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 .
DETAILED ACTION
Acknowledgement is made of applicant’s amendment which was received by the office on September 9, 2025. Claims 1-20 are currently pending and under examination.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an output device configured to output….”in claim claims 1-14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Specifically, “a display or one or more other user interface(s)” and equivalents thereof, see para. [0083] of published application 2023/0337988.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
In view of the amendment filed on 9/9/2025 clarifying the language of claim 8 the objections made against claim 8 in the office action of 6/17/2025 have been withdrawn.
Claim 20 is objected to because of the following informalities: Claim 20, ll. 1-2 should recite similar to –based at least in part—in order to fix an inadvertent typographical error. Appropriate correction is required.
Claim Rejections - 35 USC § 112
In view of the amendment filed on 9/9/2025 clarifying the language of claims 11-12 and 20 the 112 rejections made against claims 11-12 and 20 in the office action of 6/17/2025 have been withdrawn.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 recites “an output device configured to output the estimated physiological parameter or the detected physiological event to a user or a process for initiating or titrating a therapy based on the detected physiological…”, it is unclear what applicant is attempting to encompass when reciting “an output device configured to output the estimated physiological parameter or the detected physiological event to a user or a process for initiating or titrating a therapy based on the detected physiological…” Is applicant stating that the output device is configured to output the estimated physiological parameter or a process for initiating or titrating a therapy based on the detected physiological event is performed? Is applicant stating that the output device is configured to output the estimated physiological parameter or the output device is configured to perform a process for initiating or titrating a therapy based on the detected physiological event is? Clarification is required. Claim 15 recites “providing the estimated physiological parameter or the detected physiological event to a user or a process for initiating or titrating a therapy based on the detected physiological…”, it is unclear what applicant is attempting to encompass when reciting “providing the estimated physiological parameter or the detected physiological event to a user or a process for initiating or titrating a therapy based on the detected physiological…” Is applicant stating that the estimated parameter is provided to a user or a process for initiating or titrating a therapy based on the detected physiological event is performed? Is applicant stating that the estimated parameter is provided to a user or the estimated physiological parameter is provided to a process for initiating or titrating a therapy based on the detected physiological event? Clarification is required. Claims 2-14 and 16-20 directly or indirectly depend from claims 1 and 15 and are also rejected to for the reasons stated above regarding claims 1 and 15. As best understood, for the purposes of examination “providing the estimated physiological parameter or the detected physiological event to a user or a process for initiating or titrating a therapy based on the detected physiological…” has been interpreted to include providing the estimated parameter or detected event to the user.
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 an abstract idea without significantly more. Claims 1-20 are directed to “a system” and a “method” which describe one of the four statutory categories of patentable subject matter, i.e., a process and an apparatus (Step 1, Yes).
The claim limitations within claims 1 and 15 that set forth or describe the abstract idea is/are: “receiving[receive] ambulatory electrograms of the subject collected by an ambulatory medical device (AMD); determining[determine],a form factor[physical characteristic] indicative of a body placement or use configuration of at least one sensor using the ambulatory electrograms, serially detecting[detect] a physiological event or estimating [estimate] a physiological parameter in the subject using the ambulatory electrograms and the physiological information sensed by [and determined or confirmed physical characteristic of] the at least one sensor; and providing[output] the estimated physiological parameter or the detected physiological event to a user or a process.” The reasons that the limitations is/are considered an abstract idea is/are the following: The limitations of “receiving[receive] ambulatory electrograms of the subject collected by an ambulatory medical device (AMD); determining[determine],a form factor[physical characteristic] indicative of a body placement or use configuration of at least one sensor using the ambulatory electrograms, serially detecting[detect] a physiological event or estimating [estimate] a physiological parameter in the subject using the ambulatory electrograms and the physiological information sensed by [and determined or confirmed physical characteristic of] the at least one sensor; and providing[output] the estimated physiological parameter or the detected physiological event to a user or a process.” recite performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “computing device configured to” and “via a computing device” nothing in the claim precludes the steps from practically being performed in the mind. For example the “receiving[receive] ambulatory electrograms of the subject collected by an ambulatory medical device (AMD)” encompasses the user, receiving a printout of the ambulatory electrograms or observing the electrocardiogram on a screen; determining[determine],a form factor[physical characteristic] of at least one sensor using the ambulatory electrograms” encompasses the user, with the aid of pen and paper, using the electrograms to determine a physical characteristic of the sensor based on observed abnormalities in the signal; serially detecting[detect] a physiological event or estimating [estimate] a physiological parameter in the subject using the ambulatory electrograms and the physiological information sensed by [and determined or confirmed physical characteristic of] the at least one sensor” encompasses the user, with the aid of pen and paper, determining a physiological event or estimating a physiological parameter based on the electrograms; and providing[output] the estimated physiological parameter or the detected physiological event to a user or a process.” encompasses the user providing or writing the estimated physiological parameter or the detected physiological event on a sheet of paper. There is nothing to suggest an undue level of complexity in the steps of “receiving[receive] ambulatory electrograms of the subject collected by an ambulatory medical device (AMD); determining[determine],a form factor[physical characteristic] of at least one sensor using the ambulatory electrograms, serially detecting[detect] a physiological event or estimating [estimate] a physiological parameter in the subject using the ambulatory electrograms and the physiological information sensed by [and determined or confirmed physical characteristic of] the at least one sensor; and providing[output] the estimated physiological parameter or the detected physiological event to a user or a process.” If a claim limitation, under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls with the “Mental Processes” grouping of abstract ideas. Accordingly the claims recite an abstract idea. Although not drawn to the same subject matter, the claimed limitation(s) is/are similar to the concepts that have been identified as abstract by the courts, such as: collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016), selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis in SAP America Inc. v. Investpic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed Cir. 2018). Thus, the claim(s) are directed to a judicial exception and fall squarely within the realm of "abstract ideas," which is a patent-ineligible concept (Step 2A: Prong One YES).
Analyzing the claims as whole, the claim does not include additional elements/steps that integrate the judicial exception into a practical application. Claims 1 and 15 do not include additional elements that integrate the mental process into a practical application. The additionally recited element(s) appended to the abstract idea include: “collected by an ambulatory medical device”, “computing device configured to”, “via a computing device” and “an output device configured to”. The additional elements of “collected by an ambulatory medical device”, merely: add insignificant extra-solution activity, reciting “collected by an ambulatory medical device”, is recited at a high level of generality (i.e. as a general means of gathering and pre-processing cardiac data) and is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity. All uses of the recited judicial exception require the pre-solution activity of data gathering, see MPEP 2106.05(g). As discussed above with respect to integration of abstract idea into a practical application, the additional element of “computing device configured to”, “via a computing device” and “an output device configured to” amount to no more than mere instruction to apply the exception using generic computer components. The “computing device configured to”, “via a computing device” and “an output device configured to”.are purely general-purpose computer components recited as carrying out the general-purpose computer functions of processing data, storing and displaying to enable the abstract process. As such, this/these recitation(s) is/are nothing more than nominal recitation(s) of a computer covering an abstract concept. See Bancorp Servs. v. Sun Life Assurance Co., 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Circ. 2012). See also Mayo Collaborative Services v. Prometheus Laboratories Inc., 101 USPQ2d 1961 (U.S. 2012), which establishes that a claim cannot simply state the abstract idea and add the words "apply it” (Step 2A, Prong Two, NO).
Analyzing the claims as whole for an inventive concept, the claims do not include additional elements/steps that are sufficient to amount to significantly more than the judicial exception. Claims 1 and 15 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. e.g., all elements are directed to extra-solution activity using well-understood, routine, conventional activity previously known to the industry and amount to elements that have been recognized as well-understood, routine and conventional activity in particular fields such as receiving, storing and retrieving information in memory Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, see MPEP 2106.05(d)(II) or purely general-purpose computer components recited as carrying out the general-purpose computer function of processing data and storing to enable the abstract process. The use of a “computing device configured to”, “via a computing device” and “an output device configured to” are elements that have been recognized as well-understood, routine and conventional activity previously known to the industry for analyzing electrocardiogram information as evidence by US 2021/0396131 to Cao et al. and/or US 2020/0297230 to Thakur et al., see elements within rejection below. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Similarly, when considered as an ordered combination, the additional components/steps of the claim(s) add nothing that is not already present when the steps are considered separately (Step 2B: NO). The claims are not patent eligible.
Claim(s) 2-14 and 16-20 depend directly or indirectly from claim(s) 1 and 15. Therefore, the dependent claims rely upon the same abstract idea as the independent claim(s), as set forth above. Additionally, the dependent claims do nothing more than further limiting the abstract idea while failing to qualify as "significantly more", and the specificity of an abstract idea does not make it any "less abstract" as it is still directed to concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work subject matter. The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely a. further describe the abstract idea, wherein to detect the physiological event…perform parallel computing (claim 13) b. describe pre-solution activity (or the structure used for such activity), the AMD configured to collect the ambulatory electrograms (claim 9) and c. further amount to no more than mere instruction to apply the exception using generic computer components, using a “trained machine learning model” (claim 2-8, 11-12, 16-18 and 20), “multiple processors” (claim 14) d. recite a “therapy circuit” which is recited at high level of generality that covers any therapy system and amounts to mere instruction to “apply” the abstract idea in a generic way, see MPEP 2106.05(f). Stating a therapy circuit “configured to initiate or adjust therapy…”, is not a positive limitation and is akin to a step of prescribing a vaccine or medication or a step of making a syringe operable to dispense a medication because it does not require the therapy actually being used by or on the patient as it is not an affirmative limitation because it merely indicates how the invention might be used, see MPEP 2106.04(d)(2). Further, “therapy circuit” “configured to initiate or adjust a therapy..” (i.e. making a computerized system operable to dispense therapy) is not an affirmative treatment limitation because it merely indicates how the claimed invention might be used. It does not require any treatment, specific or otherwise, see MPEP 2106.04(d)(2) (claims 10 and 19). Therefore, the dependent claim(s) are also not patent eligible for the reasons discussed above.
The instantly rejected claim(s) are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. In the interest of advancing prosecution, the examiner suggests: providing evidence, for example, delineating how the abstract idea and/or additional elements appended to the abstract idea results in an improvement to the technology/technical field, which can show eligibility and/or adding a practical application of the claimed method outside of the computer (e.g. delivering a specific therapy to the subject based on the detected physiological event or estimated physiological parameter). See MPEP § 716.01(c) for examples of providing evidence supported by an appropriate affidavit or declaration. For additional guidance, applicant is directed generally to MPEP § 2106.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1,9 and 13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/0369131 to Cao et al. (Cao) (previously cited).
In reference to at least claim 1
Cao discloses a system for detecting physiological events in a subject (e.g. abnormality event information, abstract), the system comprising: a computing device configured to (e.g. processor 12): receive ambulatory electrograms of the subject collected by an ambulatory medical device (AMD) (e.g. collects and records signals generated by electrophysiological activities of heart cells in a single-lead or multi-lead form through non-invasive electrocardiogram examination to obtain the electrocardiogram data.”, para. [0089]) ; determine or confirm a physical characteristic of at least one sensor using the ambulatory electrograms (e.g. “performing a judgment of signal abnormality on the data segment with the heartbeat interval greater than or equal to the preset interval determination threshold to determine whether the data segment is an abnormal signal; [0121] wherein the identification of the abnormal signal includes whether there are electrode peeling off, low voltage, etc”, para. [0117]), the physical characteristic including a sensor type or a form factor of the at least one sensor indicative of a body placement or use configuration of the at least one sensor (e.g. “performing a judgment of signal abnormality on the data segment with the heartbeat interval greater than or equal to the preset interval determination threshold to determine whether the data segment is an abnormal signal; [0121] wherein the identification of the abnormal signal includes whether there are electrode peeling off, low voltage, etc”, the “electrode peeling off”, low voltage etc. is being interpreted as a “form factor indicative of…a use configuration of the at least one sensor”, para. [0117]); and detect a physiological event or estimate a physiological parameter in the subject using the ambulatory electrograms and the determined or confirmed physical characteristic of the at least one sensor (e.g. “the electrocardiogram data are analyzed and calculated, especially in the case of the introduction of artificial intelligence (AI) technology, arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals”, para. [0097]); and an output device configured to output the estimated physiological parameter or the detected physiological event to a user (e.g. “The alarm information may be output locally in the ambulatory monitoring device, which may be simply a preset sound alarm, photoelectric alarm, or a voice alarm, information display alarm”, para. [0215]).
In reference to at least claim 9
Cao discloses comprising the AMD configured to collect the ambulatory electrograms of the subject continuously or periodically via one or more attachable or implantable electrodes, the AMD including at least one of: an insertable cardiac monitor; a subcutaneous implantable cardioverter-defibrillator; or a wearable or holdable cardiac monitor (e.g. The ambulatory monitoring device may be a single-lead or multi-lead wearable electrocardiogram monitor”, para. [0085]).
In reference to at least claim 13
Cao discloses wherein the computing device is configured to perform parallel computing to estimate multiple physiological parameters or to detect multiple physiological events substantially concurrently (“arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals, and analysis and classification are performed on the electrocardiogram events,”, para. [0097]).
Claim Rejections - 35 USC § 103
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.
Claim(s) 2-8, 10-12, 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0369131 to Cao et al. (Cao) in view of US 2020/0297230 to Thakur et al. (Thakur).
In reference to at least claim 2
Cao teaches a system according to claim 1 and further discloses, wherein to detect the physiological event, the computing device is configured to apply the received ambulatory electrograms to a trained machine learning model to estimate the physiological parameter or to detect the physiological event in the subject using the ambulatory electrograms and the physiologic information sensed by the at least one sensor (e.g. “the electrocardiogram data are analyzed and calculated, especially in the case of the introduction of artificial intelligence (AI) technology, arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals”, para. [0097]),
However, Cao does not explicitly teach wherein the at least one sensor is configured to sense from the subject physiological information different than the ambulatory electrogram.
Thakur in the same field of physiological event detection discloses a system (e.g. 100) that receives ambulatory electrograms collected from an ambulatory medical device (e.g. known to use “intracardiac or subcutaneous electrogram (EGM) acquired by an ambulatory monitor.”, para. [0042]; “ambulatory system 105 associated with the patient”, para. [0047]-[0049]; “cardiac information” such as ECG or an EGM, para. [0066]) and at least one sensor is configured to sense from the subject physiological information different than the ambulatory electrogram (e.g. multiple sensors including cardiac acceleration, respiration, impedance, cardiac and physical activity, Fig. 2, para. [0063]; “It is to be understood that the physiologic sensors 211-215 illustrated in FIG. 2 are non-limiting examples of sensors”, therefore other sensor may also be used, para. [0068]) wherein to detect the physiological event, the computing device is configured to apply the received ambulatory electrograms to a trained machine learning model (e.g. “using a machine-learning (ML) model”, para. [0078], [0092], [0097]) to estimate the physiological parameter or to detect the physiological event in the subject using the ambulatory electrograms and the physiologic information sensed by the at least one sensor (e.g. fed into an arrhythmia risk stratifier 230 to assess cardiac arrhythmia risk or predict further cardiac arrhythmia event, para. [0069], [0092]). Thakur discloses that the present invention proactively identify patients at high risk of atrial tachyarrhythmia like AF, predicts future AF events before an occurrence of clinical manifestations, such as electrophysiological presentation or patient being symptomatic (e.g. para. [0043], [0045], [0098]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Cao to include the at least one sensor being configured to sense from the subject physiological information different than the ambulatory electrogram and detecting the physiological event in the subject using the ambulatory electrograms and the physiologic information sensed by the at least one sensor, as taught by Thakur, in order to proactively identify patients at high risk of atrial tachyarrhythmia like AF before the patient become symptomatic improving patient outcome and reducing healthcare cost associated with AF and EHF management (e.g. ‘230 para. [0043], [0045]).
In reference to at least claim 3
Cao modified by Thakur renders obvious a system according to claim 2. Cao further discloses the electrocardiogram being analyzed using artificial intelligence (e.g. “the electrocardiogram data are analyzed and calculated, especially in the case of the introduction of artificial intelligence (AI) technology, arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals”, para. [0097]) including trained models based on self-learning (e.g. para. [0101]).
Thakur discloses constructing a training dataset including ambulatory electrograms collected from a patient population and assessments of physiological parameters or physiological events in the patient population; and training a machine learning model using the constructed training dataset until a convergence or training stopping criterion is satisfied, the trained machine learning model representing a correspondence between the ambulatory electrograms of the patient population and the physiological parameters or the physiological events in the patient population (e.g. “The ML model may be trained using sensor data from patient population, and produce an arrhythmia risk indication corresponding to the signal metric measurements as input to the model.”, para. [0078]-[0079]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Cao to include constructing a training dataset including ambulatory electrograms collected from a patient population and assessments of physiological parameters or physiological events in the patient population and training a machine learning model using the constructed training dataset until a convergence or training stopping criterion is satisfied, as taught by Thakur, in order to provide an accurate machine learning model for arrhythmia risk indication that proactively identify patients at high risk of atrial tachyarrhythmia like AF before the patient become symptomatic (e.g. ‘230, para. [0078]).
In reference to at least claim 4
Cao modified by Thakur renders obvious a system according to claim 2. Cao further discloses wherein the training module is configured to train the machine learning model using a deep learning algorithm comprising a deep neural network (e.g. “CNN model..method in deep learning”, para. [0098]; “deep learning”, para. [0099]-[0100], [0129]). Thakur further discloses wherein the training module is configured to train the machine learning model using a deep learning algorithm comprising a deep neural network (e.g. “neural network model”, para. [0078]).
In reference to at least claim 5
Cao modified by Thakur renders obvious a system according to claim 2. Cao further discloses wherein the computing device is configured to apply the received ambulatory electrograms to the trained machine learning model to estimate the physiological parameter including at least one of: a cardiac parameter (e.g. “heartbeat data”, para. [0024]; “ST segment change”, para. [0097]; “heart rate parameters”, para. [0168]); a respiratory parameter; a circulating biomarker; or a systemic or local fluid status.
In reference to at least claim 6
Cao modified by Thakur renders obvious a system according to claim 2. Cao further discloses wherein the computing device is configured to apply the received ambulatory electrograms to the trained machine learning model to detect the physiological event including at least one of: a cardiac arrhythmia (e.g. “arrhythmia analysis”, para. [0080], [0097]); a worsening heart failure event; a heart failure comorbidity condition; a neurological condition; or a response to medication.
In reference to at least claim 7
Cao modified by Thakur renders obvious a system according to claim 2. Cao further discloses wherein the computing device is further configured to apply the received ambulatory electrograms to the trained machine learning model to detect an operating status of the AMD (e.g. “ disturbance problems caused by main disturbance sources such as electrode peeling off, exercise interference and electrostatic interference,”, para. [0116]-[0117]).
In reference to at least claim 8
Cao modified by Thakur renders obvious a system according to claim 7. Cao further discloses wherein the operating status of the AMD indicates at least one of: a change in position, posture, or orientation of the AMD; or a change in an device-tissue interface of the AMD (e.g. “disturbance problems caused by main disturbance sources such as electrode peeling off, exercise interference and electrostatic interference,”, “electrode peeling off”, low voltage etc. being interpreted as “change in an device-tissue interface” para. [0116]-[0117]).
In reference to at least claim 10
Cao teaches a system according to claim 1 and further discloses using electrocardiogram data to analyze and calculate arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals (e.g. “the electrocardiogram data are analyzed and calculated, especially in the case of the introduction of artificial intelligence (AI) technology, arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals”, para. [0097]),
However, Cao does not explicitly teach a therapy circuit configured to initiate or adjust a therapy to the subject based on the estimated physiological parameter or the detected physiological event.
Thakur in the same field of physiological event detection discloses a system (e.g. 100) that receives ambulatory electrograms collected from an ambulatory medical device (e.g. known to use “intracardiac or subcutaneous electrogram (EGM) acquired by an ambulatory monitor.”, para. [0042]; “ambulatory system 105 associated with the patient”, para. [0047]-[0049]; “cardiac information” such as ECG or an EGM, para. [0066]) and at least one sensor is configured to sense from the subject physiological information different than the ambulatory electrogram (e.g. multiple sensors including cardiac acceleration, respiration, impedance, cardiac and physical activity, Fig. 2, para. [0063]; “It is to be understood that the physiologic sensors 211-215 illustrated in FIG. 2 are non-limiting examples of sensors”, therefore other sensor may also be used, para. [0068]) wherein to detect the physiological event, the computing device is configured to apply the received ambulatory electrograms to a trained machine learning model (e.g. “using a machine-learning (ML) model”, para. [0078], [0092], [0097]) to estimate the physiological parameter or to detect the physiological event in the subject using the ambulatory electrograms and the physiologic information sensed by the at least one sensor (e.g. fed into an arrhythmia risk stratifier 230 to assess cardiac arrhythmia risk or predict further cardiac arrhythmia event, para. [0069], [0092]) and a therapy circuit configured to initiate or adjust a therapy to the subject based on the estimated physiological parameter or the detected physiological event (e.g. “a therapy unit that may generate and deliver one or more therapies. The therapy may be delivered to the patient 102 via the lead system 108 and the associated electrodes. The therapies may include electrical, magnetic, or other types of therapy. The therapy may include anti-arrhythmic therapy to treat an arrhythmia or to treat or control one or more complications from arrhythmia, such as syncope, congestive heart failure, or stroke, among others.”, para. [0052], “The optional therapy circuit 250 may be configured to deliver a therapy to the patient in response to the arrhythmic risk indication satisfying a condition”, para. [0062], [0083]). Thakur discloses that the present invention proactively identify patients at high risk of atrial tachyarrhythmia like AF, predicts future AF events before an occurrence of clinical manifestations, such as electrophysiological presentation or patient being symptomatic (e.g. para. [0043], [0045], [0098]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Cao to include a therapy circuit configured to initiate or adjust a therapy to the subject based on the estimated physiological parameter or the detected physiological event, as taught by Thakur, in order to treat the identified arrhythmia improving patient outcome (e.g. ‘230, para. [0045]).
In reference to at least claim 11
Cao modified by Thakur renders obvious a system according to claim 2. Cao further discloses apply the received ambulatory electrograms to the trained machine learning model to estimate the physiological parameter (e.g. “the electrocardiogram data are analyzed and calculated, especially in the case of the introduction of artificial intelligence (AI) technology, arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals”, para. [0097]); and based at least in part on the estimated physiological, trigger at least one of the at least one sensor to directly measure the physiological parameter (e.g. “alarm information of the electrocardiogram event information, as well as a plurality of pieces of electrocardiogram data within a preset time period before and after acquisition time (i.e., a time corresponding to alarm time information) for obtaining the electrocardiogram data for which the alarm information is generated.”, para. [0017]-[0018], [0217]).
In reference to at least claim 12
Cao modified by Thakur renders obvious a system according to claim 11. Cao further discloses wherein the computing device includes a calibration circuit configured to adjust the trained machine learning model based at least on the directly measured physiological parameter. (e.g. “heartbeat classification and the like performed on the electrocardiogram data are all based on trained models based on artificial intelligence self-learning to obtain output results, with a fast analysis speed and a high accuracy.”, para. [0098], [0101], since the artificial intelligence is self-learning some form of calibration circuit is present to adjust the trained machine learning model based on the measured data).
In reference to at least claim 14
Cao teaches a system according to claim 1 and further discloses a computing device configured to (e.g. processor 12).
However, Cao does not explicitly teach wherein the computing device includes multiple processors or a multi-core processor comprising multiple computing units, each of the multiple processors or the multiple computing units configured to apply a portion of the ambulatory electrograms of the subject to a respectively trained machine learning model to estimate a respective physiological parameter or to detect a respective physiological event in the subject.
Thakur in the same field of physiological event detection discloses a system (e.g. 100) that receives ambulatory electrograms collected from an ambulatory medical device (e.g. known to use “intracardiac or subcutaneous electrogram (EGM) acquired by an ambulatory monitor.”, para. [0042]; “ambulatory system 105 associated with the patient”, para. [0047]-[0049]; “cardiac information” such as ECG or an EGM, para. [0066]) and at least one sensor is configured to sense from the subject physiological information different than the ambulatory electrogram (e.g. multiple sensors including cardiac acceleration, respiration, impedance, cardiac and physical activity, Fig. 2, para. [0063]; “It is to be understood that the physiologic sensors 211-215 illustrated in FIG. 2 are non-limiting examples of sensors”, therefore other sensor may also be used, para. [0068]) wherein to detect the physiological event, the computing device is configured to apply the received ambulatory electrograms to a trained machine learning model (e.g. “using a machine-learning (ML) model”, para. [0078], [0092], [0097]) to estimate the physiological parameter or to detect the physiological event in the subject using the ambulatory electrograms and the physiologic information sensed by the at least one sensor (e.g. fed into an arrhythmia risk stratifier 230 to assess cardiac arrhythmia risk or predict further cardiac arrhythmia event, para. [0069], [0092]) and wherein the computing device includes multiple processors or a multi-core processor comprising multiple computing units, each of the multiple processors or the multiple computing units configured to apply a portion of the ambulatory electrograms of the subject to a respectively trained machine learning model to estimate a respective physiological parameter or to detect a respective physiological event in the subject (e.g. multiple processors for evaluation of data including the arrhythmia risk stratifier 230, para. [0069] and an external system 125 that includes data processor(s) for evaluating “the physiologic or functional signals received by the AMD”, para. [0055], [0059]). Thakur discloses that the present invention proactively identify patients at high risk of atrial tachyarrhythmia like AF, predicts future AF events before an occurrence of clinical manifestations, such as electrophysiological presentation or patient being symptomatic (e.g. para. [0043], [0045], [0098]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Cao to include the computing device including multiple processors or a multi-core processor comprising multiple computing units to evaluate various segments of the ambulatory electrogram signals, as taught by Thakur, in order to verify that a medical event that is detected is a true positive detection (‘230, para. [0055]).
In reference to at least claim 15
Cao discloses a method for detecting physiological events in a subject, the method comprising: receiving ambulatory electrograms of the subject collected by an ambulatory medical device (AMD) (e.g. collects and records signals generated by electrophysiological activities of heart cells in a single-lead or multi-lead form through non-invasive electrocardiogram examination to obtain the electrocardiogram data.”, para. [0089]); determining, via a computing device, a form factor indicative of a body placement or use configuration of at least one sensor using the ambulatory electrograms (e.g. “performing a judgment of signal abnormality on the data segment with the heartbeat interval greater than or equal to the preset interval determination threshold to determine whether the data segment is an abnormal signal; [0121] wherein the identification of the abnormal signal includes whether there are electrode peeling off, low voltage, etc”, the “electrode peeling off”, low voltage, etc. is being interpreted as a “form factor” of the at least one sensor, para. [0117]), serially detecting a physiological event (e.g. “the electrocardiogram data are analyzed and calculated, especially in the case of the introduction of artificial intelligence (AI) technology, arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals”, para. [0097]) or estimating a physiological parameter in the subject using the ambulatory electrograms and the physiological information sensed by the at least one sensor; and providing the estimated physiological parameter or the detected physiological event to a user (e.g. “The alarm information may be output locally in the ambulatory monitoring device, which may be simply a preset sound alarm, photoelectric alarm, or a voice alarm, information display alarm”, para. [0215]) or a process for initiating or titrating therapy based on the detected physiological event or the estimated physiological parameter.
However, Cao does not explicitly teach wherein the at least one sensor is configured to sense from the subject physiological information different than the ambulatory electrogram.
Thakur in the same field of physiological event detection discloses a system (e.g. 100) that receives ambulatory electrograms collected from an ambulatory medical device (e.g. known to use “intracardiac or subcutaneous electrogram (EGM) acquired by an ambulatory monitor.”, para. [0042]; “ambulatory system 105 associated with the patient”, para. [0047]-[0049]; “cardiac information” such as ECG or an EGM, para. [0066]) and at least one sensor is configured to sense from the subject physiological information different than the ambulatory electrogram (e.g. multiple sensors including cardiac acceleration, respiration, impedance, cardiac and physical activity, Fig. 2, para. [0063]; “It is to be understood that the physiologic sensors 211-215 illustrated in FIG. 2 are non-limiting examples of sensors”, therefore other sensor may also be used, para. [0068]) wherein to detect the physiological event, the computing device is configured to apply the received ambulatory electrograms to a trained machine learning model (e.g. “using a machine-learning (ML) model”, para. [0078], [0092], [0097]) to estimate the physiological parameter or to detect the physiological event in the subject using the ambulatory electrograms and the physiologic information sensed by the at least one sensor (e.g. fed into an arrhythmia risk stratifier 230 to assess cardiac arrhythmia risk or predict further cardiac arrhythmia event, para. [0069], [0092]). Thakur discloses that the present invention proactively identify patients at high risk of atrial tachyarrhythmia like AF, predicts future AF events before an occurrence of clinical manifestations, such as electrophysiological presentation or patient being symptomatic (e.g. para. [0043], [0045], [0098]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Cao to include the at least one sensor is configured to sense from the subject physiological information different than the ambulatory electrogram and detecting the physiological event in the subject using the ambulatory electrograms and the physiologic information sensed by the at least one sensor, as taught by Thakur, in order to proactively identify patients at high risk of atrial tachyarrhythmia like AF before the patient become symptomatic improving patient outcome and reducing healthcare cost associated with AF and EHF management (e.g. ‘230, para. [0043], [0045]).
In reference to at least claim 16
Cao modified by Thakur renders obvious a method according to claim 15. Cao further discloses wherein serially detecting the physiological event includes applying the received ambulatory electrograms to a trained machine learning model to estimate the physiological parameter or to detect the physiological event in the subject (e.g. “artificial intelligence technology”, para. [0097]; “all based on trained models based on artificial intelligence self-learning to obtain output results, with a fast analysis speed and a high accuracy”, para. [0101], [0146]).
In reference to at least claim 17
Cao modified by Thakur renders obvious a method according to claim 16. Cao further discloses the electrocardiogram being analyzed using artificial intelligence (e.g. “the electrocardiogram data are analyzed and calculated, especially in the case of the introduction of artificial intelligence (AI) technology, arrhythmia analysis long intermittent arrest, flutter and fibrillation, conduction block, premature beat and escape beat, bradycardia, tachycardia, ST segment change detection are performed on the collected digital signals”, para. [0097]) including trained models based on self-learning (e.g. para. [0101]).
Thakur discloses constructing a training dataset including ambulatory electrograms collected from a patient population and assessments of physiological parameters or physiological events in the patient population; and training a machine learning model using the constructed training dataset until a convergence or training stopping criterion is satisfied, the trained machine learning model representing a correspondence between the ambulatory electrograms of the patient population and the physiological parameters or the physiological events in the patient population (e.g. “The ML model may be trained using sensor data from patient population, and produce an arrhythmia risk indication corresponding to the signal metric measurements as input to the model.”, para. [0078]-[0079]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Cao to include constructing a training dataset including ambulatory electrograms collected from a patient population and assessments of physiological parameters or physiological events in the patient population; and training a machine learning model using the constructed training dataset until a convergence or training stopping criterion is satisfied, as taught by Thakur, in order to provide an accurate machine learning model for arrhythmia risk indication that proactively identify patients at high risk of atrial tachyarrhythmia like AF before the patient become symptomatic (e.g. ‘230. para. [0078]).
In reference to at least claim 18
Cao modified by Thakur renders obvious a method according to claim 16. Cao further discloses applying the received ambulatory electrograms to the trained machine learning model and detecting an operating status of the AMD (e.g. “ disturbance problems caused by mai