CTNF 18/861,874 CTNF 98504 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Preliminary Amendment This Office Action is responsive to the amendment filed on 30 Oct 2024. As directed by the amendment: claims 3-5, 7, 9-12, and 15 have been amended, claims 6, 8, and 13-14 have been canceled, and claims 16-24 have been added. Thus, claims 1-5, 7, 9-12, and 15-24 are presently pending in this application. Claim Objections 07-29-01 AIA Claim s 3 and 18 are objected to because of the following informalities: Claim 3: “the pacing capture classification machine” in lines 10-11 should read “the pacing capture classification machine learning model ” for consistency with parent claim 1 Claim 18: “the pacing capture classification machine” on page 9, lines 1-2 should read “the pacing capture classification machine learning model ” for consistency with parent claim 15 Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-5, 7, 9-12, and 15-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Determination as to whether a claim satisfies the criteria for subject matter eligibility is a stepwise process (MPEP 2016). Step 1: Does the claim fall within a statutory category of invention? Claim 1 recites a machine (system), and claim 15 recites a manufacture (non-transitory computer-readable medium), which are within the four statutory categories. Therefore, claims 1 and 15 are directed to a statutory category of invention. Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Claims 1 and 15 are directed to an abstract idea. Claim 1 is directed to a medical device system, comprising: a memory configured to store a first cardiac signal sensed following delivery of a ventricular conduction system (VCS) pacing pulse; processing circuitry configured to: receive the first cardiac signal sensed following delivery of the VCS pacing pulse; apply a pacing capture classification machine learning model to at least the first cardiac signal; determine, based on the applied pacing capture classification machine learning model, a capture type classification of the VCS pacing pulse from among a plurality of capture types; and generate an output based on the capture type classification; and a user interface configured to, in response to the generated output, present a representation of the capture type classification associated with the delivered ventricular conduction system pacing pulse. Claim 15 is directed to a non-transitory computer-readable storage medium storing a set of instructions which, which executed by processing circuitry of a medical device system, cause the medical device system to perform the same steps as recited in claim 1. The limitations of applying a machine learning model, and determining a capture type classification based on a machine learning model, as drafted, under their broadest reasonable interpretations, are merely mental processes, because these steps are akin to having a doctor or other human actor performing these operations with pen and paper. For example, “applying a pacing capture classification machine learning model to at least the first cardiac signal” encompasses nothing more than a human actor mentally evaluating at least the first cardiac signal according to the criteria of the machine learning model. The limitation of “receiving the first cardiac signal” encompasses nothing more than a human actor collecting this information by hand. The limitation of “a memory configured to store a first cardiac signal” indicates that the data may be stored for analysis and display at a later time. Therefore, a human actor can perform the analysis with pen and paper. The limitation “present a representation of the capture type classification” encompasses nothing more than a human actor drawing out representation on a piece of paper. Therefore, claims 1 and 15 recite an abstract idea. Claims 2-5, 7, and 9-12 depend on claim 1; claims 16-24 depend on claim 15. These dependent claims only recite additional features of the analysis described in claims 1 and 15, which may also be performed by a human actor mentally and using a pen and paper. For example, claims 2 and 16 recite “determin[ing] the capture type classification based on the pacing capture classification learning model applied to at least the first template beat signal and the first cardiac signal”, which encompasses nothing more than a human actor evaluating at least the first template beat signal and the first cardiac signal according to the criteria of the machine learning model. Therefore, claims 1-5, 7, 9-12, and 15-24 recite an abstract idea. Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? This judicial exception is not integrated into a practical application. Claims 1 and 15 only recite the additional limitations “a memory” and “processing circuitry”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Paragraphs [0115], [0130], and [0232] of the specification describe the memory and processing circuitry at a high level of generality. These generic processor and memory limitations are no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore claims 1 and 15 do not integrate the judicial exception into a practical application. Thus, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. As described above, dependent claims 2-5, 7, 9-12, and 16-24 only recite other limitations of applying the machine learning model and determining a capture type, which may be done mentally by a human actor and/or with a pen and paper. Step 2B: Does the claim include additional elements that are sufficient to amount to significantly more than the judicial exception? The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As explained above with respect to the integration of the judicial exception into a practical application (Step 2A, Prong 2), the additional elements of using computer components to perform the process steps amounts to no more than mere instructions to apply the judicial exception using generic computer elements. The structural elements recited in claims 1 and 15 are “a memory” and “processing circuitry”. These additional elements are recited at a high level of generality (i.e. most generic computers would be known to have these components). Paragraphs [0115], [0130], and [0232] of the specification describe the processor and non-transitory computer-readable medium at a high level of generality, and only provides conventional, well-known computing functions that do not add meaningful limits to practicing the abstract idea. Therefore, claims 1-5, 7, 9-12, and 15-24 are not patent-eligible under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-2, 4, 7, 9-12, 15-17, and 20-24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cao et al. (US 20220062645 A1), hereinafter Cao . 07-15-02-aia The applied reference has a common applicant with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. Regarding claim 1, Cao discloses a medical device system (Fig. 1, paragraph [0023], system 10), comprising: a memory (Fig. 3, paragraph [0034], memory 72; paragraph [0032]) configured to store a first cardiac signal sensed following delivery of a ventricular conduction system (VCS) pacing pulse (paragraph [0021], "Samples of evoked responses are collected for each classification"); processing circuitry (Fig. 3, paragraph [0034], processing circuitry 70, pacing analysis circuitry 34) configured to: receive the first cardiac signal sensed following delivery of the VCS pacing pulse (paragraph [0028], "From time to time, shortly after delivering the electrical stimulation (e.g., within 90 to 120 milliseconds, etc.), the cardiac signal analysis circuitry measures an evoked electrical response (sometimes referred to as an 'evoked response') characterizing the depolarization wave of the heart muscle of heart 12"); apply a pacing capture classification machine learning model to at least the first cardiac signal (paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response"; paragraph [0040], "Classification model 35 is generated using supervised machine learning"); determine, based on the applied pacing capture classification machine learning model, a capture type classification of the VCS pacing pulse from among a plurality of capture types (paragraph [0039], "pacing analysis circuitry 34 may classify each evoked response as 'selective capture,' 'non-selective capture,' 'RV capture,' or 'no capture.'"); and generate an output based on the capture type classification (paragraph [0040], "Classification model 35 is a model that ... outputs the classification"); and a user interface (Fig. 1, paragraph [0023], external device 24) configured to, in response to the generated output, present a representation of the capture type classification associated with the delivered ventricular conduction system pacing pulse (paragraph [0026], "external device 24 takes the form of a handheld computing device, computer workstation or networked computing device that includes a user interface for presenting information to and receiving input from a user"; paragraph [0050], "pacemaker 800 may take actions based on the classification of the evoked response, such as adjusting pacing parameters and/or providing an alert (e.g., to external device 24)"). Regarding claim 2, Cao discloses the medical device system of claim 1, as explained above. Cao further discloses: the memory is further configured to store a first template beat signal corresponding to a first VCS pacing pulse output (paragraph [0021], "Samples of evoked responses are collected for each classification"); and the processing circuitry is further configured to: determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template beat signal and the first cardiac signal (paragraph [0021], "These prepared samples are used to generate one or more training sets and one or more test sets. The training sets used to train a machine learning algorithm to classify pacing captures"; paragraph [0040], "Classification model 35 is a model that takes, as input, the physiologically significant features (sometimes referred to as the “classification features”). and outputs the classification. ... To train the classification model 35, training sets and validation sets are generated (collectively referred to as “ML sets”). The ML set are generated by collecting sample evoked responses (e.g., the NF, FF, and DFF potentials of evoked responses) for each classification. Features are extracted or derived from the sample evoked response"). Regarding claim 4, Cao discloses the medical device system of claim 2, as explained above. Cao further discloses that: the memory is further configured to store a plurality of template beat signals comprising the first template beat signal (paragraph [0021], "Samples of evoked responses are collected for each classification"), where each of the plurality of template beat signals corresponds to one VCS pacing pulse output of a plurality of VCS pacing pulse outputs (paragraph [0045], "the pacing test may provide a series of pacing such that IMD 16 may extract FF, NF, and DFF potentials for multiple beats. In some such examples, one or more of the finals pacing stimulus signals may change one or more pacing parameters (e.g., amplitude, pulse width, etc.) such that a new pacing parameter may be compared to existing pacing parameters"); and the processing circuitry is further configured to: input each of the plurality of template beat signals and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0021], "These prepared samples are used to generate one or more training sets and one or more test sets. The training sets used to train a machine learning algorithm to classify pacing captures"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the plurality of template beat signals and the first cardiac signal (paragraph [0040], "Classification model 35 is a model that takes, as input, the physiologically significant features (sometimes referred to as the “classification features”). and outputs the classification. ... To train the classification model 35, training sets and validation sets are generated (collectively referred to as “ML sets”). The ML set are generated by collecting sample evoked responses (e.g., the NF, FF, and DFF potentials of evoked responses) for each classification. Features are extracted or derived from the sample evoked response"). Regarding claim 7, Cao discloses the medical device system of claim 1, as explained above. Cao further discloses that the processing circuitry is further configured to: determine a derivative signal from the first cardiac signal (paragraph [0028], "The cardiac signal analysis circuitry may also determine differential far field (DFF) potential (e.g., a first order differential of the FF potential, etc.)"; paragraph [0039], "Pacing analysis circuitry 34 generates a DFF potential"); input the first cardiac signal to the pacing capture classification machine learning model by inputting at least the derivative signal (paragraph [0028], "the cardiac EGM of an evoked response may include physiologically meaningful features. These physiologically meaningful features are extracted by pacing analysis circuitry 34. Pacing analysis circuitry 34 then uses classification model 35"; paragraph [0039], "The portions extracted from the cardiac EMGs of the FF potential, the DFF potential, and/or the NF potential may be collectively referred to as the “evoked response” of that test pacing stimulation. For each evoked response, pacing analysis circuitry 34 extracts and/or derives physiologically significant features from the evoked response. Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the derivative signal (paragraph [0028], "Pacing analysis circuitry 34 then uses classification model 35 to classify the evoked response"; paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"). Regarding claim 9, Cao discloses the medical device system of claim 1, as explained above. Cao further discloses that: the memory is further configured to store a pacing pulse output of the delivered VCS pacing pulse (paragraph [0045], amplitude, pulse width); and the processing circuitry is further configured to: input at least the pacing pulse output and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0046], "IMD 16 classifies the beat using model 35 and the features and parameters"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the pacing pulse output and the first cardiac signal (paragraph [0046], "IMD 16 classifies the beat using model 35 and the features and parameters"). Regarding claim 10, Cao discloses the medical device system of claim 1, as explained above. Cao further discloses that the processing circuitry is further configured to: determine a feature of the first cardiac signal (paragraph [0039], "pacing analysis circuitry 34 extracts and/or derives physiologically significant features from the evoked response"); input the feature of the first cardiac signal and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the feature of the first cardiac signal and the first cardiac signal (paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"). Regarding claim 11, Cao discloses the medical device system of claim 1, as explained above. Cao further discloses that the processing circuitry is further configured to: receive training cardiac signal datasets obtained from a plurality of patients (paragraph [0021], "the model may be optimized (e.g., the training set customized) based on a certain population (e.g., a shared location and/or a shared set of demographic and physical data, etc.) and/or individual patient data. ... Samples of evoked responses are collected for each classification"), the training cardiac signal datasets comprising a plurality of training cardiac signals each sensed following delivery of a VCS pacing pulse (paragraph [0028], "From time to time, shortly after delivering the electrical stimulation (e.g., within 90 to 120 milliseconds, etc.), the cardiac signal analysis circuitry measures an evoked electrical response (sometimes referred to as an 'evoked response') characterizing the depolarization wave of the heart muscle of heart 12"), wherein the VCS pacing pulses associated with the plurality of training cardiac signals comprise VCS pacing pulses delivered at a plurality of different pacing pulse outputs (paragraph [0045], "IMD 16 uses the pacing test to, for example, determine whether the current stimulation level of pacing is providing effective capture and whether a different amplitude of stimulation (e.g., an amplitude that uses a lower voltage, etc.) may still provide effective capture. ... one or more of the finals pacing stimulus signals may change one or more pacing parameters (e.g., amplitude, pulse width, etc.) such that a new pacing parameter may be compared to existing pacing parameters"); train the pacing capture classification machine learning model with the training cardiac signal datasets according to a machine learning algorithm (paragraph [0021], "These prepared samples are used to generate one or more training sets and one or more test sets. The training sets used to train a machine learning algorithm to classify pacing captures"); and apply the pacing capture classification machine learning model trained with the training cardiac signal datasets to at least the first cardiac signal (paragraph [0022], "The model is downloaded to the IMD with the physiologically meaningful features that are used to classify the pacing"). Regarding claim 12, Cao discloses the medical device system of claim 1, as explained above. Cao further discloses that the processing circuitry is further configured to: receive a plurality of cardiac signals comprising the first cardiac signal (paragraph [0045], "the pacing test may provide a series of pacing such that IMD 16 may extract FF, NF, and DFF potentials for multiple beats"), each of the plurality of cardiac signals associated with a VCS pacing pulse, wherein the VCS pacing pulses associated with the plurality of cardiac signals comprise VCS pacing pulses delivered at a plurality of different pacing pulse outputs (paragraph [0045], "the pacing test may provide a series of pacing such that IMD 16 may extract FF, NF, and DFF potentials for multiple beats. In some such examples, one or more of the finals pacing stimulus signals may change one or more pacing parameters (e.g., amplitude, pulse width, etc.) such that a new pacing parameter may be compared to existing pacing parameters"); input each of the plurality of cardiac signals to the pacing capture classification machine learning model (paragraph [0046], "IMD 16 classifies the beat using model 35 and the features and parameters (716). When there is another beat to classify (“YES” at 718), IMD 16 selects the next beat (708)"); determine a capture threshold for at least one capture type of the plurality of capture types based on the capture type classifications (paragraph [0046], "IMD 16 extracts features from the FF, NF and DFF electrograms and, in some examples, derive parameters based on features and parameters used by model 35 to classify the beats (e.g., a specific list of features provided when model 35 is downloaded into IMD 16, etc.) ... IMD 116 may set the amplitude of the stimulus based on the lowest amplitude (e.g., with an added safety margin) that was classified as 'selective capture.'"); and determine an operating pacing pulse output based on the capture threshold determined for the at least one capture type (paragraph [0046], "IMD 16 may further take actions in response to the classification, such as adjusting the amplitude and/or pulse width of the stimulation"); and the user interface is further configured to generate a display of the operating pacing pulse output for a user to accept and confirm (paragraph [0026], "external device 24 takes the form of a handheld computing device, computer workstation or networked computing device that includes a user interface for presenting information to and receiving input from a user"). Regarding claim 15, Cao discloses a non-transitory, computer-readable storage medium (Fig. 3, paragraph [0034], memory 72; paragraph [0032]) storing a set of instructions which, when executed by processing circuitry of a medical device system (Fig. 3, paragraph [0034], processing circuitry 70, pacing analysis circuitry 34), cause the medical device system to: store in a memory of the medical device system a first cardiac signal sensed following delivery of a ventricular conduction system (VCS) pacing pulse (paragraph [0028], "From time to time, shortly after delivering the electrical stimulation (e.g., within 90 to 120 milliseconds, etc.), the cardiac signal analysis circuitry measures an evoked electrical response (sometimes referred to as an 'evoked response') characterizing the depolarization wave of the heart muscle of heart 12"); apply a pacing capture classification machine learning model to at least the first cardiac signal (paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response"; paragraph [0040], "Classification model 35 is generated using supervised machine learning"); determine, based on the applied pacing capture classification machine learning model, a capture type classification of the VCS pacing pulse from among a plurality of capture types (paragraph [0039], "pacing analysis circuitry 34 may classify each evoked response as 'selective capture,' 'non-selective capture,' 'RV capture,' or 'no capture.'"); and generate an output based on the capture type classification (paragraph [0040], "Classification model 35 is a model that ... outputs the classification"); and in response to the generated output, present a representation of the determined capture type associated with the delivered ventricular conduction system pacing pulse by a user interface of the medical device system (paragraph [0026], "external device 24 takes the form of a handheld computing device, computer workstation or networked computing device that includes a user interface for presenting information to and receiving input from a user"; paragraph [0050], "pacemaker 800 may take actions based on the classification of the evoked response, such as adjusting pacing parameters and/or providing an alert (e.g., to external device 24)"). Regarding claim 16, Cao discloses the storage medium of claim 15, as explained above. Cao further discloses instructions that cause the medical device system to: store a first template beat signal corresponding to a first VCS pacing pulse output (paragraph [0021], "Samples of evoked responses are collected for each classification"); and input to the pacing capture classification machine learning model at least the first template beat signal and the first cardiac signal (paragraph [0021], "These prepared samples are used to generate one or more training sets and one or more test sets. The training sets used to train a machine learning algorithm to classify pacing captures"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template beat signal and the first cardiac signal (paragraph [0040], "Classification model 35 is a model that takes, as input, the physiologically significant features (sometimes referred to as the “classification features”). and outputs the classification. ... To train the classification model 35, training sets and validation sets are generated (collectively referred to as “ML sets”). The ML set are generated by collecting sample evoked responses (e.g., the NF, FF, and DFF potentials of evoked responses) for each classification. Features are extracted or derived from the sample evoked response"). Regarding claim 17, Cao discloses the storage medium of claim 16, as explained above. Cao further discloses instructions that cause the medical device system to: store a plurality template beat signals comprising the first template beat signal (paragraph [0021], "Samples of evoked responses are collected for each classification"), where each of the plurality of template beat signals corresponds to one VCS pacing pulse output of a plurality of VCS pacing pulse outputs (paragraph [0045], "the pacing test may provide a series of pacing such that IMD 16 may extract FF, NF, and DFF potentials for multiple beats. In some such examples, one or more of the finals pacing stimulus signals may change one or more pacing parameters (e.g., amplitude, pulse width, etc.) such that a new pacing parameter may be compared to existing pacing parameters"); and input each of the plurality of template beat signals and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0021], "These prepared samples are used to generate one or more training sets and one or more test sets. The training sets used to train a machine learning algorithm to classify pacing captures"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the plurality of template beat signals and the first cardiac signal (paragraph [0040], "Classification model 35 is a model that takes, as input, the physiologically significant features (sometimes referred to as the “classification features”). and outputs the classification. ... To train the classification model 35, training sets and validation sets are generated (collectively referred to as “ML sets”). The ML set are generated by collecting sample evoked responses (e.g., the NF, FF, and DFF potentials of evoked responses) for each classification. Features are extracted or derived from the sample evoked response"). Regarding claim 20, Cao discloses the storage medium of claim 15, as explained above. Cao further discloses instructions that cause the medical device system to: determine a derivative signal from the first cardiac signal (paragraph [0028], "The cardiac signal analysis circuitry may also determine differential far field (DFF) potential (e.g., a first order differential of the FF potential, etc.)"; paragraph [0039], "Pacing analysis circuitry 34 generates a DFF potential"); input the first cardiac signal to the pacing capture classification machine learning model by inputting at least the derivative signal (paragraph [0028], "the cardiac EGM of an evoked response may include physiologically meaningful features. These physiologically meaningful features are extracted by pacing analysis circuitry 34. Pacing analysis circuitry 34 then uses classification model 35"; paragraph [0039], "The portions extracted from the cardiac EMGs of the FF potential, the DFF potential, and/or the NF potential may be collectively referred to as the “evoked response” of that test pacing stimulation. For each evoked response, pacing analysis circuitry 34 extracts and/or derives physiologically significant features from the evoked response. Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the derivative signal (paragraph [0028], "Pacing analysis circuitry 34 then uses classification model 35 to classify the evoked response"; paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"). Regarding claim 21, Cao discloses the storage medium of claim 15, as explained above. Cao further discloses instructions that cause the medical device system to: store a pacing pulse output of the delivered VCS pacing pulse (paragraph [0045], amplitude, pulse width); and input at least the pacing pulse output and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0046], "IMD 16 classifies the beat using model 35 and the features and parameters"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the pacing pulse output and the first cardiac signal (paragraph [0046], "IMD 16 classifies the beat using model 35 and the features and parameters"). Regarding claim 22, Cao discloses the storage medium of claim 15, as explained above. Cao further discloses instructions that cause the medical device system to: determine a feature of the first cardiac signal (paragraph [0039], "pacing analysis circuitry 34 extracts and/or derives physiologically significant features from the evoked response"); input the feature of the first cardiac signal and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the feature of the first cardiac signal and the first cardiac signal (paragraph [0039], "Pacing analysis circuitry 34 uses classification model 35 to classify each evoked response based on these physiologically significant features"). Regarding claim 23, Cao discloses the storage medium of claim 15, as explained above. Cao further discloses instructions that cause the medical device system to: receive training cardiac signal datasets obtained from a plurality of patients (paragraph [0021], "the model may be optimized (e.g., the training set customized) based on a certain population (e.g., a shared location and/or a shared set of demographic and physical data, etc.) and/or individual patient data. ... Samples of evoked responses are collected for each classification"), the training cardiac signal datasets comprising a plurality of training cardiac signals each sensed following delivery of a VCS pacing pulse (paragraph [0028], "From time to time, shortly after delivering the electrical stimulation (e.g., within 90 to 120 milliseconds, etc.), the cardiac signal analysis circuitry measures an evoked electrical response (sometimes referred to as an 'evoked response') characterizing the depolarization wave of the heart muscle of heart 12"), wherein the VCS pacing pulses associated with the plurality of training cardiac signals comprise VCS pacing pulses delivered at a plurality of different pacing pulse outputs (paragraph [0045], "IMD 16 uses the pacing test to, for example, determine whether the current stimulation level of pacing is providing effective capture and whether a different amplitude of stimulation (e.g., an amplitude that uses a lower voltage, etc.) may still provide effective capture. ... one or more of the finals pacing stimulus signals may change one or more pacing parameters (e.g., amplitude, pulse width, etc.) such that a new pacing parameter may be compared to existing pacing parameters"); train the pacing capture classification machine learning model with the training cardiac signal datasets according to a machine learning algorithm (paragraph [0021], "These prepared samples are used to generate one or more training sets and one or more test sets. The training sets used to train a machine learning algorithm to classify pacing captures"); and apply the pacing capture classification machine learning model trained with the training cardiac signal datasets to at least the first cardiac signal (paragraph [0022], "The model is downloaded to the IMD with the physiologically meaningful features that are used to classify the pacing"). Regarding claim 24, Cao discloses the storage medium of claim 15, as explained above. Cao further discloses instructions that cause the medical device system to: receive a plurality of cardiac signals comprising the first cardiac signal (paragraph [0045], "the pacing test may provide a series of pacing such that IMD 16 may extract FF, NF, and DFF potentials for multiple beats"), each of the plurality of cardiac signals associated with a VCS pacing pulse, wherein the VCS pacing pulses associated with the plurality of cardiac signals comprise VCS pacing pulses delivered at a plurality of different pacing pulse outputs (paragraph [0045], "the pacing test may provide a series of pacing such that IMD 16 may extract FF, NF, and DFF potentials for multiple beats. In some such examples, one or more of the finals pacing stimulus signals may change one or more pacing parameters (e.g., amplitude, pulse width, etc.) such that a new pacing parameter may be compared to existing pacing parameters"); input each of the plurality of cardiac signals to the pacing capture classification machine learning model (paragraph [0046], "IMD 16 classifies the beat using model 35 and the features and parameters (716). When there is another beat to classify (“YES” at 718), IMD 16 selects the next beat (708)"); determine a capture type classification of each of the VCS pacing pulses delivered at the plurality of different pacing pulse outputs associated with the plurality of cardiac signals based on the pacing capture classification machine learning model (paragraph [0046], "IMD 116 may perform pacing tests with reduced stimulus amplitudes until the beats change from a 'selective capture' classification to a 'non-selective capture' classification"); determine a capture threshold for at least one capture type of the plurality of capture types based on the capture type classifications (paragraph [0046], "IMD 16 extracts features from the FF, NF and DFF electrograms and, in some examples, derive parameters based on features and parameters used by model 35 to classify the beats (e.g., a specific list of features provided when model 35 is downloaded into IMD 16, etc.) ... IMD 116 may set the amplitude of the stimulus based on the lowest amplitude (e.g., with an added safety margin) that was classified as 'selective capture.'"); and determine an operating pacing pulse output based on the capture threshold determined for the at least one capture type (paragraph [0046], "IMD 16 may further take actions in response to the classification, such as adjusting the amplitude and/or pulse width of the stimulation"); and the user interface is further configured to generate a display of the operating pacing pulse output for a user to accept and confirm (paragraph [0026], "external device 24 takes the form of a handheld computing device, computer workstation or networked computing device that includes a user interface for presenting information to and receiving input from a user"). Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 3, 5, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. (US 20220062645 A1), hereinafter Cao, in view of Walker et al. (US 20220219000 A1), hereinafter Walker . Regarding claim 3, Cao discloses the medical device system of claim 1, as explained above. Cao further discloses that: the memory is further configured to store a first template beat signal corresponding to a first VCS pacing pulse output (paragraph [0021], "Samples of evoked responses are collected for each classification"). Cao does not explicitly disclose that the processing circuitry is further configured to: determine a first template difference signal from the first cardiac signal and the first template beat signal; input at least the first template difference signal and the first cardiac signal to the pacing capture classification machine learning model; and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template difference signal and the first cardiac signal. However, Walker teaches techniques for pacing assistance and automation (Abstract) wherein processing circuitry is configured to: determine a first template difference signal from the first cardiac signal and the first template beat signal (paragraph [0028], "the sensor analysis component 102 determines a difference between sensor data (e.g., an ECG signal) generated by the various sensors that detect biological conditions as described above after an electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing"); input at least the first template difference signal and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0023], "The sensor analysis component 102 may continue to input data associated with one or multiple biological parameters over time"; paragraph [0028], "the capture component 106 may input the difference into the machine-learned model 108"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template difference signal and the first cardiac signal (paragraph [0028], "If the difference between the sensor data after the electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing is greater than or equal to a threshold difference (e.g., 50%, 20%, 10% difference, etc.), the capture component 106 determines that the electrical stimulation has likely caused capture. ... If the difference between the sensor data after the electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing is less than the threshold difference, the capture component 106 determines that the electrical stimulation has likely failed to cause capture."). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cao with the teachings of Walker so that the processing circuitry is further configured to: determine a first template difference signal from the first cardiac signal and the first template beat signal; input at least the first template difference signal and the first cardiac signal to the pacing capture classification machine learning model; and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template difference signal and the first cardiac signal, because doing so improves accuracy of a capture determination over systems that may rely on a single biological parameter (Walker, paragraph [0016]). Regarding claim 5, the medical device system of claim 3 is obvious over Cao and Walker, as explained above. Walker further teaches that: the memory is further configured to store a plurality of template beat signals comprising the first template beat signal, where each template beat signal corresponds to one VCS pacing pulse output of a plurality of VCS pacing pulse outputs (paragraph [0052], "The pacing device 100 stores the sensor data received prior to the electrical stimulation (also referred to as “historical sensor data”) for later comparison with sensor data received during pacing. In some instances, the historical sensor data is from a past pacing setting, such as a previous electrical stimulation that was administered at a previous, different current amplitude. Alternatively or additionally, the historical sensor data is from data prior to achieving capture, and/or is from after capture has been achieved while monitoring to ensure that capture is maintained over time"); and the processing circuitry is further configured to: determine a plurality of template difference signals, comprising the first template difference signal, by determining a template difference signal from each one of the plurality of template beat signals and the first cardiac signal (paragraph [0055], "the pacing device 100 determines a difference in sensor data values at different times, such as by comparing the sensor data received after the electrical stimulation to the sensor data received prior to the electrical stimulation. For example, the sensor analysis component 102 determines a difference between sensor data (e.g., an ECG pulse) generated after an electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing"); and input the plurality of template difference signals and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0056], "the capture component 106 may input the difference into the machine-learned model 108"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the plurality of template difference signals and the first cardiac signal (paragraph [0056], "receive a likelihood that the electrical stimulation caused capture from the machine-learned model based on the difference"). Regarding claim 18, Cao discloses the storage medium of claim 15, as explained above. Cao further discloses instructions that cause the medical device system to: store a first template beat signal corresponding to a first VCS pacing pulse output (paragraph [0021], "Samples of evoked responses are collected for each classification"). Cao does not explicitly disclose instructions that cause the medical device system to: determine a first template difference signal from the first cardiac signal and the first template beat signal; input at least the first template difference signal and the first cardiac signal to the pacing capture classification machine learning model; and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template difference signal and the first cardiac signal. However, Walker teaches techniques for pacing assistance and automation (Abstract) wherein processing circuitry is configured to: determine a first template difference signal from the first cardiac signal and the first template beat signal (paragraph [0028], "the sensor analysis component 102 determines a difference between sensor data (e.g., an ECG signal) generated by the various sensors that detect biological conditions as described above after an electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing"); input at least the first template difference signal and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0023], "The sensor analysis component 102 may continue to input data associated with one or multiple biological parameters over time"; paragraph [0028], "the capture component 106 may input the difference into the machine-learned model 108"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template difference signal and the first cardiac signal (paragraph [0028], "If the difference between the sensor data after the electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing is greater than or equal to a threshold difference (e.g., 50%, 20%, 10% difference, etc.), the capture component 106 determines that the electrical stimulation has likely caused capture. ... If the difference between the sensor data after the electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing is less than the threshold difference, the capture component 106 determines that the electrical stimulation has likely failed to cause capture."). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cao with the teachings of Walker so that the processing circuitry is further configured to: determine a first template difference signal from the first cardiac signal and the first template beat signal; input at least the first template difference signal and the first cardiac signal to the pacing capture classification machine learning model; and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the first template difference signal and the first cardiac signal, because doing so improves accuracy of a capture determination over systems that may rely on a single biological parameter (Walker, paragraph [0016]). Regarding claim 19, the storage medium of claim 18 is obvious over Cao and Walker, as explained above. Walker further teaches instructions that cause the medical device system to: store a plurality of template beat signals comprising the first template beat signal, where each template beat signal corresponds to one VCS pacing pulse output of a plurality of VCS pacing pulse outputs (paragraph [0052], "The pacing device 100 stores the sensor data received prior to the electrical stimulation (also referred to as “historical sensor data”) for later comparison with sensor data received during pacing. In some instances, the historical sensor data is from a past pacing setting, such as a previous electrical stimulation that was administered at a previous, different current amplitude. Alternatively or additionally, the historical sensor data is from data prior to achieving capture, and/or is from after capture has been achieved while monitoring to ensure that capture is maintained over time"); and determine a plurality of template difference signals, comprising the first template difference signal, by determining a template difference signal from each one of the plurality of template beat signals and the first cardiac signal (paragraph [0055], "the pacing device 100 determines a difference in sensor data values at different times, such as by comparing the sensor data received after the electrical stimulation to the sensor data received prior to the electrical stimulation. For example, the sensor analysis component 102 determines a difference between sensor data (e.g., an ECG pulse) generated after an electrical stimulation was administered and historical sensor data collected prior to administration of electrical stimulation during pacing"); and input the plurality of template difference signals and the first cardiac signal to the pacing capture classification machine learning model (paragraph [0056], "the capture component 106 may input the difference into the machine-learned model 108"); and determine the capture type classification based on the pacing capture classification machine learning model applied to at least the plurality of template difference signals and the first cardiac signal (paragraph [0056], "receive a likelihood that the electrical stimulation caused capture from the machine-learned model based on the difference") . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Giorgis et al. (US 20120022607 A1) discloses using machine learning algorithms (paragraphs [0029], [0092], [0094]) to confirm the presence or absence of ventricular capture (paragraph [0023]) Botzer et al. (US 20220193422 A1) discloses using machine learning algorithms to determine whether a pacing signal is captured from at least a portion of an anatomical structure (paragraph [0029]) Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE SISON whose telephone number is (703)756-4661. The examiner can normally be reached 8 am - 5 pm PT, Mon - Fri. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer McDonald can be reached at (571) 270-3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHRISTINE SISON/Examiner, Art Unit 3796 /Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792 Application/Control Number: 18/861,874 Page 2 Art Unit: 3796 Application/Control Number: 18/861,874 Page 4 Art Unit: 3796 Application/Control Number: 18/861,874 Page 5 Art Unit: 3796 Application/Control Number: 18/861,874 Page 6 Art Unit: 3796 Application/Control Number: 18/861,874 Page 7 Art Unit: 3796 Application/Control Number: 18/861,874 Page 8 Art Unit: 3796 Application/Control Number: 18/861,874 Page 9 Art Unit: 3796 Application/Control Number: 18/861,874 Page 10 Art Unit: 3796 Application/Control Number: 18/861,874 Page 11 Art Unit: 3796 Application/Control Number: 18/861,874 Page 12 Art Unit: 3796 Application/Control Number: 18/861,874 Page 13 Art Unit: 3796 Application/Control Number: 18/861,874 Page 14 Art Unit: 3796 Application/Control Number: 18/861,874 Page 15 Art Unit: 3796 Application/Control Number: 18/861,874 Page 16 Art Unit: 3796 Application/Control Number: 18/861,874 Page 17 Art Unit: 3796 Application/Control Number: 18/861,874 Page 18 Art Unit: 3796 Application/Control Number: 18/861,874 Page 19 Art Unit: 3796 Application/Control Number: 18/861,874 Page 20 Art Unit: 3796 Application/Control Number: 18/861,874 Page 21 Art Unit: 3796 Application/Control Number: 18/861,874 Page 22 Art Unit: 3796 Application/Control Number: 18/861,874 Page 23 Art Unit: 3796 Application/Control Number: 18/861,874 Page 24 Art Unit: 3796 Application/Control Number: 18/861,874 Page 25 Art Unit: 3796 Application/Control Number: 18/861,874 Page 26 Art Unit: 3796 Application/Control Number: 18/861,874 Page 27 Art Unit: 3796 Application/Control Number: 18/861,874 Page 28 Art Unit: 3796 Application/Control Number: 18/861,874 Page 29 Art Unit: 3796
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