers includes assigning the medical imaging exam to at least one peer reviewer most competent for that type of medical imaging exam, which the Examiner is interpreting at least one peer reviewer to encompass an external institution as the peer reviewer does not have to be within the reading physician’s institution);
instructing a display to display a graphical user interface (GUI) comprising the exam and an indication of the flagging proximate to the exam, and the threshold number of exams (See [0028], [0035]: The computer device generates a random peer review worklist from a database table of available peer reviewers (e.g., on request), and the computer device generates a random peer review worklist from a database table of available peer reviewers (e.g., on request), and the machine learning algorithm is configured to analyze various categories of candidate medical imaging exams and to determine the types of medical imaging exams that are more prone to errors, which the Examiner is interpreting the determined types of medical imaging exams that are more prone to errors to encompass an indication of the flagging proximate to the exam, and the threshold number of exams as the medical imaging exams with high scores will be selected for peer review based on a threshold value ([0051]));
identifying one or more priors based on attributes of the exam (See [0040]-[0042], [0048]: The computer device can train a classifier to convert input data (exam, reading physician, and review data (scores)) into a feature presentation and predict a review score for each reader, and the medical imaging exam and the reading physician data as feature vectors, details of the imaging exam can include a body part, impressions, findings, annotations, measurements, anatomy segmentation, modality, procedure, priors, number of slices, computer-aided diagnosis (CAD) results, raw image data, or the like);
transmitting the package to the external institution (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting a quality assurance (QA) lead person to encompass the external institution);
utilizing, via the processor, a machine learning model to gather the user input and user preferences over time (See [0050]: Once trained the machine learning algorithms can be used to select a subset of candidate completed imaging exam for review and can assign a selected exam to a particular reviewer, the machine learning algorithms used by the system can learn specialties, preferences, which the Examiner is interpreting the machine learning algorithms used by the system can learn specialties, preferences to encompass a machine learning model to gather the user input and user preferences over time) and to utilize the user input and the user preferences gathered over time to train and to update a packaging application (See [0050]-[0052]: The input data, and ground truth score are used to update the online machine learning algorithm, if the online machine learning algorithm is a neural network, the update will be provided to the network weights using gradient descent, and the updating of the machine learning algorithm is an online machine learning update, which the Examiner is interpreting input data, and ground truth score are used to update the online machine learning algorithm to encompass the user input and the user preferences gathered over time to train and to update the packaging application) to automatically perform the operations of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time (See [0048]-[0049]: Both machine learning algorithms generated a predicted review score and the scores can be combined, such as by averaging, to determine a final predicted review score for an imaging exam, which the Examiner is interpreting determine a final predicted review score for an imaging exam to encompass automatically perform the acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time when combined Chung’s teachings on “determining that a number corresponding to a plurality of exams is greater than the threshold number of exams” described below); and
packaging the exam with additional data based on user input via the display (See Fig. 4 and [0051]-[0053]: The computer device provides review score and text for the selected medical imaging exam at step 328 is also provided, at feedback step 352 to the reading physician 360 and/or to a quality assurance (QA) lead person, which the Examiner is interpreting the review score and text for the selected medical imaging exam to encompass packaging the exam with additional data based on user input via the display ([0052]).)
While Sargent teaches the computer-readable medium as described above, Sargent may not explicitly teach determining that a number corresponding to a plurality of exams is greater than threshold number of exams;
determining that the number corresponding to the plurality of exams is greater than the threshold number of exams, dynamically flagging, without user intervention, the exam of the plurality of exams for transmission to the external institution to review based on the number being greater than the threshold number of exams.
Chung teaches a non-transitory computer-readable medium for determining that a number corresponding to a plurality of exams is greater than threshold number of exams (See [0150]-[0151], [0165]: The ranking of the readers for which the new order can be inserted in their respective schedule is performed as a function of at least one parameter, the parameter can include the minimum due-in-time requirement, the maximum due-in-time requirement, the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, and conditions where a reader may be overloaded with work can be detected, which the Examiner is interpreting conditions where a reader may be overloaded with work can be detected to encompass determining a number corresponding to a plurality of exams is greater than a threshold number of exams);
determining that the number corresponding to the plurality of exams is greater than the threshold number of exams, dynamically flagging, without user intervention, the exam of the plurality of exams for transmission to the external institution to review based on the number being greater than the threshold number of exams (See [0175]-[0178]: Various adequate methods or criteria for measuring the quality of a reader schedule can be used, the reader schedule quality is a function of at least one parameter, examples include the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, and the method is configurable for different optimization criteria based on site, client, or system state such as when overload conditions are detected for readers, which the Examiner is interpreting overload conditions to encompass dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution as the overload condition identifies a number corresponding to the plurality of exams is greater than the threshold number, and the reassignment policies to encompass transmission to an external institution.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the non-transitory computer-readable medium of Sargent to include determining that a number corresponding to a plurality of exams is greater than threshold number of exams; determining that the number corresponding to the plurality of exams is greater than the threshold number of exams, dynamically flagging, without user intervention, the exam of the plurality of exams for transmission to the external institution to review based on the number being greater than the threshold number of exams as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
Khorasani teaches a non-transitory computer-readable medium for compressing the exam and the one or more priors into a package (See [0009]: Employing a compression engine or encoder 215 and a DICOM Enabled Server Application 217 in a PACS server 202 within a PACS Station 203 to compress medical images prior to storing them.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the non-transitory computer-readable medium of Sargent/Chung to include compressing the exam and the one or more priors into a package as taught by Khorasani. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent/Chung with Khorasani with the motivation of enhance the efficiency of transferring large volumes of Medical Images to remote stations (See Background of Khorasani in Paragraph [0012]).
As per claim 18, Sargent/Chung/Khorasani discloses the non-transitory computer-readable medium of claim 17 as described above. Sargent further teaches wherein the computer-readable code, when executed by the one or more processors, causes the one or more processors to perform further operations comprising:
assigning a score to each exam of the plurality of exams based on routing rules (See [0040]-[0042]: The computer device can train a classifier to convert input data (exam, reading physician, and review data (scores)) into a feature presentation and predict a review score for each reader, and the medical imaging exam and the reading physician data as feature vectors, which the Examiner is interpreting the medical imaging exam as a feature vector to encompass assigning a score to each of the plurality of exams); and
adjusting the routing rules based on additional user input (See [0028]-[0029]: After a medical imaging exam is selected for review and a reviewer is assigned, embodiments described herein can provide a user interface that allows a reviewer to efficiently and effectively provide feedback on the medical imaging exam.)
While Sargent teaches the non-transitory computer-readable medium as described above, Sargent may not explicitly teach wherein the computer-readable code, when executed by the one or more processors, causes the one or more processors to perform further operations comprising: instructing a second GUI comprising the plurality of exams ordered from a highest score to a lowest score, the indication of the flagging proximate to the exam, and the routing rules, wherein the routing rules comprise an attribute, a criteria, and a weight.
Chung teaches a non-transitory computer-readable medium wherein the computer-readable code, when executed by the one or more processors, causes the one or more processors to perform further operations comprising: instructing a second GUI comprising the plurality of exams ordered from a highest score to a lowest score, the indication of the flagging proximate to the exam, and the routing rules (See [0087], [0151]: Examples of parameters that may be used for the ranking of the readers comprise the total expected reading time for the reader schedule, the average expected reading time, the minimum expected reading time, the maximum expected reading time, the variance in expected reading time, the total RVU value for the reader schedule, the average RVU value, the minimum RVU value, the maximum RVU value, the variance in RVU value, the total slack value, the minimum slack value, the maximum slack value, the average slack value, the variance in slack value, the minimum due-in-time requirement, the maximum due-in-time requirement, the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, which the Examiner is interpreting the number of orders having a routine priority to encompass a priority, the number of orders having a stat priority to encompass a patient class as the stat priority is a high priority or critical priority, the number of orders having a stat priority to encompass a type of exam, and the maximum RVU value to encompass a maximum number of priors as the priors could affect the measure of work effort ([0116]), and the determined match scores may be transmitted to a display unit to be displayed to encompass display, via the display, a second GUI comprising the plurality of exams from a highest score to a lowest score, routing rules, and the indication of the flagging when combined with Sargent’s teachings of the medical imaging exam as a feature vector), wherein the routing rules comprise an attribute, a criteria, and a weight (See [0149]-[0151]: Examples of parameters that may be used for the ranking of the readers comprise the total expected reading time for the reader schedule, the average expected reading time, the minimum expected reading time, the maximum expected reading time, the variance in expected reading time, the total RVU value for the reader schedule, the average RVU value, the minimum RVU value, the maximum RVU value, the variance in RVU value, the total slack value, the minimum slack value, the maximum slack value, the average slack value, the variance in slack value, the minimum due-in-time requirement, the maximum due-in-time requirement, the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the non-transitory computer-readable medium of Sargent to include instructing a second GUI comprising the plurality of exams ordered from a highest score to a lowest score, the indication of the flagging proximate to the exam, and the routing rules, wherein the routing rules comprise an attribute, a criteria, and a weight as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 19, Sargent/Chung/Khorasani discloses the non-transitory computer-readable medium of claims 17-18 as described above. Sargent further teaches wherein the computer- readable code, when executed by the one or more processors, causes the one or more processors to perform further operations comprising:
adjusting the routing rules based on the additional user input (See [0028]-[0029]: After a medical imaging exam is selected for review and a reviewer is assigned, embodiments described herein can provide a user interface that allows a reviewer to efficiently and effectively provide feedback on the medical imaging exam.)
While Sargent teaches a non-transitory computer-readable medium wherein the computer- readable code, when executed by the one or more processors, causes the one or more processors to perform further operations comprising: adjusting the routing rules based on the additional user input, Sargent may not explicitly teach wherein the computer- readable code, when executed by the one or more processors, causes the one or more processors to perform further operations comprising: receiving additional user input assigning the exam from a worklist to an in-house reader.
Chung teaches a non-transitory computer-readable medium wherein the computer- readable code, when executed by the one or more processors, causes the one or more processors to perform further operations comprising:
receiving additional user input assigning the exam from a worklist to an in-house reader (See [0167], [0176]-[0177]: The optimization criteria can be based on at least one parameter such as SLA penalties, order priority, order RVU, order ER, order location, or the like, which the Examiner is interpreting the optimization criteria and order location to encompass an in-house reader as the optimization criteria can vary from site to site, and client to client ([0173]).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the non-transitory computer-readable medium of Sargent to include receiving additional user input assigning the exam from a worklist to an in-house reader as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
As per claim 20, Sargent/Chung/Khorasani discloses the non-transitory computer-readable medium of claim 17 as described above. Sargent may not explicitly teach wherein determining the number of the plurality of exams comprises: identifying a number of in-house readers and a schedule of the in-house readers based on the plurality of work schedules; estimating the number of exams the in-house readers can review over a period of time, wherein the estimation is the threshold number; and comparing the estimation to the number of the plurality of exams.
Chung teaches a non-transitory computer-readable medium wherein determining the number of the plurality of exams comprises: identifying a number of in-house readers and a schedule of the in-house readers based on the plurality of work schedules (See Paragraph [0124]: Each reader has a schedule of assigned orders that he or she has to analyze, and each assigned order has a corresponding due-in-time requirement by which it has to be analyzed);
estimating the number of exams the in-house readers can review over a period of time, wherein the estimation is the threshold number (See [0185]-[0186]: The at risk level of a reader for missing a given order's due in time requirement can be quantified as a function of estimated completion time past due in time, which the Examiner is interpreting estimated completion time past due in time to encompass estimating the number of exams the in-house readers can review over a period of time); and
comparing the estimation to the number of the plurality of exams (See [0164]-[0165]: The workload from the outstanding orders contained in a reader schedule can be measured and compared against their remaining work capacity for a shift.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the non-transitory computer-readable medium of Sargent to include determining the number of the plurality of exams comprises: identifying a number of in-house readers and a schedule of the in-house readers based on the plurality of work schedules; estimating the number of exams the in-house readers can review over a period of time, wherein the estimation is the threshold number; and comparing the estimation to the number of the plurality of exams as taught by Chung. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Sargent with Chung with the motivation of providing an improved method and system of selecting readers to analyze a given radiology order (See Background of Chung in Paragraph [0006]).
Response to Arguments
In the Remarks filed on February 9, 2026, the Applicant argues that the newly amended and/or added claims overcome the Claim Objection(s), 35 U.S.C. 101 rejection(s), and 35 U.S.C. 103 rejection(s). The Examiner acknowledges that the newly added and/or amended claims overcome the previous Claim Objection(s). However, the Examiner does not acknowledge that the newly added and/or amended claims overcome the newly added Claim Objection(s), 35 U.S.C. 101 rejection(s), and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) Applicant respectfully submits that the recitations of independent claims 1, 11, and 17 cannot reasonably be construed as fundamental economic principles or practices, or as commercial or legal interactions. Additionally, Applicant contends that the recitations of independent claims 1, 11, and 17 cannot reasonably be construed as managing personal behavior or managing relationships or interactions between people, at least because the present claims do not recite actions that tell humans how to do something. Indeed, because the certain methods of organizing human activity are not to be extended beyond the enumerated groupings, Applicant respectfully submits that independent claims 1, 11, and 17 cannot reasonably be interpreted as reciting certain methods of organizing human activity. As such, independent claims 1, 11, and 17 are not directed to an abstract idea, and thus, are patent eligible;
(2) even assuming, arguendo, that the claims recite certain methods of organizing human activity, Applicant submits that the claims "integrate the recited exception into a practical application of the exception." MPEP § 2106.04(II)(A)(2). In the Office Action, the Examiner indicated that the claims do not integrate the allegedly abstract ideas into a practical application. See Office Action, pages 5-7. Applicant respectfully disagrees and notes that § 2106 of the MPEP states that "[a] claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." MPEP § 2106.04(d)(I). Applicant respectfully submits that the claims recite specific features of performed techniques. For example, independent claim 1 recites "predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time," "determining a number corresponding to a plurality of exams is greater than the threshold number," "dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution," "displaying, via a display, a graphical user interface (GUI) comprising the exam and an indication of the flagging proximate to the exam," "packaging the exam with additional data based on user input via the display," "transmitting the packaged exam to the external institution," and "utilizing a machine learning model to gather the user input and user preferences over time and to utilize the user input and the user preferences gathered over time to train and to update the packaging application to automatically perform the acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time; determining that the number corresponding to the plurality of exams is greater than the threshold number, dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution; and packaging the exam with additional data based on user input via the display" which Applicant submits could not be practically performed by a person following rules or instructions as the person cannot perform an action on data (e.g., a worklist) prior to being made aware of the data in question. Furthermore, Applicant submits that the person cannot display a GUI, even if the person was instructed to do so. Even furthermore, Applicant submits that a person cannot act as a machine learning model to gather user input and user preferences over time and to utilize the user input and the user preferences gathered over time to train and to update a packaging application. Applicant respectfully submits that independent claims 11 and 17 include similar recitations as independent claim 1. In this way, the claims incorporate the alleged abstract ideas into a practical application and are not merely "a drafting effort designed to monopolize the judicial exception." Id Furthermore, the MPEP states that claims directed to "an improvement to [some] other technology or technical field" may provide an indication that an abstract idea has been incorporated into a practical application. See MPEP § 2106.04(d)(I). Here, as described in more detail below, Applicant notes that independent claims 1, 11, and 17 recite features that are patentably distinct from the cited references. Additionally, Applicant submits that the present claims improve the technical field of work item distribution by providing guidance corresponding to a recommended action to decrease tum-around time for review. Additionally, the specification discloses that "[i]n this way, the packaging application may automate the steps of identifying relevant priors, packaging the exam and priors, transmitting the package in response to user input, and decompressing a response package received from the third-party reader." Id at paragraph 26. In this way, the present claims improve the technical field of work item distribution by providing guidance corresponding to a recommended action to decrease tum-around time for review. As such, Applicant respectfully submits that even if independent claims 1, 11, and 17 allegedly recite abstract ideas, the claims integrate the judicial exception into a practical application by imposing meaningful limitations on the judicial exception such that the claims are more than a drafting effort to monopolize the judicial exception;
(3) under Step 2B of the test outlined in the MPEP, if additional elements recited by the claims amount to "significantly more" than the judicial exception, then the claim is eligible under 35 U.S.C. § 101. See MPEP § 2106.05. Additional claim elements may amount to "significantly more," for example, by providing an inventive concept by adding a particular limitation or combination of limitations that are not well-understood, routine, or conventional. See id Even if the Examiner determines that independent claims 1, 11, and 17 are directed to a judicial exception and do not incorporate the alleged judicial exception into a practical application, independent claims 1, 11, and 17 recite an inventive concept. Applicant submits that the recited features of independent claims 1, 11, and 17 are novel and non-obvious, thereby making the recited features of independent claims 1, 11, and 17 not well-understood, routine, conventional activity in the field. As such, Applicant respectfully submits that the claims recite an inventive concept under Step 2B of the test outlined in the MPEP and are directed to patentable subject matter. For at least these reasons among others, Applicant respectfully requests withdrawal of the rejections under 35 U.S.C. § 101;
(4) Sargent and Chung, taken alone or in hypothetical combination, fail to teach or suggest "predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time", as recited in independent claim 1 and similarly recited in independent claim 11. Sargent relates to using machine learning to assign peer reviews of medical exams and to predict peer review scores. Sargent, Abstract. The Examiner solely relied on paragraphs 29-31 and 46 of Sargent for allegedly teaching this recitation. In contrast to the Examiner's assertion, predicting an exam score as disclosed in Sargent is not the same as predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time. Office Action, p. 13. Also, these portions of Sargent (as well as the rest of Sargent) are completely silent with regard to predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time. Chung fails to obviate these deficiencies. Indeed, the Examiner solely relied on Chung for allegedly teaching determining a number corresponding to a plurality of exams is greater than the threshold number. Id, p. 15. In addition, Sargent and Chung, taken alone or in hypothetical combination, fail to teach or suggest "determining that a number corresponding to a plurality of exams is greater than the threshold number of exams", as recited in independent claim 1 and similarly recited in independent claim 11. The Examiner admitted that "Sargent may not explicitly teach determining that a number corresponding to a plurality of exams is greater than the threshold number." Office Action, p. 15. Instead, the Examiner solely relied on Chung for allegedly teaching determining a number corresponding to a plurality of exams is greater than the threshold number. Id Chung relates to selecting readers analyzing radiology orders based on sub-specialty. Chung, Abstract. The Examiner solely relied on paragraphs 150, 151, and 165 of Chung for allegedly teaching this recitation. Id In contrast to the Examiner's assertion, determining that an individual reviewer is overwhelmed as disclosed in Chung is not the same as determining that a number corresponding to a plurality of exams is greater than the threshold number of exams. Id Also, these portions of Chung (as well as the rest of Chung) are completely silent with regard to determining that a number corresponding to a plurality of exams is greater than the threshold number of exams. Thus, Chung fails to obviate the deficiencies of Sargent;
(5) Sargent and Chung, taken alone or in hypothetical combination, fail to teach or suggest "dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution", as recited in independent claim 1 and similarly recited in independent claim 11. Sargent relates to using machine learning to assign peer reviews of medical exams and to predict peer review scores. Sargent, Abstract. The Examiner solely relied on paragraphs 35 and 36 of Sargent for allegedly teaching this recitation. In contrast to the Examiner's assertion, determining the types of medical imaging exams that are prone to error as disclosed in Sargent is not the same as dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution. Office Action, pp. 13 and 14. Also, these portions of Sargent (as well as the rest of Sargent) are completely silent with regard to dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution. Chung fails to obviate these deficiencies. Indeed, the Examiner solely relied on Chung for allegedly teaching determining a number corresponding to a plurality of exams is greater than the threshold number. Id, p. 15. For at least these reasons, Applicant respectfully submits that Sargent and Chung, taken alone or in hypothetical combination, do not teach or suggest all of the recitations of independent claims 1 and 11, and thus cannot support a prima facie case of obviousness with respect to these claims. Based on their dependencies on independent claims 1 and 11, as well as the elements therein, Applicant submits that Sargent and Chung do not teach every element of claims 4-10, 12, 13, 15, 16, and 21, and thus cannot support a prima facie case of obviousness with respect to these claims. Accordingly, Applicant respectfully requests withdrawal of the rejection of claims 1, 4-13, 15, 16, and 21 under 35 U.S.C. § 103 and allowance of the same;
(6) Sargent, Chung, and Khorasani, taken alone or in hypothetical combination, fail to teach or suggest "predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time", as recited in independent claim 17. Sargent relates to using machine learning to assign peer reviews of medical exams and to predict peer review scores. Sargent, Abstract. The Examiner solely relied on paragraphs 29-31 and 46 of Sargent for allegedly teaching this recitation. In contrast to the Examiner's assertion, predicting an exam score as disclosed in Sargent is not the same as predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time. Office Action, p. 35. Also, these portions of Sargent (as well as the rest of Sargent) are completely silent with regard to predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time. Chung fails to obviate these deficiencies. Indeed, the Examiner solely relied on Chung for allegedly teaching determining a number corresponding to a plurality of exams is greater than the threshold number. Id, p. 36. Khorasani fails to obviate these deficiencies. Indeed, the Examiner solely relied on Chung for allegedly teaching compressing the exam and the one or more priors into a package. Id, p. 37. In addition, Sargent, Chung, and Khorasani, taken alone or in hypothetical combination, fail to teach or suggest "determining that a number corresponding to a plurality of exams is greater than threshold number of exams", as recited in independent claim 17. The Examiner admitted that "Sargent may not explicitly teach determining that a number corresponding to a plurality of exams is greater than the threshold number." Office Action, p. 36. Instead, the Examiner solely relied on Chung for allegedly teaching determining a number corresponding to a plurality of exams is greater than the threshold number. Id Chung relates to selecting readers analyzing radiology orders based on sub-specialty. Chung, Abstract. The Examiner solely relied on paragraphs 150, 151, and 165 of Chung for allegedly teaching this recitation. Id, p. 37. In contrast to the Examiner's assertion, determining that an individual reviewer is overwhelmed as disclosed in Chung is not the same as determining that a number corresponding to a plurality of exams is greater than the threshold number of exams. Id Also, these portions of Chung (as well as the rest of Chung) are completely silent with regard to determining that a number corresponding to a plurality of exams is greater than the threshold number of exams. Thus, Chung fails to obviate the deficiencies of Sargent. Khorasani fails to obviate these deficiencies. Indeed, the Examiner solely relied on Chung for allegedly teaching compressing the exam and the one or more priors into a package. Id, p. 37. Further, Sargent, Chung, and Khorasani, taken alone or in hypothetical combination, fail to teach or suggest "dynamically flagging, without user intervention, an exam of the plurality of exams for transmission to an external institution to review based on the number being greater than the threshold number of exams", as recited in independent claim 17. Sargent relates to using machine learning to assign peer reviews of medical exams and to predict peer review scores. Sargent, Abstract. The Examiner solely relied on paragraphs 35 and 36 of Sargent for allegedly teaching this recitation. Office Action. p. 35. In contrast to the Examiner's assertion, determining the types of medical imaging exams that are prone to error as disclosed in Sargent is not the same as dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution. Id Also, these portions of Sargent (as well as the rest of Sargent) are completely silent with regard to dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution. Chung fails to obviate these deficiencies. Indeed, the Examiner solely relied on Chung for allegedly teaching determining a number corresponding to a plurality of exams is greater than the threshold number. Id, p. 36. Khorasani fails to obviate these deficiencies. Indeed, the Examiner solely relied on Chung for allegedly teaching compressing the exam and the one or more priors into a package. Id, p. 37. For at least these reasons, Applicant respectfully submits that Sargent, Chung, and Khorasani, taken alone or in hypothetical combination, do not teach or suggest all of the recitations of independent claim 17, and thus cannot support a prima facie case of obviousness with respect to this claim. Based on their dependencies on independent claims 1, 11, and 17, as well as the elements therein, Applicant submits that Sargent, Chung, and Khorasani do not teach every element of claims 2, 14, and 18-20, and thus cannot support a prima facie case of obviousness with respect to these claims. Accordingly, Applicant respectfully requests withdrawal of the rejection of claims 2, 14, and 17-20 under 35 U.S.C. § 103 and allowance of the same
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claimed limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior or relationships between people (including following rules or instructions) which is a subgrouping of Certain Methods of Organizing Human Activity. The Examiner maintains that the Applicant’s claims recite a series of steps that a person managing a workflow for radiologists could follow. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea and adding insignificant extra-solution activity. The Examiner maintains that independent claim 1’s recitations are not specific techniques as the Applicant’s claimed limitations determine predictions, determine if a number is greater than a threshold, make a determination that the number is greater than a threshold, including more information with an exam, gathering information on a user’s preferences and information about the user, making another determination if a number is greater than a threshold, and including information with an exam for another person to view. The claims do not recite specific features of performed techniques as the claims are reciting a person interacting with a generic computer component and data, while following instructions. The Examiner does not acknowledge that “a person following rules or instructions as the person cannot perform an action on data (e.g., a worklist) prior to being made aware of the data in question” is not represented in the claim language as computer software would also need to be made aware of the data in question to perform an action. The Applicant’s first claimed limitation is “predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time;”, a person following rules or instructions can make this prediction without any information as the claim language does not specify that the prediction must be accurate or correct, only that there is a prediction. Additionally, claim 1 recites “displaying, the exam and an indication of the flagging proximate to the exam;” that amounts to insignificant extra-solution selecting a particular data source or type of data to be manipulated activity (See MPEP 2106.05(g)), and “…via a display, a graphical user interface (GUI) comprising…” amounts to no more than general purpose computer components programmed to perform the abstract idea. The Applicant’s recitation of “a machine learning model” is similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The Examiner maintains that the Applicant’s claims are similar to “iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48” (See MPEP 2106.05(a)(II)), which the courts have indicated may not be sufficient to show an improvement to technology. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (3), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”) Insignificant, extra solution, data gathering activity and selecting a particular data source or type of data to be manipulated has been found to not amount to significantly more than an abstract idea (See MPEP 2106.05(g)). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea. Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2016.05(h) and Affinity Labs of Texas v. DirectTV, LLC, 838 F.3d 1253, 120 USP12d 1201 (Fed. Cir. 2016)). The 35 U.S.C. 101 rejection(s) stand.
In response to argument (4), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that Sargent’s teachings in Paragraphs [0029]-[0031], [0046] teaches "predicting a threshold number of exams that a plurality of reviewers of an institution can review over a period of time" as recited in independent claim 1 as the machine learning algorithm analyzes other environment features including time of day and exam details for other medical exams to provide trends to the reading physician to encompass a plurality of reviewers of an institution can review over a period of time, and Paragraphs [0048]-[0049] teaches that both machine learning algorithms generated a predicted review score and the scores can be combined, such as by averaging, to determine a final predicted review score for an imaging exam, which the Examiner is interpreting determine a final predicted review score for an imaging exam to encompass automatically perform the acts of predicting the threshold number of exams that the plurality of reviewers of the institution can review over the period of time when combined Chung’s teachings on “determining that a number corresponding to a plurality of exams is greater than the threshold number of exams”. The Examiner maintains that Chung’s teachings in Paragraphs [0150]-[0151], [0165] teaches “determining that a number corresponding to a plurality of exams is greater than the threshold number of exams” as the Examiner is interpreting conditions where a reader may be overloaded with work can be detected to encompass determining that a number corresponding to a plurality of exams is greater than a threshold number of exams. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (5), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the combination of Sargent/Chung teaches the “dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution” as recited in claim 1 as Sargent in Paragraphs [0035]-[0036] that the machine learning algorithm is configured to analyze various categories of candidate medical imaging exams and to determine the types of medical imaging exams that are more prone to errors, which the Examiner is interpreting determine the types of medical imaging exams that are more prone to errors to encompass dynamically flagging, without user intervention, an exam of the plurality of exams, and the assigning of the medical imaging exams to the selected peer reviewers includes assigning the medical imaging exam to at least one peer reviewer most competent for that type of medical imaging exam, which the Examiner is interpreting at least one peer reviewer to encompass an external institution as the peer reviewer does not have to be within the reading physician’s institution as Chung teaches in Paragraphs [0150]-[0151], [0165] teaches “determining that a number corresponding to a plurality of exams is greater than the threshold number of exams” as the Examiner is interpreting conditions where a reader may be overloaded with work can be detected to encompass determining that a number corresponding to a plurality of exams is greater than a threshold number of exams. Further, Chung is relied upon to teach “determining that the number corresponding to the plurality of exams is greater than the threshold number, dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution” in Paragraphs [0175]-[0178]: Various adequate methods or criteria for measuring the quality of a reader schedule can be used, the reader schedule quality is a function of at least one parameter, examples include the number of orders contained in the reader schedule, the number of orders having a stat priority, the number of orders to be urgently analyzed, the number of orders having a routine priority, the proportion of stat/routine orders, or the like, and the method is configurable for different optimization criteria based on site, client, or system state such as when overload conditions are detected for readers, which the Examiner is interpreting overload conditions to encompass dynamically flagging, based on the number being greater than the threshold number of exams and without user intervention, an exam of the plurality of exams for transmission to an external institution as the overload condition identifies a number corresponding to the plurality of exams is greater than the threshold number, and the reassignment policies to encompass transmission to an external institution. The 35 U.S.C. 103 rejection(s) stand.
In response to argument (6), the Examiner does not find the Applicant’s argument(s) persuasive. Khorasani is relied upon in independent claim 17 to teach “compressing the exam and the one or more priors into a package” which the Examiner maintains the rejection as described above in the 35 U.S.C. 103 rejection(s). The Applicant’s arguments in argument (6) are addressed above in response to arguments (4)-(5) as the Applicant’s arguments are in reference to the 35 U.S.C. 103 rejections combination of Sargent and Chung. The 35 U.S.C. 103 rejection(s) stand.
Conclusion
THIS ACTION IS MADE FINAL. 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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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683