DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
Claims 1, 17, and 19 recite similar language of “determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and determining one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves.” While the specification discloses the following, it differs from the claim language:
¶246-one metric is the average time lapsed between a given activation at a given electrode and activations at all (or some) other sites on this wavefront. This time lapse can be represented as a positive value if the electrode activates before another electrode, or as a negative value in the opposite case. This time lapse could also be 0 in case of simultaneous activations or in cases where one of the electrodes is not activated in that wavefront;
¶251-wLEAP scores would be ranked in a line of electrodes along a given vector. The most ordered line of electrodes would correspond to the paired direction for guidance. This can be done in multiple parallel lines within the sensing array and averaged to provide an overall vector. In general, the system may determine a sequential ordering of the set of electrical signals by calculating a gradient based on time shift that maximizes cross-correlation between the electrical signals;
¶272; and
¶295-the treatment system 1770 determines directional guidance 1780 for steering the catheter to another region of interest.
Claim 2 recites “a site where localized therapy can improve biological function in the patient after therapy.” While the specification discloses the following, it does not disclose improving biological function as claimed:
¶292-the treatment assessment model 1790 verifies success of an ablation procedure at a particular region of interest. The treatment assessment model collects signals measured by the heart treatment device 105 after the ablation procedure has been performed. The electrical signals may be analyzed to determine whether the source region is still contributing to or affecting the heart rhythm disorder. Several metrics are used to verify the success of an ablation procedure.
Claims 3, 18, and 20 recite similar language of “the training set of electrical signals annotated with reference times of tissue activation.” While the specification discloses the following, it does not disclose reference times as claimed:
¶5-the algorithm may be a machine-learning model trained with a training dataset including electrical signals annotated with activation times.
Claim 4 recites “identifying the one or more times of tissue activation in the electrical signal above a threshold likelihood.” While the specification discloses the following, it does not disclose identifying the one or more times as claimed:
¶7-identifying one or more peaks in the activation likelihood timeseries above a threshold activation likelihood as the one or more activations in the electrical signal.
Claim 5 recites “wherein determining the score for each wave comprises: determining the score based on the likelihoods of the times of tissue activation from the subset of electrical signals for the wave.” While the specification discloses the following, it differs from the claim language:
¶251-a subset of the wLEAP scores would be averaged using a predefined template for each direction, such as all the top electrodes for the “up” direction. In yet another embodiment 2030, wLEAP scores would be ranked in a line of electrodes along a given vector; and
¶269-a subset of the electrophysiological signals to infer the probability that a feature (FIG. 18) is present in the area neighboring the subset electrodes.
Claim 6 recites “determining a signal quality score for each electrical signal based on the likelihoods of the one or more times of tissue activation in the electrical signal.” While the specification discloses the following, it does not disclose the likelihoods of the one or more times as claimed:
¶33-determining a signal quality score for each electrical signal based on the annotated activations.
Claim 12 recites “determining a direction of conduction for each wave based on the earliest activated electrode and the latest activated electrode; and combining the directions of conduction for the plurality of waves weighted based on the scores.” While the specification discloses the following, it does not disclose scores or weighting as claimed:
¶232-to group activations at different electrodes, the system examines pairs of activations in neighboring electrodes. For a pair of activations to be considered as part of the same wavefront, certain criteria must be met. Specifically, the spatial distance and time difference between these activations must be compatible with conduction velocity values falling within physiological limits. In one embodiment, the range is 20-100 cm/second, but this can be altered using classification tools by taking into account patient-specific comorbidities, such as diabetes, heart failure and advanced age associated with slower conduction, or the lack of comorbidities associated with faster conduction.
Claim 13 recites “wherein determining the score for each wave further comprises determining the score based on tissue conduction velocity and spatial distance between the sensing electrodes on the catheter.” While the specification discloses the following, it does not disclose scores as claimed:
¶13-wherein determining the one or more distinct waves of the heart rhythm disorder comprises defining, for each pair of neighboring sensing electrodes, a time between consecutive activations based on the spatial distance between the neighboring sensing electrodes and tissue conduction velocity.
Claim 15 recites “determining that conduction directions of sub-regions of the catheter converge to one location.” While the specification discloses the following, it does not disclose converging to one location as claimed:
¶22-wherein determining that the catheter is positioned at the one critical location is based on determining that directions of sub-regions of the catheter indicate a critical site.
Claim 16 recites “wherein determining the guidance direction comprises determining the guidance direction to steer the catheter away from the scar tissue.” While the specification discloses the following, it does not disclose steering the catheter away from the scar tissue as claimed:
¶153-this may include scar (very low voltage from tissue elimination) or edema (attenuated voltage due to insulation of tissue from catheter by fluid) due to ablation. This may also include rotational activity, which may arise in patients with AF after ablation of a site of focal activity, or of complex fractionated activity, or that leaves a small gap (absence of scar). Upon detection of this feature, the operator may elect to ablate at this location, or repeat steps 240 to 285 before making this determination, or the physician may wish to repeat 295 from earlier steps.
Claim Objections
Claims 1, 12, 17, and 19 are objected to because of the following informalities: “the scores,” see claim 1, line 11 for example. Only a singular score was positively recited previously, Applicant is encouraged to change “the scores” to recite –each score—to properly refer back to “a score,” see claim 1, line 8 for example. Appropriate correction is required.
Claims 5 and 6 are objected to because of the following informalities: the limitation of “the likelihoods.” Previous claim 4 recites “output a likelihood timeseries indicating likelihood of an activation in the electrical signal over time; and identifying the one or more times of tissue activation in the electrical signal above a threshold likelihood,” which claims 5 and 6 depend from. Applicant is encouraged to change “the likelihoods” in claims 5 and 6 since a plurality were not previously recited in claim 4. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: the limitation of “electrical signals from neighboring sensing electrodes.” Applicant is encouraged to change the limitation to recite –the electrical signals from the neighboring sensing electrodes—to correctly refer back to the previously recited electrical signals and neighboring sensing electrodes. Appropriate correction is required.
Claims 9 and 10 are objected to because of the following informalities: “the direction” in the last line. Applicant is encouraged to change the limitation to recite –the one or more guidance directions—for proper antecedent basis. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: “the guidance direction” in line 4. Applicant is encouraged to change the limitation to recite –the one or more guidance directions—for proper antecedent basis. Appropriate correction is required.
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.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 19-20 are rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
Claim 19 includes the limitations of “a plurality of sensing electrodes for measuring electrical activity of tissue” in lines 2-3 and “measured by the plurality of sensing electrodes on the catheter in contact with human tissue” in lines 8-9. As such, the limitation requires that a plurality of sensing electrodes would encompass being in contact with a patient (human organism) under the broadest reasonable interpretation. Applicant should be recommended to respectively change the claimed limitations to --a plurality of sensing electrodes configured for measuring electrical activity of tissue—and --measured by the plurality of sensing electrodes on the catheter configured to be in contact with human tissue-- in order to overcome these 101 rejections. Dependent claim 20 is rejected for the same deficiency in independent claim 19.
Claims 1-13 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea.
Step 1
The claimed invention in claims 1-13 and 16-20 are directed to statutory subject matter as the claims recite a method, a non-transitory computer-readable storage medium, and a system for generation of a graphical user interface for steering a catheter towards a critical site of a biological rhythm disorder of a patient.
Step 2A, Prong One
Regarding claims 1, 17, and 19, the recited steps are directed to a mental process of performing concepts in a human mind or by a human using a pen and paper (see MPEP 2106.04(a)(2) subsection (III)).
Regarding claims 1, 17, and 19, the limitations of “identifying one or more times of tissue activation in each electrical signal in a set of electrical signals; identifying a plurality of waves of the biological rhythm disorder over time, wherein each wave is based upon times of tissue activation from a subset of electrical signals; determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and determining one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional receiving a print out of electrical signals, identifying one or more times of tissue activation in each electrical signal, identifying a plurality of waves of the biological rhythm disorder over time, determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode, and determining one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder based on a ranking of the scores for the plurality of waves.
Step 2A, Prong Two
For claims 1, 17, and 19, the judicial exception is not integrated into a practical application. In particular, claims 1, 17, and 19 recite “a catheter comprising a plurality of sensing electrodes for measuring electrical activity of tissue; a control system; a computer processor; and generating a graphical user interface for steering a catheter towards a critical site of a biological rhythm disorder.” The catheter comprising a plurality of sensing electrode for measuring electrical activity of tissue amounts to nothing more than pre-solution activity of data gathering. The control system, a computer processor, and a graphical user interface are recited at a high-level of generality and amount to nothing more than parts of a generic computer. Additionally, the limitation of generating a graphical user interface for steering a catheter towards a critical site of a biological rhythm disorder amounts to nothing more than post-solution activity. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into
a practical application, the additional element of a catheter comprising a plurality of sensing electrodes for measuring electrical activity of tissue amounts to nothing more than mere pre-solution activity of data gathering, which does not amount to an inventive concept. Moreover, the catheter comprising a plurality of sensing electrodes for measuring electrical activity of tissue is recited at a high level of generality and are well-understood, routine, and conventional structures as evidenced by US 20190046063: ¶33-a multi-electrode catheter as in a conventional technology, US 20070100232: ¶2-electrode catheters of this type are known in particular in the field of electric physiology for detecting and treating stimulus conduction malfunctions in the heart and are also referred to as EP catheters (electrophysiology catheters), US 20150133759: ¶43-conventional invasive mapping procedures, using one or more catheters with multiple electrodes, can only provide electropotential mapping while the catheters are in the heart, and the mapping is only valid at the positions of the electrodes, and US 20210186608: ¶75-known ablation catheters incorporate sensing electrodes at the tip (E1, distal electrode) and at a relatively proximal position along the catheter the shank (E2, second electrode), commonly provided as a ring electrode. Further, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Regarding dependent claims 1, 17, and 19, the limitations of claims 2-13, 16, 18, and 20 further define the limitations already indicated as being directed to the abstract idea.
Claims 2, 5, and 11-13 are further directed to the abstract idea indicated in claim 1.
Regarding claims 3, 18, and 20, Applicant includes details regarding an activation detection model/deep-learning neural network which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting.
Regarding claim 4, Applicant includes details regarding the activation detection model which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. The limitation of “identifying the one or more times of tissue activation in the electrical signal above a threshold likelihood” is a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, this limitation is nothing more than a medical professional receiving a printout of an electrical signal and identifying the one or more times of tissue activation in the electrical signal above a threshold likelihood.
Regarding claim 6, the limitations of “determining a signal quality score for each electrical signal based on the likelihoods of the one or more times of tissue activation in the electrical signal; and identifying a first electrical signal as unusable based on the signal quality score for the first electrical signal being below a threshold signal quality score” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional receiving a print out of an electrical signal, determining a signal quality score for each electrical signal based on the likelihoods of the one or more times of tissue activation in the electrical signal, and identifying a first electrical signal as unusable based on the signal quality score for the first electrical signal being below a threshold signal quality score.
Regarding claim 7, the limitation of “in response to identifying the first electrical signal as unusable, reconstructing a synthetic electrical signal for use in place of the first electrical signal based on electrical signals of neighboring sensing electrodes” is a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, this limitation is nothing more than a medical professional using pen and paper to reconstruct a synthetic electrical signal for use in place of the first electrical signal based on print outs of electrical signals of neighboring sensing electrodes.
Regarding claim 8, the limitation of “wherein reconstructing the synthetic electrical signal comprises interpolation or extrapolation of electrical signals from neighboring sensing electrodes” is a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, this limitation is nothing more than a medical professional using pen and paper to reconstruct the synthetic electrical signal doing interpolation or extrapolation of electrical signals from neighboring sensing electrodes using pen and paper.
Regarding claim 9, Applicant includes details regarding a machine learning model which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. The limitations of “identify a location of one or more critical sites for the biological rhythm disorder in relation to patterns of activation sequence; identifying…a location of the critical site for the biological rhythm disorder in the patient; and identifying the direction towards the location of the critical site” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional receiving a print out of electrical signal data, identifying a location of one or more critical sites for the biological rhythm disorder in relation to patterns of activation sequence, identifying a location of the critical site for the biological rhythm disorder in the patient, and identifying the direction towards the location of the critical site.
Regarding claim 10, Applicant includes details regarding a machine learning model which is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. The limitations of “identify critical sites ablated in patients with previously successful therapy in relation to patterns of the series of activation sequences; identifying…a location of the critical site for the biological rhythm disorder in the patient; and identifying the direction towards the location of the one or more critical sites.” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional receiving a print out of electrical signal data, identifying critical sites ablated in patients with previously successful therapy in relation to patterns of the series of activation sequences, identifying a location of the critical site for the biological rhythm disorder in the patient, and identifying the direction towards the location of the one or more critical sites.
Claim 14 would be considered a practical application. Dependent claim 15 depends from claim 14.
Regarding claim 16, the limitation of “determining that the catheter is positioned at scar tissue upon identifying one or more electrical signal has a voltage below a threshold voltage, wherein determining the guidance direction comprises determining the guidance direction to steer the catheter away from the scar tissue” is a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, this limitation is nothing more than a medical professional receiving a printout of an electrical signal, identifying one or more electrical signal has a voltage below a threshold voltage, and determining the guidance direction to steer the catheter away from the scar tissue.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 9-10, 13-14, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Narayan (US 20210259765 filed on 2/20/21) in view of Zrihem (US 20170202515 filed on 1/12/17).
Regarding claim 1, Narayan teaches a method for generation of a graphical user interface for steering a catheter towards a critical site of a biological rhythm disorder of a patient (¶204-the catheter continues to be steered incrementally towards the source or other target region in step 1250 until an indication is provided that the target has been located, the catheter positioning will be controlled by a physician, in which case the system may provide some form of notification, e.g., a visual display on a display device, an audible tone, or a combination thereof, to indicate to the physician where to move the catheter to follow the guidance direction toward the target), the method comprising: identifying one or more times of tissue activation in each electrical signal in a set of electrical signals (¶190-identify actual activation time (onset) and recovery time (offset) in complex rhythms in the human heart; ¶191-194) measured by a plurality of sensing electrodes on the catheter in contact with human tissue (¶51-an ablation catheter for treating electrical rhythm disorders includes an array of sensor electrodes to detect electrical signals; ¶53-a catheter configured to be placed in contact with a tissue surface, the catheter comprising a flexible body having a contact surface; an array of sensor electrodes arranged within the flexible body, each sensor electrode configured to detect electrical signals from the tissue surface); identifying a plurality of waves of the biological rhythm disorder over time, wherein each wave is based upon times of tissue activation from a subset of electrical signals (¶188-analyzes the electrical waves; ¶190-MAPs provide one of the few methods to identify actual activation time (onset) and recovery time (offset) in complex rhythms in the human heart. Phase 0 ( 1022 ) of the MAP indicates onset time, and phase 3 ( 1024 ) of the MAP indicates the offset time during any electrical rhythm in the tissue; Fig. 10; ¶191-194; ¶229), determining one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (¶62-determining the guidance direction to the target region outside the directionality map, steering the device to a subsequent position in the guidance direction to the target region; ¶53-generate movement instructions to move the catheter toward the target region); and generating the graphical user interface on an electronic display depicting a visual representation of the catheter and the one or more guidance directions (¶72-a display unit of sensed signals, indicating directional guidance for the sensor to move towards a source region of interest, and to indicate when this region has been reached; ¶204-the catheter positioning will be controlled by a physician, in which case the system may provide some form of notification, e.g., a visual display on a display device; ¶225-controller 1360 detects and determines the location and/or the guidance direction to the source or other target region, then generates one or more indicators (e.g., visual and/or audio) of the location and/or the guidance direction to the physician, the physician may direct movement of shaft 1320 via a user interface 1365 in communication with the controller (and motor 1326 ), allowing the physician to control movement of the spade 1310, examples of user interfaces that can be included in embodiments include a handle, joystick, mouse, or trackball on a computer).
However, Narayan does not explicitly teach determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves.
Zrihem relates to systems and methods for determining regions of interest to be ablated for treatment of cardiac arrhythmia, such as atrial fibrillation, and, more particularly, to systems and methods for detecting atrial fibrillation rotational activity pattern (RAP) sources to determine a region of interest of the heart for ablation (¶2). Zrihem further teaches the invention using the following steps:
determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode (¶28-RAP detection may include activation based algorithms such as analyzing activation waves according to spatio-temporal manifestations and identifying centers of sources of activation to determine potential RAP sources; ¶103-provide RAP score information (e.g., a value) which indicate a likelihood or probability that a potential RAP source is detected using the algorithm. For example, score information may be based on a similarity between two or more atrial activations over different cycles; ¶56-detected by a corresponding electrode activated earlier than neighboring electrodes though restricted by neighbors activated by the same wave; Fig. 7; Figs. 8A-8B); and based on a ranking of the scores for the plurality of waves (¶111-score information for one or more algorithms (e.g., Activation Based Algorithm and Outer Circle To Inner Circle Activation Spread algorithm) may be provided and used to determine a potential ablation ROI; ¶86-a RAP is valid if a percentage of the electrodes is equal to or greater than a predetermined similarity threshold (e.g., 50%); ¶89-a RAP is valid if the head to toe distance is less than a predetermined threshold distance (e.g., 25 mm); ¶90-parameters are extracted for the RAP analysis; Figs. 9A-9D; Figs. 10A-10F), wherein the guidance directions indicate directions of conduction for the plurality of waves (¶50-determine whether the catheter should be repositioned; ¶83-the conduction path from electrode to a neighbor electrode is determined by the maximum transition from the electrode to its neighbor. For example, if electrode A 2 is participating in a RAP with 5 cycles, three wave propagate from A 2 to B 2 , a fourth wave propagates from A 2 to A 1 and electrode A 2 is missing from the fifth wave, then the RAP is considered to propagate from A 2 to B 2 for the static representation; ¶107-identify a wave front direction of activation to determine the origin of activation for a rotational activation pattern, catheter is moved toward the center of the rotational activity; ¶68-for each electrode currently being investigated in a window of time, a “simple wave” of conduction is defined by the most probable electrode from which the wave is propagating toward the electrode being investigated and according to the most probable electrode to which the wave is propagating from the electrode being investigated. The most probable electrodes are neighboring electrodes having the closest activation with valid conduction velocities (above 0.1 and below 15 mm/sec); ¶73).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves of Zrihem in order for determining regions of interest to be ablated for treatment of cardiac arrhythmia (Zrihem, ¶2).
Regarding claim 2, the combination of Narayan and Zrihem teaches the method of claim 1, wherein the critical site is one of: a site where localized therapy can modify the biological rhythm disorder (Narayan, ¶88- “Ablation energy” refers to energy used to modify tissue. The tissue being modified may correspond to a source region or other target region for an electrical rhythm disorder. Modification of the tissue affects one or more electrical rhythms generated at the source region); a site where localized therapy can eliminate the biological rhythm disorder on long-term follow-up (Narayan, ¶36-elimination of the disease with specific therapy; ¶185-step 870 assesses the response to therapy, particularly if the region of interest has been eliminated. If not, therapy is repeated; ¶186-the process returns to step 850 , navigating to and ablating regions of interest until they are all eliminated; ¶205-successful treatment entails correction of the electrical rhythms and elimination of target regions that would affect the electrical rhythms); and a site where localized therapy can improve biological function in the patient after therapy (Narayan, ¶18-identify and locate source regions or other target regions to treat biological rhythm disorders using a personalized digital medicine approach; ¶38-response to therapy personalized to that individual; ¶42; ¶67).
Regarding claim 9, the combination of Narayan and Zrihem teaches the method of claim 1, wherein determining the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region) comprises: applying a machine learning model trained using a series of activation sequences in a plurality of patients and biological features in the plurality of patients (Narayan, ¶58-applying a trained machine learning model to the electrical signals, wherein the machine learning model is trained on training examples comprising electrical signals of a human heart and known target regions of the heart rhythm disorder; ¶190-employs analytical techniques such as machine learning to identify activation onset and offset times from electrograms, calibrated to the ground-truth annotated in MAP recordings, indicated as line 1028; ¶191-194), the machine learning model configured to identify a location of one or more critical sites for the biological rhythm disorder in relation to patterns of activation sequence (Narayan, ¶189-actiation patterns, when such patterns are found, the inventive system will suggest navigation towards this detected target type; ¶191-the trained system can accurately identify activation onset and offset, making it possible to accurately map activation paths; ¶193-to identify the location from which activation emanates outwards, even during complex fibrillatory rhythms, and the direction in which the path would lead); identifying from the machine learning model a location of the critical site for the biological rhythm disorder in the patient (Narayan, ¶38-predicting the locations of sources for a biological rhythm disorder, helping to guide navigation to said source; ¶53-determine a location of a target region of a heart rhythm disorder); and identifying the direction towards the location of the critical site (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region).
Regarding claim 10, the combination of Narayan and Zrihem teaches the method of claim 1, wherein determining the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region) comprises: applying a machine learning model trained using a series of activation sequences in a plurality of patients and biological features in the plurality of patients (Narayan, ¶58-applying a trained machine learning model to the electrical signals, wherein the machine learning model is trained on training examples comprising electrical signals of a human heart and known target regions of the heart rhythm disorder; ¶190-employs analytical techniques such as machine learning to identify activation onset and offset times from electrograms, calibrated to the ground-truth annotated in MAP recordings, indicated as line 1028; ¶191-194), the machine learning model configured to identify critical sites ablated in patients with previously successful therapy in relation to patterns of the series of activation sequences (Narayan, ¶38-PDPs indicate the relevance of biological and clinical data for the rhythm disorder in that individual, which may be unclear to experts, using systems and methods trained on previously labeled datasets in which a specific therapy was or was not successful. This enables the identification of individuals with and without treatable forms of the disorder, such as predicting the locations of sources for a biological rhythm disorder, helping to guide navigation to said source, predicting the type and size of said source, and the likely response to therapy personalized to that individual; ¶189-actiation patterns, when such patterns are found, the inventive system will suggest navigation towards this detected target type; ¶191-the trained system can accurately identify activation onset and offset, making it possible to accurately map activation paths; ¶149); identifying from the machine learning model a location of the critical site for the biological rhythm disorder in the patient (Narayan, ¶38-predicting the locations of sources for a biological rhythm disorder, helping to guide navigation to said source; ¶53-determine a location of a target region of a heart rhythm disorder); and identifying the direction towards the location of the one or more critical sites (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region).
Regarding claim 13, the combination of Narayan and Zrihem teaches the method of claim 1, wherein determining the score for each wave further comprises determining the score based on tissue conduction velocity and spatial distance between the sensing electrodes on the catheter (Zrihem, ¶111-score information for one or more algorithms (e.g., Activation Based Algorithm and Outer Circle To Inner Circle Activation Spread algorithm) may be provided and used to determine a potential ablation ROI; ¶68-the most probable electrodes are neighboring electrodes having the closest activation with valid conduction velocities (above 0.1 and below 15 mm/sec); ¶89-a RAP is valid if the head to toe distance is less than a predetermined threshold distance (e.g., 25 mm); ¶90-parameters are extracted for the RAP analysis, smaller distances are factors indicating higher likelihood of a RAP; Figs. 9A-9D; Figs. 10A-10F).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include determining the score for each wave further comprises determining the score based on tissue conduction velocity and spatial distance between the sensing electrodes on the catheter of Zrihem in order for determining regions of interest to be ablated for treatment of cardiac arrhythmia (Zrihem, ¶2).
Regarding claim 14, the combination of Narayan and Zrihem teaches the method of claim 1, further comprising: determining that the catheter is positioned at the critical site of the biological rhythm disorder based on the plurality of waves; and transmitting control instructions to cause delivery of ablation energy at the critical site of the biological rhythm disorder (Narayan, ¶72-indicate when this region has been reached; ¶195-directionality analysis is used to guide the ablation catheter to the target region of interest, for example, a source for the arrhythmia. The ablation catheter is then analyzed to determine a ratio (percentage) of the number of electrodes that are covered by the region of interest in step 1180. This is achieved by determining the area of the sensor that covers the predicted region of interest. In step 1185, a determination is made as to whether the area ratio exceeds a predicted ratio. If so, the therapy is applied at this site in step 1190; ¶188).
Regarding claim 17, Narayan teaches a non-transitory computer-readable storage medium storing instructions for generation of a graphical user interface for steering a catheter towards a critical site of a biological rhythm disorder (¶204-the catheter continues to be steered incrementally towards the source or other target region in step 1250 until an indication is provided that the target has been located, the catheter positioning will be controlled by a physician, in which case the system may provide some form of notification, e.g., a visual display on a display device, an audible tone, or a combination thereof, to indicate to the physician where to move the catheter to follow the guidance direction toward the target), the instructions, when executed by a computer processor, cause the computer processor to perform operations (¶243-software programs tangibly embodied in a processor-readable medium and may be executed by a processor) comprising: identifying one or more times of tissue activation in each electrical signal in a set of electrical signals (¶190-identify actual activation time (onset) and recovery time (offset) in complex rhythms in the human heart; ¶191-194) measured by a plurality of sensing electrodes on the catheter in contact with human tissue (¶51-an ablation catheter for treating electrical rhythm disorders includes an array of sensor electrodes to detect electrical signals; ¶53-a catheter configured to be placed in contact with a tissue surface, the catheter comprising a flexible body having a contact surface; an array of sensor electrodes arranged within the flexible body, each sensor electrode configured to detect electrical signals from the tissue surface); identifying a plurality of waves of the biological rhythm disorder over time, wherein each wave comprises times of tissue activation from a subset of electrical signals (¶188-analyzes the electrical waves; ¶190-MAPs provide one of the few methods to identify actual activation time (onset) and recovery time (offset) in complex rhythms in the human heart. Phase 0 ( 1022 ) of the MAP indicates onset time, and phase 3 ( 1024 ) of the MAP indicates the offset time during any electrical rhythm in the tissue; Fig. 10; ¶191-194; ¶229); determining one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (¶62-determining the guidance direction to the target region outside the directionality map, steering the device to a subsequent position in the guidance direction to the target region; ¶53-generate movement instructions to move the catheter toward the target region); and generating the graphical user interface on an electronic display depicting a visual representation of the catheter and the one or more guidance directions (¶72-a display unit of sensed signals, indicating directional guidance for the sensor to move towards a source region of interest, and to indicate when this region has been reached; ¶204-the catheter positioning will be controlled by a physician, in which case the system may provide some form of notification, e.g., a visual display on a display device; ¶225-controller 1360 detects and determines the location and/or the guidance direction to the source or other target region, then generates one or more indicators (e.g., visual and/or audio) of the location and/or the guidance direction to the physician, the physician may direct movement of shaft 1320 via a user interface 1365 in communication with the controller (and motor 1326 ), allowing the physician to control movement of the spade 1310, examples of user interfaces that can be included in embodiments include a handle, joystick, mouse, or trackball on a computer).
However, Narayan does not explicitly teach determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves.
Zrihem teaches determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode (¶28-RAP detection may include activation based algorithms such as analyzing activation waves according to spatio-temporal manifestations and identifying centers of sources of activation to determine potential RAP sources; ¶103-provide RAP score information (e.g., a value) which indicate a likelihood or probability that a potential RAP source is detected using the algorithm. For example, score information may be based on a similarity between two or more atrial activations over different cycles; ¶56-detected by a corresponding electrode activated earlier than neighboring electrodes though restricted by neighbors activated by the same wave; Fig. 7; Figs. 8A-8B); and based on a ranking of the scores for the plurality of waves (¶111-score information for one or more algorithms (e.g., Activation Based Algorithm and Outer Circle To Inner Circle Activation Spread algorithm) may be provided and used to determine a potential ablation ROI; ¶86-a RAP is valid if a percentage of the electrodes is equal to or greater than a predetermined similarity threshold (e.g., 50%); ¶89-a RAP is valid if the head to toe distance is less than a predetermined threshold distance (e.g., 25 mm); ¶90-parameters are extracted for the RAP analysis; Figs. 9A-9D; Figs. 10A-10F), wherein the guidance directions indicate directions of conduction for the plurality of waves (¶50-determine whether the catheter should be repositioned; ¶83-the conduction path from electrode to a neighbor electrode is determined by the maximum transition from the electrode to its neighbor. For example, if electrode A 2 is participating in a RAP with 5 cycles, three wave propagate from A 2 to B 2 , a fourth wave propagates from A 2 to A 1 and electrode A 2 is missing from the fifth wave, then the RAP is considered to propagate from A 2 to B 2 for the static representation; ¶107-identify a wave front direction of activation to determine the origin of activation for a rotational activation pattern, catheter is moved toward the center of the rotational activity; ¶68-for each electrode currently being investigated in a window of time, a “simple wave” of conduction is defined by the most probable electrode from which the wave is propagating toward the electrode being investigated and according to the most probable electrode to which the wave is propagating from the electrode being investigated. The most probable electrodes are neighboring electrodes having the closest activation with valid conduction velocities (above 0.1 and below 15 mm/sec); ¶73).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves of Zrihem in order for determining regions of interest to be ablated for treatment of cardiac arrhythmia (Zrihem, ¶2).
Regarding claim 19, Narayan teaches a system (¶3) comprising: a catheter comprising a plurality of sensing electrodes for measuring electrical activity of tissue (¶51-an ablation catheter for treating electrical rhythm disorders includes an array of sensor electrodes to detect electrical signals; ¶53-a catheter configured to be placed in contact with a tissue surface, the catheter comprising a flexible body having a contact surface; an array of sensor electrodes arranged within the flexible body, each sensor electrode configured to detect electrical signals from the tissue surface); and a control system (¶51-controller; ¶239-control system) configured to generate a graphical user interface for steering a catheter towards a critical site of a biological rhythm disorder (¶204-the catheter continues to be steered incrementally towards the source or other target region in step 1250 until an indication is provided that the target has been located, the catheter positioning will be controlled by a physician, in which case the system may provide some form of notification, e.g., a visual display on a display device, an audible tone, or a combination thereof, to indicate to the physician where to move the catheter to follow the guidance direction toward the target), the control system configured to: identify one or more times of tissue activation in each electrical signal in a set of electrical signals (¶190-identify actual activation time (onset) and recovery time (offset) in complex rhythms in the human heart; ¶191-194) measured by the plurality of sensing electrodes on the catheter in contact with human tissue (¶51-an ablation catheter for treating electrical rhythm disorders includes an array of sensor electrodes to detect electrical signals; ¶53-a catheter configured to be placed in contact with a tissue surface, the catheter comprising a flexible body having a contact surface; an array of sensor electrodes arranged within the flexible body, each sensor electrode configured to detect electrical signals from the tissue surface); identify a plurality of waves of the biological rhythm disorder over time, wherein each wave comprises times of tissue activation from a subset of electrical signals (¶188-analyzes the electrical waves; ¶190-MAPs provide one of the few methods to identify actual activation time (onset) and recovery time (offset) in complex rhythms in the human heart. Phase 0 ( 1022 ) of the MAP indicates onset time, and phase 3 ( 1024 ) of the MAP indicates the offset time during any electrical rhythm in the tissue; Fig. 10; ¶191-194; ¶229); determine one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (¶62-determining the guidance direction to the target region outside the directionality map, steering the device to a subsequent position in the guidance direction to the target region; ¶53-generate movement instructions to move the catheter toward the target region); and generate the graphical user interface on an electronic display depicting a visual representation of the catheter and the one or more guidance directions (¶72-a display unit of sensed signals, indicating directional guidance for the sensor to move towards a source region of interest, and to indicate when this region has been reached; ¶204-the catheter positioning will be controlled by a physician, in which case the system may provide some form of notification, e.g., a visual display on a display device; ¶225-controller 1360 detects and determines the location and/or the guidance direction to the source or other target region, then generates one or more indicators (e.g., visual and/or audio) of the location and/or the guidance direction to the physician, the physician may direct movement of shaft 1320 via a user interface 1365 in communication with the controller (and motor 1326 ), allowing the physician to control movement of the spade 1310, examples of user interfaces that can be included in embodiments include a handle, joystick, mouse, or trackball on a computer).
However, Narayan does not explicitly teach determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves.
Zrihem teaches to determine a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode (¶28-RAP detection may include activation based algorithms such as analyzing activation waves according to spatio-temporal manifestations and identifying centers of sources of activation to determine potential RAP sources; ¶103-provide RAP score information (e.g., a value) which indicate a likelihood or probability that a potential RAP source is detected using the algorithm. For example, score information may be based on a similarity between two or more atrial activations over different cycles; ¶56-detected by a corresponding electrode activated earlier than neighboring electrodes though restricted by neighbors activated by the same wave; Fig. 7; Figs. 8A-8B); and based on a ranking of the scores for the plurality of waves (¶111-score information for one or more algorithms (e.g., Activation Based Algorithm and Outer Circle To Inner Circle Activation Spread algorithm) may be provided and used to determine a potential ablation ROI; ¶86-a RAP is valid if a percentage of the electrodes is equal to or greater than a predetermined similarity threshold (e.g., 50%); ¶89-a RAP is valid if the head to toe distance is less than a predetermined threshold distance (e.g., 25 mm); ¶90-parameters are extracted for the RAP analysis; Figs. 9A-9D; Figs. 10A-10F), wherein the guidance directions indicate directions of conduction for the plurality of waves (¶50-determine whether the catheter should be repositioned; ¶83-the conduction path from electrode to a neighbor electrode is determined by the maximum transition from the electrode to its neighbor. For example, if electrode A 2 is participating in a RAP with 5 cycles, three wave propagate from A 2 to B 2 , a fourth wave propagates from A 2 to A 1 and electrode A 2 is missing from the fifth wave, then the RAP is considered to propagate from A 2 to B 2 for the static representation; ¶107-identify a wave front direction of activation to determine the origin of activation for a rotational activation pattern, catheter is moved toward the center of the rotational activity; ¶68-for each electrode currently being investigated in a window of time, a “simple wave” of conduction is defined by the most probable electrode from which the wave is propagating toward the electrode being investigated and according to the most probable electrode to which the wave is propagating from the electrode being investigated. The most probable electrodes are neighboring electrodes having the closest activation with valid conduction velocities (above 0.1 and below 15 mm/sec); ¶73).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves of Zrihem in order for determining regions of interest to be ablated for treatment of cardiac arrhythmia (Zrihem, ¶2).
Claims 3-4, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of Zrihem as applied to claims 1, 17, and 19 above, and further in view of Bort (US 20220338923 filed on 4/15/22) and Ravuna (US 20220051091 filed on 8/12/20).
Regarding claim 3, the combination of Narayan and Zrihem teaches the method of claim 1. However, the combination of Narayan and Zrihem does not explicitly teach wherein identifying the one or more times of tissue activation comprises: applying an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal, wherein the activation detection model is a deep-learning neural network trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects, the training set of electrical signals annotated with reference times of tissue activation.
Bort teaches wherein identifying the one or more times of tissue activation comprises: applying an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal (¶138-gradient-weighted Class Activation Mapping (Grad-CAM), which identifies the most critical nodes as the largest output weights multiplied by output's backpropagated gradients with respect to the final convolutional layer; ¶183- spatial activation or timing between the body surface and catheter inputs for a specific heart rhythm disorder in a specific patient; ¶185-differences in patterns of electrical activation over time; ¶71), wherein the activation detection model is a deep-learning neural network (¶143-apply one or more machine learning and deep learning techniques; ¶197) trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects (¶197-deep learning classifiers trained with previous and stored clinical data; ¶144-145).
Bort relates generally to a non-invasive medical device and, more specifically, to a body surface device that may be used in place or in conjunction to a catheter for treating electrical rhythm disorders (¶2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein identifying the one or more times of tissue activation comprises: applying an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal, wherein the activation detection model is a deep-learning neural network trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects of Bort in order to identify key regions of interest and guide the physician towards critical regions for treatment (Bort, ¶4).
While Bort teaches a set of training samples (Bort, ¶144), the combination does not teach the training set of electrical signals annotated with reference times of tissue activation.
Ravuna teaches the training set of electrical signals annotated with reference times of tissue activation (¶63- FIG. 2 is a schematic illustration of a workflow describing training and deployment of an algorithm (e.g., one comprising an ANN and a trainable preprocessing model) for the detection of an activation time in an electrogram (EGM), according to an embodiment of the present invention. As seen, collection of a training set of EGMs is done at numerous sites 202, where, at each site, a physician collects numerous correlated bipolar and unipolar EGMs that the physician verifies as correctly annotated with activation times (e.g., such as the annotation 102 of FIG. 1). The physician may manually correct wrong annotations of activations. This verified set of annotations is used during an ANN training session at a training station 204 in order to fine-tune annotations performed by the ANN during training by minimizing a loss function of the ANN, so as to generate a set of ANN parameters {{right arrow over (w)}}, and preprocessing parameters {{right arrow over (x)}}).
Ravuna relates generally to analysis of intracardiac electrophysiological signals, and specifically to evaluation of electrical propagation in the heart using machine leaning (ML) (¶1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include the training set of electrical signals annotated with reference times of tissue activation of Ravuna in order for diagnosing arrhythmias using an EP timing diagram map, called a local activation time (LAT) map, of regions of the cardiac chamber wall tissue (Ravuna, ¶29).
Regarding claim 4, the combination of Narayan, Zrihem, Bort, and Ravuna teaches the method of claim 3, wherein applying the activation detection model to each electrical signal comprises: inputting the electrical signal into the activation detection model to output a likelihood timeseries indicating likelihood of an activation in the electrical signal over time (Ravuna, ¶77-deep learning model; ¶18-identifying the activation includes generating, by the ML model, a respective probability for each of multiple possible activation values in the window of interest); and identifying the one or more times of tissue activation in the electrical signal above a threshold likelihood (Ravuna, ¶19-identifying the activation includes selecting one of (i) an activation with the highest probability, (ii) one or more activations with a probability above a given threshold, and (iii) one or more activations with a probability above a variable threshold).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein applying the activation detection model to each electrical signal comprises: inputting the electrical signal into the activation detection model to output a likelihood timeseries indicating likelihood of an activation in the electrical signal over time; and identifying the one or more times of tissue activation in the electrical signal above a threshold likelihood of Ravuna in order for diagnosing arrhythmias using an EP timing
diagram map, called a local activation time (LAT) map, of regions of the cardiac chamber wall tissue
(Ravuna, ¶29).
Regarding claim 18, the combination of Narayan and Zrihem teaches the non-transitory computer-readable storage medium of claim 17. However, the combination of Narayan and Zrihem does not explicitly teach wherein identifying the one or more times of tissue activation comprises: applying an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal, wherein the activation detection model is a deep-learning neural network trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects, the training set of electrical signals annotated with reference times of tissue activation.
Bort teaches wherein identifying the one or more times of tissue activation comprises: applying an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal (¶138-gradient-weighted Class Activation Mapping (Grad-CAM), which identifies the most critical nodes as the largest output weights multiplied by output's backpropagated gradients with respect to the final convolutional layer; ¶183- spatial activation or timing between the body surface and catheter inputs for a specific heart rhythm disorder in a specific patient; ¶185-differences in patterns of electrical activation over time; ¶71), wherein the activation detection model is a deep-learning neural network (¶143-apply one or more machine learning and deep learning techniques; ¶197) trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects (¶197-deep learning classifiers trained with previous and stored clinical data; ¶144-145).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein identifying the one or more times of tissue activation comprises: applying an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal, wherein the activation detection model is a deep-learning neural network trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects of Bort in order to identify key regions of interest and guide the physician towards critical regions for treatment (Bort, ¶4).
While Bort teaches a set of training samples (Bort, ¶144), the combination does not teach the training set of electrical signals annotated with reference times of tissue activation.
Ravuna teaches the training set of electrical signals annotated with reference times of tissue activation (¶63- FIG. 2 is a schematic illustration of a workflow describing training and deployment of an algorithm (e.g., one comprising an ANN and a trainable preprocessing model) for the detection of an activation time in an electrogram (EGM), according to an embodiment of the present invention. As seen, collection of a training set of EGMs is done at numerous sites 202, where, at each site, a physician collects numerous correlated bipolar and unipolar EGMs that the physician verifies as correctly annotated with activation times (e.g., such as the annotation 102 of FIG. 1). The physician may manually correct wrong annotations of activations. This verified set of annotations is used during an ANN training session at a training station 204 in order to fine-tune annotations performed by the ANN during training by minimizing a loss function of the ANN, so as to generate a set of ANN parameters {{right arrow over (w)}}, and preprocessing parameters {{right arrow over (x)}}).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include the training set of electrical signals annotated with reference times of tissue activation of Ravuna in order for diagnosing arrhythmias using an EP timing diagram map, called a local activation time (LAT) map, of regions of the cardiac chamber wall tissue (Ravuna, ¶29).
Regarding claim 20, the combination of Narayan and Zrihem teaches the system of claim 19. However, the combination of Narayan and Zrihem does not explicitly teach wherein the control system being configured to identify the one or more times of tissue activation comprises being configured to: apply an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal, wherein the activation detection model is a deep-learning neural network trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects, the training set of electrical signals annotated with reference times of tissue activation.
Bort teaches wherein the control system being configured to identify the one or more times of tissue activation comprises being configured to: apply an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal (¶138-gradient-weighted Class Activation Mapping (Grad-CAM), which identifies the most critical nodes as the largest output weights multiplied by output's backpropagated gradients with respect to the final convolutional layer; ¶183-spatial activation or timing between the body surface and catheter inputs for a specific heart rhythm disorder in a specific patient; ¶185-differences in patterns of electrical activation over time; ¶71), wherein the activation detection model is a deep-learning neural network (¶143-apply one or more machine learning and deep learning techniques; ¶197) trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects (¶197-deep learning classifiers trained with previous and stored clinical data; ¶144-145).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein the control system being configured to identify the one or more times of tissue activation comprises being configured to: apply an activation detection model to each electrical signal to identify the one or more times of tissue activation in the electrical signal, wherein the activation detection model is a deep-learning neural network trained using a training set of electrical signals measured from one or more human tissues from one or more training subjects of Bort in order to identify key regions of interest and guide the physician towards critical regions for treatment (Bort, ¶4).
While Bort teaches a set of training samples (Bort, ¶144), the combination does not teach the training set of electrical signals annotated with reference times of tissue activation.
Ravuna teaches the training set of electrical signals annotated with reference times of tissue activation (¶63- FIG. 2 is a schematic illustration of a workflow describing training and deployment of an algorithm (e.g., one comprising an ANN and a trainable preprocessing model) for the detection of an activation time in an electrogram (EGM), according to an embodiment of the present invention. As seen, collection of a training set of EGMs is done at numerous sites 202, where, at each site, a physician collects numerous correlated bipolar and unipolar EGMs that the physician verifies as correctly annotated with activation times (e.g., such as the annotation 102 of FIG. 1). The physician may manually correct wrong annotations of activations. This verified set of annotations is used during an ANN training session at a training station 204 in order to fine-tune annotations performed by the ANN during training by minimizing a loss function of the ANN, so as to generate a set of ANN parameters {{right arrow over (w)}}, and preprocessing parameters {{right arrow over (x)}}).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include the training set of electrical signals annotated with reference times of tissue activation of Ravuna in order for diagnosing arrhythmias using an EP timing diagram map, called a local activation time (LAT) map, of regions of the cardiac chamber wall tissue (Ravuna, ¶29).
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of Zrihem, and further in view of Bort and Ravuna as applied to claim 4 above, and further in view of Weiss (US 20230277112 filed on 2/9/23).
Regarding claim 5, the combination of Narayan, Zrihem, Bort, and Ravuna teaches the method of claim 4. However, the combination of Narayan, Zrihem, Bort, and Ravuna does not explicitly teach wherein determining the score for each wave comprises: determining the score based on the likelihoods of the times of tissue activation from the subset of electrical signals for the wave.
Weiss teaches wherein determining the score for each wave comprises: determining the score based on the likelihoods of the times of tissue activation from the subset of electrical signals for the wave (¶16-EP map quality according to the quality score (i.e., “smart index”) of the graded data points; ¶52-wherein the quality score comprises a weighted scoring of at least two of (i) signal ( 40 ) pattern matching, (ii) annotated cycle length, (iii) LAT stability, (iv) complex/simple data point flagging, (v) electrode contact pressure index, (vi) electrode position stability (vii) respiratory flax, and (vii) signal SNR, and (viii) sharpness of signal deflection).
Weiss relates generally to cardiac electrophysiological (EP) mapping, and particularly to automation of generation of data points for cardiac EP maps (¶2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein determining the score for each wave comprises: determining the score based on the likelihoods of the times of tissue activation from the subset of electrical signals for the wave of Weiss in order to ensure that its settings produce a sufficient quality (e.g., relevant, stable) of data points (e.g., for an EP map) (Weiss, ¶11).
Regarding claim 6, the combination of Narayan, Zrihem, Bort, and Ravuna teaches the method of claim 4. However, the combination of Narayan, Zrihem, Bort, and Ravuna does not explicitly teach determining a signal quality score for each electrical signal based on the likelihoods of the one or more times of tissue activation in the electrical signal; and identifying a first electrical signal as unusable based on the signal quality score for the first electrical signal being below a threshold signal quality score.
Weiss teaches determining a signal quality score for each electrical signal based on the likelihoods of the one or more times of tissue activation in the electrical signal (¶16-EP map quality according to the quality score (i.e., “smart index”) of the graded data points; ¶52-wherein the quality score comprises a weighted scoring of at least two of (i) signal ( 40 ) pattern matching, (ii) annotated cycle length, (iii) LAT stability, (iv) complex/simple data point flagging, (v) electrode contact pressure index, (vi) electrode position stability (vii) respiratory flax, and (vii) signal SNR, and (viii) sharpness of signal deflection); and identifying a first electrical signal as unusable based on the signal quality score for the first electrical signal being below a threshold signal quality score (¶16-user-selected threshold value of the quality score, so the user can fine-tune the EP map quality by changing a quality scoring threshold below which candidate data points are not considered; ¶55-reject any data point whose quality score is below the threshold; ¶27).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include determining a signal quality score for each electrical signal based on the likelihoods of the one or more times of tissue activation in the electrical signal; and identifying a first electrical signal as unusable based on the signal quality score for the first electrical signal being below a threshold signal quality score of Weiss in order to ensure that its settings produce a sufficient quality (e.g., relevant, stable) of data points (e.g., for an EP map) (Weiss, ¶11).
Regarding claim 7, the combination of Narayan, Zrihem, Bort, Ravuna, and Weiss teaches the method of claim 6, further comprising: in response to identifying the first electrical signal as unusable, reconstructing a synthetic electrical signal for use in place of the first electrical signal based on electrical signals of neighboring sensing electrodes (Weiss, ¶16-where the quantity of data points is too low (e.g., spatially sparse), the physician can collect additional data points so as to improve the EP map quality in such regions).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include in response to identifying the first electrical signal as unusable, reconstructing a synthetic electrical signal for use in place of the first electrical signal based on electrical signals of neighboring sensing electrodes of Weiss in order to ensure that its settings produce a sufficient quality (e.g., relevant, stable) of data points (e.g., for an EP map) (Weiss, ¶11).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of Zrihem, and further in view of Bort, Ravuna, and Weiss as applied to claim 7 above, and further in view of Laughner (US 20150254419 filed on 2/24/15).
Regarding claim 8, the combination of Narayan, Zrihem, Bort, Ravuna, and Weiss teaches the method of claim 7. However, the combination of Narayan, Zrihem, Bort, Ravuna, and Weiss does not explicitly teach wherein reconstructing the synthetic electrical signal comprises interpolation or extrapolation of electrical signals from neighboring sensing electrodes.
Laughner teaches wherein reconstructing the synthetic electrical signal comprises interpolation or extrapolation of electrical signals from neighboring sensing electrodes (¶59-desirable to interpolate activation times for missing signal data and populate and/or fill in the activation time map 72 accordingly, when selecting an interpolation method, it may be desirable to select a method that incorporates the relative distance between neighboring electrodes and utilizes those distances in an algorithm to estimate unknown data points).
Laughner relates to medical devices, and methods for using medical devices. More particularly, the present disclosure pertains to medical devices for mapping cardiac tissue and methods for displaying mapping data (¶2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein reconstructing the synthetic electrical signal comprises interpolation or extrapolation of electrical signals from neighboring sensing electrodes of Laughner in order to indicate of an area of diseased or abnormal cellular tissue (Laughner,¶55).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of Zrihem as applied to claim 1 above, and further in view of Harley (US 9002442 filed on 9/23/11).
Regarding claim 11, the combination of Narayan and Zrihem teaches the method of claim 1. However, the combination of Narayan and Zrihem does not teach wherein identifying each wave of the plurality of waves comprises identifying a sequential ordering of the set of electrical signals by calculating a gradient based on time shift that maximizes cross-correlation between the electrical signals.
Harley teaches wherein identifying each wave of the plurality of waves comprises identifying a sequential ordering of the set of electrical signals by calculating a gradient based on time shift that maximizes cross-correlation between the electrical signals (col. 19 and lines 43-45-generating an intracavitary electric field and measuring the resulting voltage gradients; col. 2 and lines 23-28-synchronizing the signals can include aligning the plurality of additional data signals with the templates representing the exemplary beat of interest. Aligning the plurality of additional data signals with the templates representing the exemplary beat of interest can include computing a cross-correlation to align the template and the data signals; col. 15 and lines 51-54-a cross-correlation value is computed for each channel, and the reference points are defined when the average cross-correlation between all of the template and synchronization signals reach a maximum).
Harley relates to the determination and representation of physiological information relating to a heart surface (col. 1 and lines 15-16).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein identifying each wave of the plurality of waves comprises identifying a sequential ordering of the set of electrical signals by calculating a gradient based on time shift that maximizes cross-correlation between the electrical signals of Harley in order to identify different beats to generate a cardiac map (Harley, col. 16 and lines 38-41) for determining the source of arrhythmias and in guiding therapeutic treatment (Harley, col. 10 and lines 38-39).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of Zrihem as applied to claim 1 above, and further in view of Laughner.
Regarding claim 12, the combination of Narayan and Zrihem teaches the method of claim 1. However, the combination of Narayan and Zrihem does not explicitly teach wherein identifying the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder comprises: determining a direction of conduction for each wave based on the earliest activated electrode and the latest activated electrode; and combining the directions of conduction for the plurality of waves weighted based on the scores.
Laughner teaches wherein identifying the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (¶48-ablation catheter 16 may include a flexible catheter body 34 that carries one or more ablation electrodes 36; ¶49-generate position-identifying output for display on device 40 that aids the physician in guiding ablation electrode(s) 36 into contact with tissue at the site identified for ablation) comprises: determining a direction of conduction for each wave based on the earliest activated electrode and the latest activated electrode (¶44-electrodes 24 may be configured to detect activation signals of the intrinsic physiological activity within the anatomical structure (e.g., the activation times of cardiac activity); ¶68-conduction velocity vectors for electrodes 24. Thus, both the magnitude and direction of the wavefront sensed at electrodes 24 can be displayed); and combining the directions of conduction for the plurality of waves weighted based on the scores (¶23-displaying the set of metric data as an interpolated activation map and/or a set of conduction velocity vectors; ¶68-conduction velocity vectors for electrodes 24. Thus, both the magnitude and direction of the wavefront sensed at electrodes 24 can be displayed; ¶45).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein identifying the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder comprises: determining a direction of conduction for each wave based on the earliest activated electrode and the latest activated electrode; and combining the directions of conduction for the plurality of waves weighted based on the scores of Laughner in order to identify the site or sites within the heart appropriate for a diagnostic and/or treatment procedure (Laughner,¶45).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of Zrihem as applied to claim 14 above, and further in view of Briggs (WO 2017192617 filed on 5/2/17).
Regarding claim 15, the combination of Narayan and Zrihem teaches the method of claim 14. However, the combination of Narayan and Zrihem does not teach wherein determining that the catheter is positioned at the critical site comprises: determining that conduction directions at successive time periods converge to one location; determining that conduction directions of sub-regions of the catheter converge to one location; identifying a critical feature for the biological rhythm disorder by applying a feature identification model to the electrical signals; or determining that a conduction direction of the biological rhythm disorder is a near-zero vector.
Briggs teaches wherein determining that the catheter is positioned at the critical site comprises: determining that a conduction direction of the biological rhythm disorder is a near-zero vector (¶28- used to identify a focal source as zero sum rotation in all directions (i.e. centrifugal activation) from a region of tissue. These regions (or sites) can be targeted for treatment (such as ablation) as described below; ¶41-rhythm disorders, this precession area is very small (effectively zero, but actually nonzero due to slight stochastic changes in functional property of tissue over time); ¶31-a vector is constructed that indicates the direction of activation between electrode sites in a pair and the speed of conduction between them, based upon differences in activation time and the relative distance).
Briggs relates generally to biological rhythm disorders. More specifically, the present application is directed to a system and method of identifying a source (or sources) of a biological rhythm disorder, such as a heart rhythm disorder, by analyzing whether there exists continuous or interrupted activation associated with a source of a heart rhythm disorder (e.g., using a metric of progressive rotational or focal activation in relation to one or more spatial elements associated with the source of the heart rhythm disorder) (¶4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein determining that the catheter is positioned at the critical site comprises: determining that a conduction direction of the biological rhythm disorder is a near-zero vector of Briggs in order to identify sources of various rhythm disorders and directly using this information to treat the rhythm disorders (Briggs, ¶13).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Narayan in view of Zrihem as applied to claim 1 above, and further in view of Bort.
Regarding claim 16, the combination of Narayan and Zrihem teaches the method of claim 1, further comprising: determining that the catheter is positioned at scar tissue upon identifying one or more electrical signal has a voltage below a threshold voltage (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region; ¶113-small channels of viable tissue within fibrosis or scar regions of low voltage; ¶160-low voltage (such as <0.1 mV); ¶181-regions of low voltage suggesting scar). However, the combination of Narayan and Zrihem does not explicitly teach wherein determining the guidance direction comprises determining the guidance direction to steer the catheter away from the scar tissue.
Bort teaches wherein determining the guidance direction comprises determining the guidance direction to steer the catheter away from the scar tissue (¶110-guiding the selection of sites for cardiac resynchronization therapy pacing. This may also include pacing sites that avoid pre-existing scars where signals are very small or attenuated).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Narayan to include wherein determining the guidance direction comprises determining the guidance direction to steer the catheter away from the scar tissue of Bort in order to avoid pre-existing scars where signals are very small or attenuated (Bort, ¶110).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-4, 6-11, 13-15, and 17-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-25 of U.S. Patent No. 12290370 in view of Narayan (US 20210259765) and Zrihem (US 20170202515).
This is a nonstatutory double patenting rejection. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the U.S. Patent to include the subject matter in Narayan and Zrihem as shown below.
Claims of the Present Application (19/169730)
Claims of US Patent 12290370
Secondary Reference Narayan (US 20210259765)
Secondary Reference Zrihem (US 20170202515)
1, 17, 19
1, 13, 25
Narayan teaches identifying a plurality of waves of the biological rhythm disorder over time, wherein each wave is based upon times of tissue activation from a subset of electrical signals (¶188-analyzes the electrical waves; ¶190-MAPs provide one of the few methods to identify actual activation time (onset) and recovery time (offset) in complex rhythms in the human heart. Phase 0 ( 1022 ) of the MAP indicates onset time, and phase 3 ( 1024 ) of the MAP indicates the offset time during any electrical rhythm in the tissue; Fig. 10; ¶191-194; ¶229); and to steer the catheter towards the critical site of the biological rhythm disorder (¶62-determining the guidance direction to the target region outside the directionality map, steering the device to a subsequent position in the guidance direction to the target region; ¶53-generate movement instructions to move the catheter toward the target region). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include identifying a plurality of waves of the biological rhythm disorder over time, wherein each wave is based upon times of tissue activation from a subset of electrical signals and to steer the catheter towards the critical site of the biological rhythm disorder of Narayan in order for personalized identification and therapy for electrical rhythm disorders (Narayan, ¶3).
Zrihem teaches determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode (¶28-RAP detection may include activation based algorithms such as analyzing activation waves according to spatio-temporal manifestations and identifying centers of sources of activation to determine potential RAP sources; ¶103-provide RAP score information (e.g., a value) which indicate a likelihood or probability that a potential RAP source is detected using the algorithm. For example, score information may be based on a similarity between two or more atrial activations over different cycles; ¶56-detected by a corresponding electrode activated earlier than neighboring electrodes though restricted by neighbors activated by the same wave; Fig. 7; Figs. 8A-8B); and based on a ranking of the scores for the plurality of waves (¶111-score information for one or more algorithms (e.g., Activation Based Algorithm and Outer Circle To Inner Circle Activation Spread algorithm) may be provided and used to determine a potential ablation ROI; ¶86-a RAP is valid if a percentage of the electrodes is equal to or greater than a predetermined similarity threshold (e.g., 50%); ¶89-a RAP is valid if the head to toe distance is less than a predetermined threshold distance (e.g., 25 mm); ¶90-parameters are extracted for the RAP analysis; Figs. 9A-9D; Figs. 10A-10F), wherein the guidance directions indicate directions of conduction for the plurality of waves (¶50-determine whether the catheter should be repositioned; ¶83-the conduction path from electrode to a neighbor electrode is determined by the maximum transition from the electrode to its neighbor. For example, if electrode A 2 is participating in a RAP with 5 cycles, three wave propagate from A 2 to B 2 , a fourth wave propagates from A 2 to A 1 and electrode A 2 is missing from the fifth wave, then the RAP is considered to propagate from A 2 to B 2 for the static representation; ¶107-identify a wave front direction of activation to determine the origin of activation for a rotational activation pattern, catheter is moved toward the center of the rotational activity; ¶68-for each electrode currently being investigated in a window of time, a “simple wave” of conduction is defined by the most probable electrode from which the wave is propagating toward the electrode being investigated and according to the most probable electrode to which the wave is propagating from the electrode being investigated. The most probable electrodes are neighboring electrodes having the closest activation with valid conduction velocities (above 0.1 and below 15 mm/sec); ¶73). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include determining a score for each wave of the plurality of waves based on a time delay between an earliest activated electrode and a latest activated electrode; and based on a ranking of the scores for the plurality of waves, wherein the guidance directions indicate directions of conduction for the plurality of waves of Zrihem in order for determining regions of interest to be ablated for treatment of cardiac arrhythmia (Zrihem, ¶2).
2
Narayan teaches wherein the critical site is one of: a site where localized therapy can modify the biological rhythm disorder (Narayan, ¶88- “Ablation energy” refers to energy used to modify tissue. The tissue being modified may correspond to a source region or other target region for an electrical rhythm disorder. Modification of the tissue affects one or more electrical rhythms generated at the source region); a site where localized therapy can eliminate the biological rhythm disorder on long-term follow-up (Narayan, ¶36-elimination of the disease with specific therapy; ¶185-step 870 assesses the response to therapy, particularly if the region of interest has been eliminated. If not, therapy is repeated; ¶186-the process returns to step 850 , navigating to and ablating regions of interest until they are all eliminated; ¶205-successful treatment entails correction of the electrical rhythms and elimination of target regions that would affect the electrical rhythms); and a site where localized therapy can improve biological function in the patient after therapy (Narayan, ¶18-identify and locate source regions or other target regions to treat biological rhythm disorders using a personalized digital medicine approach; ¶38-response to therapy personalized to that individual; ¶42; ¶67). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include wherein the critical site is one of: a site where localized therapy can modify the biological rhythm disorder; a site where localized therapy can eliminate the biological rhythm disorder on long-term follow-up; and a site where localized therapy can improve biological function in the patient after therapy of Narayan in order for personalized identification and therapy for electrical rhythm disorders (Narayan, ¶3).
3, 18, 20
1, 25
4
1, 25
6
3, 4
7
4, 5
8
5, 9
9
Narayan teaches wherein determining the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region) comprises: applying a machine learning model trained using a series of activation sequences in a plurality of patients and biological features in the plurality of patients (Narayan, ¶58-applying a trained machine learning model to the electrical signals, wherein the machine learning model is trained on training examples comprising electrical signals of a human heart and known target regions of the heart rhythm disorder; ¶190-employs analytical techniques such as machine learning to identify activation onset and offset times from electrograms, calibrated to the ground-truth annotated in MAP recordings, indicated as line 1028; ¶191-194), the machine learning model configured to identify a location of one or more critical sites for the biological rhythm disorder in relation to patterns of activation sequence (Narayan, ¶189-actiation patterns, when such patterns are found, the inventive system will suggest navigation towards this detected target type; ¶191-the trained system can accurately identify activation onset and offset, making it possible to accurately map activation paths; ¶193-to identify the location from which activation emanates outwards, even during complex fibrillatory rhythms, and the direction in which the path would lead); identifying from the machine learning model a location of the critical site for the biological rhythm disorder in the patient (Narayan, ¶38-predicting the locations of sources for a biological rhythm disorder, helping to guide navigation to said source; ¶53-determine a location of a target region of a heart rhythm disorder); and identifying the direction towards the location of the critical site (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include wherein determining the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder comprises: applying a machine learning model trained using a series of activation sequences in a plurality of patients and biological features in the plurality of patients, the machine learning model configured to identify a location of one or more critical sites for the biological rhythm disorder in relation to patterns of activation sequence; identifying from the machine learning model a location of the critical site for the biological rhythm disorder in the patient; and identifying the direction towards the location of the critical site of Narayan in order for personalized identification and therapy for electrical rhythm disorders (Narayan, ¶3).
10
Narayan teaches wherein determining the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region) comprises: applying a machine learning model trained using a series of activation sequences in a plurality of patients and biological features in the plurality of patients (Narayan, ¶58-applying a trained machine learning model to the electrical signals, wherein the machine learning model is trained on training examples comprising electrical signals of a human heart and known target regions of the heart rhythm disorder; ¶190-employs analytical techniques such as machine learning to identify activation onset and offset times from electrograms, calibrated to the ground-truth annotated in MAP recordings, indicated as line 1028; ¶191-194), the machine learning model configured to identify critical sites ablated in patients with previously successful therapy in relation to patterns of the series of activation sequences (Narayan, ¶38-PDPs indicate the relevance of biological and clinical data for the rhythm disorder in that individual, which may be unclear to experts, using systems and methods trained on previously labeled datasets in which a specific therapy was or was not successful. This enables the identification of individuals with and without treatable forms of the disorder, such as predicting the locations of sources for a biological rhythm disorder, helping to guide navigation to said source, predicting the type and size of said source, and the likely response to therapy personalized to that individual; ¶189-actiation patterns, when such patterns are found, the inventive system will suggest navigation towards this detected target type; ¶191-the trained system can accurately identify activation onset and offset, making it possible to accurately map activation paths; ¶149); identifying from the machine learning model a location of the critical site for the biological rhythm disorder in the patient (Narayan, ¶38-predicting the locations of sources for a biological rhythm disorder, helping to guide navigation to said source; ¶53-determine a location of a target region of a heart rhythm disorder); and identifying the direction towards the location of the one or more critical sites (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region, and, if not optimally positioned, to compute directionality to the target region and generate movement instructions to move the catheter toward the target region). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include wherein determining the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder comprises: applying a machine learning model trained using a series of activation sequences in a plurality of patients and biological features in the plurality of patients, the machine learning model configured to identify critical sites ablated in patients with previously successful therapy in relation to patterns of the series of activation sequences; identifying from the machine learning model a location of the critical site for the biological rhythm disorder in the patient; and identifying the direction towards the location of the one or more critical sites of Narayan in order for personalized identification and therapy for electrical rhythm disorders (Narayan, ¶3).
11
10
13
1, 7, 25
14
1, 14-18, 25
15
15-18
Claim 5 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-25 of U.S. Patent No. 12290370 in view of Narayan, Zrihem, and Weiss (US 20230277112).
This is a nonstatutory double patenting rejection. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the U.S. Patent to include the subject matter in Narayan, Zrihem, and Weiss as shown below.
Claims of the Present Application (19/169730)
Claims of US Patent 12290370
Secondary Reference Narayan (US 20210259765)
Secondary Reference Zrihem (US 20170202515)
Secondary Reference Weiss (US 20230277112)
5
1, 21, 22, 25
Weiss teaches wherein determining the score for each wave comprises: determining the score based on the likelihoods of the times of tissue activation from the subset of electrical signals for the wave (¶16-EP map quality according to the quality score (i.e., “smart index”) of the graded data points; ¶52-wherein the quality score comprises a weighted scoring of at least two of (i) signal ( 40 ) pattern matching, (ii) annotated cycle length, (iii) LAT stability, (iv) complex/simple data point flagging, (v) electrode contact pressure index, (vi) electrode position stability (vii) respiratory flax, and (vii) signal SNR, and (viii) sharpness of signal deflection). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include wherein determining the score for each wave comprises: determining the score based on the likelihoods of the times of tissue activation from the subset of electrical signals for the wave of Weiss in order to ensure that its settings produce a sufficient quality (e.g., relevant, stable) of data points (e.g., for an EP map) (Weiss, ¶11).
Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-25 of U.S. Patent No. 12290370 in view of Narayan, Zrihem, and Laughner (US 20150254419).
This is a nonstatutory double patenting rejection. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the U.S. Patent to include the subject matter in Narayan, Zrihem, and Laughner as shown below.
Claims of the Present Application (19/169730)
Claims of US Patent 12290370
Secondary Reference Narayan (US 20210259765)
Secondary Reference Zrihem (US 20170202515)
Secondary Reference Laughner (US 20150254419)
12
Laughner teaches wherein identifying the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder (¶48-ablation catheter 16 may include a flexible catheter body 34 that carries one or more ablation electrodes 36; ¶49-generate position-identifying output for display on device 40 that aids the physician in guiding ablation electrode(s) 36 into contact with tissue at the site identified for ablation) comprises: determining a direction of conduction for each wave based on the earliest activated electrode and the latest activated electrode (¶44-electrodes 24 may be configured to detect activation signals of the intrinsic physiological activity within the anatomical structure (e.g., the activation times of cardiac activity); ¶68-conduction velocity vectors for electrodes 24. Thus, both the magnitude and direction of the wavefront sensed at electrodes 24 can be displayed); and combining the directions of conduction for the plurality of waves weighted based on the scores (¶23-displaying the set of metric data as an interpolated activation map and/or a set of conduction velocity vectors; ¶68-conduction velocity vectors for electrodes 24. Thus, both the magnitude and direction of the wavefront sensed at electrodes 24 can be displayed; ¶45). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include wherein identifying the one or more guidance directions to steer the catheter towards the critical site of the biological rhythm disorder comprises: determining a direction of conduction for each wave based on the earliest activated electrode and the latest activated electrode; and combining the directions of conduction for the plurality of waves weighted based on the scores of Laughner in order to identify the site or sites within the heart appropriate for a diagnostic and/or treatment procedure (Laughner,¶45).
Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-25 of U.S. Patent No. 12290370 in view of Narayan, Zrihem, and Bort (US 20220338923).
This is a nonstatutory double patenting rejection. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the U.S. Patent to include the subject matter in Narayan, Zrihem, and Bort as shown below.
Claims of the Present Application (19/169730)
Claims of US Patent 12290370
Secondary Reference Narayan (US 20210259765)
Secondary Reference Zrihem (US 20170202515)
Secondary Reference Bort (US 20220338923)
16
Narayan teaches determining that the catheter is positioned at scar tissue upon identifying one or more electrical signal has a voltage below a threshold voltage (Narayan, ¶53-determine whether the catheter is optimally positioned at the target region; ¶113-small channels of viable tissue within fibrosis or scar regions of low voltage; ¶160-low voltage (such as <0.1 mV); ¶181-regions of low voltage suggesting scar). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include determining that the catheter is positioned at scar tissue upon identifying one or more electrical signal has a voltage below a threshold voltage of Narayan in order for personalized identification and therapy for electrical rhythm disorders (Narayan, ¶3).
Bort teaches wherein determining the guidance direction comprises determining the guidance direction to steer the catheter away from the scar tissue (¶110-guiding the selection of sites for cardiac resynchronization therapy pacing. This may also include pacing sites that avoid pre-existing scars where signals are very small or attenuated). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of the US Patent to include wherein determining the guidance direction comprises determining the guidance direction to steer the catheter away from the scar tissue of Bort in order to avoid pre-existing scars where signals are very small or attenuated (Bort, ¶110).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210085204: relates to medical systems, and in particular, but not exclusively, to analysis of electrical activity (¶1).
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