DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Final Office Action is in response to the Amendment and Remarks filed 6/10/2026. Claims 1, 4, 7, 11, 12, 15 and 18 are amended. Claims 21-22 are new. Claims 1-22 are currently pending and considered herein.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites, wherein the abstract elements are not emboldened:
A computing system comprising memory storing instructions and processing circuitry of one or more devices, including a server, a computing device of a primary adjudicator, and a computing device of a secondary adjudicator, configured to execute those instructions to: receive, at the server, episode data of a cardiac episode from a medical device; identify, by the server, a region of the episode data; provide, based on a first input from the primary adjudicator and via the computing device of the secondary adjudicator, a notification to the secondary adjudicator; display, in response to the secondary adjudicator interacting with the computing device of the secondary adjudicator in response to the notification and in a primary view, the region of the episode data onto a display of the computing device of the secondary adjudicator; receive, by the computing device of the secondary adjudicator, a second input; and transmit, by the computing device of the secondary adjudicator and to the server, the second input received from the secondary adjudicator in response to the episode data.
Independent claims 11 and 12 recite substantially similar limitations. The claimed invention is directed to the abstract idea of collecting patient information including episode data of a cardiac episode that is monitored, analyzing the information, and generating notifications based on the analyses.
The limitations of “a primary adjudicator, and a secondary adjudicator, receive episode data of a cardiac episode; identify a region of the episode data; provide, based on a first input from the primary adjudicator, a notification to the secondary adjudicator; in response to the secondary adjudicator interacting [and] in response to the notification and in a primary view, [displaying] the region of the episode data; receive from the secondary adjudicator, a second input; [and] the second input received from the secondary adjudicator in response to the episode data,” as drafted, is a process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as organizing human activity. For example, but for the generic “computing system comprising memory storing instructions and processing circuitry of one or more devices, including a server, a computing device” and display, the claim recites an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. The claim recites as a whole a method of organizing human activity because the limitations include a method that allows users to access myriad patient data, analyze the data and determine whether certain conditions are met based on the analyses. This is a method of managing interactions between people. The mere nominal recitation of a generic computer devices, server and display does not take the claims out of the method of organizing human interactions grouping. The additional limitations amount to computer methods for further implementing/applying the abstract idea. The additional limitations generally link the abstract idea to a technological environment or field of use. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a “computing system comprising memory storing instructions and processing circuitry of one or more devices, including a server, a computing device” and display. The computer and/or medical devices and functions in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying selected information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The limitations seem to monopolize the abstract idea of patient analysis and diagnoses and general techniques between a physician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a “computing system comprising memory storing instructions and processing circuitry of one or more devices, including a server, a computing device” and display amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements are used to generally link the abstract idea to a technological environment. The additional elements, when considered separately and as an ordered combination, do not add significantly more as these limitations simply apply the exception in a generic computer environment, and do not provide an inventive concept.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea. Claims 2 and 13 further specifies episode data and limits the abstract idea. Claims 3, 14 describe a machine learning model and identifying a region and claim 22 describes the machine learning model as minimizing a cost function to determine a region, and the machine learning model, region and cost function are recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the machine learning model and minimizing a cost function does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 4 and 15 defines a region that is indicated to be displayed and further limits the abstract idea. Claims 5 and 16 include a display and screen which are recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the display and screen does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 7 and 18 describe receiving and selecting a question and further limits the abstract idea. Claims 8-9 and 19-20 describe receiving a selection and further limits the abstract idea. Claim 10 describes a zoom or enhancement of episode data, which is recited at a high level of generality such that it amounts to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the zoom enhancement of episode data does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claim 21 describes human adjudicators and further limits the abstract idea. Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 11-17 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2020/0352466 A1 to Chakravarthy et al., hereinafter “Chakravarthy,” in view of U.S. 2018/0060512 A1 to Sorenson et al., hereinafter “Sorenson.”
Regarding claim 1, Chakravarthy discloses A computing system comprising memory storing instructions and processing circuitry of one or more devices, including a server, a computing device of a primary adjudicator (See Chakravarthy at Paras. [0030]-[0043] (computer devices, memory, server), [0076]-[0081] (“[T]he machine learning model is trained with a plurality of ECG episodes annotated by a clinician or a monitoring center for arrhythmias of several different types. In one example, machine learning system 150 applies the machine learning model to take one or several subsegments of a normalized input ECG signal and generates arrhythmia labels and a likelihood of an occurrence of the arrhythmia.”); Figs. 1, 4-10), and a computing device of a secondary adjudicator, configured to execute those instructions to: receive, at the server, episode data of a cardiac episode from a medical device (See id. at least at Paras. [0011]-[0012] (“receiving, by a computing device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient sensed by a medical device; obtaining, by the computing device, a first classification of arrhythmia in the patient determined by feature-based delineation of the received cardiac electrogram data, wherein the feature-based delineation identifies first cardiac features present in the cardiac electrogram data that coincide with the first classification of arrhythmia in the patient; determining, by the computing device, that one or more episodes of arrhythmia of the first classification have previously occurred in the patient; in response to determining that the one or more episodes of arrhythmia of the first classification have previously occurred in the patient, applying, by the computing device, a machine learning model.”), [0039]-[0045] (“[C]omputing system 24 receives cardiac electrogram data of patient 4 sensed by implantable medical device 10. Computing system 24 obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in patient 4 […] computing system 24 outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia. Computing system 24 may receive, in response to the report, one or more adjustments to one or more parameters used by implantable medical device 10 to sense the cardiac electrogram data of patient 4.”), [0047]-[0054] (“A clinician or other user may retrieve data from IMD 10 using external device 12, or by using another local or networked computing device configured to communicate with processing circuitry 50 via communication circuitry 54. The clinician may also program parameters of IMD 10 using external device 12 or another local or networked computing device. In some examples, the clinician may select one or more parameters defining how IMD 10 senses cardiac electrogram data of patient.”), [0060]-[0061], [0080], [0104]-[0107], [0119]; Figs. 1, 4-10); identify, by the server, a region of the episode data (See id. at least at Paras. [0007]-[0012], [0026], [0039]-[0043], [0053]-[0056] (“[P]rocessing circuitry 50 identifies one or more features of a T-wave of an electrocardiogram of patient 4 and applies a model to the one or more identified features to detect an episode of cardiac arrhythmia in patient 4.”), [0080], [0104]-[0107]; Figs. 6-10).
Chakravarthy may not specifically describe but Sorenson teaches to provide, based on a first input from the primary adjudicator and via the computing device of the secondary adjudicator, a notification to the secondary adjudicator (The Specification describes at Paras. [0028] (“FIG. 9 is a conceptual diagram illustrating an example user interface for secondary adjudicators to analyze cardiac episode data and make an assessment.”), [0035], [0048] (“interface 210 may represent techniques involving a first adjudicator (e.g., nursing staff, doctors, care givers, technicians, or specialists). In some examples, the first adjudicator may communicate with CareLinkTM software interface by interacting with the first computing device of computing devices 100. Tactile and visual sensors (e.g., cameras, tactile screens, buttons, and accelerometers) may receive the actions of the first adjudicator as inputs or input data from the sensors.”), [0059] (“Phone application 211 may be configured to display, to the secondary adjudicator, the input questions (244). In some examples, displaying the input questions may include displaying input questions received from primary adjudicator. For example, received questions or selection of questions from the primary adjudicator may correspond to questions displayed to the secondary adjudicator.”), [0070]). See Sorenson at least at Abstract (Peer Review System); Paras. [0025]-[0029] (“The system can be a system that can connect one or more medical institutes at one or more locations […] The target findings are either held in blind confidence to see if the physician agrees independently, or the findings are presented within the physician interpretation process to evoke responses, and any feedback, adjustments, agreement or disagreement are captured and utilized […] prospective application of the Peer Review System to pre-process studies which have not been read, allowing real-time physician first-time interpretations of medical image studies to prospectively incorporate parallel blinded or unblinded automatically generated Peer Review System findings.”), [0037]-[0038] (peer review system, images reviewed by a set of physicians – a first input reviewed by a first adjudicator and sent to a second adjudicator (Figs. 6A, 6B for primary and subsequent reviewers), [0039]-[0040] (“The physician confirmations and rejections as well as other collectable workflow inputs they provide using the Peer Review System can be used as training data.”), [0041]-[0043] (“The image processing engines are configured to perform image processing operations to detect any abnormal findings of the medical images, or to optimize clinical workflow in accordance with the preferences or computer-observed working ways of the end-user (based on the system that they are using for interpretation, or in an end-user customized manner as part of the Peer Review System functionality) and to generate a first result describing the abnormal findings or preferred presentation of the images and normal and/or abnormal findings. The physician input represents the second result. The Peer Review System detects agreement or disagreement in the results and findings and sends alerts for further adjudication given the discordant results, or it records the differences and provides these to the owner of the algorithm/engine allowing them to govern whether this feedback is accepted (i.e. whether or not the physician input should be accepted as truth, and whether this study should be included in a new or updated cohort.”), [0098] (“[A] PACS, sends a set of images to primary review system 602 representing a first set of physicians as primary reviewers. The reviewers of primary review system 602 review and send the findings back to PACS 601. A portion (e.g., 5%) of the images reviewed by the primary reviewers is sent to peer review system 603 representing a second set of reviewers as secondary or peer reviewers. The secondary reviewers review the images to generate a second result that provides second opinion findings.”); Claims 3-11 (“[C]omparing the first result and the second result against a third result performed by a clinical study system, wherein the clinical study system is configured to detect any abnormal image, wherein the first review system is a peer review system with respect to the clinical study system; and validating abnormal findings of the clinical study system based on the first result, the second result, and the third result.”); Figs. 1-7, 10 (first and second physician findings)); display, in response to the secondary adjudicator interacting with the computing device of the secondary adjudicator in response to the notification and in a primary view, the region of the episode data onto a display of the computing device of the secondary adjudicator (See id. at least at Paras. [0041]-[0043] (“The physician input represents the second result. The Peer Review System detects agreement or disagreement in the results and findings and sends alerts for further adjudication given the discordant results, or it records the differences and provides these to the owner of the algorithm/engine allowing them to govern whether this feedback is accepted (i.e. whether or not the physician input should be accepted as truth, and whether this study should be included in a new or updated cohort.”), [0104], [0116]-[0117] (“[A] first physician can review a first study and determine that the images in the first study did not contain lung nodules. The first study can be processed by a first lung nodule engine. The first lung nodule engine can flag the first study as having lung nodules without displaying any claims/findings in the first study such that a second physician would not know which engine was used or what findings were detected by the first lung nodule engine. The second physician can review the first study and confirm that the first study has lung nodules.”), [0177]-[0178] (graphical display of selected regions for reviewing), [0190]; Claims 5-6 (alert, i.e., notification), Figs. 1-7, 9-10, 17); receive, by the computing device of the secondary adjudicator, a second input (The Specification describes at Paras. [0060]-[0062] (“Phone application 211 may be configured to receive, as answers, inputs from secondary adjudicator (245). In some examples, answers may include observations, comments, and direct responses to the input questions […] The inputs may include inputs as answers received from the secondary adjudicator, addressing questions provided or selected by the primary adjudicator.”); See Sorenson at least at Paras. [0041]-[0043], [0104] (“[T]he findings from the second set of physicians can be sent to the image processing server 110, the workstation, the PACS server, or any combination thereof.”), [0116]-[0118] (“[A] first physician can review a first study and determine that the images in the first study did not contain lung nodules. The first study can be processed by a first lung nodule engine. The first lung nodule engine can flag the first study as having lung nodules without displaying any claims/findings in the first study such that a second physician would not know which engine was used or what findings were detected by the first lung nodule engine. The second physician can review the first study and confirm that the first study has lung nodules.” This review and feedback or confirmation is the input by the second adjudicator.); Figs. 1-7, 9-10, 17); and transmit, by the computing device of the secondary adjudicator and to the server, the second input received from the secondary adjudicator in response to the episode data (See id. at least at Paras. [0102], [0104] (“[T]he findings from the second set of physicians can be sent to the image processing server 110, the workstation, the PACS server, or any combination thereof.”), [0116]-[0118] (“The first study can be processed by a first engine in the image processing server 110. The first engine can flag the first study as having the first finding and output the first study as the machine learned possibly higher likelihood of disease studies without indicating any claims/findings on or with the first study. The second physician can review the first study and confirm that the first study has the first finding. The tracking engine can track the first study findings of the first physician, the second physician, the first engine, or any combination thereof. A comparator engine (not shown) can compare the findings of the first physician, the second physician, the first engine, or any combination thereof to determine if the first engine can be validated via a double-blinded read. The tracking engine can keep track of whether operations of the first engine was validated or invalidated.”), [0120]-[0122]; Figs. 1-7, 9-10, 17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy to incorporate the teachings of Sorenson and provide episode data and another adjudicator and devices. Sorenson is directed to a medical imaging informatics peer review system. Incorporating the medical imaging peer review system of Sorenson with the arrythmia detection and adjudication of Chakravarthy would thereby increase the applicability, utility, and efficacy of multi-party adjudication of cardiac episodes.
Regarding claim 2, Chakravarthy as modified by Sorenson discloses the limitations of claim 1 and Chakravarthy further discloses wherein the episode data comprises cardiac electrogram or electrocardiogram data (See Chakravarthy at least at Abstract; Paras. [0006]-[0012]; Figs. 4-10).
Regarding claim 3, Chakravarthy as modified by Sorenson discloses the limitations of claim 1 and Chakravarthy further discloses wherein the processing circuitry is configured to apply the episode data to a machine learning model to determine the region (See id. at least at Paras. [0077]-[0081] (“[T]he machine learning model is trained with a plurality of ECG episodes annotated by a clinician or a monitoring center for arrhythmias of several different types. In one example, machine learning system 150 applies the machine learning model to take one or several subsegments of a normalized input ECG signal and generates arrhythmia labels and a likelihood of an occurrence of the arrhythmia.”); Figs. 1, 4-10).
Regarding claim 4, Chakravarthy as modified by Sorenson discloses the limitations of claim 1 and Chakravarthy further discloses wherein the server is configured to determine based on the first input from the primary adjudicator, the region of the episode data (See id. at least at Paras. [0033], [0047], [0059]-[0061], [0077]-[0081] (“[C]omputing system 24 may apply QRS detection delineation and noise flagging (e.g., is the beat noisy or not) to the cardiac electrogram data to provide arrhythmia characteristics and/or cardiac features for detected episodes of arrhythmia (e.g., an average heartrate during an episode of atrial fibrillation, a duration of a pause). Further, computing system 24 may apply feature delineation to guide notification and reporting criteria for system 2.”), [0099]-[0107] (annotates detected arrhythmia)).
Regarding claim 5, Chakravarthy as modified by Sorenson discloses the limitations of claim 1 and Sorenson further teaches wherein the computing device of the secondary adjudicator is configured to display the notification on a screen of the computing device of the secondary adjudicator (See Sorenson at least at Abstract; Paras. [0041]-[0043] (“The physician input represents the second result. The Peer Review System detects agreement or disagreement in the results and findings and sends alerts for further adjudication given the discordant results, or it records the differences and provides these to the owner of the algorithm/engine allowing them to govern whether this feedback is accepted (i.e. whether or not the physician input should be accepted as truth, and whether this study should be included in a new or updated cohort.”), [0104], [0116]-[0117] (“[A] first physician can review a first study and determine that the images in the first study did not contain lung nodules. The first study can be processed by a first lung nodule engine. The first lung nodule engine can flag the first study as having lung nodules without displaying any claims/findings in the first study such that a second physician would not know which engine was used or what findings were detected by the first lung nodule engine. The second physician can review the first study and confirm that the first study has lung nodules.”), [0177]-[0178] (graphical display of selected regions for reviewing), [0190]; Claims 5-6 (alert, i.e., notification), Figs. 1-7, 9-10, 17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy to incorporate the teachings of Sorenson and provide displaying adjudication information or images to a secondary adjudicator. Sorenson is directed to a medical imaging informatics peer review system. Incorporating the medical imaging peer review system of Sorenson with the arrythmia detection and adjudication of Chakravarthy would thereby increase the applicability, utility, and efficacy of multi-party adjudication of cardiac episodes.
Regarding claim 6, Chakravarthy as modified by Sorenson discloses the limitations of claim 1 and Chakravarthy further discloses wherein the server is configured to save the input received from the secondary adjudicator (See Chakravarthy at least at Paras. [0029]-[0043]; Figs. 1-10).
Regarding claims 11 and 12, claims 11 and 12 recite substantially the same limitations as included in independent claim 1. Thus, the claims are rejected for the same reasoning and under the same grounds of rejection as applied to claim 1, above.
Regarding claims 13-17, claims 13-17 recite substantially the same limitations as included in claims 2-6, respectively. Thus, claims 13-17 are rejected for the same reasoning and under the same grounds of rejection as applied to claims 2-6, above.
Regarding claim 21, Chakravarthy as modified by Sorenson disclose the limitations of claim 1 and Sorenson further teaches wherein the primary adjudicator and the secondary adjudicator are human adjudicators (See id. at least at Paras. [0102]-[0105] (“The data source 601 can send the total studies with findings by the first set of physicians 602 to the server 110 […] [T]he findings from the second set of physicians can be sent to the image processing server 110, the workstation, the PACS server, or any combination thereof.”), [0114]-[0118] (“The first study can be processed by a first engine in the image processing server 110. The first engine can flag the first study as having the first finding and output the first study as the machine learned possibly higher likelihood of disease studies without indicating any claims/findings on or with the first study. The second physician can review the first study and confirm that the first study has the first finding. The tracking engine can track the first study findings of the first physician, the second physician, the first engine, or any combination thereof. A comparator engine (not shown) can compare the findings of the first physician, the second physician, the first engine, or any combination thereof to determine if the first engine can be validated via a double-blinded read. The tracking engine can keep track of whether operations of the first engine was validated or invalidated.”), [0120]-[0122]; Figs. 1-7, 9-10, 17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy to incorporate the teachings of Sorenson and provide human adjudicators. Sorenson is directed to a medical imaging informatics peer review system. Incorporating the medical imaging peer review system of Sorenson with the arrythmia detection and adjudication of Chakravarthy would thereby increase the applicability, utility, and efficacy of computing multi-party adjudication of cardiac episodes.
Claims 7-9 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy, in view of Sorenson and further in view of U.S. 2010/0106036 A1 to Dong et al., hereinafter “Dong.”
Regarding claim 7, Chakravarthy as modified by Sorenson discloses the limitations of claim 1. The references may not specifically describe but Dong teaches wherein the computing device of the primary adjudicator is configured to receive the first input, the first input comprising an input question from the primary adjudicator, wherein the server is configured to cause the computing device of the secondary adjudicator to present the input question to the secondary adjudicator (See Dong at least at Paras. [0094]-[0098], [0109]-[0116]; Figs. 1-4, 7-12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy and Mahajan to incorporate the teachings of Dong and provide a question present from an adjudicator to a device. Dong is directed to arrythmia detection and training systems and methods. Incorporating the arrythmia detection and training systems as in Dong with the medical imaging peer review system of Sorenson and the arrythmia detection, adjudication and machine learning of Chakravarthy would thereby increase the applicability, utility, and efficacy of multi-party adjudication of health incidences.
Regarding claim 8, Chakravarthy as modified by Sorenson and Dong disclose the limitations of claim 7 and Dong further teaches wherein the computing device of the primary adjudicator is configured to receive, from the primary adjudicator, a selection from among input questions determined by application of the episode data to a machine learning model (See id. at least at Paras. [0057]-[0061] (parameter selection and episode data), [0066]-[0071], [0094]-[0098], [0109]-[0116] (“Learning module 506 analyzes the data provided from the various information sources, including the data collected by the patient system 200 and external information sources, and may be implemented via a neural network (or equivalent) system to perform, for example, probabilistic calculations.”); Figs. 1-4, 7-12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy and Mahajan to incorporate the teachings of Dong and provide a question present from an adjudicator to a device. Dong is directed to arrythmia detection and training systems and methods. Incorporating the arrythmia detection and training systems as in Dong with the medical imaging peer review system of Sorenson and the arrythmia detection, adjudication and machine learning of Chakravarthy would thereby increase the applicability, utility, and efficacy of facilitating multi-party adjudication of cardiac episodes.
Regarding claim 9, Chakravarthy as modified by Sorenson discloses the limitations of claim 1. The references may not specifically describe but Dong teaches wherein the computing device of the primary adjudicator is configured to receive, from the primary adjudicator, a selection indicating the identity of the secondary adjudicator (See Dong at least at Paras. [0031]-[0036], [0060]-[0065], [0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy and Mahajan to incorporate the teachings of Dong and provide a question present from an adjudicator to a device. Dong is directed to arrythmia detection and training systems and methods. Incorporating the arrythmia detection and training systems as in Dong with the medical imaging peer review system of Sorenson and the arrythmia detection, adjudication and machine learning of Chakravarthy would improve the peer-to-peer system to facilitate multi-party adjudication of health episodes.
Regarding claims 18-20, claims 18-20 recite substantially the same limitations as included in claims 7-9, respectively. Thus, claims 18-20 are rejected for the same reasoning and under the same grounds of rejection as applied to claims 7-9, above.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy, in view of Sorenson and further in view of U.S. 2018/0137244 A1 to Sorenson et al., hereinafter “Sorenson ‘244.”
Regarding claim 10, Chakravarthy as modified by Sorenson discloses the limitations of claim 1. The references may not specifically describe but Sorenson ‘244 teaches wherein to display the region the computing device of the secondary adjudicator is configured to zoom in on the region (See Sorenson ‘244 at least at Paras. [0177]-[0180] (peer review), [0201] (“User interactions can include, but are not limited to, user's preference for study layouts, states of image display, adjusting display protocols, image properties, preferences, work patterns, mouse selection, mouse movement, series move, image setting change, tool selection, tool settings, mouse mode, image navigation, image manipulation, series navigation, comparison study selection, navigation, zoom, layout, overlay data preferences, contour changes, any other movement or selection performed on the client, or any combination thereof.”), [0218]-[0223] (GUI and functions for manipulating images), [0238]-[0240], Figs. 8-15, 18-22, 25-28).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy and Sorenson to incorporate the teachings of Sorenson ‘244 and provide a display and enhancement to a region for an adjudicator. Sorenson ‘244 is directed to medical imaging identification and interpretation and peer review. Incorporating the medical imaging identification as in Sorenson with the medical imaging peer review system of Sorenson and the arrythmia detection, adjudication and machine learning of Chakravarthy would thereby improve the multi-party adjudication of medical images and diagnoses.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy, in view of Sorenson and further in view of U.S. 2019/0346523 A1 to Li et al., hereinafter “Li.”
Regarding claim 22, Regarding claim 21, Chakravarthy as modified by Sorenson discloses the limitations of claim 3. The references may not specifically describe but Li teaches wherein to determine the region, the machine learning model is configured to: generate a cost function; and minimize the cost function (See Li at least at Paras. [0005]-[0007], [0038]-[0039] (minimizing cost function); Claims 14-16, 21, 23, 25; Figs. 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy and Sorenson to incorporate the teachings of Li and provide minimizing a cost function. Li is directed to tensor imaging for MRI. Incorporating the tensor imaging for MRI and minimizing cost function as in Li with the medical imaging peer review system of Sorenson and the arrythmia detection, adjudication and machine learning of Chakravarthy would thereby increase the applicability, utility, and efficacy of computing multi-party adjudication of cardiac episodes.
Response to Arguments
Applicant’s amendments and remarks filed June 10, 2026 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under relevant sections of the MPEP. The arguments at pages 7-13 of Applicant’s Remarks are not persuasive. At pages 7-10 the Examiner disagrees that there is not an abstract idea. The Examiner disagrees that the claim “requires specialized, non-generic hardware,” to follow rules or instructions as organizing human activity. The claimed episode data shared and analyzed by adjudicators use computers as a tool to employ the abstract idea of making determinations about a patient’s health indication. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The Examiner disagrees at Pages 10-11 that there is any practical application of the judicial exception, or that there is a technological improvement to the underlying computer components in the claim as a whole. The interactions of the limitations are merely analyzing and accessing data that is displayed between multiple adjudicators, without more. Further, there is no clear technological improvement that is described in the claims or the Specification for any underlying computer technology. Finally, the Examiner disagrees at Pages 12-13 that the claim recites significantly more than the abstract idea. The combination of providing regions of interest and notifying secondary adjudicators based on an input from a primary adjudicator is part of the abstract idea, and not significantly more than it. For at least these reasons and those stated in the rejection above, the claims are not patent eligible.
Applicant’s arguments pertaining to prior art rejections are not persuasive. The amended claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments pertaining to prior art references of the Applicant’s Remarks at Page 14 are moot in light of at least new reference Sorenson. Thus, the cited prior art reads on the broad claim limitations and are in the same field of endeavor, namely, peer review and adjudication of health conditions and other determinations. Therefore, the claims are rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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/WILLIAM T. MONTICELLO/Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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