DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Nonfinal Office Action is in response to the Application filed 1/30/2025. Claims 1-20 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 1-20 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, 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, 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, an input; and transmit, by the computing device of the secondary adjudicator and to the server, the 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, the secondary adjudicator, a notification to the secondary adjudicator; in response to the secondary adjudicator interacting [and] in response to the notification, [displaying] the region of the episode data; receive from the secondary adjudicator, an input; [and] the 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 computer system including reciting training a machine learning model (claims 3, 14), and a “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, display and training a machine learning model does not take the claims out of the method of organizing human interactions grouping. The additional limitations amount to computer methods for further implementing the abstract idea of organizing human activity. Thus, the claims recite an abstract idea.
The claims also recited an abstract idea including mental processes. But for the generic reciting of training a machine learning model (claims 3, 14), and a “computing system comprising memory storing instructions and processing circuitry of one or more devices, including a server, a computing device” and display, nothing in the claims is precluded from being performed in the mind. For example, a physician can collect the patient data and analyze the EKG and determine if there is an episode or risk or not based on the analyses. 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 the generic reciting training a machine learning model (claims 3, 14), and 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 the generic reciting training a machine learning model (claims 3, 14), and 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 when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
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 and 14 describe a machine learning model, 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 machine learning model 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. 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 and 11-17 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. 2019/0232067 A1 to Mahajan et al., hereinafter “Mahajan.”
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 Mahajan teaches to provide, via the computing device of the secondary adjudicator, a notification to the secondary adjudicator (See Mahajan at least at Paras. [0004]-[0025] (“The IMDs may generate patient alert notification upon a detection of a particular health condition or a medical event, such as a cardiac arrhythmia or worsening heart failure (WHF) […] the user interface that may be configured to present at least a portion of a first arrhythmia episode generated by the medical device and to receive from the user an adjudication decision and a first episode characterization of the first arrhythmia episode. The episode management circuit […]”), [0058]-[0062]; Figs. 1-6); display, in response to the secondary adjudicator interacting with the computing device of the secondary adjudicator in response to the notification, the region of the episode data onto a display of the computing device of the secondary adjudicator (See Mahajan at least at Abstract; Paras. [0006]-[0012], [0038] (“FIG. 4 illustrates an example of at least a portion of a user interface for displaying, and interactive user adjudication, of a medical event episode.”), [0041], [0053]-[0058] (“[T]he external device 120 or the remote device 124 may respectively include display units for displaying the physiologic or functional signals, or alerts, alarms, emergency calls, or other forms of warnings to signal the detection of arrhythmia. In some examples, the external system 125 may include an external data processor configured to analyze the physiologic or functional signals received by the AMD 110, and to confirm or reject the detection of the medical events.”), [0080]-[0089] (“FIG. 4 illustrates an example of at least a portion of a user interface 400 for displaying, and interactive user adjudication, of a medical event episode. The user interface portion 400, which is an embodiment of the display unit of the user interface 230, includes a display of information of a medical event episode generated by a medical device, such as the AMD 310.”), [0092]-[0096]; Figs. 3-6); receive, by the computing device of the secondary adjudicator, an input (See id. at least at Paras. [0006]-[0012], [0024]-[0025], [0058]-[0062], [0080]-[0089] (“The user interface portion 400 may include a display zone allowing for interface adjudication of the medical event as presented on the user interface portion 400. On an adjudication type zone 422, a user (e.g., a clinician) may provide an adjudication decision, such as an arrhythmia type, or a designation of a true positive detection (indicating an agreement with the device-detected arrhythmia type shown in the detection summary 416) or a false positive detection (indicating a disagreement with the device-detected arrhythmia type shown in the detection summary 416). On an episode characterization zone 424, the user may select an episode characterization from a plurality of pre-set characterizations.”), [0092]-[0096]; Figs. 1-6); and transmit, by the computing device of the secondary adjudicator and to the server, the input received from the secondary adjudicator in response to the episode data (See id. at least at Paras. [0032]-[0033], [0080]-[0089] (The communication circuit 315 may transmit the detected arrhythmia episodes (including physiologic data and device-generated detection results) to the external system 320 via the communication link 115. […] the user interface 230 may prompt a user to recommend programming the detection algorithm […] the displayed information may include patient identification and episode identifier 412 and physiologic data 414. A user may interactively select a patient and an episode for display, such as by using respective drop-down lists (as shown), check boxes, radio buttons, list boxes, buttons, toggles, text fields, among other input control elements on the user interface 400. The physiologic data 414 may be sensed using electrodes or physiologic sensors in communication with the medical device, and collected during, or alternatively before or after, the detected medical event.”), [0092]-[0096]; Figs. 1-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Chakravarthy to incorporate the teachings of Mahajan and provide episode data and another adjudicator and devices. Mahajan is directed to systems and methods for presenting arrhythmia episodes. Incorporating the system for presenting certain heart episodes as in Mahajan with the arrythmia detection and machine learning of Chakravarthy would thereby increase the applicability, utility, and efficacy of the claimed computing system to facilitate multi-party adjudication of cardiac episodes.
Regarding claim 2, Chakravarthy as modified by Mahajan disclose 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 Mahajan disclose 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 Mahajan disclose the limitations of claim 1 and Chakravarthy further discloses wherein the server is configured to determine based on an 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 Mahajan disclose the limitations of claim 1 and Mahajan 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 Mahajan at least at Abstract; Paras. [0006]-[0012], [0038] (“FIG. 4 illustrates an example of at least a portion of a user interface for displaying, and interactive user adjudication, of a medical event episode.”), [0041], [0053]-[0058] (“[T]he external device 120 or the remote device 124 may respectively include display units for displaying the physiologic or functional signals, or alerts, alarms, emergency calls, or other forms of warnings to signal the detection of arrhythmia. In some examples, the external system 125 may include an external data processor configured to analyze the physiologic or functional signals received by the AMD 110, and to confirm or reject the detection of the medical events.”), [0080]-[0089] (“FIG. 4 illustrates an example of at least a portion of a user interface 400 for displaying, and interactive user adjudication, of a medical event episode. The user interface portion 400, which is an embodiment of the display unit of the user interface 230, includes a display of information of a medical event episode generated by a medical device, such as the AMD 310.”), [0092]-[0096]; Figs. 3-6).
Regarding claim 6, Chakravarthy as modified by Mahajan disclose 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.
Claims 7-9 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chakravarthy, in view of Mahajan and further in view of U.S. 2010/0106036 A1 to Dong et al., hereinafter “Dong.”
Regarding claim 7, Chakravarthy as modified by Mahajan disclose 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 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 system for presenting certain heart episodes as in Mahajan and the arrythmia detection and machine learning of Chakravarthy would thereby increase the applicability, utility, and efficacy of the claimed computing system to facilitate multi-party adjudication of cardiac episodes.
Regarding claim 8, Chakravarthy as modified by Mahajan 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).
Regarding claim 9, Chakravarthy as modified by Mahajan disclose 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 system for presenting certain heart episodes as in Mahajan and the arrythmia detection and machine learning of Chakravarthy would thereby increase the applicability, utility, and efficacy of the claimed computing system to facilitate multi-party adjudication of cardiac 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 Mahajan and further in view of U.S. 2018/0137244 A1 to Sorenson et al., hereinafter “Sorenson.”
Regarding claim 10, Chakravarthy as modified by Mahajan disclose the limitations of claim 1. The references may not specifically describe but Sorenson teaches wherein to display the region the computing device of the secondary adjudicator is configured to zoom in on the region (See Sorenson at least at Paras. [0177]-[0180] (peer review), [0201], [0218]-[0223], [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 Mahajan to incorporate the teachings of Sorenson and provide a display and enhancement to a region for an adjudicator. Sorenson is directed to medical imaging identification and interpretation and peer review. Incorporating the medical imaging identification as in Sorenson with the system for presenting certain heart episodes as in Mahajan and the arrythmia detection and machine learning of Chakravarthy would thereby increase the applicability, utility, and efficacy of the claimed computing system to facilitate multi-party adjudication of cardiac episodes.
Conclusion
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/WILLIAM T. MONTICELLO/ Examiner, Art Unit 3682
/FONYA M LONG/ Supervisory Patent Examiner, Art Unit 3682